The emission of greenhouse gases poses enormous pressure on current carbon emissions and carbon reduction. Accurate quantification of carbon emissions from coal-fired power plants is of great significance for achieving the dual carbon goal. To enable enterprises to better understand their carbon emissions, this study constructs a carbon emission model and carbon emission data accounting model for coal-fired power plants. Case data calculations and a carbon emission reduction analysis were conducted. The experiment showcases that the carbon sensitivity of the inner side of the boiler under control conditions is higher than that of the operating parameters controlled on the inner side of the steam turbine, with a maximum total value of 16.67 g/MJ; the annual average low calorific value of coal remains between 16,000 kJ/kg; the activity level of coal remains between 30,000 TJ; and the oxidation probability of coal char during combustion fluctuates, with a maximum of 99.8%. In the calculation of coal-fired carbon emissions, the fitting difference between the emissions of generator unit 1 and generator unit 2 is maintained within 2%. Overall, the CO₂ emissions of power plants involved in the study are generally high. The model built through this study has well analyzed the carbon emissions of power plants. It is of great significance for the actual carbon emission reduction of coal-fired power plants.

Introduction: Carbon emissions from the healthcare sector have drawn increasing attention as global climate change intensifies. Achieving carbon neutrality in healthcare is vital for sustainable development, yet the complexity of stakeholder interactions and regulatory mechanisms creates substantial challenges.Methods: This study develops a dynamic evolutionary game model with time-delay effects to examine the strategic behaviors of four stakeholders: government, public hospitals, pharmaceutical enterprises, and the public. Stability analysis is applied to determine equilibrium strategies, while MATLAB simulations explore the impacts of regulatory, financial, and reputational parameters on system dynamics.Results: The findings reveal that government regulation is constrained by high costs and limited credibility, indicating the need to reduce costs and enhance credibility through market-based carbon trading and information-driven supervision. Public hospitals' weak compliance, driven by short-term profit motives, can be improved by strengthening financial incentives and penalties. Pharmaceutical enterprises' self-discipline is promoted by raising fines, reinforcing reputational incentives, and expanding public oversight channels. Public participation exerts a significant influence on carbon neutrality outcomes, and optimized online engagement mechanisms coupled with flexible compensation ratios are found to enhance effectiveness. Time delays accelerate system convergence but do not alter the evolutionary direction.Discussion: These results underscore the critical role of multi-stakeholder interaction in advancing healthcare carbon neutrality. The study provides both theoretical insights and practical policy implications for designing more effective carbon neutrality strategies in the healthcare sector.

The value of personal data can only be unlocked through efficient circulation. This study explores a multi-party collaborative mechanism for personal-data trading, aiming to improve data quality and market vitality via incentive-compatible institutional design, thereby supporting the high-quality development of the digital economy. Symmetry enhancement refers to the use of strategies and mechanisms to narrow the information gap among data controllers, operators, and demanders, enabling all parties to facilitate personal-data transactions on relatively equal footing. Drawing on evolutionary-game theory, we construct a tripartite dynamic-game model that incorporates data controllers, data operators, and data demanders. We analyze how initial willingness, payoff structures, breach costs, and risk factors (e.g., data leakage) shape each party’s strategic choices (cooperate vs. defect) and their evolutionary trajectories, in search of stable equilibrium conditions and core incentive mechanisms for a healthy market. We find that (1) the initial willingness to cooperate among participants is the foundation of a virtuous cycle; (2) the net revenue of data products significantly influences operators’ and demanders’ propensity to cooperate; and (3) the severity of breach penalties and the potential losses from data leakage jointly affect the strategies of all three parties, serving as key levers for maintaining market trust and compliance. Accordingly, we recommend strengthening contract enforcement and trust-building; refining the legal and regulatory framework for data rights confirmation, circulation, trading, and security; and promoting stable supply–demand cooperation and market education to enhance awareness of data value and compliance, thereby stimulating individuals’ willingness to authorize the use of their data and maximizing its value.

E-commerce platforms involve interactions among platform governance, manufacturer pricing, consumer perception, and managerial cognition. This study examines how platform overconfidence and consumer product-fit perception accuracy affect pricing, profitability, and operational mode selection. Product-fit perception accuracy denotes the probability that consumers correctly perceive the mismatch between their preferences and product attributes. We develop a two-period dynamic game involving two competing platforms and two manufacturers and compare resale and agency modes under simultaneous and sequential decisions. Equilibrium analysis shows that, under the agency mode, overconfidence increases promotional allowances and manufacturers’ first-period and switching-purchase prices, while its effect on repeat-purchase prices depends on a mismatch-cost threshold. Under the resale mode, overconfidence raises wholesale prices only within an intermediate range, and product-fit perception accuracy can have either a positive or negative effect depending on the level of overconfidence. Sequential decision-making further produces different pricing responses for leader and follower platforms due to differences in commitment and information conditions. Numerical analysis under the specified parameter settings indicates that resale tends to generate higher platform profits when consumers’ perceived product-quality level is high. When this level is low, the relative profitability of the two modes becomes more sensitive to overconfidence, product-fit perception accuracy, and decision position, with agency becoming more profitable in some parameter regions. These results characterize how governance mode and decision timing shape the economic consequences of managerial overconfidence and product-fit information within the modeled platform setting.