Investments in energy efficiency with government environmental sensitiveness: An application of geometric programming and game theory
Bibliographic record
Abstract
To maintain a competitive advantage, manufacturers of household appliances should promote the product’s energy efficiency, considering the impact on customer purchasing behavior. Since the product’s energy efficiency and pricing policies influence customers’ purchasing decisions, manufacturers confront significant challenges in balancing costs and demand since they must consider their profit-maximizing objective and government regulations. The Stackelberg game framework represents the interactions between the government, the leader, and a manufacturer, the follower, incorporating the government’s involvement in environmentally dependent social welfare under a tax structure. This paper proposes closed-form equilibrium using a game theory approach and geometric programming (GP) to solve the government’s and manufacturers’ non-linear decision models. The analytical results offer insight into the management’s approach to the product’s energy efficiency. The findings demonstrate that when clients’ concerns about energy-saving grow, the net payoff to the total manufacturer revenue ratio continuously decreases. The outcomes imply that the manufacturer must allocate significant revenue to tax expenditures in markets with more price-sensitive clients. As a motivation for research, this paper explores the application of the proposed model by examining a numerical example of a real-world refrigerator manufacturer case to obtain further managerial insight.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".