diff --git a/figures/diagrams/eeio-overview.svg b/figures/diagrams/eeio-overview.svg new file mode 100644 index 00000000..d890a244 --- /dev/null +++ b/figures/diagrams/eeio-overview.svg @@ -0,0 +1,54 @@ + + + + + + + + + + + + + + + + + Input-output tables + Dollars flowing between + sectors and to final users + V, U → A, L + + + + + + + + + + CO₂, CH₄, … + Environmental extension + Greenhouse gases emitted + by each sector + E → B, C + + + + + + + + EEIO model + Emissions traced through + every round of suppliers + + + + + + Emission factors + D, N + kg CO₂e per $ of purchases, + by sector and country + diff --git a/figures/figures.csv b/figures/figures.csv index 500ca58c..095dd2ce 100644 --- a/figures/figures.csv +++ b/figures/figures.csv @@ -5,3 +5,4 @@ io-tables-to-a,io/index.md,diagram (SVG),hand-made,,,draft io-mrio-blocks,io/index.md,diagram (SVG),hand-made (after CEDA team slide 19),,,draft 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 diff --git a/intro/what-is-eeio.md b/intro/what-is-eeio.md index 81b23f39..0083c8b0 100644 --- a/intro/what-is-eeio.md +++ b/intro/what-is-eeio.md @@ -3,32 +3,39 @@ title: "What is an EEIO model?" --- :::{tip} Summary -*To be written.* +An environmentally-extended input-output ({term}EEIO) model combines data of how outputs produced by sectors in an economy serve as inputs for other sectors along with environmental attributes of each sector. When the economic data is extended with environmental data this way, such as with GHG emissions, the resulting model can produce emission factors that represent the GHG emissions across a whole supply chain per dollar spent on a sector's products. ::: -:::{note} Draft placeholder -:class: dropdown -Plan §3.1.1 · target: **RC (Oct 27)** +:::{figure} ../figures/diagrams/eeio-overview.svg +:label: fig-eeio-overview +:alt: Two grey boxes on the left feed arrows into a central box labeled EEIO model. The upper box, input-output tables, shows a small grid of cells and is described as dollars flowing between sectors and to final users, with the letters V, U, A, and L. The lower box, environmental extension, shows a row of shaded cells labeled CO₂, CH₄, and so on, and is described as greenhouse gases emitted by each sector, with the letters E, B, and C. The EEIO model traces emissions through every round of suppliers. An arrow from it leads to a highlighted box, emission factors D and N, in kilograms of CO₂e per dollar of purchases, by sector and country. +An EEIO model combines input-output tables with an environmental extension to produce emission factors. +::: +## Where EEIO comes from -**Planned content** +EEIO grew out of economic input-output analysis when economist Wassily Leontief added the pollution generated by each sector to his model of the economy [@leontiefEnvironmentalRepercussionsEconomic1970], and it has been a basis for carbon footprinting of nations, organizations, and products over the last few decades [@minxInputOutputAnalysisCarbon2009]. -- Why spend-based supply chain emission factors exist and what they answer: the cradle-to-gate emissions per dollar of purchases from sector X in country Y +## The parts of the model -**Key visuals** +Each data component is put into a matrix, named by a letter that is common in input-output analysis and used throughout these docs (see [](../reference/notation.md)). Input-output analysis then combines the matrices with matrix algebra. -- 🖼 Story graphic: a $1 furniture purchase → the furniture maker buys wood, metal, electricity, transport → each of those buys from others, some abroad -::: +- **Input-output tables.** The supply (`V`) and use (`U`) tables record which sectors produce which products, and which products each sector and final user buys. From them come the direct requirements (`A`), the inputs a sector buys per dollar of its output, and the total requirements (`L`, the Leontief inverse), which add up the purchases through every round of suppliers. See [](../io/index.md). +- **Environmental extension.** Emissions of each gas by each sector (`E`), divided by the sector's output, give the direct emission intensities (`B`). Characterization factors (`C`) convert each gas to CO₂-equivalent. See [](../ee/index.md). +- **Emission factors.** Combining the two gives the direct emission factors, `D = C × B`, for what a sector emits itself, and the total emission factors, `N = C × B × L`, for what is emitted across its whole supply chain. + +## EEIO and life cycle assessment + +An EEIO model has the same matrix structure as the models used for life cycle assessment (LCA) [@suhDevelopingSectoralEnvironmental2005]. In both, a matrix of the inputs each activity needs is inverted to trace every upstream supplier, and a matrix of emissions per unit of each activity is multiplied through it. The difference is that EEIO describes sectors in dollars, where LCA usually describes individual processes in physical units. Because of this shared structure, EEIO data can serve as background data in LCA, as discussed in {cite:t}`ingwersenConsideringInputoutputDerived2026`. -## Learn more +## Multi-regional models -- **From the field:** USEEIO supply chain factors [@ingwersen_supply_2022] -- **Cornerstone papers:** *to be added* -- **Code:** *to be added* +A single-country model assumes imported products are made the same way, and with the same emissions, as products made at home. A multi-regional input-output ({term}`MRIO`) model instead links countries' input-output tables through international trade, so each import carries the supply chain and emissions as it was produced in the origin country. For the companies reporting to the Carbon Disclosure Project (CDP), in one study using an MRIO model instead of a U.S. single-region model raised their estimated upstream emissions by about 10% [@davisImportanceMultiregionalAccounting2025]. The Cornerstone model is an MRIO model; see [](../io/index.md#from-single-country-tables-to-the-multi-region-model). + + +## Further reading + +For a book-length introduction to EEIO and its use in LCA, see {cite:t}`hendricksonEnvironmentalLifeCycle2006a`. +++ -:::{div} -:class: page-status -Applies to: RC · Last reviewed: RC -::: diff --git a/myst.yml b/myst.yml index 8e9f796e..b86fc29f 100644 --- a/myst.yml +++ b/myst.yml @@ -20,14 +20,7 @@ project: output: exports/cornerstone-eeio-guide.pdf toc: - file: index.md - - title: Introduction to EEIO models - children: - - file: intro/what-is-eeio.md - - file: intro/io-part.md - - file: intro/multi-regional.md - - file: intro/ee-part.md - - file: intro/emission-factors.md - - file: intro/uses-and-limits.md + - file: intro/what-is-eeio.md - title: The IO model children: - file: io/index.md diff --git a/references.bib b/references.bib index 91a8d448..058b7f28 100644 --- a/references.bib +++ b/references.bib @@ -476,3 +476,129 @@ @online{eiaInternationalEnergyStatistics2026 url = {https://www.eia.gov/international/data/world}, urldate = {2026-10-21}, } + +@article{davisImportanceMultiregionalAccounting2025, + title = {The Importance of Multiregional Accounting for Corporate Carbon Emissions}, + author = {Davis, Steven J. and Dumit, Andrew and Li, Mo and Maldonado, Yohanna and Steffen, Michael and Stevenson, Martha and Boldyreva, Tatiana and Suh, Sangwon}, + date = {2025-12-20}, + journaltitle = {Nat Commun}, + issn = {2041-1723}, + doi = {10.1038/s41467-025-67759-5}, + url = {https://www.nature.com/articles/s41467-025-67759-5}, + urldate = {2026-01-07}, + abstract = {Abstract Corporations routinely use environmentally-extended input-output models to estimate and report greenhouse gas emissions upstream in their supply chains. However, the most widely used models assume that supply chains and emissions intensities of industries match those of a single region—usually the U.S. or the U.K. Here, we use a high-resolution multiregional input-output model to demonstrate the scale and pattern of emissions that may be missed by single-region models. We find that the upstream emissions of the companies reporting to CDP are together greater by 2.0 gigatons of CO 2 -equivalent emissions (\textasciitilde 10\%) when estimated by a multiregional model instead of a U.S.-based single-region model, with the largest differences in manufacturing sectors of moderate emissions intensity. Widespread adoption of multiregional models could thus improve the accuracy of corporate emissions inventories and help prioritize primary data collection and emissions reduction efforts, often by shifting focus to energy- and emissions-intensive sectors of industrializing nations.}, + langid = {english}, +} + +@book{hendricksonEnvironmentalLifeCycle2006a, + title = {Environmental {{Life Cycle Assessment}} of {{Goods}} and {{Services}}: {{An Input}}-Output {{Approach}}}, + shorttitle = {Environmental {{Life Cycle Assessment}} of {{Goods}} and {{Services}}}, + author = {Hendrickson, Chris T. and Lave, Dr Lester B. and Matthews, H. Scott}, + date = {2006}, + publisher = {Resources for the Future}, + location = {Washington DC}, + abstract = {Environmental life cycle assessment is often thought of as cradle to grave and therefore as the most complete accounting of the environmental costs and benefits of a product or service. However, as anyone who has done an environmental life cycle assessment knows, existing tools have many problems: data is difficult to assemble and life cycle studies take months of effort. A truly comprehensive analysis is prohibitive, so analysts are often forced to simply ignore many facets of life cycle impacts. But the focus on one aspect of a product or service can result in misleading indications if that aspect is benign while other aspects pollute or are otherwise unsustainable. This book summarizes the EIO-LCA method, explains its use in relation to other life cycle assessment models, and provides sample applications and extensions of the model into novel areas. A final chapter explains the free, easy-to-use software tool available on a companion website. (www.eiolca.net) The software tool provides a wealth of data, summarizing the current U.S. economy in 500 sectors with information on energy and materials use, pollution and greenhouse gas discharges, and other attributes like associated occupational deaths and injuries. The joint project of twelve faculty members and over 20 students working together over the past ten years at the Green Design Institute of Carnegie Mellon University, the EIO-LCA has been applied to a wide range of products and services. It will prove useful for research, industry, and in economics, engineering, or interdisciplinary classes in green design.}, + isbn = {978-1-933115-23-8}, + langid = {english}, + pagetotal = {271}, + keywords = {Nature / Ecology,Nature / Environmental Conservation & Protection,Nature / Natural Resources} +} + +@article{ingwersenConsideringInputoutputDerived2026, + title = {Considering Input-Output Derived Data as Background Life Cycle Inventory Data}, + author = {Ingwersen, Wesley}, + date = {2026-09}, + journaltitle = {Int J Life Cycle Assess}, + volume = {31}, + number = {9}, + pages = {164}, + issn = {0948-3349, 1614-7502}, + doi = {10.1007/s11367-026-02735-9}, + url = {https://link.springer.com/10.1007/s11367-026-02735-9}, + urldate = {2026-08-27}, + langid = {english} +} + +@article{ingwersenUSEEIOV20US2022a, + title = {{{USEEIO}} v2.0, {{The US Environmentally-Extended Input-Output Model}} v2.0}, + author = {Ingwersen, Wesley W. and Li, Mo and Young, Ben and Vendries, Jorge and Birney, Catherine}, + date = {2022-05-03}, + journaltitle = {Sci Data}, + volume = {9}, + number = {1}, + pages = {194}, + publisher = {Nature Publishing Group}, + issn = {2052-4463}, + doi = {10.1038/s41597-022-01293-7}, + url = {https://www.nature.com/articles/s41597-022-01293-7}, + urldate = {2022-08-08}, + abstract = {USEEIO v2.0 is an environmental-economic model of US goods and services that can be used for life cycle assessment, footprinting, national prioritization, and related applications. This paper describes the development of the model and accompanies the release of a full model dataset as well as various supporting datasets of national environmental totals by US industry. Novel methodological elements since USEEIO v1 models include waste sector disaggregation, final demand vectors for US consumption and production, a domestic form of the model that can be used to separate domestic and foreign impacts, and price adjustment matrices for converting outputs to purchaser price and in various US dollar years. Improvements in modeling national totals of industry and environmental flows are described. The model is validated through reproduction of national totals from input data sources and through analysis of changes from the most recent complete USEEIO model that can be explained based on data updates or method changes. The model datasets can all be reproduced with open source software packages.}, + eid = {1}, + langid = {english}, + keywords = {Environmental impact,Industry}, +} + +@article{leontiefEnvironmentalRepercussionsEconomic1970, + title = {Environmental Repercussions and the Economic Structure: {{An}} Input-Output Approach}, + author = {Leontief, Wassily}, + date = {1970-08}, + journaltitle = {The Review of Economics and Statistics}, + volume = {52}, + number = {3}, + eprint = {1926294}, + eprinttype = {jstor}, + pages = {262--271}, + publisher = {The MIT Press}, + doi = {10.2307/1926294}, + url = {https://www.jstor.org/stable/1926294}, + langid = {english} +} + +@article{minxInputOutputAnalysisCarbon2009, + ids = {minx_inputoutput_2009}, + title = {Input-{{Output Analysis}} and {{Carbon Footprinting}}: {{An Overview}} of {{Applications}}}, + shorttitle = {Input-{{Output Analysis}} and {{Carbon Footprinting}}}, + author = {Minx, J.c. and Wiedmann, T. and Wood, R. and Peters, G.p. and Lenzen, M. and Owen, A. and Scott, K. and Barrett, J. and Hubacek, K. and Baiocchi, G. and Paul, A. and Dawkins, E. and Briggs, J. and Guan, D. and Suh, S. and Ackerman, F.}, + date = {2009-09}, + journaltitle = {Economic Systems Research}, + volume = {21}, + number = {3}, + pages = {187--216}, + issn = {09535314}, + doi = {10.1080/09535310903541298}, + url = {http://search.ebscohost.com/login.aspx?direct=true&AuthType=ip,uid&db=buh&AN=49234930&site=bsi-live}, + urldate = {2019-10-29}, + abstract = {This article provides an overview of how generalised multi-regional input-output models can be used for carbon footprint applications. We focus on the relevance and suitability of such evidence to inform decision making. Such an overview is currently missing. Drawing on UK results, we cover carbon footprint applications in seven areas: national emissions inventories and trade, emission drivers, economic sectors, supply chains, organisations, household consumption and lifestyles as well as sub-national emission inventories. The article highlights the multiple uses of generalised multi-regional input-output models for carbon footprinting and concludes by highlighting important avenues for future research.}, + keywords = {Carbon footprint,CONSUMPTION (Economics),Consumption-based accounting,DECISION making,ECOLOGICAL impact,Emission inventory,INPUT-output analysis,Multi-regional input-output model,Multi-regional input–output model,SUPPLY chains,Trade}, +} + +@article{suhDevelopingSectoralEnvironmental2005, + title = {Developing a Sectoral Environmental Database for Input–Output Analysis: The Comprehensive Environmental Data Archive of the {{US}}}, + shorttitle = {Developing a Sectoral Environmental Database for Input–Output Analysis: The Comprehensive Environmental Data Archive of the {{US}}}, + author = {Suh, S}, + date = {2005}, + journaltitle = {Economic Systems Research}, + volume = {17}, + number = {4}, + pages = {449--469} +} + +@article{yangUSEEIONewTransparent2017, + ids = {yang_useeio_2017}, + title = {{{USEEIO}}: A {{New}} and {{Transparent United States Environmentally-Extended Input-Output Model}}}, + shorttitle = {{{USEEIO}}}, + author = {Yang, Yi and Ingwersen, Wesley W. and Hawkins, Troy R. and Srocka, Michael and Meyer, David E.}, + date = {2017-08}, + journaltitle = {J CLEAN PROD}, + volume = {158}, + eprint = {30344374}, + eprinttype = {pubmed}, + pages = {308--318}, + issn = {0959-6526}, + doi = {10.1016/j.jclepro.2017.04.150}, + url = {https://www.ncbi.nlm.nih.gov/pubmed/?term=USEEIO}, + abstract = {National-scope environmental life cycle models of goods and services may be used for many purposes, not limited to quantifying impacts of production and consumption of nations, assessing organization-wide impacts, identifying purchasing hotspots, analyzing environmental impacts of policies, and performing streamlined life cycle assessment. USEEIO is a new environmentally-extended input-output model of the United States fit for such purposes and other sustainable materials management applications. USEEIO melds data on economic transactions between 389 industry sectors with environmental data for these sectors covering land, water, energy and mineral usage and emissions of greenhouse gases, criteria air pollutants, nutrients and toxics, to build a life cycle model of 385 US goods and services. In comparison with existing US models, USEEIO is more current with most data representing year 2013, more extensive in its coverage of resources and emissions, more deliberate and detailed in its interpretation and combination of data sources, and includes formal data quality evaluation and description. USEEIO is assembled with a new Python module called the IO Model Builder capable of assembling and calculating results of user-defined input-output models and exporting the models into LCA software. The model and data quality evaluation capabilities are demonstrated with an analysis of the environmental performance of an average hospital in the US. All USEEIO files are publicly available bringing a new level of transparency for environmentally-extended input-output models.}, + langid = {english}, + pmcid = {PMC6192422}, + keywords = {Data quality assessment,Environmentally-extended input-output (EEIO),Life cycle assessment,Product hotspot analysis,Streamlined life cycle assessment,Sustainable materials management}, +}