Decisions in Power Supply Chain with Emission Reduction Effort of Coal-Fired Power Plant under the Power Market Reform
Bibliographic record
Abstract
Sustainability in power supply chain has been supported by emission reduction of coal-fired power generation and increasing renewable energy power generation. Under the power market reform of direct power purchase transactions, this paper focuses on the channel selection and emission reduction decisions of power supply chain. From the theoretical perspective, this paper develops the decision-making models of centralized and decentralized power supply chain, which consist of one renewable energy power generation enterprise, one coal-fired power plant and one power grid enterprise. The optimal strategies of power quantities and profits for power supply chain members and their corresponding numerical experiments are analyzed in different cases. The results show that there are qA1Nc* qB1Lc* and eBNc*>eBLc* for coal-fired power plant B, which indicate that the direct power purchase channel in the centralized scenario is conducive to promoting the transaction quantity of renewable energy power generation, as well as the on-grid power quantity and emission reduction efforts of coal-fired power plant B. Furthermore, the profit of whole power supply chain could be enhanced by the increasing on-grid power preference coefficient of coal-fired power generation, subsidy for renewable energy power generation and preference coefficient for clean production, and by the decreasing emission reduction cost coefficient of coal-fired power plant. Additionally, the emission reduction effort of coal-fired power plant is positively relevant with preference coefficient for clean production, whereas it is negatively relevant with power grid wheeling charge, emission reduction cost coefficient and subsidy for renewable energy power generation. Our findings can provide useful managerial insights for policymakers and enterprises in the sustainability of power supply chain.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".