Promoting Pro-environmental Beliefs and Behaviour: Choose-Your-Own Story Futuristic Climate Game
Bibliographic record
Abstract
How can we address climate scepticism and increase public support for ambitious pro-environmental policies? This study investigates the potential of future-oriented perspective taking, using an innovative and futuristic choose-your-own-adventure narrative game. This cutting-edge intervention involves living in the life of a future self and making choices related to hypothetical climate crises. The choose-your-own-adventure game was integrated into online survey experiments in the United Kingdom (N = 1,738) and the United States (N = 1,290). We found that participation in the game elicited strong emotional responses in individuals, making them more empathetic, but also more hopeless and sad. Imagining their future self during the climate game enhanced people's willingness to engage in future discussions about climate change among the UK respondents. Yet, the intervention did little to transform people's pro-environmental beliefs, policy support, or willingness to sign a climate petition. Causal mediation analyses reveal that these null effects hide important direct and indirect effects. Empathic concern mediates significant positive indirect effect of climate game on people's pro-environmental beliefs, but negative indirect effect on willingness to sign the climate petition. Empathy seems to shape environmental beliefs and behaviours in diverse ways, highlighting the complex and nuanced relationship between them. These findings offer important implications for recent research on the role of emotions in climate change communication, environmental psychology, and policymaking. We also present a unique approach to fostering empathy for the environment and future generations through an engaging choose-your-own-adventure game.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".