Influencing pro-environmental behaviors through visual arts: a scoping review of research designs and state of knowledge
Bibliographic record
Abstract
Background: Collaboration between scientists and artists is a growing trend driven notably by the need to take action against climate change. Consequently, the role of art is evolving to serve purposes beyond communication in the scientific community. With artistic projects increasingly becoming a form of intervention to influence pro-environmental behaviors, diverse methods are being deployed to assess their influence on behavior, yet little effort has been made to document coherently this emerging diversity of research designs and learnings. Objective: The aim of this study is to explore methodological approaches used to assess the effects of visual arts in promoting pro-environmental behaviors to fight climate change and determine whether some general trends can be observed. Methodology: We conducted a scoping review to identify empirical papers published between 2001 and 2024, from which 8 studies were selected. The selected papers assessed the influence of artistic intervention on pro-environmental behaviors and its defining variables. Results: We found six redundant attributes in research designs. Four of them (mixed-method approach, literature-based conceptual basis, longitudinal studies, baseline environmental profiles) can help support better research designs, while two others (supplementary material, co-creation) can enhance the influence of science-art projects on behavior. We also found that, among the diverse types of art forms (movies, festivals, exhibitions, murals, immersive installation), none were able to directly and significantly affect behaviors, but they could all affect environmental attitudes or awareness to some degree. Originality: This scoping review stands out from prior studies by highlighting the challenges and opportunities in assessing the influence of art on pro-environmental behaviors, as well as by exploring the role of visual arts for engaging the public in such behaviors.
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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 | 0.058 | 0.215 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.027 | 0.025 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".