Environmental Impact Assessment of Power Development Project: Lessons from Thailand Experiences
Bibliographic record
Abstract
Environmental impact assessment (EIA) is an important part of environmental and public health regulations. Increasing demand of electric consumption in Thailand is a major challenge for authorities trying to ensure satisfactory supply. However, the adverse impacts of development projects are powerful public concerns. Preparations of adequate EIA reports contribute to enhancing overall effectiveness of the EIA process. A sample of 3 EIA reports relating to power development projects was examined, to identify problems and to investigate typical strong and weak points of environmental impact statements, for effective implementation of EIA in Thailand. The competent authority has a well formulated environmental legislation and EIA guidelines and uses a listing method as a key quality control instrument for accuracy and veracity of the reports. Comprehensive descriptions of the major issues and the adverse impacts were defined based on the nature of the construction and operation phases, with adequate information to inform on existing attributes and situations appearing in all critiqued samples. The most important lessons are that there was an absence or weakness in: (i) information on baseline conditions and site–specific information (ii) public participation and (iii) communication involvement of all stakeholders. Increased enforcement could be based on strong, well–written law. The establishment of an independent review committee’s regulation, widespread public participation in every step of the EIA process and formulation of a code of conduct for the consultants, are strongly recommended.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".