Institutionalizing the urban governance of climate change adaptation: Results of an international survey
Bibliographic record
Abstract
Three hundred and fifty municipalities across five continents participated in the Urban Climate Change Governance Survey (UCGS). Conducted at MIT in partnership with ICLEI – Local Governments for Sustainability, the UCGS provides a first of its kind look at the governance networks that municipalities are creating to address climate change. Drawing from these results, this paper analyses the institutional governance structures that surround local government work on climate change adaptation. Results show an integration of adaptation and mitigation planning, and a mainstreaming of adaptation planning into other long-range and sectoral plans. Seventy-three percent of respondents stated that their local government’s are engaging with both adaptation and mitigation, and 75% are integrating adaptation into long-range or sectoral plans. However, many critical municipal agencies – including those responsible for water, waste water, health, and building codes – remain on the margins of urban adaptation efforts. Internal institutional networks of governance are inextricably linked to efforts to address a problem like adaptation, which does not fit neatly into individual institutional silos. The results of the UCGS show where these networks have so far been made, how they have been created, and which local government actors have yet to be effectively engaged.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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 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".