Early engagement and co-benefits strengthen cities’ climate commitments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Cities can lead the way in tackling climate change through robust climate actions (that is, measures taken to limit climate change or its impacts). However, escalating crises due to pandemics, conflict and climate change pose challenges to ambitious and sustained city climate action. Here we use global data on 793 cities from the Carbon Disclosure Project 2021 platform to assess how the COVID-19 crisis has affected cities’ reported climate commitments and actions and the factors associated with these impacts. We find climate actions persist despite funding shortfalls; yet only 43% of cities have implemented green recovery interventions. Co-benefits of climate action (for example, health outcomes) and early engagement on sustainability issues (for example, via climate networks) are associated with sustained climate action and finance during COVID-19 and green recovery interventions. Cities should strengthen sustainability co-benefits and relationships with coalitions of actors to support durable climate commitments during crises.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it