Municipal Sustainability and Climate Planning: A Study of 38 Canadian Local Governments’ Plans and Reports
Bibliographic record
Abstract
Even with the benefits of sustainability and climate change reporting, there is limited information on how municipalities are reporting on performance for external stakeholders in comparison with private sector organizations. The purpose of this research was to gain an understanding of the current state of sustainability and climate change reporting at the local level and to investigate the extent to which municipalities across Ontario, Canada, report. We used content analysis to identify the presence or non-presence of information on the websites of 38 municipalities and analyzed the results using descriptive statistics. Our analysis showed that the sample municipalities were not widely reporting on sustainability or climate change performance. Also, we identified a gap between the number of plans and reports produced by sample municipalities, with the latter being less common, indicating a need for an improved evaluation of plan implementation. Further, we found that a provincial regulation that required municipalities to make their energy conservation and demand management plans public did not guarantee publication of the plan on a municipality’s website. This study contributes to the growing field of sustainability and climate change planning and reporting by local governments and offers empirical evidence specific to Ontario, Canada.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".