Characterization of Renewable Energy Utilization Mode for Air-Environmental Quality Improvement through an Inexact Factorial Optimization Approach
Bibliographic record
Abstract
Energy-related environmental problems have been hot spot issues in regional energy system sustainable development. Thus, comprehensive planning of energy systems management is important for social and economic development, as well as environmental sustainability. In addition, uncertainties and complexities, as well as their potential interactions pose a great challenge for effective management in energy and environmental system. This study proposes a stochastic factorial energy systems management model to conduct uncertainties and risks in the energy systems, as well as handle their interaction effects among different environmental policies. The developed method can not only tackle uncertainties expressed as probability distributions and even interval values, but also be applied to determine decision alternatives associated with multiple economic penalties if the formulated environmental policy targets are violated. Meanwhile, by introducing the factorial technology, it can analyze a parameter’s impact on the system and their coordination effect. To verify the feasibility and effectiveness of the proposed method, the developed model was applied to a hypothetical case study for energy structure optimization under considering energy supply, SO2 emissions reduction, and environmental quality requirements. Multiple facilities, related environmental pollutants, and energy demand levels were taken into account. Moreover, the key factors of the system and their interaction effect were discovered. The results indicated that the developed method can resolve meritorious uncertainties in decision-making and analysis, generate effective management programming under multi-levels of the proposed energy and environmental systems. The method can be used for supporting the adjustment for allocating fossil fuels and renewable energy resources, analyzing the tradeoff between conflicting economic and environmental objectives and formulating the local policies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".