Improving economic assessment and decision-making for managed retreat through CBA+: a targeted literature review
Bibliographic record
Abstract
There is growing recognition that managed retreat, also known as strategic relocation, could be an increasingly important adaptation measure in the face of climate change and rising natural hazard risk. However, managed retreat’s potential benefits are limited by challenges in funding, negative participant experiences, public and political opposition, uncertainty in long-term climate change and natural hazard risk, and equity concerns, all of which increase the complexity of managed retreat decision-making. While there is some research on how economic assessment tools can be used to aid in managed retreat decision-making, there is a knowledge gap in how these practises contribute to both the causes and potential resolution of the challenges associated with managed retreat. To begin to fill this gap, this paper presents a targeted literature review on the nexus between managed retreat, cost–benefit analysis of climate change adaptation and natural hazard risk reduction, and alternative economic assessment and decision-making tools. We identify connections between economic assessment practises and the primary challenges associated with managed retreat and then present several avenues where changes or additions to standard economic assessment approaches such as cost–benefit analysis (which we collectively refer to as ‘CBA+’) could lead to better managed retreat outcomes. Finally, we present a framework and 10 key principles that summarise key aspects of CBA+ to help agencies involved in managed retreat improve outcomes through economic assessment and decision-making process design. The most important key principles are the context- and community-specific design of economic assessment and decision-making processes, and the need for ongoing and thorough community engagement and co-production.
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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 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".