Summary Report: The Climate Impact of Congressional Infrastructure and Budget Bills
Bibliographic record
Abstract
This summary report of the REPEAT Project (repeatproject.org) describes the national-scale impacts of the Infrastructure Investment and Jobs Act (IIJA, H.R. 3684), which was signed into law in November 2021, and the Build Back Better Act (BBBA, H.R. 5376), which passed the House of Representatives on November 19, 2021 but remains stalled in the Senate. To track the impacts of Congressional negotiations, we also model the original version of the Build Back Better Act introduced in September 2021 (H.R. 5376, H. Rept. 117-130) . The report also presents two ‘benchmark’ scenarios: Frozen Policies, which captures the impacts of federal policies and regulations as of the start of the 117th Congress and inauguration of President Biden in January 2021; and Net-Zero Pathway, a cost-optimized pathway to reduce economy-wide U.S. greenhouse gas emissions 50% below 2005 levels by 2030 and to net-zero by 2050. This report contains macro-energy system modeling results including impact on carbon dioxide emissions, clean energy and electric vehicle deployment, fossil energy use, and more, along with estimated impacts on U.S. energy expenditures, capital investment in energy supply infrastructure, energy supply-related employment changes and improvements in air pollution and public health. All quantitative results from this study are available via an interactive data portal at repeatproject.org.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.059 | 0.023 |
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".