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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".