Climate Change Adaptation through Renewable Energy: The Cases of Australia, Canada, and the United Kingdom
Bibliographic record
Abstract
In recent years, climate change has escalated significantly, with forecasts indicating that this trend will further accelerate in the future. Renewable energy systems play a crucial role in global efforts to mitigate climate change due to their minimal greenhouse gas emissions. These systems also have the potential to facilitate the energy sector’s adaptation to climate change, given their decentralized nature, which enhances the resilience of energy infrastructure to extreme climate events. Nevertheless, existing literature predominantly focuses on their role in global mitigation efforts, often overlooking their significant adaptation capacity, particularly as reflected in national policies. This study seeks to bridge this gap through a qualitative examination of how renewable energy is incorporated into climate change adaptation policies in three countries: Australia, Canada, and the United Kingdom. It highlights a growing awareness of the role of renewable energy within these countries’ adaptation policies. However, while there is consensus on the importance of policy factors such as local focus, research initiatives, and risk assessment in utilizing renewable energy for adaptation, this study reveals that the actual deployment of renewable energy remains largely centered on mitigation efforts, partly neglecting crucial adaptation needs in the energy sector, such as geographical and technological diversification.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".