Local solutions to a global problem? : Canadian municipal policy responses to climate change
Bibliographic record
Abstract
Urbanization and global warming are two of the most pressing issues facing humanity over the next 50 years. Why do some local governments enact more climate change mitigation policies than others? What makes some cities leaders in urban sustainability, while others lag far behind? Over the past decade, global climate change negotiations have repeatedly failed to produce binding commitments and robust responses by national governments. These failures have led academics and practitioners to put increasing emphasis on the potential for sub-national governments, including cities, to undertake commitments that might substitute for national action on climate change. Applying concepts from the comparative public policy literature to the study of urban politics, this dissertation puts forward and tests a new theory to explain variation in Canadian cities’ climate change policy. I find that political economy factors reduce the likelihood that cities will adopt climate change policy that will significantly reduce greenhouse gas emissions, but the presence of independent municipal environment departments makes the adoption of such policy more likely. This dissertation employs a systematic and explicit process tracing methodology. It examines the decision-making of four Canadian cities (Brampton, Toronto, Winnipeg and Vancouver) across four policy areas (landfill gas management, fleet services, cycling infrastructure and building standards). The analysis is based on data gathered from primary and secondary sources and expert interviews with over 70 local politicians, bureaucrats, journalists, and NGO and business representatives. This dissertation argues that cities cannot solve the climate change challenge on their own, but knowledge of the dynamics of climate change mitigation policy adoption at the local level may permit scholars and practitioners to increase the effectiveness of municipal governments’ climate change policy choices.
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How this classification was reachedexpand
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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".