The Role of Pilot Projects in Urban Climate Change Policy Innovation
Bibliographic record
Abstract
Cities are taking a leadership role in addressing global climate change and reducing greenhouse gas (GHG) emissions, but policy innovations are needed to help cities move from goals to outcomes. Pilot projects are one means by which cities are experimenting with new ways of governing and financing climate change mitigation. In this paper, we develop a framework for understanding the role of pilot projects in urban policy innovation: their emergence and rationale, and the means by which they ultimately scale up and out to reduce GHG emissions. We use this framework to evaluate a pilot project for retrofitting social housing buildings in Toronto. We find the initial pilot project helped address the challenges of pursuing deep retrofits of social housing. Scaling these lessons up to the city level required overcoming challenges to financing and coordinating a larger project; scaling out to the provincial level revealed institutional and political obstacles to pursuing the co‐benefits of deep building retrofits in social housing. Bridging agents play an important role in both scaling processes. The analysis reveals the additive nature of urban policy innovation and the dynamic interplay of change agents and institutional and political context in innovation processes.
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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 | 0.022 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".