The Carbon City Index (CCI): A Consumption Based, Regional Input-Output Analysis of Carbon Emissions
Bibliographic record
Abstract
This paper presents a consumption-based Carbon City Index for CO2 emissions in a city. The index is derived from regional consumption and not from regional production. It includes imports and exports of emissions, factual emission developments, green investments as well as low carbon city development policies and stakeholder engagement. The index is based on a multi-region input-output model used in most parts of the world for more than half a century. We demonstrate the index through comparative case studies of three Danish<br/>regions: a rural region with a city center, the municipality of Sønderborg, a mid-sized city region, the municipality of Odense, and a metropolitan area, the municipality of Copenhagen. We demonstrate how city initiatives implemented to reduce emissions are translated into easy to access input-output parameters changes and how the index transparently assesses the emission impact of various possible municipal climate plans over time. As such, the index promotes the export of solutions from one region on another, as it enables policy makers to look elsewhere for best practices and test them on their own city before potential implementation. The index facilitates an easy to use and transparent comparison of factual and planned emission policies in different cities and can inform regional sustainability discussion and contribute to the dissemination of solutions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".