Greenhouse gas emissions from cities: comparison of international inventory frameworks
Bibliographic record
Abstract
Credibly and consistently reporting greenhouse gas (GHG) emissions from cities and urban areas enables policy-makers and practitioners to contribute to addressing the challenge of climate change by meeting mitigation targets, and is critical to overall good municipal management. Good reporting allows for transparency, verification, and replication over time. This study provides an understanding of the GHG emissions inventory protocols and methodologies as they apply to cities. Though the inventories generally use common terminology, the differences in inventorying approaches are many, and the implications of the inventorying results at the city level are important to climate change policy and decision-makers. A compilation of GHG emissions inventory protocols is developed along with an analysis of their characteristics and inherent differences. Seven protocols are investigated: four are applied to Shanghai's community emissions; four to New York City's corporate emissions (i.e. those from municipal activities); and two to the reporting of Paris' emissions, including upstream components. The results show a significant degree of variability among the protocols.
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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 | 0.026 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".