Factors contributing to climate adaptation lag in practice: Insights from local and territorial government interactions
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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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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.020 | 0.025 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".