Governing intersectional climate justice: Tactics and lessons from Barcelona
Bibliographic record
Abstract
Abstract Cities and local governments are important actors in the global governance of climate change; however, the specific governance principles and arrangements that enable urban climate plans and policies to realize commitments to social equity and justice remain largely unexplored. This article uses the City of Barcelona, Spain, as a critical case study of emerging “intersectional climate justice” practice, where plans to build resilience to climate change are pursued in conjunction with efforts to tackle structural inequalities in accessing the built environment, health services, energy, housing, and transportation experienced by frontline communities. The study illustrates how Barcelona and its community partners do this through four different categories of governance and decision‐making tactics, which include: (1) experimenting with disruptive planning strategies; (2) working transversally across agencies and actors to institutionalize climate justice over time; (3) putting care at the center of urban planning; and (4) mobilizing place‐based approaches to tackle intersecting vulnerabilities of frontline residents. These tactics seek to redistribute the benefits of climate‐resilient infrastructures more fairly and to enhance participatory processes more meaningfully. Finally, we assess the limitations and challenges of mobilizing these tactics in everyday urban politics. Barcelona's experience contributes to research on climate governance by challenging the notion of distinct waves of governance and revealing concurrent dimensions of climate urbanism that coexist spatially and temporally. Our research also illustrates lessons for fairer climate governance in the city, where different tactics are mobilized to address structural and intersecting socioeconomic vulnerabilities that exacerbate the experience of climate change of frontline residents.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".