Environmental Impact Assessment of Onshore Wind Power Plants: A Case Study of a 50 MW Wind Power Plant in Northeastern Thailand
Bibliographic record
Abstract
This research aims to assess the environmental feasibility of a wind power plant by investigating its noise disturbances, shadow flicker, and zones of visual influence. The model is applied as a case study for a 50 MW wind power plant, located in the Nakhon Ratchasima province of northeastern Thailand. The acoustic noise emissions were analyzed using the sound propagation and absorption models under the wind conditions on the site studied. The shadow flicker around each wind turbine generator, in terms of the number of hours per year, was also simulated along with the analysis of the zones of visual influence according to the number of wind turbines that can be seen by an observer from a certain distance. The results show a maximum sound level of 47 dBA, within the allowed limits of the 50 dBA legislation of the Department of Pollution Control of the Royal Thai Government. Similarly, the shadow flicker within 1 km of the wind turbines is 10 hours/year, well below the international standard of 30 hours/year. Results of the zones of visual influence indicate that between 15 and 20 turbines are visible from observation points surrounding the potential wind power plant. The results applied to this case study suggest that the potential wind power plant is well-suited regarding its environmental impacts and should typically not incur negative impacts for the local communities. Studies like these are vital to gaining the trust of the communities living near wind power plants to address their concerns and minimize opposition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".