IDEAS home Printed from https://ideas.repec.org/a/eee/streco/v51y2019icp67-76.html
   My bibliography  Save this article

Factor decomposition of China’s industrial electricity consumption using structural decomposition analysis

Author

Listed:
  • Yu, Miao
  • Zhao, Xintong
  • Gao, Yuning

Abstract

We analyzed changes in China’s industrial electricity consumption using a structural decomposition model based on input-output analysis. China’s industrial electricity consumption changes during 2007–2012 were decomposed into four factors: electricity intensity, technology-input structural, final demand structure and total final demand. The results showed that changes in total final demand contributed most to increases in China’s industrial electricity consumption, which increased electricity consumption by 2091.34 billion kW h. As for aggregate demand, increased investment, urban residential consumption, and exports all played major roles. However, increases in total rural residents’ consumption and total inventory had little effect on the electricity consumption of various industrial sectors. The key reason for reductions in industrial electricity consumption was the decrease in electricity intensity in the heavy manufacturing industry, the service industry, and the energy industry. The decline in electricity consumption intensity in China reduced electricity consumption by 446.21 billion kW h.

Suggested Citation

  • Yu, Miao & Zhao, Xintong & Gao, Yuning, 2019. "Factor decomposition of China’s industrial electricity consumption using structural decomposition analysis," Structural Change and Economic Dynamics, Elsevier, vol. 51(C), pages 67-76.
  • Handle: RePEc:eee:streco:v:51:y:2019:i:c:p:67-76
    DOI: 10.1016/j.strueco.2019.08.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0954349X19301055
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.strueco.2019.08.002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zan Yang & Ying Fan & Liqing Zhao, 2018. "A Reexamination of Housing Price and Household Consumption in China: The Dual Role of Housing Consumption and Housing Investment," The Journal of Real Estate Finance and Economics, Springer, vol. 56(3), pages 472-499, April.
    2. Meng, Bo & Wang, Jianguo & Andrew, Robbie & Xiao, Hao & Xue, Jinjun & Peters, Glen P., 2017. "Spatial spillover effects in determining China's regional CO2 emissions growth: 2007–2010," Energy Economics, Elsevier, vol. 63(C), pages 161-173.
    3. Tso, Geoffrey K.F. & Yau, Kelvin K.W., 2007. "Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks," Energy, Elsevier, vol. 32(9), pages 1761-1768.
    4. Pérez-García, Julián & Moral-Carcedo, Julián, 2016. "Analysis and long term forecasting of electricity demand trough a decomposition model: A case study for Spain," Energy, Elsevier, vol. 97(C), pages 127-143.
    5. Bo Meng & Yaxiong Zhang & Satoshi Inomata, 2013. "Compilation And Applications Of Ide-Jetro'S International Input-Output Tables," Economic Systems Research, Taylor & Francis Journals, vol. 25(1), pages 122-142, March.
    6. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
    7. Zhao, Xiaoli & Li, Na & Ma, Chunbo, 2012. "Residential energy consumption in urban China: A decomposition analysis," Energy Policy, Elsevier, vol. 41(C), pages 644-653.
    8. Xie, Rui & Wang, Fangfang & Chevallier, Julien & Zhu, Bangzhu & Zhao, Guomei, 2018. "Supply-side structural effects of air pollutant emissions in China: A comparative analysis," Structural Change and Economic Dynamics, Elsevier, vol. 46(C), pages 89-95.
    9. Sanquist, Thomas F. & Orr, Heather & Shui, Bin & Bittner, Alvah C., 2012. "Lifestyle factors in U.S. residential electricity consumption," Energy Policy, Elsevier, vol. 42(C), pages 354-364.
    10. Kankal, Murat & AkpInar, Adem & Kömürcü, Murat Ihsan & Özsahin, Talat Sükrü, 2011. "Modeling and forecasting of Turkey's energy consumption using socio-economic and demographic variables," Applied Energy, Elsevier, vol. 88(5), pages 1927-1939, May.
    11. Nidhi Bagaria & Saba Ismail, 2019. "Export Performance of China: A Constant Market Share Analysis," Frontiers of Economics in China-Selected Publications from Chinese Universities, Higher Education Press, vol. 14(1), pages 110-130, March.
    12. Chen, Cheng-Zhong & Lin, Zhen-Shan, 2008. "Multiple timescale analysis and factor analysis of energy ecological footprint growth in China 1953-2006," Energy Policy, Elsevier, vol. 36(5), pages 1666-1678, May.
    13. Baker, Keith J. & Rylatt, R. Mark, 2008. "Improving the prediction of UK domestic energy-demand using annual consumption-data," Applied Energy, Elsevier, vol. 85(6), pages 475-482, June.
    14. Ang, B.W. & Xu, X.Y. & Su, Bin, 2015. "Multi-country comparisons of energy performance: The index decomposition analysis approach," Energy Economics, Elsevier, vol. 47(C), pages 68-76.
    15. Zan Yang & Ying Fan & Cindy Hiu-ying Cheung, 2017. "Housing assets to the elderly in urban China: to fund or to hedge?," Housing Studies, Taylor & Francis Journals, vol. 32(5), pages 638-658, July.
    16. Chunding Li & Chuantian He & Chuangwei Lin, 2018. "Economic Impacts of the Possible China–US Trade War," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 54(7), pages 1557-1577, May.
    17. Zhou, Dequn & Zhou, Xiaoyong & Xu, Qing & Wu, Fei & Wang, Qunwei & Zha, Donglan, 2018. "Regional embodied carbon emissions and their transfer characteristics in China," Structural Change and Economic Dynamics, Elsevier, vol. 46(C), pages 180-193.
    18. Terence Tai Leung Chong & Xiaoyang Li, 2019. "Understanding the China–US trade war: causes, economic impact, and the worst-case scenario," Economic and Political Studies, Taylor & Francis Journals, vol. 7(2), pages 185-202, April.
    19. Zhang, Haiyan & Lahr, Michael L., 2014. "China's energy consumption change from 1987 to 2007: A multi-regional structural decomposition analysis," Energy Policy, Elsevier, vol. 67(C), pages 682-693.
    20. Boqiang Lin & Kui Liu, 2016. "How Efficient Is China’s Heavy Industry? A Perspective of Input–Output Analysis," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 52(11), pages 2546-2564, November.
    21. Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
    22. Inglesi-Lotz, Roula & Blignaut, James N., 2011. "South Africa’s electricity consumption: A sectoral decomposition analysis," Applied Energy, Elsevier, vol. 88(12), pages 4779-4784.
    23. Rafindadi, Abdulkadir Abdulrashid & Ozturk, Ilhan, 2016. "Effects of financial development, economic growth and trade on electricity consumption: Evidence from post-Fukushima Japan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1073-1084.
    24. Geem, Zong Woo & Roper, William E., 2009. "Energy demand estimation of South Korea using artificial neural network," Energy Policy, Elsevier, vol. 37(10), pages 4049-4054, October.
    25. Zhifu Mi & Jing Meng & Dabo Guan & Yuli Shan & Malin Song & Yi-Ming Wei & Zhu Liu & Klaus Hubacek, 2017. "Chinese CO2 emission flows have reversed since the global financial crisis," Nature Communications, Nature, vol. 8(1), pages 1-10, December.
    26. Erik Dietzenbacher & Bart Los, 1998. "Structural Decomposition Techniques: Sense and Sensitivity," Economic Systems Research, Taylor & Francis Journals, vol. 10(4), pages 307-324.
    27. Jones, Rory V. & Fuertes, Alba & Lomas, Kevin J., 2015. "The socio-economic, dwelling and appliance related factors affecting electricity consumption in domestic buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 901-917.
    28. Okushima, Shinichiro & Tamura, Makoto, 2011. "Identifying the sources of energy use change: Multiple calibration decomposition analysis and structural decomposition analysis," Structural Change and Economic Dynamics, Elsevier, vol. 22(4), pages 313-326.
    29. Ekonomou, L., 2010. "Greek long-term energy consumption prediction using artificial neural networks," Energy, Elsevier, vol. 35(2), pages 512-517.
    30. Lan, Jun & Malik, Arunima & Lenzen, Manfred & McBain, Darian & Kanemoto, Keiichiro, 2016. "A structural decomposition analysis of global energy footprints," Applied Energy, Elsevier, vol. 163(C), pages 436-451.
    31. Imhotep P. Alagidede & Tamara E. Mughogho, 2019. "Capital Account Liberalization and Capital Flows to Sub-Saharan Africa: A Panel Threshold Approach," Working Papers 203, Economic Research Southern Africa.
    32. Lin, Boqiang & Liu, Chang, 2016. "Why is electricity consumption inconsistent with economic growth in China?," Energy Policy, Elsevier, vol. 88(C), pages 310-316.
    33. Li, Yingzhu & Shi, Xunpeng & Yao, Lixia, 2016. "Evaluating energy security of resource-poor economies: A modified principle component analysis approach," Energy Economics, Elsevier, vol. 58(C), pages 211-221.
    34. Lin, Jiang & Kahrl, Fredrich & Liu, Xu, 2018. "A regional analysis of excess capacity in China’s power systems," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt44j2w0d0, Department of Agricultural & Resource Economics, UC Berkeley.
    35. Brounen, Dirk & Kok, Nils & Quigley, John M., 2012. "Residential energy use and conservation: Economics and demographics," European Economic Review, Elsevier, vol. 56(5), pages 931-945.
    36. He, Yaoyao & Qin, Yang & Wang, Shuo & Wang, Xu & Wang, Chao, 2019. "Electricity consumption probability density forecasting method based on LASSO-Quantile Regression Neural Network," Applied Energy, Elsevier, vol. 233, pages 565-575.
    37. Yawen Han & Shigemi Kagawa & Fumiya Nagashima & Keisuke Nansai, 2019. "Sources of China’s Fossil Energy-Use Change," Energies, MDPI, vol. 12(4), pages 1-16, February.
    38. Chen, Shaoqing & Chen, Bin, 2015. "Urban energy consumption: Different insights from energy flow analysis, input–output analysis and ecological network analysis," Applied Energy, Elsevier, vol. 138(C), pages 99-107.
    39. Ang, B.W. & Zhang, F.Q., 2000. "A survey of index decomposition analysis in energy and environmental studies," Energy, Elsevier, vol. 25(12), pages 1149-1176.
    40. Bianco, Vincenzo & Manca, Oronzio & Nardini, Sergio, 2009. "Electricity consumption forecasting in Italy using linear regression models," Energy, Elsevier, vol. 34(9), pages 1413-1421.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jiang, Rui & Wu, Peng & Song, Yongze & Wu, Chengke & Wang, Peng & Zhong, Yun, 2022. "Factors influencing the adoption of renewable energy in the U.S. residential sector: An optimal parameters-based geographical detector approach," Renewable Energy, Elsevier, vol. 201(P1), pages 450-461.
    2. Abbasi, Kashif Raza & Shahbaz, Muhammad & Zhang, Jinjun & Irfan, Muhammad & Alvarado, Rafael, 2022. "Analyze the environmental sustainability factors of China: The role of fossil fuel energy and renewable energy," Renewable Energy, Elsevier, vol. 187(C), pages 390-402.
    3. Guang, Fengtao & Wen, Le & Sharp, Basil, 2022. "Energy efficiency improvements and industry transition: An analysis of China's electricity consumption," Energy, Elsevier, vol. 244(PA).
    4. Liu, Xiaorui & Sun, Tao & Feng, Qiang & Zhang, Di, 2020. "Dynamic nonlinear influence of urbanization on China’s electricity consumption: Evidence from dynamic economic growth threshold effect," Energy, Elsevier, vol. 196(C).
    5. Karmellos, M. & Kosmadakis, V. & Dimas, P. & Tsakanikas, A. & Fylaktos, N. & Taliotis, C. & Zachariadis, T., 2021. "A decomposition and decoupling analysis of carbon dioxide emissions from electricity generation: Evidence from the EU-27 and the UK," Energy, Elsevier, vol. 231(C).
    6. Lin, Boqiang & Huang, Chenchen, 2023. "How will promoting the digital economy affect electricity intensity?," Energy Policy, Elsevier, vol. 173(C).
    7. Yu, Miao & Meng, Bo & Li, Rong, 2022. "Analysis of China's urban household indirect carbon emissions drivers under the background of population aging," Structural Change and Economic Dynamics, Elsevier, vol. 60(C), pages 114-125.
    8. Fang, Debin & Hao, Peng & Yu, Qian & Wang, Jiancheng, 2020. "The impacts of electricity consumption in China's key economic regions," Applied Energy, Elsevier, vol. 267(C).
    9. Han, Yang, 2022. "The impact of the COVID-19 pandemic on China's economic structure: An input–output approach," Structural Change and Economic Dynamics, Elsevier, vol. 63(C), pages 181-195.
    10. Jiang, Shan & Zhu, Yongnan & He, Guohua & Wang, Qingming & Lu, Yajing, 2020. "Factors influencing China’s non-residential power consumption: Estimation using the Kaya–LMDI methods," Energy, Elsevier, vol. 201(C).
    11. Andreoni, Valeria, 2022. "Drivers of coal consumption changes: A decomposition analysis for Chinese regions," Energy, Elsevier, vol. 242(C).
    12. Han, Yang & Zhang, Haotian & Zhao, Yong, 2021. "Structural evolution of real estate industry in China: 2002-2017," Structural Change and Economic Dynamics, Elsevier, vol. 57(C), pages 45-56.

    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. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    2. Chen, Guangwu & Zhu, Yuhan & Wiedmann, Thomas & Yao, Lina & Xu, Lixiao & Wang, Yafei, 2019. "Urban-rural disparities of household energy requirements and influence factors in China: Classification tree models," Applied Energy, Elsevier, vol. 250(C), pages 1321-1335.
    3. Shao, Zhen & Chao, Fu & Yang, Shan-Lin & Zhou, Kai-Le, 2017. "A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 123-136.
    4. Satre-Meloy, Aven, 2019. "Investigating structural and occupant drivers of annual residential electricity consumption using regularization in regression models," Energy, Elsevier, vol. 174(C), pages 148-168.
    5. Guo, Peiyang & Lam, Jacqueline C.K. & Li, Victor O.K., 2019. "Drivers of domestic electricity users’ price responsiveness: A novel machine learning approach," Applied Energy, Elsevier, vol. 235(C), pages 900-913.
    6. Nsangou, Jean Calvin & Kenfack, Joseph & Nzotcha, Urbain & Ngohe Ekam, Paul Salomon & Voufo, Joseph & Tamo, Thomas T., 2022. "Explaining household electricity consumption using quantile regression, decision tree and artificial neural network," Energy, Elsevier, vol. 250(C).
    7. Lan, Jun & Malik, Arunima & Lenzen, Manfred & McBain, Darian & Kanemoto, Keiichiro, 2016. "A structural decomposition analysis of global energy footprints," Applied Energy, Elsevier, vol. 163(C), pages 436-451.
    8. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
    9. Angelopoulos, Dimitrios & Siskos, Yannis & Psarras, John, 2019. "Disaggregating time series on multiple criteria for robust forecasting: The case of long-term electricity demand in Greece," European Journal of Operational Research, Elsevier, vol. 275(1), pages 252-265.
    10. Gui, Shusen & Mu, Hailin & Li, Nan, 2014. "Analysis of impact factors on China's CO2 emissions from the view of supply chain paths," Energy, Elsevier, vol. 74(C), pages 405-416.
    11. Zhang, Chi & Zhou, Kaile & Yang, Shanlin & Shao, Zhen, 2017. "On electricity consumption and economic growth in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 353-368.
    12. Li, Jia Shuo & Zhou, H.W. & Meng, Jing & Yang, Q. & Chen, B. & Zhang, Y.Y., 2018. "Carbon emissions and their drivers for a typical urban economy from multiple perspectives: A case analysis for Beijing city," Applied Energy, Elsevier, vol. 226(C), pages 1076-1086.
    13. Huang, He & Hong, Jingke & Wang, Xianzhu & Chang-Richards, Alice & Zhang, Jingxiao & Qiao, Bei, 2022. "A spatiotemporal analysis of the driving forces behind the energy interactions of the Chinese economy: Evidence from static and dynamic perspectives," Energy, Elsevier, vol. 239(PB).
    14. Fernández González, P. & Presno, M.J. & Landajo, M., 2015. "Regional and sectoral attribution to percentage changes in the European Divisia carbonization index," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1437-1452.
    15. 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).
    16. Yuehjen E. Shao & Yi-Shan Tsai, 2018. "Electricity Sales Forecasting Using Hybrid Autoregressive Integrated Moving Average and Soft Computing Approaches in the Absence of Explanatory Variables," Energies, MDPI, vol. 11(7), pages 1-22, July.
    17. Jones, Rory V. & Fuertes, Alba & Lomas, Kevin J., 2015. "The socio-economic, dwelling and appliance related factors affecting electricity consumption in domestic buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 901-917.
    18. PU, Zhengning & YUE, Shujing & GAO, Peng, 2020. "The driving factors of China's embodied carbon emissions," Technological Forecasting and Social Change, Elsevier, vol. 153(C).
    19. Jeong, Kwangbok & Koo, Choongwan & Hong, Taehoon, 2014. "An estimation model for determining the annual energy cost budget in educational facilities using SARIMA (seasonal autoregressive integrated moving average) and ANN (artificial neural network)," Energy, Elsevier, vol. 71(C), pages 71-79.
    20. Sun-Youn Shin & Han-Gyun Woo, 2022. "Energy Consumption Forecasting in Korea Using Machine Learning Algorithms," Energies, MDPI, vol. 15(13), pages 1-20, July.

    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:eee:streco:v:51:y:2019:i:c:p:67-76. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/525148 .

    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.