IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i8p2731-d789393.html
   My bibliography  Save this article

Household Energy Consumption Patterns and Carbon Emissions for the Megacities—Evidence from Guangzhou, China

Author

Listed:
  • Lu Jiang

    (Academy of Plateau Science and Sustainability, People’s Government of Qinghai Province and Beijing Normal University, Xining 810016, China
    Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
    Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510030, China)

  • Bowenpeng Ding

    (College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China)

  • Xiaonan Shi

    (Northeast Institute of Geography and Agroecology, CAS, Jilin 130102, China)

  • Chunhua Li

    (Academy of Plateau Science and Sustainability, People’s Government of Qinghai Province and Beijing Normal University, Xining 810016, China)

  • Yamei Chen

    (Academy of Plateau Science and Sustainability, People’s Government of Qinghai Province and Beijing Normal University, Xining 810016, China)

Abstract

Over the last 20 years, energy consumption in the residential sector in China has grown rapidly, and the growth is faster than that of any other energy form. To assess the limitations of the spatial characteristics of household energy consumption in urban areas, this paper selected Guangzhou as the research area. Specifically, the old town, core area, central area and peri-urban areas, which best reflect the evolutionary characteristics and spatial differentiation of households, were assessed. Based on the surveyed database of community-scale household energy consumption (N = 1097), the spatial heterogeneity of household energy consumption and carbon emissions at the community scale were assessed through exploratory spatial data analysis and the standard deviation ellipse method. The results report that (1) the main sources of energy consumption in Guangzhou households were water heating equipment, kitchen equipment and refrigeration equipment, which were related to the climatic conditions and cultural traditions of the city. (2) There was significant spatial heterogeneity in the carbon emissions from household domestic energy use in Guangzhou. (3) The economic level, the effects of the Lingnan culture and the characteristics of residents are the main drivers influencing the spatial characteristics of household energy consumption and carbon emissions in Guangzhou. We propose that policy development should actively promote energy-efficient equipment, such as water heating and cooling equipment, in urban households and take full account of the basic household energy needs of residents in old urban and suburban areas while promoting the development of low-carbon buildings.

Suggested Citation

  • Lu Jiang & Bowenpeng Ding & Xiaonan Shi & Chunhua Li & Yamei Chen, 2022. "Household Energy Consumption Patterns and Carbon Emissions for the Megacities—Evidence from Guangzhou, China," Energies, MDPI, vol. 15(8), pages 1-21, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:8:p:2731-:d:789393
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/8/2731/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/8/2731/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Zhifu Mi & Jiali Zheng & Jing Meng & Jiamin Ou & Klaus Hubacek & Zhu Liu & D’Maris Coffman & Nicholas Stern & Sai Liang & Yi-Ming Wei, 2020. "Economic development and converging household carbon footprints in China," Nature Sustainability, Nature, vol. 3(7), pages 529-537, July.
    3. De Lauretis, Simona & Ghersi, Frédéric & Cayla, Jean-Michel, 2017. "Energy consumption and activity patterns: An analysis extended to total time and energy use for French households," Applied Energy, Elsevier, vol. 206(C), pages 634-648.
    4. Golley, Jane & Meng, Xin, 2012. "Income inequality and carbon dioxide emissions: The case of Chinese urban households," Energy Economics, Elsevier, vol. 34(6), pages 1864-1872.
    5. Jihoon Min & Zeke Hausfather & Qi Feng Lin, 2010. "A High‐Resolution Statistical Model of Residential Energy End Use Characteristics for the United States," Journal of Industrial Ecology, Yale University, vol. 14(5), pages 791-807, October.
    6. Moshiri, Saeed, 2015. "The effects of the energy price reform on households consumption in Iran," Energy Policy, Elsevier, vol. 79(C), pages 177-188.
    7. Xie, Lunyu & Yan, Haosheng & Zhang, Shuhan & Wei, Chu, 2020. "Does urbanization increase residential energy use? Evidence from the Chinese residential energy consumption survey 2012," China Economic Review, Elsevier, vol. 59(C).
    8. Shimei Wu & Xinye Zheng & Chu Wei, 2017. "Measurement of inequality using household energy consumption data in rural China," Nature Energy, Nature, vol. 2(10), pages 795-803, October.
    9. Zaneta Simanaviciene & Virgilijus Dirma & Arturas Simanavicius, 2017. "Psychological Factors Influence On Energy Efficiency In Households," Working Papers 112/2017, Institute of Economic Research, revised May 2017.
    10. Li, Jiajia & Zhang, Jian & Zhang, Dayong & Ji, Qiang, 2019. "Does gender inequality affect household green consumption behaviour in China?," Energy Policy, Elsevier, vol. 135(C).
    11. Estiri, Hossein, 2014. "Building and household X-factors and energy consumption at the residential sector," Energy Economics, Elsevier, vol. 43(C), pages 178-184.
    12. Yang Wang & Xiaoli Yue & Hong’ou Zhang & Yongxian Su & Jing Qin, 2021. "Relationship between Urban Floating Population Distribution and Livability Environment: Evidence from Guangzhou’s Urban District, China," Sustainability, MDPI, vol. 13(23), pages 1-15, December.
    13. Jiang, Lu & Yu, Lu & Xue, Bing & Chen, Xingpeng & Mi, Zhifu, 2020. "Who is energy poor? Evidence from the least developed regions in China," Energy Policy, Elsevier, vol. 137(C).
    14. Zaneta Simanaviciene & Virgilijus Dirma & Arturas Simanavicius, 2017. "Psychological factors influence on energy efficiency in households," Oeconomia Copernicana, Institute of Economic Research, vol. 8(4), pages 671-684, December.
    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. Yamei Chen & Lu Jiang, 2022. "Influencing Factors of Direct Carbon Emissions of Households in Urban Villages in Guangzhou, China," IJERPH, MDPI, vol. 19(24), pages 1-14, December.
    2. Yanyi Zhu & Youpei Hu, 2023. "The Correlation between Urban Form and Carbon Emissions: A Bibliometric and Literature Review," Sustainability, MDPI, vol. 15(18), pages 1-28, September.
    3. Jingjing Chen & Yangyang Lin & Xiaojun Wang & Bingjing Mao & Lihong Peng, 2022. "Direct and Indirect Carbon Emission from Household Consumption Based on LMDI and SDA Model: A Decomposition and Comparison Analysis," Energies, MDPI, vol. 15(14), pages 1-22, July.

    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, Shaoyue & Xu, Xiangbo & Li, Chang & Zhang, Linxiu & Sun, Mingxing, 2021. "Energy consumption inequality decrease with energy consumption increase: Evidence from rural China at micro scale," Energy Policy, Elsevier, vol. 159(C).
    2. Lei, Mingyu & Cai, Wenjia & Liu, Wenling & Wang, Can, 2022. "The heterogeneity in energy consumption patterns and home appliance purchasing preferences across urban households in China," Energy, Elsevier, vol. 253(C).
    3. Wang, Na & Fu, Xiaodong & Wang, Shaobin & Yang, Hao & Li, Zhen, 2022. "Convergence characteristics and distribution patterns of residential electricity consumption in China: An urban-rural gap perspective," Energy, Elsevier, vol. 254(PB).
    4. Xu, Shang & Zhang, Jun, 2023. "The welfare impacts of removing coal subsidies in rural China," Energy Economics, Elsevier, vol. 118(C).
    5. Shi, Xunpeng & Wang, Keying & Cheong, Tsun Se & Zhang, Hongwu, 2020. "Prioritizing driving factors of household carbon emissions: An application of the LASSO model with survey data," Energy Economics, Elsevier, vol. 92(C).
    6. Zhang, Hongwu & Shi, Xunpeng & Cheong, Tsun Se & Wang, Keying, 2020. "Convergence of carbon emissions at the household level in China: A distribution dynamics approach," Energy Economics, Elsevier, vol. 92(C).
    7. Xinkuo Xu & Liyan Han, 2017. "Diverse Effects of Consumer Credit on Household Carbon Emissions at Quantiles: Evidence from Urban China," Sustainability, MDPI, vol. 9(9), pages 1-25, September.
    8. Li, Jiajia & Li, Houjian, 2022. "Spiritual support or living support: Which alleviates solid fuel use for rural households in ethnical minority regions of China?," Renewable Energy, Elsevier, vol. 189(C), pages 479-491.
    9. Nguyen, Trung Thanh & Nguyen, Thanh-Tung & Hoang, Viet-Ngu & Wilson, Clevo & Managi, Shunsuke, 2019. "Energy transition, poverty and inequality in Vietnam," Energy Policy, Elsevier, vol. 132(C), pages 536-548.
    10. Lu Jiang & Xingpeng Chen & Bing Xue, 2019. "Features, Driving Forces and Transition of the Household Energy Consumption in China: A Review," Sustainability, MDPI, vol. 11(4), pages 1-20, February.
    11. Sun, Shuyu & Tong, Kangkang, 2024. "Rural-urban inequality in energy use sufficiency and efficiency during a rapid urbanization period," Applied Energy, Elsevier, vol. 364(C).
    12. Chalal, M.L. & Medjdoub, B. & Bezai, N. & Bull, R. & Zune, M., 2022. "Visualisation in energy eco-feedback systems: A systematic review of good practice," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    13. Zhu, Mengshu & Huang, Ying & Wang, Si-Nuo & Zheng, Xinye & Wei, Chu, 2023. "Characteristics and patterns of residential energy consumption for space cooling in China: Evidence from appliance-level data," Energy, Elsevier, vol. 265(C).
    14. Fang Shen & Zibibula Simayi & Shengtian Yang & Yusuyunjiang Mamitimin & Xiaofen Zhang & Yunyi Zhang, 2023. "A Bibliometric Review of Household Carbon Footprint during 2000–2022," Sustainability, MDPI, vol. 15(7), pages 1-25, April.
    15. Zhang, Yimeng & Wang, Feng & Zhang, Bing, 2023. "The impacts of household structure transitions on household carbon emissions in China," Ecological Economics, Elsevier, vol. 206(C).
    16. Estiri, Hossein, 2014. "Energy Planning in the Big Data Era: A Theme Study of the Residential Sector," EconStor Conference Papers 106936, ZBW - Leibniz Information Centre for Economics.
    17. Wang, Yao & Du, Zhili, 2024. "Has energy poverty entangled the households by hindering the filial generation?," Energy Policy, Elsevier, vol. 186(C).
    18. Zihan Zhang & Enping Li & Guowei Zhang, 2023. "How Efficient China’s Tiered Pricing Is for Household Electricity: Evidence from Survey Data," Sustainability, MDPI, vol. 15(2), pages 1-17, January.
    19. Yulin Liu & Min Zhang & Rujia Liu, 2020. "The Impact of Income Inequality on Carbon Emissions in China: A Household-Level Analysis," Sustainability, MDPI, vol. 12(7), pages 1-22, March.
    20. Lanre Olatomiwa & James Garba Ambafi & Umar Suleiman Dauda & Omowunmi Mary Longe & Kufre Esenowo Jack & Idowu Adetona Ayoade & Isah Ndakara Abubakar & Alabi Kamilu Sanusi, 2023. "A Review of Internet of Things-Based Visualisation Platforms for Tracking Household Carbon Footprints," Sustainability, MDPI, vol. 15(20), pages 1-32, October.

    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:jeners:v:15:y:2022:i:8:p:2731-:d:789393. 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.