U.S. cities’ integration and evaluation of equity considerations into climate action plans
Bibliographic record
Abstract
Abstract While cities in the United States play an active role developing and implementing climate policy, urban centers are often sites of socio-spatial inequity. Thus, we explore how cities grapple with these inequities in their Climate Action Plans (CAPs). While CAPs can empower cities to engage in equitable planning practices that prioritize marginalized communities, little empirical research examines how equity goals are measured and evaluated. We find that among large U.S. cities with CAPs, less than one third include measurable indicators to evaluate progress towards achieving equity goals. Across climate adaptation and mitigation planning, nineteen cities consider equity goals as they relate to ten thematic areas, six outcomes, and five dimensions of equity. We suggest ways forward for cities to develop, implement, and measure a diverse and holistic set of equity indicators to use in their climate planning efforts and beyond.
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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 | 0.023 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".