Cities Leading on Climate Action: A Comparison of Los Angeles’s and Boston’s Green New Deals
Bibliographic record
Abstract
Background: The Green New Deal (GND) is a promising approach to simultaneously address the climate crisis and inequality through a programme of targeted public investment. It is a visionary framework for governments to radically shift their climate policy to focus on decarbonization, inequity, and a just transition. Extant GND literature primarily focuses on national plans, the European Green Deal, and increasingly on the need for a Global GND. However, cities across the United States and globally have also begun to adopt GNDs. While there is a burgeoning literature on municipal climate action, the emergence of city-level GNDs has not been addressed. Methods: This project includes a qualitative comparative analysis of Boston’s and Los Angeles’s GNDs, including elite interviews with relevant policymakers and stakeholders. We employ Vogel and Henstra’s comparative local policy analysis to analyze the policy content and policy process of each GND. Results: GNDs are an impactful tool for cities to address climate, inequity, and justice. They are considered more comprehensive, holistic, specific, and coordinated than previous municipal climate plans. We also discuss the reasons that each city adopted a GND, as well as the factors that shaped them. We conclude by providing lessons for policymakers across North America in adopting their own city-level GNDs. Conclusion: Boston and Los Angeles are useful examples for other cities considering adopting a GND. The GND is an exciting approach to support policymakers and organizations in creating a more equitable and sustainable future for everyone.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".