The ‘boomerang effect’: insights for improved climate action
Bibliographic record
Abstract
States have been negotiating climate mitigation actions centred around greenhouse gas emissions for several decades. In the wake of the Paris Agreement, a significant body of research has emerged reflecting on the unintended negative consequences of climate mitigation action. More recently, this research includes a focus on climate adaptation actions. The negative impacts have, together, been labelled ‘maladaptation’. Maladaptation as articulated in the literature takes many forms: e.g. displacement of communities from traditional lands such as forests and pasture, violent conflict at different scales, resource capture by elites. In this article, we argue in support of a careful delineation between local-level side effects of climate action and negative effects reaching back to the state (through different pathways and at different levels). The latter we label ‘boomerang effects’. We illustrate, through several examples, the pathways leading from climate action to local impact to boomerang effect, arguing that careful articulation of policy and program decisions, actions and effects upon the state provide support for improved policy making. Climate action is necessary, and necessarily must be better informed in order to achieve the broadest socio-ecological benefits possible.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 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".