The economy and environmental attitudes: Does a good economy make citizens care more about the environment?
Bibliographic record
Abstract
The relationship between economic conditions and environmental attitudes has been hotly debated in academic and public discourse. Some contend that economic prosperity strengthens environmental attitudes, while others argue that no such relationship exists. We shed light on the economy-environmental importance relationship by incorporating both nonlinear effects and public perceptions of the economy. Two countries with comparable economic performance and cultural heritage, Australia and Canada, are investigated. Using opinion survey data in a time-series analysis over a 20-year period, we find that GDP per capita has a nonlinear relationship with environmental attitudes in both countries, and the inclusion of perceptions of the economy significantly improves the prediction of environmental attitudes. In both Australia and Canada, a U-shaped relationship is observed; initial improvements in economic conditions tend to worsen environmental attitudes, but past a certain threshold, further economic improvements result in increased environmental concern. Perceptions of the economy too showed analogous trends. In both countries, positive perceptions of the economy were associated with strong environmental attitudes; at least in Canada, negative perceptions of the economy were also positively associated with environmental attitudes. The nonlinear effects of the economy as well as perceptions of the economy helps shed light on the current inconclusive literature on the relationship between the economy and citizens' pro-environmental support. This is an important consideration given the critical importance to the future sustainability of the natural environment.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".