Pro-Environmental Behaviour: Psychometric assessment and relationship with psychological variables in a Portuguese community sample
Bibliographic record
Abstract
Introduction Climate change is one of the main global challenges of the 21st century due to its consequences for the environment, the economy and health. Given that human behaviour exists at the forefront of many of the environmental issues we face, it is crucial to effectively measure pro-environmental behaviours (PEB). Stanley et al. (2021) proposed the PEB Scale (PEBS), a bifactorial measure including eight items asking about personal behaviours and eight collective actions that revealed good psychometric properties. To the best of our knowledge, there is no instrument that validly assesses these parameters in the general Portuguese population. Objectives To analyze the psychometric properties of the Portuguese version of PEBS and to explore its relationship with individual factors. Methods A community sample of 599 Portuguese adults (64.6% women; mean age=34.40±16.18) answered to the Portuguese preliminary version of PEBS and to the validated instruments: Climate Change Distress and Impairment Scale/CC-DIS, Big3 Perfectionism Scale–Short version/BTPS-SF, HEXACO-60 and Toronto and Coimbra Prosocial Behaviour Questionnaire/ProBeQ. SPSS 29 and AMOS-29 were used for Exploratory Factor Analysis (EFA; with a subsample of n=291) and Confirmatory Factor Analysis (CFA; n=308), respectively. Results EFA revealed a 3-factor solution with an explained variance of 60.52% confirmed by parallel analysis. With CFA, fit indices were found to be acceptable for first and second-order models (X²/df=4,0741; CFI =.8638; TLI=.8382; GFI=.8515; RMSEA= .0887, p<.001). Cronbach’s alphas were of .871 for the total scale (16 items), .857 for F1 (Collective actions/CA; 5 items), .832 for F2 (Personal behaviours/PB; 8 items) and .736 for F3 (Political actions/PA; 3 items). PEBS correlated with CC-DIS (r=.48 with total PEBS, r=.42 with CA and PB and r=.21 with PA, p<.01), ProBeQ (r=.24 with total PEBS, r=.12 with CA, r=.37 with PB and r=-.14 with PA, p<.01), Narcissistic Perfectionism (r=.08, p<.05 with CA and r=.20, p<.01 with PA), Honesty-Humility (r=-.15, p<.01 with PA), Emotionality (r=-.12, p<.01 with PB and r=.09, p<.05 with PA), Extraversion (r=.09, p<.05 with CA and r=.12, p<.01 with PA), Agreeableness (r=.11 with total PEBS, r=.12 with CA and r=.14 with PA, p<.01) and Conscientiousness (r=.09, p<.05 with total PEBS). When the correlated variables were inserted as predictors in linear multiple regression models where CA, PB and PA were dependent variables, they explained 19.8% (R2=.198), 24.6% (R2=.246) and 14.9% (R2=.149) of their variance, respectively (all p<.001). Conclusions PEBS shows adequate psychometric properties, therefore, it can be used to measure PEB in the Portuguese population, namely, to analyze the efficacy of pro-climate interventions and campaigns. These initiatives should take into consideration the role of individual factors (such as personality traits) in PEB. Disclosure of Interest None Declared
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".