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Energy development and management in the Middle East: A holistic analysis

2024· article· en· W4404231397 on OpenAlexaff
Simin Tazikeh, Omid Mohammadzadeh, Sohrab Zendehboudi, Noori M. Cata Saady, Talib M. Albayati, Ioannis Chatzis

Bibliographic record

VenueEnergy Conversion and Management · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsUniversity of WaterlooMemorial University of Newfoundland
Fundersnot available
KeywordsMiddle EastEnergy managementEnergy (signal processing)Energy developmentEngineeringPolitical scienceEnergy conservationPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

The Middle East (ME) has undergone substantial changes in the energy landscape in recent years due to considerable variations in energy demand trends, economic/political upheaval, and climate change. The ME energy heavily relies on limited fossil fuel resources, which cause adverse climate change. Considering its geographical location, this region has huge potential for developing clean and sustainable energy resources, which will simultaneously satisfy its increasing energy demand and address climate change concerns. This review paper provides a comprehensive overview of non-renewable and renewable energy resources and their current status and future prospects in the ME. Moreover, it discusses in detail the energy utilization, management, and challenges associated with their development in the ME. Further, it examines the adverse effects of energy development on environment and health. The cost of energy development and current market status in the ME are also precisely analyzed. In particular, this review paper systematically assesses the energy policies and frameworks in the ME with consideration of political relations and governmental regulations. The outcomes of this study confirm that energy transition to renewable resources in the ME requires investment, research, and precise frameworks and policies. Therefore, the ME still has to go a long way to reliably count on renewable energy as the main energy source.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.927
Threshold uncertainty score0.531

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.024
GPT teacher head0.205
Teacher spread0.181 · 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 designNot applicable
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

Citations20
Published2024
Admission routes1
Has abstractyes

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