Financing and Management Strategies for Expanding Green Development Projects: A Case Study of Energy Corporation in China’s Renewable Energy Sector Using Machine Learning (ML) Modeling

This study investigates potential financing and management strategies that the Energy Corporation, a Chinese renewable energy company, could adopt in order to expand its green development projects. While China has made significant advancements in renewable energy, its heavy reliance on fossil fuels necessitates a shift towards a more sustainable energy system. To analyze the factors driving and impeding sustainability, this article provides an overview of China’s energy sector and policies. Through case studies of the Energy Corporation and other prominent renewable energy companies, the study showcases a range of demonstration projects, financing models, and management technologies that have the potential to accelerate the growth of sustainable initiatives. Recommendations from expert interviews are also provided, covering areas such as optimizing investment, monitoring distributed assets, and balancing social and environmental impacts. The results show that the Energy Corporation can effectively develop wind, solar, and energy efficiency projects nationwide by leveraging partnerships, utilizing green bonds, employing big data platforms, and engaging stakeholders, while also setting sustainability benchmarks. With a strategic approach, Energy Corporation aims to invest USD 1 billion over the next five years, targeting a renewable energy capacity of 5000 MW and a 20% reduction in CO 2 emissions. Achieving these goals would position Chinese companies as global leaders in the transition to renewable energy. The study also utilized an artificial neural network (ANN) to analyze the impact of increasing green jobs and renewable energy capacities on CO 2 emission reduction and economic growth. The results indicate that green jobs have a more significant effect on reducing CO 2 emissions compared to renewable energy capacities. When green jobs increased while energy capacities remained constant, substantial CO 2 reductions were observed, but the economic growth was only 1%. However, when there was a moderate increase in jobs alongside a four-fold increase in renewable energy capacities, economic growth reached 4%. The neural network’s prediction errors were deemed acceptable based on linear regression analysis and experimental results.