Phasing Out Fossil Fuels: Determinants of Production Cuts and Implications for an International Agreement
Bibliographic record
Abstract
Abstract Fossil fuel producers have a major role to play in curbing greenhouse gas emissions through supply-side initiatives. Yet, no study has systematically assessed the determinants of efforts to constrain fossil fuel production for climate purposes. To contribute to climate change mitigation efforts, this article develops a conceptual framework for factors potentially affecting country-level initiatives to keep fossil fuels in the ground. Using data for 124 countries with fossil fuel reserves for 2006–2019 and multivariate Poisson regression analysis, we identify factors influencing the use of such constraints by national governments. Results show that although dependence on fossil fuel rents reduces the likelihood of constraint measures, the size of fossil fuel reserves or production does not impact it. Richer countries are also more likely to use constraints. Organization of Petroleum Exporting Countries membership constitutes a barrier to having moratoria on fossil fuel extraction. These results can help identify potential members for new fossil fuel supply-side initiatives and coalitions.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.000 |
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".