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

Author

Listed:
  • 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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    1. Damilola Elizabeth Babatunde & Ambrose Anozie & James Omoleye, 2020. "Artificial Neural Network and its Applications in the Energy Sector An Overview," International Journal of Energy Economics and Policy, Econjournals, vol. 10(2), pages 250-264.
    2. Tan Yigitcanlar & Kevin C. Desouza & Luke Butler & Farnoosh Roozkhosh, 2020. "Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature," Energies, MDPI, vol. 13(6), pages 1-38, March.
    3. Di Vaio, Assunta & Palladino, Rosa & Hassan, Rohail & Escobar, Octavio, 2020. "Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review," Journal of Business Research, Elsevier, vol. 121(C), pages 283-314.
    4. Deng, Yue & Jiang, Wanyi & Wang, Zeyu, 2023. "Economic resilience assessment and policy interaction of coal resource oriented cities for the low carbon economy based on AI," Resources Policy, Elsevier, vol. 82(C).
    5. Tan Yigitcanlar & Federico Cugurullo, 2020. "The Sustainability of Artificial Intelligence: An Urbanistic Viewpoint from the Lens of Smart and Sustainable Cities," Sustainability, MDPI, vol. 12(20), pages 1-24, October.
    6. Xu, Xiaofeng & Wei, Zhifei & Ji, Qiang & Wang, Chenglong & Gao, Guowei, 2019. "Global renewable energy development: Influencing factors, trend predictions and countermeasures," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
    7. Monirul Islam Miskat & Protap Sarker & Hemal Chowdhury & Tamal Chowdhury & Md Salman Rahman & Nazia Hossain & Piyal Chowdhury & Sadiq M. Sait, 2023. "Current Scenario of Solar Energy Applications in Bangladesh: Techno-Economic Perspective, Policy Implementation, and Possibility of the Integration of Artificial Intelligence," Energies, MDPI, vol. 16(3), pages 1-27, February.
    8. Nam, KiJeon & Hwangbo, Soonho & Yoo, ChangKyoo, 2020. "A deep learning-based forecasting model for renewable energy scenarios to guide sustainable energy policy: A case study of Korea," Renewable and Sustainable Energy Reviews, Elsevier, vol. 122(C).
    9. Yildizbasi, Abdullah, 2021. "Blockchain and renewable energy: Integration challenges in circular economy era," Renewable Energy, Elsevier, vol. 176(C), pages 183-197.
    10. Wang, Zeyu & Zhang, Shuting & Zhao, Yuanyuan & Chen, Chuan & Dong, Xiufang, 2023. "Risk prediction and credibility detection of network public opinion using blockchain technology," Technological Forecasting and Social Change, Elsevier, vol. 187(C).
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