RETRACTED: Analysis of the Legal Framework Governing Gas Flaring in Nigeria’s Upstream Petroleum Sector and the Need for Overhauling
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
Nigeria is rated the number one producer of crude oil in Africa. Still, oil exploration activities have resulted in a high rate of gas flaring due to weak enforcement of the anti-gas flaring laws by the regulatory authorities. Associated natural gas is generated from oil production, and it is burnt in large volumes, thereby leading to the emission of greenhouse gases and waste of natural resources which could have generated billions of dollars for the Federal Government of Nigeria. There are concerns that if nothing is done to curtail this menace, humans and the environment will be imperiled due to its negative consequences. There is therefore a need to decrease gas flaring by replicating the strategies applied in the selected case study countries to combat the menace. It is relevant to carry out this analysis to reduce greenhouse gas emissions in the oil industry for the sustainability of the energy sector and to generate more revenues for the government. This study provides guidelines for legislatures on suitable approaches to adopt for formulating an anti-flaring legal framework. The study is a comparative analysis of national legal regimes on gas flaring in Nigeria, Canada, the United Kingdom, Saudi Arabia, and Norway. The study adopts a doctrinal legal research method, a point-by-point comparative approach with a library-based legal research method. The study finds that weak enforcement of laws is a critical factor responsible for the menace. It recommends the use of more advanced technologies, a sophisticated mixture of regulations and non-regulatory incentives such as fiscal policies and gas market restructuring, and proffers further suggestions based on the lessons learnt from the selected case study countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".