Do Excuses Matter? Dispositions, Justifications and Pro-environmental Behaviour
Bibliographic record
Abstract
Since environmental degradation principally results from human activities, it is critical to understand what impedes individuals from behaving sustainably. Environmental awareness and concern are insufficient for promulgating behavioural shifts. The extant literature recognises the importance of perceived behavioural control and personal accountability for environmental outcomes, the context-specific nature of when various attitudes shape pro-environmental behaviours (PEBs), and that perceptions of situational factors can impede PEBs. The purpose of this research is to propose another explanation for the attitude–behaviour gap: the justifications that consumers summon when explaining why they do not engage in PEBs. According to cognitive dissonance theory, individuals seek to justify their behaviours against their inconsistent stated attitudes to reduce psychological tension. We propose that different justifications be activated depending on the nature, difficulty and trade-offs required. Using a survey-based approach and employing structural equations modelling, this research validates a scale for capturing the justifications for inaction and then examines how the relationships between attitudes (i.e. environmental locus of control (ELOC)) and PEBs are mediated by these justifications. The scale development process yielded a typology of 12 justifications: environmental moderation, moral licencing, indifference, non-salience, scepticism, transportation infrastructure, product infrastructure, social encouragement, social discouragement, economic priority, economic cost and institutional. Both ELOC and justifications had strong direct effects on the three categories of PEBs, and there were also instances of indirect effects of ELOC – mediated by justifications – on behaviour. Implications for theory, practice and public policy are discussed.
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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 | 0.005 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".