Impact assessment for renewable energy development: analysis of impacts and mitigation practices for wind energy in western Canada
Bibliographic record
Abstract
Impact assessment can play an important role in global energy transition, delivering knowledge to identify and manage the impacts of renewable energy projects. Yet, there are enduring concerns about IA’s efficacy for renewable energy development. Based on content analysis of IA applications for wind energy development in Canada, this paper examines the environmental and social impacts typically assessed across wind energy projects and the mitigation solutions proposed. Results indicate considerable imbalance between biophysical versus social impacts, including mitigation solutions. IAs include far more solutions for managing biophysical impacts than social ones, with impact-to-mitigation ratios of 1:4.3 and 1:1.3 respectively. Most mitigations focus on impact minimisation, followed by avoidance, and are often vague and imprecise regarding the timing, methods of implementation, and responsibility. Notwithstanding common impacts, mitigation actions that were common across projects were too vague or imprecise to support transferable practice to find efficiencies in assessment. Improved understanding the impacts of renewable energy projects and mitigation solutions, and learning from one project to the next, are foundational to advancing the role of IA the transition to renewable energy.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".