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Record W2617911058 · doi:10.4209/aaqr.2016.09.0418

An Integrated Air Quality Model and Optimization Model for Regional Economic and Environmental Development: A Case Study of Tangshan, China

2017· article· en· W2617911058 on OpenAlexaff
Xiaowei Sun, Shuiyuan Cheng, Jianbing Li, Wei Wen

Bibliographic record

VenueAerosol and Air Quality Research · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsUniversity of Northern British Columbia
FundersNational Natural Science Foundation of China
KeywordsChinaQuality (philosophy)Air quality indexEnvironmental qualityEnvironmental scienceGeographyMeteorologyPolitical science

Abstract

fetched live from OpenAlex

An interval programming optimization model was formulated to develop effective and feasible regional economic structure adjustment plan using Tangshan Municipality in China as a case study. The optimization model was coupled with a WRF-CAMx-PSAT air quality simulation system through the estimated industrial emission sensitivity coefficients and equivalent coefficients for PM2.5 concentrations. Seven categories of industries were examined, and the results indicated that industries with higher emission sensitivity coefficients should be given priority for control. The effectiveness of the obtained optimal schemes was further assessed by the air quality simulation system. It indicated that PM2.5 concentrations in Tangshan would decrease by [33.5%, 39.3%] than those in 2013. This study provided an effective method framework for industries to maximize profits while meeting certain air quality constraints under uncertainty through the coupling of air quality simulation and optimization models.

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)0.003
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 score0.607
Threshold uncertainty score0.789

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.394
GPT teacher head0.416
Teacher spread0.022 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

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