Technical pathways to deep decarbonization in cities: Eight best practice case studies of transformational climate mitigation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As the urgency for climate action heightens, local governments and stakeholders are developing pathways towards deep decarbonization at the local level and committing to community-wide greenhouse gas reductions of 80–100% by 2050 or earlier. Urban areas are the largest place-based source of greenhouse gas emissions, accounting for 71%–76% of global emissions. Local governments have direct and indirect control of over a significant proportion of emissions that occur within their municipalities. However, there remains a gap in knowledge about the local technical and policy pathways that are being developed in order to achieve deep decarbonization and how these pathways vary for different size cities. This study qualitatively analyzes eight local government deep decarbonization plans of cities that range in size from eight thousand to nine million people. We analyze emerging patterns among the cities, while also considering the impacts of the population size and the national context. Each city has unique circumstances and priorities when it comes to decarbonization, and not all cities prioritize their highest emitting sectors for decarbonization. We find that emerging technical pathways to deep decarbonization focus on five priority sectors (electricity, buildings, transportation, waste, and carbon sinks and storage), but also that several local governments are developing innovative strategies beyond what is described in the literature for decarbonizing the priority sectors within their jurisdiction and are expanding the scope of their plans to include emerging areas in GHG mitigation such as scope 3 and embodied greenhouse gas emissions.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it