This research contributes to the overarching objectives of achieving carbon neutrality and enhancing environmental governance by examining the role of artificial intelligence-enhanced multi-energy optimization in rural energy planning within the broader context of a sustainable energy economy. By proposing an innovative planning framework that accounts for geographical and economic disparities across rural regions, this study specifically targets the optimization of energy systems in X County of Yantai City, Y County of Luoyang City, and Z County of Lanzhou City. Furthermore, it establishes a foundation for integrating these localized approaches into broader national carbon-neutral efforts and assessments of green total factor productivity. The comparative analysis of energy demand, conservation, efficiency, and economic metrics among these counties underscores the potential of tailored solutions to significantly advance low-carbon practices in agriculture, urban development, and industry. Additionally, the insights derived from this study offer a deeper understanding of the dynamics between government and enterprise in environmental governance, empirically supporting the Porter hypothesis, which postulates that stringent environmental policies can foster innovation and competitiveness. The rural coal-coupled biomass power generation model introduced in this work represents the convergence of green economy principles and financial systems, serving as a valuable guide for decision-making in decisions aimed at sustainable consumption and production. Moreover, this research underscores the importance of resilient and adaptable energy systems, proposing a pathway for evaluating emission trading markets and promoting sustainable economic recovery strategies that align with environmental sustainability goals.

This paper explores methodologies to enhance the integration of a green supply chain circular economy within smart cities by incorporating machine learning technology. To refine the precision and effectiveness of the prediction model, the gravitational algorithm is introduced to optimize parameter selection in the support vector machine model. A nationwide prediction model for green supply chain economic development efficiency is meticulously constructed by leveraging public economic, environmental, and demographic data. A comprehensive empirical analysis follows, revealing a noteworthy reduction in mean squared error and root mean squared error with increasing iterations, reaching a minimum of 0.007 and 0.103, respectively—figures that are the lowest among all considered machine learning models. Moreover, the mean absolute percentage error value is remarkably low at 0.0923. The data illustrate a gradual decline in average prediction error and standard deviation throughout the model optimization process, indicative of both model convergence and heightened prediction accuracy. These results underscore the significant potential of machine learning technology in optimizing supply chain and circular economy management. The paper provides valuable insights for decision-makers and researchers navigating the landscape of sustainable development.

Introduction: This paper aims to explore the intersection of corporate social responsibility (CSR) and public health within the context of digital platforms. Specifically, the paper explores the impact of digital platforms on the sustainable development practices of enterprises, seeking to comprehend how these platforms influence the implementation of environmental protection policies, resource management, and social responsibility initiatives.Methods: To assess the impact of digital platforms on corporate environmental behavior, we conducted a questionnaire survey targeting employees in private enterprises. This survey aimed to evaluate the relationship between the adoption of digital platforms and the implementation of environmental protection policies and practices.Results: Analysis of the survey responses revealed a significant positive correlation between the use of digital platforms and the environmental protection behavior of enterprises (r=0.523;p<0.001), Moreover, the presence of innovative environmental protection technologies on these platforms was found to positively influence the enforcement of environmental policies, with a calculated impact ratio of (a∗b/c=55.31%). An intermediary analysis highlighted that environmental innovation technology plays a mediating role in this process. Additionally, adjustment analysis showed that enterprises of various sizes and industries respond differently to digital platforms, indicating the need for tailored environmental policiesDiscussion: These findings underscore the pivotal role of digital platforms in enhancing CSR efforts and public health by fostering improved environmental practices among corporations. The mediating effect of environmental innovation technologies suggests that digital platforms not only facilitate direct environmental actions but also enhance the efficiency and effectiveness of such initiatives through technological advances. The variability in response by different enterprises points to the importance of customizable strategies in policy formulation. By offering empirical evidence of digital platforms’ potential to advance CSR and public health through environmental initiatives, this paper contributes to the ongoing dialogue on sustainable development goals. It provides practical insights for enterprises and policy implications for governments striving to craft more effective environmental policies and strategies.

With the rapid development of information technology, digital platforms (DPs) have emerged as an indispensable aspect of the entrepreneurial landscape, which has a profound impact on entrepreneurial innovation and practice. However, there remains a gap in understanding the specific ways in which DPs influence the entrepreneurial process. Therefore, within the framework of information management theory, this study aims to deepen the understanding of how DPs foster entrepreneurial innovation and enhance entrepreneurial effectiveness, thereby enhancing the overall efficiency of DPs. Firstly, the concept of information management in contemporary social enterprise management is thoroughly examined and reviewed to establish a robust theoretical foundation for further research. Secondly, the conceptual framework and core elements of DPs are extensively discussed, elucidating their role and function in the entrepreneurial process. On this basis, a theoretical model illustrating the impact of DPs on entrepreneurial innovation and activities is formulated, along with six specific research hypotheses. These hypotheses encompass the effects of DPs on entrepreneur behavior, enterprise development, and the platform–entrepreneur relationship. To verify the validity of these hypotheses, a questionnaire survey involving 398 entrepreneurs is conducted to facilitate in-depth communication. Simultaneously, the various attributes of entrepreneurs are categorized, and the resulting classifications are thoroughly analyzed and evaluated. The results show that the DP plays a positive role in promoting the development of platform enterprises, improving intrapreneurship activities, and enhancing the ability of intrapreneurship. In addition, DPs can effectively regulate the relationship between platform enterprises and entrepreneurs and promote the self-development of entrepreneurs. Furthermore, the entrepreneurial growth process has positively influenced platform enterprises. This finding strongly supports the practical application of DPs and provides valuable guidance for entrepreneurs to leverage them effectively in fostering innovation and entrepreneurial activities in the digital era. Meanwhile, this offers crucial insights for platform enterprises on enhancing their service offerings to entrepreneurs and fostering the sustainable development of the entrepreneurial ecosystem. In summary, through rigorous theoretical discourse and empirical analysis, this study emphasizes the significant role of DPs in the entrepreneurial journey, providing robust backing for their continued advancement and utilization in the entrepreneurial realm.