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Record W4405937618 · doi:10.33002/jelp040301

Appraisal of the Legal Framework and Regulation on Automobile Emissions: Nigeria Perspectives

2024· article· en· W4405937618 on OpenAlexvenueno aff
Eric Omo Enakireru, Gaga Wilson Ekakitie

Bibliographic record

VenueJournal of Environmental Law & Policy · 2024
Typearticle
Languageen
FieldEnergy
TopicEnergy, Environment, and Transportation Policies
Canadian institutionsnot available
Fundersnot available
KeywordsEnforcementBusinessDeveloping countryLegislationEconomic growthEconomicsPolitical scienceLaw

Abstract

fetched live from OpenAlex

The paper examined the efficacy of legal framework and enforcement of vehicular emission in Nigeria. The paper posits that the vast technological improvement in the manufacturing of automobiles and awareness in developed countries, the need to dispose of their obsolete and end of life vehicles for better environmental friendly vehicles, the interest of developing countries, like Nigeria, to meet up with global trends, improve their standard of living and alleviate poverty rate has turned them to willing recipients of used vehicles. This has a large share in the despoliation of air quality in the country by the release of high amounts of pollutants into the Nigerian atmosphere, thereby, causing headaches, loss of vision, anemia, forgetfulness and fatigue, high blood pressure, heart and circulatory diseases, cause abnormal foetal development and others. The article adopts the doctrinal approach of analytically and comparatively used of legal rules founded in primary sources; and statutes to critic the efficacy of the legal regimes in force in Nigeria. The objective of this paper was to further critic the efficacy of extant laws and regulations in Nigeria, the impact and enforcement challenges of pollution from vehicular emissions and its adverse effect on the global environmental concern. The paper concludes and recommended that all the facilities, officials and resources required should be made available to ensure that there is continuous and sound monitoring exercise of the Nigerian environment especially in areas of vehicular emissions devoid of the peculiar mix of polities, in order to properly audit the quality of air.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.927
Threshold uncertainty score0.424

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.262
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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