A GIS-Multicriteria Approach to Analyzing Noise and Visual Impacts of Wind Farms
Bibliographic record
Abstract
Land-use conflicts in facility siting can trigger public opposition in communities. A negative public perception, such as the Not-in-my-backyard (NIMBY) attitude, is a planning issue that is strongly associated with some types of siting decisions. After the Feed-in-Tariff (FIT) program through the Green Energy Act was introduced in Ontario in 2009, a large number of wind farm developments were proposed and implemented. Public concerns regarding the noise and aesthetic impacts of wind turbines have created public resistance and caused project delays. More importantly, the wind farm siting decision making process is a top-down process, which overrides the power of municipalities and ignores public concerns towards wind farms. \n \nIn this thesis, a Geographic Information System (GIS)-based multi-criteria decision analysis (MCDA) siting approach has been developed, which is capable of representing the potential noise and visual impacts caused by wind turbines in a wind farm siting process. After identifying a sample of feasible sites in Southern Ontario, the noise and visual impact assessment approaches were applied to estimate the affected-population by wind farm sites. The changes of suitability levels within each feasible site can be determined after the integration of noise and visual criteria with the common siting criteria, which include physical, environmental, planning and economic factors. This siting approach is generalizable, which means it can be applied to other facility developments that have potential noise and visual impacts to the public. The results illustrate the spatial changes of suitability level before and after introducing the noise and visual criteria into the siting process. Planners and decision makers could potentially apply this siting approach to address public concerns in the future wind farm siting decisions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".