Utilizing support vector machines to foster sustainable development and innovation in the clean energy sector via green finance.

As the global demand for clean energy continues to grow, the sustainable development of clean energy projects has become an important topic of research. in order to optimize the performance and sustainability of clean energy projects, this work explores the environmental and economic benefits of the clean energy industry. through the use of Support Vector Machine (SVM) Multi-factor models and a bi-level multi-objective approach, this work conducts comprehensive assessment and optimization. with wind power base a as a case study, the work describes the material consumption of wind turbines, transportation energy consumption and carbon dioxide (CO2) emissions, and infrastructure material consumption through descriptive statistics. Moreover, this work analyzes the characteristics of different wind turbine models in depth. On one hand, the SVM multi-factor model is used to predict and assess the profitability of Wind Power Base A. On the other hand, a bi-level multi-objective approach is applied to optimize the number of units, internal rate of return within the project, and annual average equivalent utilization hours of the Wind Power Base A. The research results indicate that in March, the WilderHill New Energy Global Innovation Index (NEX) was 0.91053, while the predicted value of the SVM multi-factor model was 0.98596. The predicted value is slightly higher than the actual value, demonstrating the model's good grasp of future returns. The cumulative rate of return of Wind Power Base A is 18.83%, with an annualized return of 9.47%, exceeding the market performance by 1.68%. Under the optimization of the bi-level multi-objective approach, the number of units at Wind Power Base A decreases from the original 7004 to 5860, with total purchase and transportation costs remaining basically unchanged. The internal rate of return of the project increases from 8% to 9.3%, and the annual equivalent utilization hours increase to 2044 h, comprehensively improving the investment return and utilization efficiency of the wind power base. Through optimization, significant improvements are achieved in terAs the global demand for clean energy continues to grow, the sustainable development of clean energy projects has become an important topic of research. In order to optimize the performance and sustainability of clean energy projects, this work explores the environmental and economic benefits of the clean energy industry. Through the use of Support Vector Machine (SVM) multi-factor models and a bi-level multi-objective approach, this work conducts comprehensive assessment and optimization. With Wind Power Base A as a case study, the work describes the material consumption of wind turbines, transportation energy consumption and carbon dioxide (CO2) emissions, and infrastructure material consumption through descriptive statistics. Moreover, this work analyzes the characteristics of different wind turbine models in depth. On one hand, the SVM multi-factor model is used to predict and assess the profitability of Wind Power Base A. On the other hand, a bi-level multi-objective approach is applied to optimize the number of units, internal rate of return within the project, and annual average equivalent utilization hours of the Wind Power Base A. The research results indicate that in March, the WilderHill New Energy Global Innovation Index (NEX) was 0.91053, while the predicted value of the SVM multi-factor model was 0.98596. The predicted value is slightly higher than the actual value, demonstrating the model's good grasp of future returns. The cumulative rate of return of Wind Power Base A is 18.83%, with an annualized return of 9.47%, exceeding the market performance by 1.68%. Under the optimization of the bi-level multi-objective approach, the number of units at Wind Power Base A decreases from the original 7004 to 5860, with total purchase and transportation costs remaining basically unchanged. The internal rate of return of the project increases from 8% to 9.3%, and the annual equivalent utilization hours increase to 2044 h, comprehensively improving the investment return and utilization efficiency of the wind power base. Through optimization, significant improvements are achieved in terms of the number of units, internal rate of return within the project, and annual average equivalent utilization hours at Wind Power Base A. The number of units decreases to 5860, with total purchase and transportation costs remaining basically unchanged, the internal rate of return increases to 9.3%, and annual equivalent utilization hours increase to 2044 h. Energy consumption and CO2 emissions are significantly reduced, with energy consumption decreasing by 0.68 × 109 kgce and CO2 emissions decreasing by 1.29 × 109 kg. The optimization effects are mainly concentrated in the production and installation stages, with emission reductions achieved through the recycling and disposal of materials consumed in the early stages. In terms of investment benefits, environmental benefits are enhanced, with a 13.93% reduction in CO2 emissions. Moreover, there is improved energy efficiency, with the energy input-output ratio increasing from 7.73 to 9.31. This indicates that the Wind Power Base A project has significant environmental and energy efficiency advantages in the clean energy industry. This work innovatively provides a comprehensive assessment and optimization scheme for clean energy projects and predicts the profitability of Wind Power Base A using SVM multi-factor models. Besides, this work optimizes key parameters of the project using a bi-level multi-objective approach, thus comprehensively improving the investment return and utilization efficiency of the wind power base. This work provides innovative methods and strong data support for the development of the clean energy industry, which is of great significance for promoting sustainable development under the backdrop of green finance.

The influence of digital platform on the implementation of corporate social responsibility: from the perspective of environmental science development to explore its potential role in public health.

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 policies. Discussion: 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.

Utilizing data platform management to implement "5W" analysis framework for preventing and controlling corruption in grassroots government.

In the era of information technology advancement, big data analysis has emerged as a crucial tool for government governance. Despite this, corruption remains a challenge at the grass-roots level, primarily attributed to information asymmetry. To enhance the efficacy of corruption prevention and control in grass-roots government, this study introduces the concept of data platform management and integrates it with the "5W" (Who, What, When, Where, Why) analysis framework. The research is motivated by the observation that existing studies on corruption prevention primarily concentrate on the formulation of laws and regulations, neglecting the potential improvement in actual effectiveness through the utilization of data platforms and analytical frameworks. The research employs methodologies grounded in the Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis framework, the Plan, Do, Check, Act (PDCA) cycle analysis framework, and the 5W analysis framework. Throughout the iterative process of implementing data platform management, various timeframes are established, and the impact of the three models is evaluated using indicators such as public participation and government satisfaction. The research reveals that the SWOT framework can formulate targeted strategies, the PDCA framework continuously optimizes work processes, and the 5W framework profoundly explores the root causes of corruption. The outcomes indicate a 10.76% increase in the public participation level score with the 5W model, rising from 71.67%, and a 23.24% increase in the governance efficiency score, reaching 66.12%. The SWOT model excels in case handling prescription and corruption reporting rate. The synergistic application of the three models demonstrates a positive impact. In conclusion, the amalgamation of data platform management and a multi-model approach effectively enhances the corruption prevention capabilities of grass-roots governments, offering insights for the establishment of transparent and efficient grass-roots governance.