Local planning for climate induced relocation: A systematic review
Bibliographic record
Abstract
Climate change impacts are worsening globally, urging local governments to respond via adaptation. Structural protective adaptation measures continue to be favoured by residents and local decision-makers, while relocation remains a lower priority. Yet, relocation may be the only viable and effective long-term strategy for reducing climate change related community vulnerability. International and national climate policy has started to incorporate content on relocation. However, there is a gap in our understanding of how climate induced relocation is influenced by and considered in local government planning and policy tools. Employing a systematic literature review method, we collected 1261 records, and analyzed 192 peer reviewed articles published between 1980 and 2022 in order to respond to three critical questions: 1) What literature exists on local planning for relocation? 2) How do local planning tools and policies influence the process and outcomes of relocation? 3) What are the knowledge gaps in local planning for relocation? Results reveal that scholarship on local planning for climate induced relocation has grown considerably, with attention paid to resident perceptions of risk and adaptation options, planning tools to implement relocation, relocation outcomes, and factors facilitating and constraining relocation. Included scholarship agrees that rigorous and proactive planning is critical to foster effective and equitable relocation. However, we uncovered a lack of scholarship that provides a comprehensive review of relocation planning processes, particularly in Northern contexts. Additionally, our analysis reveals that less attention is paid to exploring how, and which local planning tools consider and influence relocation.
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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 | 0.020 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".