Where a 5-digit NAICS contains only a single 6-digit child NAICS (e.g., 56291), flows are automatically assigned to that sector. https://echo.epa.gov/trends/loading-tool/water-pollution-search (U.S. Environmental Protection Agency, 2018). 36. 28 for all flows at a 1% tolerance, with a <1% difference assumed to be expected due to rounding errors65 both for the full model and the domestic model, where Ld Eq. Once all the requirements are installed, the generation of v2.0 takes place in a single buildModel function to load the various data components and build the model. New procedures for preparing and integrating these datasets into the model are described in the Procedure for Model Building section. https://www.bls.gov/opub/hom/ (U.S. Bureau of Labor Statistics, 2020). Water_national_2015_m1 was created primarily using water withdrawal data accessed from the USGS National Water Information System Web Interface45. When making these improvements, it is recommended to focus first on categories with the largest impact on the organizations total GHG inventory. Secure .gov websites use HTTPS 54, 30913102, https://doi.org/10.1021/acs.est.9b06024 (2020). ~98% of commodities have a value of 10.025. Manfred Lenzen, Arne Geschke, Heinz Schandl, Helmut Haberl, Dominik Wiedenhofer, Marina Fischer-Kowalski, Arnulf Grubler, Charlie Wilson, Hugo Valin, Richard Wood, Daniel D. Moran, Konstantin Stadler, Scientific Data This decrease is likely a result of fuel source changes in the electricity production over this period69. All nomenclature used is defined in the Table 4. 21. where lc is the column representing the commodity of interest from the L matrix, and dn is the transposed row representing the indicator of interest from the D matrix. Waste management and remediation services fell out of the top 20 due to the disaggregation of the waste sectors in v2.0. 26. Major uses of land in the united states 2012. https://www.ers.usda.gov/webdocs/publications/84880/eib-178.pdf?v=6791.3 (US Department of Agriculture, 2017). Estimating industry land use with the MLU as the primary data source is an update from v1.2, where land use was calculated by summing USDA CoA, Bureau of Land Management (BLM) Public Land Statistics (PLS), EIA Commercial (CBECS), and EIA Manufacturing (MECS) land use with the MLUs statistics for forest land, transportation, national defense, and grazing land38,39,40,41,42. They have been updated for 201756. The USGS publishes state-level water withdrawal estimates for nine broad categories: Aquaculture, Domestic, Industrial, Irrigation Crop, Irrigation Golf Courses, Livestock, Mining, Public Supply, and Thermoelectric Power. Zhuang, X. The most common sources listed in the table are: To apply the EF Hub scope 1 and 2 factors, the organization can first define the GHG generating activity for each relevant source category, then apply the appropriate factors for stationary combustion, mobile combustion, fugitive emissions, electricity, heat, or steam. Also, change in inventories, y, is positive for commodities produced but not sold, and negative for commodities consumed from a previous years production. Perspect. Other agricultural commodities show the inverse change in v2.0, where the agricultural output in v2.0 is higher and thus the pesticide release and related impact intensities are lower. The practical guidance below provides further suggestions on calculating scope 3 emissions. The economic data base year for v2.0 is 2012, corresponding to the latest detailed IO tables10. PubMed Central The v2 industry output and commodity output totals for each commodity and industry in the model were both found to be within 1% of the original totals. We use three kinds of cookies on our websites: required, functional, and advertising. Official websites use .gov National totals of flows (physical movements of specific resources, emissions or employment) by industries are used as the sources of environmental and employment data. JavaScript appears to be disabled on this computer. Ingwersen, W., Li, M. & Young, B. v2.0 is a single region model with the 50 states of the United States modeled as a single region. ACID in utilities, manufacturing and transportation sectors is largely driven by criteria air pollutant emissions like sulfur dioxide (SO2) and nitrogen oxides (NOX). A new water withdrawal sector attribution model was developed, referred to as Water_national_2015_m1. 30, where V' is the transposed model Make table, which is normalized by multiplying it by the diagonalized form of the inverse of model output, x. The report states that a critical contribution is the development of a waste input-output model designed to capture the material inputs (production sectors like mining, energy, materials, parts and products) and outputs (waste and material separation and reprocessing, of products, services and wastes). Therefore, BEA code 1111A0 should be connected to all these NAICS codes in order to form a complete BEA-NAICS correspondence. Report out, Q&A and closing. This is an update from v1.1, where value added data were taken from BEA Summary level Use tables for more recent years and adjusted as described in the documentation5. Industrial and Mining water withdrawals are proportionally allocated using BLS QCEW employment data47. For v2.0, Waste management and remediation services is disaggregated into the seven sectors shown in Table5. USEEIO v2.0 was built in useeior v1.0.061. Top 20 commodities by composite impact score for models v2.0 and v1.2 calculated using (a) the total US production demand vector and the direct perspective and (b) using the total US consumption demand vector and the final perspective. Sci Data 9, 194 (2022). Emission Factor Database Last modified 23 Nov 2020 1 min read This viewer presents selected emission factors and abatement efficiencies included in the EMEP/EEA Guidebook 2019. The second ranking is done with Hf calculated where y is the US consumption vector, yc (see Eq. Wolsky, A. M. Disaggregating Input-Output Models. Water data for the nation 2015. https://waterdata.usgs.gov/nwis (U.S. Geological Survey, 2018). This EPA report describes supply chain GHG emission factors prepared with versions of the USEEIO, which are life cycle models of all categories of goods and services and industries in the US economy. Google Scholar. However, the overall effects of any allocation scheme to the imports and exports sectors is fairly small, as they account for a small portion of total commodity use. In Water_national_2015_m1, employment data are used to allocate water withdrawals to relevant sectors identified by USGS. https://www.epa.gov/ozone-layer-protection/international-actions-montreal-protocol-substances-deplete-ozone-layer (2015). Irrigation Golf Courses water withdrawals are assigned to NAICS 713910. v2 models represent a second generation of USEEIO models built using an improved technical infrastructure9. Federal government (defense) climbed from 17th to 7th due to an increased relative amount of HTOX. We define consumption as final use within the US of all goods and services that are both produced and sold within the US or imported. 158, 308318, https://doi.org/10.1016/j.jclepro.2017.04.150 (2017). In v1, the Scrap commodity was removed from the model following a methodology presented by BEA for deriving a total requirements matrix11. Sign up for the Nature Briefing newsletter what matters in science, free to your inbox daily. This paper presents a summary of the complete v2.0 model attributes and model creation with a focus on describing methodological updates since the publication of the original USEEIO methodology. Pairing the domestic proportion with the ranking figure (Fig. )2, therefore all environmental data was adjusted to be in 2013 US Dollars (USD). 13. In v1.2, withdrawals were calculated for 37 crops published in the 2008 USDA Irrigation and Water Management Survey (IWMS). The calculated per capita GHG in the US production view of 15.57 MTCO2e/person is reasonably close to a World Bank tabulated estimate of 15.77 MTCO2e/person in 201266, considering USEEIO v2.0 is a mixed year model with the GHG emissions data representing 2016 intensity in 2012 USD. liter) emission factor for fuel source (for example, kg CO2 e/liter)) + ((quantity of refrigerant leakage (kg) emission factor for refrigerant (kg CO2 e/kg)) + process emissions) These data are difficult to obtain given the scarcity in publicly available waste management pricing data, the level of aggregation of the waste management sectors, and the differences in prices and materials used by each waste management activity. In other words, the monetary and physical flows occur in the same direction: makers of the waste treatment commodity receive both the physical waste to be disposed and the money for the disposal service. These tables are also known as the benchmark tables because they are based on the US Economic Census which is conducted every five years and the tables correspond to the Census year11. Emissions (carbon intensity) associated with fuel combustion. The difference between the summed state level data and published national MLU pasture and grazed land data were attributed to animal type using a national average of the USDA CoA data. Li, M. & Ingwersen, W. H_r and H_f matrices of USEEIOv1.2 and v2.0.1-411. It may also yield insights into changes in US industry environmental performance that may be of interest to users that have used a v1 model either directly or via an interface like the SMM Prioritization Tools. The SAS data provides the total expenses of more detailed sectors within Waste management and remediation services. Tobacco, cotton, sugarcane, peanuts, sugar beets, herbs and spices, and other crops fell 14 places, apparently to decreased water consumption. Inventory of u.s. Greenhouse gas emissions and sinks: 19902016. Expanding the definition of industrial water use allowed for calculation of impact intensities for industries not previously captured, such as industries within wholesale trade, retail trade, and professional and business services. Purchaser price reflects the producers price plus sale and transportation margins11. For xz to be in year y USD, the year of the IO data, x, must first be price adjusted using Eq. FLOWSA v1.0.1. Truck transportation, Water transportation, Rail transportation are commodities in set m for transportation). 2010 Manufacturing Energy Consumption Survey. The data have been updated for 201729. In the production vector, the direct imports both used by final consumers and industries are removed. Ingwersen, W., Li, M., Young, B., Vendries, J. Derivation of these demand vectors is described in depth in the Final Demand section, since these have not been previously described in USEEIO documentation. Mercury emissions to air drive the increase seen in Cement (9% contribution to HTOX), that arenot due to change in emissions, but rather toxicity characterization that was not present in v1.2. Young, B., Li, M. & Ingwersen, W. Direct impact coefficients (D matrix) of USEEIOv1.2 and v2.0.1-411. Garvey, T. & Ingwersen, W. USEEIO Elementary Flows and Life Cycle Impact Assessment (LCIA) Characterization Factors. The general equation for emissions estimation is: E = A x EF x (1-ER/100) where: E = emissions; A = activity rate; conceptualized USEEIO v2.0, led the methodological development for model building and validation, contributed to useeior and flowsa software, supervised the team, administered the project, and led writing the manuscript. These values are included in the WasteDisaggregation_Make sheet of the primary data record, in the Make table intersection rows. U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-20/001, 2020. Changes in GHG intensity were less than 0.5kg CO2e/$ for >95% of sectors. J. Econ. Quarterly census of employment and wages 2012. https://www.bls.gov/cew/downloadable-data-files.htm (U.S. Bureau of Labor Statistics, 2020). Land use now differentiates urban and rural residential housing land by incorporating values from the Major Uses of Land report36. Information is ordered by the respective Nomenclature For Reporting (NFR) source category code. This then must be further transformed into commodity form before use, which is done so by multiplication with the market shares matrix, Vn, which itself is obtained from the model Make table and the model commodity output, q. Ingwersen, W., Yang, Y., Gilkey, K. & Li, M. USEEIOv1.1 - Satellite Tables. To solve the first problem, BEA-NAICS correspondence for these sectors is approximated after careful inspection and comparison of their definitions in BEA and NAICS systems. J.V. 23. Services to buildings and dwellings (561700): the entirety of the 562000 commodity produced by this sector is assigned to the Remediation services sector (562910), as it is assumed that the services provided by this sector deal with site-specific remediation. AC-17-SS-1 https://www.nass.usda.gov/Publications/AgCensus/2017/Online_Resources/Farm_and_Ranch_Irrigation_Survey/fris.pdf (U.S. Department of Agriculture, 2019). USEEIO v2.0, The US Environmentally-Extended Input-Output Model v2.0, $$A=U{\widehat{x}}^{-1}V{\widehat{q}}^{-1}$$, $${B}_{I,y}={E}_{I,z}{\widehat{x}}_{z,y}^{-1}$$, $${x}_{i,y}={x}_{i,a}\ast {\rho }_{i,z- > y}$$, $${\rho }_{i,z- > y}=\frac{p{i}_{i,y}}{p{i}_{i,z}}$$, $${\varPhi }_{c},y=\frac{{q}_{PRO,c,y}}{{q}_{PUR,c,y}}$$, $${q}_{PUR,c,y}={q}_{c}{P}_{c,y}+{t}_{c,y}{P}_{t,y}+{w}_{c,y}{P}_{w,y}+{r}_{c,y}{P}_{r,y}$$, $${P}_{m,y}=\frac{{\sum }_{c\in m}{q}_{c,y}{P}_{c,y}}{{\sum }_{c\in m}{q}_{c,y}}$$, $${y}_{p}={y}_{c}+{y}_{e}+{y}_{m}+{y}_{\delta }$$, $$r{c}_{f},n=\frac{{m}_{f}\circ {c}_{n}^{{\prime} }}{\sum \left({m}_{f}\circ {c}_{n}^{{\prime} }\right)}$$, $$r{c}_{c},n=\frac{{l}_{c}\circ {d}_{n}^{{\prime} }}{\sum ({l}_{c}\circ {d}_{n}^{{\prime} })}$$, $${A}_{d}={U}_{d}{\widehat{x}}^{-1}\ast V{\widehat{q}}^{-1}$$, $${E}_{c}={({C}_{m}{E}_{i}^{{\prime} })}^{{\prime} }$$, $${C}_{m}={V}^{{\prime} }{\widehat{x}}^{-1}$$, $${B}_{\chi ,c}={B}_{i}\,\circ \,\chi V{\widehat{q}}^{-1}$$, $$i=w{\widehat{x}}^{-1}V{\widehat{q}}^{-1}L$$, $${H}_{i,c}={{\rm{\$}}}_{c}{N}_{i,c}{P}_{c,y}{\varPhi }_{c,y}$$, https://doi.org/10.1038/s41597-022-01293-7. Ei, a national total of flow by industry per year consisting of the concatenation of all the satellite tables described above, is available in varying years. The decline in impact intensity for Tobacco, cotton, sugarcane, peanuts, sugar beets, herbs and spices and other crops is attributed to correcting an error in the v1.2 calculation. In v1 models, a base economic year of 2013 different than the IO year of the economic data was used. For the disaggregation procedure, we assume that each disaggregated industry only produces its own disaggregated commodity; in other words, there is no off-diagonal production of waste management services in the intersection. Mineral commodity summary 2014. https://s3-us-west-2.amazonaws.com/prd-wret/assets/palladium/production/mineral-pubs/mcs/mcs2014.pdf (U.S. Geological Survey, 2014). Data for commercial hazardous waste are sourced from the Resource Conversation Recovery Act Biennial Report, the same source as used in v1.2. This method for creating the A matrix is based on the industry- technology assumption, wherein the manufacture of the primary and any secondary commodities by an industry uses the same production requirements, and the commodity requirements are based therefore on the mix of industries that produce that commodity, weighted by their relative share of total commodity output16. That is, for each row in the waste management columns, the original value is multiplied by these default percentages and assigned to the corresponding disaggregated column along that row. Scope 3 emissions, also referred to as value chain emissions, often represent the majority of an organizations total greenhouse gas (GHG) emissions. The result is available in the National Criteria and Hazardous Air Pollutant Totals By Industry 2017 v1.1 dataset32. The original relation between the environmental data in the form of national totals by industry, E, and the model economic data uses the model industry output, as described in Eq. http://edap-data-commons.s3.amazonaws.com/data_commons_search.html (2021). The first complete and peer-reviewed USEEIO model, v1.0, was released in early 2017 and described in Yang et al.2 and related datasets3,4. This equation is shown in Eq. The validation results show that the model passes the check shown in Eq. For v2.0, we derive two primary final demand vectors, a production vector and a consumption vector. famous lacrosse players female,

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