Local responses to regional mandates: assessing municipal greenhouse gas emissions reduction targets in British Columbia
Bibliographic record
Abstract
Local governments around the world face external and internal pressures to adopt climate change mitigation strategies. Provincial legislation in the Canadian province of British Columbia has recently mandated that all municipalities adopt targets for reducing greenhouse-gas emissions. Lack of specificity in the legislation gives rise to the possibility that even if compliance with the legislation is universal it could nonetheless result in minimal reductions in emissions releases. This article examines the response to the legislation of twenty municipalities in British Columbia’s most populous regions. We hypothesized that noncompliance would be rampant and that cities with large populations, high residential densities, lower growth rates, and prior climate change planning work would set more ambitious targets. However, findings indicate that municipal targets vary widely in terms of intensity, target year, and type of reduction and have little or no relationship to population, residential density, or growth rate. We found 90% compliance and some correlation between prior planning activities related to climate change and target intensity. Findings also indicate that despite the wide range of emissions targets by each municipality, provincial per capita targets would be met if each municipality were to achieve the targets that they have set by the 2050 target year.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".