Behavioural insights and environmental sustainability: Key findings and policy implications from a systematic review
Bibliographic record
Abstract
Both private and public actors increasingly apply behavioural insights (BI) across various social and economic contexts, particularly in highly developed countries such as Canada. Grounded in extensive theory and research, BI can help develop novel ways to improve policy outcomes, including environmental sustainability. A unique strength of BI interventions, particularly notable in this domain, is their ability to promote "nudges" for sustainable environmental behaviours while preserving individual choice. Over the past decade, several governments-including those of Canada and the U.K.-have established dedicated BI units to inform public policies, especially in response to escalating environmental concerns and sustainability objectives. This review, based on comprehensive and interdisciplinary database searches, synthesizes the existing literature on the domains, types, and empirical evidence related to the use of BI interventions for environmental sustainability. The findings reveal that BI interventions show substantial promise in promoting environmentally sustainable behaviours. However, critical research gaps persist, including the role of individual attitudes and socio-demographic factors in determining the effectiveness of BI interventions, the application of experimental designs to establish causality, the interaction between BI tools and conventional policy instruments, and the measurement of the long-term persistence of BI-induced behavioural changes. An interdisciplinary approach, combined with a nuanced understanding of critical variables, can meaningfully address these gaps in the literature and fully realize the potential of behavioural insights for environmental sustainability governance.
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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 | 0.032 | 0.148 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.020 | 0.027 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".