Does Global Climate Policy Promote Low-Carbon Cities? Lessons Learnt From The CDM
Bibliographic record
Abstract
An increasing proportion of greenhouse gas emissions is produced in urban areas in industrializing and developing countries. Recent research shows that per capita emissions in cities like Bangkok, Cape Town or Shanghai have already reached the level of cities like London, New York or Toronto. Large parts of the building stock and service infrastructure in cities in rapidly developing countries is built in the coming decade or two. Decisions taken in this sector today may therefore lock in a high emissions path.\nBased upon a survey of projects under the Clean Development Mechanism (CDM) of the Kyoto Protocol, we find that only about 1% of CDM projects have been submitted by municipalities, mostly in the waste management sector. This low participation is probably due to a lack of technical know how to develop CDM projects and an absence of motivation due to the long project cycle and the limited “visibility” of the projects for the electorate. Projects in the buildings and transport sector are rare, mainly due to heavy methodological challenges. A case study of the city network ICLEI and its experience with cities’ participation in the CDM adds insights from the practitioner side. \nWe conclude that CDM reforms may make it easier for municipalities to engage in the CDM, and that new forms of cooperation between municipalities and project developers, potentially facilitated by ICLEI, are required to help to realize the urban CDM potential.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".