Determinants of Small and Rural Local Governments’ Renewable Energy Program Adoption in Cascadia
Bibliographic record
Abstract
Aim: This study aimed to investigate the determinants of renewable energy policy adoption by small and rural local governments in Cascadia. Background: Small and rural local governments currently face many ongoing and numerous new challenges that complicate their task of sustaining current public services and programs. How government officials adapt to these changes can affect the long-term viability of local governments in both the U.S. and Canadian contexts. Objective: This study has examined the presence or absence of renewable energy programs in small and rural local governments in the “Cascadia” region of Canada and the U.S. (British Columbia, Oregon, and Washington). Methods: Using surveys and interviews of Cascadia local government leaders during the summer and fall of 2023, correlates of renewable energy policy adoption have been examined, including cultural, demographic, economic, and political factors. Results: Key findings have indicated cities, experiencing population growth, and those with a progressive political orientation to be more inclined to adopt renewable energy policies. Conversely, remote communities have demonstrated a lower propensity for such adoption. Financial constraints, evidenced by the impact of inflation and the necessity for service cutbacks, have been found to negatively correlate with the consideration of renewable energy policies. Conclusion: This study has indicated renewable energy projects to be more often found or contemplated in areas being politically liberal, densely populated, and not predominantly rural. It could be beneficial in shifting the perception of renewable energy from being predominantly an environmental concern to being recognized for its economic benefits.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".