From 83f3290f924d9971283d0aee54ac4bd65676881d Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Wed, 21 Oct 2026 12:53:43 -0700
Subject: [PATCH 01/47] docs(factors): describe the release workbook and update
 cadence

Rewrite "What's in a release" around the release workbook (cover, producer and
purchaser price tabs, auxiliary tabs) and add an update-cadence section with a
release-cycle figure. The figure is a hand-drawn SVG generated by
figures/scripts/release_cycle.py and listed in figures/figures.csv.

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 factors/whats-in-a-release.md      |  79 +++++++++++++++++++---
 figures/diagrams/release-cycle.png | Bin 0 -> 98534 bytes
 figures/diagrams/release-cycle.svg |  89 +++++++++++++++++++++++++
 figures/figures.csv                |   1 +
 figures/scripts/release_cycle.py   | 103 +++++++++++++++++++++++++++++
 5 files changed, 262 insertions(+), 10 deletions(-)
 create mode 100644 figures/diagrams/release-cycle.png
 create mode 100644 figures/diagrams/release-cycle.svg
 create mode 100644 figures/scripts/release_cycle.py

diff --git a/factors/whats-in-a-release.md b/factors/whats-in-a-release.md
index d7ca1f8a..4498ac7b 100644
--- a/factors/whats-in-a-release.md
+++ b/factors/whats-in-a-release.md
@@ -3,25 +3,84 @@ title: "What's in a release"
 ---
 
 :::{tip} Summary
-*To be written.*
+The Cornerstone EEIO model is updated annually and versioned with the year of the release, so Cornerstone EEIO 2026 is released in 2026 and the 2027 version will be released in 2027. Every year, the releases will be publicly available in October. Each release publishes emission factors as a CSV file covering 405 sectors across 148 countries, 15 regions and one global average. To download the latest version, check out zenodo.
 :::
 
-:::{note} Draft placeholder
-:class: dropdown
-Plan §3.4.1 · target: **RC (Oct 27)**
+## The release workbook
 
+The release is one Excel workbook that contains: a cover, a data dictionary, a producer-price tab and a purchaser-price tab for each year the data is available for, and five auxiliary tabs.
 
-**Planned content**
+### Cover
 
-- Files and columns, described from the published tables: `CornerstoneSupplyChainGHG_CO2e_USD{year}.csv`, one file per dollar year; commodity code and name; GHG; unit (kg CO₂e per dollar, purchaser price); the three factor columns: without margins, margins, with margins
-- What isn't in the table and why (electricity, government sectors)
-- Optional `M` and `N` matrices; tables for other countries (format to confirm)
+The first tab introduces the dataset and the release details:
 
-**Key visuals**
+- Description: cradle-to-gate greenhouse gas emission factors in kg CO₂e per USD, by good or service, location and year, in producer and purchaser price. Prices are in current-year dollars, so the dollar year matches the data year.
+- Coverage: 148 countries and 21 regions, and 405 categories of goods and services (government services are not covered). The total and each gas (CO₂, CH₄, N₂O, HFCs, PFCs, NF₃, SF₆) are given in CO₂e, annually from 2018 to 2026.
+- Publisher and license: Cornerstone Sustainability Data Initiative, under a Creative Commons Attribution 4.0 International license.
 
-- 🖼 Annotated table extract
+Below these details, the cover lists every other tab with a one-line description and points readers to the data dictionary and the model documentation.
+
+### Producer and purchaser price tabs
+
+There are two tabs for each year, with identical layouts: `{YEAR}PRO` for producer prices and `{YEAR}PUR` for purchaser prices. The producer price is the cost to the producer plus their profits and taxes paid. The purchaser price adds the value of sales and distribution. Read more about how to choose between purchaser and producer prices in [](prices-and-margins.md).
+
+Each row is one sector in one location, and the columns are:
+
+| Column | Meaning |
+|---|---|
+| Code | Good or service code, based on the BEA input-output commodity codes, which derive from NAICS. There are 405 categories. |
+| Name | Name of the good or service. |
+| Location | A UN three-letter country code, with additions for multi-country regions. |
+| Total GHGs | Sum of all greenhouse gases, in kg CO₂e per USD. |
+| CO2 | Carbon dioxide, in kg CO₂e per USD. |
+| CH4 | Methane, in kg CO₂e per USD. |
+| N2O | Nitrous oxide, in kg CO₂e per USD. |
+| HFCs | Hydrofluorocarbons, in kg CO₂e per USD. |
+| PFCs | Perfluorocarbons, in kg CO₂e per USD. |
+| NF3 | Nitrogen trifluoride, in kg CO₂e per USD. |
+| SF6 | Sulfur hexafluoride, in kg CO₂e per USD. |
+
+Values are in current-year dollars: a factor in a 2024 tab applies to spend in 2024 dollars.
+
+### Auxiliary tabs
+
+The data dictionary and five other tabs support the data tabs. The data dictionary is the second tab in the workbook; the others follow the price tabs.
+
+| Tab | What it contains |
+|---|---|
+| Data Dictionary | Definitions of each field in the data tabs. |
+| Exchange rates | Local currency units per US dollar for 148 countries: country code and name, currency, unit, and one column per year from 2018 to 2026. Rates come from Open Exchange Rates (openexchangerates.org) and are Cornerstone's recommended rates for converting spend in local currency before applying the factors. |
+| GWP | The 100-year global warming potentials from the IPCC AR6 that express each gas in CO₂e: CO₂ 1, CH₄ 27.9 (to confirm), N₂O 273, SF₆ 24,300 and NF₃ 17,400. HFCs and PFCs are already in CO₂e, so their factor is 1. |
+| Cornerstone \<\> NAICS mapping | The correspondence between NAICS 2022 codes and Cornerstone codes: NAICS code and name, Cornerstone code and title, and a weight. When a NAICS industry is split across several Cornerstone codes, the weights show each share and add up to 1. |
+| GoodsServices | The 405 sectors with their code, name and description. Cornerstone recommends using this description to map spend categories to emission factors. |
+| Locations | Every location code and name used in the model: countries by their three-letter code, and regions. |
+
+## Update cadence
+
+The Cornerstone EEIO follows an annual update cycle. Every year we release a version named after the release year, so Cornerstone 2027 will be released in 2027. Releases are publicly available every October so companies can plan accordingly.
+
+The Cornerstone EEIO combines USEEIO and CEDA, and it is developed in two parallel versions, each with its own release cycle (see {numref}`release-cycle`).
+
+**Research version.** This version is updated continuously and has many releases. It is where new science is developed and tested.
+
+**Corporate version.** This version is released every October with data updates, bug fixes and small improvements. Its methods stay stable for five years, so companies can make plans, set targets and measure progress against a consistent model.
+
+**Major updates.** Every five years, a major update brings the science proven in the research version into the corporate version and starts a new five-year period of stable methods. The first period covers 2026 to 2030, and the next major update is in 2031.
+
+:::{figure} ../figures/diagrams/release-cycle.svg
+:label: release-cycle
+:alt: USEEIO and CEDA merge into the Cornerstone EEIO, which follows a research version updated continuously with many releases, and a corporate version released every October with methods stable for five years and a major update every five years.
+:width: 100%
+
+USEEIO and CEDA merge into the Cornerstone EEIO, which follows two parallel versions. The research version is updated continuously, with many releases. The corporate version is released every October with data updates, bug fixes and small improvements, and keeps its methods stable for five years. Every five years, a major update brings proven science from the research version into the corporate version.
 :::
 
+## Learn more
+
+- **From the field:** *to be added*
+- **Cornerstone papers:** *to be added*
+- **Code:** *to be added*
+
 +++
 
 :::{div}
diff --git a/figures/diagrams/release-cycle.png b/figures/diagrams/release-cycle.png
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diff --git a/figures/diagrams/release-cycle.svg b/figures/diagrams/release-cycle.svg
new file mode 100644
index 00000000..33b9a305
--- /dev/null
+++ b/figures/diagrams/release-cycle.svg
@@ -0,0 +1,89 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="680" height="376" viewBox="0 0 680 376" role="img">
+<title>USEEIO and CEDA merge into Cornerstone EEIO, which follows a research version and a corporate version from 2026 to 2040</title>
+<desc>USEEIO and CEDA merge into the Cornerstone EEIO. The research version is updated continuously with many releases. The corporate version is released every October and keeps its methodology stable for five years. Every five years a major update brings proven science from the research version into a new set of methods.</desc>
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+<rect x="264" y="24" width="376" height="88" rx="10" fill="#F6F5FF" stroke="#534AB7" stroke-width="1"/>
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+<text class="ts" x="278" y="62" text-anchor="start" dominant-baseline="central" fill="#534AB7">Continuously updated, with many releases</text>
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+<text class="th" x="278" y="178" text-anchor="start" dominant-baseline="central" fill="#085041">Corporate version</text>
+<text class="ts" x="626" y="178" text-anchor="end" dominant-baseline="central" fill="#0F6E56">For targets and reporting</text>
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+<circle cx="435.5" cy="262" r="5.5" fill="#E1F5EE" stroke="#0F6E56" stroke-width="1"/>
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+<polygon points="410,154 405.5,146 414.5,146" fill="#D85A30"/>
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+<text class="ts" x="544" y="135" text-anchor="start" dominant-baseline="central">Proven science</text>
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+<circle cx="46" cy="356" r="6" fill="#EEEDFE" stroke="#534AB7" stroke-width="1"/>
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diff --git a/figures/figures.csv b/figures/figures.csv
index dbb5bacc..b3ca9adf 100644
--- a/figures/figures.csv
+++ b/figures/figures.csv
@@ -1 +1,2 @@
 id,page,type,data_source,generator_script,owner,status
+release-cycle,factors/whats-in-a-release.md,diagram,none (hand-drawn schematic),figures/scripts/release_cycle.py,Yohanna Maldonado,draft
diff --git a/figures/scripts/release_cycle.py b/figures/scripts/release_cycle.py
new file mode 100644
index 00000000..52117348
--- /dev/null
+++ b/figures/scripts/release_cycle.py
@@ -0,0 +1,103 @@
+"""Write figures/diagrams/release-cycle.svg: the Cornerstone EEIO development and release cycle.
+
+Hand-drawn schematic (no data source). Run from the repo root:
+    python3 figures/scripts/release_cycle.py
+"""
+from pathlib import Path
+
+OUT = Path(__file__).resolve().parents[1] / "diagrams" / "release-cycle.svg"
+W, H = 680, 376
+
+INK, MUTED, LINE = "#2C2C2A", "#5F5E5A", "#888780"
+PURPLE = dict(fill="#EEEDFE", stroke="#534AB7", ink="#3C3489", line="#7F77DD", tint="#F6F5FF")
+TEAL = dict(fill="#E1F5EE", stroke="#0F6E56", ink="#085041", tint="#F2FAF6")
+CORAL = dict(fill="#FAECE7", stroke="#993C1D", line="#D85A30")
+GRAY = dict(fill="#F1EFE8", stroke="#5F5E5A")
+
+els = []
+add = els.append
+
+
+def text(x, y, s, cls="t", anchor="start", fill=None):
+    f = f' fill="{fill}"' if fill else ""
+    add(f'<text class="{cls}" x="{x}" y="{y}" text-anchor="{anchor}" dominant-baseline="central"{f}>{s}</text>')
+
+
+def rect(x, y, w, h, fill, stroke, rx=8, sw=1):
+    add(f'<rect x="{x}" y="{y}" width="{w}" height="{h}" rx="{rx}" fill="{fill}" stroke="{stroke}" stroke-width="{sw}"/>')
+
+
+def dot(cx, cy, r, c):
+    add(f'<circle cx="{cx}" cy="{cy}" r="{r}" fill="{c["fill"]}" stroke="{c["stroke"]}" stroke-width="1"/>')
+
+
+def right_arrow(x1, x2, y, color=LINE):
+    add(f'<line x1="{x1}" y1="{y}" x2="{x2 - 6}" y2="{y}" stroke="{color}" stroke-width="1.5"/>')
+    add(f'<polygon points="{x2},{y} {x2 - 8},{y - 4.5} {x2 - 8},{y + 4.5}" fill="{color}"/>')
+
+
+def down_arrow(x, y1, y2, color=LINE):
+    add(f'<line x1="{x}" y1="{y1}" x2="{x}" y2="{y2 - 6}" stroke="{color}" stroke-width="1.5"/>')
+    add(f'<polygon points="{x},{y2} {x - 4.5},{y2 - 8} {x + 4.5},{y2 - 8}" fill="{color}"/>')
+
+
+# predecessors merge into Cornerstone EEIO
+rect(40, 80, 84, 40, GRAY["fill"], GRAY["stroke"]); text(82, 100, "USEEIO", "th", "middle")
+rect(40, 204, 84, 40, GRAY["fill"], GRAY["stroke"]); text(82, 224, "CEDA", "th", "middle")
+right_arrow(124, 146, 100); right_arrow(124, 146, 224)
+rect(148, 24, 98, 282, "#FFFFFF", LINE)
+text(197, 156, "Cornerstone", "th", "middle"); text(197, 174, "EEIO", "th", "middle")
+
+# lane 1: research version
+LX, LW = 264, 376
+rect(LX, 24, LW, 88, PURPLE["tint"], PURPLE["stroke"], rx=10)
+text(LX + 14, 44, "Research version", "th", fill=PURPLE["ink"])
+text(LX + LW - 14, 44, "Tests new science", "ts", "end", fill=PURPLE["stroke"])
+text(LX + 14, 62, "Continuously updated, with many releases", "ts", fill=PURPLE["stroke"])
+add(f'<line x1="{LX + 14}" y1="92" x2="{LX + LW - 14}" y2="92" stroke="{PURPLE["line"]}" stroke-width="2"/>')
+for cx in (284, 310, 336, 362, 382, 436, 462, 488, 512, 556, 582, 604, 620):
+    dot(cx, 92, 6, PURPLE)
+right_arrow(248, 262, 68, PURPLE["line"])
+
+# lane 2: corporate version
+rect(LX, 158, LW, 148, TEAL["tint"], TEAL["stroke"], rx=10)
+text(LX + 14, 178, "Corporate version", "th", fill=TEAL["ink"])
+text(LX + LW - 14, 178, "For targets and reporting", "ts", "end", fill=TEAL["stroke"])
+text(LX + 14, 196, "One release every October, stable methods", "ts", fill=TEAL["stroke"])
+right_arrow(248, 262, 229, TEAL["stroke"])
+bands = [(272, "2026"), (396, "2031"), (520, "2036")]
+for x0, yr in bands:
+    rect(x0, 208, 114, 88, "#FFFFFF", LINE)
+    text(x0 + 57, 224, f"{yr} methods", "th", "middle")
+    text(x0 + 57, 241, "Stable 5 years", "ts", "middle")
+    for k in range(5):
+        cx = x0 + 22 + 17.5 * k
+        dot(cx, 262, 8, CORAL) if k == 0 else dot(cx, 262, 5.5, TEAL)
+    text(x0 + 22, 283, yr, "ts", "middle")
+    text(x0 + 92, 283, str(int(yr) + 4), "ts", "middle")
+
+# proven science: research into the major updates
+for x0 in (396, 520):
+    down_arrow(x0 + 14, 116, 154, CORAL["line"])
+    text(x0 + 24, 135, "Proven science", "ts")
+
+text(340, 328, "Each October release: data updates, bug fixes and small improvements", "ts", "middle")
+
+# legend
+dot(46, 356, 6, PURPLE); text(58, 356, "Research release", "ts")
+dot(196, 356, 5.5, TEAL); text(208, 356, "Annual release", "ts")
+dot(332, 356, 8, CORAL); text(346, 356, "Major update, every 5 years", "ts")
+
+svg = f'''<svg xmlns="http://www.w3.org/2000/svg" width="{W}" height="{H}" viewBox="0 0 {W} {H}" role="img">
+<title>USEEIO and CEDA merge into Cornerstone EEIO, which follows a research version and a corporate version from 2026 to 2040</title>
+<desc>USEEIO and CEDA merge into the Cornerstone EEIO. The research version is updated continuously with many releases. The corporate version is released every October and keeps its methodology stable for five years. Every five years a major update brings proven science from the research version into a new set of methods.</desc>
+<style>
+text{{font-family:Helvetica,Arial,sans-serif}}
+.t{{font-size:14px;fill:{INK}}} .th{{font-size:14px;font-weight:500;fill:{INK}}} .ts{{font-size:12px;fill:{MUTED}}}
+</style>
+<rect width="{W}" height="{H}" fill="#FFFFFF"/>
+{chr(10).join(els)}
+</svg>
+'''
+OUT.write_text(svg, encoding="utf-8")
+print("wrote", OUT)

From 90530d215be2439d49ab25c1dc0954b35bc4a4b0 Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Wed, 21 Oct 2026 13:29:52 -0700
Subject: [PATCH 02/47] docs: keep both update-cadence versions as unpublished
 drafts

Move the two update-cadence options (two parallel versions with a time series,
and a two-year version lifecycle) out of "What's in a release" into drafts/,
and exclude drafts/** from the build so neither is published yet. Add the
lifecycle figure, its generator script and inventory row.

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 README.md                              |   1 +
 drafts/update-cadence-version-1.md     |  25 ++++
 drafts/update-cadence-version-2.md     |  21 ++++
 factors/whats-in-a-release.md          |  20 ----
 figures/diagrams/release-cycle.png     | Bin 98534 -> 105874 bytes
 figures/diagrams/release-cycle.svg     | 159 +++++++++++++------------
 figures/diagrams/release-lifecycle.png | Bin 0 -> 101731 bytes
 figures/diagrams/release-lifecycle.svg |  85 +++++++++++++
 figures/figures.csv                    |   1 +
 figures/scripts/release_cycle.py       |  67 ++++++-----
 figures/scripts/release_lifecycle.py   |  81 +++++++++++++
 myst.yml                               |   1 +
 12 files changed, 330 insertions(+), 131 deletions(-)
 create mode 100644 drafts/update-cadence-version-1.md
 create mode 100644 drafts/update-cadence-version-2.md
 create mode 100644 figures/diagrams/release-lifecycle.png
 create mode 100644 figures/diagrams/release-lifecycle.svg
 create mode 100644 figures/scripts/release_lifecycle.py

diff --git a/README.md b/README.md
index a4ccd853..b4d57e9b 100644
--- a/README.md
+++ b/README.md
@@ -30,6 +30,7 @@ myst build --html --strict   # the same build CI runs
 | `releases/` | One page per model release |
 | `versions/` | Version records (one data file per release) |
 | `figures/` | Hand-made diagrams, figure generator scripts, `figures.csv` inventory |
+| `drafts/` | Unpublished drafts kept in the repo; excluded from the build in `myst.yml` |
 | `data/` | Small cached data extracts for figures |
 | `references.bib` | Bibliography; add entries as pages cite them, reusing keys from the `papers` repo where they exist |
 | `_parts/` | Navbar, footer, and custom CSS |
diff --git a/drafts/update-cadence-version-1.md b/drafts/update-cadence-version-1.md
new file mode 100644
index 00000000..0fc37e2e
--- /dev/null
+++ b/drafts/update-cadence-version-1.md
@@ -0,0 +1,25 @@
+---
+title: "Update cadence, version 1 (draft, not published)"
+---
+
+:::{note} Draft
+Option 1 for the update-cadence section of [](../factors/whats-in-a-release.md): two parallel versions, with five-year stable methods and a new time series at each major update. Not published; this folder is excluded in `myst.yml`.
+:::
+
+The Cornerstone EEIO follows an annual update cycle. Every year we release a version named after the release year, so Cornerstone 2027 will be released in 2027. Releases are publicly available every October so companies can plan accordingly. Each release builds on one time series of emission factors: Cornerstone EEIO 2026 publishes the 2017 to 2026 series, and Cornerstone EEIO 2027 only adds the 2027 emission factors to it, so results stay continuous from one year to the next.
+
+The Cornerstone EEIO is developed in two parallel versions, each with its own release cycle (see {numref}`release-cycle`).
+
+**Research version.** This version is updated continuously and has many releases. It is where new science is developed and tested.
+
+**Corporate version.** This version is released every October with data updates, bug fixes and small improvements. Each annual release adds that year's emission factors to the existing time series. Its methods stay stable for five years, so companies can make plans, set targets and measure progress against a consistent model.
+
+**Major updates.** Every five years, a major update brings the science proven in the research version into the corporate version and starts a new time series built with the updated methods. The following annual releases extend that series for five years. The first series, built with the 2026 methods, starts in 2017 and is extended through 2030, and the next major update is in 2031.
+
+:::{figure} ../figures/diagrams/release-cycle.svg
+:label: release-cycle
+:alt: USEEIO and CEDA merge into the Cornerstone EEIO, which follows a research version updated continuously with many releases, and a corporate version released every October. Each annual release adds one year to the existing time series, and a major update every five years starts a new time series.
+:width: 100%
+
+USEEIO and CEDA merge into the Cornerstone EEIO, which follows two parallel versions. The research version is updated continuously, with many releases. The corporate version is released every October with data updates, bug fixes and small improvements, and keeps its methods stable for five years. Each annual release adds one year to the existing time series, and every five years a major update brings proven science from the research version into the corporate version and starts a new time series.
+:::
diff --git a/drafts/update-cadence-version-2.md b/drafts/update-cadence-version-2.md
new file mode 100644
index 00000000..c6a5f224
--- /dev/null
+++ b/drafts/update-cadence-version-2.md
@@ -0,0 +1,21 @@
+---
+title: "Update cadence, version 2 (draft, not published)"
+---
+
+:::{note} Draft
+Option 2 for the update-cadence section of [](../factors/whats-in-a-release.md): a version lifecycle, with a new methodology version about every two years. Not published; this folder is excluded in `myst.yml`.
+:::
+
+The Cornerstone EEIO follows a version lifecycle, similar to the long-term support model used in software. About every two years, we release a new version with a large methodology update, named after its release year (for example, the 2026 methods and the 2028 methods).
+
+Each version is supported for about five years. For at least three years, it receives annual updates every October with data updates and bug fixes, and its methods do not change. It then moves into extended support for two more years.
+
+Companies can adopt a version when they are ready and know they will have about five years of support to set targets and measure progress, while new versions keep improving the model.
+
+:::{figure} ../figures/diagrams/release-lifecycle.svg
+:label: release-lifecycle
+:alt: About every two years a new methodology version is released. Each version has at least three years of annual maintenance and data updates, then two years of extended support.
+:width: 100%
+
+About every two years, a new version with a large methodology update is released. Each version receives annual maintenance and data updates for at least three years, then two years of extended support, so companies can rely on about five years of support from the version they adopt.
+:::
diff --git a/factors/whats-in-a-release.md b/factors/whats-in-a-release.md
index 4498ac7b..8290e3b3 100644
--- a/factors/whats-in-a-release.md
+++ b/factors/whats-in-a-release.md
@@ -55,26 +55,6 @@ The data dictionary and five other tabs support the data tabs. The data dictiona
 | GoodsServices | The 405 sectors with their code, name and description. Cornerstone recommends using this description to map spend categories to emission factors. |
 | Locations | Every location code and name used in the model: countries by their three-letter code, and regions. |
 
-## Update cadence
-
-The Cornerstone EEIO follows an annual update cycle. Every year we release a version named after the release year, so Cornerstone 2027 will be released in 2027. Releases are publicly available every October so companies can plan accordingly.
-
-The Cornerstone EEIO combines USEEIO and CEDA, and it is developed in two parallel versions, each with its own release cycle (see {numref}`release-cycle`).
-
-**Research version.** This version is updated continuously and has many releases. It is where new science is developed and tested.
-
-**Corporate version.** This version is released every October with data updates, bug fixes and small improvements. Its methods stay stable for five years, so companies can make plans, set targets and measure progress against a consistent model.
-
-**Major updates.** Every five years, a major update brings the science proven in the research version into the corporate version and starts a new five-year period of stable methods. The first period covers 2026 to 2030, and the next major update is in 2031.
-
-:::{figure} ../figures/diagrams/release-cycle.svg
-:label: release-cycle
-:alt: USEEIO and CEDA merge into the Cornerstone EEIO, which follows a research version updated continuously with many releases, and a corporate version released every October with methods stable for five years and a major update every five years.
-:width: 100%
-
-USEEIO and CEDA merge into the Cornerstone EEIO, which follows two parallel versions. The research version is updated continuously, with many releases. The corporate version is released every October with data updates, bug fixes and small improvements, and keeps its methods stable for five years. Every five years, a major update brings proven science from the research version into the corporate version.
-:::
-
 ## Learn more
 
 - **From the field:** *to be added*
diff --git a/figures/diagrams/release-cycle.png b/figures/diagrams/release-cycle.png
index 57ef71472969f0bc01f689f3a7e4be267f6bb77d..14cb48198427d6ba4acc6ca0d64096bb7aed3d01 100644
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diff --git a/figures/diagrams/release-lifecycle.svg b/figures/diagrams/release-lifecycle.svg
new file mode 100644
index 00000000..254f129e
--- /dev/null
+++ b/figures/diagrams/release-lifecycle.svg
@@ -0,0 +1,85 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="680" height="334" viewBox="0 0 680 334" role="img">
+<title>Cornerstone version lifecycle: a new methodology version about every two years, each with three years of annual updates and two years of extended support</title>
+<desc>About every two years a new version with a large methodology update is released. Each version gets at least three years of maintenance with an annual data update every October, then two years of extended support, so companies can adopt a version and rely on about five years of support.</desc>
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+<text class="ts" x="352" y="284" text-anchor="start" dominant-baseline="central">Extended support, 2 years</text>
+<circle cx="47" cy="306" r="7" fill="#FAECE7" stroke="#993C1D" stroke-width="1"/>
+<text class="ts" x="62" y="306" text-anchor="start" dominant-baseline="central">Large methodology update: a new version</text>
+<circle cx="337" cy="306" r="5" fill="#FFFFFF" stroke="#1D9E75" stroke-width="1.5"/>
+<text class="ts" x="352" y="306" text-anchor="start" dominant-baseline="central">Annual update, every October</text>
+</svg>
diff --git a/figures/figures.csv b/figures/figures.csv
index b3ca9adf..d3202c6c 100644
--- a/figures/figures.csv
+++ b/figures/figures.csv
@@ -1,2 +1,3 @@
 id,page,type,data_source,generator_script,owner,status
 release-cycle,factors/whats-in-a-release.md,diagram,none (hand-drawn schematic),figures/scripts/release_cycle.py,Yohanna Maldonado,draft
+release-lifecycle,factors/whats-in-a-release.md,diagram,none (hand-drawn schematic),figures/scripts/release_lifecycle.py,Yohanna Maldonado,draft (option 2)
diff --git a/figures/scripts/release_cycle.py b/figures/scripts/release_cycle.py
index 52117348..95e18a73 100644
--- a/figures/scripts/release_cycle.py
+++ b/figures/scripts/release_cycle.py
@@ -6,12 +6,12 @@
 from pathlib import Path
 
 OUT = Path(__file__).resolve().parents[1] / "diagrams" / "release-cycle.svg"
-W, H = 680, 376
+W, H = 680, 404
 
 INK, MUTED, LINE = "#2C2C2A", "#5F5E5A", "#888780"
 PURPLE = dict(fill="#EEEDFE", stroke="#534AB7", ink="#3C3489", line="#7F77DD", tint="#F6F5FF")
 TEAL = dict(fill="#E1F5EE", stroke="#0F6E56", ink="#085041", tint="#F2FAF6")
-CORAL = dict(fill="#FAECE7", stroke="#993C1D", line="#D85A30")
+CORAL = dict(fill="#FAECE7", stroke="#993C1D", line="#D85A30", ink="#712B13")
 GRAY = dict(fill="#F1EFE8", stroke="#5F5E5A")
 
 els = []
@@ -42,55 +42,58 @@ def down_arrow(x, y1, y2, color=LINE):
 
 
 # predecessors merge into Cornerstone EEIO
-rect(40, 80, 84, 40, GRAY["fill"], GRAY["stroke"]); text(82, 100, "USEEIO", "th", "middle")
-rect(40, 204, 84, 40, GRAY["fill"], GRAY["stroke"]); text(82, 224, "CEDA", "th", "middle")
-right_arrow(124, 146, 100); right_arrow(124, 146, 224)
-rect(148, 24, 98, 282, "#FFFFFF", LINE)
-text(197, 156, "Cornerstone", "th", "middle"); text(197, 174, "EEIO", "th", "middle")
+rect(40, 84, 62, 40, GRAY["fill"], GRAY["stroke"]); text(71, 104, "USEEIO", "th", "middle")
+rect(40, 214, 62, 40, GRAY["fill"], GRAY["stroke"]); text(71, 234, "CEDA", "th", "middle")
+right_arrow(102, 118, 104); right_arrow(102, 118, 234)
+rect(120, 24, 98, 290, "#FFFFFF", LINE)
+text(169, 160, "Cornerstone", "th", "middle"); text(169, 178, "EEIO", "th", "middle")
 
 # lane 1: research version
-LX, LW = 264, 376
+LX, LW = 238, 402
 rect(LX, 24, LW, 88, PURPLE["tint"], PURPLE["stroke"], rx=10)
 text(LX + 14, 44, "Research version", "th", fill=PURPLE["ink"])
 text(LX + LW - 14, 44, "Tests new science", "ts", "end", fill=PURPLE["stroke"])
 text(LX + 14, 62, "Continuously updated, with many releases", "ts", fill=PURPLE["stroke"])
 add(f'<line x1="{LX + 14}" y1="92" x2="{LX + LW - 14}" y2="92" stroke="{PURPLE["line"]}" stroke-width="2"/>')
-for cx in (284, 310, 336, 362, 382, 436, 462, 488, 512, 556, 582, 604, 620):
+for cx in (262, 284, 306, 328, 350, 384, 440, 462, 486, 520, 570, 592, 614):
     dot(cx, 92, 6, PURPLE)
-right_arrow(248, 262, 68, PURPLE["line"])
+right_arrow(222, 236, 68, PURPLE["line"])
 
 # lane 2: corporate version
-rect(LX, 158, LW, 148, TEAL["tint"], TEAL["stroke"], rx=10)
+rect(LX, 158, LW, 156, TEAL["tint"], TEAL["stroke"], rx=10)
 text(LX + 14, 178, "Corporate version", "th", fill=TEAL["ink"])
 text(LX + LW - 14, 178, "For targets and reporting", "ts", "end", fill=TEAL["stroke"])
 text(LX + 14, 196, "One release every October, stable methods", "ts", fill=TEAL["stroke"])
-right_arrow(248, 262, 229, TEAL["stroke"])
-bands = [(272, "2026"), (396, "2031"), (520, "2036")]
+right_arrow(222, 236, 234, TEAL["stroke"])
+bands = [(246, "2026"), (378, "2031"), (510, "2036")]
 for x0, yr in bands:
-    rect(x0, 208, 114, 88, "#FFFFFF", LINE)
-    text(x0 + 57, 224, f"{yr} methods", "th", "middle")
-    text(x0 + 57, 241, "Stable 5 years", "ts", "middle")
-    for k in range(5):
-        cx = x0 + 22 + 17.5 * k
-        dot(cx, 262, 8, CORAL) if k == 0 else dot(cx, 262, 5.5, TEAL)
-    text(x0 + 22, 283, yr, "ts", "middle")
-    text(x0 + 92, 283, str(int(yr) + 4), "ts", "middle")
-
-# proven science: research into the major updates
-for x0 in (396, 520):
-    down_arrow(x0 + 14, 116, 154, CORAL["line"])
-    text(x0 + 24, 135, "Proven science", "ts")
-
-text(340, 328, "Each October release: data updates, bug fixes and small improvements", "ts", "middle")
+    rect(x0, 208, 124, 96, "#FFFFFF", LINE)
+    text(x0 + 62, 224, f"{yr} methods", "th", "middle")
+    text(x0 + 62, 241, "Stable 5 years", "ts", "middle")
+    # time series bar: one wide block (new series) then one small block per annual release
+    rect(x0 + 6, 256, 66, 22, CORAL["fill"], CORAL["stroke"], rx=3)
+    if yr == "2026":  # later series coverage is not decided yet, so those blocks stay blank
+        text(x0 + 39, 267, "2017–2026", "ts", "middle", fill=CORAL["ink"])
+    for k in range(4):
+        rect(x0 + 76 + 11 * k, 256, 8, 22, TEAL["fill"], TEAL["stroke"], rx=2)
+    text(x0 + 39, 292, yr, "ts", "middle")
+    text(x0 + 118, 292, str(int(yr) + 4), "ts", "end")
+
+# proven science: research into the major updates (arrows point at the new-series blocks)
+for x0 in (378, 510):
+    down_arrow(x0 + 39, 116, 154, CORAL["line"])
+    text(x0 + 47, 135, "Proven science", "ts")
+
+text(340, 336, "Each October release: data updates, bug fixes and small improvements", "ts", "middle")
 
 # legend
-dot(46, 356, 6, PURPLE); text(58, 356, "Research release", "ts")
-dot(196, 356, 5.5, TEAL); text(208, 356, "Annual release", "ts")
-dot(332, 356, 8, CORAL); text(346, 356, "Major update, every 5 years", "ts")
+dot(46, 362, 6, PURPLE); text(58, 362, "Research release", "ts")
+rect(190, 357, 10, 10, TEAL["fill"], TEAL["stroke"], rx=2); text(208, 362, "Annual release: adds that year to the time series", "ts")
+rect(40, 377, 16, 10, CORAL["fill"], CORAL["stroke"], rx=2); text(64, 382, "Major update, every 5 years: starts a new time series", "ts")
 
 svg = f'''<svg xmlns="http://www.w3.org/2000/svg" width="{W}" height="{H}" viewBox="0 0 {W} {H}" role="img">
 <title>USEEIO and CEDA merge into Cornerstone EEIO, which follows a research version and a corporate version from 2026 to 2040</title>
-<desc>USEEIO and CEDA merge into the Cornerstone EEIO. The research version is updated continuously with many releases. The corporate version is released every October and keeps its methodology stable for five years. Every five years a major update brings proven science from the research version into a new set of methods.</desc>
+<desc>USEEIO and CEDA merge into the Cornerstone EEIO. The research version is updated continuously with many releases. The corporate version is released every October and keeps its methodology stable for five years. A major update every five years brings proven science from the research version into a new time series, and each annual release adds one year to the existing series.</desc>
 <style>
 text{{font-family:Helvetica,Arial,sans-serif}}
 .t{{font-size:14px;fill:{INK}}} .th{{font-size:14px;font-weight:500;fill:{INK}}} .ts{{font-size:12px;fill:{MUTED}}}
diff --git a/figures/scripts/release_lifecycle.py b/figures/scripts/release_lifecycle.py
new file mode 100644
index 00000000..b9682afc
--- /dev/null
+++ b/figures/scripts/release_lifecycle.py
@@ -0,0 +1,81 @@
+"""Write figures/diagrams/release-lifecycle.svg: the Cornerstone EEIO version lifecycle (option 2).
+
+Hand-drawn schematic (no data source). A new methodology version about every two years;
+each gets 3+ years of annual maintenance and data updates, then 2 years of extended support.
+Run from the repo root:
+    python3 figures/scripts/release_lifecycle.py
+"""
+from pathlib import Path
+
+OUT = Path(__file__).resolve().parents[1] / "diagrams" / "release-lifecycle.svg"
+W, H = 680, 334
+INK, MUTED, LINE = "#2C2C2A", "#5F5E5A", "#888780"
+TEAL = dict(fill="#E1F5EE", stroke="#0F6E56", ink="#085041", mark="#1D9E75")
+GRAY = dict(fill="#F1EFE8", stroke="#5F5E5A", ink="#444441")
+CORAL = dict(fill="#FAECE7", stroke="#993C1D")
+
+X0, PY = 170, 39.1
+x = lambda year: round(X0 + (year - 2026) * PY, 1)
+
+els = []
+add = els.append
+
+
+def text(px, py, s, cls="ts", anchor="start", fill=None):
+    f = f' fill="{fill}"' if fill else ""
+    add(f'<text class="{cls}" x="{px}" y="{py}" text-anchor="{anchor}" dominant-baseline="central"{f}>{s}</text>')
+
+
+def rect(px, py, w, h, c, rx=0):
+    add(f'<rect x="{px}" y="{py}" width="{w}" height="{h}" rx="{rx}" fill="{c["fill"]}" stroke="{c["stroke"]}" stroke-width="1"/>')
+
+
+def marker(cx, cy):
+    add(f'<circle cx="{cx}" cy="{cy}" r="5" fill="#FFFFFF" stroke="{TEAL["mark"]}" stroke-width="1.5"/>')
+
+
+# time axis
+add(f'<line x1="{X0}" y1="236" x2="{x(2038)}" y2="236" stroke="{LINE}" stroke-width="1"/>')
+for yr in range(2026, 2039):
+    add(f'<line x1="{x(yr)}" y1="236" x2="{x(yr)}" y2="242" stroke="{LINE}" stroke-width="1"/>')
+    text(x(yr), 254, yr, "ts", "middle")
+
+# one row per version
+for i, yr in enumerate((2026, 2028, 2030, 2032)):
+    cy = 48 + 52 * i
+    s, m, e = x(yr), x(yr + 3), x(yr + 5)
+    text(40, cy, f"{yr} methods", "th")
+    rect(s, cy - 17, round(m - s, 1), 34, TEAL, rx=3)
+    text(round((s + m) / 2 + 4, 1), cy - 7, "Annual updates", "ts", "middle", fill=TEAL["ink"])
+    for k in range(3):
+        marker(round(s + PY * (k + 0.5), 1), cy + 8)
+    rect(m, cy - 17, round(e - m, 1), 34, GRAY, rx=3)
+    text(round((m + e) / 2, 1), cy, "2 years", "ts", "middle", fill=GRAY["ink"])
+    add(f'<circle cx="{s}" cy="{cy}" r="8" fill="{CORAL["fill"]}" stroke="{CORAL["stroke"]}" stroke-width="1"/>')
+
+text(381, 48, "About 5 years of support per version")
+add(f'<line x1="{x(2026)}" y1="74" x2="{x(2028)}" y2="74" stroke="{LINE}" stroke-width="1"/>')
+for yr in (2026, 2028):
+    add(f'<line x1="{x(yr)}" y1="69" x2="{x(yr)}" y2="79" stroke="{LINE}" stroke-width="1"/>')
+text(round((x(2026) + x(2028)) / 2, 1), 88, "~2 years", "ts", "middle")
+
+# legend
+rect(40, 278, 14, 12, TEAL, rx=2); text(62, 284, "Maintenance and data updates, 3+ years")
+rect(330, 278, 14, 12, GRAY, rx=2); text(352, 284, "Extended support, 2 years")
+add(f'<circle cx="47" cy="306" r="7" fill="{CORAL["fill"]}" stroke="{CORAL["stroke"]}" stroke-width="1"/>')
+text(62, 306, "Large methodology update: a new version")
+marker(337, 306); text(352, 306, "Annual update, every October")
+
+svg = f'''<svg xmlns="http://www.w3.org/2000/svg" width="{W}" height="{H}" viewBox="0 0 {W} {H}" role="img">
+<title>Cornerstone version lifecycle: a new methodology version about every two years, each with three years of annual updates and two years of extended support</title>
+<desc>About every two years a new version with a large methodology update is released. Each version gets at least three years of maintenance with an annual data update every October, then two years of extended support, so companies can adopt a version and rely on about five years of support.</desc>
+<style>
+text{{font-family:Helvetica,Arial,sans-serif}}
+.th{{font-size:14px;font-weight:500;fill:{INK}}} .ts{{font-size:12px;fill:{MUTED}}}
+</style>
+<rect width="{W}" height="{H}" fill="#FFFFFF"/>
+{chr(10).join(els)}
+</svg>
+'''
+OUT.write_text(svg, encoding="utf-8")
+print("wrote", OUT)
diff --git a/myst.yml b/myst.yml
index 1b9a0469..90bddf97 100644
--- a/myst.yml
+++ b/myst.yml
@@ -13,6 +13,7 @@ project:
     - README.md
     - CONTRIBUTING.md
     - _parts/**
+    - drafts/**
   exports:
     - id: guide-pdf
       format: typst

From 455aeb9a5234e159e4010ca016e3da32e02d764e Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Wed, 21 Oct 2026 13:33:14 -0700
Subject: [PATCH 03/47] docs(factors): restore the shared update-cadence
 paragraph

The annual-release and time-series paragraph applies to every cadence option,
so it stays on the published page. The two option drafts now start after it.

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 drafts/update-cadence-version-1.md | 4 +---
 drafts/update-cadence-version-2.md | 2 +-
 factors/whats-in-a-release.md      | 4 ++++
 3 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/drafts/update-cadence-version-1.md b/drafts/update-cadence-version-1.md
index 0fc37e2e..abc199e6 100644
--- a/drafts/update-cadence-version-1.md
+++ b/drafts/update-cadence-version-1.md
@@ -3,11 +3,9 @@ title: "Update cadence, version 1 (draft, not published)"
 ---
 
 :::{note} Draft
-Option 1 for the update-cadence section of [](../factors/whats-in-a-release.md): two parallel versions, with five-year stable methods and a new time series at each major update. Not published; this folder is excluded in `myst.yml`.
+Option 1 for the update-cadence section of [](../factors/whats-in-a-release.md), to follow the shared opening paragraph on annual releases: two parallel versions, with five-year stable methods and a new time series at each major update. Not published; this folder is excluded in `myst.yml`.
 :::
 
-The Cornerstone EEIO follows an annual update cycle. Every year we release a version named after the release year, so Cornerstone 2027 will be released in 2027. Releases are publicly available every October so companies can plan accordingly. Each release builds on one time series of emission factors: Cornerstone EEIO 2026 publishes the 2017 to 2026 series, and Cornerstone EEIO 2027 only adds the 2027 emission factors to it, so results stay continuous from one year to the next.
-
 The Cornerstone EEIO is developed in two parallel versions, each with its own release cycle (see {numref}`release-cycle`).
 
 **Research version.** This version is updated continuously and has many releases. It is where new science is developed and tested.
diff --git a/drafts/update-cadence-version-2.md b/drafts/update-cadence-version-2.md
index c6a5f224..2db26956 100644
--- a/drafts/update-cadence-version-2.md
+++ b/drafts/update-cadence-version-2.md
@@ -3,7 +3,7 @@ title: "Update cadence, version 2 (draft, not published)"
 ---
 
 :::{note} Draft
-Option 2 for the update-cadence section of [](../factors/whats-in-a-release.md): a version lifecycle, with a new methodology version about every two years. Not published; this folder is excluded in `myst.yml`.
+Option 2 for the update-cadence section of [](../factors/whats-in-a-release.md), to follow the shared opening paragraph on annual releases: a version lifecycle, with a new methodology version about every two years. Not published; this folder is excluded in `myst.yml`.
 :::
 
 The Cornerstone EEIO follows a version lifecycle, similar to the long-term support model used in software. About every two years, we release a new version with a large methodology update, named after its release year (for example, the 2026 methods and the 2028 methods).
diff --git a/factors/whats-in-a-release.md b/factors/whats-in-a-release.md
index 8290e3b3..6dd7987c 100644
--- a/factors/whats-in-a-release.md
+++ b/factors/whats-in-a-release.md
@@ -55,6 +55,10 @@ The data dictionary and five other tabs support the data tabs. The data dictiona
 | GoodsServices | The 405 sectors with their code, name and description. Cornerstone recommends using this description to map spend categories to emission factors. |
 | Locations | Every location code and name used in the model: countries by their three-letter code, and regions. |
 
+## Update cadence
+
+The Cornerstone EEIO follows an annual update cycle. Every year we release a version named after the release year, so Cornerstone 2027 will be released in 2027. Releases are publicly available every October so companies can plan accordingly. Each release builds on one time series of emission factors: Cornerstone EEIO 2026 publishes the 2017 to 2026 series, and Cornerstone EEIO 2027 only adds the 2027 emission factors to it, so results stay continuous from one year to the next.
+
 ## Learn more
 
 - **From the field:** *to be added*

From ac96d949d7e56a4fdb199d1e4ff203f34f74e864 Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Wed, 21 Oct 2026 14:32:58 -0700
Subject: [PATCH 04/47] docs(factors): reword the last sentence of the
 update-cadence paragraph

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 factors/whats-in-a-release.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/factors/whats-in-a-release.md b/factors/whats-in-a-release.md
index 6dd7987c..739b3020 100644
--- a/factors/whats-in-a-release.md
+++ b/factors/whats-in-a-release.md
@@ -57,7 +57,7 @@ The data dictionary and five other tabs support the data tabs. The data dictiona
 
 ## Update cadence
 
-The Cornerstone EEIO follows an annual update cycle. Every year we release a version named after the release year, so Cornerstone 2027 will be released in 2027. Releases are publicly available every October so companies can plan accordingly. Each release builds on one time series of emission factors: Cornerstone EEIO 2026 publishes the 2017 to 2026 series, and Cornerstone EEIO 2027 only adds the 2027 emission factors to it, so results stay continuous from one year to the next.
+The Cornerstone EEIO follows an annual update cycle. Every year we release a version named after the release year, so Cornerstone 2027 will be released in 2027. Releases are publicly available every October so companies can plan accordingly. Each release builds on one time series of emission factors. For example, Cornerstone EEIO 2026 publishes the 2017 to 2026 series, and Cornerstone EEIO 2027 adds only the 2027 emission factors to it. This keeps results continuous from one year to the next.
 
 ## Learn more
 

From ea42a93a4997bfb0ec998af745cc1c72c90aa05a Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Wed, 21 Oct 2026 14:34:34 -0700
Subject: [PATCH 05/47] docs(factors): shorten the update-cadence paragraph

Drop the time-series sentences from the shared paragraph, matching the working
Google Doc.

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 factors/whats-in-a-release.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/factors/whats-in-a-release.md b/factors/whats-in-a-release.md
index 739b3020..dfb1a6fa 100644
--- a/factors/whats-in-a-release.md
+++ b/factors/whats-in-a-release.md
@@ -57,7 +57,7 @@ The data dictionary and five other tabs support the data tabs. The data dictiona
 
 ## Update cadence
 
-The Cornerstone EEIO follows an annual update cycle. Every year we release a version named after the release year, so Cornerstone 2027 will be released in 2027. Releases are publicly available every October so companies can plan accordingly. Each release builds on one time series of emission factors. For example, Cornerstone EEIO 2026 publishes the 2017 to 2026 series, and Cornerstone EEIO 2027 adds only the 2027 emission factors to it. This keeps results continuous from one year to the next.
+The Cornerstone EEIO follows an annual update cycle. Every year we release a version named after the release year, so Cornerstone 2027 will be released in 2027. Releases are publicly available every October so companies can plan accordingly.
 
 ## Learn more
 

From e69988d76b28cadd527e6162000529f3e9b56f3e Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Wed, 21 Oct 2026 14:39:55 -0700
Subject: [PATCH 06/47] docs(factors): align "What's in a release" with the
 working Doc

Drop "as a CSV file" from the summary, rename the section to "The release",
and reword its opening sentence.

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 factors/whats-in-a-release.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/factors/whats-in-a-release.md b/factors/whats-in-a-release.md
index dfb1a6fa..385a1278 100644
--- a/factors/whats-in-a-release.md
+++ b/factors/whats-in-a-release.md
@@ -3,12 +3,12 @@ title: "What's in a release"
 ---
 
 :::{tip} Summary
-The Cornerstone EEIO model is updated annually and versioned with the year of the release, so Cornerstone EEIO 2026 is released in 2026 and the 2027 version will be released in 2027. Every year, the releases will be publicly available in October. Each release publishes emission factors as a CSV file covering 405 sectors across 148 countries, 15 regions and one global average. To download the latest version, check out zenodo.
+The Cornerstone EEIO model is updated annually and versioned with the year of the release, so Cornerstone EEIO 2026 is released in 2026 and the 2027 version will be released in 2027. Every year, the releases will be publicly available in October. Each release publishes emission factors covering 405 sectors across 148 countries, 15 regions and one global average. To download the latest version, check out zenodo.
 :::
 
-## The release workbook
+## The release
 
-The release is one Excel workbook that contains: a cover, a data dictionary, a producer-price tab and a purchaser-price tab for each year the data is available for, and five auxiliary tabs.
+The release includes one Excel workbook that contains: a cover, a data dictionary, a producer-price tab and a purchaser-price tab for each year the data is available for, and five auxiliary tabs.
 
 ### Cover
 

From 62c7b01b04f04b0cfce88926501f70b266c9e1aa Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Wed, 21 Oct 2026 15:01:53 -0700
Subject: [PATCH 07/47] docs(figures): add waterfall charts vs. USEEIO and vs.
 CEDA

Charts for the release candidate page, built from the percentage steps on
slides 17 and 25 of the Practitioners Forum deck. The listed steps do not sum
exactly to the stated net change, so the remainder is drawn as "Other" until
the science team confirms the attribution.

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 figures/diagrams/waterfall-vs-ceda.png   | Bin 0 -> 106195 bytes
 figures/diagrams/waterfall-vs-ceda.svg   | 484 +++++++++++++++++++++++
 figures/diagrams/waterfall-vs-useeio.png | Bin 0 -> 101590 bytes
 figures/diagrams/waterfall-vs-useeio.svg | 424 ++++++++++++++++++++
 figures/figures.csv                      |   2 +
 figures/scripts/waterfalls.py            | 102 +++++
 6 files changed, 1012 insertions(+)
 create mode 100644 figures/diagrams/waterfall-vs-ceda.png
 create mode 100644 figures/diagrams/waterfall-vs-ceda.svg
 create mode 100644 figures/diagrams/waterfall-vs-useeio.png
 create mode 100644 figures/diagrams/waterfall-vs-useeio.svg
 create mode 100644 figures/scripts/waterfalls.py

diff --git a/figures/diagrams/waterfall-vs-ceda.png b/figures/diagrams/waterfall-vs-ceda.png
new file mode 100644
index 0000000000000000000000000000000000000000..a9238f33f12b9f6f9ae3c7c9ead87e02390c2ffb
GIT binary patch
literal 106195
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+    <text style="font-weight: 500; font-size: 11px; font-family: 'Helvetica', 'Arial', 'DejaVu Sans'; text-anchor: middle; fill: #2c2c2a" x="548.686522" y="135.485539" transform="rotate(-0 548.686522 135.485539)">−0.5%</text>
+   </g>
+   <g id="text_25">
+    <text style="font-weight: 500; font-size: 11px; font-family: 'Helvetica', 'Arial', 'DejaVu Sans'; text-anchor: middle; fill: #2c2c2a" x="634.689301" y="144.649548" transform="rotate(-0 634.689301 144.649548)">−0.7%</text>
+   </g>
+   <g id="text_26">
+    <text style="font-weight: 500; font-size: 11px; font-family: 'Helvetica', 'Arial', 'DejaVu Sans'; fill: #2c2c2a" transform="translate(703.871533 144.721112)">0.2448</text>
+    <text style="font-weight: 500; font-size: 11px; font-family: 'Helvetica', 'Arial', 'DejaVu Sans'; fill: #2c2c2a" transform="translate(692.108408 156.5798)">(−1.0% net)</text>
+   </g>
+   <g id="text_27">
+    <text style="font-size: 12.5px; font-family: 'Helvetica', 'Arial', 'DejaVu Sans'; text-anchor: start; fill: #2c2c2a" x="59.244706" y="21.184412" transform="rotate(-0 59.244706 21.184412)">US production-weighted emission factor: Cornerstone 2026 vs. USEEIO v1.4</text>
+   </g>
+  </g>
+  <g id="text_28">
+   <text style="font-size: 9px; font-family: 'Helvetica', 'Arial', 'DejaVu Sans'; text-anchor: start; fill: #5f5e5a" x="9.504" y="399.168" transform="rotate(-0 9.504 399.168)">Axis starts at 0.22. Increases in coral, decreases in teal.</text>
+  </g>
+ </g>
+ <defs>
+  <clipPath id="p5594dd5821">
+   <rect x="59.244706" y="35.184412" width="720.875294" height="311.146647"/>
+  </clipPath>
+ </defs>
+ <defs>
+  <pattern id="hb7257ac3db" patternUnits="userSpaceOnUse" x="0" y="0" width="72" height="72">
+   <rect x="0" y="0" width="73" height="73" fill="#b4b2a9"/>
+   <path d="M -36 36 
+L 36 -36 
+M -33 39 
+L 39 -33 
+M -30 42 
+L 42 -30 
+M -27 45 
+L 45 -27 
+M -24 48 
+L 48 -24 
+M -21 51 
+L 51 -21 
+M -18 54 
+L 54 -18 
+M -15 57 
+L 57 -15 
+M -12 60 
+L 60 -12 
+M -9 63 
+L 63 -9 
+M -6 66 
+L 66 -6 
+M -3 69 
+L 69 -3 
+M 0 72 
+L 72 0 
+M 3 75 
+L 75 3 
+M 6 78 
+L 78 6 
+M 9 81 
+L 81 9 
+M 12 84 
+L 84 12 
+M 15 87 
+L 87 15 
+M 18 90 
+L 90 18 
+M 21 93 
+L 93 21 
+M 24 96 
+L 96 24 
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+M 30 102 
+L 102 30 
+M 33 105 
+L 105 33 
+M 36 108 
+L 108 36 
+" style="fill: #888780; stroke: #888780; stroke-width: 1.0; stroke-linecap: butt; stroke-linejoin: miter"/>
+  </pattern>
+ </defs>
+</svg>
diff --git a/figures/figures.csv b/figures/figures.csv
index d3202c6c..65ea8e04 100644
--- a/figures/figures.csv
+++ b/figures/figures.csv
@@ -1,3 +1,5 @@
 id,page,type,data_source,generator_script,owner,status
 release-cycle,factors/whats-in-a-release.md,diagram,none (hand-drawn schematic),figures/scripts/release_cycle.py,Yohanna Maldonado,draft
 release-lifecycle,factors/whats-in-a-release.md,diagram,none (hand-drawn schematic),figures/scripts/release_lifecycle.py,Yohanna Maldonado,draft (option 2)
+waterfall-vs-useeio,releases/rc.md (draft),chart,"Roundtable 2 deck, slide 17 (percent steps)",figures/scripts/waterfalls.py,Yohanna Maldonado,draft (steps do not sum to the net change)
+waterfall-vs-ceda,releases/rc.md (draft),chart,"Roundtable 2 deck, slide 25 (percent steps)",figures/scripts/waterfalls.py,Yohanna Maldonado,draft (steps do not sum to the net change)
diff --git a/figures/scripts/waterfalls.py b/figures/scripts/waterfalls.py
new file mode 100644
index 00000000..17dfe6f9
--- /dev/null
+++ b/figures/scripts/waterfalls.py
@@ -0,0 +1,102 @@
+"""Write the two release-candidate waterfall charts to figures/diagrams/.
+
+Source: the Practitioners Forum Roundtable 2 deck (slides 17 and 25). Steps are the percentage
+changes shown on the slides, converted to kg CO2e per USD against each baseline. The listed steps
+do not sum exactly to the net change on the slides, so the remainder is drawn as "Other" until the
+science team confirms the attribution.
+
+Run from the repo root (needs matplotlib):
+    uv run --with matplotlib python figures/scripts/waterfalls.py
+"""
+from pathlib import Path
+
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt  # noqa: E402
+
+OUT = Path(__file__).resolve().parents[1] / "diagrams"
+INK, MUTED = "#2C2C2A", "#5F5E5A"
+UP, DOWN, TOTAL, OTHER = "#D85A30", "#1D9E75", "#5F5E5A", "#B4B2A9"
+plt.rcParams.update({"font.family": ["Helvetica", "Arial", "DejaVu Sans"], "font.size": 11,
+                     "svg.fonttype": "none"})
+
+CHARTS = {
+    "waterfall-vs-useeio": dict(
+        title="US production-weighted emission factor: Cornerstone 2026 vs. USEEIO v1.4",
+        ylabel="kg CO$_2$e per 2024 USD",
+        base_label="USEEIO\nv1.4", base=0.2474, end_label="Cornerstone\n2026", end=0.2448,
+        steps=[("Model\nreconciliation", -3.5), ("US\ndata", 0.4), ("Nowcasting &\nfacility data", -5.8),
+               ("Trade\nimpact", 9.0), ("Benchmark-\ninformed\nimprovements", -0.5)],
+        ylim=(0.220, 0.262), net="−1.0% net",
+    ),
+    "waterfall-vs-ceda": dict(
+        title="Global production-weighted emission factor: Cornerstone 2026 vs. CEDA 2025",
+        ylabel="kg CO$_2$e per 2024 USD",
+        base_label="CEDA\n2025", base=0.6957, end_label="Cornerstone\n2026", end=0.5281,
+        steps=[("Model\nreconciliation", 0.0), ("US\ndata", 0.1), ("Nowcasting &\nfacility\ndata", -0.7),
+               ("Bug\nfixes", -0.4), ("UK & global\ntrade data", 0.0), ("China\ndata", 7.8),
+               ("Europe &\nother global\ndata", -1.6), ("ABSR", -10.7), ("Benchmark-\ninformed\nimprovements", -19.0)],
+        ylim=(0.50, 0.76), net="−24.1% net",
+    ),
+}
+
+
+def draw(name, c):
+    labels, bottoms, heights, colors, texts = [], [], [], [], []
+    level = c["base"]
+    labels.append(c["base_label"]); bottoms.append(0); heights.append(c["base"]); colors.append(TOTAL)
+    texts.append((c["base"], f'{c["base"]:.4f}'))
+    for label, pct in c["steps"]:
+        delta = c["base"] * pct / 100
+        new = level + delta
+        labels.append(label); bottoms.append(min(level, new)); heights.append(abs(delta))
+        colors.append(UP if delta > 0 else DOWN)
+        txt = "~0%" if pct == 0 else f"{pct:+.1f}%".replace("-", "−")
+        texts.append((max(level, new), txt))
+        level = new
+    other = c["end"] - level
+    labels.append("Other"); bottoms.append(min(level, c["end"])); heights.append(abs(other)); colors.append(OTHER)
+    pct_other = other / c["base"] * 100
+    texts.append((max(level, c["end"]), f"{pct_other:+.1f}%".replace("-", "−")))
+    labels.append(c["end_label"]); bottoms.append(0); heights.append(c["end"]); colors.append(TOTAL)
+    texts.append((c["end"], f'{c["end"]:.4f}\n({c["net"]})'))
+
+    fig, ax = plt.subplots(figsize=(11, 5.6), dpi=170)
+    xs = range(len(labels))
+    bars = ax.bar(xs, heights, bottom=bottoms, color=colors, width=0.62, zorder=3)
+    bars[-2].set_hatch("////"); bars[-2].set_edgecolor("#888780"); bars[-2].set_linewidth(0.6)
+    # connectors between bars
+    run = [c["base"]]
+    for h, b, col in list(zip(heights, bottoms, colors))[1:-2]:
+        run.append(b + h if col == UP else b)
+    top = c["base"]
+    pos = c["base"]
+    levels = [c["base"]]
+    for _, pct in c["steps"]:
+        pos += c["base"] * pct / 100; levels.append(pos)
+    levels.append(c["end"])
+    for i in range(len(levels)):
+        ax.plot([i + 0.31, i + 1 - 0.31], [levels[i], levels[i]], color="#B4B2A9", lw=0.8, zorder=2)
+    span = c["ylim"][1] - c["ylim"][0]
+    for x, (y, t) in zip(xs, texts):
+        ax.text(x, y + span * 0.012, t, ha="center", va="bottom", color=INK, fontsize=11, fontweight="medium")
+    ax.set_xticks(list(xs)); ax.set_xticklabels(labels, fontsize=9, color=INK)
+    ax.set_ylim(*c["ylim"]); ax.set_ylabel(c["ylabel"], color=MUTED)
+    ax.set_title(c["title"], loc="left", fontsize=12.5, color=INK, pad=14)
+    ax.tick_params(axis="y", colors=MUTED, length=0); ax.tick_params(axis="x", length=0)
+    ax.grid(axis="y", color="#E8E6DF", lw=0.8, zorder=0)
+    for s in ("top", "right", "left"):
+        ax.spines[s].set_visible(False)
+    ax.spines["bottom"].set_color("#B4B2A9")
+    fig.text(0.012, 0.01, f"Axis starts at {c['ylim'][0]:.2f}. Increases in coral, decreases in teal.",
+             color=MUTED, fontsize=9)
+    fig.tight_layout(rect=(0, 0.03, 1, 1))
+    for ext in ("png", "svg"):
+        fig.savefig(OUT / f"{name}.{ext}", facecolor="white")
+    plt.close(fig)
+    print(name, "baseline", c["base"], "end", c["end"], "other", round(other, 4), f"({pct_other:+.2f}%)")
+
+
+for n, c in CHARTS.items():
+    draw(n, c)

From 6fb27ba0fc87eb85400c9f0266600360fa5c55cf Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Wed, 21 Oct 2026 15:48:02 -0700
Subject: [PATCH 08/47] docs(figures): drop the "Other" bar from the waterfall
 charts

The final bar shows the stated end value and the last step is not forced to
land on it, until the science team confirms the attribution.

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 figures/diagrams/waterfall-vs-ceda.png   | Bin 106195 -> 104165 bytes
 figures/diagrams/waterfall-vs-ceda.svg   | 376 +++++++++--------------
 figures/diagrams/waterfall-vs-useeio.png | Bin 101590 -> 98878 bytes
 figures/diagrams/waterfall-vs-useeio.svg | 310 +++++++------------
 figures/scripts/waterfalls.py            |  13 +-
 5 files changed, 266 insertions(+), 433 deletions(-)

diff --git a/figures/diagrams/waterfall-vs-ceda.png b/figures/diagrams/waterfall-vs-ceda.png
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diff --git a/figures/scripts/waterfalls.py b/figures/scripts/waterfalls.py
index 17dfe6f9..7786504d 100644
--- a/figures/scripts/waterfalls.py
+++ b/figures/scripts/waterfalls.py
@@ -55,27 +55,16 @@ def draw(name, c):
         txt = "~0%" if pct == 0 else f"{pct:+.1f}%".replace("-", "−")
         texts.append((max(level, new), txt))
         level = new
-    other = c["end"] - level
-    labels.append("Other"); bottoms.append(min(level, c["end"])); heights.append(abs(other)); colors.append(OTHER)
-    pct_other = other / c["base"] * 100
-    texts.append((max(level, c["end"]), f"{pct_other:+.1f}%".replace("-", "−")))
     labels.append(c["end_label"]); bottoms.append(0); heights.append(c["end"]); colors.append(TOTAL)
     texts.append((c["end"], f'{c["end"]:.4f}\n({c["net"]})'))
 
     fig, ax = plt.subplots(figsize=(11, 5.6), dpi=170)
     xs = range(len(labels))
     bars = ax.bar(xs, heights, bottom=bottoms, color=colors, width=0.62, zorder=3)
-    bars[-2].set_hatch("////"); bars[-2].set_edgecolor("#888780"); bars[-2].set_linewidth(0.6)
-    # connectors between bars
-    run = [c["base"]]
-    for h, b, col in list(zip(heights, bottoms, colors))[1:-2]:
-        run.append(b + h if col == UP else b)
-    top = c["base"]
     pos = c["base"]
     levels = [c["base"]]
     for _, pct in c["steps"]:
         pos += c["base"] * pct / 100; levels.append(pos)
-    levels.append(c["end"])
     for i in range(len(levels)):
         ax.plot([i + 0.31, i + 1 - 0.31], [levels[i], levels[i]], color="#B4B2A9", lw=0.8, zorder=2)
     span = c["ylim"][1] - c["ylim"][0]
@@ -95,7 +84,7 @@ def draw(name, c):
     for ext in ("png", "svg"):
         fig.savefig(OUT / f"{name}.{ext}", facecolor="white")
     plt.close(fig)
-    print(name, "baseline", c["base"], "end", c["end"], "other", round(other, 4), f"({pct_other:+.2f}%)")
+    print(name, "baseline", c["base"], "end", c["end"], "last step level", round(levels[-1], 4))
 
 
 for n, c in CHARTS.items():

From d885586e3d192b98958983a2c58fb6e445522498 Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Wed, 21 Oct 2026 16:07:32 -0700
Subject: [PATCH 09/47] docs(releases): write the release candidate page from
 the working Doc

Replace the placeholder with the RC summary, what changed, the China, Europe and
US-imports examples, and the emission-factor comparison, with the five figures.
Change-log links stay as plain text until those pages exist in the repo.

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 images/rc-china-battery.png     | Bin 0 -> 16040 bytes
 images/rc-europe-power.png      | Bin 0 -> 45961 bytes
 images/rc-factor-comparison.png | Bin 0 -> 72515 bytes
 releases/rc.md                  | 127 +++++++++++++++++++++++++++++---
 4 files changed, 115 insertions(+), 12 deletions(-)
 create mode 100644 images/rc-china-battery.png
 create mode 100644 images/rc-europe-power.png
 create mode 100644 images/rc-factor-comparison.png

diff --git a/images/rc-china-battery.png b/images/rc-china-battery.png
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literal 0
HcmV?d00001

diff --git a/releases/rc.md b/releases/rc.md
index 05c06d5d..83def634 100644
--- a/releases/rc.md
+++ b/releases/rc.md
@@ -1,23 +1,126 @@
 ---
-title: Release candidate (2024–2026 factors)
+title: "Cornerstone EEIO 2026 - RC"
 ---
 
-:::{note} Draft placeholder
-:class: dropdown
-Plan §8.1 · target: **RC (Oct 27)**. Written by hand for the RC; generated from the version record from v1.0 on. Open: which model version the RC is built on (v0.6?) and which dollar years it publishes (plan Q9, Q10).
-:::
+## Release date: October 27th, 2026
+
+## Summary
+
+The Cornerstone EEIO 2026 - RC (Release Candidate) is the first stage of a phased approach to releasing the Cornerstone EEIO 2026 model, the successor of USEEIO and CEDA. It is a global environmentally-extended input-output (EEIO) model that produces spend-based emission factors, in kg CO₂e per USD for 405 sectors across 148 countries, plus 15 regional averages and one global average. It uses 2024 as the base year and is available to use for 2024, 2025 and 2026 measurements.
+
+This release candidate will be superseded in December by the release of the Cornerstone EEIO 2026 final version, which will include a time series from 2017 to 2026.
+
+Download this release on Zenodo here.
+
+**Why have a Release Candidate?**
+
+2026 is an unprecedented year of development because it is the first time we are combining CEDA and USEEIO into one model and also the first time we are releasing a time series. The intention behind having a two phased approach to the release is to give companies additional time for impact assessments on their most recent year of measurements, while the developers finish the validation process for the time series. This enables companies to understand footprint changes before applying the time series.
 
-## Release summary
-*Release date, model version, Zenodo DOI, download links.*
+**Will there be any changes for years 2024 to 2026 once the time series is released?**
+
+We do not foresee significant changes, but some sector-country combinations could be impacted by improvements driven from the time series model development. Users should consider this version as a release candidate and wait for the final 2026 version of this model that will be released in early December for reporting and rebaselining purposes.
+
+**What to expect from the time series?**
+
+The time series (2017-2026) will use the same methodology for all years and only change the underlying data that represents each country’s economic structure and emissions data. For example, in the US each year of the dataset will use the US electricity grid emissivity for each year, capturing any decarbonization on this sector year over year.
+
+This is the first time that either USEEIO or CEDA releases a time series built this way. In the past CEDA has released a time series built with inflation adjustments for years prior and after the base year. Moving forward the Cornerstone EEIO will only apply inflation adjustments to the years after the base year, for which there is no reported data (e.g. 2025 and 2026 are inflation adjusted from the 2024 base year).
+
+To learn more about the methodology behind Cornerstone EEIO refer to [the IO model](../io/index.md) and [the GHG extension](../ee/index.md).
 
 ## What changed
-*Changes since the previous release and how much they moved `D` and `N`. Includes: FIGARO replaced ICIO for FIGARO-covered Tier 2 countries (v0.6).*
 
-## Methods and data vintages
-*Rendered from `versions/rc.yaml`.*
+Cornerstone EEIO 2026 combines USEEIO’s detailed picture of the US economy with CEDA’s global coverage and trade modeling, representing a step change improvement to both models.
+
+Three groups of improvements sit behind the changes.
+
+1. **A more current picture of the US economy that accounts for imports**. US input-output tables are rebuilt for every year from 2017 to 2024, manufacturing uses facility-reported emissions, and imports are modeled where they are made.
+2. **Newer data for Europe, the UK and China, and better gap-filling for the rest.** Europe moves from the OECD’s ICIO tables to Eurostat’s FIGARO (through 2023). China moves to the latest National Bureau of Statistics tables (through 2023), with more sectors. Countries without data are filled with the auto-balanced structural reflection method.
+3. **A validation process that feeds back into the model.** Third-party benchmarks review the sectors behind about 50% of global emissions. Gaps are traced and fixed in the data or the method and documented when unresolved.
+
+Together these make the 2017–2026 time series possible.
+
+**Why do these improvements matter?**
+
+They translate real world changes to corporate footprints. Three examples show how:
+
+### New data for China
+
+Cornerstone adopts the most recent economic data released by the Chinese National Bureau of Statistics (2023 vs. 2020), which reflects methodology changes, changes in the economic structure, and sector-specific decarbonization.
+
+For example, China’s battery factor rises by ~30% driven by a change in the composition of battery production in China. From 2020 to 2023, battery production more than tripled, but more importantly it shifted to require more Lithium-ion and less Lead-acid as inputs. This is because the majority of the market growth in batteries are in EVs and grid storage that require lithium-ion. Lithium-ion needs lithium chemicals and power-hungry cell factories, being more carbon intensive per dollar than lead-acid batteries, driving the emission factor up.
+
+:::{figure} ../images/rc-china-battery.png
+:label: rc-china-battery
+:alt: Bar chart of China battery production. In 2020, output was 382 GWh, mostly lead-acid. In 2023, output was 1,183 GWh, mostly lithium-ion.
+:width: 60%
+
+China battery production in 2020 and 2023, by battery type. Output is in GWh and approximate.
+:::
+
+### New data for Europe
+
+Cornerstone adopts the most recent release of FIGARO, Eurostat’s official input-output and emissions dataset. FIGARO is more granular than the OECD dataset for Europe, is available through 2023 vs. 2020, and is updated every year, matching Cornerstone’s release cadence.
 
-## Validation and benchmarking
-*Headline results.*
+This data update lowers European factors by about 4% in median. The most significant change is in electricity factors, which fall in every EU country. This change is driven by an increase in renewable energy and by an increase in energy prices due to the gas-supply shocks the Ukraine war has caused in Europe. For example, Germany’s power factor fell by 60%, as renewables grew from 35% to over 50% of generation and wholesale power prices more than doubled.
+
+:::{figure} ../images/rc-europe-power.png
+:label: rc-europe-power
+:alt: Germany's power emission factor falls by 60% and the Netherlands' by 77%, with the share of the change attributed to a cleaner grid and to higher prices for each.
+:width: 60%
+
+Change in the power emission factor in Germany and the Netherlands, and what drove it.
+:::
+
+### Incorporation of imports in the US
+
+USEEIO treated the US as a closed economy: every product used in the US was assumed to be made in the US. Cornerstone uses trade data to model each import in the country that produced it. Many imports come from more emissions-intensive economies such as China and India, so US factors tend to rise by 10 to 30% on average. This is the main upward pressure and the main reason for the change for USEEIO users. One example is light trucks and utility vehicles. The US factor rises by ~24.1%, mostly because the majority of the components of these vehicles are manufactured in South East Asia.
+
+## How do the emission factors change?
+
+The charts compare the new factors with each model they replace. Emission factors are production-weighted totals in kg CO₂e per 2024 USD.
+
+:::{figure} ../images/rc-factor-comparison.png
+:label: rc-factor-comparison
+:alt: Two waterfall charts. Against CEDA 2025, the factor goes from 0.6957 to 0.5281 (24.1% lower), with data updates adding 6.3% and methodology changes removing 28.6%. Against USEEIO v1.4 for the US, it goes from 0.2474 to 0.2448 (1.0% lower), with data updates removing 4.2% and methodology changes adding 3.3%.
+:width: 100%
+
+Production-weighted emission factors for Cornerstone 2026 against CEDA 2025 and USEEIO v1.4, split into data updates and methodology changes.
+:::
+
+### Drivers of the change vs. USEEIO
+
+:::{figure} ../figures/diagrams/waterfall-vs-useeio.png
+:label: waterfall-vs-useeio
+:alt: Waterfall chart of the US emission factor from USEEIO v1.4 (0.2474) to Cornerstone 2026 (0.2448), by driver.
+:width: 100%
+
+Change in the US production-weighted emission factor from USEEIO v1.4 to Cornerstone 2026, by driver. Percentages are changes relative to the USEEIO baseline.
+:::
+
+### Drivers of the change vs. CEDA
+
+:::{figure} ../figures/diagrams/waterfall-vs-ceda.png
+:label: waterfall-vs-ceda
+:alt: Waterfall chart of the global emission factor from CEDA 2025 (0.6957) to Cornerstone 2026 (0.5281), by driver.
+:width: 100%
+
+Change in the global production-weighted emission factor from CEDA 2025 to Cornerstone 2026, by driver. Percentages are changes relative to the CEDA 2025 baseline.
+:::
+
+:::{important} Your footprint may not follow the median
+A company’s change depends on which sectors and countries its spend falls in. A small median can hide large moves in individual sectors, in both directions.
+:::
+
+For a more detailed change log against the USEEIO or CEDA, please refer to Change Log vs. USEEIO and Change Log vs. CEDA.
 
 ## Give feedback
+
 [Open an issue on GitHub](https://github.com/cornerstone-data/eeio-docs/issues/new).
+
++++
+
+:::{div}
+:class: page-status
+Applies to: RC · Last reviewed: RC
+:::

From 8a3dce2cbe1a0e7ba7554f16717ec4c512fcf5a2 Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Wed, 21 Oct 2026 16:15:20 -0700
Subject: [PATCH 10/47] docs(releases): restructure the release candidate page

Use the standard Summary tip, move the three questions into dropdowns, make
"Why do these improvements matter?" a section with the three examples under it,
redraw the three charts with white backgrounds and titles and sources in the
captions, keep only Germany in the Europe example, and remove the two driver
sections (they move to the change logs).

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 figures/scripts/rc_figures.py   | 111 ++++++++++++++++++++++++++++++++
 images/rc-china-battery.png     | Bin 16040 -> 21564 bytes
 images/rc-europe-power.png      | Bin 45961 -> 0 bytes
 images/rc-factor-comparison.png | Bin 72515 -> 59387 bytes
 images/rc-germany-power.png     | Bin 0 -> 27565 bytes
 releases/rc.md                  |  61 +++++++-----------
 6 files changed, 133 insertions(+), 39 deletions(-)
 create mode 100644 figures/scripts/rc_figures.py
 delete mode 100644 images/rc-europe-power.png
 create mode 100644 images/rc-germany-power.png

diff --git a/figures/scripts/rc_figures.py b/figures/scripts/rc_figures.py
new file mode 100644
index 00000000..b080db32
--- /dev/null
+++ b/figures/scripts/rc_figures.py
@@ -0,0 +1,111 @@
+"""Write the three charts on the release candidate page to images/.
+
+Redrawn from the Practitioners Forum Roundtable 2 deck so they read cleanly in the text: white
+background, no title and no source line on the image (those live in the figure caption).
+
+- rc-factor-comparison.png: emission factors, Cornerstone 2026 vs CEDA 2025 and vs USEEIO v1.4.
+  Step sizes are the slide labels; the methodology bar is drawn to the stated end value.
+- rc-china-battery.png: China battery production. Totals are the slide labels. The split by
+  battery type is read off the slide graphic, so it is approximate.
+- rc-germany-power.png: Germany's power factor, share of the change from a cleaner grid vs prices.
+
+Run from the repo root (needs matplotlib):
+    uv run --with matplotlib python figures/scripts/rc_figures.py
+"""
+from pathlib import Path
+
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt  # noqa: E402
+
+OUT = Path(__file__).resolve().parents[2] / "images"
+INK, MUTED = "#2C2C2A", "#5F5E5A"
+BASE, END, UP, DOWN = "#B4B2A9", "#5F5E5A", "#D85A30", "#1D9E75"
+DARK_TEAL, LIGHT_TEAL = "#0F6E56", "#9FE1CB"
+plt.rcParams.update({"font.family": ["Helvetica", "Arial", "DejaVu Sans"], "font.size": 11})
+
+
+def clean(ax, bottom=True):
+    for s in ("top", "right", "left"):
+        ax.spines[s].set_visible(False)
+    ax.spines["bottom"].set_color("#B4B2A9")
+    ax.set_yticks([])
+    ax.tick_params(axis="x", length=0, colors=INK)
+    if not bottom:
+        ax.spines["bottom"].set_visible(False)
+
+
+def panel(ax, start_label, start, steps, end_label, end, net):
+    """steps: [(label, pct)] in order; the last step is drawn to land on `end`."""
+    level = start
+    xs, labels = [0], [start_label]
+    ax.bar(0, start, color=BASE, width=0.62)
+    ax.text(0, start * 1.015, f"{start:.4f}", ha="center", va="bottom", color=INK, fontweight="medium")
+    prev_top = start
+    for i, (label, pct) in enumerate(steps, start=1):
+        new = level + start * pct / 100
+        if i == len(steps):
+            new = end
+        lo, hi = min(level, new), max(level, new)
+        ax.bar(i, hi - lo, bottom=lo, color=UP if pct > 0 else DOWN, width=0.62)
+        txt = f"{pct:+.1f}%".replace("-", "−")
+        ax.text(i, hi * 1.015, txt, ha="center", va="bottom", color=UP if pct > 0 else DOWN, fontweight="medium")
+        ax.plot([i - 0.69, i - 0.31], [level, level], color="#B4B2A9", lw=0.9, ls=(0, (3, 3)))
+        level = new
+        xs.append(i); labels.append(label)
+    n = len(steps) + 1
+    ax.plot([n - 0.69, n - 0.31], [level, level], color="#B4B2A9", lw=0.9, ls=(0, (3, 3)))
+    ax.bar(n, end, color=END, width=0.62)
+    ax.text(n, end * 1.015, f"{end:.4f}\n{net} net", ha="center", va="bottom", color=INK, fontweight="medium", linespacing=1.3)
+    xs.append(n); labels.append(end_label)
+    ax.set_xticks(xs); ax.set_xticklabels(labels, fontsize=10)
+    ax.set_ylim(0, start * 1.28)
+    clean(ax)
+
+
+def comparison():
+    fig, axes = plt.subplots(1, 2, figsize=(11, 3.7), dpi=200)
+    panel(axes[0], "CEDA\n2025", 0.6957, [("Data\nupdates", 6.3), ("Methodology\nchanges", -28.6)],
+          "Cornerstone\n2026", 0.5281, "−24.1%")
+    panel(axes[1], "USEEIO\nv1.4 (US)", 0.2474, [("Data\nupdates", -4.2), ("Methodology\nchanges", 3.3)],
+          "Cornerstone\n2026", 0.2448, "−1.0%")
+    fig.tight_layout(w_pad=3)
+    fig.savefig(OUT / "rc-factor-comparison.png", facecolor="white")
+    plt.close(fig)
+
+
+def china():
+    fig, ax = plt.subplots(figsize=(6, 4), dpi=200)
+    data = {"2020": (150, 232), "2023": (946, 237)}  # (lithium-ion, lead-acid) GWh, approximate split
+    for x, (yr, (li, pb)) in enumerate(data.items()):
+        ax.bar(x, pb, color=LIGHT_TEAL, width=0.5)
+        ax.bar(x, li, bottom=pb, color=DARK_TEAL, width=0.5)
+        ax.text(x, li + pb + 20, f"{li + pb:,} GWh", ha="center", va="bottom", fontweight="medium", color=INK)
+    ax.text(1.29, 237 + 946 / 2, "Lithium-ion", va="center", color=DARK_TEAL, fontweight="medium")
+    ax.text(1.29, 237 / 2, "Lead-acid", va="center", color="#3C8F77", fontweight="medium")
+    ax.set_xticks([0, 1]); ax.set_xticklabels(list(data), fontsize=11)
+    ax.set_xlim(-0.5, 1.95); ax.set_ylim(0, 1350)
+    clean(ax)
+    fig.tight_layout()
+    fig.savefig(OUT / "rc-china-battery.png", facecolor="white")
+    plt.close(fig)
+
+
+def germany():
+    fig, ax = plt.subplots(figsize=(8, 1.7), dpi=200)
+    ax.barh(0, 25, color=DARK_TEAL, height=0.5)
+    ax.barh(0, 75, left=25, color=LIGHT_TEAL, height=0.5)
+    ax.text(12.5, 0, "~25%", ha="center", va="center", color="white", fontweight="medium")
+    ax.text(62.5, 0, "~75%", ha="center", va="center", color=DARK_TEAL, fontweight="medium")
+    ax.text(0, -0.5, "Cleaner grid", ha="left", va="top", color=DARK_TEAL, fontweight="medium")
+    ax.text(100, -0.5, "Higher prices", ha="right", va="top", color="#3C8F77", fontweight="medium")
+    ax.text(0, 0.42, "Share of the 60% fall in Germany's power factor", ha="left", va="bottom", color=MUTED, fontsize=10)
+    ax.set_xlim(0, 100); ax.set_ylim(-0.95, 0.85); ax.axis("off")
+    fig.tight_layout()
+    fig.savefig(OUT / "rc-germany-power.png", facecolor="white")
+    plt.close(fig)
+
+
+comparison(); china(); germany()
+print("wrote", *(p.name for p in sorted(OUT.glob("rc-*.png"))))
diff --git a/images/rc-china-battery.png b/images/rc-china-battery.png
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diff --git a/releases/rc.md b/releases/rc.md
index 83def634..13dd39f7 100644
--- a/releases/rc.md
+++ b/releases/rc.md
@@ -4,29 +4,32 @@ title: "Cornerstone EEIO 2026 - RC"
 
 ## Release date: October 27th, 2026
 
-## Summary
-
+:::{tip} Summary
 The Cornerstone EEIO 2026 - RC (Release Candidate) is the first stage of a phased approach to releasing the Cornerstone EEIO 2026 model, the successor of USEEIO and CEDA. It is a global environmentally-extended input-output (EEIO) model that produces spend-based emission factors, in kg CO₂e per USD for 405 sectors across 148 countries, plus 15 regional averages and one global average. It uses 2024 as the base year and is available to use for 2024, 2025 and 2026 measurements.
 
 This release candidate will be superseded in December by the release of the Cornerstone EEIO 2026 final version, which will include a time series from 2017 to 2026.
 
 Download this release on Zenodo here.
+:::
 
-**Why have a Release Candidate?**
-
+:::{note} Why have a Release Candidate?
+:class: dropdown
 2026 is an unprecedented year of development because it is the first time we are combining CEDA and USEEIO into one model and also the first time we are releasing a time series. The intention behind having a two phased approach to the release is to give companies additional time for impact assessments on their most recent year of measurements, while the developers finish the validation process for the time series. This enables companies to understand footprint changes before applying the time series.
+:::
 
-**Will there be any changes for years 2024 to 2026 once the time series is released?**
-
+:::{note} Will there be any changes for years 2024 to 2026 once the time series is released?
+:class: dropdown
 We do not foresee significant changes, but some sector-country combinations could be impacted by improvements driven from the time series model development. Users should consider this version as a release candidate and wait for the final 2026 version of this model that will be released in early December for reporting and rebaselining purposes.
+:::
 
-**What to expect from the time series?**
-
+:::{note} What to expect from the time series?
+:class: dropdown
 The time series (2017-2026) will use the same methodology for all years and only change the underlying data that represents each country’s economic structure and emissions data. For example, in the US each year of the dataset will use the US electricity grid emissivity for each year, capturing any decarbonization on this sector year over year.
 
 This is the first time that either USEEIO or CEDA releases a time series built this way. In the past CEDA has released a time series built with inflation adjustments for years prior and after the base year. Moving forward the Cornerstone EEIO will only apply inflation adjustments to the years after the base year, for which there is no reported data (e.g. 2025 and 2026 are inflation adjusted from the 2024 base year).
 
 To learn more about the methodology behind Cornerstone EEIO refer to [the IO model](../io/index.md) and [the GHG extension](../ee/index.md).
+:::
 
 ## What changed
 
@@ -40,7 +43,7 @@ Three groups of improvements sit behind the changes.
 
 Together these make the 2017–2026 time series possible.
 
-**Why do these improvements matter?**
+## Why do these improvements matter?
 
 They translate real world changes to corporate footprints. Three examples show how:
 
@@ -52,10 +55,10 @@ For example, China’s battery factor rises by ~30% driven by a change in the co
 
 :::{figure} ../images/rc-china-battery.png
 :label: rc-china-battery
-:alt: Bar chart of China battery production. In 2020, output was 382 GWh, mostly lead-acid. In 2023, output was 1,183 GWh, mostly lithium-ion.
-:width: 60%
+:alt: Stacked bar chart of China battery production. In 2020, output was 382 GWh, mostly lead-acid. In 2023, output was 1,183 GWh, mostly lithium-ion.
+:width: 65%
 
-China battery production in 2020 and 2023, by battery type. Output is in GWh and approximate.
+**China battery production, 2020 and 2023.** Output in GWh. The split by battery type is approximate. Sources: NBS input-output tables 2020 and 2023; MIIT; China lead-acid battery industry statistics.
 :::
 
 ### New data for Europe
@@ -64,12 +67,12 @@ Cornerstone adopts the most recent release of FIGARO, Eurostat’s official inpu
 
 This data update lowers European factors by about 4% in median. The most significant change is in electricity factors, which fall in every EU country. This change is driven by an increase in renewable energy and by an increase in energy prices due to the gas-supply shocks the Ukraine war has caused in Europe. For example, Germany’s power factor fell by 60%, as renewables grew from 35% to over 50% of generation and wholesale power prices more than doubled.
 
-:::{figure} ../images/rc-europe-power.png
-:label: rc-europe-power
-:alt: Germany's power emission factor falls by 60% and the Netherlands' by 77%, with the share of the change attributed to a cleaner grid and to higher prices for each.
-:width: 60%
+:::{figure} ../images/rc-germany-power.png
+:label: rc-germany-power
+:alt: Bar showing that about 25% of the fall in Germany's power emission factor comes from a cleaner grid and about 75% from higher prices.
+:width: 100%
 
-Change in the power emission factor in Germany and the Netherlands, and what drove it.
+**Germany's power emission factor falls by 60%.** About 25% of the change comes from a cleaner grid, as renewables grew from 35% to over 50% of generation, and about 75% from higher prices, as wholesale power prices more than doubled, from about €44 to €95 per MWh. Sources: Ember, Öko-Institut, SMARD, Vattenfall.
 :::
 
 ### Incorporation of imports in the US
@@ -78,34 +81,14 @@ USEEIO treated the US as a closed economy: every product used in the US was assu
 
 ## How do the emission factors change?
 
-The charts compare the new factors with each model they replace. Emission factors are production-weighted totals in kg CO₂e per 2024 USD.
+The charts compare the new factors with each model they replace.
 
 :::{figure} ../images/rc-factor-comparison.png
 :label: rc-factor-comparison
 :alt: Two waterfall charts. Against CEDA 2025, the factor goes from 0.6957 to 0.5281 (24.1% lower), with data updates adding 6.3% and methodology changes removing 28.6%. Against USEEIO v1.4 for the US, it goes from 0.2474 to 0.2448 (1.0% lower), with data updates removing 4.2% and methodology changes adding 3.3%.
 :width: 100%
 
-Production-weighted emission factors for Cornerstone 2026 against CEDA 2025 and USEEIO v1.4, split into data updates and methodology changes.
-:::
-
-### Drivers of the change vs. USEEIO
-
-:::{figure} ../figures/diagrams/waterfall-vs-useeio.png
-:label: waterfall-vs-useeio
-:alt: Waterfall chart of the US emission factor from USEEIO v1.4 (0.2474) to Cornerstone 2026 (0.2448), by driver.
-:width: 100%
-
-Change in the US production-weighted emission factor from USEEIO v1.4 to Cornerstone 2026, by driver. Percentages are changes relative to the USEEIO baseline.
-:::
-
-### Drivers of the change vs. CEDA
-
-:::{figure} ../figures/diagrams/waterfall-vs-ceda.png
-:label: waterfall-vs-ceda
-:alt: Waterfall chart of the global emission factor from CEDA 2025 (0.6957) to Cornerstone 2026 (0.5281), by driver.
-:width: 100%
-
-Change in the global production-weighted emission factor from CEDA 2025 to Cornerstone 2026, by driver. Percentages are changes relative to the CEDA 2025 baseline.
+**Emission factors fall 24.1% against CEDA 2025 and 1.0% against USEEIO v1.4 (US).** Production-weighted totals in kg CO₂e per 2024 USD, split into data updates and methodology changes.
 :::
 
 :::{important} Your footprint may not follow the median

From 2865d6abc0e65c81488afd4ff9173a899d2ebf7b Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Wed, 21 Oct 2026 16:21:00 -0700
Subject: [PATCH 11/47] docs(releases): add the change log pages vs. USEEIO and
 vs. CEDA

Two child pages under the release candidate page, written from the working
Docs, each with its waterfall chart (title and axis note now in the caption).
The release candidate page links to both, and the combined draft page is gone.

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 figures/diagrams/waterfall-vs-ceda.png   | Bin 104165 -> 79171 bytes
 figures/diagrams/waterfall-vs-ceda.svg   | 316 +++++++++++------------
 figures/diagrams/waterfall-vs-useeio.png | Bin 98878 -> 75604 bytes
 figures/diagrams/waterfall-vs-useeio.svg | 250 +++++++++---------
 figures/scripts/waterfalls.py            |   5 +-
 myst.yml                                 |   3 +
 releases/changelog-ceda.md               |  98 +++++++
 releases/changelog-useeio.md             |  99 +++++++
 releases/rc.md                           |   2 +-
 9 files changed, 479 insertions(+), 294 deletions(-)
 create mode 100644 releases/changelog-ceda.md
 create mode 100644 releases/changelog-useeio.md

diff --git a/figures/diagrams/waterfall-vs-ceda.png b/figures/diagrams/waterfall-vs-ceda.png
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+    <text style="font-weight: 500; font-size: 11px; font-family: 'Helvetica', 'Arial', 'DejaVu Sans'; text-anchor: middle; fill: #2c2c2a" x="122.699947" y="125.894981" transform="rotate(-0 122.699947 125.894981)">0.2474</text>
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+    <text style="font-weight: 500; font-size: 11px; font-family: 'Helvetica', 'Arial', 'DejaVu Sans'; fill: #2c2c2a" transform="translate(699.844212 135.489207)">0.2448</text>
+    <text style="font-weight: 500; font-size: 11px; font-family: 'Helvetica', 'Arial', 'DejaVu Sans'; fill: #2c2c2a" transform="translate(688.081087 147.347895)">(−1.0% net)</text>
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-   </g>
-  </g>
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-   <text style="font-size: 9px; font-family: 'Helvetica', 'Arial', 'DejaVu Sans'; text-anchor: start; fill: #5f5e5a" x="9.504" y="399.168" transform="rotate(-0 9.504 399.168)">Axis starts at 0.22. Increases in coral, decreases in teal.</text>
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diff --git a/figures/scripts/waterfalls.py b/figures/scripts/waterfalls.py
index 7786504d..b94866f4 100644
--- a/figures/scripts/waterfalls.py
+++ b/figures/scripts/waterfalls.py
@@ -72,15 +72,12 @@ def draw(name, c):
         ax.text(x, y + span * 0.012, t, ha="center", va="bottom", color=INK, fontsize=11, fontweight="medium")
     ax.set_xticks(list(xs)); ax.set_xticklabels(labels, fontsize=9, color=INK)
     ax.set_ylim(*c["ylim"]); ax.set_ylabel(c["ylabel"], color=MUTED)
-    ax.set_title(c["title"], loc="left", fontsize=12.5, color=INK, pad=14)
     ax.tick_params(axis="y", colors=MUTED, length=0); ax.tick_params(axis="x", length=0)
     ax.grid(axis="y", color="#E8E6DF", lw=0.8, zorder=0)
     for s in ("top", "right", "left"):
         ax.spines[s].set_visible(False)
     ax.spines["bottom"].set_color("#B4B2A9")
-    fig.text(0.012, 0.01, f"Axis starts at {c['ylim'][0]:.2f}. Increases in coral, decreases in teal.",
-             color=MUTED, fontsize=9)
-    fig.tight_layout(rect=(0, 0.03, 1, 1))
+    fig.tight_layout()
     for ext in ("png", "svg"):
         fig.savefig(OUT / f"{name}.{ext}", facecolor="white")
     plt.close(fig)
diff --git a/myst.yml b/myst.yml
index 90bddf97..7dee6446 100644
--- a/myst.yml
+++ b/myst.yml
@@ -55,6 +55,9 @@ project:
     - file: releases/index.md
       children:
         - file: releases/rc.md
+          children:
+            - file: releases/changelog-useeio.md
+            - file: releases/changelog-ceda.md
     - file: faq.md
 site:
   template: book-theme
diff --git a/releases/changelog-ceda.md b/releases/changelog-ceda.md
new file mode 100644
index 00000000..031dccdb
--- /dev/null
+++ b/releases/changelog-ceda.md
@@ -0,0 +1,98 @@
+---
+title: "Change log vs. CEDA"
+---
+
+:::{tip} Summary
+Cornerstone EEIO 2026 replaces CEDA 2025. The global production-weighted emission factor falls by 24.1%, from 0.6957 to 0.5281 kg CO₂e per 2024 USD. Data updates raise factors (+6.3%) and methodology changes lower them by more (−28.6%). Four changes drive about 95% of the impact: benchmark-informed improvements, auto-balanced structural reflection (ABSR), new data for China, and new data for Europe.
+:::
+
+## The changes at a glance
+
+Emission factors are global production-weighted totals, in kg CO₂e per 2024 USD.
+
+| | Cornerstone 2026 vs. CEDA 2025 |
+|---|---|
+| Emission factor, before and after | 0.6957 to 0.5281 |
+| **Net change** | **−24.1%** |
+| Data updates | +6.3% |
+| Methodology changes | −28.6% |
+| Median change in a company's total footprint | −13.7% |
+| Companies whose footprint moves by more than 20% | 24% |
+
+*Company figures come from a sample of 400 corporate footprints that combine spend-based and activity data.*
+
+:::{important} Your footprint may not follow the median
+A company's change depends on which sectors and countries its spend falls in. A small median can hide large moves in individual sectors, in both directions.
+:::
+
+## What the science improvements mean in practice
+
+1. **A more current picture of the US economy that accounts for imports.** US input-output tables are rebuilt for every year from 2017 to 2024, manufacturing uses facility-reported emissions, and imports are modeled where they are made.
+2. **Newer data for Europe, the UK and China, and better gap-filling for the rest.** Europe moves from the OECD's ICIO tables to Eurostat's FIGARO (through 2023). China moves to the latest National Bureau of Statistics tables (through 2023), with more sectors. Countries without data are filled with the auto-balanced structural reflection method.
+3. **A validation process that feeds back into the model.** Third-party benchmarks review the sectors behind about 50% of global emissions. Gaps are traced and fixed in the data or the method, not overridden in the output.
+
+## What drives the change for CEDA users
+
+:::{figure} ../figures/diagrams/waterfall-vs-ceda.png
+:label: waterfall-vs-ceda
+:alt: Waterfall chart of the global emission factor from CEDA 2025 (0.6957) to Cornerstone 2026 (0.5281), by driver. The largest are benchmark-informed improvements (−19.0%), ABSR (−10.7%) and China data (+7.8%).
+:width: 100%
+
+**Change in the global production-weighted emission factor from CEDA 2025 to Cornerstone 2026, by driver.** Percentages are changes relative to the CEDA 2025 baseline. Increases are in coral and decreases in teal. The vertical axis starts at 0.50.
+:::
+
+The smaller changes are model reconciliation (about 0%), US data (+0.1%), nowcasting and facility-reported data (−0.7%), bug fixes (−0.4%), and UK and global trade data (about 0%).
+
+### Benchmark-informed improvements
+
+This is the largest driver. See the benchmarking section below.
+
+### Auto-balanced structural reflection (ABSR)
+
+Structural reflection is the method CEDA uses to give every country the same sectors. ABSR improves it. It achieves two goals that are hard to deliver together in a global model:
+
+- All countries share the same 405 sectors, so emission factors compare like for like.
+- Each economy balances production and consumption, in both money and emissions.
+
+### New data for China
+
+China's National Bureau of Statistics released benchmark input-output tables for 2023. The last update was for 2020. The new tables come from China's 5th National Economic Census and have more detail: 211 sectors instead of 153.
+
+This raises China's emission factors by about 10% at the median. It reflects methodology changes, changes in the economic structure, and sector-specific decarbonization. For example, China's battery factor rises by about 30%. Battery production grew from 382 GWh in 2020 to 1,183 GWh in 2023, and lithium-ion batteries, which are about twice as carbon-intensive per dollar as lead-acid, took a larger share.
+
+### New data for Europe
+
+Cornerstone adopts FIGARO, Eurostat's official input-output and emissions dataset. FIGARO is more granular than the OECD dataset for Europe, is available through 2023, and is updated every year, matching Cornerstone's release cadence.
+
+This lowers European factors by about 4% at the median. Electricity factors fall in every EU country. For example, Germany's power factor falls by 60%, as renewables grew from 35% to over 50% of generation. The Netherlands' falls by 77%, after a coal ban and a gas-heavy grid.
+
+## Third-party benchmarking
+
+Cornerstone uses benchmark data built from about 1,000 sources to review the key sectors that drive about 50% of global emissions. Each review ends in one of three outcomes:
+
+- **Sharper benchmark.** Scope and price assumptions are aligned, so the external reference is directly comparable to the model.
+- **Model improvement.** The gap is traced to a data source or an allocation method, and fixed with more data or a corrected implementation.
+- **Known discrepancy.** The difference is documented, because of a limitation in how the model is built or a gap in the data.
+
+About 70% of country-sector pairs with a verified benchmark are within 25% of it. 21% are 25–50% apart and 10% are 50% or more apart.
+
+| Outcome | Example | Gap before and after |
+|---|---|---|
+| Sharper benchmark | US electricity: the benchmark covered only private utilities, while the model also includes state and local utilities. Aligning scope gives a benchmark of 2.37 against the model's 2.44 kg CO₂e per USD. | 15% to 3% |
+| Model improvement | Brazil dairy: the model was allocating methane from beef to dairy for lack of country-specific data. Brazil's national accounts of emissions and output fixed it. | 51% to 5% |
+| Known discrepancy | US oil and gas: the benchmark roughly doubles methane using aerial and satellite measurements. The model uses the EPA inventory, as for every other sector. Resolving this needs more research. | 22% (stays) |
+
+For the equivalent change log against USEEIO, see [](changelog-useeio.md).
+
+## Learn more
+
+- **From the field:** *to be added*
+- **Cornerstone papers:** *to be added*
+- **Code:** *to be added*
+
++++
+
+:::{div}
+:class: page-status
+Applies to: RC · Last reviewed: RC
+:::
diff --git a/releases/changelog-useeio.md b/releases/changelog-useeio.md
new file mode 100644
index 00000000..d0835bc7
--- /dev/null
+++ b/releases/changelog-useeio.md
@@ -0,0 +1,99 @@
+---
+title: "Change log vs. USEEIO"
+---
+
+:::{tip} Summary
+Cornerstone EEIO 2026 is the continuation of the USEEIO Supply Chain GHG Emission Factors v1.4. For US factors, the net change is on median −1.0%, from 0.2474 to 0.2448 kg CO₂e per 2024 USD (production-weighted).
+
+However, the net figure hides large changes that offset each other. Three changes drive about 95% of the impact: model reconciliation, nowcasting and facility-reported data, and the modeling of imports.
+:::
+
+## The changes at a glance
+
+Emission factors are production-weighted totals for the United States, in kg CO₂e per 2024 USD.
+
+| | Cornerstone 2026 vs. USEEIO v1.4 |
+|---|---|
+| Emission factor, before and after | 0.2474 to 0.2448 |
+| **Net change** | **−1.0%** |
+| Data updates | −4.2% |
+| Methodology changes | +3.3% |
+| Median change in a company's total footprint | −1.4% |
+| Companies whose footprint moves by more than 20% | 6% |
+
+*Company figures come from a sample of 400 corporate footprints that combine spend-based and activity data.*
+
+:::{important} Your footprint may not follow the median
+A company's change depends on which sectors and countries its spend falls in. A small median can hide large moves in individual sectors, in both directions.
+:::
+
+## What drives the change for USEEIO users
+
+:::{figure} ../figures/diagrams/waterfall-vs-useeio.png
+:label: waterfall-vs-useeio
+:alt: Waterfall chart of the US emission factor from USEEIO v1.4 (0.2474) to Cornerstone 2026 (0.2448), by driver: model reconciliation, US data, nowcasting and facility data, trade impact, and benchmark-informed improvements.
+:width: 100%
+
+**Change in the US production-weighted emission factor from USEEIO v1.4 to Cornerstone 2026, by driver.** Percentages are changes relative to the USEEIO baseline. Increases are in coral and decreases in teal. The vertical axis starts at 0.22.
+:::
+
+### Model reconciliation
+
+The USEEIO and CEDA teams reviewed 18 differences between the two models for the US. For each, they adopted one model's approach or proposed an improvement. Selected decisions:
+
+| Topic | USEEIO | CEDA | Cornerstone |
+|---|---|---|---|
+| Waste | Split into 7 sectors | One sector | USEEIO: split into 7 |
+| Aluminum | Primary and secondary separate | Merged | USEEIO: separate |
+| Home appliances | One sector, as BEA | Four sectors | USEEIO: one sector |
+| Accounting-only commodities | Kept in the tables | Removed, output recalculated | CEDA: remove |
+| Electric utilities | 3 separate industries | One aggregated industry | CEDA: aggregate |
+| Transit | Government and private separate | Aggregated | CEDA: aggregate |
+| Households | Tracked as sector F01000 | Not always tracked | USEEIO: track F01000 |
+
+Each decision is documented in the open discussion log (see Learn more).
+
+### Nowcasting and facility-reported data
+
+BEA publishes detailed (about 400-sector) US input-output tables only every 10 years, with a five-year lag. The latest is for 2017. Nowcasting rebuilds these tables for every year from 2017 to 2024, in BEA's own structure, using the same official statistics BEA uses.
+
+Where most facilities in a sector report their emissions, those reports replace estimates based on fuel use data that is updated about every four years. The 2024 data includes 6,127 facility reports across 84 industries. More than 50% of the economic data behind the factors is now backed by reported annual data, beyond inflation adjustments.
+
+### Trade impact: modeling imports
+
+USEEIO treated the US as a closed economy: every product used in the US was assumed to be made in the US. Cornerstone uses trade data to model each import in the country that produced it. Many imports come from more emissions-intensive economies such as China and India, so US factors tend to rise by 10 to 30% on average. This is the main upward pressure and the main reason for the change for USEEIO users.
+
+For example, about 40% of the US crude oil mix is imported. Emissions from that oil are allocated to the producing countries, and Canadian oil is up to twice as emissions-intensive as US oil.
+
+**Case study: light trucks and utility vehicles.** The US factor rises by 24.1% (0.2925 to 0.3628 kg CO₂e per USD), mostly because of trade effects.
+
+## Third-party benchmarking
+
+Cornerstone uses benchmark data built from about 1,000 sources to review the key sectors that drive about 50% of global emissions. Each review ends in one of three outcomes:
+
+- **Sharper benchmark.** Scope and price assumptions are aligned, so the external reference is directly comparable to the model.
+- **Model improvement.** The gap is traced to a data source or an allocation method, and fixed with more data or a corrected implementation.
+- **Known discrepancy.** The difference is documented, because of a limitation in how the model is built or a gap in the data.
+
+About 70% of country-sector pairs with a verified benchmark are within 25% of it. 21% are 25–50% apart and 10% are 50% or more apart.
+
+| Outcome | Example | Gap before and after |
+|---|---|---|
+| Sharper benchmark | US electricity: the benchmark covered only private utilities, while the model also includes state and local utilities. Aligning scope gives a benchmark of 2.37 against the model's 2.44 kg CO₂e per USD. | 15% to 3% |
+| Model improvement | Brazil dairy: the model was allocating methane from beef to dairy for lack of country-specific data. Brazil's national accounts of emissions and output fixed it. | 51% to 5% |
+| Known discrepancy | US oil and gas: the benchmark roughly doubles methane using aerial and satellite measurements. The model uses the EPA inventory, as for every other sector. Resolving this needs more research. | 22% (stays) |
+
+For the equivalent change log against CEDA, see [](changelog-ceda.md).
+
+## Learn more
+
+- **From the field:** *to be added*
+- **Cornerstone papers:** *to be added*
+- **Code:** [Methods discussion log](https://github.com/cornerstone-data/methods/discussions), the record of each model reconciliation decision.
+
++++
+
+:::{div}
+:class: page-status
+Applies to: RC · Last reviewed: RC
+:::
diff --git a/releases/rc.md b/releases/rc.md
index 13dd39f7..15f4879d 100644
--- a/releases/rc.md
+++ b/releases/rc.md
@@ -95,7 +95,7 @@ The charts compare the new factors with each model they replace.
 A company’s change depends on which sectors and countries its spend falls in. A small median can hide large moves in individual sectors, in both directions.
 :::
 
-For a more detailed change log against the USEEIO or CEDA, please refer to Change Log vs. USEEIO and Change Log vs. CEDA.
+For a more detailed change log against the USEEIO or CEDA, please refer to [](changelog-useeio.md) and [](changelog-ceda.md).
 
 ## Give feedback
 

From 47b0fe530048297d42317d9fb9ba8f9cbf033612 Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Wed, 21 Oct 2026 16:26:40 -0700
Subject: [PATCH 12/47] docs(releases): fix the light-truck example and add
 where US auto parts come from

Replace the unsupported claim that most components come from South East Asia
with the public trade picture (about half from Mexico and Canada, about a
quarter from China, Japan and South Korea) and add a chart of parts imports by
origin, with the ITA/MNI source in the caption.

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 figures/scripts/rc_figures.py  |  34 ++++++++++++++++++++++++++++++++-
 images/rc-us-parts-imports.png | Bin 0 -> 67771 bytes
 releases/rc.md                 |  10 +++++++++-
 3 files changed, 42 insertions(+), 2 deletions(-)
 create mode 100644 images/rc-us-parts-imports.png

diff --git a/figures/scripts/rc_figures.py b/figures/scripts/rc_figures.py
index b080db32..1eeb311f 100644
--- a/figures/scripts/rc_figures.py
+++ b/figures/scripts/rc_figures.py
@@ -8,6 +8,10 @@
 - rc-china-battery.png: China battery production. Totals are the slide labels. The split by
   battery type is read off the slide graphic, so it is approximate.
 - rc-germany-power.png: Germany's power factor, share of the change from a cleaner grid vs prices.
+- rc-us-parts-imports.png: where US auto parts imports came from in 2024 (all vehicle types, so an
+  indicator for light trucks rather than a measure of that sector). Source: International Trade
+  Administration auto parts data as reported by MNI, March 2025 (www.mnimarkets.com). Europe (11.9%) and
+  ASEAN (almost 7%) are combined regions in the source; the remainder is derived (100% minus the listed shares).
 
 Run from the repo root (needs matplotlib):
     uv run --with matplotlib python figures/scripts/rc_figures.py
@@ -107,5 +111,33 @@ def germany():
     plt.close(fig)
 
 
-comparison(); china(); germany()
+def parts_origin():
+    shares = [  # (label, percent, colour group)
+        ("Mexico", 41.0, "na"), ("Europe (combined)", 11.9, "other"), ("Canada", 10.0, "na"),
+        ("China", 9.3, "asia"), ("Japan", 8.0, "asia"), ("ASEAN (combined)", 7.0, "other"),
+        ("South Korea", 6.4, "asia"),
+    ]
+    rest = 100 - sum(v for _, v, _ in shares)
+    shares.append(("Other (remainder)", rest, "rest"))
+    colours = {"na": DARK_TEAL, "asia": "#1D9E75", "other": LIGHT_TEAL, "rest": "#D3D1C7"}
+    fig, ax = plt.subplots(figsize=(7.5, 4.2), dpi=200)
+    for i, (label, v, g) in enumerate(shares):
+        ax.barh(i, v, color=colours[g], height=0.62)
+        txt = f"{v:.1f}%" if label not in ("ASEAN (combined)", "Other (remainder)") else f"~{v:.0f}%"
+        ax.text(v + 0.8, i, txt, va="center", color=INK, fontweight="medium")
+    ax.set_yticks(range(len(shares))); ax.set_yticklabels([l for l, _, _ in shares], color=INK)
+    ax.invert_yaxis(); ax.set_xlim(0, 48)
+    for sp in ("top", "right", "bottom"):
+        ax.spines[sp].set_visible(False)
+    ax.spines["left"].set_color("#B4B2A9")
+    ax.set_xticks([]); ax.tick_params(axis="y", length=0)
+    handles = [plt.Rectangle((0, 0), 1, 1, color=colours[k]) for k in ("na", "asia", "other", "rest")]
+    ax.legend(handles, ["North America", "East Asia", "Other regions", "Not allocated"], loc="lower right",
+              frameon=False, fontsize=9.5, labelcolor=INK)
+    fig.tight_layout()
+    fig.savefig(OUT / "rc-us-parts-imports.png", facecolor="white")
+    plt.close(fig)
+
+
+comparison(); china(); germany(); parts_origin()
 print("wrote", *(p.name for p in sorted(OUT.glob("rc-*.png"))))
diff --git a/images/rc-us-parts-imports.png b/images/rc-us-parts-imports.png
new file mode 100644
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diff --git a/releases/rc.md b/releases/rc.md
index 15f4879d..906ee7cd 100644
--- a/releases/rc.md
+++ b/releases/rc.md
@@ -77,7 +77,15 @@ This data update lowers European factors by about 4% in median. The most signifi
 
 ### Incorporation of imports in the US
 
-USEEIO treated the US as a closed economy: every product used in the US was assumed to be made in the US. Cornerstone uses trade data to model each import in the country that produced it. Many imports come from more emissions-intensive economies such as China and India, so US factors tend to rise by 10 to 30% on average. This is the main upward pressure and the main reason for the change for USEEIO users. One example is light trucks and utility vehicles. The US factor rises by ~24.1%, mostly because the majority of the components of these vehicles are manufactured in South East Asia.
+USEEIO treated the US as a closed economy: every product used in the US was assumed to be made in the US. Cornerstone uses trade data to model each import in the country that produced it. Many imports come from more emissions-intensive economies such as China and India, so US factors tend to rise by 10 to 30% on average. This is the main upward pressure and the main reason for the change for USEEIO users. One example is light trucks and utility vehicles. The US factor rises by ~24.1%, mostly because many of their components are now modeled as imports from the countries that make them. Public trade data shows where US auto parts come from: about half from Mexico and Canada, and about a quarter from China, Japan and South Korea (see {numref}`rc-us-parts-imports`).
+
+:::{figure} ../images/rc-us-parts-imports.png
+:label: rc-us-parts-imports
+:alt: Horizontal bar chart of the origin of US auto parts imports in 2024. Mexico 41%, Europe combined 11.9%, Canada 10%, China 9.3%, Japan 8.0%, ASEAN combined about 7%, South Korea 6.4%, other about 6%.
+:width: 75%
+
+**Where US auto parts imports come from, 2024.** Share of the value of US imports of motor vehicle parts, by country or region of origin. The data covers parts for all vehicle types, so it is an indicator for light trucks and utility vehicles, not a measure for that sector. Europe and ASEAN are combined regions in the source, and the remainder is not allocated by the source. Source: International Trade Administration auto parts data, as reported by [MNI (March 2025)](https://www.mnimarkets.com/articles/us-auto-tariffs-impact-broad-based-across-regions-1743036084321).
+:::
 
 ## How do the emission factors change?
 

From 502d2ac62b474fb4f4e1aea8fdfca6630fa25cb7 Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Wed, 21 Oct 2026 16:30:07 -0700
Subject: [PATCH 13/47] docs(releases): show light-truck component origin as
 one stacked bar

Replace the parts-imports bar chart with a single horizontal stacked bar: the
domestic share (47%, mean US and Canadian parts content of 87 US-assembled light
trucks, NHTSA AALA MY2025) and the imported share split by origin using the
2024 ITA/MNI auto parts import mix without Canada. Sources and method are in the
caption.

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 figures/scripts/rc_figures.py  |  69 ++++++++++++++++++++-------------
 images/rc-us-parts-imports.png | Bin 67771 -> 58328 bytes
 releases/rc.md                 |   8 ++--
 3 files changed, 47 insertions(+), 30 deletions(-)

diff --git a/figures/scripts/rc_figures.py b/figures/scripts/rc_figures.py
index 1eeb311f..efcdf593 100644
--- a/figures/scripts/rc_figures.py
+++ b/figures/scripts/rc_figures.py
@@ -8,10 +8,14 @@
 - rc-china-battery.png: China battery production. Totals are the slide labels. The split by
   battery type is read off the slide graphic, so it is approximate.
 - rc-germany-power.png: Germany's power factor, share of the change from a cleaner grid vs prices.
-- rc-us-parts-imports.png: where US auto parts imports came from in 2024 (all vehicle types, so an
-  indicator for light trucks rather than a measure of that sector). Source: International Trade
-  Administration auto parts data as reported by MNI, March 2025 (www.mnimarkets.com). Europe (11.9%) and
-  ASEAN (almost 7%) are combined regions in the source; the remainder is derived (100% minus the listed shares).
+- rc-us-parts-imports.png: estimated origin of the components of a US-assembled light truck, as one
+  stacked bar. Domestic share (US and Canada) = 47%, the unweighted mean of the NHTSA AALA parts
+  content for 87 US-assembled light trucks, model year 2025 (as compiled on Wikipedia,
+  "List of automobiles manufactured in the United States"; ranges taken at their midpoint). The
+  remaining 53% is split by origin using the 2024 mix of US auto parts imports (International Trade
+  Administration data as reported by MNI, March 2025) with Canada removed and the rest rescaled. The
+  mix covers parts for all vehicle types, Europe and ASEAN are combined regions in the source, and
+  "Other" is the remainder; so the split is indicative, not a measurement for this sector.
 
 Run from the repo root (needs matplotlib):
     uv run --with matplotlib python figures/scripts/rc_figures.py
@@ -112,31 +116,44 @@ def germany():
 
 
 def parts_origin():
-    shares = [  # (label, percent, colour group)
-        ("Mexico", 41.0, "na"), ("Europe (combined)", 11.9, "other"), ("Canada", 10.0, "na"),
-        ("China", 9.3, "asia"), ("Japan", 8.0, "asia"), ("ASEAN (combined)", 7.0, "other"),
-        ("South Korea", 6.4, "asia"),
-    ]
-    rest = 100 - sum(v for _, v, _ in shares)
-    shares.append(("Other (remainder)", rest, "rest"))
-    colours = {"na": DARK_TEAL, "asia": "#1D9E75", "other": LIGHT_TEAL, "rest": "#D3D1C7"}
-    fig, ax = plt.subplots(figsize=(7.5, 4.2), dpi=200)
-    for i, (label, v, g) in enumerate(shares):
-        ax.barh(i, v, color=colours[g], height=0.62)
-        txt = f"{v:.1f}%" if label not in ("ASEAN (combined)", "Other (remainder)") else f"~{v:.0f}%"
-        ax.text(v + 0.8, i, txt, va="center", color=INK, fontweight="medium")
-    ax.set_yticks(range(len(shares))); ax.set_yticklabels([l for l, _, _ in shares], color=INK)
-    ax.invert_yaxis(); ax.set_xlim(0, 48)
-    for sp in ("top", "right", "bottom"):
-        ax.spines[sp].set_visible(False)
-    ax.spines["left"].set_color("#B4B2A9")
-    ax.set_xticks([]); ax.tick_params(axis="y", length=0)
-    handles = [plt.Rectangle((0, 0), 1, 1, color=colours[k]) for k in ("na", "asia", "other", "rest")]
-    ax.legend(handles, ["North America", "East Asia", "Other regions", "Not allocated"], loc="lower right",
-              frameon=False, fontsize=9.5, labelcolor=INK)
+    domestic = 47.0                       # US and Canadian content, mean of 87 light trucks (AALA, MY2025)
+    imports = 100 - domestic
+    mix = [("Mexico", 41.0), ("Europe (combined)", 11.9), ("China", 9.3), ("Japan", 8.0),
+           ("ASEAN (combined)", 7.0), ("South Korea", 6.4)]
+    mix.append(("Other", 100 - 10.0 - sum(v for _, v in mix)))   # 10.0 = Canada, removed below
+    total = sum(v for _, v in mix)         # 90.0: shares of parts imports excluding Canada
+    raw = [(n, imports * v / total) for n, v in mix]
+    # round to whole percentages that sum to 100 (largest remainder)
+    parts = [("Domestic components\n(US and Canada)", domestic)] + raw
+    floors = [int(v) for _, v in parts]
+    order = sorted(range(len(parts)), key=lambda i: parts[i][1] - floors[i], reverse=True)
+    for i in order[: 100 - sum(floors)]:
+        floors[i] += 1
+    colours = ["#5F5E5A", "#0F6E56", "#378ADD", "#D85A30", "#BA7517", "#7F77DD", "#D4537E", "#B4B2A9"]
+    dark_text = {7}
+    fig, ax = plt.subplots(figsize=(9.5, 3.1), dpi=200)
+    left = 0
+    handles = []
+    for i, ((name, _), pct) in enumerate(zip(parts, floors)):
+        ax.barh(0, pct, left=left, color=colours[i], height=0.55, edgecolor="white", linewidth=1.2)
+        ax.text(left + pct / 2, 0, f"{pct}%", ha="center", va="center", fontsize=10.5, fontweight="medium",
+                color=INK if i in dark_text else "white")
+        handles.append(plt.Rectangle((0, 0), 1, 1, color=colours[i]))
+        left += pct
+    d = floors[0]
+    for x0, x1, label in ((0, d, "Made in the US and Canada"), (d, 100, "Imported from the rest of the world")):
+        ax.plot([x0 + 0.4, x1 - 0.4], [0.5, 0.5], color="#888780", lw=1)
+        ax.text((x0 + x1) / 2, 0.58, label, ha="center", va="bottom", color=INK, fontsize=10.5)
+    ax.set_xlim(0, 100); ax.set_ylim(-0.45, 0.95); ax.axis("off")
+    labels = [n.replace("\n", " ") for n, _ in parts]
+    row_major = [0, 4, 1, 5, 2, 6, 3, 7]      # matplotlib fills legend columns first; this reads in bar order
+    handles, labels = [handles[i] for i in row_major], [labels[i] for i in row_major]
+    ax.legend(handles, labels, loc="upper center", bbox_to_anchor=(0.5, -0.02), ncol=4, frameon=False,
+              fontsize=9.5, labelcolor=INK, columnspacing=1.6, handlelength=1.1, handleheight=1.0)
     fig.tight_layout()
     fig.savefig(OUT / "rc-us-parts-imports.png", facecolor="white")
     plt.close(fig)
+    print("light-truck component origin (%):", dict(zip([n.replace("\n", " ") for n, _ in parts], floors)), "sum", sum(floors))
 
 
 comparison(); china(); germany(); parts_origin()
diff --git a/images/rc-us-parts-imports.png b/images/rc-us-parts-imports.png
index abf669d606211c923c3beb3e940907bd6eb57533..2bf8487e6695eb0cdcf1c8b948861e6f49f20025 100644
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diff --git a/releases/rc.md b/releases/rc.md
index 906ee7cd..bdf931ad 100644
--- a/releases/rc.md
+++ b/releases/rc.md
@@ -77,14 +77,14 @@ This data update lowers European factors by about 4% in median. The most signifi
 
 ### Incorporation of imports in the US
 
-USEEIO treated the US as a closed economy: every product used in the US was assumed to be made in the US. Cornerstone uses trade data to model each import in the country that produced it. Many imports come from more emissions-intensive economies such as China and India, so US factors tend to rise by 10 to 30% on average. This is the main upward pressure and the main reason for the change for USEEIO users. One example is light trucks and utility vehicles. The US factor rises by ~24.1%, mostly because many of their components are now modeled as imports from the countries that make them. Public trade data shows where US auto parts come from: about half from Mexico and Canada, and about a quarter from China, Japan and South Korea (see {numref}`rc-us-parts-imports`).
+USEEIO treated the US as a closed economy: every product used in the US was assumed to be made in the US. Cornerstone uses trade data to model each import in the country that produced it. Many imports come from more emissions-intensive economies such as China and India, so US factors tend to rise by 10 to 30% on average. This is the main upward pressure and the main reason for the change for USEEIO users. One example is light trucks and utility vehicles. The US factor rises by ~24.1%, mostly because many of their components are now modeled as imports from the countries that make them. Public data suggests that about half of the components of a US-assembled light truck are imported, with Mexico the largest source, followed by Europe and East Asia (see {numref}`rc-us-parts-imports`).
 
 :::{figure} ../images/rc-us-parts-imports.png
 :label: rc-us-parts-imports
-:alt: Horizontal bar chart of the origin of US auto parts imports in 2024. Mexico 41%, Europe combined 11.9%, Canada 10%, China 9.3%, Japan 8.0%, ASEAN combined about 7%, South Korea 6.4%, other about 6%.
-:width: 75%
+:alt: One horizontal stacked bar showing the estimated origin of light-truck components. Domestic, US and Canada, 47%. Imports: Mexico 24%, Europe combined 7%, China 5%, Japan 5%, ASEAN combined 4%, South Korea 4%, other 4%.
+:width: 100%
 
-**Where US auto parts imports come from, 2024.** Share of the value of US imports of motor vehicle parts, by country or region of origin. The data covers parts for all vehicle types, so it is an indicator for light trucks and utility vehicles, not a measure for that sector. Europe and ASEAN are combined regions in the source, and the remainder is not allocated by the source. Source: International Trade Administration auto parts data, as reported by [MNI (March 2025)](https://www.mnimarkets.com/articles/us-auto-tariffs-impact-broad-based-across-regions-1743036084321).
+**Estimated origin of the components of a US-assembled light truck.** The domestic share is the average US and Canadian parts content of 87 light trucks assembled in the US, from the American Automobile Labeling Act data for model year 2025 (unweighted, ranges at their midpoint; the data does not separate the US from Canada). The import split applies the 2024 mix of US auto parts imports by origin, excluding Canada, to the imported share. That mix covers parts for all vehicle types, Europe and ASEAN are combined regions in the source, and Other is the remainder, so the split is indicative. Sources: [NHTSA AALA parts content data](https://www.nhtsa.gov/part-583-american-automobile-labeling-act-reports), as compiled on [Wikipedia](https://en.wikipedia.org/wiki/List_of_automobiles_manufactured_in_the_United_States); International Trade Administration data as reported by [MNI (March 2025)](https://www.mnimarkets.com/articles/us-auto-tariffs-impact-broad-based-across-regions-1743036084321).
 :::
 
 ## How do the emission factors change?

From e8d1d451dcc785282b95255832757654ff9eb866 Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Wed, 21 Oct 2026 16:31:19 -0700
Subject: [PATCH 14/47] docs(releases): separate the US from Canada in the
 light-truck component chart

The AALA data reports US and Canadian content together, so Canada is estimated
from its 10% share of US auto parts imports (about 6% of components), leaving
about 41% made in the US. The caption states the method and its limits.

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 figures/scripts/rc_figures.py  |  70 ++++++++++++++++++++-------------
 images/rc-us-parts-imports.png | Bin 58328 -> 49605 bytes
 releases/rc.md                 |   6 +--
 3 files changed, 45 insertions(+), 31 deletions(-)

diff --git a/figures/scripts/rc_figures.py b/figures/scripts/rc_figures.py
index efcdf593..9b644503 100644
--- a/figures/scripts/rc_figures.py
+++ b/figures/scripts/rc_figures.py
@@ -9,13 +9,14 @@
   battery type is read off the slide graphic, so it is approximate.
 - rc-germany-power.png: Germany's power factor, share of the change from a cleaner grid vs prices.
 - rc-us-parts-imports.png: estimated origin of the components of a US-assembled light truck, as one
-  stacked bar. Domestic share (US and Canada) = 47%, the unweighted mean of the NHTSA AALA parts
-  content for 87 US-assembled light trucks, model year 2025 (as compiled on Wikipedia,
-  "List of automobiles manufactured in the United States"; ranges taken at their midpoint). The
-  remaining 53% is split by origin using the 2024 mix of US auto parts imports (International Trade
-  Administration data as reported by MNI, March 2025) with Canada removed and the rest rescaled. The
-  mix covers parts for all vehicle types, Europe and ASEAN are combined regions in the source, and
-  "Other" is the remainder; so the split is indicative, not a measurement for this sector.
+  stacked bar. US + Canada together = 47%, the unweighted mean of the NHTSA AALA parts content for 87
+  US-assembled light trucks, model year 2025 (as compiled on Wikipedia, "List of automobiles
+  manufactured in the United States"; ranges at their midpoint). The AALA data does not separate the
+  US from Canada, so Canada is estimated: it supplies 10% of US auto parts imports (International Trade
+  Administration data as reported by MNI, March 2025), so with total imports I (incl. Canada),
+  Canada = 0.10 * I and the rest of the world = 0.90 * I = 53%, giving I = 58.9%, Canada = 5.9% and
+  the US = 47% - 5.9% = 41.1%. The other origins use the same 2024 import mix (parts for all vehicle
+  types; Europe and ASEAN are combined regions in the source; Other is the remainder). Indicative only.
 
 Run from the repo root (needs matplotlib):
     uv run --with matplotlib python figures/scripts/rc_figures.py
@@ -116,44 +117,57 @@ def germany():
 
 
 def parts_origin():
-    domestic = 47.0                       # US and Canadian content, mean of 87 light trucks (AALA, MY2025)
-    imports = 100 - domestic
-    mix = [("Mexico", 41.0), ("Europe (combined)", 11.9), ("China", 9.3), ("Japan", 8.0),
+    us_canada = 47.0                       # US and Canadian content, mean of 87 light trucks (AALA, MY2025)
+    mix = [("Mexico", 41.0), ("Europe (combined)", 11.9), ("Canada", 10.0), ("China", 9.3), ("Japan", 8.0),
            ("ASEAN (combined)", 7.0), ("South Korea", 6.4)]
-    mix.append(("Other", 100 - 10.0 - sum(v for _, v in mix)))   # 10.0 = Canada, removed below
-    total = sum(v for _, v in mix)         # 90.0: shares of parts imports excluding Canada
-    raw = [(n, imports * v / total) for n, v in mix]
-    # round to whole percentages that sum to 100 (largest remainder)
-    parts = [("Domestic components\n(US and Canada)", domestic)] + raw
+    mix.append(("Other", 100 - sum(v for _, v in mix)))            # remainder of parts imports (6.4%)
+    canada_share = dict(mix)["Canada"] / 100
+    rest_of_world = 100 * (1 - canada_share) * 0 + sum(v for n, v in mix if n != "Canada")  # 90.0
+    imports_total = (100 - us_canada) / (rest_of_world / 100)     # imports incl. Canada, as % of components
+    raw = [(n, imports_total * v / 100) for n, v in mix]          # each origin's share of components
+    us = us_canada - dict(raw)["Canada"]
+    parts = [("Made in the US", us)] + raw
     floors = [int(v) for _, v in parts]
     order = sorted(range(len(parts)), key=lambda i: parts[i][1] - floors[i], reverse=True)
     for i in order[: 100 - sum(floors)]:
         floors[i] += 1
-    colours = ["#5F5E5A", "#0F6E56", "#378ADD", "#D85A30", "#BA7517", "#7F77DD", "#D4537E", "#B4B2A9"]
-    dark_text = {7}
+    # draw order: US first, then imports by size
+    idx = [0] + sorted(range(1, len(parts)), key=lambda i: (-floors[i], i))
+    parts = [parts[i] for i in idx]; floors = [floors[i] for i in idx]
+    palette = {"Made in the US": "#5F5E5A", "Mexico": "#0F6E56", "Canada": "#5DCAA5", "Europe (combined)": "#378ADD",
+               "China": "#D85A30", "Japan": "#BA7517", "ASEAN (combined)": "#7F77DD", "South Korea": "#D4537E",
+               "Other": "#B4B2A9"}
+    dark_text = {"Canada", "Other"}
     fig, ax = plt.subplots(figsize=(9.5, 3.1), dpi=200)
     left = 0
     handles = []
-    for i, ((name, _), pct) in enumerate(zip(parts, floors)):
-        ax.barh(0, pct, left=left, color=colours[i], height=0.55, edgecolor="white", linewidth=1.2)
+    for (name, _), pct in zip(parts, floors):
+        ax.barh(0, pct, left=left, color=palette[name], height=0.55, edgecolor="white", linewidth=1.2)
         ax.text(left + pct / 2, 0, f"{pct}%", ha="center", va="center", fontsize=10.5, fontweight="medium",
-                color=INK if i in dark_text else "white")
-        handles.append(plt.Rectangle((0, 0), 1, 1, color=colours[i]))
+                color=INK if name in dark_text else "white")
+        handles.append(plt.Rectangle((0, 0), 1, 1, color=palette[name]))
         left += pct
     d = floors[0]
-    for x0, x1, label in ((0, d, "Made in the US and Canada"), (d, 100, "Imported from the rest of the world")):
+    for x0, x1, label in ((0, d, "Made in the US"), (d, 100, "Imported")):
         ax.plot([x0 + 0.4, x1 - 0.4], [0.5, 0.5], color="#888780", lw=1)
         ax.text((x0 + x1) / 2, 0.58, label, ha="center", va="bottom", color=INK, fontsize=10.5)
     ax.set_xlim(0, 100); ax.set_ylim(-0.45, 0.95); ax.axis("off")
-    labels = [n.replace("\n", " ") for n, _ in parts]
-    row_major = [0, 4, 1, 5, 2, 6, 3, 7]      # matplotlib fills legend columns first; this reads in bar order
-    handles, labels = [handles[i] for i in row_major], [labels[i] for i in row_major]
-    ax.legend(handles, labels, loc="upper center", bbox_to_anchor=(0.5, -0.02), ncol=4, frameon=False,
-              fontsize=9.5, labelcolor=INK, columnspacing=1.6, handlelength=1.1, handleheight=1.0)
+    labels = [n for n, _ in parts]
+    n = len(labels); rows = (n + 4) // 5
+    cols = (n + rows - 1) // rows
+    order_rm = [r + c * rows for r in range(rows) for c in range(cols) if r + c * rows < n]
+    pad = [i for i in range(n) if i not in order_rm]
+    # matplotlib fills legend columns first; reorder so it reads in bar order across rows
+    grid = [[None] * cols for _ in range(rows)]
+    for k in range(n):
+        grid[k // cols][k % cols] = k
+    seq = [grid[r][c] for c in range(cols) for r in range(rows) if grid[r][c] is not None]
+    ax.legend([handles[i] for i in seq], [labels[i] for i in seq], loc="upper center", bbox_to_anchor=(0.5, -0.02),
+              ncol=cols, frameon=False, fontsize=9.5, labelcolor=INK, columnspacing=1.6, handlelength=1.1, handleheight=1.0)
     fig.tight_layout()
     fig.savefig(OUT / "rc-us-parts-imports.png", facecolor="white")
     plt.close(fig)
-    print("light-truck component origin (%):", dict(zip([n.replace("\n", " ") for n, _ in parts], floors)), "sum", sum(floors))
+    print("light-truck component origin (%):", dict(zip(labels, floors)), "sum", sum(floors), "| imports incl. Canada:", round(imports_total, 1))
 
 
 comparison(); china(); germany(); parts_origin()
diff --git a/images/rc-us-parts-imports.png b/images/rc-us-parts-imports.png
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diff --git a/releases/rc.md b/releases/rc.md
index bdf931ad..d55d5da5 100644
--- a/releases/rc.md
+++ b/releases/rc.md
@@ -77,14 +77,14 @@ This data update lowers European factors by about 4% in median. The most signifi
 
 ### Incorporation of imports in the US
 
-USEEIO treated the US as a closed economy: every product used in the US was assumed to be made in the US. Cornerstone uses trade data to model each import in the country that produced it. Many imports come from more emissions-intensive economies such as China and India, so US factors tend to rise by 10 to 30% on average. This is the main upward pressure and the main reason for the change for USEEIO users. One example is light trucks and utility vehicles. The US factor rises by ~24.1%, mostly because many of their components are now modeled as imports from the countries that make them. Public data suggests that about half of the components of a US-assembled light truck are imported, with Mexico the largest source, followed by Europe and East Asia (see {numref}`rc-us-parts-imports`).
+USEEIO treated the US as a closed economy: every product used in the US was assumed to be made in the US. Cornerstone uses trade data to model each import in the country that produced it. Many imports come from more emissions-intensive economies such as China and India, so US factors tend to rise by 10 to 30% on average. This is the main upward pressure and the main reason for the change for USEEIO users. One example is light trucks and utility vehicles. The US factor rises by ~24.1%, mostly because many of their components are now modeled as imports from the countries that make them. Public data suggests that about 60% of the components of a US-assembled light truck are imported, with Mexico the largest source, followed by Europe and Canada (see {numref}`rc-us-parts-imports`).
 
 :::{figure} ../images/rc-us-parts-imports.png
 :label: rc-us-parts-imports
-:alt: One horizontal stacked bar showing the estimated origin of light-truck components. Domestic, US and Canada, 47%. Imports: Mexico 24%, Europe combined 7%, China 5%, Japan 5%, ASEAN combined 4%, South Korea 4%, other 4%.
+:alt: One horizontal stacked bar showing the estimated origin of light-truck components. Made in the US 41%. Imported: Mexico 24%, Europe combined 7%, Canada 6%, China 5%, Japan 5%, ASEAN combined 4%, South Korea 4%, other 4%.
 :width: 100%
 
-**Estimated origin of the components of a US-assembled light truck.** The domestic share is the average US and Canadian parts content of 87 light trucks assembled in the US, from the American Automobile Labeling Act data for model year 2025 (unweighted, ranges at their midpoint; the data does not separate the US from Canada). The import split applies the 2024 mix of US auto parts imports by origin, excluding Canada, to the imported share. That mix covers parts for all vehicle types, Europe and ASEAN are combined regions in the source, and Other is the remainder, so the split is indicative. Sources: [NHTSA AALA parts content data](https://www.nhtsa.gov/part-583-american-automobile-labeling-act-reports), as compiled on [Wikipedia](https://en.wikipedia.org/wiki/List_of_automobiles_manufactured_in_the_United_States); International Trade Administration data as reported by [MNI (March 2025)](https://www.mnimarkets.com/articles/us-auto-tariffs-impact-broad-based-across-regions-1743036084321).
+**Estimated origin of the components of a US-assembled light truck.** US and Canadian content together is 47%, the average of 87 light trucks assembled in the US, from the American Automobile Labeling Act data for model year 2025 (unweighted, ranges at their midpoint). That data does not separate the US from Canada, so Canada is estimated from its 10% share of US auto parts imports, which gives about 6% of components from Canada and 41% made in the US. The other origins apply the 2024 mix of US auto parts imports to the imported share. That mix covers parts for all vehicle types, Europe and ASEAN are combined regions in the source, and Other is the remainder, so the split is indicative. Sources: [NHTSA AALA parts content data](https://www.nhtsa.gov/part-583-american-automobile-labeling-act-reports), as compiled on [Wikipedia](https://en.wikipedia.org/wiki/List_of_automobiles_manufactured_in_the_United_States); International Trade Administration data as reported by [MNI (March 2025)](https://www.mnimarkets.com/articles/us-auto-tariffs-impact-broad-based-across-regions-1743036084321).
 :::
 
 ## How do the emission factors change?

From 6600ed79b806cd8b6183b163ffdd35c995c1ab3a Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Wed, 21 Oct 2026 18:14:49 -0700
Subject: [PATCH 15/47] docs(releases): simplify the light-truck origin figure

Short caption with the two sources, and plain "Europe" and "ASEAN" labels.

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 figures/scripts/rc_figures.py  |   8 ++++----
 images/rc-us-parts-imports.png | Bin 49605 -> 45427 bytes
 releases/rc.md                 |   4 ++--
 3 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/figures/scripts/rc_figures.py b/figures/scripts/rc_figures.py
index 9b644503..42ca1c54 100644
--- a/figures/scripts/rc_figures.py
+++ b/figures/scripts/rc_figures.py
@@ -118,8 +118,8 @@ def germany():
 
 def parts_origin():
     us_canada = 47.0                       # US and Canadian content, mean of 87 light trucks (AALA, MY2025)
-    mix = [("Mexico", 41.0), ("Europe (combined)", 11.9), ("Canada", 10.0), ("China", 9.3), ("Japan", 8.0),
-           ("ASEAN (combined)", 7.0), ("South Korea", 6.4)]
+    mix = [("Mexico", 41.0), ("Europe", 11.9), ("Canada", 10.0), ("China", 9.3), ("Japan", 8.0),
+           ("ASEAN", 7.0), ("South Korea", 6.4)]
     mix.append(("Other", 100 - sum(v for _, v in mix)))            # remainder of parts imports (6.4%)
     canada_share = dict(mix)["Canada"] / 100
     rest_of_world = 100 * (1 - canada_share) * 0 + sum(v for n, v in mix if n != "Canada")  # 90.0
@@ -134,8 +134,8 @@ def parts_origin():
     # draw order: US first, then imports by size
     idx = [0] + sorted(range(1, len(parts)), key=lambda i: (-floors[i], i))
     parts = [parts[i] for i in idx]; floors = [floors[i] for i in idx]
-    palette = {"Made in the US": "#5F5E5A", "Mexico": "#0F6E56", "Canada": "#5DCAA5", "Europe (combined)": "#378ADD",
-               "China": "#D85A30", "Japan": "#BA7517", "ASEAN (combined)": "#7F77DD", "South Korea": "#D4537E",
+    palette = {"Made in the US": "#5F5E5A", "Mexico": "#0F6E56", "Canada": "#5DCAA5", "Europe": "#378ADD",
+               "China": "#D85A30", "Japan": "#BA7517", "ASEAN": "#7F77DD", "South Korea": "#D4537E",
                "Other": "#B4B2A9"}
     dark_text = {"Canada", "Other"}
     fig, ax = plt.subplots(figsize=(9.5, 3.1), dpi=200)
diff --git a/images/rc-us-parts-imports.png b/images/rc-us-parts-imports.png
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diff --git a/releases/rc.md b/releases/rc.md
index d55d5da5..e8fa6a73 100644
--- a/releases/rc.md
+++ b/releases/rc.md
@@ -81,10 +81,10 @@ USEEIO treated the US as a closed economy: every product used in the US was assu
 
 :::{figure} ../images/rc-us-parts-imports.png
 :label: rc-us-parts-imports
-:alt: One horizontal stacked bar showing the estimated origin of light-truck components. Made in the US 41%. Imported: Mexico 24%, Europe combined 7%, Canada 6%, China 5%, Japan 5%, ASEAN combined 4%, South Korea 4%, other 4%.
+:alt: One horizontal stacked bar showing the estimated origin of light-truck components. Made in the US 41%. Imported: Mexico 24%, Europe 7%, Canada 6%, China 5%, Japan 5%, ASEAN 4%, South Korea 4%, other 4%.
 :width: 100%
 
-**Estimated origin of the components of a US-assembled light truck.** US and Canadian content together is 47%, the average of 87 light trucks assembled in the US, from the American Automobile Labeling Act data for model year 2025 (unweighted, ranges at their midpoint). That data does not separate the US from Canada, so Canada is estimated from its 10% share of US auto parts imports, which gives about 6% of components from Canada and 41% made in the US. The other origins apply the 2024 mix of US auto parts imports to the imported share. That mix covers parts for all vehicle types, Europe and ASEAN are combined regions in the source, and Other is the remainder, so the split is indicative. Sources: [NHTSA AALA parts content data](https://www.nhtsa.gov/part-583-american-automobile-labeling-act-reports), as compiled on [Wikipedia](https://en.wikipedia.org/wiki/List_of_automobiles_manufactured_in_the_United_States); International Trade Administration data as reported by [MNI (March 2025)](https://www.mnimarkets.com/articles/us-auto-tariffs-impact-broad-based-across-regions-1743036084321).
+**Estimated origin of the components of a US-assembled light truck.** Sources: [NHTSA AALA parts content data](https://www.nhtsa.gov/part-583-american-automobile-labeling-act-reports); [International Trade Administration data as reported by MNI (March 2025)](https://www.mnimarkets.com/articles/us-auto-tariffs-impact-broad-based-across-regions-1743036084321).
 :::
 
 ## How do the emission factors change?

From b938cfc13e9299368b351be610eb201a69ec6d9a Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Thu, 22 Oct 2026 10:27:00 -0700
Subject: [PATCH 16/47] docs: link Zenodo and record the DOI for the release
 candidate

Link the word Zenodo on the release candidate page and in the "What's in a
release" summary, and fill zenodo_doi in the RC version record. The pages link
through zenodo.org/doi/... because MyST's strict build rejects an unresolved
doi.org link, and the DOI does not resolve until the record is published.

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 factors/whats-in-a-release.md | 2 +-
 releases/rc.md                | 2 +-
 versions/rc.yaml              | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/factors/whats-in-a-release.md b/factors/whats-in-a-release.md
index 385a1278..28a77b1b 100644
--- a/factors/whats-in-a-release.md
+++ b/factors/whats-in-a-release.md
@@ -3,7 +3,7 @@ title: "What's in a release"
 ---
 
 :::{tip} Summary
-The Cornerstone EEIO model is updated annually and versioned with the year of the release, so Cornerstone EEIO 2026 is released in 2026 and the 2027 version will be released in 2027. Every year, the releases will be publicly available in October. Each release publishes emission factors covering 405 sectors across 148 countries, 15 regions and one global average. To download the latest version, check out zenodo.
+The Cornerstone EEIO model is updated annually and versioned with the year of the release, so Cornerstone EEIO 2026 is released in 2026 and the 2027 version will be released in 2027. Every year, the releases will be publicly available in October. Each release publishes emission factors covering 405 sectors across 148 countries, 15 regions and one global average. To download the latest version, check out [Zenodo](https://zenodo.org/doi/10.5281/zenodo.23396891).
 :::
 
 ## The release
diff --git a/releases/rc.md b/releases/rc.md
index e8fa6a73..c2c976f3 100644
--- a/releases/rc.md
+++ b/releases/rc.md
@@ -9,7 +9,7 @@ The Cornerstone EEIO 2026 - RC (Release Candidate) is the first stage of a phase
 
 This release candidate will be superseded in December by the release of the Cornerstone EEIO 2026 final version, which will include a time series from 2017 to 2026.
 
-Download this release on Zenodo here.
+Download this release on [Zenodo](https://zenodo.org/doi/10.5281/zenodo.23396891) here.
 :::
 
 :::{note} Why have a Release Candidate?
diff --git a/versions/rc.yaml b/versions/rc.yaml
index 8a8b0c44..e01b8fa0 100644
--- a/versions/rc.yaml
+++ b/versions/rc.yaml
@@ -4,7 +4,7 @@
 release: rc
 model_version: v0.6  # to confirm (plan Q9)
 release_date: 2026-10-27
-zenodo_doi: null
+zenodo_doi: 10.5281/zenodo.23396891
 base_year: 2024
 dollar_years: [2024, 2025, 2026]  # to confirm (plan Q10)
 gwp: AR6-100

From d02d7e9e7df340eafa3520484aa7c44cc3819806 Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Thu, 22 Oct 2026 10:30:49 -0700
Subject: [PATCH 17/47] docs(releases): reword the "Why have a Release
 Candidate?" answer

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 releases/rc.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/releases/rc.md b/releases/rc.md
index c2c976f3..f4d77069 100644
--- a/releases/rc.md
+++ b/releases/rc.md
@@ -14,7 +14,7 @@ Download this release on [Zenodo](https://zenodo.org/doi/10.5281/zenodo.23396891
 
 :::{note} Why have a Release Candidate?
 :class: dropdown
-2026 is an unprecedented year of development because it is the first time we are combining CEDA and USEEIO into one model and also the first time we are releasing a time series. The intention behind having a two phased approach to the release is to give companies additional time for impact assessments on their most recent year of measurements, while the developers finish the validation process for the time series. This enables companies to understand footprint changes before applying the time series.
+2026 is an unprecedented year of development because it is the first time we are combining CEDA and USEEIO into one model and also the first time we are releasing a time series. The intention behind having a two phased approach to the release is to give companies additional time for impact assessments on their most recent year of measurements, while the time series is under development. This enables companies to understand footprint changes before applying the time series.
 :::
 
 :::{note} Will there be any changes for years 2024 to 2026 once the time series is released?

From 541a283f37de6bd5f7659d3a0a0cf2cad5197c1d Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Thu, 22 Oct 2026 10:32:44 -0700
Subject: [PATCH 18/47] docs(releases): add the enterprise demo link and round
 the light-truck figure

Add the sentence pointing to the enterprise demo after the change-log links, and
write the light-truck increase as ~24%.

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 releases/rc.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/releases/rc.md b/releases/rc.md
index f4d77069..7c565102 100644
--- a/releases/rc.md
+++ b/releases/rc.md
@@ -77,7 +77,7 @@ This data update lowers European factors by about 4% in median. The most signifi
 
 ### Incorporation of imports in the US
 
-USEEIO treated the US as a closed economy: every product used in the US was assumed to be made in the US. Cornerstone uses trade data to model each import in the country that produced it. Many imports come from more emissions-intensive economies such as China and India, so US factors tend to rise by 10 to 30% on average. This is the main upward pressure and the main reason for the change for USEEIO users. One example is light trucks and utility vehicles. The US factor rises by ~24.1%, mostly because many of their components are now modeled as imports from the countries that make them. Public data suggests that about 60% of the components of a US-assembled light truck are imported, with Mexico the largest source, followed by Europe and Canada (see {numref}`rc-us-parts-imports`).
+USEEIO treated the US as a closed economy: every product used in the US was assumed to be made in the US. Cornerstone uses trade data to model each import in the country that produced it. Many imports come from more emissions-intensive economies such as China and India, so US factors tend to rise by 10 to 30% on average. This is the main upward pressure and the main reason for the change for USEEIO users. One example is light trucks and utility vehicles. The US factor rises by ~24%, mostly because many of their components are now modeled as imports from the countries that make them. Public data suggests that about 60% of the components of a US-assembled light truck are imported, with Mexico the largest source, followed by Europe and Canada (see {numref}`rc-us-parts-imports`).
 
 :::{figure} ../images/rc-us-parts-imports.png
 :label: rc-us-parts-imports
@@ -103,7 +103,7 @@ The charts compare the new factors with each model they replace.
 A company’s change depends on which sectors and countries its spend falls in. A small median can hide large moves in individual sectors, in both directions.
 :::
 
-For a more detailed change log against the USEEIO or CEDA, please refer to [](changelog-useeio.md) and [](changelog-ceda.md).
+For a more detailed change log against the USEEIO or CEDA, please refer to [](changelog-useeio.md) and [](changelog-ceda.md). For even additional details or custom support on how to navigate the changes in your footprint, sign up for a demo of the enterprise product [here](https://watershed.com/en-GB/solutions/ceda).
 
 ## Give feedback
 

From f0839ad9b75f48a713e2043a5ad6d067a04c4b96 Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Thu, 22 Oct 2026 10:33:01 -0700
Subject: [PATCH 19/47] docs(releases): say FIGARO is available as a time
 series through 2023

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 releases/rc.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/releases/rc.md b/releases/rc.md
index 7c565102..64f875f3 100644
--- a/releases/rc.md
+++ b/releases/rc.md
@@ -63,7 +63,7 @@ For example, China’s battery factor rises by ~30% driven by a change in the co
 
 ### New data for Europe
 
-Cornerstone adopts the most recent release of FIGARO, Eurostat’s official input-output and emissions dataset. FIGARO is more granular than the OECD dataset for Europe, is available through 2023 vs. 2020, and is updated every year, matching Cornerstone’s release cadence.
+Cornerstone adopts the most recent release of FIGARO, Eurostat’s official input-output and emissions dataset. FIGARO is more granular than the OECD dataset for Europe, is available as a time series through 2023, and is updated every year, matching Cornerstone’s release cadence.
 
 This data update lowers European factors by about 4% in median. The most significant change is in electricity factors, which fall in every EU country. This change is driven by an increase in renewable energy and by an increase in energy prices due to the gas-supply shocks the Ukraine war has caused in Europe. For example, Germany’s power factor fell by 60%, as renewables grew from 35% to over 50% of generation and wholesale power prices more than doubled.
 

From de938d870bd5f9500ec9ca8409175681e993d244 Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Thu, 22 Oct 2026 10:35:36 -0700
Subject: [PATCH 20/47] docs(releases): remove the "Give feedback" section from
 the release candidate page

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 releases/rc.md | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/releases/rc.md b/releases/rc.md
index 64f875f3..867f12eb 100644
--- a/releases/rc.md
+++ b/releases/rc.md
@@ -105,10 +105,6 @@ A company’s change depends on which sectors and countries its spend falls in.
 
 For a more detailed change log against the USEEIO or CEDA, please refer to [](changelog-useeio.md) and [](changelog-ceda.md). For even additional details or custom support on how to navigate the changes in your footprint, sign up for a demo of the enterprise product [here](https://watershed.com/en-GB/solutions/ceda).
 
-## Give feedback
-
-[Open an issue on GitHub](https://github.com/cornerstone-data/eeio-docs/issues/new).
-
 +++
 
 :::{div}

From c4bac452a3564099986b602a547657da20503264 Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Thu, 22 Oct 2026 10:36:14 -0700
Subject: [PATCH 21/47] docs(releases): show the release date and stage in the
 page header

Use the front matter subtitle and date, replacing the "Release date" heading, so
every release page can carry the same header (stage and ISO date).

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 releases/rc.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/releases/rc.md b/releases/rc.md
index 867f12eb..7b446574 100644
--- a/releases/rc.md
+++ b/releases/rc.md
@@ -1,9 +1,9 @@
 ---
 title: "Cornerstone EEIO 2026 - RC"
+subtitle: Release candidate
+date: 2026-10-27
 ---
 
-## Release date: October 27th, 2026
-
 :::{tip} Summary
 The Cornerstone EEIO 2026 - RC (Release Candidate) is the first stage of a phased approach to releasing the Cornerstone EEIO 2026 model, the successor of USEEIO and CEDA. It is a global environmentally-extended input-output (EEIO) model that produces spend-based emission factors, in kg CO₂e per USD for 405 sectors across 148 countries, plus 15 regional averages and one global average. It uses 2024 as the base year and is available to use for 2024, 2025 and 2026 measurements.
 

From 23568287b6675599c4df48bcacb7b4535d508c6c Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Thu, 22 Oct 2026 10:37:12 -0700
Subject: [PATCH 22/47] docs(releases): label the release date explicitly in
 the page header

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 releases/rc.md | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/releases/rc.md b/releases/rc.md
index 7b446574..1ec414c2 100644
--- a/releases/rc.md
+++ b/releases/rc.md
@@ -1,7 +1,6 @@
 ---
 title: "Cornerstone EEIO 2026 - RC"
-subtitle: Release candidate
-date: 2026-10-27
+subtitle: "Release candidate · Release date: October 27, 2026"
 ---
 
 :::{tip} Summary

From 88100d14b5032f73e0144267f674ee059729f604 Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Thu, 22 Oct 2026 10:55:06 -0700
Subject: [PATCH 23/47] docs: correct the region count to 21

The model has 21 UN regions plus a global average, not 15.

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 factors/whats-in-a-release.md | 2 +-
 releases/rc.md                | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/factors/whats-in-a-release.md b/factors/whats-in-a-release.md
index 28a77b1b..076b301e 100644
--- a/factors/whats-in-a-release.md
+++ b/factors/whats-in-a-release.md
@@ -3,7 +3,7 @@ title: "What's in a release"
 ---
 
 :::{tip} Summary
-The Cornerstone EEIO model is updated annually and versioned with the year of the release, so Cornerstone EEIO 2026 is released in 2026 and the 2027 version will be released in 2027. Every year, the releases will be publicly available in October. Each release publishes emission factors covering 405 sectors across 148 countries, 15 regions and one global average. To download the latest version, check out [Zenodo](https://zenodo.org/doi/10.5281/zenodo.23396891).
+The Cornerstone EEIO model is updated annually and versioned with the year of the release, so Cornerstone EEIO 2026 is released in 2026 and the 2027 version will be released in 2027. Every year, the releases will be publicly available in October. Each release publishes emission factors covering 405 sectors across 148 countries, 21 regions and one global average. To download the latest version, check out [Zenodo](https://zenodo.org/doi/10.5281/zenodo.23396891).
 :::
 
 ## The release
diff --git a/releases/rc.md b/releases/rc.md
index 1ec414c2..8f3b1420 100644
--- a/releases/rc.md
+++ b/releases/rc.md
@@ -4,7 +4,7 @@ subtitle: "Release candidate · Release date: October 27, 2026"
 ---
 
 :::{tip} Summary
-The Cornerstone EEIO 2026 - RC (Release Candidate) is the first stage of a phased approach to releasing the Cornerstone EEIO 2026 model, the successor of USEEIO and CEDA. It is a global environmentally-extended input-output (EEIO) model that produces spend-based emission factors, in kg CO₂e per USD for 405 sectors across 148 countries, plus 15 regional averages and one global average. It uses 2024 as the base year and is available to use for 2024, 2025 and 2026 measurements.
+The Cornerstone EEIO 2026 - RC (Release Candidate) is the first stage of a phased approach to releasing the Cornerstone EEIO 2026 model, the successor of USEEIO and CEDA. It is a global environmentally-extended input-output (EEIO) model that produces spend-based emission factors, in kg CO₂e per USD for 405 sectors across 148 countries, plus 21 regional averages and one global average. It uses 2024 as the base year and is available to use for 2024, 2025 and 2026 measurements.
 
 This release candidate will be superseded in December by the release of the Cornerstone EEIO 2026 final version, which will include a time series from 2017 to 2026.
 

From d915aaf08dd78d504a317977130a0bd62fc444fa Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Thu, 22 Oct 2026 12:04:32 -0700
Subject: [PATCH 24/47] docs(releases): split the release candidate changes
 into three child pages

Under the release candidate page: "Summary of changes vs. USEEIO", "Summary of
changes vs. CEDA" and "Change log" (the full table of data and methodology
changes plus the model reconciliation decisions). Rename the two earlier change-log
pages into the summaries, write the USEEIO summary from the working Doc, redraw its
four charts (summary, company histogram, drivers, cement), add a matching CEDA
summary chart, and link the RC page to all three.

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 figures/diagrams/waterfall-vs-useeio.png      | Bin 75604 -> 0 bytes
 figures/diagrams/waterfall-vs-useeio.svg      | 340 ------------------
 figures/figures.csv                           |   6 +-
 figures/scripts/rc_summary_figures.py         | 165 +++++++++
 figures/scripts/waterfalls.py                 |  10 +-
 images/cement-ef.png                          | Bin 0 -> 12549 bytes
 images/company-footprint-change.png           | Bin 0 -> 23943 bytes
 images/drivers-vs-useeio.png                  | Bin 0 -> 102442 bytes
 images/summary-vs-ceda.png                    | Bin 0 -> 45951 bytes
 images/summary-vs-useeio.png                  | Bin 0 -> 45313 bytes
 myst.yml                                      |   5 +-
 releases/change-log.md                        |  63 ++++
 releases/changelog-useeio.md                  |  99 -----
 releases/rc.md                                |   2 +-
 .../{changelog-ceda.md => summary-ceda.md}    |  33 +-
 releases/summary-useeio.md                    |  80 +++++
 16 files changed, 328 insertions(+), 475 deletions(-)
 delete mode 100644 figures/diagrams/waterfall-vs-useeio.png
 delete mode 100644 figures/diagrams/waterfall-vs-useeio.svg
 create mode 100644 figures/scripts/rc_summary_figures.py
 create mode 100644 images/cement-ef.png
 create mode 100644 images/company-footprint-change.png
 create mode 100644 images/drivers-vs-useeio.png
 create mode 100644 images/summary-vs-ceda.png
 create mode 100644 images/summary-vs-useeio.png
 create mode 100644 releases/change-log.md
 delete mode 100644 releases/changelog-useeio.md
 rename releases/{changelog-ceda.md => summary-ceda.md} (74%)
 create mode 100644 releases/summary-useeio.md

diff --git a/figures/diagrams/waterfall-vs-useeio.png b/figures/diagrams/waterfall-vs-useeio.png
deleted file mode 100644
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-    <text style="font-weight: 500; font-size: 11px; font-family: 'Helvetica', 'Arial', 'DejaVu Sans'; text-anchor: middle; fill: #2c2c2a" x="617.670624" y="123.853654" transform="rotate(-0 617.670624 123.853654)">−0.5%</text>
-   </g>
-   <g id="text_24">
-    <text style="font-weight: 500; font-size: 11px; font-family: 'Helvetica', 'Arial', 'DejaVu Sans'; fill: #2c2c2a" transform="translate(699.844212 135.489207)">0.2448</text>
-    <text style="font-weight: 500; font-size: 11px; font-family: 'Helvetica', 'Arial', 'DejaVu Sans'; fill: #2c2c2a" transform="translate(688.081087 147.347895)">(−1.0% net)</text>
-   </g>
-  </g>
- </g>
- <defs>
-  <clipPath id="ped732dd9cd">
-   <rect x="59.244706" y="11.88" width="720.875294" height="346.547059"/>
-  </clipPath>
- </defs>
-</svg>
diff --git a/figures/figures.csv b/figures/figures.csv
index 65ea8e04..6fd2b52e 100644
--- a/figures/figures.csv
+++ b/figures/figures.csv
@@ -1,5 +1,9 @@
 id,page,type,data_source,generator_script,owner,status
 release-cycle,factors/whats-in-a-release.md,diagram,none (hand-drawn schematic),figures/scripts/release_cycle.py,Yohanna Maldonado,draft
 release-lifecycle,factors/whats-in-a-release.md,diagram,none (hand-drawn schematic),figures/scripts/release_lifecycle.py,Yohanna Maldonado,draft (option 2)
-waterfall-vs-useeio,releases/rc.md (draft),chart,"Roundtable 2 deck, slide 17 (percent steps)",figures/scripts/waterfalls.py,Yohanna Maldonado,draft (steps do not sum to the net change)
 waterfall-vs-ceda,releases/rc.md (draft),chart,"Roundtable 2 deck, slide 25 (percent steps)",figures/scripts/waterfalls.py,Yohanna Maldonado,draft (steps do not sum to the net change)
+summary-vs-useeio,releases/summary-useeio.md,chart,"Change log working Doc (summary chart)",figures/scripts/rc_summary_figures.py,Yohanna Maldonado,draft
+summary-vs-ceda,releases/summary-ceda.md,chart,"Roundtable 2 deck, slide 15 (percent steps)",figures/scripts/rc_summary_figures.py,Yohanna Maldonado,draft
+company-footprint-change,releases/summary-useeio.md,chart,"Change log working Doc (histogram; heights read off the image)",figures/scripts/rc_summary_figures.py,Yohanna Maldonado,draft (approximate)
+drivers-vs-useeio,releases/summary-useeio.md,chart,"Change log working Doc (driver waterfall)",figures/scripts/rc_summary_figures.py,Yohanna Maldonado,draft (steps do not sum to the net change)
+cement-ef,releases/summary-useeio.md,chart,"Change log working Doc (values 4.35 and 5.21; unit to confirm)",figures/scripts/rc_summary_figures.py,Yohanna Maldonado,draft
diff --git a/figures/scripts/rc_summary_figures.py b/figures/scripts/rc_summary_figures.py
new file mode 100644
index 00000000..2d1fd4e2
--- /dev/null
+++ b/figures/scripts/rc_summary_figures.py
@@ -0,0 +1,165 @@
+"""Write the charts for the release candidate "Summary of changes" pages to images/.
+
+Redrawn from the Doc "Change log" working draft so they read cleanly in the text: white background,
+no title and no source line on the image (those live in the figure caption).
+
+- summary-vs-useeio.png: US emission factor, USEEIO v1.4 to Cornerstone 2026, split into data updates
+  and methodology changes (+0.4% and -1.2%; 0.2474 to 0.2454, -0.8% net).
+- summary-vs-ceda.png: global emission factor, CEDA 2025 to Cornerstone 2026 (+6.3% and -28.6%;
+  0.6957 to 0.5281, -24.1% net). The methodology bar is drawn to the stated end value.
+- company-footprint-change.png: distribution of the change in the footprint of 400 corporate
+  footprints against USEEIO (median -1.3%). Bar heights are relative and were read off the draft's
+  histogram, so they are approximate; the y-axis is not labelled.
+- drivers-vs-useeio.png: the drivers of the change against USEEIO (percentages from the draft). The
+  steps do not sum to the net change stated in the draft; the last step is not forced to land on it.
+- cement-ef.png: cement emission factor, USEEIO vs Cornerstone (4.35 and 5.21, values from the draft).
+
+Run from the repo root (needs matplotlib):
+    uv run --with matplotlib python figures/scripts/rc_summary_figures.py
+"""
+from pathlib import Path
+
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt  # noqa: E402
+from matplotlib.patches import Rectangle  # noqa: E402
+
+OUT = Path(__file__).resolve().parents[2] / "images"
+INK, MUTED = "#2C2C2A", "#5F5E5A"
+BASE, END = "#B4B2A9", "#5F5E5A"
+DATA, METHOD = "#D85A30", "#1D9E75"          # data update, methodology change
+plt.rcParams.update({"font.family": ["Helvetica", "Arial", "DejaVu Sans"], "font.size": 11})
+
+
+def clean(ax, left=False):
+    for s in ("top", "right") + (() if left else ("left",)):
+        ax.spines[s].set_visible(False)
+    ax.spines["bottom"].set_color("#B4B2A9")
+    if not left:
+        ax.set_yticks([])
+    ax.tick_params(axis="x", length=0, colors=INK)
+
+
+def legend(ax, loc="lower right", anchor=(1.0, 1.0)):
+    handles = [plt.Rectangle((0, 0), 1, 1, color=DATA), plt.Rectangle((0, 0), 1, 1, color=METHOD)]
+    ax.legend(handles, ["Data update", "Methodology"], loc=loc, bbox_to_anchor=anchor, ncol=2, frameon=False,
+              fontsize=10, labelcolor=INK, borderaxespad=0.2)
+
+
+def summary(name, start_label, start, steps, end, net, size=(7.2, 3.9)):
+    """steps: [(label, pct, colour)]; the last step is drawn to land on `end`."""
+    fig, ax = plt.subplots(figsize=size, dpi=200)
+    level = start
+    ax.bar(0, start, color=BASE, width=0.6)
+    ax.text(0, start * 1.015, f"{start:.4f}", ha="center", va="bottom", color=INK, fontweight="medium")
+    xs, labels = [0], [start_label]
+    for i, (label, pct, colour) in enumerate(steps, start=1):
+        new = end if i == len(steps) else level + start * pct / 100
+        lo, hi = min(level, new), max(level, new)
+        ax.bar(i, hi - lo, bottom=lo, color=colour, width=0.6)
+        ax.text(i, hi * 1.015, f"{pct:+.1f}%".replace("-", "−"), ha="center", va="bottom", color=colour, fontweight="medium")
+        ax.plot([i - 0.7, i - 0.3], [level, level], color="#B4B2A9", lw=0.9, ls=(0, (3, 3)))
+        level = new
+        xs.append(i); labels.append(label)
+    n = len(steps) + 1
+    ax.plot([n - 0.7, n - 0.3], [level, level], color="#B4B2A9", lw=0.9, ls=(0, (3, 3)))
+    ax.bar(n, end, color=END, width=0.6)
+    ax.text(n, end * 1.015, f"{end:.4f}\n{net} net", ha="center", va="bottom", color=INK, fontweight="medium", linespacing=1.3)
+    xs.append(n); labels.append("Cornerstone\n2026")
+    ax.set_xticks(xs); ax.set_xticklabels(labels, fontsize=10)
+    ax.set_ylim(0, start * 1.3)
+    clean(ax); legend(ax)
+    fig.tight_layout()
+    fig.savefig(OUT / name, facecolor="white", bbox_inches="tight")
+    plt.close(fig)
+
+
+def histogram():
+    # (centre of the 2.5-point bin in %, relative height read off the draft's histogram)
+    inside = [(-16.3, 16), (-13.9, 19), (-11.3, 52), (-8.9, 93), (-6.2, 111), (-3.8, 100), (-1.5, 272), (1.2, 152),
+              (3.6, 55), (6.2, 31), (8.6, 16), (11.3, 16), (13.7, 13), (16.0, 7), (18.7, 10)]
+    outside = [(-48.9, 1), (-33.8, 1), (-28.8, 4), (-24.0, 4), (-21.4, 7), (21.1, 4), (23.6, 7), (31.3, 1), (36.2, 1),
+               (38.7, 1), (43.8, 1), (46.3, 1), (68.8, 1), (76.3, 1)]
+    fig, ax = plt.subplots(figsize=(7.2, 3.3), dpi=200)
+    ax.axvspan(-20, 20, color="#EFEDE6", zorder=0)
+    ax.bar([x for x, _ in inside], [h for _, h in inside], width=2.3, color="#C9CAD1", zorder=2)
+    ax.bar([x for x, _ in outside], [h if h > 2 else 3 for _, h in outside], width=2.3, color=METHOD, zorder=2)
+    ax.axvline(-1.3, color="#A33F2E", ls=(0, (4, 3)), lw=1.4, zorder=3)
+    ax.axvline(0, color=INK, lw=0.8, zorder=3)
+    ax.text(-4, 300, "Median −1.3%", ha="right", va="center", color="#A33F2E", fontweight="medium")
+    ax.text(0, -52, "Within ±20%", ha="center", va="top", color=MUTED, fontsize=9.5)
+    ax.set_xlim(-100, 100); ax.set_ylim(0, 330)
+    ax.set_xticks([-100, -50, -20, 0, 20, 50, 100])
+    ax.set_xticklabels(["−100%", "−50%", "−20%", "0%", "+20%", "+50%", "+100%"], color=INK)
+    clean(ax)
+    fig.tight_layout()
+    fig.savefig(OUT / "company-footprint-change.png", facecolor="white")
+    plt.close(fig)
+
+
+def drivers():
+    base, end = 0.2474, 0.2454
+    steps = [("Model\nreconciliation", -3.5, METHOD, 1), ("US\ndata", 0.4, DATA, None),
+             ("Nowcasting &\nfacility reported\ndata", -5.8, METHOD, 2), ("Trade\nimpact", 8.6, METHOD, 3),
+             ("Benchmark-\ninformed\nimprovements", 0.2, METHOD, None)]
+    fig, ax = plt.subplots(figsize=(9.6, 5.0), dpi=200)
+    ylim = (0.215, 0.262)
+    span = ylim[1] - ylim[0]
+    # group bands behind the steps
+    for x0, x1, label, colour in ((0.5, 3.5, "US MODEL", "#F1EEE8"), (3.5, 5.5, "GLOBAL MODEL (US PORTION)", "#E8EEEE")):
+        ax.add_patch(Rectangle((x0 + 0.04, ylim[0]), x1 - x0 - 0.08, span, fc=colour, ec="none", zorder=0))
+        ax.text((x0 + x1) / 2, ylim[1] - span * 0.045, label, ha="center", va="center", color=MUTED, fontsize=9.5,
+                fontweight="medium")
+    ax.bar(0, base - ylim[0], bottom=ylim[0], color=BASE, width=0.6, zorder=2)
+    ax.text(0, base + span * 0.012, f"{base:.4f}", ha="center", va="bottom", color=INK, fontweight="medium")
+    level = base
+    xs, labels = [0], ["Baseline\nUSEEIO v1.4"]
+    for i, (label, pct, colour, badge) in enumerate(steps, start=1):
+        new = level + base * pct / 100
+        lo, hi = min(level, new), max(level, new)
+        ax.bar(i, hi - lo, bottom=lo, color=colour, width=0.6, zorder=2)
+        ax.text(i, hi + span * 0.012, f"{pct:+.1f}%".replace("-", "−"), ha="center", va="bottom", color=colour,
+                fontweight="medium")
+        if badge:
+            ax.text(i + 0.36, hi + span * 0.03, str(badge), ha="center", va="center", color="white", fontsize=9, fontweight="medium",
+                    bbox=dict(boxstyle="circle,pad=0.3", fc=INK, ec="none"), zorder=4)
+        ax.plot([i - 0.7, i - 0.3], [level, level], color="#B4B2A9", lw=0.9, ls=(0, (3, 3)), zorder=1)
+        level = new
+        xs.append(i); labels.append(label)
+    n = len(steps) + 1
+    ax.plot([n - 0.7, n - 0.3], [level, level], color="#B4B2A9", lw=0.9, ls=(0, (3, 3)), zorder=1)
+    ax.bar(n, end - ylim[0], bottom=ylim[0], color=END, width=0.6, zorder=2)
+    ax.text(n, end + span * 0.012, f"{end:.4f}\n−0.8% net", ha="center", va="bottom", color=INK, fontweight="medium", linespacing=1.3)
+    xs.append(n); labels.append("Cornerstone\n2026")
+    ax.set_xticks(xs); ax.set_xticklabels(labels, fontsize=9)
+    ax.set_xlim(-0.6, n + 0.6); ax.set_ylim(*ylim)
+    ax.set_ylabel("kg CO$_2$e per 2024 USD", color=MUTED)
+    ax.tick_params(axis="y", colors=MUTED, length=0)
+    ax.grid(axis="y", color="#E8E6DF", lw=0.8, zorder=0)
+    clean(ax, left=True); ax.spines["left"].set_visible(False)
+    legend(ax)
+    fig.tight_layout()
+    fig.savefig(OUT / "drivers-vs-useeio.png", facecolor="white", bbox_inches="tight")
+    plt.close(fig)
+
+
+def cement():
+    fig, ax = plt.subplots(figsize=(6.2, 2.0), dpi=200)
+    for y, (label, v, colour, bold) in enumerate((("USEEIO", 4.35, BASE, False), ("Cornerstone", 5.21, "#0F6E56", True))):
+        ax.barh(y, v, color=colour, height=0.5)
+        ax.text(v + 0.08, y, f"{v:.2f}", va="center", color=INK, fontweight="medium" if bold else "normal")
+    ax.set_yticks([0, 1]); ax.set_yticklabels(["USEEIO", "Cornerstone"], color=INK)
+    ax.invert_yaxis(); ax.set_xlim(0, 6.2)
+    for s in ("top", "right", "bottom"):
+        ax.spines[s].set_visible(False)
+    ax.spines["left"].set_color("#B4B2A9"); ax.set_xticks([]); ax.tick_params(axis="y", length=0)
+    fig.tight_layout()
+    fig.savefig(OUT / "cement-ef.png", facecolor="white")
+    plt.close(fig)
+
+
+summary("summary-vs-useeio.png", "USEEIO\nv1.4 (US)", 0.2474, [("Data\nupdates", 0.4, DATA), ("Methodology\nchanges", -1.2, METHOD)], 0.2454, "−0.8%")
+summary("summary-vs-ceda.png", "CEDA\n2025", 0.6957, [("Data\nupdates", 6.3, DATA), ("Methodology\nchanges", -28.6, METHOD)], 0.5281, "−24.1%")
+histogram(); drivers(); cement()
+print("wrote", *(p.name for p in sorted(OUT.glob("*.png"))))
diff --git a/figures/scripts/waterfalls.py b/figures/scripts/waterfalls.py
index b94866f4..a5a816c8 100644
--- a/figures/scripts/waterfalls.py
+++ b/figures/scripts/waterfalls.py
@@ -1,6 +1,6 @@
 """Write the two release-candidate waterfall charts to figures/diagrams/.
 
-Source: the Practitioners Forum Roundtable 2 deck (slides 17 and 25). Steps are the percentage
+Source: the Practitioners Forum Roundtable 2 deck (slide 25). The USEEIO driver chart is now drawn by rc_summary_figures.py. Steps are the percentage
 changes shown on the slides, converted to kg CO2e per USD against each baseline. The listed steps
 do not sum exactly to the net change on the slides, so the remainder is drawn as "Other" until the
 science team confirms the attribution.
@@ -22,14 +22,6 @@
                      "svg.fonttype": "none"})
 
 CHARTS = {
-    "waterfall-vs-useeio": dict(
-        title="US production-weighted emission factor: Cornerstone 2026 vs. USEEIO v1.4",
-        ylabel="kg CO$_2$e per 2024 USD",
-        base_label="USEEIO\nv1.4", base=0.2474, end_label="Cornerstone\n2026", end=0.2448,
-        steps=[("Model\nreconciliation", -3.5), ("US\ndata", 0.4), ("Nowcasting &\nfacility data", -5.8),
-               ("Trade\nimpact", 9.0), ("Benchmark-\ninformed\nimprovements", -0.5)],
-        ylim=(0.220, 0.262), net="−1.0% net",
-    ),
     "waterfall-vs-ceda": dict(
         title="Global production-weighted emission factor: Cornerstone 2026 vs. CEDA 2025",
         ylabel="kg CO$_2$e per 2024 USD",
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diff --git a/myst.yml b/myst.yml
index 7dee6446..4ddbe4ef 100644
--- a/myst.yml
+++ b/myst.yml
@@ -56,8 +56,9 @@ project:
       children:
         - file: releases/rc.md
           children:
-            - file: releases/changelog-useeio.md
-            - file: releases/changelog-ceda.md
+            - file: releases/summary-useeio.md
+            - file: releases/summary-ceda.md
+            - file: releases/change-log.md
     - file: faq.md
 site:
   template: book-theme
diff --git a/releases/change-log.md b/releases/change-log.md
new file mode 100644
index 00000000..e217ca4b
--- /dev/null
+++ b/releases/change-log.md
@@ -0,0 +1,63 @@
+---
+title: "Change log"
+---
+
+:::{tip} Summary
+This table summarizes all the data and methodology changes behind Cornerstone EEIO 2026, compared to both USEEIO v1.4 and CEDA 2025.
+:::
+
+| Type of change | Category | What changed? |
+|---|---|---|
+| Data update | US data | Updated the U.S. GHG emissions data (totals and by sector) released by the **University of Maryland Greenhouse Gas Inventory and Analysis (UMD GHGIA)** [(link)](https://ghgi.cgs.umd.edu/data.html) from **2023 to 2024**. |
+| Data update | UK data | Updated the UK IO tables released by the **U.K. Office for National Statistics** [(link)](https://www.ons.gov.uk/economy/nationalaccounts/supplyandusetables/datasets/ukinputoutputanalyticaltablesdetailed) from **2022 to 2023**, incorporating the most recent benchmark IO tables. Updated the U.K. GHG emissions data released by the **U.K. Office for National Statistics** [(link)](https://www.ons.gov.uk/economy/environmentalaccounts/datasets/ukenvironmentalaccountsatmosphericemissionsgreenhousegasemissionsbyeconomicsectorandgasunitedkingdom) from **2022 to 2023**, incorporating the most recent benchmark IO tables. |
+| Data update | China data | Updated the China IO tables released by the **National Bureau of Statistics of China (NBS)** [(link)](https://www.stats.gov.cn/sj/ndsj/2025/html/sm03.htm) from **2020 to 2023**, incorporating the most recent benchmark IO tables. Updated the China GHG emissions data released by **Our World in Data (OWID)** [(link)](https://github.com/owid/co2-data), **Community Emissions Data System (CEDS)** [(link)](https://zenodo.org/records/10904361), and **Emissions Database for Global Atmospheric Research (EDGAR)** [(link)](https://edgar.jrc.ec.europa.eu/dataset_ghg80) from **2020 to 2023**, incorporating the most recent benchmark IO tables. |
+| Data update | Europe & other global data | Updated the tier 2 country IO tables using Eurostat's Full International and Global Accounts for Research in Input-Output Analysis (FIGARO) [(link)](https://ec.europa.eu/eurostat/en/web/esa-supply-use-input-tables/database) for **2023** data where available and otherwise to **2020 OECD IO tables** [(link)](https://www.oecd.org/en/data/datasets/inter-country-input-output-tables.html). Updated the tier 2 country emissions data using **FIGARO's 2023 data** [(link)](https://ec.europa.eu/eurostat/en/web/esa-supply-use-input-tables/database) where available and otherwise to **2020 OECD IO tables** [(link)](https://www.oecd.org/en/data/datasets/inter-country-input-output-tables.html). |
+| Data update | Global trade data | Updated global trade data released by the **United Nations Comtrade (BACI)** [(link)](https://www.cepii.fr/DATA_DOWNLOAD/baci/doc/baci_webpage.html) from **2023 to 2024**. Global trade data is supplemented with **FIGARO trade data** [(link)](https://ec.europa.eu/eurostat/en/web/esa-supply-use-input-tables/database) to fill gaps in BACI data. |
+| Methodology improvements | Model reconciliation | USEEIO and CEDA teams reviewed 18 differences between the models for the US and either adopted one of the model's approaches or proposed an improvement. The differences can be categorized into 3 types: sectors schema, treatment of economic data and GHG attribution. For details on the differences, see the [model reconciliation section](#change-log-model-reconciliation) below. |
+| Methodology improvements | Auto-balancing structural reflection (ABSR) | Improved disaggregation of non-US economic data into detailed sectors using a new block scaling method. |
+| Methodology improvements | US nowcasting and facility reported data | Estimated detailed 2024 US IO tables from primary sources from 2017 to 2024, replicating the tables produced by the BEA. Used facility reported data to allocate emissions of manufacturing sectors, instead of survey data. |
+| Methodology improvements | Bug fixes | Corrected errors in HS mapping. Corrected double application of AR5 to AR6 conversion. Corrected loading of the UK IO table. |
+| Methodology improvements | Benchmark informed improvements | Leveraged third-party benchmark data created with ~1,000 data sources to review key sectors that drive ~50% of global emissions, producing one of three outcomes: benchmark improvements, model improvements or documentation of known discrepancies. More on why we benchmark in [](../benchmarking/index.md). |
+
+(change-log-model-reconciliation)=
+## Model reconciliation
+
+The USEEIO and the US portion of CEDA had 18 methodological differences. Cornerstone EEIO adopted either one of the approaches for each difference or improved the approach. For more details on each topic, see our open discussions on [GitHub](https://github.com/cornerstone-data/methods/discussions).
+
+### Sectors schema
+
+| Item | USEEIO | CEDA | Cornerstone |
+|---|---|---|---|
+| **Waste sector** ([#8](https://github.com/cornerstone-data/methods/discussions/8)) | Split into 7 sectors | Single sector | **USEEIO: split into 7** |
+| **Aluminum** ([#9](https://github.com/cornerstone-data/methods/discussions/9)) | Primary and secondary separate | Merged | **USEEIO: separate** |
+| **Home appliances** ([#24](https://github.com/cornerstone-data/methods/discussions/24)) | One sector, as BEA | Four sectors | **USEEIO: one sector** |
+| **Accounting-only commodities** ([#1](https://github.com/cornerstone-data/methods/discussions/1)) | Kept in tables (inferred) | Removed, output recalculated | **CEDA: remove** |
+
+### Economic data
+
+| Item | USEEIO | CEDA | Cornerstone |
+|---|---|---|---|
+| **Redefinitions** ([#3](https://github.com/cornerstone-data/methods/discussions/3)) | Before | After | **CEDA: after** |
+| **Electric utilities** ([#7](https://github.com/cornerstone-data/methods/discussions/7)) | 3 separate industries | One aggregated industry | **CEDA: aggregate** |
+| **Transit** ([#44](https://github.com/cornerstone-data/methods/discussions/44)) | Government and private separate | Aggregated | **CEDA: aggregate** |
+| **B matrix steps** ([#16](https://github.com/cornerstone-data/methods/discussions/16)) | Adjust price, divide, convert | Divide, convert, adjust price | **USEEIO** |
+| **Scrap** ([#5](https://github.com/cornerstone-data/methods/discussions/5)) | Adjusted out in v1, kept in v2 | Adjusted out of Make table | **Adjust scrap out** |
+
+### GHG attribution
+
+| Item | USEEIO | CEDA | Cornerstone |
+|---|---|---|---|
+| **Electricity across sectors** ([#33](https://github.com/cornerstone-data/methods/discussions/33)) | Split across 3 electric sectors | All in one sector (221100) | **CEDA: no split** |
+| **Core GHG schema** ([#47](https://github.com/cornerstone-data/methods/discussions/47)) | NAICS codes, mapped to BEA later | BEA sectors directly | **Hybrid: NAICS plus BEA** |
+| **Mobile Combustion Emissions** ([#39](https://github.com/cornerstone-data/methods/discussions/39)) | GHGI Table 3-13 by vehicle type | Two-layer fuel allocation, wide government spread | **Hybrid: Updated approach** |
+| **Households** ([#41](https://github.com/cornerstone-data/methods/discussions/41)) | Tracked as sector F01000 | Not always tracked | **USEEIO: track F01000** |
+| **Use table for attribution** ([#45](https://github.com/cornerstone-data/methods/discussions/45)) | Supply-Use, before redefinitions | Make/Use, after redefinitions | **CEDA: after redefinitions** |
+
+For the summaries of these changes, see [](summary-useeio.md) and [](summary-ceda.md).
+
++++
+
+:::{div}
+:class: page-status
+Applies to: RC · Last reviewed: RC
+:::
diff --git a/releases/changelog-useeio.md b/releases/changelog-useeio.md
deleted file mode 100644
index d0835bc7..00000000
--- a/releases/changelog-useeio.md
+++ /dev/null
@@ -1,99 +0,0 @@
----
-title: "Change log vs. USEEIO"
----
-
-:::{tip} Summary
-Cornerstone EEIO 2026 is the continuation of the USEEIO Supply Chain GHG Emission Factors v1.4. For US factors, the net change is on median −1.0%, from 0.2474 to 0.2448 kg CO₂e per 2024 USD (production-weighted).
-
-However, the net figure hides large changes that offset each other. Three changes drive about 95% of the impact: model reconciliation, nowcasting and facility-reported data, and the modeling of imports.
-:::
-
-## The changes at a glance
-
-Emission factors are production-weighted totals for the United States, in kg CO₂e per 2024 USD.
-
-| | Cornerstone 2026 vs. USEEIO v1.4 |
-|---|---|
-| Emission factor, before and after | 0.2474 to 0.2448 |
-| **Net change** | **−1.0%** |
-| Data updates | −4.2% |
-| Methodology changes | +3.3% |
-| Median change in a company's total footprint | −1.4% |
-| Companies whose footprint moves by more than 20% | 6% |
-
-*Company figures come from a sample of 400 corporate footprints that combine spend-based and activity data.*
-
-:::{important} Your footprint may not follow the median
-A company's change depends on which sectors and countries its spend falls in. A small median can hide large moves in individual sectors, in both directions.
-:::
-
-## What drives the change for USEEIO users
-
-:::{figure} ../figures/diagrams/waterfall-vs-useeio.png
-:label: waterfall-vs-useeio
-:alt: Waterfall chart of the US emission factor from USEEIO v1.4 (0.2474) to Cornerstone 2026 (0.2448), by driver: model reconciliation, US data, nowcasting and facility data, trade impact, and benchmark-informed improvements.
-:width: 100%
-
-**Change in the US production-weighted emission factor from USEEIO v1.4 to Cornerstone 2026, by driver.** Percentages are changes relative to the USEEIO baseline. Increases are in coral and decreases in teal. The vertical axis starts at 0.22.
-:::
-
-### Model reconciliation
-
-The USEEIO and CEDA teams reviewed 18 differences between the two models for the US. For each, they adopted one model's approach or proposed an improvement. Selected decisions:
-
-| Topic | USEEIO | CEDA | Cornerstone |
-|---|---|---|---|
-| Waste | Split into 7 sectors | One sector | USEEIO: split into 7 |
-| Aluminum | Primary and secondary separate | Merged | USEEIO: separate |
-| Home appliances | One sector, as BEA | Four sectors | USEEIO: one sector |
-| Accounting-only commodities | Kept in the tables | Removed, output recalculated | CEDA: remove |
-| Electric utilities | 3 separate industries | One aggregated industry | CEDA: aggregate |
-| Transit | Government and private separate | Aggregated | CEDA: aggregate |
-| Households | Tracked as sector F01000 | Not always tracked | USEEIO: track F01000 |
-
-Each decision is documented in the open discussion log (see Learn more).
-
-### Nowcasting and facility-reported data
-
-BEA publishes detailed (about 400-sector) US input-output tables only every 10 years, with a five-year lag. The latest is for 2017. Nowcasting rebuilds these tables for every year from 2017 to 2024, in BEA's own structure, using the same official statistics BEA uses.
-
-Where most facilities in a sector report their emissions, those reports replace estimates based on fuel use data that is updated about every four years. The 2024 data includes 6,127 facility reports across 84 industries. More than 50% of the economic data behind the factors is now backed by reported annual data, beyond inflation adjustments.
-
-### Trade impact: modeling imports
-
-USEEIO treated the US as a closed economy: every product used in the US was assumed to be made in the US. Cornerstone uses trade data to model each import in the country that produced it. Many imports come from more emissions-intensive economies such as China and India, so US factors tend to rise by 10 to 30% on average. This is the main upward pressure and the main reason for the change for USEEIO users.
-
-For example, about 40% of the US crude oil mix is imported. Emissions from that oil are allocated to the producing countries, and Canadian oil is up to twice as emissions-intensive as US oil.
-
-**Case study: light trucks and utility vehicles.** The US factor rises by 24.1% (0.2925 to 0.3628 kg CO₂e per USD), mostly because of trade effects.
-
-## Third-party benchmarking
-
-Cornerstone uses benchmark data built from about 1,000 sources to review the key sectors that drive about 50% of global emissions. Each review ends in one of three outcomes:
-
-- **Sharper benchmark.** Scope and price assumptions are aligned, so the external reference is directly comparable to the model.
-- **Model improvement.** The gap is traced to a data source or an allocation method, and fixed with more data or a corrected implementation.
-- **Known discrepancy.** The difference is documented, because of a limitation in how the model is built or a gap in the data.
-
-About 70% of country-sector pairs with a verified benchmark are within 25% of it. 21% are 25–50% apart and 10% are 50% or more apart.
-
-| Outcome | Example | Gap before and after |
-|---|---|---|
-| Sharper benchmark | US electricity: the benchmark covered only private utilities, while the model also includes state and local utilities. Aligning scope gives a benchmark of 2.37 against the model's 2.44 kg CO₂e per USD. | 15% to 3% |
-| Model improvement | Brazil dairy: the model was allocating methane from beef to dairy for lack of country-specific data. Brazil's national accounts of emissions and output fixed it. | 51% to 5% |
-| Known discrepancy | US oil and gas: the benchmark roughly doubles methane using aerial and satellite measurements. The model uses the EPA inventory, as for every other sector. Resolving this needs more research. | 22% (stays) |
-
-For the equivalent change log against CEDA, see [](changelog-ceda.md).
-
-## Learn more
-
-- **From the field:** *to be added*
-- **Cornerstone papers:** *to be added*
-- **Code:** [Methods discussion log](https://github.com/cornerstone-data/methods/discussions), the record of each model reconciliation decision.
-
-+++
-
-:::{div}
-:class: page-status
-Applies to: RC · Last reviewed: RC
-:::
diff --git a/releases/rc.md b/releases/rc.md
index 8f3b1420..d7ddb5a3 100644
--- a/releases/rc.md
+++ b/releases/rc.md
@@ -102,7 +102,7 @@ The charts compare the new factors with each model they replace.
 A company’s change depends on which sectors and countries its spend falls in. A small median can hide large moves in individual sectors, in both directions.
 :::
 
-For a more detailed change log against the USEEIO or CEDA, please refer to [](changelog-useeio.md) and [](changelog-ceda.md). For even additional details or custom support on how to navigate the changes in your footprint, sign up for a demo of the enterprise product [here](https://watershed.com/en-GB/solutions/ceda).
+For a more detailed summary of the changes against USEEIO or CEDA, please refer to [](summary-useeio.md) and [](summary-ceda.md). The full list of data and methodology changes is in the [](change-log.md). For even additional details or custom support on how to navigate the changes in your footprint, sign up for a demo of the enterprise product [here](https://watershed.com/en-GB/solutions/ceda).
 
 +++
 
diff --git a/releases/changelog-ceda.md b/releases/summary-ceda.md
similarity index 74%
rename from releases/changelog-ceda.md
rename to releases/summary-ceda.md
index 031dccdb..65d569f5 100644
--- a/releases/changelog-ceda.md
+++ b/releases/summary-ceda.md
@@ -1,36 +1,23 @@
 ---
-title: "Change log vs. CEDA"
+title: "Summary of changes vs. CEDA"
 ---
 
 :::{tip} Summary
 Cornerstone EEIO 2026 replaces CEDA 2025. The global production-weighted emission factor falls by 24.1%, from 0.6957 to 0.5281 kg CO₂e per 2024 USD. Data updates raise factors (+6.3%) and methodology changes lower them by more (−28.6%). Four changes drive about 95% of the impact: benchmark-informed improvements, auto-balanced structural reflection (ABSR), new data for China, and new data for Europe.
 :::
 
-## The changes at a glance
+:::{figure} ../images/summary-vs-ceda.png
+:label: summary-vs-ceda
+:alt: Waterfall chart of the global emission factor. From CEDA 2025 at 0.6957, data updates add 6.3% and methodology changes remove 28.6%, ending at 0.5281 for Cornerstone 2026, 24.1% lower.
+:width: 80%
 
-Emission factors are global production-weighted totals, in kg CO₂e per 2024 USD.
-
-| | Cornerstone 2026 vs. CEDA 2025 |
-|---|---|
-| Emission factor, before and after | 0.6957 to 0.5281 |
-| **Net change** | **−24.1%** |
-| Data updates | +6.3% |
-| Methodology changes | −28.6% |
-| Median change in a company's total footprint | −13.7% |
-| Companies whose footprint moves by more than 20% | 24% |
-
-*Company figures come from a sample of 400 corporate footprints that combine spend-based and activity data.*
+**Cornerstone 2026 against CEDA 2025 (global).** Global production-weighted total emission factor, in kg CO₂e per 2024 USD, split into data updates and methodology changes.
+:::
 
 :::{important} Your footprint may not follow the median
-A company's change depends on which sectors and countries its spend falls in. A small median can hide large moves in individual sectors, in both directions.
+A company's change depends on which sectors and countries its spend falls in. A small median can hide large moves in individual sectors, in both directions. From a sample of 400 corporate footprints, the median company will experience a decrease of 13.7%, and 24% of companies will see their footprint move by more than 20%.
 :::
 
-## What the science improvements mean in practice
-
-1. **A more current picture of the US economy that accounts for imports.** US input-output tables are rebuilt for every year from 2017 to 2024, manufacturing uses facility-reported emissions, and imports are modeled where they are made.
-2. **Newer data for Europe, the UK and China, and better gap-filling for the rest.** Europe moves from the OECD's ICIO tables to Eurostat's FIGARO (through 2023). China moves to the latest National Bureau of Statistics tables (through 2023), with more sectors. Countries without data are filled with the auto-balanced structural reflection method.
-3. **A validation process that feeds back into the model.** Third-party benchmarks review the sectors behind about 50% of global emissions. Gaps are traced and fixed in the data or the method, not overridden in the output.
-
 ## What drives the change for CEDA users
 
 :::{figure} ../figures/diagrams/waterfall-vs-ceda.png
@@ -62,7 +49,7 @@ This raises China's emission factors by about 10% at the median. It reflects met
 
 ### New data for Europe
 
-Cornerstone adopts FIGARO, Eurostat's official input-output and emissions dataset. FIGARO is more granular than the OECD dataset for Europe, is available through 2023, and is updated every year, matching Cornerstone's release cadence.
+Cornerstone adopts FIGARO, Eurostat's official input-output and emissions dataset. FIGARO is more granular than the OECD dataset for Europe, is available as a time series through 2023, and is updated every year, matching Cornerstone's release cadence.
 
 This lowers European factors by about 4% at the median. Electricity factors fall in every EU country. For example, Germany's power factor falls by 60%, as renewables grew from 35% to over 50% of generation. The Netherlands' falls by 77%, after a coal ban and a gas-heavy grid.
 
@@ -82,7 +69,7 @@ About 70% of country-sector pairs with a verified benchmark are within 25% of it
 | Model improvement | Brazil dairy: the model was allocating methane from beef to dairy for lack of country-specific data. Brazil's national accounts of emissions and output fixed it. | 51% to 5% |
 | Known discrepancy | US oil and gas: the benchmark roughly doubles methane using aerial and satellite measurements. The model uses the EPA inventory, as for every other sector. Resolving this needs more research. | 22% (stays) |
 
-For the equivalent change log against USEEIO, see [](changelog-useeio.md).
+For the full list of data and methodology changes, see the [](change-log.md). For the equivalent summary against USEEIO, see [](summary-useeio.md).
 
 ## Learn more
 
diff --git a/releases/summary-useeio.md b/releases/summary-useeio.md
new file mode 100644
index 00000000..5664154a
--- /dev/null
+++ b/releases/summary-useeio.md
@@ -0,0 +1,80 @@
+---
+title: "Summary of changes vs. USEEIO"
+---
+
+:::{tip} Summary
+Cornerstone EEIO 2026 is the continuation of the USEEIO Supply Chain GHG Emission Factors v1.4. For US factors, the net change is on median −0.8% in kg CO₂e per 2024 USD (production-weighted), driven by a combination of data and methodology changes.
+:::
+
+:::{figure} ../images/summary-vs-useeio.png
+:label: summary-vs-useeio
+:alt: Waterfall chart of the US emission factor. From USEEIO v1.4 at 0.2474, data updates add 0.4% and methodology changes remove 1.2%, ending at 0.2454 for Cornerstone 2026, 0.8% lower.
+:width: 80%
+
+**Cornerstone 2026 against USEEIO v1.4 (US).** US production-weighted total emission factor, in kg CO₂e per 2024 USD, split into data updates and methodology changes.
+:::
+
+:::{important} Your footprint may not follow the median
+A company's change depends on which sectors and countries its spend falls in. A small median can hide large moves in individual sectors, in both directions. From a sample of 400 corporate footprints, the median company will experience a decrease of 1.3% but there is a range of possibilities, as shown in the histogram below.
+:::
+
+:::{figure} ../images/company-footprint-change.png
+:label: company-footprint-change
+:alt: Histogram of the change in the footprint of 400 companies against USEEIO. Most companies change by less than 20% in either direction, clustered around zero, with a median of a 1.3% decrease and a few companies changing by more than 20%.
+:width: 90%
+
+**Change in the footprint of 400 companies, Cornerstone 2026 against USEEIO v1.4.** Each bar groups companies by the percentage change in their total footprint. Bars outside the shaded ±20% band are in green. Bar heights are approximate.
+:::
+
+## What drives the change for USEEIO users
+
+Three changes drive ~95% of the impact: model reconciliation, nowcasting and facility-reported data, and the modeling of imports.
+
+:::{figure} ../images/drivers-vs-useeio.png
+:label: drivers-vs-useeio
+:alt: Waterfall chart of the US emission factor from USEEIO v1.4 (0.2474) to Cornerstone 2026 (0.2454), by driver. Model reconciliation lowers it by 3.5%, US data adds 0.4%, nowcasting and facility reported data lower it by 5.8%, trade impact adds 8.6% and benchmark-informed improvements add 0.2%. The first three steps belong to the US model and the last two to the US portion of the global model.
+:width: 100%
+
+**Change in the US production-weighted emission factor from USEEIO v1.4 to Cornerstone 2026, by driver.** Percentages are changes relative to the USEEIO baseline. Numbers 1 to 3 mark the three drivers described below. The vertical axis starts at 0.215.
+:::
+
+### Model reconciliation
+
+The USEEIO and CEDA teams reviewed 18 differences between the two models for the US. For each, they adopted one model's approach or proposed an improvement. The differences can be categorized into 3 types: sectors schema, treatment of economic data and GHG attribution. For details on the differences, see the [model reconciliation section](#change-log-model-reconciliation) of the change log.
+
+One example of these differences is using the BEA after redefinition input output tables instead of the before redefinition tables. A detailed explanation of why this is the approach can be found on GitHub [here](https://github.com/cornerstone-data/methods/discussions/3).
+
+### Nowcasting and facility-reported data
+
+BEA publishes detailed (about 400-sector) US input-output tables only every 10 years, with a five-year lag. The latest is for 2017. Nowcasting rebuilds these tables for every year from 2017 to 2024, in BEA's own structure, using the same official statistics BEA uses. This methodology improvement enables building the time series and providing a more current picture of the US economy. More than 50% of the economic data behind the factors is now backed by reported annual data, beyond inflation adjustments.
+
+Cornerstone EEIO also uses facility-level reported emissions data from the US EPA's Greenhouse Gas Reporting Program (GHGRP) where most of the facilities in a sector report them, in place of using estimates based on 4-yr frequency fuel use data. The 2024 data includes 6,127 facility reports across 84 industries. This improvement provides a more accurate picture of the US economy. For example, the US sector emissions now match the totals reported across 87 cement plants (61 MT) in 2024. USEEIO undercounted them by ~20%, driving an increase in emission factors in kg CO₂e/USD.
+
+:::{figure} ../images/cement-ef.png
+:label: cement-ef
+:alt: Bar chart of the cement sector emission factor. USEEIO 4.35, Cornerstone 5.21.
+:width: 60%
+
+**Cement sector emission factor, USEEIO v1.4 and Cornerstone 2026.**
+:::
+
+### Trade impact: modeling imports
+
+USEEIO treated the US as a closed economy: every product used in the US was assumed to be made in the US. Cornerstone uses trade data to model each import in the country that produced it. Many imports come from more emissions-intensive economies such as China and India, so US factors tend to rise by 10 to 30% on average. This is the main upward pressure and the main reason for the change for USEEIO users.
+
+For example, about 40% of the US crude oil mix is imported. Emissions from that oil are allocated to the producing countries, and Canadian oil is up to twice as emissions-intensive as US oil.
+
+For the full list of data and methodology changes, see the [](change-log.md). For the equivalent summary against CEDA, see [](summary-ceda.md).
+
+## Learn more
+
+- **From the field:** *to be added*
+- **Cornerstone papers:** *to be added*
+- **Code:** [Methods discussion log](https://github.com/cornerstone-data/methods/discussions), the record of each model reconciliation decision.
+
++++
+
+:::{div}
+:class: page-status
+Applies to: RC · Last reviewed: RC
+:::

From 1bebca5085d1a2ae9b588acf833a499376423aa7 Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Thu, 22 Oct 2026 12:11:10 -0700
Subject: [PATCH 25/47] =?UTF-8?q?docs(releases):=20sync=20summary=20pages?=
 =?UTF-8?q?=20to=20the=20latest=20Doc=20edits=20(CEDA=20=E2=88=9223.3%,=20?=
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MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Rewrite the CEDA summary from the Doc: new net change, footprint histogram, driver
waterfall and China/Europe text. Add the light-truck imports example to the USEEIO page.

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
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-    <text style="font-weight: 500; font-size: 11px; font-family: 'Helvetica', 'Arial', 'DejaVu Sans'; text-anchor: middle; fill: #2c2c2a" x="603.313478" y="42.912546" transform="rotate(-0 603.313478 42.912546)">−10.7%</text>
-   </g>
-   <g id="text_28">
-    <text style="font-weight: 500; font-size: 11px; font-family: 'Helvetica', 'Arial', 'DejaVu Sans'; text-anchor: middle; fill: #2c2c2a" x="665.547382" y="142.127712" transform="rotate(-0 665.547382 142.127712)">−19.0%</text>
-   </g>
-   <g id="text_29">
-    <text style="font-weight: 500; font-size: 11px; font-family: 'Helvetica', 'Arial', 'DejaVu Sans'; fill: #2c2c2a" transform="translate(710.96074 302.651608)">0.5281</text>
-    <text style="font-weight: 500; font-size: 11px; font-family: 'Helvetica', 'Arial', 'DejaVu Sans'; fill: #2c2c2a" transform="translate(696.139099 314.510296)">(−24.1% net)</text>
-   </g>
-  </g>
- </g>
- <defs>
-  <clipPath id="p9465a0c9f6">
-   <rect x="53.103529" y="11.88" width="727.016471" height="346.533824"/>
-  </clipPath>
- </defs>
-</svg>
diff --git a/figures/figures.csv b/figures/figures.csv
index 6fd2b52e..d5bebddf 100644
--- a/figures/figures.csv
+++ b/figures/figures.csv
@@ -1,9 +1,10 @@
 id,page,type,data_source,generator_script,owner,status
 release-cycle,factors/whats-in-a-release.md,diagram,none (hand-drawn schematic),figures/scripts/release_cycle.py,Yohanna Maldonado,draft
 release-lifecycle,factors/whats-in-a-release.md,diagram,none (hand-drawn schematic),figures/scripts/release_lifecycle.py,Yohanna Maldonado,draft (option 2)
-waterfall-vs-ceda,releases/rc.md (draft),chart,"Roundtable 2 deck, slide 25 (percent steps)",figures/scripts/waterfalls.py,Yohanna Maldonado,draft (steps do not sum to the net change)
 summary-vs-useeio,releases/summary-useeio.md,chart,"Change log working Doc (summary chart)",figures/scripts/rc_summary_figures.py,Yohanna Maldonado,draft
 summary-vs-ceda,releases/summary-ceda.md,chart,"Roundtable 2 deck, slide 15 (percent steps)",figures/scripts/rc_summary_figures.py,Yohanna Maldonado,draft
 company-footprint-change,releases/summary-useeio.md,chart,"Change log working Doc (histogram; heights read off the image)",figures/scripts/rc_summary_figures.py,Yohanna Maldonado,draft (approximate)
 drivers-vs-useeio,releases/summary-useeio.md,chart,"Change log working Doc (driver waterfall)",figures/scripts/rc_summary_figures.py,Yohanna Maldonado,draft (steps do not sum to the net change)
+drivers-vs-ceda,releases/summary-ceda.md,chart,"Change log working Doc (driver waterfall)",figures/scripts/rc_summary_figures.py,Yohanna Maldonado,draft (steps sum to -23.2% against the -23.3% net)
+company-footprint-change-ceda,releases/summary-ceda.md,chart,"Change log working Doc (histogram)",figures/scripts/rc_summary_figures.py,Yohanna Maldonado,draft (bar heights approximate)
 cement-ef,releases/summary-useeio.md,chart,"Change log working Doc (values 4.35 and 5.21; unit to confirm)",figures/scripts/rc_summary_figures.py,Yohanna Maldonado,draft
diff --git a/figures/scripts/rc_summary_figures.py b/figures/scripts/rc_summary_figures.py
index 2d1fd4e2..6e238107 100644
--- a/figures/scripts/rc_summary_figures.py
+++ b/figures/scripts/rc_summary_figures.py
@@ -5,8 +5,12 @@
 
 - summary-vs-useeio.png: US emission factor, USEEIO v1.4 to Cornerstone 2026, split into data updates
   and methodology changes (+0.4% and -1.2%; 0.2474 to 0.2454, -0.8% net).
-- summary-vs-ceda.png: global emission factor, CEDA 2025 to Cornerstone 2026 (+6.3% and -28.6%;
-  0.6957 to 0.5281, -24.1% net). The methodology bar is drawn to the stated end value.
+- summary-vs-ceda.png: global emission factor, CEDA 2025 to Cornerstone 2026 (+5.8% and -27.5%;
+  0.6942 to 0.5324, -23.3% net). The methodology bar is drawn to the stated end value.
+- company-footprint-change-ceda.png: the same histogram against CEDA (median -14.0%); bar heights read
+  off the draft, so approximate.
+- drivers-vs-ceda.png: the drivers of the change against CEDA (percentages from the draft; the steps sum
+  to -23.2% against the -23.3% net, and the last step is drawn to land on the end value).
 - company-footprint-change.png: distribution of the change in the footprint of 400 corporate
   footprints against USEEIO (median -1.3%). Bar heights are relative and were read off the draft's
   histogram, so they are approximate; the y-axis is not labelled.
@@ -75,52 +79,43 @@ def summary(name, start_label, start, steps, end, net, size=(7.2, 3.9)):
     plt.close(fig)
 
 
-def histogram():
-    # (centre of the 2.5-point bin in %, relative height read off the draft's histogram)
-    inside = [(-16.3, 16), (-13.9, 19), (-11.3, 52), (-8.9, 93), (-6.2, 111), (-3.8, 100), (-1.5, 272), (1.2, 152),
-              (3.6, 55), (6.2, 31), (8.6, 16), (11.3, 16), (13.7, 13), (16.0, 7), (18.7, 10)]
-    outside = [(-48.9, 1), (-33.8, 1), (-28.8, 4), (-24.0, 4), (-21.4, 7), (21.1, 4), (23.6, 7), (31.3, 1), (36.2, 1),
-               (38.7, 1), (43.8, 1), (46.3, 1), (68.8, 1), (76.3, 1)]
+def histogram(name, median, inside, outside, label_x, label_y, ymax):
+    """inside/outside: [(centre of the 2.5-point bin in %, relative height read off the draft's histogram)]."""
     fig, ax = plt.subplots(figsize=(7.2, 3.3), dpi=200)
     ax.axvspan(-20, 20, color="#EFEDE6", zorder=0)
     ax.bar([x for x, _ in inside], [h for _, h in inside], width=2.3, color="#C9CAD1", zorder=2)
     ax.bar([x for x, _ in outside], [h if h > 2 else 3 for _, h in outside], width=2.3, color=METHOD, zorder=2)
-    ax.axvline(-1.3, color="#A33F2E", ls=(0, (4, 3)), lw=1.4, zorder=3)
+    ax.axvline(median, color="#A33F2E", ls=(0, (4, 3)), lw=1.4, zorder=3)
     ax.axvline(0, color=INK, lw=0.8, zorder=3)
-    ax.text(-4, 300, "Median −1.3%", ha="right", va="center", color="#A33F2E", fontweight="medium")
-    ax.text(0, -52, "Within ±20%", ha="center", va="top", color=MUTED, fontsize=9.5)
-    ax.set_xlim(-100, 100); ax.set_ylim(0, 330)
+    ax.text(label_x, label_y, f"Median {median:.1f}%".replace("-", "−"), ha="right", va="center", color="#A33F2E", fontweight="medium")
+    ax.text(0, -ymax * 0.16, "Within ±20%", ha="center", va="top", color=MUTED, fontsize=9.5)
+    ax.set_xlim(-100, 100); ax.set_ylim(0, ymax)
     ax.set_xticks([-100, -50, -20, 0, 20, 50, 100])
     ax.set_xticklabels(["−100%", "−50%", "−20%", "0%", "+20%", "+50%", "+100%"], color=INK)
     clean(ax)
     fig.tight_layout()
-    fig.savefig(OUT / "company-footprint-change.png", facecolor="white")
+    fig.savefig(OUT / name, facecolor="white")
     plt.close(fig)
 
 
-def drivers():
-    base, end = 0.2474, 0.2454
-    steps = [("Model\nreconciliation", -3.5, METHOD, 1), ("US\ndata", 0.4, DATA, None),
-             ("Nowcasting &\nfacility reported\ndata", -5.8, METHOD, 2), ("Trade\nimpact", 8.6, METHOD, 3),
-             ("Benchmark-\ninformed\nimprovements", 0.2, METHOD, None)]
+def drivers(name, base_label, base, end, net, steps, bands, ylim, n_ticks_label=9, force_last=False):
+    """steps: [(label, pct, colour, badge)]; bands: [(x0, x1, label, colour)]. The last step lands on `end` only with force_last."""
     fig, ax = plt.subplots(figsize=(9.6, 5.0), dpi=200)
-    ylim = (0.215, 0.262)
     span = ylim[1] - ylim[0]
-    # group bands behind the steps
-    for x0, x1, label, colour in ((0.5, 3.5, "US MODEL", "#F1EEE8"), (3.5, 5.5, "GLOBAL MODEL (US PORTION)", "#E8EEEE")):
+    for x0, x1, label, colour in bands:
         ax.add_patch(Rectangle((x0 + 0.04, ylim[0]), x1 - x0 - 0.08, span, fc=colour, ec="none", zorder=0))
         ax.text((x0 + x1) / 2, ylim[1] - span * 0.045, label, ha="center", va="center", color=MUTED, fontsize=9.5,
                 fontweight="medium")
     ax.bar(0, base - ylim[0], bottom=ylim[0], color=BASE, width=0.6, zorder=2)
     ax.text(0, base + span * 0.012, f"{base:.4f}", ha="center", va="bottom", color=INK, fontweight="medium")
     level = base
-    xs, labels = [0], ["Baseline\nUSEEIO v1.4"]
+    xs, labels = [0], [base_label]
     for i, (label, pct, colour, badge) in enumerate(steps, start=1):
-        new = level + base * pct / 100
+        new = end if (force_last and i == len(steps)) else level + base * pct / 100
         lo, hi = min(level, new), max(level, new)
-        ax.bar(i, hi - lo, bottom=lo, color=colour, width=0.6, zorder=2)
-        ax.text(i, hi + span * 0.012, f"{pct:+.1f}%".replace("-", "−"), ha="center", va="bottom", color=colour,
-                fontweight="medium")
+        ax.bar(i, max(hi - lo, span * 0.002), bottom=lo, color=colour, width=0.6, zorder=2)
+        text = "~0%" if abs(pct) < 0.05 else f"{pct:+.1f}%".replace("-", "−")
+        ax.text(i, hi + span * 0.012, text, ha="center", va="bottom", color=colour, fontweight="medium")
         if badge:
             ax.text(i + 0.36, hi + span * 0.03, str(badge), ha="center", va="center", color="white", fontsize=9, fontweight="medium",
                     bbox=dict(boxstyle="circle,pad=0.3", fc=INK, ec="none"), zorder=4)
@@ -130,9 +125,9 @@ def drivers():
     n = len(steps) + 1
     ax.plot([n - 0.7, n - 0.3], [level, level], color="#B4B2A9", lw=0.9, ls=(0, (3, 3)), zorder=1)
     ax.bar(n, end - ylim[0], bottom=ylim[0], color=END, width=0.6, zorder=2)
-    ax.text(n, end + span * 0.012, f"{end:.4f}\n−0.8% net", ha="center", va="bottom", color=INK, fontweight="medium", linespacing=1.3)
+    ax.text(n, end + span * 0.012, f"{end:.4f}\n{net} net", ha="center", va="bottom", color=INK, fontweight="medium", linespacing=1.3)
     xs.append(n); labels.append("Cornerstone\n2026")
-    ax.set_xticks(xs); ax.set_xticklabels(labels, fontsize=9)
+    ax.set_xticks(xs); ax.set_xticklabels(labels, fontsize=n_ticks_label)
     ax.set_xlim(-0.6, n + 0.6); ax.set_ylim(*ylim)
     ax.set_ylabel("kg CO$_2$e per 2024 USD", color=MUTED)
     ax.tick_params(axis="y", colors=MUTED, length=0)
@@ -140,10 +135,28 @@ def drivers():
     clean(ax, left=True); ax.spines["left"].set_visible(False)
     legend(ax)
     fig.tight_layout()
-    fig.savefig(OUT / "drivers-vs-useeio.png", facecolor="white", bbox_inches="tight")
+    fig.savefig(OUT / name, facecolor="white", bbox_inches="tight")
     plt.close(fig)
 
 
+def drivers_useeio():
+    steps = [("Model\nreconciliation", -3.5, METHOD, 1), ("US\ndata", 0.4, DATA, None),
+             ("Nowcasting &\nfacility reported\ndata", -5.8, METHOD, 2), ("Trade\nimpact", 8.6, METHOD, 3),
+             ("Benchmark-\ninformed\nimprovements", 0.2, METHOD, None)]
+    bands = ((0.5, 3.5, "US MODEL", "#F1EEE8"), (3.5, 5.5, "GLOBAL MODEL (US PORTION)", "#E8EEEE"))
+    drivers("drivers-vs-useeio.png", "Baseline\nUSEEIO v1.4", 0.2474, 0.2454, "−0.8%", steps, bands, (0.215, 0.262))
+
+
+def drivers_ceda():
+    steps = [("Model\nreconciliation", 0.0, METHOD, None), ("US\ndata", 0.1, DATA, None),
+             ("Nowcasting &\nfacility reported\ndata", -0.7, METHOD, None), ("Bug\nfixes", -0.2, METHOD, None),
+             ("UK & global\ntrade data", 0.0, DATA, None), ("China\ndata", 7.6, DATA, 1),
+             ("Europe & other\nglobal data", -1.8, DATA, 2), ("ABSR", -11.0, METHOD, 3),
+             ("Benchmark-\ninformed\nimprovements", -17.2, METHOD, 4)]
+    bands = ((0.5, 3.5, "US MODEL", "#F1EEE8"), (3.5, 9.5, "GLOBAL MODEL", "#E8EEEE"))
+    drivers("drivers-vs-ceda.png", "Baseline\nCEDA 2025", 0.6942, 0.5324, "−23.3%", steps, bands, (0.50, 0.80), 8.5, force_last=True)
+
+
 def cement():
     fig, ax = plt.subplots(figsize=(6.2, 2.0), dpi=200)
     for y, (label, v, colour, bold) in enumerate((("USEEIO", 4.35, BASE, False), ("Cornerstone", 5.21, "#0F6E56", True))):
@@ -160,6 +173,16 @@ def cement():
 
 
 summary("summary-vs-useeio.png", "USEEIO\nv1.4 (US)", 0.2474, [("Data\nupdates", 0.4, DATA), ("Methodology\nchanges", -1.2, METHOD)], 0.2454, "−0.8%")
-summary("summary-vs-ceda.png", "CEDA\n2025", 0.6957, [("Data\nupdates", 6.3, DATA), ("Methodology\nchanges", -28.6, METHOD)], 0.5281, "−24.1%")
-histogram(); drivers(); cement()
+summary("summary-vs-ceda.png", "CEDA\n2025", 0.6942, [("Data\nupdates", 5.8, DATA), ("Methodology\nchanges", -27.5, METHOD)], 0.5324, "−23.3%")
+histogram("company-footprint-change.png", -1.3,
+          [(-16.3, 16), (-13.9, 19), (-11.3, 52), (-8.9, 93), (-6.2, 111), (-3.8, 100), (-1.5, 272), (1.2, 152),
+           (3.6, 55), (6.2, 31), (8.6, 16), (11.3, 16), (13.7, 13), (16.0, 7), (18.7, 10)],
+          [(-48.9, 1), (-33.8, 1), (-28.8, 4), (-24.0, 4), (-21.4, 7), (21.1, 4), (23.6, 7), (31.3, 1), (36.2, 1),
+           (38.7, 1), (43.8, 1), (46.3, 1), (68.8, 1), (76.3, 1)], -4, 300, 330)
+histogram("company-footprint-change-ceda.png", -14.0,
+          [(-18.7, 231), (-16.3, 244), (-11.5, 211), (-9.0, 184), (-6.5, 184), (-4.0, 132), (-1.4, 310),
+           (1.2, 5), (6.0, 5), (8.5, 5)],
+          [(-38.8, 19), (-36.5, 12), (-34.2, 12), (-31.4, 19), (-28.9, 72), (-26.6, 98), (-24.0, 164), (-21.5, 164)],
+          -16.5, 330, 350)
+drivers_useeio(); drivers_ceda(); cement()
 print("wrote", *(p.name for p in sorted(OUT.glob("*.png"))))
diff --git a/figures/scripts/waterfalls.py b/figures/scripts/waterfalls.py
deleted file mode 100644
index a5a816c8..00000000
--- a/figures/scripts/waterfalls.py
+++ /dev/null
@@ -1,80 +0,0 @@
-"""Write the two release-candidate waterfall charts to figures/diagrams/.
-
-Source: the Practitioners Forum Roundtable 2 deck (slide 25). The USEEIO driver chart is now drawn by rc_summary_figures.py. Steps are the percentage
-changes shown on the slides, converted to kg CO2e per USD against each baseline. The listed steps
-do not sum exactly to the net change on the slides, so the remainder is drawn as "Other" until the
-science team confirms the attribution.
-
-Run from the repo root (needs matplotlib):
-    uv run --with matplotlib python figures/scripts/waterfalls.py
-"""
-from pathlib import Path
-
-import matplotlib
-
-matplotlib.use("Agg")
-import matplotlib.pyplot as plt  # noqa: E402
-
-OUT = Path(__file__).resolve().parents[1] / "diagrams"
-INK, MUTED = "#2C2C2A", "#5F5E5A"
-UP, DOWN, TOTAL, OTHER = "#D85A30", "#1D9E75", "#5F5E5A", "#B4B2A9"
-plt.rcParams.update({"font.family": ["Helvetica", "Arial", "DejaVu Sans"], "font.size": 11,
-                     "svg.fonttype": "none"})
-
-CHARTS = {
-    "waterfall-vs-ceda": dict(
-        title="Global production-weighted emission factor: Cornerstone 2026 vs. CEDA 2025",
-        ylabel="kg CO$_2$e per 2024 USD",
-        base_label="CEDA\n2025", base=0.6957, end_label="Cornerstone\n2026", end=0.5281,
-        steps=[("Model\nreconciliation", 0.0), ("US\ndata", 0.1), ("Nowcasting &\nfacility\ndata", -0.7),
-               ("Bug\nfixes", -0.4), ("UK & global\ntrade data", 0.0), ("China\ndata", 7.8),
-               ("Europe &\nother global\ndata", -1.6), ("ABSR", -10.7), ("Benchmark-\ninformed\nimprovements", -19.0)],
-        ylim=(0.50, 0.76), net="−24.1% net",
-    ),
-}
-
-
-def draw(name, c):
-    labels, bottoms, heights, colors, texts = [], [], [], [], []
-    level = c["base"]
-    labels.append(c["base_label"]); bottoms.append(0); heights.append(c["base"]); colors.append(TOTAL)
-    texts.append((c["base"], f'{c["base"]:.4f}'))
-    for label, pct in c["steps"]:
-        delta = c["base"] * pct / 100
-        new = level + delta
-        labels.append(label); bottoms.append(min(level, new)); heights.append(abs(delta))
-        colors.append(UP if delta > 0 else DOWN)
-        txt = "~0%" if pct == 0 else f"{pct:+.1f}%".replace("-", "−")
-        texts.append((max(level, new), txt))
-        level = new
-    labels.append(c["end_label"]); bottoms.append(0); heights.append(c["end"]); colors.append(TOTAL)
-    texts.append((c["end"], f'{c["end"]:.4f}\n({c["net"]})'))
-
-    fig, ax = plt.subplots(figsize=(11, 5.6), dpi=170)
-    xs = range(len(labels))
-    bars = ax.bar(xs, heights, bottom=bottoms, color=colors, width=0.62, zorder=3)
-    pos = c["base"]
-    levels = [c["base"]]
-    for _, pct in c["steps"]:
-        pos += c["base"] * pct / 100; levels.append(pos)
-    for i in range(len(levels)):
-        ax.plot([i + 0.31, i + 1 - 0.31], [levels[i], levels[i]], color="#B4B2A9", lw=0.8, zorder=2)
-    span = c["ylim"][1] - c["ylim"][0]
-    for x, (y, t) in zip(xs, texts):
-        ax.text(x, y + span * 0.012, t, ha="center", va="bottom", color=INK, fontsize=11, fontweight="medium")
-    ax.set_xticks(list(xs)); ax.set_xticklabels(labels, fontsize=9, color=INK)
-    ax.set_ylim(*c["ylim"]); ax.set_ylabel(c["ylabel"], color=MUTED)
-    ax.tick_params(axis="y", colors=MUTED, length=0); ax.tick_params(axis="x", length=0)
-    ax.grid(axis="y", color="#E8E6DF", lw=0.8, zorder=0)
-    for s in ("top", "right", "left"):
-        ax.spines[s].set_visible(False)
-    ax.spines["bottom"].set_color("#B4B2A9")
-    fig.tight_layout()
-    for ext in ("png", "svg"):
-        fig.savefig(OUT / f"{name}.{ext}", facecolor="white")
-    plt.close(fig)
-    print(name, "baseline", c["base"], "end", c["end"], "last step level", round(levels[-1], 4))
-
-
-for n, c in CHARTS.items():
-    draw(n, c)
diff --git a/images/company-footprint-change-ceda.png b/images/company-footprint-change-ceda.png
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diff --git a/releases/summary-ceda.md b/releases/summary-ceda.md
index 65d569f5..671f27fd 100644
--- a/releases/summary-ceda.md
+++ b/releases/summary-ceda.md
@@ -3,55 +3,67 @@ title: "Summary of changes vs. CEDA"
 ---
 
 :::{tip} Summary
-Cornerstone EEIO 2026 replaces CEDA 2025. The global production-weighted emission factor falls by 24.1%, from 0.6957 to 0.5281 kg CO₂e per 2024 USD. Data updates raise factors (+6.3%) and methodology changes lower them by more (−28.6%). Four changes drive about 95% of the impact: benchmark-informed improvements, auto-balanced structural reflection (ABSR), new data for China, and new data for Europe.
+Cornerstone EEIO 2026 is the continuation of CEDA 2025. For global factors, the net change is on median −23.3% in kg CO₂e per 2024 USD (production-weighted), driven by a combination of data and methodology changes.
 :::
 
 :::{figure} ../images/summary-vs-ceda.png
 :label: summary-vs-ceda
-:alt: Waterfall chart of the global emission factor. From CEDA 2025 at 0.6957, data updates add 6.3% and methodology changes remove 28.6%, ending at 0.5281 for Cornerstone 2026, 24.1% lower.
+:alt: Waterfall chart of the global emission factor. From CEDA 2025 at 0.6942, data updates add 5.8% and methodology changes remove 27.5%, ending at 0.5324 for Cornerstone 2026, 23.3% lower.
 :width: 80%
 
 **Cornerstone 2026 against CEDA 2025 (global).** Global production-weighted total emission factor, in kg CO₂e per 2024 USD, split into data updates and methodology changes.
 :::
 
 :::{important} Your footprint may not follow the median
-A company's change depends on which sectors and countries its spend falls in. A small median can hide large moves in individual sectors, in both directions. From a sample of 400 corporate footprints, the median company will experience a decrease of 13.7%, and 24% of companies will see their footprint move by more than 20%.
+A company's change depends on which sectors and countries its spend falls in. A small median can hide large moves in individual sectors, in both directions. From a sample of 400 corporate footprints, the median company will experience a decrease of 14% but there is a range of possibilities, as shown in the histogram below.
+:::
+
+:::{figure} ../images/company-footprint-change-ceda.png
+:label: company-footprint-change-ceda
+:alt: Histogram of the change in the footprint of 400 companies against CEDA. Nearly all companies decrease, mostly between 0% and 40%, with a median of a 14% decrease. A few companies change by less than 1% or increase slightly.
+:width: 90%
+
+**Change in the footprint of 400 companies, Cornerstone 2026 against CEDA 2025.** Each bar groups companies by the percentage change in their total footprint. Bars outside the shaded ±20% band are in green. Bar heights are approximate.
 :::
 
 ## What drives the change for CEDA users
 
-:::{figure} ../figures/diagrams/waterfall-vs-ceda.png
-:label: waterfall-vs-ceda
-:alt: Waterfall chart of the global emission factor from CEDA 2025 (0.6957) to Cornerstone 2026 (0.5281), by driver. The largest are benchmark-informed improvements (−19.0%), ABSR (−10.7%) and China data (+7.8%).
+Four changes drive ~95% of the impact: China data, Europe and global data, ABSR and benchmark-informed improvements.
+
+:::{figure} ../images/drivers-vs-ceda.png
+:label: drivers-vs-ceda
+:alt: Waterfall chart of the global emission factor from CEDA 2025 (0.6942) to Cornerstone 2026 (0.5324), by driver. China data adds 7.6%, Europe and other global data lower it by 1.8%, ABSR lowers it by 11.0% and benchmark-informed improvements lower it by 17.2%. Model reconciliation, US data, nowcasting, bug fixes and UK and global trade data are each within 0.7% of zero.
 :width: 100%
 
-**Change in the global production-weighted emission factor from CEDA 2025 to Cornerstone 2026, by driver.** Percentages are changes relative to the CEDA 2025 baseline. Increases are in coral and decreases in teal. The vertical axis starts at 0.50.
+**Change in the global production-weighted emission factor from CEDA 2025 to Cornerstone 2026, by driver.** Percentages are changes relative to the CEDA 2025 baseline. Numbers 1 to 4 mark the four drivers described below. The vertical axis starts at 0.50.
 :::
 
-The smaller changes are model reconciliation (about 0%), US data (+0.1%), nowcasting and facility-reported data (−0.7%), bug fixes (−0.4%), and UK and global trade data (about 0%).
-
-### Benchmark-informed improvements
+### New data for China
 
-This is the largest driver. See the benchmarking section below.
+Cornerstone adopts the most recent economic data released by the Chinese National Bureau of Statistics (2023 vs. 2020), which reflects methodology changes, changes in the economic structure, and sector-specific decarbonization.
 
-### Auto-balanced structural reflection (ABSR)
+For example, China's battery factor rises by ~30% driven by a change in the composition of battery production in China. From 2020 to 2023, battery production more than tripled, but more importantly it shifted to require more lithium-ion and less lead-acid as inputs. This is because the majority of the market growth in batteries is in EVs and grid storage, which require lithium-ion. Lithium-ion needs lithium chemicals and power-hungry cell factories, being more carbon intensive per dollar than lead-acid batteries, driving the emission factor up.
 
-Structural reflection is the method CEDA uses to give every country the same sectors. ABSR improves it. It achieves two goals that are hard to deliver together in a global model:
+:::{figure} ../images/rc-china-battery.png
+:label: summary-china-battery
+:alt: Stacked bar chart of China battery production. In 2020, output was 382 GWh, mostly lead-acid. In 2023, output was 1,183 GWh, mostly lithium-ion.
+:width: 65%
 
-- All countries share the same 405 sectors, so emission factors compare like for like.
-- Each economy balances production and consumption, in both money and emissions.
+**China battery production, 2020 and 2023.** Output in GWh. The split by battery type is approximate. Sources: NBS input-output tables 2020 and 2023; MIIT; China lead-acid battery industry statistics.
+:::
 
-### New data for China
+### New data for Europe
 
-China's National Bureau of Statistics released benchmark input-output tables for 2023. The last update was for 2020. The new tables come from China's 5th National Economic Census and have more detail: 211 sectors instead of 153.
+Cornerstone adopts the most recent release of FIGARO, Eurostat's official input-output and emissions dataset. FIGARO is more granular than the OECD dataset for Europe, is available as a time series through 2023, and is updated every year, matching Cornerstone's release cadence.
 
-This raises China's emission factors by about 10% at the median. It reflects methodology changes, changes in the economic structure, and sector-specific decarbonization. For example, China's battery factor rises by about 30%. Battery production grew from 382 GWh in 2020 to 1,183 GWh in 2023, and lithium-ion batteries, which are about twice as carbon-intensive per dollar as lead-acid, took a larger share.
+This data update lowers European factors by about 4% in median. The most significant change is in electricity factors, which fall in every EU country. This change is driven by an increase in renewable energy and by an increase in energy prices due to the gas-supply shocks the Ukraine war has caused in Europe. For example, Germany's power factor fell by 60%, as renewables grew from 35% to over 50% of generation and wholesale power prices more than doubled.
 
-### New data for Europe
+### Auto-balanced structural reflection (ABSR)
 
-Cornerstone adopts FIGARO, Eurostat's official input-output and emissions dataset. FIGARO is more granular than the OECD dataset for Europe, is available as a time series through 2023, and is updated every year, matching Cornerstone's release cadence.
+Structural reflection is the method CEDA uses to give every country the same sectors. ABSR improves it. It achieves two goals that are hard to deliver together in a global model:
 
-This lowers European factors by about 4% at the median. Electricity factors fall in every EU country. For example, Germany's power factor falls by 60%, as renewables grew from 35% to over 50% of generation. The Netherlands' falls by 77%, after a coal ban and a gas-heavy grid.
+- All countries share the same 405 sectors, so emission factors compare like for like.
+- Each economy balances production and consumption, in both money and emissions.
 
 ## Third-party benchmarking
 
diff --git a/releases/summary-useeio.md b/releases/summary-useeio.md
index 5664154a..dea22253 100644
--- a/releases/summary-useeio.md
+++ b/releases/summary-useeio.md
@@ -62,7 +62,15 @@ Cornerstone EEIO also uses facility-level reported emissions data from the US EP
 
 USEEIO treated the US as a closed economy: every product used in the US was assumed to be made in the US. Cornerstone uses trade data to model each import in the country that produced it. Many imports come from more emissions-intensive economies such as China and India, so US factors tend to rise by 10 to 30% on average. This is the main upward pressure and the main reason for the change for USEEIO users.
 
-For example, about 40% of the US crude oil mix is imported. Emissions from that oil are allocated to the producing countries, and Canadian oil is up to twice as emissions-intensive as US oil.
+One example is light trucks and utility vehicles. The US factor rises by ~24%, mostly because many of their components are now modeled as imports from the countries that make them. Public data suggests that about 60% of the components of a US-assembled light truck are imported, with Mexico the largest source, followed by Europe and Canada (see {numref}`us-parts-imports`).
+
+:::{figure} ../images/rc-us-parts-imports.png
+:label: us-parts-imports
+:alt: One horizontal stacked bar showing the estimated origin of light-truck components. Made in the US 41%. Imported: Mexico 24%, Europe 7%, Canada 6%, China 5%, Japan 5%, ASEAN 4%, South Korea 4%, other 4%.
+:width: 100%
+
+**Estimated origin of the components of a US-assembled light truck.** Sources: [NHTSA AALA parts content data](https://www.nhtsa.gov/part-583-american-automobile-labeling-act-reports); [International Trade Administration data as reported by MNI (March 2025)](https://www.mnimarkets.com/articles/us-auto-tariffs-impact-broad-based-across-regions-1743036084321).
+:::
 
 For the full list of data and methodology changes, see the [](change-log.md). For the equivalent summary against CEDA, see [](summary-ceda.md).
 

From d40fbe6d9ead32b12a49d655c04a6eaffcf3ca65 Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Thu, 22 Oct 2026 12:16:09 -0700
Subject: [PATCH 26/47] docs(releases): add kg CO2e per USD unit to the cement
 chart

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 figures/scripts/rc_summary_figures.py |   1 +
 images/cement-ef.png                  | Bin 12549 -> 17187 bytes
 releases/summary-useeio.md            |   4 ++--
 3 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/figures/scripts/rc_summary_figures.py b/figures/scripts/rc_summary_figures.py
index 6e238107..52b269dd 100644
--- a/figures/scripts/rc_summary_figures.py
+++ b/figures/scripts/rc_summary_figures.py
@@ -167,6 +167,7 @@ def cement():
     for s in ("top", "right", "bottom"):
         ax.spines[s].set_visible(False)
     ax.spines["left"].set_color("#B4B2A9"); ax.set_xticks([]); ax.tick_params(axis="y", length=0)
+    ax.set_xlabel("kg CO$_2$e per USD", color=MUTED, fontsize=9.5, loc="left")
     fig.tight_layout()
     fig.savefig(OUT / "cement-ef.png", facecolor="white")
     plt.close(fig)
diff --git a/images/cement-ef.png b/images/cement-ef.png
index 60fee687602438d8e1564dd509877d7e3662a527..cbd05475a636e0f81fa7c62e19b729820131ec40 100644
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diff --git a/releases/summary-useeio.md b/releases/summary-useeio.md
index dea22253..440979e5 100644
--- a/releases/summary-useeio.md
+++ b/releases/summary-useeio.md
@@ -52,10 +52,10 @@ Cornerstone EEIO also uses facility-level reported emissions data from the US EP
 
 :::{figure} ../images/cement-ef.png
 :label: cement-ef
-:alt: Bar chart of the cement sector emission factor. USEEIO 4.35, Cornerstone 5.21.
+:alt: Bar chart of the cement sector emission factor in kg CO₂e per USD. USEEIO 4.35, Cornerstone 5.21.
 :width: 60%
 
-**Cement sector emission factor, USEEIO v1.4 and Cornerstone 2026.**
+**Cement sector emission factor, USEEIO v1.4 and Cornerstone 2026.** In kg CO₂e per USD.
 :::
 
 ### Trade impact: modeling imports

From ca67d9d4e7974df30894368dac8dbae100a7e2f4 Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Thu, 22 Oct 2026 12:16:36 -0700
Subject: [PATCH 27/47] docs(releases): bring back bullets in the change log
 table

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 releases/change-log.md | 67 ++++++++++++++++++++++++++++++++++--------
 1 file changed, 55 insertions(+), 12 deletions(-)

diff --git a/releases/change-log.md b/releases/change-log.md
index e217ca4b..f8f85cc0 100644
--- a/releases/change-log.md
+++ b/releases/change-log.md
@@ -6,18 +6,61 @@ title: "Change log"
 This table summarizes all the data and methodology changes behind Cornerstone EEIO 2026, compared to both USEEIO v1.4 and CEDA 2025.
 :::
 
-| Type of change | Category | What changed? |
-|---|---|---|
-| Data update | US data | Updated the U.S. GHG emissions data (totals and by sector) released by the **University of Maryland Greenhouse Gas Inventory and Analysis (UMD GHGIA)** [(link)](https://ghgi.cgs.umd.edu/data.html) from **2023 to 2024**. |
-| Data update | UK data | Updated the UK IO tables released by the **U.K. Office for National Statistics** [(link)](https://www.ons.gov.uk/economy/nationalaccounts/supplyandusetables/datasets/ukinputoutputanalyticaltablesdetailed) from **2022 to 2023**, incorporating the most recent benchmark IO tables. Updated the U.K. GHG emissions data released by the **U.K. Office for National Statistics** [(link)](https://www.ons.gov.uk/economy/environmentalaccounts/datasets/ukenvironmentalaccountsatmosphericemissionsgreenhousegasemissionsbyeconomicsectorandgasunitedkingdom) from **2022 to 2023**, incorporating the most recent benchmark IO tables. |
-| Data update | China data | Updated the China IO tables released by the **National Bureau of Statistics of China (NBS)** [(link)](https://www.stats.gov.cn/sj/ndsj/2025/html/sm03.htm) from **2020 to 2023**, incorporating the most recent benchmark IO tables. Updated the China GHG emissions data released by **Our World in Data (OWID)** [(link)](https://github.com/owid/co2-data), **Community Emissions Data System (CEDS)** [(link)](https://zenodo.org/records/10904361), and **Emissions Database for Global Atmospheric Research (EDGAR)** [(link)](https://edgar.jrc.ec.europa.eu/dataset_ghg80) from **2020 to 2023**, incorporating the most recent benchmark IO tables. |
-| Data update | Europe & other global data | Updated the tier 2 country IO tables using Eurostat's Full International and Global Accounts for Research in Input-Output Analysis (FIGARO) [(link)](https://ec.europa.eu/eurostat/en/web/esa-supply-use-input-tables/database) for **2023** data where available and otherwise to **2020 OECD IO tables** [(link)](https://www.oecd.org/en/data/datasets/inter-country-input-output-tables.html). Updated the tier 2 country emissions data using **FIGARO's 2023 data** [(link)](https://ec.europa.eu/eurostat/en/web/esa-supply-use-input-tables/database) where available and otherwise to **2020 OECD IO tables** [(link)](https://www.oecd.org/en/data/datasets/inter-country-input-output-tables.html). |
-| Data update | Global trade data | Updated global trade data released by the **United Nations Comtrade (BACI)** [(link)](https://www.cepii.fr/DATA_DOWNLOAD/baci/doc/baci_webpage.html) from **2023 to 2024**. Global trade data is supplemented with **FIGARO trade data** [(link)](https://ec.europa.eu/eurostat/en/web/esa-supply-use-input-tables/database) to fill gaps in BACI data. |
-| Methodology improvements | Model reconciliation | USEEIO and CEDA teams reviewed 18 differences between the models for the US and either adopted one of the model's approaches or proposed an improvement. The differences can be categorized into 3 types: sectors schema, treatment of economic data and GHG attribution. For details on the differences, see the [model reconciliation section](#change-log-model-reconciliation) below. |
-| Methodology improvements | Auto-balancing structural reflection (ABSR) | Improved disaggregation of non-US economic data into detailed sectors using a new block scaling method. |
-| Methodology improvements | US nowcasting and facility reported data | Estimated detailed 2024 US IO tables from primary sources from 2017 to 2024, replicating the tables produced by the BEA. Used facility reported data to allocate emissions of manufacturing sectors, instead of survey data. |
-| Methodology improvements | Bug fixes | Corrected errors in HS mapping. Corrected double application of AR5 to AR6 conversion. Corrected loading of the UK IO table. |
-| Methodology improvements | Benchmark informed improvements | Leveraged third-party benchmark data created with ~1,000 data sources to review key sectors that drive ~50% of global emissions, producing one of three outcomes: benchmark improvements, model improvements or documentation of known discrepancies. More on why we benchmark in [](../benchmarking/index.md). |
+```{list-table}
+:header-rows: 1
+:widths: 15 20 65
+
+* - Type of change
+  - Category
+  - What changed?
+* - Data update
+  - US data
+  -
+    - Updated the U.S. GHG emissions data (totals and by sector) released by the **University of Maryland Greenhouse Gas Inventory and Analysis (UMD GHGIA)** [(link)](https://ghgi.cgs.umd.edu/data.html) from **2023 to 2024**.
+* - Data update
+  - UK data
+  -
+    - Updated the UK IO tables released by the **U.K. Office for National Statistics** [(link)](https://www.ons.gov.uk/economy/nationalaccounts/supplyandusetables/datasets/ukinputoutputanalyticaltablesdetailed) from **2022 to 2023**, incorporating the most recent benchmark IO tables.
+    - Updated the U.K. GHG emissions data released by the **U.K. Office for National Statistics** [(link)](https://www.ons.gov.uk/economy/environmentalaccounts/datasets/ukenvironmentalaccountsatmosphericemissionsgreenhousegasemissionsbyeconomicsectorandgasunitedkingdom) from **2022 to 2023**, incorporating the most recent benchmark IO tables.
+* - Data update
+  - China data
+  -
+    - Updated the China IO tables released by the **National Bureau of Statistics of China (NBS)** [(link)](https://www.stats.gov.cn/sj/ndsj/2025/html/sm03.htm) from **2020 to 2023**, incorporating the most recent benchmark IO tables.
+    - Updated the China GHG emissions data released by **Our World in Data (OWID)** [(link)](https://github.com/owid/co2-data), **Community Emissions Data System (CEDS)** [(link)](https://zenodo.org/records/10904361), and **Emissions Database for Global Atmospheric Research (EDGAR)** [(link)](https://edgar.jrc.ec.europa.eu/dataset_ghg80) from **2020 to 2023**, incorporating the most recent benchmark IO tables.
+* - Data update
+  - Europe & other global data
+  -
+    - Updated the tier 2 country IO tables using Eurostat's Full International and Global Accounts for Research in Input-Output Analysis (FIGARO) [(link)](https://ec.europa.eu/eurostat/en/web/esa-supply-use-input-tables/database) for **2023** data where available and otherwise to **2020 OECD IO tables** [(link)](https://www.oecd.org/en/data/datasets/inter-country-input-output-tables.html).
+    - Updated the tier 2 country emissions data using **FIGARO's 2023 data** [(link)](https://ec.europa.eu/eurostat/en/web/esa-supply-use-input-tables/database) where available and otherwise to **2020 OECD IO tables** [(link)](https://www.oecd.org/en/data/datasets/inter-country-input-output-tables.html).
+* - Data update
+  - Global trade data
+  -
+    - Updated global trade data released by the **United Nations Comtrade (BACI)** [(link)](https://www.cepii.fr/DATA_DOWNLOAD/baci/doc/baci_webpage.html) from **2023 to 2024**.
+    - Global trade data is supplemented with **FIGARO trade data** [(link)](https://ec.europa.eu/eurostat/en/web/esa-supply-use-input-tables/database) to fill gaps in BACI data.
+* - Methodology improvements
+  - Model reconciliation
+  -
+    - USEEIO and CEDA teams reviewed 18 differences between the models for the US and either adopted one of the model's approaches or proposed an improvement. The differences can be categorized into 3 types: sectors schema, treatment of economic data and GHG attribution. For details on the differences, see the [model reconciliation section](#change-log-model-reconciliation) below.
+* - Methodology improvements
+  - Auto-balancing structural reflection (ABSR)
+  -
+    - Improved disaggregation of non-US economic data into detailed sectors using a new block scaling method.
+* - Methodology improvements
+  - US nowcasting and facility reported data
+  -
+    - Estimated detailed 2024 US IO tables from primary sources from 2017 to 2024, replicating the tables produced by the BEA.
+    - Used facility reported data to allocate emissions of manufacturing sectors, instead of survey data.
+* - Methodology improvements
+  - Bug fixes
+  -
+    - Corrected errors in HS mapping.
+    - Corrected double application of AR5 to AR6 conversion.
+    - Corrected loading of the UK IO table.
+* - Methodology improvements
+  - Benchmark informed improvements
+  -
+    - Leveraged third-party benchmark data created with ~1,000 data sources to review key sectors that drive ~50% of global emissions, producing one of three outcomes: benchmark improvements, model improvements or documentation of known discrepancies. More on why we benchmark in [](../benchmarking/index.md).
+```
 
 (change-log-model-reconciliation)=
 ## Model reconciliation

From 942c438137fc57a996bda4fa6ff7319a84e6da50 Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Thu, 22 Oct 2026 12:19:25 -0700
Subject: [PATCH 28/47] docs(releases): collapse footprint histogram, drop
 numbered badges from driver charts

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 figures/scripts/rc_summary_figures.py |   3 ---
 images/drivers-vs-ceda.png            | Bin 116089 -> 112189 bytes
 images/drivers-vs-useeio.png          | Bin 102442 -> 97791 bytes
 releases/summary-ceda.md              |   9 +++++----
 releases/summary-useeio.md            |   9 +++++----
 5 files changed, 10 insertions(+), 11 deletions(-)

diff --git a/figures/scripts/rc_summary_figures.py b/figures/scripts/rc_summary_figures.py
index 52b269dd..d8d32fad 100644
--- a/figures/scripts/rc_summary_figures.py
+++ b/figures/scripts/rc_summary_figures.py
@@ -116,9 +116,6 @@ def drivers(name, base_label, base, end, net, steps, bands, ylim, n_ticks_label=
         ax.bar(i, max(hi - lo, span * 0.002), bottom=lo, color=colour, width=0.6, zorder=2)
         text = "~0%" if abs(pct) < 0.05 else f"{pct:+.1f}%".replace("-", "−")
         ax.text(i, hi + span * 0.012, text, ha="center", va="bottom", color=colour, fontweight="medium")
-        if badge:
-            ax.text(i + 0.36, hi + span * 0.03, str(badge), ha="center", va="center", color="white", fontsize=9, fontweight="medium",
-                    bbox=dict(boxstyle="circle,pad=0.3", fc=INK, ec="none"), zorder=4)
         ax.plot([i - 0.7, i - 0.3], [level, level], color="#B4B2A9", lw=0.9, ls=(0, (3, 3)), zorder=1)
         level = new
         xs.append(i); labels.append(label)
diff --git a/images/drivers-vs-ceda.png b/images/drivers-vs-ceda.png
index 11d8397474a9bcb1843833ea36f490c99f5a6f9c..23aa3cd263dffc89b24dbf06f0a38e648e365722 100644
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diff --git a/releases/summary-ceda.md b/releases/summary-ceda.md
index 671f27fd..a00dca0e 100644
--- a/releases/summary-ceda.md
+++ b/releases/summary-ceda.md
@@ -14,9 +14,9 @@ Cornerstone EEIO 2026 is the continuation of CEDA 2025. For global factors, the
 **Cornerstone 2026 against CEDA 2025 (global).** Global production-weighted total emission factor, in kg CO₂e per 2024 USD, split into data updates and methodology changes.
 :::
 
-:::{important} Your footprint may not follow the median
-A company's change depends on which sectors and countries its spend falls in. A small median can hide large moves in individual sectors, in both directions. From a sample of 400 corporate footprints, the median company will experience a decrease of 14% but there is a range of possibilities, as shown in the histogram below.
-:::
+::::{important} Your footprint may not follow the median
+:class: dropdown
+A company's change depends on which sectors and countries its spend falls in. A small median can hide large moves in individual sectors, in both directions. From a sample of 400 corporate footprints, the median company will experience a decrease of 14%. Open this box to see the full range.
 
 :::{figure} ../images/company-footprint-change-ceda.png
 :label: company-footprint-change-ceda
@@ -25,6 +25,7 @@ A company's change depends on which sectors and countries its spend falls in. A
 
 **Change in the footprint of 400 companies, Cornerstone 2026 against CEDA 2025.** Each bar groups companies by the percentage change in their total footprint. Bars outside the shaded ±20% band are in green. Bar heights are approximate.
 :::
+::::
 
 ## What drives the change for CEDA users
 
@@ -35,7 +36,7 @@ Four changes drive ~95% of the impact: China data, Europe and global data, ABSR
 :alt: Waterfall chart of the global emission factor from CEDA 2025 (0.6942) to Cornerstone 2026 (0.5324), by driver. China data adds 7.6%, Europe and other global data lower it by 1.8%, ABSR lowers it by 11.0% and benchmark-informed improvements lower it by 17.2%. Model reconciliation, US data, nowcasting, bug fixes and UK and global trade data are each within 0.7% of zero.
 :width: 100%
 
-**Change in the global production-weighted emission factor from CEDA 2025 to Cornerstone 2026, by driver.** Percentages are changes relative to the CEDA 2025 baseline. Numbers 1 to 4 mark the four drivers described below. The vertical axis starts at 0.50.
+**Change in the global production-weighted emission factor from CEDA 2025 to Cornerstone 2026, by driver.** Percentages are changes relative to the CEDA 2025 baseline. The vertical axis starts at 0.50.
 :::
 
 ### New data for China
diff --git a/releases/summary-useeio.md b/releases/summary-useeio.md
index 440979e5..8995626b 100644
--- a/releases/summary-useeio.md
+++ b/releases/summary-useeio.md
@@ -14,9 +14,9 @@ Cornerstone EEIO 2026 is the continuation of the USEEIO Supply Chain GHG Emissio
 **Cornerstone 2026 against USEEIO v1.4 (US).** US production-weighted total emission factor, in kg CO₂e per 2024 USD, split into data updates and methodology changes.
 :::
 
-:::{important} Your footprint may not follow the median
-A company's change depends on which sectors and countries its spend falls in. A small median can hide large moves in individual sectors, in both directions. From a sample of 400 corporate footprints, the median company will experience a decrease of 1.3% but there is a range of possibilities, as shown in the histogram below.
-:::
+::::{important} Your footprint may not follow the median
+:class: dropdown
+A company's change depends on which sectors and countries its spend falls in. A small median can hide large moves in individual sectors, in both directions. From a sample of 400 corporate footprints, the median company will experience a decrease of 1.3%. Open this box to see the full range.
 
 :::{figure} ../images/company-footprint-change.png
 :label: company-footprint-change
@@ -25,6 +25,7 @@ A company's change depends on which sectors and countries its spend falls in. A
 
 **Change in the footprint of 400 companies, Cornerstone 2026 against USEEIO v1.4.** Each bar groups companies by the percentage change in their total footprint. Bars outside the shaded ±20% band are in green. Bar heights are approximate.
 :::
+::::
 
 ## What drives the change for USEEIO users
 
@@ -35,7 +36,7 @@ Three changes drive ~95% of the impact: model reconciliation, nowcasting and fac
 :alt: Waterfall chart of the US emission factor from USEEIO v1.4 (0.2474) to Cornerstone 2026 (0.2454), by driver. Model reconciliation lowers it by 3.5%, US data adds 0.4%, nowcasting and facility reported data lower it by 5.8%, trade impact adds 8.6% and benchmark-informed improvements add 0.2%. The first three steps belong to the US model and the last two to the US portion of the global model.
 :width: 100%
 
-**Change in the US production-weighted emission factor from USEEIO v1.4 to Cornerstone 2026, by driver.** Percentages are changes relative to the USEEIO baseline. Numbers 1 to 3 mark the three drivers described below. The vertical axis starts at 0.215.
+**Change in the US production-weighted emission factor from USEEIO v1.4 to Cornerstone 2026, by driver.** Percentages are changes relative to the USEEIO baseline. The vertical axis starts at 0.215.
 :::
 
 ### Model reconciliation

From a7379f9630e3d366f1678b5ae2d755935d8e4d00 Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Thu, 22 Oct 2026 12:24:41 -0700
Subject: [PATCH 29/47] docs(releases): remove Learn more from the summary
 pages

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 releases/summary-ceda.md   | 6 ------
 releases/summary-useeio.md | 6 ------
 2 files changed, 12 deletions(-)

diff --git a/releases/summary-ceda.md b/releases/summary-ceda.md
index a00dca0e..b6bb1ded 100644
--- a/releases/summary-ceda.md
+++ b/releases/summary-ceda.md
@@ -84,12 +84,6 @@ About 70% of country-sector pairs with a verified benchmark are within 25% of it
 
 For the full list of data and methodology changes, see the [](change-log.md). For the equivalent summary against USEEIO, see [](summary-useeio.md).
 
-## Learn more
-
-- **From the field:** *to be added*
-- **Cornerstone papers:** *to be added*
-- **Code:** *to be added*
-
 +++
 
 :::{div}
diff --git a/releases/summary-useeio.md b/releases/summary-useeio.md
index 8995626b..904dc1b3 100644
--- a/releases/summary-useeio.md
+++ b/releases/summary-useeio.md
@@ -75,12 +75,6 @@ One example is light trucks and utility vehicles. The US factor rises by ~24%, m
 
 For the full list of data and methodology changes, see the [](change-log.md). For the equivalent summary against CEDA, see [](summary-ceda.md).
 
-## Learn more
-
-- **From the field:** *to be added*
-- **Cornerstone papers:** *to be added*
-- **Code:** [Methods discussion log](https://github.com/cornerstone-data/methods/discussions), the record of each model reconciliation decision.
-
 +++
 
 :::{div}

From ab68a468265bd53baf5deb16cd547cdfa24b3152 Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Thu, 22 Oct 2026 12:26:00 -0700
Subject: [PATCH 30/47] docs(factors): remove Learn more from What's in a
 release

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 factors/whats-in-a-release.md | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/factors/whats-in-a-release.md b/factors/whats-in-a-release.md
index 076b301e..4ec355d2 100644
--- a/factors/whats-in-a-release.md
+++ b/factors/whats-in-a-release.md
@@ -59,12 +59,6 @@ The data dictionary and five other tabs support the data tabs. The data dictiona
 
 The Cornerstone EEIO follows an annual update cycle. Every year we release a version named after the release year, so Cornerstone 2027 will be released in 2027. Releases are publicly available every October so companies can plan accordingly.
 
-## Learn more
-
-- **From the field:** *to be added*
-- **Cornerstone papers:** *to be added*
-- **Code:** *to be added*
-
 +++
 
 :::{div}

From bacbe229a615ccfb11841ea3d70522020c37f252 Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Thu, 22 Oct 2026 12:29:07 -0700
Subject: [PATCH 31/47] docs(releases): embed interactive driver waterfalls in
 the summary pages

The HTML lives in figures/interactive and CI copies it into the built site.

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 .github/workflows/deploy.yml               |  2 +
 .github/workflows/pr.yml                   |  3 +
 figures/interactive/drivers-waterfall.html | 97 ++++++++++++++++++++++
 releases/summary-ceda.md                   |  5 +-
 releases/summary-useeio.md                 |  5 +-
 5 files changed, 106 insertions(+), 6 deletions(-)
 create mode 100644 figures/interactive/drivers-waterfall.html

diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml
index 0deb9c78..96b31bbd 100644
--- a/.github/workflows/deploy.yml
+++ b/.github/workflows/deploy.yml
@@ -33,6 +33,8 @@ jobs:
         run: npm install -g mystmd
       - name: Build HTML
         run: myst build --html --strict
+      - name: Copy interactive figures into the site
+        run: mkdir -p _build/html/interactive && cp figures/interactive/*.html _build/html/interactive/
       - name: Publish to the hub's eeio/ folder
         uses: peaceiris/actions-gh-pages@v4
         with:
diff --git a/.github/workflows/pr.yml b/.github/workflows/pr.yml
index fe6de9af..0f153c04 100644
--- a/.github/workflows/pr.yml
+++ b/.github/workflows/pr.yml
@@ -30,6 +30,9 @@ jobs:
       - name: Build HTML (fails on errors)
         if: github.event.action != 'closed'
         run: myst build --html --strict
+      - name: Copy interactive figures into the site
+        if: github.event.action != 'closed'
+        run: mkdir -p _build/html/interactive && cp figures/interactive/*.html _build/html/interactive/
       - name: Deploy or remove preview
         # Fork PRs can't push to gh-pages; they still get the build check above.
         if: github.event.pull_request.head.repo.full_name == github.repository
diff --git a/figures/interactive/drivers-waterfall.html b/figures/interactive/drivers-waterfall.html
new file mode 100644
index 00000000..f017b14b
--- /dev/null
+++ b/figures/interactive/drivers-waterfall.html
@@ -0,0 +1,97 @@
+<!doctype html>
+<html lang="en">
+<head>
+<meta charset="utf-8">
+<meta name="viewport" content="width=device-width, initial-scale=1">
+<title>Drivers of change</title>
+<style>
+  :root { --ink:#2C2C2A; --muted:#5F5E5A; --grid:#E8E6DF; --base:#B4B2A9; --end:#5F5E5A; --data:#D85A30; --meth:#1D9E75; --bg:#fff; }
+  @media (prefers-color-scheme: dark) { :root { --ink:#ECEBE6; --muted:#A8A69E; --grid:#3A3935; --bg:#1c1c1b; --base:#76746D; --end:#C9C7BF; } }
+  body { margin:0; padding:8px 8px 6px; background:var(--bg); color:var(--ink); font:14px/1.4 -apple-system,"Helvetica Neue",Arial,sans-serif; }
+  .bar { display:flex; justify-content:flex-end; margin-bottom:2px; }
+  .legend { display:flex; gap:16px; color:var(--muted); font-size:13px; }
+  .legend i { display:inline-block; width:12px; height:12px; margin-right:6px; vertical-align:-1px; }
+  .wrap { overflow-x:auto; } svg { display:block; overflow:visible; }
+  text { fill:var(--ink); font-size:inherit; } svg { font-size:11.5px; }
+  .muted { fill:var(--muted); }
+  .col rect.b { transition:opacity .15s; } .col { cursor:default; }
+  svg.hov .col:not(.on) { opacity:.35; }
+  #tip { position:fixed; pointer-events:none; background:var(--ink); color:var(--bg); padding:8px 10px; border-radius:6px; font-size:12.5px; max-width:260px; opacity:0; transition:opacity .1s; z-index:5; }
+  #tip b { display:block; margin-bottom:2px; }
+  .note { color:var(--muted); font-size:12px; margin-top:2px; }
+</style>
+</head>
+<body>
+<div class="bar">
+  <div class="legend"><span><i style="background:var(--data)"></i>Data update</span><span><i style="background:var(--meth)"></i>Methodology</span></div>
+</div>
+<div class="wrap"><svg id="chart" role="img" aria-label="Waterfall chart of the change in the production-weighted emission factor, by driver"></svg></div>
+<div class="note" id="note"></div>
+<div id="tip"></div>
+<script>
+const D = {
+  useeio: {
+    base:["USEEIO<br>v1.4", 0.2474], end:0.2454, net:"−0.8%", lo:0.215, hi:0.255, force:false,
+    note:"kg CO₂e per 2024 USD, production-weighted.",
+    bands:[[1,3,"US MODEL"],[4,5,"GLOBAL MODEL"]],
+    steps:[["Model<br>reconciliation",-3.5,"m","Adopting one model's approach on each of 18 differences, e.g. BEA tables after redefinitions."],
+           ["US<br>data",0.4,"d","Updated US emissions data (UMD GHGIA, 2023 to 2024)."],
+           ["Nowcasting &<br>facility data",-5.8,"m","Annual 2017–2024 US tables and EPA GHGRP facility-reported emissions."],
+           ["Trade<br>impact",8.6,"m","Imports modelled in the country that produced them instead of as US-made."],
+           ["Benchmark-<br>informed",0.2,"m","Fixes found by reviewing sectors against third-party benchmarks."]]
+  },
+  ceda: {
+    base:["CEDA<br>2025", 0.6942], end:0.5324, net:"−23.3%", lo:0.50, hi:0.80, force:true,
+    note:"kg CO₂e per 2024 USD, production-weighted.",
+    bands:[[1,3,"US MODEL"],[4,9,"GLOBAL MODEL"]],
+    steps:[["Model<br>reconciliation",0,"m","Adopting one model's approach on each of 18 US differences."],
+           ["US<br>data",0.1,"d","Updated US emissions data (UMD GHGIA, 2023 to 2024)."],
+           ["Nowcasting &<br>facility data",-0.7,"m","Annual 2017–2024 US tables and EPA GHGRP facility-reported emissions."],
+           ["Bug<br>fixes",-0.2,"m","HS mapping, AR5 to AR6 conversion and UK table loading."],
+           ["UK & global<br>trade data",0,"d","UK 2023 tables and BACI 2024 trade data."],
+           ["China<br>data",7.6,"d","China 2023 input-output tables and emissions."],
+           ["Europe & other<br>global data",-1.8,"d","FIGARO 2023 where available, otherwise OECD 2020."],
+           ["ABSR",-11.0,"m","Auto-balanced structural reflection: same 405 sectors, balanced in money and emissions."],
+           ["Benchmark-<br>informed",-17.2,"m","Fixes found by reviewing key sectors against about 1,000 third-party sources."]]
+  }
+};
+const svg = document.getElementById("chart"), tip = document.getElementById("tip"), NS = "http://www.w3.org/2000/svg";
+const css = n => getComputedStyle(document.documentElement).getPropertyValue(n).trim();
+const fmt = p => Math.abs(p) < .05 ? "~0%" : (p>0?"+":"−")+Math.abs(p).toFixed(1)+"%";
+function el(n, a, parent, txt){ const e=document.createElementNS(NS,n); for(const k in a) e.setAttribute(k,a[k]); if(txt!==undefined) e.textContent=txt; (parent||svg).appendChild(e); return e; }
+function label(x,y,html,cls,parent,anchor="middle"){ const t=el("text",{x,y,"text-anchor":anchor,class:cls||""},parent); html.split("<br>").forEach((l,i)=>el("tspan",{x,dy:i?15:0},t,l)); return t; }
+
+function draw(key){
+  const d = D[key], W=Math.max(document.querySelector(".wrap").clientWidth, 480), L=46, R=14, T=22, B=64,
+    H=Math.max(innerHeight - document.querySelector(".bar").offsetHeight - document.getElementById("note").offsetHeight - 54, 230), n=d.steps.length+2;
+  svg.setAttribute("width",W); svg.setAttribute("height",H); svg.setAttribute("viewBox",`0 0 ${W} ${H}`); svg.innerHTML="";
+  const y = v => T+(H-T-B)*(1-(v-d.lo)/(d.hi-d.lo)), cw=(W-L-R)/n, bw=cw*.62, cx=i=>L+cw*(i+.5); svg.style.fontSize = cw<80 ? "10px" : "11.5px";
+  for(let v=Math.ceil(d.lo*50)/50; v<=d.hi+1e-9; v+=(d.hi-d.lo)>.1?.05:.01){ const yy=y(v);
+    el("line",{x1:L,x2:W-R,y1:yy,y2:yy,stroke:css("--grid")}); label(L-8,yy+4,v.toFixed(v<.1?3:2),"muted",null,"end"); }
+  d.bands.forEach(([a,b,t])=>{ el("rect",{x:L+cw*a+3,y:T,width:cw*(b-a+1)-6,height:H-T-B,fill:"currentColor",opacity:.05,rx:4}); label(L+cw*(a+b+1)/2,T+16,t,"muted"); });
+  el("line",{x1:L,x2:W-R,y1:y(d.lo),y2:y(d.lo),stroke:css("--base")});
+  const cols=[]; let level=d.base[1];
+  cols.push({i:0,lo:d.lo,hi:level,c:"--base",top:level.toFixed(4),lab:d.base[0],tip:["Baseline",d.base[0].replace("<br>"," ")+": "+level.toFixed(4)+" kg CO₂e per USD"]});
+  d.steps.forEach((s,k)=>{ const nx = d.force&&k==d.steps.length-1 ? d.end : level+d.base[1]*s[1]/100;
+    cols.push({i:k+1,lo:Math.min(level,nx),hi:Math.max(level,nx),c:s[2]=="d"?"--data":"--meth",top:fmt(s[1]),lab:s[0],from:level,to:nx,tip:[s[0].replace("<br>"," ")+" · "+fmt(s[1]),s[3]]});
+    el("line",{x1:cx(k)+bw/2,x2:cx(k+1)-bw/2,y1:y(level),y2:y(level),stroke:css("--base"),"stroke-dasharray":"3 3"}); level=nx; });
+  const last=d.steps.length;
+  el("line",{x1:cx(last)+bw/2,x2:cx(last+1)-bw/2,y1:y(level),y2:y(level),stroke:css("--base"),"stroke-dasharray":"3 3"});
+  cols.push({i:last+1,lo:d.lo,hi:d.end,c:"--end",top:d.end.toFixed(4)+"\n"+d.net+" net",lab:"Cornerstone<br>2026",tip:["Cornerstone 2026",d.end.toFixed(4)+" kg CO₂e per USD ("+d.net+" net)"]});
+  cols.forEach(c=>{ const g=el("g",{class:"col"}); const h=Math.max(y(c.lo)-y(c.hi),1.5);
+    el("rect",{class:"b",x:cx(c.i)-bw/2,y:y(c.hi),width:bw,height:h,fill:css(c.c),rx:2},g);
+    const col = c.c=="--data"||c.c=="--meth" ? css(c.c) : css("--ink");
+    c.top.split("\n").forEach((l,j,a)=>{ const t=label(cx(c.i),y(c.hi)-6-(a.length-1-j)*15,l,"",g); t.style.fill=col; t.style.fontWeight=500; });
+    label(cx(c.i),H-B+18,c.lab,"",g);
+    const hit=el("rect",{x:cx(c.i)-cw/2,y:T,width:cw,height:H-T-B+50,fill:"transparent"},g);
+    g.addEventListener("mousemove",e=>{ svg.classList.add("hov"); g.classList.add("on"); tip.innerHTML=`<b>${c.tip[0]}</b>${c.tip[1]}`; tip.style.opacity=1;
+      tip.style.left=Math.min(e.clientX+14,innerWidth-280)+"px"; tip.style.top=(e.clientY+14)+"px"; });
+    g.addEventListener("mouseleave",()=>{ svg.classList.remove("hov"); g.classList.remove("on"); tip.style.opacity=0; }); });
+  document.getElementById("note").textContent=d.note;
+}
+const KEY = new URLSearchParams(location.search).get("d") === "ceda" ? "ceda" : "useeio";
+draw(KEY);
+addEventListener("resize", () => draw(KEY));
+</script>
+</body>
+</html>
diff --git a/releases/summary-ceda.md b/releases/summary-ceda.md
index b6bb1ded..888e38e5 100644
--- a/releases/summary-ceda.md
+++ b/releases/summary-ceda.md
@@ -31,12 +31,11 @@ A company's change depends on which sectors and countries its spend falls in. A
 
 Four changes drive ~95% of the impact: China data, Europe and global data, ABSR and benchmark-informed improvements.
 
-:::{figure} ../images/drivers-vs-ceda.png
+:::{iframe} ../../interactive/drivers-waterfall.html?d=ceda
 :label: drivers-vs-ceda
-:alt: Waterfall chart of the global emission factor from CEDA 2025 (0.6942) to Cornerstone 2026 (0.5324), by driver. China data adds 7.6%, Europe and other global data lower it by 1.8%, ABSR lowers it by 11.0% and benchmark-informed improvements lower it by 17.2%. Model reconciliation, US data, nowcasting, bug fixes and UK and global trade data are each within 0.7% of zero.
 :width: 100%
 
-**Change in the global production-weighted emission factor from CEDA 2025 to Cornerstone 2026, by driver.** Percentages are changes relative to the CEDA 2025 baseline. The vertical axis starts at 0.50.
+**Change in the global production-weighted emission factor from CEDA 2025 to Cornerstone 2026, by driver.** Percentages are changes relative to the CEDA 2025 baseline. Hover over a bar for details. The vertical axis does not start at zero.
 :::
 
 ### New data for China
diff --git a/releases/summary-useeio.md b/releases/summary-useeio.md
index 904dc1b3..e652dca3 100644
--- a/releases/summary-useeio.md
+++ b/releases/summary-useeio.md
@@ -31,12 +31,11 @@ A company's change depends on which sectors and countries its spend falls in. A
 
 Three changes drive ~95% of the impact: model reconciliation, nowcasting and facility-reported data, and the modeling of imports.
 
-:::{figure} ../images/drivers-vs-useeio.png
+:::{iframe} ../../interactive/drivers-waterfall.html?d=useeio
 :label: drivers-vs-useeio
-:alt: Waterfall chart of the US emission factor from USEEIO v1.4 (0.2474) to Cornerstone 2026 (0.2454), by driver. Model reconciliation lowers it by 3.5%, US data adds 0.4%, nowcasting and facility reported data lower it by 5.8%, trade impact adds 8.6% and benchmark-informed improvements add 0.2%. The first three steps belong to the US model and the last two to the US portion of the global model.
 :width: 100%
 
-**Change in the US production-weighted emission factor from USEEIO v1.4 to Cornerstone 2026, by driver.** Percentages are changes relative to the USEEIO baseline. The vertical axis starts at 0.215.
+**Change in the US production-weighted emission factor from USEEIO v1.4 to Cornerstone 2026, by driver.** Percentages are changes relative to the USEEIO baseline. Hover over a bar for details. The vertical axis does not start at zero.
 :::
 
 ### Model reconciliation

From d7a7b6743b662e6531ac230cf3e8b7db0d438484 Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Thu, 22 Oct 2026 12:36:55 -0700
Subject: [PATCH 32/47] docs(releases): remove the open-this-box hint

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 releases/summary-ceda.md   | 2 +-
 releases/summary-useeio.md | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/releases/summary-ceda.md b/releases/summary-ceda.md
index 888e38e5..a1b51204 100644
--- a/releases/summary-ceda.md
+++ b/releases/summary-ceda.md
@@ -16,7 +16,7 @@ Cornerstone EEIO 2026 is the continuation of CEDA 2025. For global factors, the
 
 ::::{important} Your footprint may not follow the median
 :class: dropdown
-A company's change depends on which sectors and countries its spend falls in. A small median can hide large moves in individual sectors, in both directions. From a sample of 400 corporate footprints, the median company will experience a decrease of 14%. Open this box to see the full range.
+A company's change depends on which sectors and countries its spend falls in. A small median can hide large moves in individual sectors, in both directions. From a sample of 400 corporate footprints, the median company will experience a decrease of 14%.
 
 :::{figure} ../images/company-footprint-change-ceda.png
 :label: company-footprint-change-ceda
diff --git a/releases/summary-useeio.md b/releases/summary-useeio.md
index e652dca3..f10ebd64 100644
--- a/releases/summary-useeio.md
+++ b/releases/summary-useeio.md
@@ -16,7 +16,7 @@ Cornerstone EEIO 2026 is the continuation of the USEEIO Supply Chain GHG Emissio
 
 ::::{important} Your footprint may not follow the median
 :class: dropdown
-A company's change depends on which sectors and countries its spend falls in. A small median can hide large moves in individual sectors, in both directions. From a sample of 400 corporate footprints, the median company will experience a decrease of 1.3%. Open this box to see the full range.
+A company's change depends on which sectors and countries its spend falls in. A small median can hide large moves in individual sectors, in both directions. From a sample of 400 corporate footprints, the median company will experience a decrease of 1.3%.
 
 :::{figure} ../images/company-footprint-change.png
 :label: company-footprint-change

From 9c2f13fc3d36d0ebb1708005b45f09c32cb75023 Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Thu, 22 Oct 2026 12:39:07 -0700
Subject: [PATCH 33/47] docs(releases): scale the interactive waterfall to
 short frames

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 figures/interactive/drivers-waterfall.html | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/figures/interactive/drivers-waterfall.html b/figures/interactive/drivers-waterfall.html
index f017b14b..c99dfe5f 100644
--- a/figures/interactive/drivers-waterfall.html
+++ b/figures/interactive/drivers-waterfall.html
@@ -63,8 +63,8 @@
 
 function draw(key){
   const d = D[key], W=Math.max(document.querySelector(".wrap").clientWidth, 480), L=46, R=14, T=22, B=64,
-    H=Math.max(innerHeight - document.querySelector(".bar").offsetHeight - document.getElementById("note").offsetHeight - 54, 230), n=d.steps.length+2;
-  svg.setAttribute("width",W); svg.setAttribute("height",H); svg.setAttribute("viewBox",`0 0 ${W} ${H}`); svg.innerHTML="";
+    avail=innerHeight - document.querySelector(".bar").offsetHeight - document.getElementById("note").offsetHeight - 54, H=Math.max(avail, 230), n=d.steps.length+2;
+  const k = avail < 230 ? Math.max(avail,120)/230 : 1; svg.setAttribute("width",W*k); svg.setAttribute("height",H*k); svg.setAttribute("viewBox",`0 0 ${W} ${H}`); svg.innerHTML="";
   const y = v => T+(H-T-B)*(1-(v-d.lo)/(d.hi-d.lo)), cw=(W-L-R)/n, bw=cw*.62, cx=i=>L+cw*(i+.5); svg.style.fontSize = cw<80 ? "10px" : "11.5px";
   for(let v=Math.ceil(d.lo*50)/50; v<=d.hi+1e-9; v+=(d.hi-d.lo)>.1?.05:.01){ const yy=y(v);
     el("line",{x1:L,x2:W-R,y1:yy,y2:yy,stroke:css("--grid")}); label(L-8,yy+4,v.toFixed(v<.1?3:2),"muted",null,"end"); }

From 1e198671430c5fdfc67020cf2c87cafa59418bc6 Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Thu, 22 Oct 2026 12:40:23 -0700
Subject: [PATCH 34/47] docs(releases): align the RC overview chart with the
 summary pages

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 figures/scripts/rc_figures.py   |  13 +++++++------
 images/rc-factor-comparison.png | Bin 59387 -> 59168 bytes
 releases/rc.md                  |   4 ++--
 3 files changed, 9 insertions(+), 8 deletions(-)

diff --git a/figures/scripts/rc_figures.py b/figures/scripts/rc_figures.py
index 42ca1c54..9cc1d352 100644
--- a/figures/scripts/rc_figures.py
+++ b/figures/scripts/rc_figures.py
@@ -57,9 +57,10 @@ def panel(ax, start_label, start, steps, end_label, end, net):
         if i == len(steps):
             new = end
         lo, hi = min(level, new), max(level, new)
-        ax.bar(i, hi - lo, bottom=lo, color=UP if pct > 0 else DOWN, width=0.62)
+        colour = "#D85A30" if label.startswith("Data") else "#1D9E75"  # data update / methodology, as on the summary pages
+        ax.bar(i, hi - lo, bottom=lo, color=colour, width=0.62)
         txt = f"{pct:+.1f}%".replace("-", "−")
-        ax.text(i, hi * 1.015, txt, ha="center", va="bottom", color=UP if pct > 0 else DOWN, fontweight="medium")
+        ax.text(i, hi * 1.015, txt, ha="center", va="bottom", color=colour, fontweight="medium")
         ax.plot([i - 0.69, i - 0.31], [level, level], color="#B4B2A9", lw=0.9, ls=(0, (3, 3)))
         level = new
         xs.append(i); labels.append(label)
@@ -75,10 +76,10 @@ def panel(ax, start_label, start, steps, end_label, end, net):
 
 def comparison():
     fig, axes = plt.subplots(1, 2, figsize=(11, 3.7), dpi=200)
-    panel(axes[0], "CEDA\n2025", 0.6957, [("Data\nupdates", 6.3), ("Methodology\nchanges", -28.6)],
-          "Cornerstone\n2026", 0.5281, "−24.1%")
-    panel(axes[1], "USEEIO\nv1.4 (US)", 0.2474, [("Data\nupdates", -4.2), ("Methodology\nchanges", 3.3)],
-          "Cornerstone\n2026", 0.2448, "−1.0%")
+    panel(axes[0], "CEDA\n2025", 0.6942, [("Data\nupdates", 5.8), ("Methodology\nchanges", -27.5)],
+          "Cornerstone\n2026", 0.5324, "−23.3%")
+    panel(axes[1], "USEEIO\nv1.4 (US)", 0.2474, [("Data\nupdates", 0.4), ("Methodology\nchanges", -1.2)],
+          "Cornerstone\n2026", 0.2454, "−0.8%")
     fig.tight_layout(w_pad=3)
     fig.savefig(OUT / "rc-factor-comparison.png", facecolor="white")
     plt.close(fig)
diff --git a/images/rc-factor-comparison.png b/images/rc-factor-comparison.png
index 56ee39e8e7955597948f1fb0a5ed009980bb39a2..442e96ae118dfd1ede2b575b6d02d2ae41f77720 100644
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diff --git a/releases/rc.md b/releases/rc.md
index d7ddb5a3..40f8d2b3 100644
--- a/releases/rc.md
+++ b/releases/rc.md
@@ -92,10 +92,10 @@ The charts compare the new factors with each model they replace.
 
 :::{figure} ../images/rc-factor-comparison.png
 :label: rc-factor-comparison
-:alt: Two waterfall charts. Against CEDA 2025, the factor goes from 0.6957 to 0.5281 (24.1% lower), with data updates adding 6.3% and methodology changes removing 28.6%. Against USEEIO v1.4 for the US, it goes from 0.2474 to 0.2448 (1.0% lower), with data updates removing 4.2% and methodology changes adding 3.3%.
+:alt: Two waterfall charts. Against CEDA 2025, the factor goes from 0.6942 to 0.5324 (23.3% lower), with data updates adding 5.8% and methodology changes removing 27.5%. Against USEEIO v1.4 for the US, it goes from 0.2474 to 0.2454 (0.8% lower), with data updates adding 0.4% and methodology changes removing 1.2%.
 :width: 100%
 
-**Emission factors fall 24.1% against CEDA 2025 and 1.0% against USEEIO v1.4 (US).** Production-weighted totals in kg CO₂e per 2024 USD, split into data updates and methodology changes.
+**Emission factors fall 23.3% against CEDA 2025 and 0.8% against USEEIO v1.4 (US).** Production-weighted totals in kg CO₂e per 2024 USD, split into data updates and methodology changes.
 :::
 
 :::{important} Your footprint may not follow the median

From fe9aac3e9903e4722c125e13937eee00d3708afb Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Thu, 22 Oct 2026 12:42:49 -0700
Subject: [PATCH 35/47] docs(releases): update the ABSR text

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 releases/summary-ceda.md | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/releases/summary-ceda.md b/releases/summary-ceda.md
index a1b51204..1d4b9d27 100644
--- a/releases/summary-ceda.md
+++ b/releases/summary-ceda.md
@@ -60,10 +60,12 @@ This data update lowers European factors by about 4% in median. The most signifi
 
 ### Auto-balanced structural reflection (ABSR)
 
-Structural reflection is the method CEDA uses to give every country the same sectors. ABSR improves it. It achieves two goals that are hard to deliver together in a global model:
+Structural reflection is the method CEDA uses to maintain the same sectoral granularity across all countries.
+
+The ABSR method is an improvement in this methodology. It achieves two goals that are hard to deliver together in a global model:
 
 - All countries share the same 405 sectors, so emission factors compare like for like.
-- Each economy balances production and consumption, in both money and emissions.
+- Each economy balances production and consumption in terms of monetary transactions and emissions.
 
 ## Third-party benchmarking
 

From a22e057d736034aaac4824f835ba272182ea37fd Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Thu, 22 Oct 2026 12:43:26 -0700
Subject: [PATCH 36/47] docs(releases): add the ABSR diagram to the CEDA
 summary

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 figures/figures.csv      |   3 ++-
 images/absr-diagram.png  | Bin 0 -> 82542 bytes
 releases/summary-ceda.md |   8 ++++++++
 3 files changed, 10 insertions(+), 1 deletion(-)
 create mode 100644 images/absr-diagram.png

diff --git a/figures/figures.csv b/figures/figures.csv
index d5bebddf..0dd62199 100644
--- a/figures/figures.csv
+++ b/figures/figures.csv
@@ -7,4 +7,5 @@ company-footprint-change,releases/summary-useeio.md,chart,"Change log working Do
 drivers-vs-useeio,releases/summary-useeio.md,chart,"Change log working Doc (driver waterfall)",figures/scripts/rc_summary_figures.py,Yohanna Maldonado,draft (steps do not sum to the net change)
 drivers-vs-ceda,releases/summary-ceda.md,chart,"Change log working Doc (driver waterfall)",figures/scripts/rc_summary_figures.py,Yohanna Maldonado,draft (steps sum to -23.2% against the -23.3% net)
 company-footprint-change-ceda,releases/summary-ceda.md,chart,"Change log working Doc (histogram)",figures/scripts/rc_summary_figures.py,Yohanna Maldonado,draft (bar heights approximate)
-cement-ef,releases/summary-useeio.md,chart,"Change log working Doc (values 4.35 and 5.21; unit to confirm)",figures/scripts/rc_summary_figures.py,Yohanna Maldonado,draft
+cement-ef,releases/summary-useeio.md,chart,"Change log working Doc (values 4.35 and 5.21; kg CO2e per USD)",figures/scripts/rc_summary_figures.py,Yohanna Maldonado,draft
+absr-diagram,releases/summary-ceda.md,diagram,"Provided by Yohanna Maldonado",,Yohanna Maldonado,draft (image provided, not generated from a script)
diff --git a/images/absr-diagram.png b/images/absr-diagram.png
new file mode 100644
index 0000000000000000000000000000000000000000..5285df81d541a7d5d2dd4ffa471dfadc5c845bc7
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diff --git a/releases/summary-ceda.md b/releases/summary-ceda.md
index 1d4b9d27..609a2616 100644
--- a/releases/summary-ceda.md
+++ b/releases/summary-ceda.md
@@ -67,6 +67,14 @@ The ABSR method is an improvement in this methodology. It achieves two goals tha
 - All countries share the same 405 sectors, so emission factors compare like for like.
 - Each economy balances production and consumption in terms of monetary transactions and emissions.
 
+:::{figure} ../images/absr-diagram.png
+:label: absr-diagram
+:alt: Diagram of the ABSR method. Countries A, B and C start with different numbers of sectors, so the sector granularity varies by country. ABSR disaggregates and balances each economy, giving every country the same 405 sectors, with row and column totals that check out.
+:width: 100%
+
+**How ABSR works.** ABSR disaggregates each country's economy into the same 405 sectors and balances it, so that production and consumption match in both money and emissions.
+:::
+
 ## Third-party benchmarking
 
 Cornerstone uses benchmark data built from about 1,000 sources to review the key sectors that drive about 50% of global emissions. Each review ends in one of three outcomes:

From 13e9d93e407c37c3bfef3d7cda680fd5cdf9f93c Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Thu, 22 Oct 2026 12:45:51 -0700
Subject: [PATCH 37/47] docs(releases): sync the third-party benchmarking
 section to the Doc

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 releases/summary-ceda.md | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/releases/summary-ceda.md b/releases/summary-ceda.md
index 609a2616..20a694b8 100644
--- a/releases/summary-ceda.md
+++ b/releases/summary-ceda.md
@@ -83,13 +83,11 @@ Cornerstone uses benchmark data built from about 1,000 sources to review the key
 - **Model improvement.** The gap is traced to a data source or an allocation method, and fixed with more data or a corrected implementation.
 - **Known discrepancy.** The difference is documented, because of a limitation in how the model is built or a gap in the data.
 
-About 70% of country-sector pairs with a verified benchmark are within 25% of it. 21% are 25–50% apart and 10% are 50% or more apart.
-
 | Outcome | Example | Gap before and after |
 |---|---|---|
-| Sharper benchmark | US electricity: the benchmark covered only private utilities, while the model also includes state and local utilities. Aligning scope gives a benchmark of 2.37 against the model's 2.44 kg CO₂e per USD. | 15% to 3% |
-| Model improvement | Brazil dairy: the model was allocating methane from beef to dairy for lack of country-specific data. Brazil's national accounts of emissions and output fixed it. | 51% to 5% |
-| Known discrepancy | US oil and gas: the benchmark roughly doubles methane using aerial and satellite measurements. The model uses the EPA inventory, as for every other sector. Resolving this needs more research. | 22% (stays) |
+| Sharper benchmark | US electricity: the benchmark covered only private utilities, while the model also includes state and local utilities. Aligning scope reduces the gap. | 15% to 3% |
+| Model improvement | Brazil dairy: the model was allocating methane from beef to dairy for lack of country-specific data. The model now uses Brazil's national accounts of emissions and output for this sector, reducing the gap. | 51% to 5% |
+| Known discrepancy | US oil and gas: the benchmark roughly doubles methane using aerial and satellite measurements. The model uses the EPA inventory, as for every other sector. This is documented as a known discrepancy. | 22% (stays) |
 
 For the full list of data and methodology changes, see the [](change-log.md). For the equivalent summary against USEEIO, see [](summary-useeio.md).
 

From 505a907de0718c6ea0c1ae0e1fb21b2264dce913 Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Thu, 22 Oct 2026 12:52:51 -0700
Subject: [PATCH 38/47] docs(releases): add the enterprise demo line to the
 three change pages

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 releases/change-log.md     | 2 ++
 releases/summary-ceda.md   | 2 ++
 releases/summary-useeio.md | 2 ++
 3 files changed, 6 insertions(+)

diff --git a/releases/change-log.md b/releases/change-log.md
index f8f85cc0..53c8876b 100644
--- a/releases/change-log.md
+++ b/releases/change-log.md
@@ -98,6 +98,8 @@ The USEEIO and the US portion of CEDA had 18 methodological differences. Corners
 
 For the summaries of these changes, see [](summary-useeio.md) and [](summary-ceda.md).
 
+For even additional details or custom support on how to navigate the changes in your footprint, sign up for a demo of the enterprise product [here](https://watershed.com/en-GB/solutions/ceda).
+
 +++
 
 :::{div}
diff --git a/releases/summary-ceda.md b/releases/summary-ceda.md
index 20a694b8..67b4548b 100644
--- a/releases/summary-ceda.md
+++ b/releases/summary-ceda.md
@@ -91,6 +91,8 @@ Cornerstone uses benchmark data built from about 1,000 sources to review the key
 
 For the full list of data and methodology changes, see the [](change-log.md). For the equivalent summary against USEEIO, see [](summary-useeio.md).
 
+For even additional details or custom support on how to navigate the changes in your footprint, sign up for a demo of the enterprise product [here](https://watershed.com/en-GB/solutions/ceda).
+
 +++
 
 :::{div}
diff --git a/releases/summary-useeio.md b/releases/summary-useeio.md
index f10ebd64..3adcccf7 100644
--- a/releases/summary-useeio.md
+++ b/releases/summary-useeio.md
@@ -74,6 +74,8 @@ One example is light trucks and utility vehicles. The US factor rises by ~24%, m
 
 For the full list of data and methodology changes, see the [](change-log.md). For the equivalent summary against CEDA, see [](summary-ceda.md).
 
+For even additional details or custom support on how to navigate the changes in your footprint, sign up for a demo of the enterprise product [here](https://watershed.com/en-GB/solutions/ceda).
+
 +++
 
 :::{div}

From 6435180be3f3ccefcc962e5aa1a16a1be3675c76 Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Fri, 23 Oct 2026 16:06:00 -0700
Subject: [PATCH 39/47] docs(factors): align What's in a release with the
 workbook and drop Downloads and citation

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 factors/downloads.md          | 24 ------------------------
 factors/whats-in-a-release.md | 20 +++++++++++---------
 faq.md                        |  2 +-
 myst.yml                      |  1 -
 4 files changed, 12 insertions(+), 35 deletions(-)
 delete mode 100644 factors/downloads.md

diff --git a/factors/downloads.md b/factors/downloads.md
deleted file mode 100644
index c795ef52..00000000
--- a/factors/downloads.md
+++ /dev/null
@@ -1,24 +0,0 @@
----
-title: "Downloads and citation"
----
-
-:::{tip} Summary
-*To be written.*
-:::
-
-:::{note} Draft placeholder
-:class: dropdown
-Plan §3.4.7 · target: **RC (Oct 27)**
-
-
-**Planned content**
-
-- Zenodo DOIs, license, attribution requirement
-:::
-
-+++
-
-:::{div}
-:class: page-status
-Applies to: RC · Last reviewed: RC
-:::
diff --git a/factors/whats-in-a-release.md b/factors/whats-in-a-release.md
index 4ec355d2..12c5178d 100644
--- a/factors/whats-in-a-release.md
+++ b/factors/whats-in-a-release.md
@@ -15,7 +15,8 @@ The release includes one Excel workbook that contains: a cover, a data dictionar
 The first tab introduces the dataset and the release details:
 
 - Description: cradle-to-gate greenhouse gas emission factors in kg CO₂e per USD, by good or service, location and year, in producer and purchaser price. Prices are in current-year dollars, so the dollar year matches the data year.
-- Coverage: 148 countries and 21 regions, and 405 categories of goods and services (government services are not covered). The total and each gas (CO₂, CH₄, N₂O, HFCs, PFCs, NF₃, SF₆) are given in CO₂e, annually from 2018 to 2026.
+- Coverage: 148 countries and 21 regions, and 405 categories of goods and services (government services are not covered). The total and each gas (CO₂, CH₄, N₂O, HFCs, PFCs, NF₃, SF₆) are given in CO₂e, annually.
+- Version, date and DOI: the release version, its release date and the Zenodo DOI.
 - Publisher and license: Cornerstone Sustainability Data Initiative, under a Creative Commons Attribution 4.0 International license.
 
 Below these details, the cover lists every other tab with a one-line description and points readers to the data dictionary and the model documentation.
@@ -31,14 +32,15 @@ Each row is one sector in one location, and the columns are:
 | Code | Good or service code, based on the BEA input-output commodity codes, which derive from NAICS. There are 405 categories. |
 | Name | Name of the good or service. |
 | Location | A UN three-letter country code, with additions for multi-country regions. |
-| Total GHGs | Sum of all greenhouse gases, in kg CO₂e per USD. |
+| Location Name | Full name of the location. |
+| Total GHGs | Sum of all greenhouse gases, in kg CO₂e per USD. Totals may not equal the sum of the gas values due to rounding. |
 | CO2 | Carbon dioxide, in kg CO₂e per USD. |
 | CH4 | Methane, in kg CO₂e per USD. |
 | N2O | Nitrous oxide, in kg CO₂e per USD. |
 | HFCs | Hydrofluorocarbons, in kg CO₂e per USD. |
 | PFCs | Perfluorocarbons, in kg CO₂e per USD. |
-| NF3 | Nitrogen trifluoride, in kg CO₂e per USD. |
 | SF6 | Sulfur hexafluoride, in kg CO₂e per USD. |
+| NF3 | Nitrogen trifluoride, in kg CO₂e per USD. |
 
 Values are in current-year dollars: a factor in a 2024 tab applies to spend in 2024 dollars.
 
@@ -49,15 +51,15 @@ The data dictionary and five other tabs support the data tabs. The data dictiona
 | Tab | What it contains |
 |---|---|
 | Data Dictionary | Definitions of each field in the data tabs. |
-| Exchange rates | Local currency units per US dollar for 148 countries: country code and name, currency, unit, and one column per year from 2018 to 2026. Rates come from Open Exchange Rates (openexchangerates.org) and are Cornerstone's recommended rates for converting spend in local currency before applying the factors. |
-| GWP | The 100-year global warming potentials from the IPCC AR6 that express each gas in CO₂e: CO₂ 1, CH₄ 27.9 (to confirm), N₂O 273, SF₆ 24,300 and NF₃ 17,400. HFCs and PFCs are already in CO₂e, so their factor is 1. |
-| Cornerstone \<\> NAICS mapping | The correspondence between NAICS 2022 codes and Cornerstone codes: NAICS code and name, Cornerstone code and title, and a weight. When a NAICS industry is split across several Cornerstone codes, the weights show each share and add up to 1. |
-| GoodsServices | The 405 sectors with their code, name and description. Cornerstone recommends using this description to map spend categories to emission factors. |
-| Locations | Every location code and name used in the model: countries by their three-letter code, and regions. |
+| Exchange Rates | Local currency units per US dollar for 221 countries and territories, with the columns Country Code, Country Name, LCU (local currency unit), Unit, and one column per year. Rates come from Open Exchange Rates (openexchangerates.org) and are Cornerstone's recommended rates for converting spend in local currency before applying the factors. |
+| GWP | The 100-year global warming potentials from the IPCC AR6 that express each gas in CO₂e: CO₂ 1, CH₄ 29.8 (fossil) or 27.0 (non-fossil), N₂O 273, SF₆ 24,300 and NF₃ 17,400. HFCs and PFCs are already in CO₂e in the published factors, so their factor is 1. |
+| NAICS to Cornerstone mapping | The correspondence between NAICS 2017 codes and Cornerstone codes, with the columns NAICS 2017 Code, NAICS Name, Cornerstone Code, Cornerstone Name and Weight. When a NAICS industry is split across several Cornerstone codes, the weights show each share and add up to 1. |
+| Sector Descriptions | The 405 sectors with their Code, Name and Description. Cornerstone recommends using this description to map spend categories to emission factors. |
+| Locations | Every location used in the model, with the columns Location, Location Name, Region, Region Name and Has Country EFs (whether the location has its own country factors). It lists 249 locations, 148 of which have country factors. |
 
 ## Update cadence
 
-The Cornerstone EEIO follows an annual update cycle. Every year we release a version named after the release year, so Cornerstone 2027 will be released in 2027. Releases are publicly available every October so companies can plan accordingly.
+The Cornerstone EEIO follows an annual update cycle. Every year we release a version named after the release year, so Cornerstone 2027 will be released in 2027. Releases are publicly available every October so companies can plan accordingly. Cornerstone EEIO 2026 will stay methodologically stable for at least the next five years, giving companies a consistent basis to rebaseline and set new targets.
 
 +++
 
diff --git a/faq.md b/faq.md
index c3c22e92..603e1928 100644
--- a/faq.md
+++ b/faq.md
@@ -70,7 +70,7 @@ Plan §3.9 · target: **RC (Oct 27)**, roughly the top 10 questions. Each answer
 ## Practical
 
 ### What license are the factors under, and what attribution is required?
-*To be written. See [](factors/downloads.md).*
+*To be written.*
 
 ### How do I report a possible error or ask a question?
 Open an issue on [GitHub](https://github.com/cornerstone-data/eeio-docs/issues).
diff --git a/myst.yml b/myst.yml
index 4ddbe4ef..7f12c541 100644
--- a/myst.yml
+++ b/myst.yml
@@ -43,7 +43,6 @@ project:
         - file: factors/choose-a-factor.md
         - file: factors/prices-and-margins.md
         - file: factors/year-and-currency.md
-        - file: factors/downloads.md
     - file: validation/index.md
     - file: benchmarking/index.md
     - title: Reference

From 3b5cf9b6e0b2801e9da3ed64ec46c33f03a8b733 Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Fri, 23 Oct 2026 21:58:37 -0700
Subject: [PATCH 40/47] docs(factors): add User guidance page under Emission
 factors

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 factors/user-guidance.md                 |  99 +++++++++++++++++++++++
 figures/figures.csv                      |   2 +
 figures/scripts/user_guidance_figures.py |  63 +++++++++++++++
 images/user-guidance-ef-by-country.png   | Bin 0 -> 52973 bytes
 images/user-guidance-worked-example.png  | Bin 0 -> 58595 bytes
 myst.yml                                 |   1 +
 6 files changed, 165 insertions(+)
 create mode 100644 factors/user-guidance.md
 create mode 100644 figures/scripts/user_guidance_figures.py
 create mode 100644 images/user-guidance-ef-by-country.png
 create mode 100644 images/user-guidance-worked-example.png

diff --git a/factors/user-guidance.md b/factors/user-guidance.md
new file mode 100644
index 00000000..0f9cf93e
--- /dev/null
+++ b/factors/user-guidance.md
@@ -0,0 +1,99 @@
+---
+title: "User guidance"
+---
+
+:::{tip} Summary
+This guidance walks you through how to apply the emission factors to your corporate carbon footprint. The factors are cradle-to-gate: they cover the emissions of producing a good or service up to the point where you buy it. They are typically used for Scope 3, categories 3.1 (purchased goods and services) and 3.2 (capital goods). They can also be used for category 3.15 (investments) to estimate financed emissions, using a company's revenue as the proxy for spend, following PCAF's guidance.
+:::
+
+This user guidance covers two topics:
+
+1. How to choose the right emission factor: the country, the year and the exchange rate.
+2. How to choose between purchaser and producer price.
+
+A separate guide explains how to think about the margin emission factors, relevant only for previous USEEIO users.
+
+## Choose the right emission factor
+
+Every emission factor is defined by a sector, a location, a year and a price basis. The steps below take you from a line in your spend data to the factor that fits it, and then to an emissions number.
+
+### Step 1: Match the purchase to a sector
+
+Find the sector that best describes what you bought, using the sector descriptions that come with the factors. Pick the sector for the final good or service you purchased. You do not add up factors along a supply chain, because each factor already includes the upstream emissions of that sector. A purchase of steel products, for example, already includes the emissions of making the steel.
+
+Each sector is related to a commodity and follows the BEA standard classification. For crosswalks to other industry classifications such as GICS, NACE and ANZSIC, please enquire about the Watershed enterprise product.
+
+### Step 2: Choose the country
+
+Use the location where the good or service was produced. When you do not know it for every supplier, work down this hierarchy and stop at the first one you have:
+
+1. Country of manufacture
+2. Country of the supplier
+3. Country of purchase
+4. Country of your company's headquarters
+
+If a country has no factor of its own for that sector, use the regional factor for the country's region, and use the global average only when neither is available.
+
+**Why country matters.** The same sector can have very different emissions per dollar in different places, because of each country's energy mix, the structure of its economy and its trade. The chart shows the 2026 producer-price factor for management consulting in six locations.
+
+:::{figure} ../images/user-guidance-ef-by-country.png
+:label: user-guidance-ef-by-country
+:alt: Horizontal bar chart of the 2026 producer-price emission factor for management consulting, in kg CO2e per USD. China 0.315, India 0.137, global average 0.0948, United Kingdom 0.0449 (highlighted), United States 0.0391, France 0.0390.
+:width: 100%
+
+**Emission factor for management consulting by location, 2026, producer price (kg CO₂e per USD).** The United Kingdom is highlighted because it is used in the worked example below.
+:::
+
+Applying one country's factor to all of your spend would misstate the emissions of suppliers elsewhere. A supplier in China has a factor about eight times the US factor for the same sector. A model with global coverage lets you use the factor of the country where the work is done.
+
+### Step 3: Choose the year
+
+Use the factor year that matches the year of your spend. Each factor is expressed in dollars of its own year, so a 2026 factor applies to spend in 2026 dollars. The factors for different years are not interchangeable: the same sector and country has a different factor for each year.
+
+- If your reporting year is not the calendar year, use the factors for the calendar year that holds most of your data. If the data splits evenly between two years, use the newer factors. For example, a reporting year from April 2025 to March 2026 has nine months in 2025 and three in 2026, so it uses the 2025 factors.
+
+### Step 4: Convert the factor to your currency
+
+The factors are per US dollar. Convert either the factor or your spend to the same currency using the recommended exchange rate information.
+
+**Factor per unit of local currency = factor per USD ÷ exchange rate**
+
+Use the rate for the year of the spend, not the rate on the day you do the calculation.
+
+### Worked example: consulting services in the United Kingdom
+
+A company paid £10,000 for management consulting services from a UK supplier in 2026. The 2026 factor for management consulting in the United Kingdom is 0.0449 kg CO₂e per USD, and the 2026 exchange rate is 0.74283 pounds per US dollar.
+
+:::{figure} ../images/user-guidance-worked-example.png
+:label: user-guidance-worked-example
+:alt: Four boxes joined by arrows. Your spend, £10,000 of management consulting in the UK in 2026. Look up the emission factor, 0.0449 kg CO2e per USD. Divide by 0.74283 to get the factor in pounds, 0.0604 kg CO2e per pound. Multiply by £10,000 to get emissions of 604 kg CO2e, or 0.60 tonnes.
+:width: 100%
+
+**From spend to emissions.** Look up the factor, convert it to pounds, and multiply by the spend.
+:::
+
+To see the breakdown by gas, apply the same steps to the factor for each gas.
+
+## Choose between purchaser and producer price
+
+Each year has two sets of factors. The producer price factors value a good at what the producer receives. The purchaser price factors value it at what the buyer pays, which adds the trade and transport margins on the way to the buyer. The same purchase therefore has a larger dollar value at purchaser price, and each dollar carries less emissions.
+
+**If you are unsure, use producer price.** Margins are hard to estimate globally, even for government statistical offices, and producer price is the more conservative choice, meaning it gives the higher emissions estimate for the same spend.
+
+| Your situation | Use |
+|---|---|
+| You are unsure what your spend includes | Producer price |
+| You are buying off-the-shelf goods at retail, so what you paid includes the usual wholesale and retail margins | Purchaser price |
+| You buy directly from the producer and account for transport and distribution separately in your footprint | Producer price |
+| You buy a service | Either: the two sets give the same factor in most service sectors |
+
+The consulting example shows the last case. The management consulting factor for the United Kingdom is 0.0449 at both producer and purchaser price, because a service has no trade margin.
+
+For goods, the choice changes the result. The 2026 factor for computers in the United Kingdom is 0.0814 kg CO₂e per USD at producer price and 0.0539 at purchaser price. In pounds, that is 0.1096 and 0.0726 kg CO₂e per £, so £10,000 of spend gives about 1,096 kg CO₂e at producer price and 726 kg CO₂e at purchaser price, a difference of about a third. Match the factor to the price basis of your spend data: if your invoice is what you paid at retail, pair it with purchaser price.
+
++++
+
+:::{div}
+:class: page-status
+Applies to: 2026-rc.1 · Last reviewed: 2026-rc.1
+:::
diff --git a/figures/figures.csv b/figures/figures.csv
index d1a3351f..2a977432 100644
--- a/figures/figures.csv
+++ b/figures/figures.csv
@@ -16,3 +16,5 @@ io-mrio-blocks,io/index.md,diagram (SVG),hand-made (after CEDA team slide 19),,,
 country-tiers-map,io/country-tiers.md,map (SVG),data/country_tiers.csv + Natural Earth 1:50m admin-0,figures/scripts/tier_map.py,,draft
 ghg-pipeline,ee/index.md,diagram (SVG),hand-made,,,draft
 eeio-overview,intro/what-is-eeio.md,diagram (SVG),hand-made,,,draft
+user-guidance-ef-by-country,factors/user-guidance.md,chart,"Cornerstone GHG Emission Factors workbook, 2026-rc.1 (2026PRO, sector 541610)",figures/scripts/user_guidance_figures.py,Yohanna Maldonado,draft
+user-guidance-worked-example,factors/user-guidance.md,diagram,"Worked example from the 2026-rc.1 workbook (factor 0.0449; GBP rate 0.74283)",figures/scripts/user_guidance_figures.py,Yohanna Maldonado,draft
diff --git a/figures/scripts/user_guidance_figures.py b/figures/scripts/user_guidance_figures.py
new file mode 100644
index 00000000..10045aef
--- /dev/null
+++ b/figures/scripts/user_guidance_figures.py
@@ -0,0 +1,63 @@
+"""Write the two graphics on the User guidance page to images/.
+
+- user-guidance-ef-by-country.png: 2026 producer-price factor for management consulting (sector 541610)
+  in six locations, kg CO2e per USD, from the 2026-rc.1 release workbook.
+- user-guidance-worked-example.png: GBP 10,000 of UK management consulting taken to kg CO2e, using the
+  0.0449 kg CO2e per USD factor and the 2026 rate of 0.74283 GBP per USD (0.0449 / 0.74283 = 0.0604).
+
+Run from the repo root (needs matplotlib):
+    uv run --with matplotlib python figures/scripts/user_guidance_figures.py
+"""
+from pathlib import Path
+
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+from matplotlib.patches import FancyBboxPatch, FancyArrowPatch
+
+OUT = Path(__file__).resolve().parents[2] / "images"
+INK, MUTED, GRID = "#1F2933", "#5B6770", "#E3E7EA"
+BASE, HI = "#A9BFCC", "#1F5F8B"
+plt.rcParams["font.family"] = "DejaVu Sans"
+
+# Chart 1: EF by location
+data = [("China", 0.315), ("India", 0.137), ("Global average", 0.0948),
+        ("United Kingdom", 0.0449), ("United States", 0.0391), ("France", 0.0390)]
+fig, ax = plt.subplots(figsize=(8, 3.6), dpi=200)
+labels = [d[0] for d in data][::-1]; vals = [d[1] for d in data][::-1]
+cols = [HI if l == "United Kingdom" else BASE for l in labels]
+bars = ax.barh(labels, vals, color=cols, height=0.62)
+for b, v in zip(bars, vals):
+    ax.text(v + 0.006, b.get_y() + b.get_height()/2, f"{v:.3g}" if v>=0.1 else f"{v:.4f}".rstrip("0") if False else (f"{v:.3f}" if v>=0.1 else f"{v:.4f}"), va="center", fontsize=10, color=INK)
+ax.set_xlim(0, 0.36)
+ax.set_xlabel("kg CO₂e per USD (2026, producer price)", fontsize=9, color=MUTED)
+ax.xaxis.grid(True, color=GRID, lw=0.8); ax.set_axisbelow(True)
+for s in ("top", "right", "left"): ax.spines[s].set_visible(False)
+ax.spines["bottom"].set_color(GRID)
+ax.tick_params(axis="y", length=0, labelsize=10, labelcolor=INK)
+ax.tick_params(axis="x", labelsize=9, labelcolor=MUTED, colors=GRID)
+fig.tight_layout(); fig.savefig(str(OUT / "user-guidance-ef-by-country.png"), facecolor="white"); plt.close(fig)
+
+# Chart 2: worked example flow
+fig, ax = plt.subplots(figsize=(10, 2.7), dpi=200)
+ax.set_xlim(0, 101); ax.set_ylim(0, 27); ax.axis("off")
+boxes = [
+    ("£10,000", "Management consulting,\nUnited Kingdom, 2026", "Your spend", BASE, INK),
+    ("0.0449", "kg CO₂e per USD", "Emission factor", BASE, INK),
+    ("0.0604", "kg CO₂e per £\n(0.0449 ÷ 0.74283)", "Factor in pounds", BASE, INK),
+    ("604 kg", "CO₂e\n(0.60 t)", "Emissions", HI, "white"),
+]
+w, gap, x = 18, 9.2, 1
+for i, (big, small, tag, fc, tc) in enumerate(boxes):
+    ax.add_patch(FancyBboxPatch((x, 3), w, 18, boxstyle="round,pad=0,rounding_size=1.5",
+                                fc=fc if fc == HI else "#EEF3F6", ec=fc, lw=1.4))
+    ax.text(x + w/2, 24.2, tag, ha="center", va="center", fontsize=9, color=MUTED)
+    ax.text(x + w/2, 15.2, big, ha="center", va="center", fontsize=16, fontweight="bold", color=tc)
+    ax.text(x + w/2, 8.2, small, ha="center", va="center", fontsize=8, color=tc if fc == HI else MUTED, linespacing=1.3)
+    if i < 3:
+        ax.text(x + w + gap/2, 15, ["look up", "÷ 0.74283", "× £10,000"][i], ha="center", va="center", fontsize=7, color=MUTED)
+        ax.add_patch(FancyArrowPatch((x + w + 0.6, 11.5), (x + w + gap - 0.6, 11.5), arrowstyle="-|>",
+                                     mutation_scale=12, color=MUTED, lw=1.4))
+    x += w + gap
+ax.text(1 + 2*(w+gap) + w + gap/2 + 0.0, 0.0, "", fontsize=1)
+fig.savefig(str(OUT / "user-guidance-worked-example.png"), facecolor="white", bbox_inches="tight", pad_inches=0.1); plt.close(fig)
diff --git a/images/user-guidance-ef-by-country.png b/images/user-guidance-ef-by-country.png
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diff --git a/myst.yml b/myst.yml
index 6f3f7e2d..aa624c41 100644
--- a/myst.yml
+++ b/myst.yml
@@ -33,6 +33,7 @@ project:
     - title: Emission factors
       children:
         - file: factors/whats-in-a-release.md
+        - file: factors/user-guidance.md
         - file: factors/choose-a-factor.md
         - file: factors/prices-and-margins.md
         - file: factors/year-and-currency.md

From 2bf394d8d07ca0f3c73e9619b1c11db868d3e945 Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Fri, 23 Oct 2026 21:58:37 -0700
Subject: [PATCH 41/47] docs(releases): rename the release candidate 2026-rc.1
 and update the Releases intro

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 benchmarking/index.md           |   4 +-
 ee/country-tiers.md             |   2 +-
 ee/index.md                     |   2 +-
 factors/choose-a-factor.md      |   2 +-
 factors/prices-and-margins.md   |   2 +-
 factors/whats-in-a-release.md   |   2 +-
 factors/year-and-currency.md    |   2 +-
 faq.md                          | 303 +++++++++++++++++++++++++++-----
 intro/ee-part.md                |   2 +-
 intro/emission-factors.md       |   2 +-
 intro/io-part.md                |   2 +-
 intro/multi-regional.md         |   2 +-
 intro/uses-and-limits.md        |   2 +-
 reference/data-sources.md       |   2 +-
 reference/glossary.md           |   2 +-
 reference/methods-by-country.md |   2 +-
 reference/notation.md           |   2 +-
 releases/change-log.md          |   2 +-
 releases/index.md               |   2 +-
 releases/rc.md                  |  10 +-
 releases/summary-ceda.md        |   2 +-
 releases/summary-useeio.md      |   2 +-
 validation/index.md             |   2 +-
 23 files changed, 286 insertions(+), 71 deletions(-)

diff --git a/benchmarking/index.md b/benchmarking/index.md
index 4e21a035..40dbc674 100644
--- a/benchmarking/index.md
+++ b/benchmarking/index.md
@@ -14,7 +14,7 @@ Plan §3.6.1 · target: **RC (Oct 27)**
 **Planned content**
 
 - Validation asks "did we build the model correctly?"; benchmarking asks "are the results plausible compared with independent evidence?" Both feed the next release
-- Headline v0.6 benchmark results for the RC
+- Headline v0.6 benchmark results for 2026-rc.1
 
 **Key visuals**
 
@@ -25,5 +25,5 @@ Plan §3.6.1 · target: **RC (Oct 27)**
 
 :::{div}
 :class: page-status
-Applies to: RC · Last reviewed: RC
+Applies to: 2026-rc.1 · Last reviewed: 2026-rc.1
 :::
diff --git a/ee/country-tiers.md b/ee/country-tiers.md
index f815ecf1..72627c1f 100644
--- a/ee/country-tiers.md
+++ b/ee/country-tiers.md
@@ -167,5 +167,5 @@ See [Validation](#validation-hub).
 
 :::{div}
 :class: page-status
-Applies to: RC · Last reviewed: RC
+Applies to: 2026-rc.1 · Last reviewed: 2026-rc.1
 :::
diff --git a/ee/index.md b/ee/index.md
index 318c2e1e..0b93d080 100644
--- a/ee/index.md
+++ b/ee/index.md
@@ -92,5 +92,5 @@ See [Validation](#validation-hub).
 
 :::{div}
 :class: page-status
-Applies to: RC · Last reviewed: RC
+Applies to: 2026-rc.1 · Last reviewed: 2026-rc.1
 :::
diff --git a/factors/choose-a-factor.md b/factors/choose-a-factor.md
index 793a5c88..5bb49577 100644
--- a/factors/choose-a-factor.md
+++ b/factors/choose-a-factor.md
@@ -44,5 +44,5 @@ Plan §3.4.2 · target: **RC (Oct 27)**
 
 :::{div}
 :class: page-status
-Applies to: RC · Last reviewed: RC
+Applies to: 2026-rc.1 · Last reviewed: 2026-rc.1
 :::
diff --git a/factors/prices-and-margins.md b/factors/prices-and-margins.md
index 91c9c6e3..f3f39bad 100644
--- a/factors/prices-and-margins.md
+++ b/factors/prices-and-margins.md
@@ -41,5 +41,5 @@ Plan §3.4.3 · target: **RC (Oct 27)**
 
 :::{div}
 :class: page-status
-Applies to: RC · Last reviewed: RC
+Applies to: 2026-rc.1 · Last reviewed: 2026-rc.1
 :::
diff --git a/factors/whats-in-a-release.md b/factors/whats-in-a-release.md
index 12c5178d..94e181ef 100644
--- a/factors/whats-in-a-release.md
+++ b/factors/whats-in-a-release.md
@@ -65,5 +65,5 @@ The Cornerstone EEIO follows an annual update cycle. Every year we release a ver
 
 :::{div}
 :class: page-status
-Applies to: RC · Last reviewed: RC
+Applies to: 2026-rc.1 · Last reviewed: 2026-rc.1
 :::
diff --git a/factors/year-and-currency.md b/factors/year-and-currency.md
index 3b845315..a87ea215 100644
--- a/factors/year-and-currency.md
+++ b/factors/year-and-currency.md
@@ -43,5 +43,5 @@ Plan §3.4.4 · target: **RC (Oct 27)**
 
 :::{div}
 :class: page-status
-Applies to: RC · Last reviewed: RC
+Applies to: 2026-rc.1 · Last reviewed: 2026-rc.1
 :::
diff --git a/faq.md b/faq.md
index 603e1928..20941aac 100644
--- a/faq.md
+++ b/faq.md
@@ -2,82 +2,297 @@
 title: Frequently asked questions
 ---
 
-:::{note} Draft placeholder
-:class: dropdown
-Plan §3.9 · target: **RC (Oct 27)**, roughly the top 10 questions. Each answer is short and links to the page with the full explanation. New questions come from support emails, GitHub issues, and user conversations, reviewed at each release.
+:::{tip} Summary
+Short answers to the questions we hear most about Cornerstone EEIO 2026. Each answer links to the page with the full explanation. Cornerstone EEIO is the successor of USEEIO and CEDA, so many answers compare it with those models.
+:::
+
+% Source notes for maintainers (not rendered).
+% - Structure adapted from the CEDA 2025 FAQ (Watershed, updated 2025-11-11).
+% - Lines marked TODO(confirm) hold claims that need sign-off before the final release.
+
+## About Cornerstone EEIO 2026
+
+:::{dropdown} What is Cornerstone EEIO 2026?
+A multi-regional environmentally-extended input-output (EEIO) model that produces spend-based greenhouse gas emission factors, in kg CO₂e per USD, for 405 sectors across 148 countries, 21 regional averages and one global average. It combines USEEIO's detailed picture of the US economy with CEDA's global coverage and trade modeling. See [](releases/rc.md).
+:::
+
+:::{dropdown} What is the Cornerstone Sustainability Data Initiative, and how does it relate to USEEIO and CEDA?
+Cornerstone launched in August 2025 to bring USEEIO and CEDA together into one open, comprehensive dataset of Scope 3 spend-based emission factors. Cornerstone EEIO 2026 is the first release of that combined model. It is the continuation of both USEEIO v1.4 and CEDA 2025. Learn more at [cornerstonedata.org](https://cornerstonedata.org/).
+:::
+
+:::{dropdown} What is changing compared with USEEIO and CEDA?
+Three groups of changes:
+
+1. **A more current US economy that accounts for imports.** US tables are rebuilt for every year from 2017 to 2024, manufacturing uses facility-reported emissions, and imports are modeled where they are made.
+2. **Newer data for Europe, the UK and China, and better gap-filling for other countries.**
+3. **A validation process that feeds back into the model.** Third-party benchmarks review the sectors behind about 50% of global emissions.
+
+The full list is in the [](releases/change-log.md). Side-by-side summaries are in [](releases/summary-useeio.md) and [](releases/summary-ceda.md).
+:::
+
+:::{dropdown} Are the changes driven by methodology or by real-world change?
+Both. Data updates reflect real-world change, such as new economic, trade and emissions data. Methodology changes reflect modeling improvements, such as modeling US imports by origin and the auto-balanced structural reflection (ABSR) method. Against CEDA 2025, data updates add about 5.8% to the global production-weighted factor and methodology changes remove about 27.5%. Against USEEIO v1.4 (US), data updates add 0.4% and methodology changes remove 1.2%.
+:::
+
+:::{dropdown} How much will my footprint change?
+It depends on which sectors and countries your spend falls in.
+
+- **Coming from CEDA 2025:** global production-weighted factors fall by 23.3%. In a sample of 400 corporate footprints, the median company sees a 14% decrease.
+- **Coming from USEEIO v1.4:** US factors change by −0.8% overall. In the same sample, the median company sees a 1.3% decrease.
+
+A small median can hide large moves in individual sectors, in both directions. See [](releases/summary-ceda.md) and [](releases/summary-useeio.md).
+:::
+
+:::{dropdown} The overall change is a decrease, so why are some of my factors increasing?
+Several drivers push individual factors up:
+
+- **US imports.** USEEIO treated the US as a closed economy. Cornerstone models each import in the country that made it. Many imports come from more emissions-intensive economies such as China and India, so US factors tend to rise by 10 to 30% on average. This is the main upward pressure for USEEIO users.
+- **Facility-reported emissions.** For example, US cement now matches the totals reported by cement plants, which USEEIO undercounted by about 20%.
+- **New country data.** For example, China's battery factor rises by about 30% as production shifted from lead-acid to lithium-ion.
+
+Other factors fall. For example, European electricity factors fall in every EU country, driven by more renewables and higher energy prices.
+:::
+
+:::{dropdown} Are these changes expected?
+Yes. They come from newer data and from better modeling, and each is documented in the [](releases/change-log.md). Where a gap against third-party benchmarks remains, it is documented as a known discrepancy (see [](benchmarking/index.md)).
+:::
+
+:::{dropdown} Why is it called a "release candidate"?
+2026 is the first time CEDA and USEEIO are combined in one model, and the first time a time series is released. The two-phase release gives companies time to assess the impact on their most recent year of measurements while the time series is under development. The release candidate (2026-rc.1) is published on October 27, 2026. The final version (2026-v0.1), with the 2017–2026 time series, follows in early December 2026. See [](releases/rc.md).
+:::
+
+:::{dropdown} Will the 2024–2026 factors change between the release candidate and the final version?
+We don't foresee significant changes, but some sector–country combinations could move because of improvements made while developing the time series. If you need numbers for reporting or rebaselining, wait for the final version.
+:::
+
+:::{dropdown} Why did the versioning change from numbers to years?
+Each release is named after the year it is published, so Cornerstone EEIO 2026 is released in 2026 and the 2027 version in 2027. This replaces the different naming schemes used by USEEIO (v1.4) and CEDA (v4.1, then CEDA 2025).
+:::
+
+:::{dropdown} When is the next update?
+Releases are published every October so companies can plan for them. The next, Cornerstone EEIO 2027, will be released in October 2027. Cornerstone EEIO 2026 will stay methodologically stable for at least five years, so you have a consistent basis to rebaseline and set targets.
+:::
+
+:::{dropdown} Is this just a price index adjustment of the previous numbers?
+No. Each release incorporates the most recent economic, trade and emissions data for each country. Price indices are applied only to years after the base year, for which no reported data exists yet (for example 2025 and 2026 are adjusted from the 2024 base year).
+:::
+
+:::{dropdown} Did the sectors change?
+Yes, slightly. Cornerstone EEIO uses 405 sectors, based on the BEA input-output commodity codes, versus 400 in CEDA. Following the model reconciliation, the waste sector is split into seven sectors and aluminum into primary and secondary. Home appliances are a single sector. A NAICS-to-Cornerstone mapping tab is included in the release, so you can map your existing categories. See the [](releases/change-log.md#change-log-model-reconciliation).
+:::
+
+:::{dropdown} Did the file format change?
+The release is one Excel workbook with a cover, a data dictionary, a producer-price tab and a purchaser-price tab for each year, and five auxiliary tabs. See [](factors/whats-in-a-release.md). 
+% TODO(confirm): whether the layout differs from the CEDA 2025 files customers know, and whether a CSV/long format is also published.
+:::
+
+## Which release to use
+
+:::{dropdown} Which release should I use for my 2026 measurements?
+Use the Cornerstone EEIO 2026 factors from the `2026PRO` or `2026PUR` tab. We recommend matching the release year to the year being measured. Until the final version is published in December, treat the factors as a release candidate.
+:::
+
+:::{dropdown} Can I use the release candidate for my 2024 and 2025 measurements?
+Yes. It has factors for 2024, 2025 and 2026 dollars. The base year is 2024, so 2024 is the year most directly backed by data, and the 2025 and 2026 factors are adjusted from it with price indices. If your reporting is being finalized, consider waiting for the final version.
+:::
+
+:::{dropdown} Why can 2026 measurements use factors built on 2024 data?
+The underlying sources are published with a lag. For example, national input-output tables and GHG inventories typically cover a year that is one to three years old. Every spend-based database inherits this lag, so it is standard to match the release to the year of measurement. Cornerstone builds 2025 and 2026 factors by adjusting the 2024 base year with price indices.
+:::
+
+:::{dropdown} My reporting year isn't the calendar year. Which factors should I use?
+Use the factors for the calendar year in which most of your data falls. If your data is split equally, use the newer set. For example, for a fiscal year from April 1, 2025 to March 31, 2026, use the 2026 factors. Alternatively, apply the 2026 factors to data from January 1, 2026 and the previous year's factors before that.
+:::
+
+:::{dropdown} I'm in mid-2026 or later. Can I already measure 2027?
+Use the 2026 factors for the portion of 2027 you've measured, and update when Cornerstone EEIO 2027 is published in October 2027. If you adjust the 2026 factors yourself, apply only a price index (for example a national price index) and don't edit the factors otherwise. Auditors generally accept this approach.
+% TODO(confirm): confirm the guidance wording with the audit/assurance team.
+:::
+
+:::{dropdown} I'm measuring a past year (2017–2023). What should I use?
+The final release, due in December 2026, will include a time series from 2017 to 2026. All years use the same methodology, and only the underlying country data changes, so each year captures decarbonization that happened that year (for example the US electricity grid). Until then, use the previous version you already use (CEDA 2025 or USEEIO v1.4).
+:::
+
+:::{dropdown} I was using CEDA or USEEIO. Do I need to rebaseline or restate previous years?
+The time series is designed for exactly this. Because every year from 2017 to 2026 is built with one methodology, you can recalculate prior years on a consistent basis and compare them. Whether to restate is a decision under your own company's policy and your target framework. Rebaselining thresholds are usually based on significance, so talk to your verifier. See [](releases/summary-ceda.md) and [](releases/summary-useeio.md) for the expected change.
+% TODO(confirm): wording about SBTi / GHG Protocol recalculation thresholds.
+:::
+
+:::{dropdown} Can I keep using CEDA 2025 or USEEIO for 2026?
+We recommend using Cornerstone EEIO 2026 for 2026 measurements, since it supersedes both. If you have a reason to stay on a previous version, apply a price index to it, and be ready to explain the choice to your verifier.
+% TODO(confirm): whether CEDA 2025 and USEEIO v1.4 stay available, and for how long.
 :::
 
 ## Using the factors
 
-### Should I use `D` or `N`?
-*Almost always `N` for purchased goods and services. Answer to be written.*
+:::{dropdown} Should I use `D` or `N`?
+Use `N`, the total emission factor, for purchased goods and services. It includes the emissions of the sector itself and of its whole supply chain. `D` covers only what the sector emits directly. Using `D` leaves out the supply chain, and adding `D` to `N` double counts. The factor tabs in the release report the total (`Total GHGs`) and each gas. See [](reference/notation.md).
+:::
+
+:::{dropdown} How do I find the right sector for a purchase? What if it spans several sectors?
+Match the purchase to the *good or service you bought*, not to the supplier's industry. Use the description in the **Sector Descriptions** tab (405 sectors), and the **NAICS to Cornerstone mapping** tab if your categories are NAICS-based. If a purchase spans several sectors, split the spend across them, or choose the one that best describes most of the spend. See [](factors/choose-a-factor.md).
+:::
+
+:::{dropdown} Do I need to add up factors across the supply chain (for example raw steel plus steel products)?
+No. Factors already include all upstream emissions, so a manufactured good already includes the raw materials. For example, the factor for a steel product includes the iron and steel mill behind it. Choose the single factor closest to what you bought. This applies to every industry, including food, metals and plastics.
+:::
+
+:::{dropdown} How do I choose between producer and purchaser price?
+The producer price is what the producer receives. The purchaser price adds trade and transport margins, and is what you pay for a ready-to-buy product. If you're unsure, use producer prices, which are more conservative. If you're confident your spend includes typical margins, for example buying off-the-shelf goods at retail, use purchaser prices. See [](factors/prices-and-margins.md).
+% TODO(confirm): align with the final guidance on prices-and-margins (the plan describes three columns).
+:::
+
+:::{dropdown} Where do the margins come from, and do they apply to every country?
+Cornerstone converts producer-price factors to purchaser-price factors using US trade and transport margins for every country, since comparable margin data isn't available everywhere. Where a country's actual margins differ, its purchaser-price factors could be too high or too low. Producer-price factors aren't affected.
+:::
+
+:::{dropdown} What year and currency are the factors in?
+Factors are in kg CO₂e per USD in current-year dollars, so a factor in the 2026 tab applies to spend in 2026 US dollars. To use spend in another currency, convert it to USD at the year-of-spend rate, using the **Exchange Rates** tab (local currency per USD for 221 countries and territories, from Open Exchange Rates), then apply the factor for that year. See [](factors/year-and-currency.md).
+:::
+
+:::{dropdown} My spend is in a different year than the factor tabs. What should I do?
+Prefer the tab for the year of your spend. If the release doesn't have your year, use a sector-specific price index to bring your spend to a year that does, rather than a general consumer price index. Cornerstone doesn't support alternative monetary adjustments beyond that, because they change the resulting factor significantly.
+% TODO(confirm): which price index the 2025/2026 adjustment uses (BEA sector-level?), to state it here.
+:::
 
-### What year and currency are the factors in, and how do I adjust my spend data to match?
-*To be written. See [](factors/year-and-currency.md).*
+:::{dropdown} Can I apply my own price index adjustments?
+For years covered by the release, use the factors as published, because they are traceable and easy to audit. Only adjust beyond the published years, and keep the adjustment limited to a price index.
+:::
 
-### Do I apply the factors to purchaser prices (what I paid) or producer prices?
-*To be written. See [](factors/prices-and-margins.md).*
+:::{dropdown} How do I use the factors for goods bought from, or made in, another country?
+Choose the country where the product was produced. If you don't know it, use this order, from most to least relevant:
 
-### How do I find the right sector for a purchase? What if it spans several sectors?
-*To be written. See [](factors/choose-a-factor.md).*
+1. Country of manufacture
+2. Country of the supplier
+3. Country of purchase
+4. Country of your company's headquarters
 
-### How do I use the factors for goods bought from, or made in, another country?
-*To be written.*
+If a country has no factors of its own, use its regional average. The **Locations** tab shows which of the 249 locations have their own country factors (148 do) and which region each belongs to.
+:::
 
-### Are the factors suitable for GHG Protocol Scope 3 reporting and target setting?
-*To be written.*
+:::{dropdown} What if my supplier is in a country with no data?
+Use the factor for the region it belongs to, or the global average if the region isn't clear. See the **Locations** tab and [](reference/methods-by-country.md).
+:::
 
-### Which release should I use, and what do I do when a new release comes out?
-*To be written.*
+:::{dropdown} What do the Scope 3 categories cover?
+The factors cover upstream emissions up to the point of purchase, so they apply to the upstream categories: purchased goods and services (1), capital goods (2), upstream transportation and distribution (4), waste generated in operations (5), business travel (6), and upstream leased assets (8), as well as the upstream part of category 3. They don't cover downstream emissions (categories 9–15).
+% TODO(confirm): category 3 treatment, and whether to list 7 (employee commuting) as supported.
+:::
+
+:::{dropdown} Can I use the factors for purchased electricity (Scope 2)?
+No. Use utility or grid emission factors with your own activity data (kWh) for Scope 2, and for any other category where you have activity data. Spend-based factors are for screening and for purchases where activity data isn't available.
+:::
+
+:::{dropdown} What aren't the factors designed for?
+Cornerstone factors are sector averages per dollar. They are not suited to comparing two products or two suppliers in the same sector, to showing the effect of a supplier's own reduction efforts, or to categories the model doesn't cover (government services, taxes, fees and interest, used assets). When you have supplier-specific or activity data, use it in place of spend-based factors. Use spend-based factors for screening, hotspotting and categories where better data isn't available.
+% TODO(confirm): link to intro/uses-and-limits.md once it is written and added to the nav in myst.yml.
+:::
+
+:::{dropdown} Are the factors suitable for GHG Protocol Scope 3 reporting and for target setting?
+Cornerstone factors are designed for spend-based Scope 3 calculations under the GHG Protocol Corporate Value Chain (Scope 3) Standard. Guidance from the GHG Protocol and from target-setting bodies sets what data quality is expected, so check the current requirements for your framework.
+% TODO(confirm): GHG Protocol third-party database listing and SBTi acceptance status for Cornerstone EEIO 2026 (CEDA was on the GHG Protocol's list).
+:::
 
 ## Understanding the results
 
-### Why did a factor change between releases?
-*To be written.*
+:::{dropdown} Why did a factor change between releases?
+Look at three things in order: the data (new tables or emissions for that country), the methodology (for example US imports, ABSR, model reconciliation) and the price year (a different dollar year changes factors even with no real change). The [](releases/change-log.md) lists every change, and the release notes give examples.
+:::
+
+:::{dropdown} Why does the same sector have very different factors in different countries?
+Three reasons: the emissions intensity of each country (for example its electricity grid), the economic structure of the sector in that country, and the trade structure, meaning where its inputs are imported from. Factors for a sector in countries with detailed data differ for real reasons, but see the next question for countries with less data.
+:::
 
-### Why do factors differ from USEEIO, CEDA, EXIOBASE, or other datasets?
-*To be written.*
+:::{dropdown} What does a country's tier mean for how much I can rely on its factors?
+Countries are grouped in four tiers by the input-output data available for them. Tier 0 (US) has detailed annual tables, Tier 1 (China, Japan, South Korea, UK) uses detailed national tables, Tier 2 (60 countries) uses coarser multi-regional tables from FIGARO or ICIO, and Tier 3 (83 countries) has no usable table and uses a template built from other countries, scaled to the country's emissions per unit of GDP and its own electricity intensity. Factors for Tier 3 countries carry more uncertainty. See [](io/country-tiers.md) and [](reference/methods-by-country.md).
+:::
 
-### Why does the same sector have very different factors in different countries?
-*To be written.*
+:::{dropdown} How do the factors compare with USEEIO, CEDA, EXIOBASE or other datasets?
+Differences come from the data vintage, the sector classification, the price basis and the treatment of trade. Compare on the same basis, meaning the same year, currency and price basis, before reading the gap. Cornerstone benchmarks against about 1,000 third-party sources for the sectors behind about 50% of global emissions, and documents the gaps that remain. See [](benchmarking/index.md).
+:::
 
-### What does a country's tier mean for how much I can rely on its factors?
-*To be written. See [](io/country-tiers.md).*
+:::{dropdown} Are emissions counted where they occur, or by where the producer is based?
+It depends on the source. National inventories (US, China) use the territorial basis: emissions within the country's borders. Air emissions accounts, FIGARO and ICIO use the residence basis: emissions by a country's resident businesses wherever they occur. The model uses each source as published. The two differ most for countries with large international transport fleets or many visitors. See [](ee/index.md).
+:::
 
-### What's included: cradle-to-gate only? Capital goods? Biogenic CO₂? Land-use change?
-*To be written.*
+:::{dropdown} What's included in the factors?
+- **Boundary:** cradle to gate, meaning all upstream emissions up to the point of purchase.
+- **Gases:** all seven (CO₂, CH₄, N₂O, HFCs, PFCs, SF₆, NF₃), reported separately and as a total in CO₂e.
+- **GWPs:** IPCC AR6 100-year values (CH₄ is 29.8 for fossil and 27.0 for non-fossil methane, and N₂O is 273). The **GWP** tab in the release lists them.
+- **Land:** farming emissions are counted. Land use, land-use change and forestry (LULUCF) is not, including carbon removals.
+- **Not counted:** household direct emissions (such as fuel burned at home), since they don't come from a producing sector.
 
-### Are emissions counted where they occur or by the producer's country of residence?
-*To be written.*
+% TODO(confirm): biogenic CO2 statement (CEDA excluded it), transport well-to-wheel treatment, and whether capital goods are treated as upstream purchases for every sector.
+:::
 
-### Do the factors come with uncertainty ranges?
-*To be written.*
+:::{dropdown} Which GWP version is used?
+IPCC AR6, 100-year time horizon. Sources that report in AR5 CO₂-equivalents are converted to AR6 gas by gas. Prior versions of USEEIO used earlier GWPs, so a share of the change versus those models comes from the GWP update. Cornerstone EEIO 2026 also fixes a double application of the AR5-to-AR6 conversion.
+:::
+
+:::{dropdown} Do the factors come with uncertainty ranges?
+Not in this release. Spend-based factors are sector averages, so uncertainty is higher for individual products, and for Tier 3 countries. See the assumptions in [](io/index.md) and the validation results in [](benchmarking/index.md).
+% TODO(confirm): whether uncertainty estimates are planned for the final version.
+:::
+
+:::{dropdown} Why did my factor for [sector] in [country] change by so much?
+Large moves in a single sector are possible, and some are explained in the release notes (for example batteries in China, power in Germany, and light trucks in the US). If you can't explain a change with the [](releases/change-log.md), please report it. See "How do I report a possible error" below.
+:::
 
 ## For verifiers and auditors
 
-### How do I trace a factor back to its data sources and methods?
-*To be written.*
+:::{dropdown} How do I trace a factor back to its data sources and methods?
+Start with [](reference/methods-by-country.md), which lists the data and methods behind every country. The [IO model](io/index.md) and [GHG extension](ee/index.md) pages describe the method step by step, and the [](releases/change-log.md) lists what changed against USEEIO and CEDA. Methodology decisions are discussed openly on [GitHub](https://github.com/cornerstone-data/methods/discussions).
+:::
+
+:::{dropdown} How do I cite a specific release?
+Cite the version, the release date and the Zenodo DOI, which are on the cover tab of the workbook. The release candidate (2026-rc.1) is archived at [doi.org/10.5281/zenodo.23396891](https://zenodo.org/doi/10.5281/zenodo.23396891). See [](releases/index.md).
+% TODO(confirm): suggested citation text, and whether the final release gets a new DOI.
+:::
+
+:::{dropdown} How is the model validated and benchmarked?
+Cornerstone reviews the sectors behind about 50% of global emissions against third-party benchmarks built from about 1,000 sources. Each review ends in a sharper benchmark, a model improvement or a documented known discrepancy. See [](benchmarking/index.md) and [](validation/index.md).
+:::
 
-### How do I cite a specific release, and where are the exact docs and data for it?
-*To be written. See [](releases/index.md).*
+:::{dropdown} Does the model follow the GHG Protocol boundary conditions?
+Cornerstone EEIO follows a cradle-to-gate boundary and includes all seven Kyoto gases. Land use change isn't included, but farming emissions are.
+% TODO(confirm): GHG Protocol minimum boundary statement and SBTi methodology questions (well-to-wheel, direct land-use change), as in the CEDA 2025 FAQ.
+:::
 
-### How is the model validated and benchmarked, and where are the results?
-*To be written. See [](validation/index.md).*
+:::{dropdown} Is the model open? Where are the code and data?
+The emission factors are free to use. Documentation and the methodology discussions are public at [github.com/cornerstone-data](https://github.com/cornerstone-data).
+% TODO(confirm): code repository availability and the license of the source data.
+:::
 
-### Is the model open? Where are the code and data?
-*To be written.*
+:::{dropdown} Which published methodology supports relying on the factors?
+The approach builds on the peer-reviewed hybrid-LCA and EEIO literature, including Suh et al. (2004) on system boundary selection with hybrid approaches and Suh (2005) on developing a sectoral environmental database for input-output analysis. See the references on each methodology page.
+% TODO(confirm): add the final reference list (carry over from CEDA 2025 FAQ, plus the multi-regional and ABSR references).
+:::
 
 ## Practical
 
-### What license are the factors under, and what attribution is required?
-*To be written.*
+:::{dropdown} Where do I download the factors?
+On [Zenodo](https://zenodo.org/doi/10.5281/zenodo.23396891). Each release is a single Excel workbook. See [](factors/whats-in-a-release.md).
+:::
 
-### How do I report a possible error or ask a question?
-Open an issue on [GitHub](https://github.com/cornerstone-data/eeio-docs/issues).
+:::{dropdown} What license are the factors under, and what attribution is required?
+The factors are published by the Cornerstone Sustainability Data Initiative under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. You can use and redistribute them, including commercially, as long as you give credit and indicate any changes.
+:::
+
+:::{dropdown} I'd like to explain the switch to Cornerstone in my report. What do you recommend?
+State the source (Cornerstone EEIO 2026 and its DOI), the base year and dollar year, the reason you switched, and that the change in your emissions has two parts: real-world and data changes, and methodology changes. If you're coming from USEEIO, mention that imports are now modeled by country of origin, which tends to increase US factors. If you're coming from CEDA, mention the new China and Europe data. Quantify the effect if you can with the release's change summaries.
+:::
+
+:::{dropdown} Can I get help applying the changes to my footprint?
+For more detail, or custom support on how the changes affect your footprint, you can sign up for a demo of the enterprise product [here](https://watershed.com/en-GB/solutions/ceda).
+:::
+
+:::{dropdown} How do I report a possible error or ask a question?
+Open an issue on [GitHub](https://github.com/cornerstone-data/eeio-docs/issues). Please include the sector code, the country, the year and the tab (`PRO` or `PUR`), so we can reproduce it.
+:::
 
 +++
 
 :::{div}
 :class: page-status
-Applies to: RC · Last reviewed: RC
+Applies to: 2026-rc.1 · Last reviewed: 2026-rc.1
 :::
diff --git a/intro/ee-part.md b/intro/ee-part.md
index c1c8ebef..5bd8006b 100644
--- a/intro/ee-part.md
+++ b/intro/ee-part.md
@@ -31,5 +31,5 @@ Plan §3.1.4 · target: **RC (Oct 27)**
 
 :::{div}
 :class: page-status
-Applies to: RC · Last reviewed: RC
+Applies to: 2026-rc.1 · Last reviewed: 2026-rc.1
 :::
diff --git a/intro/emission-factors.md b/intro/emission-factors.md
index a783cc08..46030ce3 100644
--- a/intro/emission-factors.md
+++ b/intro/emission-factors.md
@@ -32,5 +32,5 @@ Plan §3.1.5 · target: **RC (Oct 27)**
 
 :::{div}
 :class: page-status
-Applies to: RC · Last reviewed: RC
+Applies to: 2026-rc.1 · Last reviewed: 2026-rc.1
 :::
diff --git a/intro/io-part.md b/intro/io-part.md
index e22ef539..17681951 100644
--- a/intro/io-part.md
+++ b/intro/io-part.md
@@ -32,5 +32,5 @@ Plan §3.1.2 · target: **RC (Oct 27)**
 
 :::{div}
 :class: page-status
-Applies to: RC · Last reviewed: RC
+Applies to: 2026-rc.1 · Last reviewed: 2026-rc.1
 :::
diff --git a/intro/multi-regional.md b/intro/multi-regional.md
index 9684028a..03052f36 100644
--- a/intro/multi-regional.md
+++ b/intro/multi-regional.md
@@ -32,5 +32,5 @@ Plan §3.1.3 · target: **RC (Oct 27)**
 
 :::{div}
 :class: page-status
-Applies to: RC · Last reviewed: RC
+Applies to: 2026-rc.1 · Last reviewed: 2026-rc.1
 :::
diff --git a/intro/uses-and-limits.md b/intro/uses-and-limits.md
index 9192b36a..66c62194 100644
--- a/intro/uses-and-limits.md
+++ b/intro/uses-and-limits.md
@@ -31,5 +31,5 @@ Plan §3.1.7 · target: **RC (Oct 27)**
 
 :::{div}
 :class: page-status
-Applies to: RC · Last reviewed: RC
+Applies to: 2026-rc.1 · Last reviewed: 2026-rc.1
 :::
diff --git a/reference/data-sources.md b/reference/data-sources.md
index 61b9cc6b..98dcdfeb 100644
--- a/reference/data-sources.md
+++ b/reference/data-sources.md
@@ -20,5 +20,5 @@ Plan §3.8 · target: **RC (Oct 27)**
 
 :::{div}
 :class: page-status
-Applies to: RC · Last reviewed: RC
+Applies to: 2026-rc.1 · Last reviewed: 2026-rc.1
 :::
diff --git a/reference/glossary.md b/reference/glossary.md
index 30bd2ec1..f5466887 100644
--- a/reference/glossary.md
+++ b/reference/glossary.md
@@ -76,5 +76,5 @@ Territorial basis
 
 :::{div}
 :class: page-status
-Applies to: RC · Last reviewed: RC
+Applies to: 2026-rc.1 · Last reviewed: 2026-rc.1
 :::
diff --git a/reference/methods-by-country.md b/reference/methods-by-country.md
index 69d2c86f..ad9fc543 100644
--- a/reference/methods-by-country.md
+++ b/reference/methods-by-country.md
@@ -182,5 +182,5 @@ The release candidate's 2024, 2025, and 2026 factors are all built from the same
 
 :::{div}
 :class: page-status
-Applies to: RC · Last reviewed: RC
+Applies to: 2026-rc.1 · Last reviewed: 2026-rc.1
 :::
diff --git a/reference/notation.md b/reference/notation.md
index c520dbf9..30b61d4a 100644
--- a/reference/notation.md
+++ b/reference/notation.md
@@ -34,5 +34,5 @@ Team to confirm the final list and whether any others (e.g., value added, `U`/`V
 
 :::{div}
 :class: page-status
-Applies to: RC · Last reviewed: RC
+Applies to: 2026-rc.1 · Last reviewed: 2026-rc.1
 :::
diff --git a/releases/change-log.md b/releases/change-log.md
index 53c8876b..c6b53c6c 100644
--- a/releases/change-log.md
+++ b/releases/change-log.md
@@ -104,5 +104,5 @@ For even additional details or custom support on how to navigate the changes in
 
 :::{div}
 :class: page-status
-Applies to: RC · Last reviewed: RC
+Applies to: 2026-rc.1 · Last reviewed: 2026-rc.1
 :::
diff --git a/releases/index.md b/releases/index.md
index 4e406fb4..7b3213bc 100644
--- a/releases/index.md
+++ b/releases/index.md
@@ -2,6 +2,6 @@
 title: Releases
 ---
 
-One permanent page per model release: what changed and how much it moved `D` and `N`, methodology choices, data sources and vintages, validation and benchmark summaries, downloads and DOI, and a link to the archived docs snapshot (plan §7.1).
+This is where the release note and change log for every Cornerstone EEIO release will live. For direct download of the data, check out [Zenodo](https://zenodo.org/doi/10.5281/zenodo.23396891).
 
 - [](rc.md)
diff --git a/releases/rc.md b/releases/rc.md
index 40f8d2b3..89dfa349 100644
--- a/releases/rc.md
+++ b/releases/rc.md
@@ -1,12 +1,12 @@
 ---
-title: "Cornerstone EEIO 2026 - RC"
-subtitle: "Release candidate · Release date: October 27, 2026"
+title: "2026-rc.1 (Release Candidate)"
+subtitle: "Release note · Release date: October 27, 2026"
 ---
 
 :::{tip} Summary
-The Cornerstone EEIO 2026 - RC (Release Candidate) is the first stage of a phased approach to releasing the Cornerstone EEIO 2026 model, the successor of USEEIO and CEDA. It is a global environmentally-extended input-output (EEIO) model that produces spend-based emission factors, in kg CO₂e per USD for 405 sectors across 148 countries, plus 21 regional averages and one global average. It uses 2024 as the base year and is available to use for 2024, 2025 and 2026 measurements.
+The 2026-rc.1 (Release Candidate) is the first stage of a phased approach to releasing the Cornerstone EEIO 2026 model, the successor of USEEIO and CEDA. It is a global environmentally-extended input-output (EEIO) model that produces spend-based emission factors, in kg CO₂e per USD for 405 sectors across 148 countries, plus 21 regional averages and one global average. It uses 2024 as the base year and is available to use for 2024, 2025 and 2026 measurements.
 
-This release candidate will be superseded in December by the release of the Cornerstone EEIO 2026 final version, which will include a time series from 2017 to 2026.
+This release candidate will be superseded in December by the release of the 2026-v0.1 (final version), which will include a time series from 2017 to 2026.
 
 Download this release on [Zenodo](https://zenodo.org/doi/10.5281/zenodo.23396891) here.
 :::
@@ -108,5 +108,5 @@ For a more detailed summary of the changes against USEEIO or CEDA, please refer
 
 :::{div}
 :class: page-status
-Applies to: RC · Last reviewed: RC
+Applies to: 2026-rc.1 · Last reviewed: 2026-rc.1
 :::
diff --git a/releases/summary-ceda.md b/releases/summary-ceda.md
index 67b4548b..9102ab83 100644
--- a/releases/summary-ceda.md
+++ b/releases/summary-ceda.md
@@ -97,5 +97,5 @@ For even additional details or custom support on how to navigate the changes in
 
 :::{div}
 :class: page-status
-Applies to: RC · Last reviewed: RC
+Applies to: 2026-rc.1 · Last reviewed: 2026-rc.1
 :::
diff --git a/releases/summary-useeio.md b/releases/summary-useeio.md
index 3adcccf7..44129f54 100644
--- a/releases/summary-useeio.md
+++ b/releases/summary-useeio.md
@@ -80,5 +80,5 @@ For even additional details or custom support on how to navigate the changes in
 
 :::{div}
 :class: page-status
-Applies to: RC · Last reviewed: RC
+Applies to: 2026-rc.1 · Last reviewed: 2026-rc.1
 :::
diff --git a/validation/index.md b/validation/index.md
index aa86c1bb..6f22191e 100644
--- a/validation/index.md
+++ b/validation/index.md
@@ -27,5 +27,5 @@ Plan §3.5 · target: **RC (Oct 27)**
 
 :::{div}
 :class: page-status
-Applies to: RC · Last reviewed: RC
+Applies to: 2026-rc.1 · Last reviewed: 2026-rc.1
 :::

From 4c8f40a6af4a8ee04dae6109d6cadb15c60b28ce Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Fri, 23 Oct 2026 22:00:36 -0700
Subject: [PATCH 42/47] docs(faq): write the Cornerstone EEIO 2026 FAQ as
 toggles

Adapted from the CEDA 2025 FAQ and grouped into five sections: About,
Moving from USEEIO or CEDA, Using the factors, Boundaries and reporting,
and Verification, licensing and support.

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 faq.md | 217 ++++++++++++---------------------------------------------
 1 file changed, 45 insertions(+), 172 deletions(-)

diff --git a/faq.md b/faq.md
index 20941aac..dec4082c 100644
--- a/faq.md
+++ b/faq.md
@@ -6,18 +6,10 @@ title: Frequently asked questions
 Short answers to the questions we hear most about Cornerstone EEIO 2026. Each answer links to the page with the full explanation. Cornerstone EEIO is the successor of USEEIO and CEDA, so many answers compare it with those models.
 :::
 
-% Source notes for maintainers (not rendered).
-% - Structure adapted from the CEDA 2025 FAQ (Watershed, updated 2025-11-11).
-% - Lines marked TODO(confirm) hold claims that need sign-off before the final release.
-
 ## About Cornerstone EEIO 2026
 
-:::{dropdown} What is Cornerstone EEIO 2026?
-A multi-regional environmentally-extended input-output (EEIO) model that produces spend-based greenhouse gas emission factors, in kg CO₂e per USD, for 405 sectors across 148 countries, 21 regional averages and one global average. It combines USEEIO's detailed picture of the US economy with CEDA's global coverage and trade modeling. See [](releases/rc.md).
-:::
-
 :::{dropdown} What is the Cornerstone Sustainability Data Initiative, and how does it relate to USEEIO and CEDA?
-Cornerstone launched in August 2025 to bring USEEIO and CEDA together into one open, comprehensive dataset of Scope 3 spend-based emission factors. Cornerstone EEIO 2026 is the first release of that combined model. It is the continuation of both USEEIO v1.4 and CEDA 2025. Learn more at [cornerstonedata.org](https://cornerstonedata.org/).
+Cornerstone launched in August 2025 to bring USEEIO and CEDA together into one open, comprehensive dataset of Scope 3 spend-based emission factors. Cornerstone EEIO 2026 is the first release of that combined model. It is the continuation of both USEEIO v1.4 and CEDA 2025. Today Cornerstone is the home of 3 additional datasets: eGRID, the GHG EF Hub and Orbae. Learn more at [cornerstonedata.org](https://cornerstonedata.org/).
 :::
 
 :::{dropdown} What is changing compared with USEEIO and CEDA?
@@ -31,30 +23,7 @@ The full list is in the [](releases/change-log.md). Side-by-side summaries are i
 :::
 
 :::{dropdown} Are the changes driven by methodology or by real-world change?
-Both. Data updates reflect real-world change, such as new economic, trade and emissions data. Methodology changes reflect modeling improvements, such as modeling US imports by origin and the auto-balanced structural reflection (ABSR) method. Against CEDA 2025, data updates add about 5.8% to the global production-weighted factor and methodology changes remove about 27.5%. Against USEEIO v1.4 (US), data updates add 0.4% and methodology changes remove 1.2%.
-:::
-
-:::{dropdown} How much will my footprint change?
-It depends on which sectors and countries your spend falls in.
-
-- **Coming from CEDA 2025:** global production-weighted factors fall by 23.3%. In a sample of 400 corporate footprints, the median company sees a 14% decrease.
-- **Coming from USEEIO v1.4:** US factors change by −0.8% overall. In the same sample, the median company sees a 1.3% decrease.
-
-A small median can hide large moves in individual sectors, in both directions. See [](releases/summary-ceda.md) and [](releases/summary-useeio.md).
-:::
-
-:::{dropdown} The overall change is a decrease, so why are some of my factors increasing?
-Several drivers push individual factors up:
-
-- **US imports.** USEEIO treated the US as a closed economy. Cornerstone models each import in the country that made it. Many imports come from more emissions-intensive economies such as China and India, so US factors tend to rise by 10 to 30% on average. This is the main upward pressure for USEEIO users.
-- **Facility-reported emissions.** For example, US cement now matches the totals reported by cement plants, which USEEIO undercounted by about 20%.
-- **New country data.** For example, China's battery factor rises by about 30% as production shifted from lead-acid to lithium-ion.
-
-Other factors fall. For example, European electricity factors fall in every EU country, driven by more renewables and higher energy prices.
-:::
-
-:::{dropdown} Are these changes expected?
-Yes. They come from newer data and from better modeling, and each is documented in the [](releases/change-log.md). Where a gap against third-party benchmarks remains, it is documented as a known discrepancy (see [](benchmarking/index.md)).
+Both. Data updates reflect real-world change, such as new economic, trade and emissions data. Methodology changes reflect modeling improvements, such as modeling US imports by origin and the auto-balanced structural reflection (ABSR) method. See more on the [](releases/change-log.md).
 :::
 
 :::{dropdown} Why is it called a "release candidate"?
@@ -65,98 +34,58 @@ Yes. They come from newer data and from better modeling, and each is documented
 We don't foresee significant changes, but some sector–country combinations could move because of improvements made while developing the time series. If you need numbers for reporting or rebaselining, wait for the final version.
 :::
 
-:::{dropdown} Why did the versioning change from numbers to years?
-Each release is named after the year it is published, so Cornerstone EEIO 2026 is released in 2026 and the 2027 version in 2027. This replaces the different naming schemes used by USEEIO (v1.4) and CEDA (v4.1, then CEDA 2025).
-:::
-
 :::{dropdown} When is the next update?
 Releases are published every October so companies can plan for them. The next, Cornerstone EEIO 2027, will be released in October 2027. Cornerstone EEIO 2026 will stay methodologically stable for at least five years, so you have a consistent basis to rebaseline and set targets.
 :::
 
-:::{dropdown} Is this just a price index adjustment of the previous numbers?
-No. Each release incorporates the most recent economic, trade and emissions data for each country. Price indices are applied only to years after the base year, for which no reported data exists yet (for example 2025 and 2026 are adjusted from the 2024 base year).
-:::
-
 :::{dropdown} Did the sectors change?
-Yes, slightly. Cornerstone EEIO uses 405 sectors, based on the BEA input-output commodity codes, versus 400 in CEDA. Following the model reconciliation, the waste sector is split into seven sectors and aluminum into primary and secondary. Home appliances are a single sector. A NAICS-to-Cornerstone mapping tab is included in the release, so you can map your existing categories. See the [](releases/change-log.md#change-log-model-reconciliation).
-:::
+Yes, slightly. Cornerstone EEIO uses 405 sectors, based on the BEA input-output commodity codes. When the USEEIO and CEDA teams reconciled their US models, they resolved the differences in sector definitions as follows.
 
-:::{dropdown} Did the file format change?
-The release is one Excel workbook with a cover, a data dictionary, a producer-price tab and a purchaser-price tab for each year, and five auxiliary tabs. See [](factors/whats-in-a-release.md). 
-% TODO(confirm): whether the layout differs from the CEDA 2025 files customers know, and whether a CSV/long format is also published.
-:::
+**Changes compared to USEEIO**
 
-## Which release to use
+- Electric utilities are aggregated into one sector (221100). USEEIO split them across three.
+- Government and private transit are aggregated into one sector. USEEIO kept them separate.
 
-:::{dropdown} Which release should I use for my 2026 measurements?
-Use the Cornerstone EEIO 2026 factors from the `2026PRO` or `2026PUR` tab. We recommend matching the release year to the year being measured. Until the final version is published in December, treat the factors as a release candidate.
-:::
+**Changes compared to CEDA**
 
-:::{dropdown} Can I use the release candidate for my 2024 and 2025 measurements?
-Yes. It has factors for 2024, 2025 and 2026 dollars. The base year is 2024, so 2024 is the year most directly backed by data, and the 2025 and 2026 factors are adjusted from it with price indices. If your reporting is being finalized, consider waiting for the final version.
+- Waste is split into seven sectors. CEDA had a single waste sector.
+- Aluminum is split into primary and secondary sectors. CEDA merged them.
+- Home appliances are one sector, as in the BEA tables. CEDA had four.
 :::
 
-:::{dropdown} Why can 2026 measurements use factors built on 2024 data?
-The underlying sources are published with a lag. For example, national input-output tables and GHG inventories typically cover a year that is one to three years old. Every spend-based database inherits this lag, so it is standard to match the release to the year of measurement. Cornerstone builds 2025 and 2026 factors by adjusting the 2024 base year with price indices.
+:::{dropdown} Did the file format change?
+Yes. The new release is different from open CEDA and from the USEEIO v1.4. See [](factors/whats-in-a-release.md) for more details.
 :::
 
-:::{dropdown} My reporting year isn't the calendar year. Which factors should I use?
-Use the factors for the calendar year in which most of your data falls. If your data is split equally, use the newer set. For example, for a fiscal year from April 1, 2025 to March 31, 2026, use the 2026 factors. Alternatively, apply the 2026 factors to data from January 1, 2026 and the previous year's factors before that.
-:::
+## Moving from USEEIO or CEDA
 
-:::{dropdown} I'm in mid-2026 or later. Can I already measure 2027?
-Use the 2026 factors for the portion of 2027 you've measured, and update when Cornerstone EEIO 2027 is published in October 2027. If you adjust the 2026 factors yourself, apply only a price index (for example a national price index) and don't edit the factors otherwise. Auditors generally accept this approach.
-% TODO(confirm): confirm the guidance wording with the audit/assurance team.
-:::
+:::{dropdown} How much will my footprint change?
+It depends on which sectors and countries your spend falls in.
+
+- **Coming from CEDA 2025:** global production-weighted factors fall by 23.3%. In a sample of 400 corporate footprints, the median company sees a 14% decrease.
+- **Coming from USEEIO v1.4:** US factors change by −0.8% overall. In the same sample, the median company sees a 1.3% decrease.
 
-:::{dropdown} I'm measuring a past year (2017–2023). What should I use?
-The final release, due in December 2026, will include a time series from 2017 to 2026. All years use the same methodology, and only the underlying country data changes, so each year captures decarbonization that happened that year (for example the US electricity grid). Until then, use the previous version you already use (CEDA 2025 or USEEIO v1.4).
+A small median can hide large moves in individual sectors, in both directions. See [](releases/summary-ceda.md) and [](releases/summary-useeio.md).
 :::
 
 :::{dropdown} I was using CEDA or USEEIO. Do I need to rebaseline or restate previous years?
-The time series is designed for exactly this. Because every year from 2017 to 2026 is built with one methodology, you can recalculate prior years on a consistent basis and compare them. Whether to restate is a decision under your own company's policy and your target framework. Rebaselining thresholds are usually based on significance, so talk to your verifier. See [](releases/summary-ceda.md) and [](releases/summary-useeio.md) for the expected change.
-% TODO(confirm): wording about SBTi / GHG Protocol recalculation thresholds.
+We do expect the majority of companies to be above the SBTi rebaselining threshold, but every company will have a different experience and ultimately whether to restate is a decision under your own company's policy and your target framework. This year, the release of the time series is designed for exactly this purpose. Because every year from 2017 to 2026 is built with one methodology, you can recalculate prior years on a consistent basis and compare them.
 :::
 
 :::{dropdown} Can I keep using CEDA 2025 or USEEIO for 2026?
-We recommend using Cornerstone EEIO 2026 for 2026 measurements, since it supersedes both. If you have a reason to stay on a previous version, apply a price index to it, and be ready to explain the choice to your verifier.
-% TODO(confirm): whether CEDA 2025 and USEEIO v1.4 stay available, and for how long.
+We recommend using Cornerstone EEIO 2026 for 2026 measurements, since it supersedes both. If you have a reason to stay on a previous version, apply a price index to it, and explain the choice to your verifier.
 :::
 
-## Using the factors
-
-:::{dropdown} Should I use `D` or `N`?
-Use `N`, the total emission factor, for purchased goods and services. It includes the emissions of the sector itself and of its whole supply chain. `D` covers only what the sector emits directly. Using `D` leaves out the supply chain, and adding `D` to `N` double counts. The factor tabs in the release report the total (`Total GHGs`) and each gas. See [](reference/notation.md).
-:::
+:::{dropdown} I'd like to explain the switch to Cornerstone in my report. What do you recommend?
+State the source (Cornerstone EEIO 2026) and the years you are applying the model to.
 
-:::{dropdown} How do I find the right sector for a purchase? What if it spans several sectors?
-Match the purchase to the *good or service you bought*, not to the supplier's industry. Use the description in the **Sector Descriptions** tab (405 sectors), and the **NAICS to Cornerstone mapping** tab if your categories are NAICS-based. If a purchase spans several sectors, split the spend across them, or choose the one that best describes most of the spend. See [](factors/choose-a-factor.md).
+Suggested wording: "The changes this year are due to adopting Cornerstone EEIO 2026. The Cornerstone model combines CEDA's global coverage with USEEIO's detailed US data into a single model that supersedes them both. This model was adopted as an improvement in data quality, as it provides a more current picture of the global economy."
 :::
 
-:::{dropdown} Do I need to add up factors across the supply chain (for example raw steel plus steel products)?
-No. Factors already include all upstream emissions, so a manufactured good already includes the raw materials. For example, the factor for a steel product includes the iron and steel mill behind it. Choose the single factor closest to what you bought. This applies to every industry, including food, metals and plastics.
-:::
+## Using the factors
 
 :::{dropdown} How do I choose between producer and purchaser price?
 The producer price is what the producer receives. The purchaser price adds trade and transport margins, and is what you pay for a ready-to-buy product. If you're unsure, use producer prices, which are more conservative. If you're confident your spend includes typical margins, for example buying off-the-shelf goods at retail, use purchaser prices. See [](factors/prices-and-margins.md).
-% TODO(confirm): align with the final guidance on prices-and-margins (the plan describes three columns).
-:::
-
-:::{dropdown} Where do the margins come from, and do they apply to every country?
-Cornerstone converts producer-price factors to purchaser-price factors using US trade and transport margins for every country, since comparable margin data isn't available everywhere. Where a country's actual margins differ, its purchaser-price factors could be too high or too low. Producer-price factors aren't affected.
-:::
-
-:::{dropdown} What year and currency are the factors in?
-Factors are in kg CO₂e per USD in current-year dollars, so a factor in the 2026 tab applies to spend in 2026 US dollars. To use spend in another currency, convert it to USD at the year-of-spend rate, using the **Exchange Rates** tab (local currency per USD for 221 countries and territories, from Open Exchange Rates), then apply the factor for that year. See [](factors/year-and-currency.md).
-:::
-
-:::{dropdown} My spend is in a different year than the factor tabs. What should I do?
-Prefer the tab for the year of your spend. If the release doesn't have your year, use a sector-specific price index to bring your spend to a year that does, rather than a general consumer price index. Cornerstone doesn't support alternative monetary adjustments beyond that, because they change the resulting factor significantly.
-% TODO(confirm): which price index the 2025/2026 adjustment uses (BEA sector-level?), to state it here.
-:::
-
-:::{dropdown} Can I apply my own price index adjustments?
-For years covered by the release, use the factors as published, because they are traceable and easy to audit. Only adjust beyond the published years, and keep the adjustment limited to a price index.
 :::
 
 :::{dropdown} How do I use the factors for goods bought from, or made in, another country?
@@ -167,119 +96,63 @@ Choose the country where the product was produced. If you don't know it, use thi
 3. Country of purchase
 4. Country of your company's headquarters
 
-If a country has no factors of its own, use its regional average. The **Locations** tab shows which of the 249 locations have their own country factors (148 do) and which region each belongs to.
+If a country has no factors of its own, use its regional average. The **Locations** tab shows which of the 249 locations have their own country factors (148 do) and which region each belongs to. See [](factors/choose-a-factor.md).
 :::
 
 :::{dropdown} What if my supplier is in a country with no data?
-Use the factor for the region it belongs to, or the global average if the region isn't clear. See the **Locations** tab and [](reference/methods-by-country.md).
+Use the factor for the region it belongs to, or the global average if the region isn't clear. See the **Locations** tab and [](reference/methods-by-country.md), and [](factors/choose-a-factor.md).
 :::
 
 :::{dropdown} What do the Scope 3 categories cover?
 The factors cover upstream emissions up to the point of purchase, so they apply to the upstream categories: purchased goods and services (1), capital goods (2), upstream transportation and distribution (4), waste generated in operations (5), business travel (6), and upstream leased assets (8), as well as the upstream part of category 3. They don't cover downstream emissions (categories 9–15).
-% TODO(confirm): category 3 treatment, and whether to list 7 (employee commuting) as supported.
 :::
 
-:::{dropdown} Can I use the factors for purchased electricity (Scope 2)?
-No. Use utility or grid emission factors with your own activity data (kWh) for Scope 2, and for any other category where you have activity data. Spend-based factors are for screening and for purchases where activity data isn't available.
+:::{dropdown} Why does the same sector have very different factors in different countries?
+Three reasons: the emissions intensity of each country (for example its electricity grid), the economic structure of the sector in that country, and the trade structure, meaning where its inputs are imported from. Factors for a sector in countries with detailed data differ for real reasons, but see the tier question below for countries with less data.
 :::
 
-:::{dropdown} What aren't the factors designed for?
-Cornerstone factors are sector averages per dollar. They are not suited to comparing two products or two suppliers in the same sector, to showing the effect of a supplier's own reduction efforts, or to categories the model doesn't cover (government services, taxes, fees and interest, used assets). When you have supplier-specific or activity data, use it in place of spend-based factors. Use spend-based factors for screening, hotspotting and categories where better data isn't available.
-% TODO(confirm): link to intro/uses-and-limits.md once it is written and added to the nav in myst.yml.
-:::
+## Boundaries and reporting
 
 :::{dropdown} Are the factors suitable for GHG Protocol Scope 3 reporting and for target setting?
-Cornerstone factors are designed for spend-based Scope 3 calculations under the GHG Protocol Corporate Value Chain (Scope 3) Standard. Guidance from the GHG Protocol and from target-setting bodies sets what data quality is expected, so check the current requirements for your framework.
-% TODO(confirm): GHG Protocol third-party database listing and SBTi acceptance status for Cornerstone EEIO 2026 (CEDA was on the GHG Protocol's list).
-:::
-
-## Understanding the results
-
-:::{dropdown} Why did a factor change between releases?
-Look at three things in order: the data (new tables or emissions for that country), the methodology (for example US imports, ABSR, model reconciliation) and the price year (a different dollar year changes factors even with no real change). The [](releases/change-log.md) lists every change, and the release notes give examples.
-:::
-
-:::{dropdown} Why does the same sector have very different factors in different countries?
-Three reasons: the emissions intensity of each country (for example its electricity grid), the economic structure of the sector in that country, and the trade structure, meaning where its inputs are imported from. Factors for a sector in countries with detailed data differ for real reasons, but see the next question for countries with less data.
+Yes. Cornerstone factors are designed for spend-based Scope 3 calculations under the GHG Protocol Corporate Value Chain (Scope 3) Standard, and they follow its minimum boundary conditions: a cradle-to-gate boundary that covers all upstream emissions up to the point of purchase, all seven GHGs reported in CO₂e with IPCC AR6 100-year global warming potentials, and no downstream emissions.
 :::
 
-:::{dropdown} What does a country's tier mean for how much I can rely on its factors?
-Countries are grouped in four tiers by the input-output data available for them. Tier 0 (US) has detailed annual tables, Tier 1 (China, Japan, South Korea, UK) uses detailed national tables, Tier 2 (60 countries) uses coarser multi-regional tables from FIGARO or ICIO, and Tier 3 (83 countries) has no usable table and uses a template built from other countries, scaled to the country's emissions per unit of GDP and its own electricity intensity. Factors for Tier 3 countries carry more uncertainty. See [](io/country-tiers.md) and [](reference/methods-by-country.md).
+:::{dropdown} Does Cornerstone EEIO follow a cradle-to-gate boundary?
+Yes. The factors cover all upstream emissions, from raw material extraction up to the point of purchase. Downstream use and end-of-life emissions are not included.
 :::
 
-:::{dropdown} How do the factors compare with USEEIO, CEDA, EXIOBASE or other datasets?
-Differences come from the data vintage, the sector classification, the price basis and the treatment of trade. Compare on the same basis, meaning the same year, currency and price basis, before reading the gap. Cornerstone benchmarks against about 1,000 third-party sources for the sectors behind about 50% of global emissions, and documents the gaps that remain. See [](benchmarking/index.md).
+:::{dropdown} Does Cornerstone EEIO account for direct land use change and land management emissions?
+The factors do not account for direct land use change, but they do include land management emissions. Farming emissions are counted in the farm sectors, including methane from livestock and rice, N₂O from agricultural soils, and CO₂ from liming and urea. Land use, land-use change and forestry (LULUCF), including carbon removals, is not counted.
 :::
 
-:::{dropdown} Are emissions counted where they occur, or by where the producer is based?
-It depends on the source. National inventories (US, China) use the territorial basis: emissions within the country's borders. Air emissions accounts, FIGARO and ICIO use the residence basis: emissions by a country's resident businesses wherever they occur. The model uses each source as published. The two differ most for countries with large international transport fleets or many visitors. See [](ee/index.md).
+:::{dropdown} Does Cornerstone EEIO exclude any of the seven GHGs (CO₂, CH₄, N₂O, HFCs, PFCs, SF₆, NF₃)?
+No. All seven GHGs are included. The release reports each gas separately, alongside the total, in kg CO₂e per USD.
 :::
 
-:::{dropdown} What's included in the factors?
-- **Boundary:** cradle to gate, meaning all upstream emissions up to the point of purchase.
-- **Gases:** all seven (CO₂, CH₄, N₂O, HFCs, PFCs, SF₆, NF₃), reported separately and as a total in CO₂e.
-- **GWPs:** IPCC AR6 100-year values (CH₄ is 29.8 for fossil and 27.0 for non-fossil methane, and N₂O is 273). The **GWP** tab in the release lists them.
-- **Land:** farming emissions are counted. Land use, land-use change and forestry (LULUCF) is not, including carbon removals.
-- **Not counted:** household direct emissions (such as fuel burned at home), since they don't come from a producing sector.
-
-% TODO(confirm): biogenic CO2 statement (CEDA excluded it), transport well-to-wheel treatment, and whether capital goods are treated as upstream purchases for every sector.
+:::{dropdown} Are biogenic emissions included in the factors?
+No, biogenic emissions are not included.
 :::
 
 :::{dropdown} Which GWP version is used?
 IPCC AR6, 100-year time horizon. Sources that report in AR5 CO₂-equivalents are converted to AR6 gas by gas. Prior versions of USEEIO used earlier GWPs, so a share of the change versus those models comes from the GWP update. Cornerstone EEIO 2026 also fixes a double application of the AR5-to-AR6 conversion.
 :::
 
-:::{dropdown} Do the factors come with uncertainty ranges?
-Not in this release. Spend-based factors are sector averages, so uncertainty is higher for individual products, and for Tier 3 countries. See the assumptions in [](io/index.md) and the validation results in [](benchmarking/index.md).
-% TODO(confirm): whether uncertainty estimates are planned for the final version.
-:::
-
-:::{dropdown} Why did my factor for [sector] in [country] change by so much?
-Large moves in a single sector are possible, and some are explained in the release notes (for example batteries in China, power in Germany, and light trucks in the US). If you can't explain a change with the [](releases/change-log.md), please report it. See "How do I report a possible error" below.
-:::
-
-## For verifiers and auditors
+## Verification, licensing and support
 
 :::{dropdown} How do I trace a factor back to its data sources and methods?
 Start with [](reference/methods-by-country.md), which lists the data and methods behind every country. The [IO model](io/index.md) and [GHG extension](ee/index.md) pages describe the method step by step, and the [](releases/change-log.md) lists what changed against USEEIO and CEDA. Methodology decisions are discussed openly on [GitHub](https://github.com/cornerstone-data/methods/discussions).
 :::
 
 :::{dropdown} How do I cite a specific release?
-Cite the version, the release date and the Zenodo DOI, which are on the cover tab of the workbook. The release candidate (2026-rc.1) is archived at [doi.org/10.5281/zenodo.23396891](https://zenodo.org/doi/10.5281/zenodo.23396891). See [](releases/index.md).
-% TODO(confirm): suggested citation text, and whether the final release gets a new DOI.
-:::
-
-:::{dropdown} How is the model validated and benchmarked?
-Cornerstone reviews the sectors behind about 50% of global emissions against third-party benchmarks built from about 1,000 sources. Each review ends in a sharper benchmark, a model improvement or a documented known discrepancy. See [](benchmarking/index.md) and [](validation/index.md).
-:::
-
-:::{dropdown} Does the model follow the GHG Protocol boundary conditions?
-Cornerstone EEIO follows a cradle-to-gate boundary and includes all seven Kyoto gases. Land use change isn't included, but farming emissions are.
-% TODO(confirm): GHG Protocol minimum boundary statement and SBTi methodology questions (well-to-wheel, direct land-use change), as in the CEDA 2025 FAQ.
-:::
-
-:::{dropdown} Is the model open? Where are the code and data?
-The emission factors are free to use. Documentation and the methodology discussions are public at [github.com/cornerstone-data](https://github.com/cornerstone-data).
-% TODO(confirm): code repository availability and the license of the source data.
-:::
-
-:::{dropdown} Which published methodology supports relying on the factors?
-The approach builds on the peer-reviewed hybrid-LCA and EEIO literature, including Suh et al. (2004) on system boundary selection with hybrid approaches and Suh (2005) on developing a sectoral environmental database for input-output analysis. See the references on each methodology page.
-% TODO(confirm): add the final reference list (carry over from CEDA 2025 FAQ, plus the multi-regional and ABSR references).
-:::
-
-## Practical
-
-:::{dropdown} Where do I download the factors?
-On [Zenodo](https://zenodo.org/doi/10.5281/zenodo.23396891). Each release is a single Excel workbook. See [](factors/whats-in-a-release.md).
+Cite the version and the release date. For example the Cornerstone EEIO 2026-rc.1 released on October 27th, 2026.
 :::
 
 :::{dropdown} What license are the factors under, and what attribution is required?
-The factors are published by the Cornerstone Sustainability Data Initiative under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. You can use and redistribute them, including commercially, as long as you give credit and indicate any changes.
+The factors are published by the Cornerstone Sustainability Data Initiative under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. You can use and redistribute them, including commercially, as long as you give credit and indicate any changes. Please attribute the factors to Cornerstone EEIO.
 :::
 
-:::{dropdown} I'd like to explain the switch to Cornerstone in my report. What do you recommend?
-State the source (Cornerstone EEIO 2026 and its DOI), the base year and dollar year, the reason you switched, and that the change in your emissions has two parts: real-world and data changes, and methodology changes. If you're coming from USEEIO, mention that imports are now modeled by country of origin, which tends to increase US factors. If you're coming from CEDA, mention the new China and Europe data. Quantify the effect if you can with the release's change summaries.
+:::{dropdown} What does a country's tier mean for how much I can rely on its factors?
+Countries are grouped in four tiers by the input-output data available for them. Tier 0 (US) has detailed annual tables, Tier 1 (China, Japan, South Korea, UK) uses detailed national tables, Tier 2 (60 countries) uses coarser multi-regional tables from FIGARO or ICIO, and Tier 3 (83 countries) has no usable table and uses a template built from other countries. Factors for Tier 3 countries carry more uncertainty. See [](io/country-tiers.md) and [](reference/methods-by-country.md).
 :::
 
 :::{dropdown} Can I get help applying the changes to my footprint?
@@ -287,7 +160,7 @@ For more detail, or custom support on how the changes affect your footprint, you
 :::
 
 :::{dropdown} How do I report a possible error or ask a question?
-Open an issue on [GitHub](https://github.com/cornerstone-data/eeio-docs/issues). Please include the sector code, the country, the year and the tab (`PRO` or `PUR`), so we can reproduce it.
+Open an issue on [GitHub](https://github.com/cornerstone-data/eeio-docs/issues). Please include the sector code, the country, the year and the tab (`PRO` or `PUR`), so we can reproduce it. For additional support, enquire about the enterprise product [here](https://watershed.com/en-GB/solutions/ceda).
 :::
 
 +++

From f851cdbc91b35313df6cde9f9a20616d9cd1c402 Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Fri, 23 Oct 2026 22:04:34 -0700
Subject: [PATCH 43/47] docs(factors): link the Watershed enterprise product in
 User guidance

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 factors/user-guidance.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/factors/user-guidance.md b/factors/user-guidance.md
index 0f9cf93e..f5f6e14b 100644
--- a/factors/user-guidance.md
+++ b/factors/user-guidance.md
@@ -21,7 +21,7 @@ Every emission factor is defined by a sector, a location, a year and a price bas
 
 Find the sector that best describes what you bought, using the sector descriptions that come with the factors. Pick the sector for the final good or service you purchased. You do not add up factors along a supply chain, because each factor already includes the upstream emissions of that sector. A purchase of steel products, for example, already includes the emissions of making the steel.
 
-Each sector is related to a commodity and follows the BEA standard classification. For crosswalks to other industry classifications such as GICS, NACE and ANZSIC, please enquire about the Watershed enterprise product.
+Each sector is related to a commodity and follows the BEA standard classification. For crosswalks to other industry classifications such as GICS, NACE and ANZSIC, please enquire about the [Watershed enterprise product](https://watershed.com/en-GB/solutions/ceda).
 
 ### Step 2: Choose the country
 

From 1b11a2c2d2a418191b3dc023c3bfde3fe8b4450d Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Fri, 23 Oct 2026 22:04:41 -0700
Subject: [PATCH 44/47] docs(factors): remove the three How-to placeholder
 pages and point their links to User guidance

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 factors/choose-a-factor.md      | 48 ---------------------------------
 factors/prices-and-margins.md   | 45 -------------------------------
 factors/whats-in-a-release.md   |  2 +-
 factors/year-and-currency.md    | 47 --------------------------------
 faq.md                          |  8 +++---
 io/country-tiers.md             |  4 +--
 myst.yml                        |  3 ---
 reference/methods-by-country.md |  2 +-
 8 files changed, 8 insertions(+), 151 deletions(-)
 delete mode 100644 factors/choose-a-factor.md
 delete mode 100644 factors/prices-and-margins.md
 delete mode 100644 factors/year-and-currency.md

diff --git a/factors/choose-a-factor.md b/factors/choose-a-factor.md
deleted file mode 100644
index 5bb49577..00000000
--- a/factors/choose-a-factor.md
+++ /dev/null
@@ -1,48 +0,0 @@
----
-title: "How-to 1: Choose the right emission factor"
----
-
-:::{tip} Summary
-*To be written.*
-:::
-
-:::{note} Draft placeholder
-:class: dropdown
-Plan §3.4.2 · target: **RC (Oct 27)**
-
-
-**Planned content**
-
-- Match purchases to commodities (not industries); commodity codes and the NAICS crosswalk; broad categories; purchases spanning several commodities
-- Choose the country of production; supplier location as a proxy; unknown origin (default to settle)
-- Total (`N`), not direct (`D`), for purchased goods and services
-- What the factors don't cover: purchased electricity, government services, taxes/fees/interest, used assets
-- Special cases: distributors and trading companies, services, capital goods, investments
-- Matching to Scope 3 categories
-
-**Key visuals**
-
-- 🖼 Decision tree: what did you buy → from which country → is it covered?
-- 🖼 Example mapping of five common purchases
-:::
-
-## Worked example
-
-*To be written.*
-
-:::{warning} Common mistakes
-*To be written.*
-:::
-
-## Learn more
-
-- **From the field:** *to be added*
-- **Cornerstone papers:** *to be added*
-- **Code:** *to be added*
-
-+++
-
-:::{div}
-:class: page-status
-Applies to: 2026-rc.1 · Last reviewed: 2026-rc.1
-:::
diff --git a/factors/prices-and-margins.md b/factors/prices-and-margins.md
deleted file mode 100644
index f3f39bad..00000000
--- a/factors/prices-and-margins.md
+++ /dev/null
@@ -1,45 +0,0 @@
----
-title: "How-to 2: Purchaser vs. producer price, and margins"
----
-
-:::{tip} Summary
-*To be written.*
-:::
-
-:::{note} Draft placeholder
-:class: dropdown
-Plan §3.4.3 · target: **RC (Oct 27)**
-
-
-**Planned content**
-
-- The price layer cake; factors are per dollar of purchaser price
-- The three columns and when to use each (retailer/wholesaler vs. direct from producer with transport counted elsewhere; high mark-up sellers)
-- If you only have producer prices; taxes; price basis for other countries
-
-**Key visuals**
-
-- 🖼 Layer cake with the three columns mapped onto it
-- 🖼 Decision diagram: who did you buy from, and is transport counted elsewhere?
-:::
-
-## Worked example
-
-*To be written.*
-
-:::{warning} Common mistakes
-*To be written.*
-:::
-
-## Learn more
-
-- **From the field:** *to be added*
-- **Cornerstone papers:** *to be added*
-- **Code:** *to be added*
-
-+++
-
-:::{div}
-:class: page-status
-Applies to: 2026-rc.1 · Last reviewed: 2026-rc.1
-:::
diff --git a/factors/whats-in-a-release.md b/factors/whats-in-a-release.md
index 94e181ef..10f4c452 100644
--- a/factors/whats-in-a-release.md
+++ b/factors/whats-in-a-release.md
@@ -23,7 +23,7 @@ Below these details, the cover lists every other tab with a one-line description
 
 ### Producer and purchaser price tabs
 
-There are two tabs for each year, with identical layouts: `{YEAR}PRO` for producer prices and `{YEAR}PUR` for purchaser prices. The producer price is the cost to the producer plus their profits and taxes paid. The purchaser price adds the value of sales and distribution. Read more about how to choose between purchaser and producer prices in [](prices-and-margins.md).
+There are two tabs for each year, with identical layouts: `{YEAR}PRO` for producer prices and `{YEAR}PUR` for purchaser prices. The producer price is the cost to the producer plus their profits and taxes paid. The purchaser price adds the value of sales and distribution. Read more about how to choose between purchaser and producer prices in [](user-guidance.md).
 
 Each row is one sector in one location, and the columns are:
 
diff --git a/factors/year-and-currency.md b/factors/year-and-currency.md
deleted file mode 100644
index a87ea215..00000000
--- a/factors/year-and-currency.md
+++ /dev/null
@@ -1,47 +0,0 @@
----
-title: "How-to 3: Find the right EF for the purchase"
----
-
-:::{tip} Summary
-*Temp: Find the matching commodity, country/region, year, etc.*
-:::
-
-:::{note} Draft placeholder
-:class: dropdown
-Plan §3.4.4 · target: **RC (Oct 27)**
-
-
-**Planned content**
-
-- Pick the file for your spend year (RC dollar years to confirm)
-- How the 2024, 2025, and 2026 factors are made from the same data: price indices used, and the exception for Tier 3 countries, whose direct intensities are not re-priced from 2018 dollars
-- Spend in another year: sector-specific price indices vs. general CPI
-- Spend in another currency: market exchange rate for the year of spend, then the factor for that year
-- Reporting across years and recalculating when a new release comes out
-- End-to-end worked example: a small purchase ledger taken through all three guides
-
-**Key visuals**
-
-- 🖼 Flow diagram: € 2023 spend → 2023 USD → factor file → kg CO₂e
-:::
-
-## Worked example
-
-*To be written.*
-
-:::{warning} Common mistakes
-*To be written.*
-:::
-
-## Learn more
-
-- **From the field:** *to be added*
-- **Cornerstone papers:** *to be added*
-- **Code:** *to be added*
-
-+++
-
-:::{div}
-:class: page-status
-Applies to: 2026-rc.1 · Last reviewed: 2026-rc.1
-:::
diff --git a/faq.md b/faq.md
index 20941aac..9ba9c592 100644
--- a/faq.md
+++ b/faq.md
@@ -130,7 +130,7 @@ Use `N`, the total emission factor, for purchased goods and services. It include
 :::
 
 :::{dropdown} How do I find the right sector for a purchase? What if it spans several sectors?
-Match the purchase to the *good or service you bought*, not to the supplier's industry. Use the description in the **Sector Descriptions** tab (405 sectors), and the **NAICS to Cornerstone mapping** tab if your categories are NAICS-based. If a purchase spans several sectors, split the spend across them, or choose the one that best describes most of the spend. See [](factors/choose-a-factor.md).
+Match the purchase to the *good or service you bought*, not to the supplier's industry. Use the description in the **Sector Descriptions** tab (405 sectors), and the **NAICS to Cornerstone mapping** tab if your categories are NAICS-based. If a purchase spans several sectors, split the spend across them, or choose the one that best describes most of the spend. See [](factors/user-guidance.md).
 :::
 
 :::{dropdown} Do I need to add up factors across the supply chain (for example raw steel plus steel products)?
@@ -138,8 +138,8 @@ No. Factors already include all upstream emissions, so a manufactured good alrea
 :::
 
 :::{dropdown} How do I choose between producer and purchaser price?
-The producer price is what the producer receives. The purchaser price adds trade and transport margins, and is what you pay for a ready-to-buy product. If you're unsure, use producer prices, which are more conservative. If you're confident your spend includes typical margins, for example buying off-the-shelf goods at retail, use purchaser prices. See [](factors/prices-and-margins.md).
-% TODO(confirm): align with the final guidance on prices-and-margins (the plan describes three columns).
+The producer price is what the producer receives. The purchaser price adds trade and transport margins, and is what you pay for a ready-to-buy product. If you're unsure, use producer prices, which are more conservative. If you're confident your spend includes typical margins, for example buying off-the-shelf goods at retail, use purchaser prices. See [](factors/user-guidance.md).
+% TODO(confirm): align with the User guidance page (the plan describes three columns).
 :::
 
 :::{dropdown} Where do the margins come from, and do they apply to every country?
@@ -147,7 +147,7 @@ Cornerstone converts producer-price factors to purchaser-price factors using US
 :::
 
 :::{dropdown} What year and currency are the factors in?
-Factors are in kg CO₂e per USD in current-year dollars, so a factor in the 2026 tab applies to spend in 2026 US dollars. To use spend in another currency, convert it to USD at the year-of-spend rate, using the **Exchange Rates** tab (local currency per USD for 221 countries and territories, from Open Exchange Rates), then apply the factor for that year. See [](factors/year-and-currency.md).
+Factors are in kg CO₂e per USD in current-year dollars, so a factor in the 2026 tab applies to spend in 2026 US dollars. To use spend in another currency, convert it to USD at the year-of-spend rate, using the **Exchange Rates** tab (local currency per USD for 221 countries and territories, from Open Exchange Rates), then apply the factor for that year. See [](factors/user-guidance.md).
 :::
 
 :::{dropdown} My spend is in a different year than the factor tabs. What should I do?
diff --git a/io/country-tiers.md b/io/country-tiers.md
index cfebce55..84d940ab 100644
--- a/io/country-tiers.md
+++ b/io/country-tiers.md
@@ -92,13 +92,13 @@ All tables are converted to 2024 U.S. dollars. Sector lists are reconciled in th
 
 - **Assumption:** Countries publish their tables on different price bases (basic, producer, or purchaser prices, and sometimes a mix within one table). We use each table as published and assume these differences do not materially change the direct requirements (`A`).
 - **Why we make it:** Converting every table to one price basis needs product-level tax, subsidy, and margin data for each country, which is not consistently available.
-- **How it affects results:** Where product taxes or trade and transport margins are large, as for fuels, input coefficients could differ from those on a common price basis. See [](../factors/prices-and-margins.md).
+- **How it affects results:** Where product taxes or trade and transport margins are large, as for fuels, input coefficients could differ from those on a common price basis. See [](../factors/user-guidance.md).
 
 **U.S. margins for purchaser prices**
 
 - **Assumption:** Factors are converted from producer to purchaser prices using U.S. trade and transport margins for every country.
 - **Why we make it:** Margin data comparable to the U.S. tables are not available for every country.
-- **How it affects results:** Where a country's wholesale, retail, or transport margins differ from U.S. margins, its purchaser-price factors could be too high or too low. Producer-price factors are not affected. See [](../factors/prices-and-margins.md).
+- **How it affects results:** Where a country's wholesale, retail, or transport margins differ from U.S. margins, its purchaser-price factors could be too high or too low. Producer-price factors are not affected. See [](../factors/user-guidance.md).
 
 **Tier 3 countries share one recipe**
 
diff --git a/myst.yml b/myst.yml
index aa624c41..fc7bc400 100644
--- a/myst.yml
+++ b/myst.yml
@@ -34,9 +34,6 @@ project:
       children:
         - file: factors/whats-in-a-release.md
         - file: factors/user-guidance.md
-        - file: factors/choose-a-factor.md
-        - file: factors/prices-and-margins.md
-        - file: factors/year-and-currency.md
     - file: validation/index.md
     - file: benchmarking/index.md
     - title: Reference
diff --git a/reference/methods-by-country.md b/reference/methods-by-country.md
index ad9fc543..5018664b 100644
--- a/reference/methods-by-country.md
+++ b/reference/methods-by-country.md
@@ -21,7 +21,7 @@ The rest-of-world region is not in the table. Its input recipe and emission inte
 
 ## 2024, 2025, and 2026 emission factors
 
-The release candidate's 2024, 2025, and 2026 factors are all built from the same data, below. Each is expressed in its own year's dollars; see [](../factors/year-and-currency.md).
+The release candidate's 2024, 2025, and 2026 factors are all built from the same data, below. Each is expressed in its own year's dollars; see [](../factors/user-guidance.md).
 
 <!-- BEGIN country-table: generated by figures/scripts/methods_by_country.py -->
 

From 55e6b383481dab215beee683ac5f68cd0c6637dc Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Fri, 23 Oct 2026 22:05:19 -0700
Subject: [PATCH 45/47] ci(preview): build PR previews for the
 docs.cornerstonedata.org base path

The repo's Pages site now serves from the custom domain, so previews live at
/pr-preview/pr-N and the old /eeio-docs/ prefix made CSS and JS 404.

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 .github/workflows/pr.yml | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/pr.yml b/.github/workflows/pr.yml
index 0f153c04..fc38d08f 100644
--- a/.github/workflows/pr.yml
+++ b/.github/workflows/pr.yml
@@ -1,7 +1,8 @@
 # Build every PR, publish a preview, and check links.
 # Previews are served from this repo's gh-pages branch at
-#   https://cornerstone-data.github.io/eeio-docs/pr-preview/pr-<number>/
-# (or under docs.cornerstonedata.org/eeio-docs/ once the hub has its custom domain).
+#   https://docs.cornerstonedata.org/pr-preview/pr-<number>/
+# (this repo's Pages site has the custom domain docs.cornerstonedata.org, so the
+# base path is /pr-preview/pr-<number>, not /eeio-docs/...).
 name: PR build and preview
 on:
   pull_request:
@@ -17,7 +18,7 @@ jobs:
   build-preview:
     runs-on: ubuntu-latest
     env:
-      BASE_URL: /eeio-docs/pr-preview/pr-${{ github.event.number }}
+      BASE_URL: /pr-preview/pr-${{ github.event.number }}
     steps:
       - uses: actions/checkout@v4
       - uses: actions/setup-node@v4

From 3b463a014229edbf38d09fd90e6c886ee7e7c6ab Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Fri, 23 Oct 2026 22:07:59 -0700
Subject: [PATCH 46/47] docs(faq): tidy the supplier-with-no-data links

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 faq.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/faq.md b/faq.md
index ab8a3fe8..c10b717d 100644
--- a/faq.md
+++ b/faq.md
@@ -100,7 +100,7 @@ If a country has no factors of its own, use its regional average. The **Location
 :::
 
 :::{dropdown} What if my supplier is in a country with no data?
-Use the factor for the region it belongs to, or the global average if the region isn't clear. See the **Locations** tab and [](reference/methods-by-country.md), and [](factors/user-guidance.md).
+Use the factor for the region it belongs to, or the global average if the region isn't clear. See the **Locations** tab, [](reference/methods-by-country.md) and [](factors/user-guidance.md).
 :::
 
 :::{dropdown} What do the Scope 3 categories cover?

From 87f8255a9102e201a46c409969445508d25ade7b Mon Sep 17 00:00:00 2001
From: Yohanna Maldonado <yohannamaldonado@Yohannas-MacBook-Air-2.local>
Date: Fri, 23 Oct 2026 22:13:28 -0700
Subject: [PATCH 47/47] docs(faq): link FAQ answers to the release note,
 summaries and guidance pages

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
---
 faq.md | 48 ++++++++++++++++++++++++++----------------------
 1 file changed, 26 insertions(+), 22 deletions(-)

diff --git a/faq.md b/faq.md
index c10b717d..8d7e3972 100644
--- a/faq.md
+++ b/faq.md
@@ -9,7 +9,7 @@ Short answers to the questions we hear most about Cornerstone EEIO 2026. Each an
 ## About Cornerstone EEIO 2026
 
 :::{dropdown} What is the Cornerstone Sustainability Data Initiative, and how does it relate to USEEIO and CEDA?
-Cornerstone launched in August 2025 to bring USEEIO and CEDA together into one open, comprehensive dataset of Scope 3 spend-based emission factors. Cornerstone EEIO 2026 is the first release of that combined model. It is the continuation of both USEEIO v1.4 and CEDA 2025. Today Cornerstone is the home of 3 additional datasets: eGRID, the GHG EF Hub and Orbae. Learn more at [cornerstonedata.org](https://cornerstonedata.org/).
+Cornerstone launched in August 2025 to bring USEEIO and CEDA together into one open, comprehensive dataset of Scope 3 spend-based emission factors. Cornerstone EEIO 2026 is the first release of that combined model. It is the continuation of both USEEIO v1.4 and CEDA 2025. Today Cornerstone is the home of 3 additional datasets: eGRID, the GHG EF Hub and Orbae. Learn more at [cornerstonedata.org](https://cornerstonedata.org/), and see the [release candidate note](releases/rc.md) for what is in this release.
 :::
 
 :::{dropdown} What is changing compared with USEEIO and CEDA?
@@ -19,11 +19,11 @@ Three groups of changes:
 2. **Newer data for Europe, the UK and China, and better gap-filling for other countries.**
 3. **A validation process that feeds back into the model.** Third-party benchmarks review the sectors behind about 50% of global emissions.
 
-The full list is in the [](releases/change-log.md). Side-by-side summaries are in [](releases/summary-useeio.md) and [](releases/summary-ceda.md).
+Examples with numbers are in [What changed](releases/rc.md#what-changed) in the release candidate note. The full list is in the [](releases/change-log.md). Side-by-side summaries are in [](releases/summary-useeio.md) and [](releases/summary-ceda.md).
 :::
 
 :::{dropdown} Are the changes driven by methodology or by real-world change?
-Both. Data updates reflect real-world change, such as new economic, trade and emissions data. Methodology changes reflect modeling improvements, such as modeling US imports by origin and the auto-balanced structural reflection (ABSR) method. See more on the [](releases/change-log.md).
+Both. Data updates reflect real-world change, such as new economic, trade and emissions data. Methodology changes reflect modeling improvements, such as modeling US imports by origin and the auto-balanced structural reflection (ABSR) method. See the [](releases/change-log.md) and, for the size of each driver, [](releases/summary-ceda.md#what-drives-the-change-for-ceda-users) and [](releases/summary-useeio.md#what-drives-the-change-for-useeio-users).
 :::
 
 :::{dropdown} Why is it called a "release candidate"?
@@ -31,11 +31,11 @@ Both. Data updates reflect real-world change, such as new economic, trade and em
 :::
 
 :::{dropdown} Will the 2024–2026 factors change between the release candidate and the final version?
-We don't foresee significant changes, but some sector–country combinations could move because of improvements made while developing the time series. If you need numbers for reporting or rebaselining, wait for the final version.
+We don't foresee significant changes, but some sector–country combinations could move because of improvements made while developing the time series. If you need numbers for reporting or rebaselining, wait for the final version. See [](releases/rc.md) for what to expect from the time series.
 :::
 
 :::{dropdown} When is the next update?
-Releases are published every October so companies can plan for them. The next, Cornerstone EEIO 2027, will be released in October 2027. Cornerstone EEIO 2026 will stay methodologically stable for at least five years, so you have a consistent basis to rebaseline and set targets.
+Releases are published every October so companies can plan for them. The next, Cornerstone EEIO 2027, will be released in October 2027. Cornerstone EEIO 2026 will stay methodologically stable for at least five years, so you have a consistent basis to rebaseline and set targets. See [](releases/index.md) and [](factors/whats-in-a-release.md#update-cadence).
 :::
 
 :::{dropdown} Did the sectors change?
@@ -51,6 +51,8 @@ Yes, slightly. Cornerstone EEIO uses 405 sectors, based on the BEA input-output
 - Waste is split into seven sectors. CEDA had a single waste sector.
 - Aluminum is split into primary and secondary sectors. CEDA merged them.
 - Home appliances are one sector, as in the BEA tables. CEDA had four.
+
+See the [model reconciliation](releases/change-log.md#change-log-model-reconciliation) section of the change log for all 18 differences.
 :::
 
 :::{dropdown} Did the file format change?
@@ -65,27 +67,29 @@ It depends on which sectors and countries your spend falls in.
 - **Coming from CEDA 2025:** global production-weighted factors fall by 23.3%. In a sample of 400 corporate footprints, the median company sees a 14% decrease.
 - **Coming from USEEIO v1.4:** US factors change by −0.8% overall. In the same sample, the median company sees a 1.3% decrease.
 
-A small median can hide large moves in individual sectors, in both directions. See [](releases/summary-ceda.md) and [](releases/summary-useeio.md).
+A small median can hide large moves in individual sectors, in both directions. See [](releases/rc.md#how-do-the-emission-factors-change), [](releases/summary-ceda.md) and [](releases/summary-useeio.md).
 :::
 
 :::{dropdown} I was using CEDA or USEEIO. Do I need to rebaseline or restate previous years?
-We do expect the majority of companies to be above the SBTi rebaselining threshold, but every company will have a different experience and ultimately whether to restate is a decision under your own company's policy and your target framework. This year, the release of the time series is designed for exactly this purpose. Because every year from 2017 to 2026 is built with one methodology, you can recalculate prior years on a consistent basis and compare them.
+We do expect the majority of companies to be above the SBTi rebaselining threshold, but every company will have a different experience and ultimately whether to restate is a decision under your own company's policy and your target framework. This year, the release of the time series is designed for exactly this purpose. Because every year from 2017 to 2026 is built with one methodology, you can recalculate prior years on a consistent basis and compare them. See [](releases/rc.md) for what to expect from the time series, and [](releases/summary-ceda.md) and [](releases/summary-useeio.md) for how much your factors may move.
 :::
 
 :::{dropdown} Can I keep using CEDA 2025 or USEEIO for 2026?
-We recommend using Cornerstone EEIO 2026 for 2026 measurements, since it supersedes both. If you have a reason to stay on a previous version, apply a price index to it, and explain the choice to your verifier.
+We recommend using Cornerstone EEIO 2026 for 2026 measurements, since it supersedes both. If you have a reason to stay on a previous version, apply a price index to it, and explain the choice to your verifier. See [](releases/summary-ceda.md) and [](releases/summary-useeio.md) for what changed.
 :::
 
 :::{dropdown} I'd like to explain the switch to Cornerstone in my report. What do you recommend?
 State the source (Cornerstone EEIO 2026) and the years you are applying the model to.
 
 Suggested wording: "The changes this year are due to adopting Cornerstone EEIO 2026. The Cornerstone model combines CEDA's global coverage with USEEIO's detailed US data into a single model that supersedes them both. This model was adopted as an improvement in data quality, as it provides a more current picture of the global economy."
+
+For numbers you can cite, see [What changed](releases/rc.md#what-changed) in the release candidate note, [](releases/summary-ceda.md) and [](releases/summary-useeio.md).
 :::
 
 ## Using the factors
 
 :::{dropdown} How do I choose between producer and purchaser price?
-The producer price is what the producer receives. The purchaser price adds trade and transport margins, and is what you pay for a ready-to-buy product. If you're unsure, use producer prices, which are more conservative. If you're confident your spend includes typical margins, for example buying off-the-shelf goods at retail, use purchaser prices. See [](factors/user-guidance.md).
+The producer price is what the producer receives. The purchaser price adds trade and transport margins, and is what you pay for a ready-to-buy product. If you're unsure, use producer prices, which are more conservative. If you're confident your spend includes typical margins, for example buying off-the-shelf goods at retail, use purchaser prices. See [](factors/user-guidance.md#choose-between-purchaser-and-producer-price).
 :::
 
 :::{dropdown} How do I use the factors for goods bought from, or made in, another country?
@@ -96,37 +100,37 @@ Choose the country where the product was produced. If you don't know it, use thi
 3. Country of purchase
 4. Country of your company's headquarters
 
-If a country has no factors of its own, use its regional average. The **Locations** tab shows which of the 249 locations have their own country factors (148 do) and which region each belongs to. See [](factors/user-guidance.md).
+If a country has no factors of its own, use its regional average. The **Locations** tab shows which of the 249 locations have their own country factors (148 do) and which region each belongs to. See [](factors/user-guidance.md#step-2-choose-the-country).
 :::
 
 :::{dropdown} What if my supplier is in a country with no data?
-Use the factor for the region it belongs to, or the global average if the region isn't clear. See the **Locations** tab, [](reference/methods-by-country.md) and [](factors/user-guidance.md).
+Use the factor for the region it belongs to, or the global average if the region isn't clear. See the **Locations** tab, [](reference/methods-by-country.md) and [](factors/user-guidance.md#step-2-choose-the-country).
 :::
 
 :::{dropdown} What do the Scope 3 categories cover?
-The factors cover upstream emissions up to the point of purchase, so they apply to the upstream categories: purchased goods and services (1), capital goods (2), upstream transportation and distribution (4), waste generated in operations (5), business travel (6), and upstream leased assets (8), as well as the upstream part of category 3. They don't cover downstream emissions (categories 9–15).
+The factors cover upstream emissions up to the point of purchase, so they apply to the upstream categories: purchased goods and services (1), capital goods (2), upstream transportation and distribution (4), waste generated in operations (5), business travel (6), and upstream leased assets (8), as well as the upstream part of category 3. They don't cover downstream emissions (categories 9–15). See [](factors/user-guidance.md).
 :::
 
 :::{dropdown} Why does the same sector have very different factors in different countries?
-Three reasons: the emissions intensity of each country (for example its electricity grid), the economic structure of the sector in that country, and the trade structure, meaning where its inputs are imported from. Factors for a sector in countries with detailed data differ for real reasons, but see the tier question below for countries with less data.
+Three reasons: the emissions intensity of each country (for example its electricity grid), the economic structure of the sector in that country, and the trade structure, meaning where its inputs are imported from. Factors for a sector in countries with detailed data differ for real reasons, but see the tier question below for countries with less data. For worked examples, see [](releases/rc.md#new-data-for-china) and [](releases/rc.md#new-data-for-europe).
 :::
 
 ## Boundaries and reporting
 
 :::{dropdown} Are the factors suitable for GHG Protocol Scope 3 reporting and for target setting?
-Yes. Cornerstone factors are designed for spend-based Scope 3 calculations under the GHG Protocol Corporate Value Chain (Scope 3) Standard, and they follow its minimum boundary conditions: a cradle-to-gate boundary that covers all upstream emissions up to the point of purchase, all seven GHGs reported in CO₂e with IPCC AR6 100-year global warming potentials, and no downstream emissions.
+Yes. Cornerstone factors are designed for spend-based Scope 3 calculations under the GHG Protocol Corporate Value Chain (Scope 3) Standard, and they follow its minimum boundary conditions: a cradle-to-gate boundary that covers all upstream emissions up to the point of purchase, all seven GHGs reported in CO₂e with IPCC AR6 100-year global warming potentials, and no downstream emissions. See [](factors/user-guidance.md) and [](ee/index.md).
 :::
 
 :::{dropdown} Does Cornerstone EEIO follow a cradle-to-gate boundary?
-Yes. The factors cover all upstream emissions, from raw material extraction up to the point of purchase. Downstream use and end-of-life emissions are not included.
+Yes. The factors cover all upstream emissions, from raw material extraction up to the point of purchase. Downstream use and end-of-life emissions are not included. See [](factors/user-guidance.md).
 :::
 
 :::{dropdown} Does Cornerstone EEIO account for direct land use change and land management emissions?
-The factors do not account for direct land use change, but they do include land management emissions. Farming emissions are counted in the farm sectors, including methane from livestock and rice, N₂O from agricultural soils, and CO₂ from liming and urea. Land use, land-use change and forestry (LULUCF), including carbon removals, is not counted.
+The factors do not account for direct land use change, but they do include land management emissions. Farming emissions are counted in the farm sectors, including methane from livestock and rice, N₂O from agricultural soils, and CO₂ from liming and urea. Land use, land-use change and forestry (LULUCF), including carbon removals, is not counted. See [](ee/index.md#what-is-and-isnt-counted).
 :::
 
 :::{dropdown} Does Cornerstone EEIO exclude any of the seven GHGs (CO₂, CH₄, N₂O, HFCs, PFCs, SF₆, NF₃)?
-No. All seven GHGs are included. The release reports each gas separately, alongside the total, in kg CO₂e per USD.
+No. All seven GHGs are included. The release reports each gas separately, alongside the total, in kg CO₂e per USD. See [](ee/index.md#gases-and-global-warming-potentials) and [](factors/whats-in-a-release.md#producer-and-purchaser-price-tabs).
 :::
 
 :::{dropdown} Are biogenic emissions included in the factors?
@@ -134,25 +138,25 @@ No, biogenic emissions are not included.
 :::
 
 :::{dropdown} Which GWP version is used?
-IPCC AR6, 100-year time horizon. Sources that report in AR5 CO₂-equivalents are converted to AR6 gas by gas. Prior versions of USEEIO used earlier GWPs, so a share of the change versus those models comes from the GWP update. Cornerstone EEIO 2026 also fixes a double application of the AR5-to-AR6 conversion.
+IPCC AR6, 100-year time horizon. Sources that report in AR5 CO₂-equivalents are converted to AR6 gas by gas. Prior versions of USEEIO used earlier GWPs, so a share of the change versus those models comes from the GWP update. Cornerstone EEIO 2026 also fixes a double application of the AR5-to-AR6 conversion. See [](ee/index.md#gases-and-global-warming-potentials), the GWP tab described in [](factors/whats-in-a-release.md#auxiliary-tabs) and the bug fixes in the [](releases/change-log.md).
 :::
 
 ## Verification, licensing and support
 
 :::{dropdown} How do I trace a factor back to its data sources and methods?
-Start with [](reference/methods-by-country.md), which lists the data and methods behind every country. The [IO model](io/index.md) and [GHG extension](ee/index.md) pages describe the method step by step, and the [](releases/change-log.md) lists what changed against USEEIO and CEDA. Methodology decisions are discussed openly on [GitHub](https://github.com/cornerstone-data/methods/discussions).
+Start with [](reference/methods-by-country.md), which lists the data and methods behind every country. The [IO model](io/index.md) and [GHG extension](ee/index.md) pages describe the method step by step, and the [](releases/change-log.md) lists what changed against USEEIO and CEDA. Methodology decisions are discussed openly on [GitHub](https://github.com/cornerstone-data/methods/discussions). Benchmark results are in [](benchmarking/index.md).
 :::
 
 :::{dropdown} How do I cite a specific release?
-Cite the version and the release date. For example the Cornerstone EEIO 2026-rc.1 released on October 27th, 2026.
+Cite the version and the release date. For example the Cornerstone EEIO 2026-rc.1 released on October 27th, 2026. See [](factors/whats-in-a-release.md#cover) and [](releases/index.md).
 :::
 
 :::{dropdown} What license are the factors under, and what attribution is required?
-The factors are published by the Cornerstone Sustainability Data Initiative under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. You can use and redistribute them, including commercially, as long as you give credit and indicate any changes. Please attribute the factors to Cornerstone EEIO.
+The factors are published by the Cornerstone Sustainability Data Initiative under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. You can use and redistribute them, including commercially, as long as you give credit and indicate any changes. Please attribute the factors to Cornerstone EEIO. See [](factors/whats-in-a-release.md#cover).
 :::
 
 :::{dropdown} What does a country's tier mean for how much I can rely on its factors?
-Countries are grouped in four tiers by the input-output data available for them. Tier 0 (US) has detailed annual tables, Tier 1 (China, Japan, South Korea, UK) uses detailed national tables, Tier 2 (60 countries) uses coarser multi-regional tables from FIGARO or ICIO, and Tier 3 (83 countries) has no usable table and uses a template built from other countries. Factors for Tier 3 countries carry more uncertainty. See [](io/country-tiers.md) and [](reference/methods-by-country.md).
+Countries are grouped in four tiers by the input-output data available for them. Tier 0 (US) has detailed annual tables, Tier 1 (China, Japan, South Korea, UK) uses detailed national tables, Tier 2 (60 countries) uses coarser multi-regional tables from FIGARO or ICIO, and Tier 3 (83 countries) has no usable table and uses a template built from other countries. Factors for Tier 3 countries carry more uncertainty. See [](io/country-tiers.md) for the IO data, [](ee/country-tiers.md) for the emissions data and [](reference/methods-by-country.md).
 :::
 
 :::{dropdown} Can I get help applying the changes to my footprint?