Reconciling Data Years in EEIO Models and Resulting Emission Factors: Alternatives and Best Practices

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:A clear taxonomy of data year adjustment methods with empirical comparisons, useful for improving EEIO models and emission factor accuracy.

🏢実務担当者:Direct guidance on how to adjust emission factor models for data year mismatches, applicable to corporate Scope 3 reporting and carbon accounting.

🏛政策担当者:Methodological best practices that can inform disclosure standards and guidance on emission factor quality for regulatory use.

Research Question Developing environmentally-extended input-output (EEIO) models requires pairing economic input-output tables (IOTs) and environmental data for the same regions, sectors, and years.  IOTs are often not available for the same year(s) as the environmental data. And furthermore, for applications like the development of Emissions Factors (EFs) from these models that are used along with organizational spend data to compute organizational footprints, neither environmental data nor IOTs may be available to march the year of the spend data. Another challenge is to adjust price types represented by the final EF which is most often presented in purchaser price when the providing EEIO models are often in basic or producer price, but margins data used to convert to purchaser price are also not available for the target year.These differences in years of data presents an obvious conundrum for model developers who wish to provide final factors that are temporally relevant to users as well as a challenge to communicate the year(s) that the EFs represent. While creation of models with mixed year data is very common in EEIO models due to data scarcity, the specific adjustment procedures, their interpretation, and clear best practices in reconciling mixed year data are missing.  Objective and Novelty The objective of this paper is to describe common adjustment procedures used to integrate data from different years into a EEIO model, provide interpretations in understandable language, demonstrate their effects with real data, and to provide some best practices for consideration to  the broader IO and EEIO community.  Methods In this paper we step through various alternatives to adjust (or not adjust) the data years to match a target year, from the creation of a direct environmental intensity matrix (B) with ratios of environmental totals by sector and economic totals by sectors, to use with the direct (A) and total requirements (L) economic matrices derived from the IOT, and to further adjustment to a year to match the desired spend data. We describe alternatives to reconciliation procedures including making assumptions that require no changes, making inflation adjustments, and use of supporting data to perform adjustments in each step using matrix algebra and detailed explanations, and then present real examples.  Data Used To quantitatively evaluate these procedures, we use data that have been used in both the USEEIO and CEDA EEIO models to develop emission factors that have been frequently used for reporting Scope 3 GHG emissions in publicly-disclosed corporate GHG inventory reports. These data include detailed benchmark U.S. IOTs from 2017, detailed level annual industry gross output data and annual industry gross output price indices and from the U.S. Bureau of Economic Analysis from 2017 to 2023, Greenhouse Gas totals by sector datasets developed by the Cornerstone Sustainability Data Initiative from the official U.S. GHG inventory and other data sources for 2017-2023 and Global Warming Potentials from the International Panel for Climate Change. Results We attempt to explain the meaning of the different adjustment alternatives as well as compare results of the alternative adjustments.We synthesize the steps into a set of best practices for developing EEIO models and emission factors using mixed year data and for communicating essential information like the year(s) that the emission factors represent. We provide all data and source code used in this analysis through the Cornerstone bedrock github repository.  

• Zenodo https://zenodo.org/records/21136918first seen 2026-07-09 04:14:50