Understanding the Impacts of, and Mitigation Actions for, Renewable Energy Projects: A Case Study of Wind Energy in Western Canada
Bibliographic record
Abstract
Canada’s renewable energy sector is undergoing major growth – growth that will require new infrastructure to aid production and distribution. Energy transition is a complex process and knowledge gaps remain about the environmental and social impacts of renewable energy systems and how best to manage them. There has been limited research about renewable energy systems’ impacts when compared with fossil fuel energy systems. Renewable energy projects are often met by concerns about impacts and uncertain impact management, project developers, regulators, communities, and interest groups need to understand the potential adverse impacts and risks of renewable energy projects before such project are approved, as well as how best to manage those impacts. \nThe purpose of this research is to improve the current understanding about the environmental and social impacts of renewable energy projects and their mitigation solutions. Attention is focused on the wind energy sector in western Canada. The Methodology consisted of a in depth document analysis of regulatory impact assessments in western Canada. \nThe results demonstrated that environmental impacts and mitigation solution for those impacts are identified at a higher rate when compared to human impacts and related mitigation actions. Mitigation actions addressing biophysical impacts were greater in comparison with human impacts with ratios of 1:4.3 and 1:1.3 respectively. The difference in addressing environmental and human impacts were also noted during the migration hierarchy and specificity analyses. The research also identified the information presented on IAs varies considerably in regard to availability, accessibility and organization, which later contributes to the discussion of the lack of information sharing between project’s stakeholders. Addressing the issues discussed in this thesis could contribute to increasing the efficiency and efficacy of impact assessments. This research provided ways to possibly address the identified issues.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".