A Methodology for Constructing Marginal Abatement Cost Curves for Climate Action in Cities
Bibliographic record
Abstract
As drivers of climate action, cities are taking measures to reduce greenhouse gas (GHG) emissions, which if left unabated pose a challenge to meeting long-term climate targets. The economics of climate action needs to be at the forefront of climate dialogue to prioritize investments among competing mitigation measures. A marginal abatement cost (MAC) curve is an effective visualization of climate action that initiates a technical and economic discussion of the cost-effectiveness and abatement potential of such actions among local leaders, policy makers, and climate experts. More commonly demonstrated for countries, MAC curves need to be developed for cities because of their heterogeneity, which vary in their urban activities, energy supply, infrastructure stock, and commuting patterns. The methodology for constructing bottom-up MAC curves for cities is presented for technologies that offer fuel switching and/or energy efficiencies, while considering technology lifetimes, city-specific electricity and fuel prices, and emission intensities. Resulting MAC curves are unique to every city, and chart the pathway towards low-carbon growth by prioritizing measures based on cost-effectiveness. A case study of Toronto’s climate targets demonstrates the prioritization of select technologies. Leveraging MAC curves to support climate programs enables cities to strategically invest in financing climate action and designing incentives.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".