Behavioral dimensions of climate change: drivers, responses, barriers, and interventions
Bibliographic record
Abstract
Abstract This overview describes the anthropogenic drivers of global climate change, reviews the behavioral and psychological responses to its impacts (including barriers to behavior change), considers behavior‐focused intervention strategies, and suggests future directions for research. In doing so, it demonstrates why and how behavioral science is crucial for confronting the complex challenges posed by global climate change. The human dimensions of climate change are discussed, followed by descriptions of key theoretical models for explaining and predicting climate‐relevant behavior, issues and distinctions in studying human behavior in response to global climate change, an account of psychological (as opposed to structural) adaptation and its behavioral sequelae, the many psychological barriers to behavior change in this context, and behavior‐focused intervention strategies. The overview concludes with suggestions for researchers interested in advancing knowledge about behavior change and psychological responses to climate change. When knowledge about human behavior, cognitions, and psychological adaptation is integrated with that produced by researchers in related social and natural science disciplines, the result will facilitate solutions to this massive shared challenge.WIREs Clim Change2011, 2:801–827. doi: 10.1002/wcc.143 This article is categorized under: Perceptions, Behavior, and Communication of Climate Change > Behavior Change and Responses
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".