Environmental Impact Assessment for Oil and Gas Projects: A Comparative Evaluation of Canadian and Nigerian Laws
Bibliographic record
Abstract
Environmental Impact Assessment (EIA) is a tool mandated by regulatory authorities to prevent environmental degradation and foster a sustainable environment. Procedural rights to access information and participate in decision-making are understood as key components of good environmental governance. This research compares the EIA laws in Nigeria and Canada and identifies areas of improvement in the EIA processes of both countries with regards to oil and gas activities, in light of existing international norms and, with a focus on public participation and climate change.\nThe research reveals that Canada, a developed country, has a more rigorous and effective public participation process in EIA than Nigeria, a developing country. This research further reveals that while the Canadian legal framework for EIA increasingly integrates consideration of climate change impacts, this is not the case in Nigeria.\nThis study concludes that there is much room for improvement in both the Nigerian and Canadian EIA processes, especially with regard to public participation and climate change issues in order to bridge the gap between international and domestic environmental standards.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".