Canadian cities: climate change action and plans
Bibliographic record
Abstract
The individual and collective decarbonization pathways of 26 Canadian cities are assessed by evaluating data gathered from the implementation of a unique energy model, CityinSight. Although many cities in Canada have declared a climate emergency and plans are at various stages of implementation, development path change is mostly incremental. They are at the very beginning of transforming development paths that necessitate climate action planning which embraces a systems perspective and whole-city planning. The present data reveal that there are very different starting points for Canadian cities, and considerable asymmetries between municipalities, as well as the collective impact of their plans on national targets. The latency of municipalities for on-the-ground implementation of their plans means that ongoing assessments will be required to determine the impact of efforts by cities to achieve their targets. Policy relevance Cities are on the front line of implementing climate change adaptation and mitigation. Many climate researchers and practitioners have called for fundamental change and new governance arrangements to achieve even a 2°C limit to rising global temperatures. At the same time, researchers argue that Canadian cities do not have the ability to raise revenue other than through continuous development: an incentive therefore exists to keep ‘growing’ regardless of other sustainable imperatives. Transformational change is required through policy instruments and more appropriate incentives harmonized across macro-, meso-, and microlevels to create carbon-neutral development paths in the next decade. Policy harmonization, coherence, and alignment are necessary and sufficient conditions for meeting the international commitments to reducing greenhouse gas emissions. This also requires action at multiple scales with multilevel partnerships and unprecedented degrees of government collaboration and leadership.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.053 | 0.009 |
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".