From “build back the same” to transformative recovery: enablers and barriers for climate-focused pathways in post-disaster case studies across Europe
Bibliographic record
Abstract
Disasters are frequently framed as opportunities for transformative change. Yet in practice, recovery processes often restore unsustainable systems under the guise of resilience or return to normal. This article examines whether, how, and under what conditions post-disaster recovery can catalyze transformative recovery pathways with a focus on climate mitigation and adaptation. Our study presents an interdisciplinary analytical framework that integrates insights from transformative research, sustainability transitions, and resilience thinking, providing a pragmatic heuristic to navigate post-disaster recovery efforts. We apply the framework to four case studies that represent different systems triggered by different disruptions: agriculture in Italy (drought), housing in Türkiye (earthquake), mobility in Spain (flood), and energy in Ukraine (war). Our findings across the cases show that most recovery efforts fall short of reconfiguring the systems in focus, primarily reproducing pre-disaster patterns, with recovery processes commonly characterized by siloed governance, technocratic fixes, and fragmented activities. Still, disasters can also open opportunities for new climate solutions, collaborations, and narratives that can challenge existing regimes and path dependencies. This is possible through addressing the enablers and barriers that cut across different spheres of transformations. Based on the findings, we argue that transformative recovery cannot be enabled purely through risk management, technical adaptation, or return to normal, but must engage with questions of power, meaning, and governance. The study offers researchers a lens to analyze transformation potential across various types of systems and disruptions and provides policymakers and practitioners with insight into the conditions that are important for transformative recovery.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| 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".