Preview: Final REPEAT Project Findings on the Emissions Impacts of the Inflation Reduction Act and Infrastructure Investment and Jobs Act
Bibliographic record
Abstract
With the close of the 117th Congress in January 2023, REPEAT Project has completed a revised, final analysis of the climate and energy system impacts of legislation passed during this landmark session. This includes detailed analysis of the combined impacts of H.R. 5376, the Inflation Reduction Act of 2022 (IRA) and H.R. 3684, the Infrastructure Investment and Jobs Act of 2021 (IIJA). This brief report previews REPEAT Project’s final revised findings on the impact of these laws on the greenhouse gas emissions trajectory of the United States. In this revised analysis, we have updated all assumptions to reflect the latest data available at year-end 20221 and improved the quality of source data and analysis on oil and gas sector methane emissions and abatement opportunities in agriculture and forestry sectors relative to our Preliminary Report on the Inflation Reduction Act released in August, 2022. This revised analysis now includes a range of three Current Policies2 scenarios (‘Conservative’, ‘Mid-range’, and ‘Optimistic’) to better reflect uncertainty about the effectiveness of IRA provisions and the potential impacts of constraints on supply chains and other rate-limiting factors. This report also presents two benchmark scenarios: a Frozen Policies scenario which only reflects policies enacted as of the start of the 117th Congress in January 2021; and a Net-Zero Pathway scenario, which reflects a cost-effective pathway to reduce U.S. greenhouse gas emissions to 50-52% below 2005 levels by 2030 and net-zero by 2050, consistent with President Biden’s climate mitigation goals.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.115 | 0.047 |
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".