Expanding scope: the role of legitimacy and legitimation in the adoption of consumption-based carbon accounting in European cities
Bibliographic record
Abstract
Introduction In the pursuit of carbon neutrality, cities are increasingly measuring their greenhouse gas emissions. Most cities focus on territorial emissions tied to production, excluding consumption-driven emissions outside city boundaries. This gap is critical, as Scope 3 emissions can represent as much as 80% of cities’ carbon footprint. Addressing this limitation calls for urban consumption-based carbon accounting (CBCA). Yet, despite CBCA’s relevance for climate action, it has struggled to gain political legitimacy within European cities. This raises the question: How can urban CBCA achieve political legitimacy? Methods Based on semi-structured expert interviews with municipal practitioners in 17 European cities, we explore the barriers, enablers, and strategies used to enhance CBCA legitimacy. We develop a framework that describes the process of CBCA legitimation from exploration to political legitimacy. Results Results show that urban CBCA’s legitimacy rests primarily on cognitive legitimacy. Throughout the legitimation process, data plays a crucial role. Initially, access to data and clear calculation methodologies contributes to comprehensibility by making urban CBCA seem plausible and predictable. However, once CBCA comprehensibility is achieved metrics become less important than acting in ways that align with the broader understanding of consumption-based emissions. Finally, as CBCA measures affect citizens more directly, metrics resurface as means to validate impacts of past policy decisions and thus reinforce CBCA’s legitimacy. Discussion We discuss the various pitfalls and promising strategies to build political legitimacy for urban CBCA. This research contributes to the understanding of how urban CBCA legitimacy evolves over time. The legitimation framework developed can help inform policymakers in their endeavors to advance CBCA legitimation and institutionalization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".