Risk Management and Regulatory Failure in the Oil and Gas Industry in Nigeria: Reflections on the Impact of Environmental Degradation in the Niger Delta Region
Bibliographic record
Abstract
Risk management practice and effective policy intervention are critical to achieve stable environment and sustainable development. They are mechanisms for environmental management, environmental sustainability and sustainable community development for the people of the Niger Delta region. Informed by intuitive insights on the large scale of degradation in the Niger Delta, theoretical analysis of extant literature and content analysis of field interview/observation, this paper identified poor environmental risk management and regulatory failure as the bane of environmental degradation in the Niger Delta region. Why has regulatory agencies failed to protect communities against the impacts of environmental degradation and other consequences of oil and gas exploration activities? While there are enough legal and regulatory frameworks, however, weak enforcement and poor implementation of the existing regulations provides fertile ground for environmental degradation to persist. Thus, this article analyses some of the salient environmental issues as well as the regulatory and risk management failures in the oil and gas industry in Nigeria. It concludes that failure to carry out effective regulations and oversight in the oil and gas industry have resulted in environmental degradation (oil spills and gas flaring), contamination of water for fishing and farming activities, dispossession of rural farmers from their means of livelihood, poverty, migration and food shortages in the Niger Delta.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| 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".