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Artificial Intelligence-Driven Multi-Energy Optimization: Promoting Green Transition of Rural Energy Planning and Sustainable Energy Economy

Author

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  • Xiaoyan Peng

    (School of Government, Sun Yat-sen University, Guangzhou 510275, China)

  • Xin Guan

    (Guangzhou Xinhua University, Dongguan 523133, China)

  • Yanzhao Zeng

    (School of Economics and Statistics, Guangzhou University, Guangzhou 510006, China)

  • Jiali Zhang

    (School of Public Administration, Guangzhou University, Guangzhou 510006, China)

Abstract

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.

Suggested Citation

  • Xiaoyan Peng & Xin Guan & Yanzhao Zeng & Jiali Zhang, 2024. "Artificial Intelligence-Driven Multi-Energy Optimization: Promoting Green Transition of Rural Energy Planning and Sustainable Energy Economy," Sustainability, MDPI, vol. 16(10), pages 1-20, May.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:10:p:4111-:d:1394377
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    References listed on IDEAS

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