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Record W3082549821 · doi:10.18331/brj2020.7.3.4

Fossil energy price and outdoor air pollution: predictions from a QUAIDS model

2020· article· en· W3082549821 on OpenAlexvenueno aff
Seyed Reza Khatibi, Maziar Moradi‐Lakeh, Majid Kermani, Seyed Abbas Motevalian

Bibliographic record

VenueBiofuel Research Journal · 2020
Typearticle
Languageen
FieldEnergy
TopicEnergy, Environment, and Transportation Policies
Canadian institutionsnot available
Fundersnot available
KeywordsFossil fuelEnergy subsidiesOverconsumptionSubsidyEnergy consumptionAir pollutionNatural resource economicsPollutionAgricultural economicsEconomicsConsumption (sociology)Energy policyBiofuelEnvironmental scienceRenewable energyProduction (economics)Waste managementMacroeconomicsEngineering

Abstract

fetched live from OpenAlex

Cheap fossil energy leads to overconsumption of energy and hazardous levels of air pollution. In this study, we provide a framework to connect fossil energy price policy to private consumption of energy and outdoor air pollution. We used a consumer demand system and reassessed it for the recent status of the Iranian economy. We extracted household consumption information from Iran’s 2011 and 2014−2016 annual household surveys (n=154683), prices from the Central Bank of Iran’s detailed monthly price indices from 2008 to 2016, and air pollution information from Iran’s Energy Balance Sheets. We estimated that an average Iranian household would reduce its energy consumption by 2%, 16%, 29%, 38%, and 45% if energy prices were hiked by 10%, 50%, 100%, 150%, and 200%, respectively. The corresponding reductions in total outdoor air pollution in the post-hike period would be 2.6, 26.3, 47.6, 62.9, and 74.5 million tons, respectively. Besides highlighting the importance of fossil energy price policy as a short-term strategy to reduce air pollution, this study calls attention to shifting the existing subsidies on fossil fuels to sustainable sources of energy such as waste-oriented biofuels as a -long-term solution.

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 score0.690
Threshold uncertainty score0.614

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.070
GPT teacher head0.313
Teacher spread0.243 · 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

Citations31
Published2020
Admission routes1
Has abstractyes

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