Participatory mapping and spatial planning for renewable energy development: The case of ground-mount solar in rural Ontario
Bibliographic record
Abstract
Displacing fossil fuels with renewable energy resources is essential to mitigate climate change. The implementation of renewable energy systems brings stark changes to local landscapes; e.g., wind turbines dotting a rural landscape, or solar panels covering fertile land that previously supplied food. These changes can evoke strong social opposition, even among people who are generally supportive of renewable energy (RE). Research suggests that public tensions around renewable energy development are reduced through inclusive decision-making processes (i.e., improved procedural justice) as well as benefits sharing (i.e., improved distributional justice). We develop and test a process for proactive and inclusive spatial planning for RE development in a region. Our work combines participatory mapping and survey- and focus-group-based sentiment analysis in order to understand community concerns around renewable energy projects, and how those concerns are reflected spatially. We conducted a case study on ground-mount solar energy systems in the Town of Caledon, Ontario, Canada. From this study, we aimed to determine what regions and kinds of landscapes community members might find acceptable or not for new solar projects, and to facilitate dialogue about opportunities and potential impacts with the general public, key stakeholders, and influencers (utilities, land-owners, developers, municipal staff) in the locality.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".