Introduction: The growing global focus on Environmental, Social, and Governance (ESG) standards necessitates that companies optimize their corporate governance to balance economic, social, and ecological responsibilities. This study examines how the synergistic effects of Corporate Social Responsibility (CSR) and Environmental Responsibility (ER) can promote sustainable corporate development. Objective: The objective of this study is to analyze the critical elements of corporate governance structure optimization and to explore how companies can enhance their governance to achieve sustainable development through strengthened social and environmental management practices. Methods: The study uses case analysis and literature review to assess high-performing enterprises in CSR and ER integration, examining their governance, policy, and environmental strategies to uncover the factors behind their success in economic, social, and environmental spheres. Results: The research shows that optimizing governance structures markedly improves operational effectiveness. Companies need to create strong internal controls for equitable and transparent decisions, embedding CSR and ER into their strategies. CSR fulfillment builds public trust and environmental support, whereas ER improves brand reputation and competitiveness, driving sustainable and mutually advantageous development. Conclusion: The key to sustainable development in ESG practice lies in optimizing corporate governance and strengthening the synergy between social and environmental responsibilities. It is imperative for companies to build a governance structure that complies with ESG standards and to incorporate social and environmental considerations into their corporate strategies to effectively manage the triple bottom line of economic, social, and environmental performance.

With the implementation of the rural revitalization strategy, rural wisdom pension gradually becomes an important direction for the development of rural society. The purpose of this paper is to study the optimization path of rural smart pension in the context of rural revitalization. By analyzing the definition, development status and dilemma of rural wisdom pension, key factors for optimizing rural wisdom pension are proposed, and the paths for enhancing rural wisdom pension are discussed. The research results show that strengthening infrastructure construction, improving service quality, and promoting information technology application are the key paths to realize rural smart aging. This study provides theoretical guidance and policy recommendations for the implementation of rural smart aging.

This research introduces a novel framework integrating stochastic finite element analysis (FEA) with advanced circular statistical methods to optimize heat pump efficiency under material uncertainties. The proposed methodologies and optimization focus on balancing the mean efficiency and variability by adjusting the concentration parameter of the Von Mises distribution, which models directional variability in thermal conductivity. The study highlights the superiority of the Von Mises distribution in achieving more consistent and efficient thermal performance compared to the uniform distribution. We also conducted a sensitivity analysis of the parameters for further insights. The results show that optimal tuning of the concentration parameter can significantly reduce efficiency variability while maintaining a mean efficiency above the desired threshold. This demonstrates the importance of considering both stochastic effects and directional consistency in thermal systems, providing robust and reliable design strategies.

In order to further alleviate the problems of large assessment deviations, low efficiency of trading organisation and difficulties in system optimisation in medium- and long-term market trading, the article proposes an optimisation model for continuous intra-month bidding trading in the electricity market that takes into account risk hedging. Firstly, the current situation of market players’ participation in medium and long-term trading is analysed; secondly, the impact of contract trading on reducing operational risks is analysed based on the application of hedging theory in the primary and secondary markets; finally, the continuous bidding trading mechanism is designed and its optimisation effect is verified. The proposed model helps to improve the efficiency of contract trading in the secondary market, maintain the stability of market players’ returns and accelerate the formation of a unified, open, competitive and well-governed electricity market system.

This research presents a novel approach utilizing a self-enhanced chimp optimization algorithm (COA) for feature selection in crowdfunding success prediction models, which offers significant improvements over existing methods. By focusing on reducing feature redundancy and improving prediction accuracy, this study introduces an innovative technique that enhances the efficiency of machine learning models used in crowdfunding. The results from this study could have a meaningful impact on how crowdfunding campaigns are designed and evaluated, offering new strategies for creators and investors to increase the likelihood of campaign success in a rapidly evolving digital funding landscape.