Supporting Figures for 'Estimating embodied environmental flows in international imports for the USEEIO Model'
Bibliographic record
Abstract
All figures and related figures for the EPA Report (2024) Estimating embodied environmental flows in international imports for the USEEIO Model, EPA 600/R-24/116. https://cfpub.epa.gov/si/si_public_search_results.cfm?simpleSearch=0&showCriteria=2&sortBy=pubDate&searchAll=Estimating+embodied+environmental+flows+in+international+imports+for+the+USEEIO+Model&TIMSType=Published+Report&dateBeginPublishedPresented=Captions are provided below by the name of the image.IEFs = Import Emission FactorsCAPTIONSFor most figures, a result is given for each of the given years and greenhouse gases (GHG). The naming of the figures fits the patterns where the gas name and year are dynamic and represented like %GHG% and %YEAR%. GHGs are CO2, CH4, and N2O which are in the unit kg, or GWP which is total GHGs combined by global warming potential, denoted as GWP and given in the unit kg CO2e. Years including 2017, 2018, 2019, 2020, 2021, and 2022.imports_by_region_%GHG%_%YEAR% : Contribution to import emission factor by region, %YEAR%. APAC = Asia Pacific excluding CN and JP. CA = Canada. CN = China. EU = European Union. JP = Japan. MX = Mexico. ROW = Rest of World (all countries except those in listed regions.) imports_results_diff_%YEAR%: Differences per commodity in GHGs for imported commodities in %YEAR% with the IEFs and the standard approach. Where the results with the IEFs are greater, the differences are positive.imports_results_DIRECT_%YEAR%: Results for %YEAR% U.S. consumption (direct perspective) using IEFs. Standard results without IEFs shown in the black diamonds. The coupled model results are split into the blue and red bar segments.imports_results_FINAL_%YEAR%: Results for %YEAR% U.S. consumption (final perspective) using IEFs. Standard results without IEFs shown in the black diamonds. The coupled model results are split into the blue and red bar segments.imports_scatter_%GHG%_%YEAR%: GHG emission factor (kg %GHG_UNIT% / $) comparison between domestic emission factors and IEFs, %YEAR%. Commodities with no imports are not shown.imports_scatter_by_country_%GHG%_%YEAR%: Import emission factor (kg %GHG_UNIT% / $) comparison between countries. Countries contributing less than 2% of total imports by sector are not shown. Dots of the same color are from the same region.imports_scatter_time_series: Import emission factors (kg CO2e / $) for 2017-2022 in current dollars.imports_top_import_sectors_%GHG%_%YEAR% : Top 8 import sectors, %YEAR%. Contribution to total imports by region (left). Regional import emission factors (kg CO2e / $) (right). Aggregate import factors are shown in red, while domestic factors are shown in black. 311FT: Food and beverage and tobacco products, 315AL: Clothing and leather, 325: Agricultural, pharmaceutical, industrial, and commercial chemicals, 333: Machinery (except computers), 334: Computers and relevant parts, conductors, measuring devices, communication devices, 335: Lights and light fixtures, switch boards, transformers, and home appliances, 3361MV: On-road vehicles (excluding motorcycles) and accompanying parts, 339: Medical supplies, entertainment and sporting goods, fashion goods, advertising products.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.633 | 0.235 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".