Achieving energy resilience: The joint role of environmental policy stringency and environmental awareness
Bibliographic record
Abstract
This paper examines the critical interplay between environmental policy stringency, public environmental awareness, and energy resilience, leveraging a dataset for 32 OECD countries between 2004 and 2020. Further, it explores the channels through which these variables influence energy resilience. Principal component analysis (pca) is adopted to derive the multi-dimensional index of energy resilience based on three pillars, renewable energy, energy access, and energy efficiency. The findings underscore a significant positive relationship between stringent environmental policies, environmental awareness, and energy resilience. Additionally, environmental awareness and environmental policy stringency are shown to exert a positive effect on renewable energy and enhance energy access. Environmental policy stringency is found to have a significant negative impact on energy intensity, i.e. a positive impact on energy efficiency. The results demonstrate that by increasing public environmental awareness and implementing more stringent environmental policies policymakers can improve energy resilience, energy efficiency, and the share of renewable energy. The latter are considered essential elements for the environmental transition emerging as a prominent solution when addressing climate change challenges. • Studies environmental policy stringency, public environmental awareness, and energy resilience • Constructs energy resilience index using three pillars and environmental awareness using Google search data • Higher environmental awareness and more stringent environmental policies increase energy efficiency • Environmental policy stringency and awareness impact renewable energy and energy access • Environmental policy stringency reduces energy intensity and enhances energy efficiency
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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 | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".