The Impact of Scarcity on Pro-environmental Behavior in the COVID-19 Pandemic
Bibliographic record
Abstract
As the world contends with the far-ranging impacts of the COVID-19 pandemic, ongoing environmental crises have, to some extent, been neglected during the pandemic. One reason behind this shift in priorities is the scarcity mindset triggered by the pandemic. Scarcity is the feeling of having less than what is necessary, and it causes people to prioritize immediate short-term needs over long-term ones. Scarcity experienced in the pandemic can reduce the willingness to engage in pro-environmental behavior, leading to environmental degradation that increases the chance of future pandemics. To protect pro-environmental behavior, we argue that it should not be viewed as value-laden and effortful, but rather reconceptualized as actions that address a multitude of human needs including pragmatic actions that conserve resources especially during scarcity. To bolster environmental protection, systematic changes are needed to make pro-environmental behavior better integrated into people's lives, communities, and cities, such that it is more accessible, less costly, and more resilient to future disturbances.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".