A novel evolutionary game-based low-methane application in three-echelon energy supply chains
Why this work is in the frame
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Bibliographic record
Abstract
Reducing methane emissions across the energy supply chain is critical due to methane’s potent short-term global warming potential, which is significantly higher than that of carbon dioxide. The development and deployment of advanced technologies, the implementation of robust regulatory frameworks, and the fostering of collaboration among governments, industry stakeholders, and consumers are important factors in accelerating the transition to a low-methane energy supply chain. This paper proposes a novel evolutionary game framework to create a green and cost-efficient low-methane application in the three-echelon energy supply chain comprising the government, the energy company, and energy consumers. The proposed low-methane application (LMA) integrates with the high-order evolutionary game dynamics, consisting of the replication dynamics of all stakeholders, methane, and social welfare dynamics of the company and consumers. Stable equilibria are achieved through the acceptance of the LMA, which introduces a novel pricing structure aimed at establishing an affordable methane-free market in the supply chain. A Canadian case study demonstrates the robustness of the LMA, which is further reinforced through its integration into the U.S. energy supply chain, showcasing the framework’s adaptability and strategic relevance in a major global energy market. Our results suggest that the LMA establishes (1) an ecologically benign and cost-effective energy market for all stakeholders involved; (2) a threshold for affordable energy prices; (3) social welfare for both the company and consumers while simultaneously reducing methane emissions within the supply chain; and (4) long-term sustainability for the government by mitigating environmental management costs associated with methane emissions.
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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 | 0.001 | 0.001 |
| 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 it