USEEIO: A new and transparent United States environmentally-extended input-output model

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 environmentallyextended 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 userdefined 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.

Greenhouse gas emissions and energy use associated with production of individual self-selected US diets

“…As such, our reported values should be considered underestimates of actual impacts associated with food consumption in the US as they include the production impacts of processed food ingredients, but not the impacts of processing itself. Using the US Environmentally Extended Input Output model developed by US EPA [52] and an approach detailed in supporting information, we estimate that food processing not captured in our bottom-up estimates amounts to 15% of the total cradle to processor gate (including agricultural production sectors) GHGE. Packaging materials represent an additional 6%.…”

The Industry Energy Tool (IET): Documentation

“…The IO model is based on the USEEIO Model published by the EPA (Yang et al 2017; "USEEIO Input-Output Accounts" 2017), which specifies detailed commodity outputs and use on a level of six-digit NAICS code for 2013 in monetary units (389 x 389 matrix). The six-digit NAICS codes were mapped to the U.S. Bureau of Economic Analysis (BEA) IO codes, and the values having the same BEA IO codes were summed to obtain new make and use tables (78 x 78 matrices).…”

Method for endogenizing capital in the United States Environmentally‐Extended Input‐Output model

“…We assume that “Scrap,” “Used and secondhand goods,” “Noncomparable imports,” and “Rest of world adjustment” commodities do not produce any FA; Yang et al. () removed these four from their model. For a straightforward example with 2012 data, in the Y K category Nonresidential private fixed investment in structures , the χ “Health care structures” ($35.4B) is linked to three h : “Hospitals” ($23.9B), “Special care” ($4.3B), and “Medical buildings” ($7.2B).…”

“…The USEEIO was developed to support the US Environmental Protection Agency's (EPA) sustainable material management goals (Yang, Ingwersen, Hawkins, Srocka, & Meyer, 2017). EPA had evaluated three pre-existing EEIO datasets for the United States but found none satisfied the criteria of transparent, reproducible, open, and temporally relevant.…”

“…The manual creation of IO-FAA concordance tablesC × h involved comparing investment in IO DIG commodities to relevant FA investment h in purchasers' value for the same year. We assume that "Scrap," "Used and secondhand goods," "Noncomparable imports," and "Rest of world adjustment" commodities do not produce any FA; Yang et al (2017) removed these four from their model. For a straightforward example with 2012 data, in the Y K category Nonresidential private fixed investment in structures, the "Health care structures" ($35.4B) is linked to three h: "Hospitals" ($23.9B), "Special care" ($4.3B), and "Medical buildings" ($7.2B).…”