Assessing climate action progress of the City of Toronto
Bibliographic record
Abstract
The Canadian City of Toronto’s progress is evaluated for the implementation of its climate action plan, TransformTO, and its effectiveness in reducing sectoral emissions. Following a brief history of climate action in Toronto, the key climate policies and programs are subjected to a content analysis and assessed using an aggregate evaluation framework composed of qualitative indicators commonly used to track municipal climate action. The results of this assessment reveal that the city has made steady progress in reducing emissions, surpassing its 2020 greenhouse gas emissions reduction target of 30% reduction below 1990 levels. However, Toronto is not on track to meet its 2030 target of a 65% emissions reduction from 1990 levels. Without transformational action across all sectors, it is unlikely to meet the 2030 and 2040 targets. The results are intended to strengthen implementation and evaluation efforts in Toronto. The discussion will be of interest to decision-makers and practitioners who seek to accelerate implementation of municipal climate action plans. Policy relevance This paper is intended to support and strengthen the City of Toronto’s implementation of its climate action plan, TransformTO, and supporting Net Zero Strategies. Of potential relevance to policymakers in other Canadian cities is the role of ambitious top-down target-setting of the municipal organization and city at large for pursuing bold climate action, even in the face of significant constraints (e.g. provincial building code and energy grid, difficulties in accessing utilities energy use data). Policymakers may also draw insights from the Toronto context for leveraging staff and community commitment to climate action by involving them in planning and implementation of emissions reductions strategies. Useful recommendations are provided for overcoming modeling deficiencies and data limitations, while advancing transformative climate action through multi-sectoral partnerships, policies that support market transformation, the scale-up of low carbon programs and investments in low carbon infrastructure.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".