IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i12p9787-d1174561.html
   My bibliography  Save this article

Towards Sustainable Energy–Water–Environment Nexus System Considering the Interactions between Climatic, Social and Economic Factors: A Case Study of Fujian, China

Author

Listed:
  • Xiao Li

    (Department of Communication, Xiamen University of Technology, Xiamen 361024, China)

  • Yu Zhang

    (School of Environmental Science and Engineering, Xiamen University of Technology, Xiamen 361024, China)

  • Jing Liu

    (Department of Communication, Xiamen University of Technology, Xiamen 361024, China
    School of Environmental Science and Engineering, Xiamen University of Technology, Xiamen 361024, China)

  • Zuomeng Sun

    (School of Environmental Science and Engineering, Xiamen University of Technology, Xiamen 361024, China)

Abstract

This study develops a factorial Bayesian least-squares support vector machine-based energy–water–environment nexus system optimization (i.e., FBL–EWEO) model. FBL–EWEO can provide dependable predictions for electricity demand, quantify the interactions among different factors, and present optimal system planning strategies. The application to Fujian Province is driven by three global climate models (i.e., GCMs) under two SSPs, as well as two levels of economic and social factors’ growth rates. Results revealed in the planning horizon: (1) Fujian would encounter rainy and warming trends (e.g., [2.17645, 4.51247] mm/year of precipitation and [0.0072, 0.0073] °C/year of mean temperature); (2) economic, social, and climatic factors contribute 62.30%, 35.50%, and 1.47% to electricity demand variations; (3) electricity demand would grow with time (increase by [64.21, 74.79]%); (4) the ratio of new energy power would rise to [70.84, 73.53]%; (5) authorities should focus on photovoltaic and wind power plants construction (their proportions increase from [0.81, 1.83]% to [9.14, 9.56]%, [1.33, 4.16]% to [11.44, 15.58]%, respectively); and (6) air pollutants/CO 2 emissions would averagely decline [51.97, 53.90]%, and water consumption would decrease [41.77%, 42.25]%. Findings provide technical support to sustainable development.

Suggested Citation

  • Xiao Li & Yu Zhang & Jing Liu & Zuomeng Sun, 2023. "Towards Sustainable Energy–Water–Environment Nexus System Considering the Interactions between Climatic, Social and Economic Factors: A Case Study of Fujian, China," Sustainability, MDPI, vol. 15(12), pages 1-22, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9787-:d:1174561
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/12/9787/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/12/9787/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lv, J. & Li, Y.P. & Shan, B.G. & Jin, S.W. & Suo, C., 2018. "Planning energy-water nexus system under multiple uncertainties – A case study of Hebei province," Applied Energy, Elsevier, vol. 229(C), pages 389-403.
    2. Kaytez, Fazil, 2020. "A hybrid approach based on autoregressive integrated moving average and least-square support vector machine for long-term forecasting of net electricity consumption," Energy, Elsevier, vol. 197(C).
    3. Ji, Ling & Zhang, Beibei & Huang, Guohe & Wang, Peng, 2020. "A novel multi-stage fuzzy stochastic programming for electricity system structure optimization and planning with energy-water nexus - A case study of Tianjin, China," Energy, Elsevier, vol. 190(C).
    4. Ahmed, T. & Muttaqi, K.M. & Agalgaonkar, A.P., 2012. "Climate change impacts on electricity demand in the State of New South Wales, Australia," Applied Energy, Elsevier, vol. 98(C), pages 376-383.
    5. Warut Pannakkong & Thanyaporn Harncharnchai & Jirachai Buddhakulsomsiri, 2022. "Forecasting Daily Electricity Consumption in Thailand Using Regression, Artificial Neural Network, Support Vector Machine, and Hybrid Models," Energies, MDPI, vol. 15(9), pages 1-21, April.
    6. Mei, H. & Li, Y.P. & Suo, C. & Ma, Y. & Lv, J., 2020. "Analyzing the impact of climate change on energy-economy-carbon nexus system in China," Applied Energy, Elsevier, vol. 262(C).
    7. Lv, J. & Li, Y.P. & Huang, G.H. & Suo, C. & Mei, H. & Li, Y., 2020. "Quantifying the impact of water availability on China's energy system under uncertainties: A perceptive of energy-water nexus," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ma, Y. & Li, Y.P. & Mei, H. & Nie, S. & Huang, G.H. & Li, Y.F. & Suo, C., 2024. "Potential way to plan China's power system (2021–2050) for climate change mitigation," Renewable Energy, Elsevier, vol. 225(C).
    2. Qin, Pengcheng & Xu, Hongmei & Liu, Min & Xiao, Chan & Forrest, Kate E. & Samuelsen, Scott & Tarroja, Brian, 2020. "Assessing concurrent effects of climate change on hydropower supply, electricity demand, and greenhouse gas emissions in the Upper Yangtze River Basin of China," Applied Energy, Elsevier, vol. 279(C).
    3. Cheng, Yang & Jin, Lei & Fu, Haiyan & Fan, Yurui & Bai, Ruolin & Wei, Yi, 2024. "Investigating the impact of climate change and policy orientation on energy–carbon–water nexus under multi-criteria analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    4. Santágata, Daniela M. & Castesana, Paula & Rössler, Cristina E. & Gómez, Darío R., 2017. "Extreme temperature events affecting the electricity distribution system of the metropolitan area of Buenos Aires (1971–2013)," Energy Policy, Elsevier, vol. 106(C), pages 404-414.
    5. Meixia Wang, 2024. "Predicting China’s Energy Consumption and CO 2 Emissions by Employing a Novel Grey Model," Energies, MDPI, vol. 17(21), pages 1-25, October.
    6. Zamanipour, Behzad & Ghadaksaz, Hesam & Keppo, Ilkka & Saboohi, Yadollah, 2023. "Electricity supply and demand dynamics in Iran considering climate change-induced stresses," Energy, Elsevier, vol. 263(PE).
    7. D'Amico, A. & Ciulla, G. & Panno, D. & Ferrari, S., 2019. "Building energy demand assessment through heating degree days: The importance of a climatic dataset," Applied Energy, Elsevier, vol. 242(C), pages 1285-1306.
    8. Gebara, C.H. & Laurent, A., 2023. "National SDG-7 performance assessment to support achieving sustainable energy for all within planetary limits," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    9. Ana Luiza Fontenelle & Erik Nilsson & Ieda Geriberto Hidalgo & Cintia B. Uvo & Drielli Peyerl, 2022. "Temporal Understanding of the Water–Energy Nexus: A Literature Review," Energies, MDPI, vol. 15(8), pages 1-21, April.
    10. Waqar Muhammad Ashraf & Ghulam Moeen Uddin & Syed Muhammad Arafat & Sher Afghan & Ahmad Hassan Kamal & Muhammad Asim & Muhammad Haider Khan & Muhammad Waqas Rafique & Uwe Naumann & Sajawal Gul Niazi &, 2020. "Optimization of a 660 MW e Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency," Energies, MDPI, vol. 13(21), pages 1-33, October.
    11. Dai, Zhifeng & Hu, Juan & Liu, Xinheng & Yang, Mi, 2024. "ynamic time-domain and frequency-domain spillovers and portfolio strategies between climate change attention and energy-relevant markets," Energy Economics, Elsevier, vol. 134(C).
    12. Cheng, Qian & Liu, Pan & Xia, Qian & Cheng, Lei & Ming, Bo & Zhang, Wei & Xu, Weifeng & Zheng, Yalian & Han, Dongyang & Xia, Jun, 2023. "An analytical method to evaluate curtailment of hydro–photovoltaic hybrid energy systems and its implication under climate change," Energy, Elsevier, vol. 278(C).
    13. Qu, Zhijian & Xu, Juan & Wang, Zixiao & Chi, Rui & Liu, Hanxin, 2021. "Prediction of electricity generation from a combined cycle power plant based on a stacking ensemble and its hyperparameter optimization with a grid-search method," Energy, Elsevier, vol. 227(C).
    14. Atif Maqbool Khan & Artur Wyrwa, 2024. "A Survey of Quantitative Techniques in Electricity Consumption—A Global Perspective," Energies, MDPI, vol. 17(19), pages 1-38, September.
    15. Zhai, Mengyu & Huang, Guohe & Liu, Lirong & Zheng, Boyue & Guan, Yuru, 2020. "Inter-regional carbon flows embodied in electricity transmission: network simulation for energy-carbon nexus," Renewable and Sustainable Energy Reviews, Elsevier, vol. 118(C).
    16. Tsao, Yu-Chung & Thanh, Vo-Van & Lu, Jye-Chyi, 2022. "Efficiency of resilient three-part tariff pricing schemes in residential power markets," Energy, Elsevier, vol. 239(PD).
    17. Wang, Xipan & Song, Junnian & Xing, Jiahao & Duan, Haiyan & Wang, Xian'en, 2022. "System nexus consolidates coupling of regional water and energy efficiencies," Energy, Elsevier, vol. 256(C).
    18. Ding, Lili & Zhao, Zhongchao & Han, Meng, 2021. "Probability density forecasts for steam coal prices in China: The role of high-frequency factors," Energy, Elsevier, vol. 220(C).
    19. Li, Xian-Xiang, 2018. "Linking residential electricity consumption and outdoor climate in a tropical city," Energy, Elsevier, vol. 157(C), pages 734-743.
    20. Vu, D.H. & Muttaqi, K.M. & Agalgaonkar, A.P., 2015. "A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables," Applied Energy, Elsevier, vol. 140(C), pages 385-394.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9787-:d:1174561. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.