A GIS-Based Multicriteria Decision Analysis Approach on Wind Power Development: the Case Study of Nova Scotia, Canada
Bibliographic record
Abstract
The growing need for reducing the negative impacts of climate change and ensuring a constant and environmentally friendly energy supply, led the way to the exploitation of renewable energy sources. Canada has already acknowledged this trend by incorporating more power from renewables on its energy mix. Similarly, Nova Scotia has started an ambitious energy program in which the substitution of most of the fossil fuels by wind energy, will play a significant factor. The purpose of this research is to investigate all suitable locations for wind energy development in the province of Nova Scotia, under the scope of minimizing environmental impacts, increasing social acceptance and maximizing energy production. This spatial analysis is performed through the combination of Geographical Information Systems (GIS) and a Multi Criteria Decision Analysis (MCDA). The analysis of the province was based on the preferences of wind experts and administration authorities, which formed the weights assigned on eight (8) evaluation criteria. The extract of the relative weights was succeeded by using the Analytical Hierarchy Process (AHP), while their spatial dimensions were expressed by GIS software. The above procedure was possible through the application of a methodology where exclusion areas were found on the first place and the remaining areas were assessed on their level of suitability. The implementation of the GIS-MCDA methodological framework indicates that, despite the exclusion of a significant part of the province, there is still enough space to develop wind energy. The applied methodology and relevant results could be used as a Decision-Making tool by planning authorities, wind developers, and stakeholders.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.000 |
| 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".