Bibliographic record
Abstract
Recent efforts to disseminate a coordinated, international standard for urban carbon accounting reflect new interests to reframe urban environments as central to global climate goals. Advocates emphasize how local policy based on standardized accounting methods will be more effective and that global knowledge over climate progress will be enhanced. As part of a decentralized international regime, more standardized measurement tools to quantify the effects of local action are seen as central to building the legitimacy of local climate action. However, others express concerns that accountability to global metrics undermines the democratic nature of local action and that the resources required to compile more complex and comprehensive carbon inventories exceed the benefits. This article examines these debates through a spatial analysis of a new urban carbon accounting standard, the Global Protocol for Community-Scale Greenhouse Gas Initiatives (GPC). Two key spatial dimensions are interrogated: the work of the GPC in locating carbon in territorially defined space, and the effects of a common standard in making carbon mobile across geographies and deterritorializing the urban environment as part of an evolving global climate regime. I argue that examining the GPC through a spatial lens makes legible how new dynamics of power and authority are being expressed over urban environments. Following an analysis on the GPC's territorial accounting method, I turn to explore the importance of new scalar relations in elevating the role of urban environments in global politics. Particular attention is given to the popular urban measurement and reporting discourses shared by international urban policy networks that have helped coordinate new global compliance expectations for local governments. The article concludes by discussing important implications to understanding the effects of a standardized measurement framework and considerations for future research.
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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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".