Co-benefits as a rationale and co-benefits as a factor for urban climate action: linking air quality and emission reductions in Moscow, Paris, and Montreal
Bibliographic record
Abstract
Abstract If local governments reduce greenhouse gas emissions, they will not see effects unless a very large number of other actors do the same. However, reducing greenhouse gas emissions can have multiple local “co-benefits” (improved air quality, energy savings, even energy security), creating incentives for local governments to reduce emissions—if just for the local side-effects of doing so. Available empirical research yet shows a large gap between co-benefits as a rationale and an explanatory factor for climate mitigation by local governments: co-benefits are seemingly very large, but do not seem to drive local mitigation efforts. Relying on policy documents, available research, and other written sources, the present paper consists of a multiple case study addressing the link between co-benefits and climate mitigation in Moscow, Paris, and Montreal. Air quality plays a very different role in each case, ranging from a key driver of mitigation to a liability for local climate action. This heterogeneity of mechanisms in place emerges as a likely explanation for the lack of a clear empirical link between co-benefits and local mitigation in the literature. We finally discuss implications for urban climate action policy and research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".