An Examination of the Social, Legal and Political Factors that Impact the Permitting Process for Wind Energy Transmission Line Projects in Canada
Bibliographic record
Abstract
In order to mitigate the effects of climate change, energy systems are undergoing a rapid transition from fossil fuels to renewable energy sources. Renewable energy is a core component of decarbonization and climate mitigation strategies, and wind energy is one of the fastest growing and most affordable sources of renewable power. However, locations in Canada with the best onshore wind energy resources are often remote and unserved or underserved by the current electrical grid. In order to support expanded deployment of wind energy in these locations, transmission lines must be constructed or expanded. While researchers have identified a lack of sufficient transmission infrastructure as one of the most significant barriers to increasing wind energy generation capacity, no study has thoroughly examined the factors that impact the permitting process of wind energy transmission line projects in Canada. This thesis aims to fill this research gap by examining the social, legal, and political factors that impact wind energy transmission line projects in Canada. This research was composed of (1) a content analysis of transmission line permitting documents from a selection of Canadian provinces and the federal government, and (2) an online survey of professionals active in the transmission line permitting process, including energy producers, energy regulators, permitting authorities, private firms, and public policy professionals. The results of the content analysis—which revealed that permitting documents do not include information about the factors impacting permitting decisions—and low participation rate in the survey indicate a lack of transparency in the permitting process, a finding which is in accordance with institutional theory and prior research demonstrating the difficulty of studying closed government processes. Statistical and descriptive analyses of the survey data revealed a complicated relationship between permitting processes, public policy, lobbying, and public opinion. These results align with public values theory, social license to operate theory, and prior research demonstrating the importance of public consultation and community acceptance for infrastructure projects, especially projects such as above-ground transmission lines and wind turbines that have a significant aesthetic impact on the surrounding community.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".