Deep Decarbonization in Cities: Pathways, Strategies, Governance Mechanisms and Actors for Transformative Climate Action
Bibliographic record
Abstract
As the urgency for climate action heightens, local governments and stakeholders are developing short-term strategies and long-term pathways towards deep decarbonization at the local level. Urban areas are the largest place-based source of greenhouse gas emissions, accounting for 71%-76% of global emissions, and are projected to house 60% of the global population by 2030. Local governments have direct and indirect control of over 52% of emissions that occur within their municipalities. This study aims to qualitatively explore eight cases of best practice cities that are leading the way towards decarbonization. The eight cases are: Bridgewater (Nova Scotia, Canada), Park City (Utah, USA), Guelph (Ontario, Canada), Lahti (Finland), Vancouver (British Columbia, Canada), Oslo (Norway), Toronto (Ontario, Canada) and New York City (New York, USA). Cases were chosen based on the ambitiousness of climate action targets. Each Canadian case was paired with an international case similar in population size. The study was conducted to qualitatively explore the emerging best practice initiatives as well as highlight any patterns among the cities, depending on the population size and/or the national context. The method of qualitative investigation involved interviewing key municipal staff or plan managers on the pathways that are being implemented, the governance structures, the key actors and the tools being used for plan development and implementation. The results of this study fill theoretical gaps in the literature around the pathways that cities of different sizes are developing and the results help to provide understanding and insight on the key variables in deep decarbonization planning and implementation variables. Through identifying the key variables in the urban climate action literature, this study aimed to explore which of these were being addressed in climate action plans, and if cities were going beyond what the literature prescribed. The key research questions related to which sectors were the focus of emissions reduction pathways, what strategies were developed for plan development and implementation, how the plans were organized and governed, what key actors were involved. This study made contributions to the literature on decarbonization frameworks in six key areas by extending the literature to include new initiatives that leading cities are developing. The areas that this study contributes to are: decarbonizing the energy sector in small cities, increasing capacity of local carbon sinks, developing green economy targets and workforce development, formalizing communication structures, bottom up vertical integration tactics, and creating funding mechanisms. The findings from this study can be useful for practitioners working towards local deep decarbonization as well as transnational city networks such as C40, CNCA and ICLEI as it highlights emerging best practices.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".