Factors contributing to climate adaptation lag in practice: Insights from local and territorial government interactions
Why this work is in the frame
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Bibliographic record
Abstract
Local governments across the globe are facing worsening climate impacts. In response, many decision-makers have initiated processes of planning for climate change adaptation. However, implementation frequently lags in practice. Scholarship exploring adaptation lag often focuses on the role of governance, specifically as it relates to interactions between various common levels of government (e. g., provincial, state, federal). However, there is a dearth of academic literature that targets the relationship between local and territorial governments, particularly in a northern context. To contribute to the narrowing of this gap, we explore the relationship between local and territorial governments in Canada in an effort to shed light on the ways in which government interactions influence progress on adaptation. Specifically, this qualitative study focuses on three local governments (Dawson City, Haines Junction and Whitehorse) in Yukon, a territory in northwest Canada, to explore how enablers and barriers emerge and influence climate adaptation action. Results demostrate that local government decision-makers (e. g., elected officials and senior managers) are eager to adapt. However, challenges impede implementation of adaptation policies in practice. Application of an evolutionary governance lens reveals that path dependencies associated with an awareness of the need to respond to climate impacts facilitate buy-in for adaptation. In contrast, goal dependencies that prioritize mitigation over adaptation stymie momentum on adaptation. Moreover, interdependencies and complex power dynamics related to the local-territorial relationship create unclear roles, further constraining the implementation of adaptation policies in practice. Recommendations geared towards overcoming these challenges are provided.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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