Extreme events and climate adaptation‐mitigation linkages: Understanding low‐carbon transitions in the era of global urbanization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract It has become increasingly clear that cities will have to simultaneously undertake both adaptation and mitigation in response to accelerating climate change and the growing demands for meaningful climate action. Here we examine the connections between climate mitigation and climate adaptation, specifically, between low‐carbon energy systems and extreme events. The article specifically addresses the question, how do responses to extreme climate risks enhance or limit capacity to promote city‐level greenhouse gas (GHG) mitigation? As a step toward answering this question, we present a framework for considering windows of opportunity that may arise as a result of extreme events and how these windows can be exploited to foster development and implementation of low‐carbon energy strategies. Four brief case studies are used to provide empirical background and determine the impact of potential windows of opportunity. Some general conclusions are defined. In particular, the existing energy system structure is an important determinant of impact and potential for energy transitions. Well‐developed and articulated governance strategies and ready access of effective and economically efficient alternative energy technology were key to transitions. However, prospects for inequity in development and implementation of low‐carbon solutions need to be considered. Finally, exploiting windows of opportunity afforded by extreme events for developing low‐carbon economy and infrastructure also can provide resilience against those very events. These types of responses will be needed as extreme events increase in frequency and magnitude in the future, with cities as primary sites of impact and action. This article is categorized under: Vulnerability and Adaptation to Climate Change > Learning from Cases and Analogies
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it