Dynamic energy system risk management under the pressures of GHG- and pollutant-emission mitigation for Hebei Province, China
Bibliographic record
Abstract
This study develops dynamic energy system risk management model (DERM) to mitigate emission in Hebei Province energy system and support decision making under uncertainty. The DERM integrates modified fuzzy chance-constrained programming, interval linear programming, and mixed-integer programming into an energy system planning model. Nine scenarios of attitude for decision-makers ( γ = 0.9, …, 0.1) and three credibility levels ( λ = 0.9, 0.8, 0.7) for the environmental loading capacity scenarios are provided in the case study. Weather conditions (mildly, moderately, and severely smoggy weather) and environmental loading capacity of different emission (NO x , SO 2 , dust, and CO 2 ) are concurrently considered in this model. The proposed DERM effectively captures both the uncertainties and dynamics characteristics of energy systems. Its application in Hebei Province demonstrates its practical value, and the results align with the long-term planning objective of Hebei. The obtained result indicates that a pessimistic attitude of policymakers toward energy availability can significantly improve the energy system and the air quality compared to a positive attitude. Such pessimism will promote the development of new energy, especially wind power generation, resulting in a [106, 236]% increase in renewable capacity during planning period. Meanwhile, the diminutive degrees of credibility would result in a slight reduction in overall system cost, while leading to a sharp increase in the risk of system failure to the maximum acceptable environmental pollutant capacity. These findings could help to investigate uncertainty features for a domestic energy system and identify desirable attitude alternatives from decision markers under the trade-off between economic and environment.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".