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Record W4402525917 · doi:10.1016/j.nexus.2024.100327

Mediating role of energy uncertainty for environmental management in electricity generation: The evidence from Pakistan

2024· article· en· W4402525917 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEnergy Nexus · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsTrinity College
Fundersnot available
KeywordsElectricityElectricity generationNatural resource economicsBusinessEnvironmental economicsEnvironmental scienceEnvironmental resource managementEnvironmental planningEconomicsEngineeringPower (physics)PhysicsElectrical engineering

Abstract

fetched live from OpenAlex

• The study examines the relationship between CO2 intensity, aggregated and disaggregated fossil fuels, clean and nuclear energy, and as a mediating variable, energy uncertainty for Pakistan. • We employ the ARDL Bound Testing method and wavelet coherence analysis for the empirical estimations. • The overall results suggest that under the mediating effect of energy uncertainty, renewables and nuclear energy in electricity generation have a negative association with CO2 intensity whereas fossil fuels in generating electricity positively influence CO2 intensity. • The rise in energy uncertainty leads to a fall in CO2 intensity in aggregated and disaggregated analysis as well. • Wavelet coherence analysis shows that CO2 intensity and energy uncertainty depend on each other dynamically for almost the whole employed period. This groundbreaking study examines the relationship between CO2 intensity, aggregated and disaggregated fossil fuels, clean and nuclear energy, and, as a mediating variable, energy uncertainty for Pakistan during 2019M01 and 2022M10 with monthly data. To this end, the ARDL Bound Testing method is used to identify the long-run relationship of the studied factors. The empirical results suggest that under the mediating effect of energy uncertainty, renewables and nuclear energy in electricity generation have a negative association with CO2 intensity. In contrast, fossil fuels in generating electricity influence positively CO2 intensity in the aggregated analysis. Moreover, the disaggregated results under the mediating role of energy uncertainty reveal that only hydro energy reduces CO2 intensity as renewables, bioenergy, wind, and solar energy do not impact CO2 intensity. Both coal and gas energies cause a rise in CO2 intensity. Regarding nuclear energy, it also has a negative relation with CO2 intensity. The increase in energy uncertainty leads to a fall in CO2 intensity in aggregated and disaggregated analyses as well. Wavelet coherence analysis shows that CO2 intensity and energy uncertainty depend on each other dynamically for almost the whole employed period.

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.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.242
Threshold uncertainty score0.820

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)0.0010.000

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 head0.013
GPT teacher head0.236
Teacher spread0.223 · 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