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Record W4220737291 · doi:10.18280/ijsse.120108

The Impact of Environmental Energy Taxes on Nigeria’s Insecurity Situation

2022· article· en· W4220737291 on OpenAlexvenueno aff
Cordelia Onyinyechi Omodero, Stella Ogechukwu Okezie

Bibliographic record

VenueInternational Journal of Safety and Security Engineering · 2022
Typearticle
Languageen
FieldEnergy
TopicOil, Gas, and Environmental Issues
Canadian institutionsnot available
FundersCovenant University
KeywordsLanguage changeGovernment (linguistics)Order (exchange)BusinessEconomic freedomProfit (economics)Economic growthDevelopment economicsEconomicsPublic economicsFinance

Abstract

fetched live from OpenAlex

It is critical to enhance environmental energy taxes in order to offer a more secure business climate in Nigeria. Insecurity in Nigeria is severe, and it has become a component of the country's environmental problem to deal with. This research investigates the influence of environmental energy tariffs on Nigeria's insecurity condition. The research approach is cross-sectional, and secondary data from 2010 to 2020 were used in this study. According to the multiple regression results, all factors have a substantial beneficial influence on the country's insecurity management. The petroleum profit tax, gas exploration tax, and level of freedom from corruption, all have a good and substantial impact on security management. The study indicates that the government has significantly utilized environmental energy taxes to combat insecurity. However, if the country will be totally free from corruption, insecurity will be minimized. Therefore, the study urges immediate participation of international community in the Nigeria’s insecurity concerns. This move has become extremely essential to salvage the country from all ecologically connected difficulties and to guarantee peace and safety for all inhabitants. Concerning corruption, it is high time for the government to begin looking for the sponsors of insurgency and abduction in order to punish them more severely.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.761
Threshold uncertainty score0.348

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 head0.004
GPT teacher head0.204
Teacher spread0.200 · 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 designSimulation or modeling
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
Published2022
Admission routes1
Has abstractyes

Explore more

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