Environmental impact assessment and sustainable development: A critical review
Bibliographic record
Abstract
Environmental Impact Assessment (EIA) has evolved and become part of major project requirements in many countries. However, its contribution to sustainable development and reduction in poverty levels of people affected by projects has not been assessed in developing countries. The study examined environmental laws and institutions in selected African countries, compared EIA laws, procedures and practices. Their effects on sustainable development and reduction in poverty are discussed. What is found lacking is the full integration of environmental assessments into planning and decision-making processes of these countries. At present EIA is applied mainly at the project level in these countries. It concluded that Africa is on the right footing towards sound environmental protection and resource management, but increasing poverty and lack of direct investment in project communities, illiteracy and corruption remains the greatest threat to the success of EIA. It recommends that corporate social responsibility with a fixed percentage of profit be made part of EIAs. It enjoins that a critical mass of a project’s community be empowered to actively participate in the early phases of the EIA process to improve benefits to communities and society at large. Sustainable development could thus be achieved on the project level when businesses and communities cooperate for their mutual benefits.Key words: Environmental Impact Assessment, sustainable development, poverty alleviation, corporate social responsibility, institutions
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".