Towards an equitable transition: Renewable energy effect on educational outcomes in Canadian communities
Bibliographic record
Abstract
This paper presents the first causal and spatial analysis of how renewable energy deployment influences educational attainment across Canadian communities, with a focus on indigenous populations. While clean energy co-benefits are gaining policy traction, their impacts on human capital remain underexplored. We address this gap using a novel panel of 1037 renewable energy projects (1981–2021) linked to the education component of the Community Well-Being Index (CWB), a standardized measure (0–100) of high school and post-secondary attainment. Employing augmented inverse probability weighting (AIPW) alongside spatial models, we find that renewable projects increase education scores by an average of 6.11 percentage points. However, Indigenous communities see markedly smaller gains, up to 12 points lower in spatial regressions with one AIPW model estimating a −58.18-point effect, indicating major inequities. Community-owned solar and municipally managed projects yield the strongest positive spillovers. Geographically weighted regressions reveal stark spatial disparities: Alberta, British Columbia, and Saskatchewan outperform, while northern and Indigenous-majority areas lag. Education outcomes also scale with population size, pointing to local infrastructure effects. By combining causal inference, spatial analysis, and equity metrics, this study shows that without targeted policy, the energy transition risks deepening existing regional and social disparities. We advocate for culturally responsive, place-based energy policy.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".