Reforming EIA in Nigeria through Next Generation Environmental Assessment
Bibliographic record
Abstract
In 1992, Nigeria put in place a federal environmental impact assessment (EIA) regime, the Environmental Impact Assessment Act, 1992 (EIAA), which conceptualised and formalised the EIA process as an environmental planning and management tool for Nigeria. The EIA processes have evolved significantly since the EIAA was passed about thirty years ago. Contemporary thinking around the world is focussed on the concept of next generation environmental assessment (NGEA). However, the EIAA remains unchanged to date. A primary objective of this thesis is to analyse the provisions of the EIAA in the light of the NGEA concept. To do so, this thesis examines how far the former satisfies or differs from the normative ideals of the latter. Analysis in this thesis uses Canada’s new Impact Assessment Act, 2019 (IAA) as a contemporary example of NGEA norm-based legislation. Thirteen defining components of NGEA are identified from the literature and analysed generally. These include, purpose and overall role of the assessment process, application rules, assessment streams, scope of assessment considerations, impacts analysis, the nature and significance of knowledge in the assessment, meaningful public participation, clear roles and responsibilities, co-operative jurisdictional assessment, consideration of alternatives and trade-offs as core decision criteria, decision making and review system, compliance and enforcement and monitoring and continuous learning. Drawing on the IAA, the focus of the analysis is on the decision making, sustainability, climate change, public participation, and enforcement and monitoring features of NGEA. This research took stock of Nigeria’s current environmental impact assessment process under the EIAA and found that EIAA significantly falls short of the NGEA standards, and that although the IAA equally has not measured up to the NGEA standards, it is a good beginning and has a lot to offer Nigeria as a reference point for future environmental regulation and management reforms.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".