Carbon Accountability Politics: Measurement and Management of Urban Energy Transitions in Toronto and Edmonton
Bibliographic record
Abstract
Carbon accounting is typically used to express climate change progress and assess the potential impact of new policies. Yet as critical accounting scholarship already contends, carbon accounting is just one epistemological lens through which we might understand society’s complex relationships with climate and imagine alternative energy futures. In this dissertation, I explore the development of bottom-up methods for carbon accounting used at the local level, drawing attention to the nature of assumptions and exclusions that have gone into establishing a common framework to compile local territorial accounts of carbon. I then introduce a new phenomenon emerging amongst municipalities referred to as carbon accountability. Carbon accountability is about more than measurement: it is premised on institutionalizing a carbon lens in local governance, where the limited world of the carbon accounting framework more directly shapes everyday decision making in municipalities. This accountability project emerges from a new relationship between scientific expertise at the global level and local territories, mediated through the recent development of internationally standardized urban accounting protocols. Data-driven climate governance is now assumed to involve the direct application of global scientific expertise in urban planning and policymaking. Yet these accounting frameworks struggle to represent how local projects intervene in much wider urban energy processes that are the basis of global climate change. The dissertation explores carbon accountability politics in two Canadian case studies, the cities of Toronto and Edmonton. Both have recently pursued international leadership by adopting new carbon-based governance tools. In 2022, Toronto was the first City worldwide to independently commission an in-house energy-economy optimization model to lead implementation of its climate action plan, TransformTO. That same year, Edmonton released North America’s first Carbon Budget as a framework to integrate carbon into the annual financial budgeting process. Grounding the research in these case studies, I draw from interviews with municipal actors and relevant technical experts, as well as analyses of climate action planning and implementation challenges, to examine the contradictions of offering management solutions to overcome enduring local climate action constraints. The dissertation finds that expectations urban systems can be rigorously quantified to direct local action often do not align with the limits of emissions quantification and the complex nature of urban politics. I take the example of waste-to-biofuel partnerships in Toronto and Edmonton to illustrate the possible system-level contradictions of local emissions-reducing projects, emphasizing how and why urban energy processes often elude quantitative representation. In its normative goal to entrench and naturalize a carbon accounting framework, carbon accountability not only poses new democratic legitimacy concerns, it also misdirects action away from more systemic transformations of urban energy systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".