IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i22p9686-d1515585.html
   My bibliography  Save this article

Analysis of the Impact of Policies and Meteorological Factors on Industrial Electricity Demand in Jiangsu Province

Author

Listed:
  • Zhanyang Xu

    (School of Soft, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Jian Xu

    (School of Soft, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Chengxi Xu

    (Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA 92092, USA)

  • Hong Zhao

    (School of Soft, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Hongyan Shi

    (School of Soft, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Zhe Wang

    (School of Soft, Nanjing University of Information Science and Technology, Nanjing 210044, China)

Abstract

Under the strategic background of “carbon peak by 2030 and carbon neutrality by 2060”, the impact of energy policy on China’s industrial electricity demand is increasingly significant. This study focuses on the industrial electricity demand in Jiangsu Province, comprehensively considering the impact of policy and meteorological factors, and uses multivariate regression analysis to systematically explore the impact mechanisms of policy adjustments and climate change on industrial electricity demand. First, by analyzing the policy background and climate characteristics of Jiangsu Province, relevant policy and meteorological indicators are extracted, followed by a correlation analysis and the establishment of an industrial electricity multivariate regression prediction model. Finally, the evolution of the industrial electricity load in Jiangsu Province under different socio-economic pathways is forecasted. The results show the following: (1) Policy factors such as the electrification rate and self-generated electricity show significant correlation with electricity demand, as do meteorological factors such as temperature. (2) The future industrial electricity level in Jiangsu Province is expected to show a fluctuating upward trend, with industrial electricity consumption reaching 767.51 to 794.32 billion kWh by 2035. Accordingly, the forecast results are expected to guide future planning of the industrial electricity system in Jiangsu Province under the carbon neutrality scenario.

Suggested Citation

  • Zhanyang Xu & Jian Xu & Chengxi Xu & Hong Zhao & Hongyan Shi & Zhe Wang, 2024. "Analysis of the Impact of Policies and Meteorological Factors on Industrial Electricity Demand in Jiangsu Province," Sustainability, MDPI, vol. 16(22), pages 1-23, November.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:22:p:9686-:d:1515585
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/22/9686/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/22/9686/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Trotter, Ian M. & Bolkesjø, Torjus Folsland & Féres, José Gustavo & Hollanda, Lavinia, 2016. "Climate change and electricity demand in Brazil: A stochastic approach," Energy, Elsevier, vol. 102(C), pages 596-604.
    3. Ghasemi, A. & Shayeghi, H. & Moradzadeh, M. & Nooshyar, M., 2016. "A novel hybrid algorithm for electricity price and load forecasting in smart grids with demand-side management," Applied Energy, Elsevier, vol. 177(C), pages 40-59.
    4. Yu-Ling Hsiao, Cody & Wei, Xinyang & Sheng, Ni & Shao, Chengwu, 2021. "A joint test of policy contagion with application to the solar sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    5. Ahmed, T. & Vu, D.H. & Muttaqi, K.M. & Agalgaonkar, A.P., 2018. "Load forecasting under changing climatic conditions for the city of Sydney, Australia," Energy, Elsevier, vol. 142(C), pages 911-919.
    6. Zhao, Ning & You, Fengqi, 2020. "Can renewable generation, energy storage and energy efficient technologies enable carbon neutral energy transition?," Applied Energy, Elsevier, vol. 279(C).
    7. Eggimann, Sven & Usher, Will & Eyre, Nick & Hall, Jim W., 2020. "How weather affects energy demand variability in the transition towards sustainable heating," Energy, Elsevier, vol. 195(C).
    8. Durmaz, Tunç & Pommeret, Aude & Tastan, Hüseyin, 2020. "Estimation of residential electricity demand in Hong Kong under electricity charge subsidies," Energy Economics, Elsevier, vol. 88(C).
    9. Zheng, Shuguang & Huang, Guohe & Zhou, Xiong & Zhu, Xiaohang, 2020. "Climate-change impacts on electricity demands at a metropolitan scale: A case study of Guangzhou, China," Applied Energy, Elsevier, vol. 261(C).
    10. De Felice, Matteo & Alessandri, Andrea & Catalano, Franco, 2015. "Seasonal climate forecasts for medium-term electricity demand forecasting," Applied Energy, Elsevier, vol. 137(C), pages 435-444.
    11. Belaïd, Fateh & Zrelli, Maha Harbaoui, 2019. "Renewable and non-renewable electricity consumption, environmental degradation and economic development: Evidence from Mediterranean countries," Energy Policy, Elsevier, vol. 133(C).
    12. Torrini, Fabiano Castro & Souza, Reinaldo Castro & Cyrino Oliveira, Fernando Luiz & Moreira Pessanha, Jose Francisco, 2016. "Long term electricity consumption forecast in Brazil: A fuzzy logic approach," Socio-Economic Planning Sciences, Elsevier, vol. 54(C), pages 18-27.
    13. Zhang, Mingyang & Zhang, Kaiwen & Hu, Wuyang & Zhu, Bangzhu & Wang, Ping & Wei, Yi-Ming, 2020. "Exploring the climatic impacts on residential electricity consumption in Jiangsu, China," Energy Policy, Elsevier, vol. 140(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. de Oliveira, Erick Meira & Cyrino Oliveira, Fernando Luiz, 2018. "Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods," Energy, Elsevier, vol. 144(C), pages 776-788.
    2. Jin, Haowei & Guo, Jue & Tang, Lei & Du, Pei, 2024. "Long-term electricity demand forecasting under low-carbon energy transition: Based on the bidirectional feedback between power demand and generation mix," Energy, Elsevier, vol. 286(C).
    3. Li, Jinghua & Luo, Yichen & Wei, Shanyang, 2022. "Long-term electricity consumption forecasting method based on system dynamics under the carbon-neutral target," Energy, Elsevier, vol. 244(PA).
    4. Yukseltan, E. & Kok, A. & Yucekaya, A. & Bilge, A. & Aktunc, E. Agca & Hekimoglu, M., 2022. "The impact of the COVID-19 pandemic and behavioral restrictions on electricity consumption and the daily demand curve in Turkey," Utilities Policy, Elsevier, vol. 76(C).
    5. Deakin, Matthew & Bloomfield, Hannah & Greenwood, David & Sheehy, Sarah & Walker, Sara & Taylor, Phil C., 2021. "Impacts of heat decarbonization on system adequacy considering increased meteorological sensitivity," Applied Energy, Elsevier, vol. 298(C).
    6. Chabouni, Naima & Belarbi, Yacine & Benhassine, Wassim, 2020. "Electricity load dynamics, temperature and seasonality Nexus in Algeria," Energy, Elsevier, vol. 200(C).
    7. Zhou, Chenyu & Shen, Yun & Wu, Haixin & Wang, Jianhong, 2022. "Using fractional discrete Verhulst model to forecast Fujian's electricity consumption in China," Energy, Elsevier, vol. 255(C).
    8. Yabin Da & Bin Zeng & Jing-Li Fan & Jiawei Hu & Lanlan Li, 2023. "Heterogeneous responses to climate: evidence from residential electricity consumption," Climatic Change, Springer, vol. 176(8), pages 1-19, August.
    9. Qiangyi Li & Lan Yang & Shuang Huang & Yangqing Liu & Chenyang Guo, 2023. "The Effects of Urban Sprawl on Electricity Consumption: Empirical Evidence from 283 Prefecture-Level Cities in China," Land, MDPI, vol. 12(8), pages 1-27, August.
    10. Cuihui Xia & Tandong Yao & Weicai Wang & Wentao Hu, 2022. "Effect of Climate on Residential Electricity Consumption: A Data-Driven Approach," Energies, MDPI, vol. 15(9), pages 1-20, May.
    11. Galdi, Giulio & Casarin, Roberto & Ferrari, Davide & Fezzi, Carlo & Ravazzolo, Francesco, 2023. "Nowcasting industrial production using linear and non-linear models of electricity demand," Energy Economics, Elsevier, vol. 126(C).
    12. Tian, Chuyin & Huang, Guohe & Piwowar, Joseph M. & Yeh, Shin-Cheng & Lu, Chen & Duan, Ruixin & Ren, Jiayan, 2022. "Stochastic RCM-driven cooling and heating energy demand analysis for residential building," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    13. Marwen Elkamel & Lily Schleider & Eduardo L. Pasiliao & Ali Diabat & Qipeng P. Zheng, 2020. "Long-Term Electricity Demand Prediction via Socioeconomic Factors—A Machine Learning Approach with Florida as a Case Study," Energies, MDPI, vol. 13(15), pages 1-21, August.
    14. Carolina Deina & João Lucas Ferreira dos Santos & Lucas Henrique Biuk & Mauro Lizot & Attilio Converti & Hugo Valadares Siqueira & Flavio Trojan, 2023. "Forecasting Electricity Demand by Neural Networks and Definition of Inputs by Multi-Criteria Analysis," Energies, MDPI, vol. 16(4), pages 1-24, February.
    15. Barman, Mayur & Dev Choudhury, Nalin Behari, 2019. "Season specific approach for short-term load forecasting based on hybrid FA-SVM and similarity concept," Energy, Elsevier, vol. 174(C), pages 886-896.
    16. Yi Liang & Dongxiao Niu & Ye Cao & Wei-Chiang Hong, 2016. "Analysis and Modeling for China’s Electricity Demand Forecasting Using a Hybrid Method Based on Multiple Regression and Extreme Learning Machine: A View from Carbon Emission," Energies, MDPI, vol. 9(11), pages 1-22, November.
    17. Tian, Chuyin & Huang, Guohe & Lu, Chen & Zhou, Xiong & Duan, Ruixin, 2021. "Development of enthalpy-based climate indicators for characterizing building cooling and heating energy demand under climate change," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    18. João Vitor Leme & Wallace Casaca & Marilaine Colnago & Maurício Araújo Dias, 2020. "Towards Assessing the Electricity Demand in Brazil: Data-Driven Analysis and Ensemble Learning Models," Energies, MDPI, vol. 13(6), pages 1-20, March.
    19. Plaga, Leonie Sara & Bertsch, Valentin, 2023. "Methods for assessing climate uncertainty in energy system models — A systematic literature review," Applied Energy, Elsevier, vol. 331(C).
    20. Ahmed, T. & Vu, D.H. & Muttaqi, K.M. & Agalgaonkar, A.P., 2018. "Load forecasting under changing climatic conditions for the city of Sydney, Australia," Energy, Elsevier, vol. 142(C), pages 911-919.

    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:16:y:2024:i:22:p:9686-:d:1515585. 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.