IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v15y2018i11p2507-d181627.html
   My bibliography  Save this article

A Comparative Analysis of Residential Energy Consumption in Urban and Rural China: Determinants and Regional Disparities

Author

Listed:
  • Feng Dong

    (School of Management, China University of Mining and Technology, Xuzhou 221116, China)

  • Bolin Yu

    (School of Management, China University of Mining and Technology, Xuzhou 221116, China)

  • Yifei Hua

    (School of Management, China University of Mining and Technology, Xuzhou 221116, China)

  • Shuaiqing Zhang

    (School of Management, China University of Mining and Technology, Xuzhou 221116, China)

  • Yue Wang

    (School of Management, China University of Mining and Technology, Xuzhou 221116, China)

Abstract

Residential energy consumption (REC) has become increasingly important in constructing an energy-saving and environment-friendly society in China. The main purpose of this study is to provide a more in-depth analysis of the determinants of REC from an urban-rural segregation perspective, and quantify the contributions of individual determinants to the regional disparities of REC. Based on the extended STIRPAT (the stochastic impacts by regression on population, affluence, and technology) model, seemingly unrelated regression (SUR) estimation is employed to examine the impacts of various determinants of urban REC per capita (URECP) and rural REC per capita (RRECP) in a sample of China’s 30 provinces over the period 2007–2016. Then, following the results of SUR, this paper tries to explore the reasons for interprovincial disparities of URECP and RRECP by using the Shapley value decomposition. The empirical results show that income level and heating lead to an increase in URECP, while other factors, including the share of natural gas, average temperature, child dependency ratio and gross dependency ratio, significantly decrease URECP. In terms of RRECP, it is shown that old-age dependency ratio, income level and the share of coal consumption positively influence RRECP, while average temperature has a negative effect on RRECP. Specially, the effect of gross dependency ratio on RRECP is positive, indicating the non-working-age population causes more energy use than the working-age population in rural areas. According to the Shapley decomposition, rather than social-economic variables, climate and heating factors contribute the most to the interprovincial differences in URECP. Furthermore, it is found that income level is the most important factor accounting for inter-provincial differences in RRECP. The findings of this research are of great interest, not only to scholars in REC-related fields, but also to decision makers.

Suggested Citation

  • Feng Dong & Bolin Yu & Yifei Hua & Shuaiqing Zhang & Yue Wang, 2018. "A Comparative Analysis of Residential Energy Consumption in Urban and Rural China: Determinants and Regional Disparities," IJERPH, MDPI, vol. 15(11), pages 1-19, November.
  • Handle: RePEc:gam:jijerp:v:15:y:2018:i:11:p:2507-:d:181627
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/15/11/2507/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/15/11/2507/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pablo-Romero, María del P. & Pozo-Barajas, Rafael & Yñiguez, Rocío, 2017. "Global changes in residential energy consumption," Energy Policy, Elsevier, vol. 101(C), pages 342-352.
    2. Akihiro Otsuka, 2018. "Population Agglomeration and Residential Energy Consumption: Evidence from Japan," Sustainability, MDPI, vol. 10(2), pages 1-12, February.
    3. Feng Dong & Bolin Yu & Jixiong Zhang, 2018. "What Contributes to Regional Disparities of Energy Consumption in China? Evidence from Quantile Regression-Shapley Decomposition Approach," Sustainability, MDPI, vol. 10(6), pages 1-26, May.
    4. Zheng, Xinye & Wei, Chu & Qin, Ping & Guo, Jin & Yu, Yihua & Song, Feng & Chen, Zhanming, 2014. "Characteristics of residential energy consumption in China: Findings from a household survey," Energy Policy, Elsevier, vol. 75(C), pages 126-135.
    5. Guanghua Wan, 2002. "Regression-based Inequality Decomposition: Pitfalls and a Solution Procedure," WIDER Working Paper Series DP2002-101, World Institute for Development Economic Research (UNU-WIDER).
    6. Garau, Giorgio & Lecca, Patrizio & Mandras, Giovanni, 2013. "The impact of population ageing on energy use: Evidence from Italy," Economic Modelling, Elsevier, vol. 35(C), pages 970-980.
    7. Hamza, Neveen & Gilroy, Rose, 2011. "The challenge to UK energy policy: An ageing population perspective on energy saving measures and consumption," Energy Policy, Elsevier, vol. 39(2), pages 782-789, February.
    8. 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.
    9. Feng, Zhen-Hua & Zou, Le-Le & Wei, Yi-Ming, 2011. "The impact of household consumption on energy use and CO2 emissions in China," Energy, Elsevier, vol. 36(1), pages 656-670.
    10. Wang, Qiang, 2014. "Effects of urbanisation on energy consumption in China," Energy Policy, Elsevier, vol. 65(C), pages 332-339.
    11. Teixidó-Figueras, Jordi & Duro, Juan Antonio, 2015. "The building blocks of International Ecological Footprint inequality: A Regression-Based Decomposition," Ecological Economics, Elsevier, vol. 118(C), pages 30-39.
    12. Sardianou, Eleni, 2007. "Estimating energy conservation patterns of Greek households," Energy Policy, Elsevier, vol. 35(7), pages 3778-3791, July.
    13. Herrerias, M.J. & Aller, Carlos & Ordóñez, Javier, 2017. "Residential energy consumption: A convergence analysis across Chinese regions," Energy Economics, Elsevier, vol. 62(C), pages 371-381.
    14. York, Richard & Rosa, Eugene A. & Dietz, Thomas, 2003. "STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts," Ecological Economics, Elsevier, vol. 46(3), pages 351-365, October.
    15. Jonathan Morduch & Terry Sicular, 2002. "Rethinking Inequality Decomposition, With Evidence from Rural China," Economic Journal, Royal Economic Society, vol. 112(476), pages 93-106, January.
    16. Brantley Liddle, 2011. "Consumption-Driven Environmental Impact and Age Structure Change in OECD Countries," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 24(30), pages 749-770.
    17. Yue-Jun Zhang & Zhao Liu & Huan Zhang & Tai-De Tan, 2014. "The impact of economic growth, industrial structure and urbanization on carbon emission intensity in China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 73(2), pages 579-595, September.
    18. Zhang, Ming & Song, Yan & Li, Peng & Li, Huanan, 2016. "Study on affecting factors of residential energy consumption in urban and rural Jiangsu," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 330-337.
    19. Wan, Guanghua, 2004. "Accounting for income inequality in rural China: a regression-based approach," Journal of Comparative Economics, Elsevier, vol. 32(2), pages 348-363, June.
    20. Kronenberg, Tobias, 2009. "The impact of demographic change on energy use and greenhouse gas emissions in Germany," Ecological Economics, Elsevier, vol. 68(10), pages 2637-2645, August.
    21. Han, Hongyun & Wu, Shu, 2018. "Rural residential energy transition and energy consumption intensity in China," Energy Economics, Elsevier, vol. 74(C), pages 523-534.
    22. 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.
    23. Xu, Xinkuo & Han, Liyan & Lv, Xiaofeng, 2016. "Household carbon inequality in urban China, its sources and determinants," Ecological Economics, Elsevier, vol. 128(C), pages 77-86.
    24. Yamasaki, Eiji & Tominaga, Norio, 1997. "Evolution of an aging society and effect on residential energy demand," Energy Policy, Elsevier, vol. 25(11), pages 903-912, September.
    25. Rogan, Fionn & Cahill, Caiman J. & Ó Gallachóir, Brian P., 2012. "Decomposition analysis of gas consumption in the residential sector in Ireland," Energy Policy, Elsevier, vol. 42(C), pages 19-36.
    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. Ting Yue & Ruyin Long & Junli Liu & Haiwen Liu & Hong Chen, 2019. "Empirical Study on Households’ Energy-Conservation Behavior of Jiangsu Province in China: The Role of Policies and Behavior Results," IJERPH, MDPI, vol. 16(6), pages 1-16, March.
    2. Long, Ruyin & Wang, Jiaqi & Chen, Hong & Li, Qianwen & Wu, Meifen & Tan-Soo, Jie-Sheng, 2023. "Applying multilevel structural equation modeling to energy-saving behavior: The interaction of individual- and city-level factors," Energy Policy, Elsevier, vol. 174(C).
    3. Yang Tang & Yifeng Liu & Weiqiang Huo & Meng Chen & Shilong Ye & Lei Cheng, 2023. "Optimal Allocation Scheme of Renewable Energy Consumption Responsibility Weight under Renewable Portfolio Standards: An Integrated Evolutionary Game and Stochastic Optimization Approach," Energies, MDPI, vol. 16(7), pages 1-22, March.

    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. Feng Dong & Bolin Yu & Jixiong Zhang, 2018. "What Contributes to Regional Disparities of Energy Consumption in China? Evidence from Quantile Regression-Shapley Decomposition Approach," Sustainability, MDPI, vol. 10(6), pages 1-26, May.
    2. Jaehyeok Kim & Minwoo Jang & Donghyun Shin, 2019. "Examining the Role of Population Age Structure upon Residential Electricity Demand: A Case from Korea," Sustainability, MDPI, vol. 11(14), pages 1-19, July.
    3. Ha-Hyun Jo & Minwoo Jang & Jaehyeok Kim, 2020. "How Population Age Distribution Affects Future Electricity Demand in Korea: Applying Population Polynomial Function," Energies, MDPI, vol. 13(20), pages 1-17, October.
    4. Xiaofeng Lv & Kun Lin & Lingshan Chen & Yongzhong Zhang, 2022. "Does Retirement Affect Household Energy Consumption Structure? Evidence from a Regression Discontinuity Design," Sustainability, MDPI, vol. 14(19), pages 1-14, September.
    5. Zhu, Penghu & Lin, Boqiang, 2022. "Do the elderly consume more energy? Evidence from the retirement policy in urban China," Energy Policy, Elsevier, vol. 165(C).
    6. Shi, Xinjie, 2019. "Inequality of opportunity in energy consumption in China," Energy Policy, Elsevier, vol. 124(C), pages 371-382.
    7. Yongxia Ding & Wei Qu & Shuwen Niu & Man Liang & Wenli Qiang & Zhenguo Hong, 2016. "Factors Influencing the Spatial Difference in Household Energy Consumption in China," Sustainability, MDPI, vol. 8(12), pages 1-20, December.
    8. Qi, Wei & Li, Guangdong, 2020. "Residential carbon emission embedded in China's inter-provincial population migration," Energy Policy, Elsevier, vol. 136(C).
    9. Wang, Zhibao & Wei, Lijie & Zhang, Xiaoping & Qi, Guangzhi, 2023. "Impact of demographic age structure on energy consumption structure: Evidence from population aging in mainland China," Energy, Elsevier, vol. 273(C).
    10. Wang, Qiang & Lin, Jian & Zhou, Kan & Fan, Jie & Kwan, Mei-Po, 2020. "Does urbanization lead to less residential energy consumption? A comparative study of 136 countries," Energy, Elsevier, vol. 202(C).
    11. Fan, Jing-Li & Zhang, Yue-Jun & Wang, Bing, 2017. "The impact of urbanization on residential energy consumption in China: An aggregated and disaggregated analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 220-233.
    12. Huang, Wen-Hsiu, 2015. "The determinants of household electricity consumption in Taiwan: Evidence from quantile regression," Energy, Elsevier, vol. 87(C), pages 120-133.
    13. Wu, Shimei & Chen, Zhan-Ming, 2023. "Carbon inequality in China: Evidence from city-level data," China Economic Review, Elsevier, vol. 78(C).
    14. Day, Rosie, 2015. "Low carbon thermal technologies in an ageing society – What are the issues?," Energy Policy, Elsevier, vol. 84(C), pages 250-256.
    15. Ota, Toru & Kakinaka, Makoto & Kotani, Koji, 2018. "Demographic effects on residential electricity and city gas consumption in the aging society of Japan," Energy Policy, Elsevier, vol. 115(C), pages 503-513.
    16. Wang, Qiang & Wu, Shi-dai & Zeng, Yue-e & Wu, Bo-wei, 2016. "Exploring the relationship between urbanization, energy consumption, and CO2 emissions in different provinces of China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1563-1579.
    17. Xu, Xinkuo & Han, Liyan & Lv, Xiaofeng, 2016. "Household carbon inequality in urban China, its sources and determinants," Ecological Economics, Elsevier, vol. 128(C), pages 77-86.
    18. Xueting Jin & Yu Li & Dongqi Sun & Jinzhou Zhang & Ji Zheng, 2019. "Factors Controlling Urban and Rural Indirect Carbon Dioxide Emissions in Household Consumption: A Case Study in Beijing," Sustainability, MDPI, vol. 11(23), pages 1-21, November.
    19. Qiucheng Li & Jiang Hu & Bolin Yu, 2021. "Spatiotemporal Patterns and Influencing Mechanism of Urban Residential Energy Consumption in China," Energies, MDPI, vol. 14(13), pages 1-17, June.
    20. Juan Antonio Duro & Jordi Teixidó-Figueras & Emilio Padilla, 2017. "The Causal Factors of International Inequality in $$\hbox {CO}_{2}$$ CO 2 Emissions Per Capita: A Regression-Based Inequality Decomposition Analysis," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 67(4), pages 683-700, August.

    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:jijerp:v:15:y:2018:i:11:p:2507-:d:181627. 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.