A Sustainable Industry-Environment Model for the Identification of Urban Environmental Risk to Confront Air Pollution in Beijing, China
Bibliographic record
Abstract
In this study, an optimized industry-environment model is proposed for identifying environmental risk under uncertainties. The strategy associated with an emission-permit trading mechanism has been introduced into the industrial-environment regulation (model) for remitting the pressures of frequent/severe haze events in Beijing City. A dual stochastic mixed fuzzy risk analysis method with Laplace’s criterion (DSFRL) can be embedded into industry-environment issues with a trading emission-permit trading mechanism (IEST) for handling uncertainties regarded as possibility and probability distributions. Meanwhile, this can also reflect the environmental risks and corresponding system benefits due to the occurrence of a random event (such as random wind velocity). Based on the application of the proposed IEST with DSFRL, the numbers of the obtained results associated with production reduction, adjustment of industrial layout pattern, emission-permit transactions, pollutant mitigation and system benefits under various Laplace criterion cases can be analyzed. A tradeoff between production development and pollution mitigation based on the preference of policymakers can be used for rectifying current strategies with a sustainable mode, which can prompt an effort to confront air pollution
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".