IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v7y2015i12p15789-15846d59542.html
   My bibliography  Save this article

Energy Service Demand Projections and CO 2 Reduction Potentials in Rural Households in 31 Chinese Provinces

Author

Listed:
  • Rui Xing

    (Center for Social and Environmental Systems Research, National Institute for Environmental Studies, 16-2, Onogawa, Tsukuba, Ibaraki 305-8506, Japan
    These authors contributed equally to this work.)

  • Tatsuya Hanaoka

    (Center for Social and Environmental Systems Research, National Institute for Environmental Studies, 16-2, Onogawa, Tsukuba, Ibaraki 305-8506, Japan
    These authors contributed equally to this work.)

  • Yuko Kanamori

    (Center for Social and Environmental Systems Research, National Institute for Environmental Studies, 16-2, Onogawa, Tsukuba, Ibaraki 305-8506, Japan
    These authors contributed equally to this work.)

  • Hancheng Dai

    (Center for Social and Environmental Systems Research, National Institute for Environmental Studies, 16-2, Onogawa, Tsukuba, Ibaraki 305-8506, Japan
    These authors contributed equally to this work.)

  • Toshihiko Masui

    (Center for Social and Environmental Systems Research, National Institute for Environmental Studies, 16-2, Onogawa, Tsukuba, Ibaraki 305-8506, Japan
    These authors contributed equally to this work.)

Abstract

Until 2012, most of China’s population lived in rural areas with markedly different patterns of household energy consumption from those in Chinese cities. The studies so far done on residential energy use in rural Chinese households have been limited to questionnaire surveys and panel data analyses. Hardly any studies on energy demand in rural areas have considered both the climatic and economic disparities across Chinese regions. In this study we conduct a systematic analysis of the rural Chinese residential sector on a regional basis. We begin by developing a macro-model to estimate energy service demands up to 2050. Next, we apply the AIM(Asia-Pasific Integrated Model)/Enduse model, a bottom-up cost-minimization model with a detailed mitigation technology database, to estimate the mitigation potential of low-carbon technologies in rural China. Our results show that energy service demand in the rural household sector will continue to increase in regions with growing population or income conditions. However, after 2030, the rural residential energy service demand will start to decline in most Chinese regions. The impacts of efficient technologies will vary from one region to the next due to regional climatic and economic disparities. Throughout all of China, the penetration of efficient technologies can reduce CO 2 emissions by 20% to 50%. Of the technologies available, efficient lighting, biomass water heaters, and efficient electronics bring the most benefit when implemented in rural households.

Suggested Citation

  • Rui Xing & Tatsuya Hanaoka & Yuko Kanamori & Hancheng Dai & Toshihiko Masui, 2015. "Energy Service Demand Projections and CO 2 Reduction Potentials in Rural Households in 31 Chinese Provinces," Sustainability, MDPI, vol. 7(12), pages 1-14, November.
  • Handle: RePEc:gam:jsusta:v:7:y:2015:i:12:p:15789-15846:d:59542
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/7/12/15789/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/7/12/15789/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Evans, Meredydd & Yu, Sha & Song, Bo & Deng, Qinqin & Liu, Jing & Delgado, Alison, 2014. "Building energy efficiency in rural China," Energy Policy, Elsevier, vol. 64(C), pages 243-251.
    2. Chitnis, Mona & Hunt, Lester C., 2012. "What drives the change in UK household energy expenditure and associated CO2 emissions? Implication and forecast to 2020," Applied Energy, Elsevier, vol. 94(C), pages 202-214.
    3. Yu, Sha & Eom, Jiyong & Zhou, Yuyu & Evans, Meredydd & Clarke, Leon, 2014. "Scenarios of building energy demand for China with a detailed regional representation," Energy, Elsevier, vol. 67(C), pages 284-297.
    4. Dai, Hancheng & Masui, Toshihiko & Matsuoka, Yuzuru & Fujimori, Shinichiro, 2012. "The impacts of China’s household consumption expenditure patterns on energy demand and carbon emissions towards 2050," Energy Policy, Elsevier, vol. 50(C), pages 736-750.
    5. Krey, Volker & O'Neill, Brian C. & van Ruijven, Bas & Chaturvedi, Vaibhav & Daioglou, Vassilis & Eom, Jiyong & Jiang, Leiwen & Nagai, Yu & Pachauri, Shonali & Ren, Xiaolin, 2012. "Urban and rural energy use and carbon dioxide emissions in Asia," Energy Economics, Elsevier, vol. 34(S3), pages 272-283.
    6. Eom, Jiyong & Clarke, Leon & Kim, Son H. & Kyle, Page & Patel, Pralit, 2012. "China's building energy demand: Long-term implications from a detailed assessment," Energy, Elsevier, vol. 46(1), pages 405-419.
    7. Büchs, Milena & Schnepf, Sylke V., 2013. "Who emits most? Associations between socio-economic factors and UK households' home energy, transport, indirect and total CO2 emissions," Ecological Economics, Elsevier, vol. 90(C), pages 114-123.
    8. Liu, Wenling & Spaargaren, Gert & Heerink, Nico & Mol, Arthur P.J. & Wang, Can, 2013. "Energy consumption practices of rural households in north China: Basic characteristics and potential for low carbon development," Energy Policy, Elsevier, vol. 55(C), pages 128-138.
    9. Henley, Andrew & Peirson, John, 1997. "Non-linearities in Electricity Demand and Temperature: Parametric versus Non-parametric Methods," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 59(1), pages 149-162, February.
    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. 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.
    2. Zhang, Weishi & Xu, Ying & Wang, Can & Streets, David G., 2022. "Assessment of the driving factors of CO2 mitigation costs of household biogas systems in China: A LMDI decomposition with cost analysis model," Renewable Energy, Elsevier, vol. 181(C), pages 978-989.
    3. Hartin, Corinne & Link, Robert & Patel, Pralit & Mundra, Anupriya & Horowitz, Russell & Dorheim, Kalyn & Clarke, Leon, 2021. "Integrated modeling of human-earth system interactions: An application of GCAM-fusion," Energy Economics, Elsevier, vol. 103(C).
    4. Yu, Sha & Tan, Qing & Evans, Meredydd & Kyle, Page & Vu, Linh & Patel, Pralit L., 2017. "Improving building energy efficiency in India: State-level analysis of building energy efficiency policies," Energy Policy, Elsevier, vol. 110(C), pages 331-341.
    5. Yu, Sha & Eom, Jiyong & Evans, Meredydd & Clarke, Leon, 2014. "A long-term, integrated impact assessment of alternative building energy code scenarios in China," Energy Policy, Elsevier, vol. 67(C), pages 626-639.
    6. Huo, Tengfei & Ma, Yuling & Xu, Linbo & Feng, Wei & Cai, Weiguang, 2022. "Carbon emissions in China's urban residential building sector through 2060: A dynamic scenario simulation," Energy, Elsevier, vol. 254(PA).
    7. Selima Sultana & Nastaran Pourebrahim & Hyojin Kim, 2018. "Household Energy Expenditures in North Carolina: A Geographically Weighted Regression Approach," Sustainability, MDPI, vol. 10(5), pages 1-22, May.
    8. Pan, Xunzhang & Chen, Wenying & Zhou, Sheng & Wang, Lining & Dai, Jiaquan & Zhang, Qi & Zheng, Xinzhu & Wang, Hailin, 2020. "Implications of near-term mitigation on China's long-term energy transitions for aligning with the Paris goals," Energy Economics, Elsevier, vol. 90(C).
    9. Zeng, Cheng & Liu, Shuli & Shukla, Ashish, 2017. "Adaptability research on phase change materials based technologies in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 145-158.
    10. 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.
    11. Beiser-McGrath, Liam & Busemeyer, Marius R., 2023. "Carbon inequality and support for carbon taxation," LSE Research Online Documents on Economics 120925, London School of Economics and Political Science, LSE Library.
    12. Berardi, Umberto, 2017. "A cross-country comparison of the building energy consumptions and their trends," Resources, Conservation & Recycling, Elsevier, vol. 123(C), pages 230-241.
    13. 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).
    14. Dirks, James A. & Gorrissen, Willy J. & Hathaway, John H. & Skorski, Daniel C. & Scott, Michael J. & Pulsipher, Trenton C. & Huang, Maoyi & Liu, Ying & Rice, Jennie S., 2015. "Impacts of climate change on energy consumption and peak demand in buildings: A detailed regional approach," Energy, Elsevier, vol. 79(C), pages 20-32.
    15. De Cian, Enrica & Dasgupta, Shouro & Hof, Andries F. & van Sluisveld, Mariësse A.E. & Köhler, Jonathan & Pfluger, Benjamin & van Vuuren, Detlef P., 2020. "Actors, decision-making, and institutions in quantitative system modelling," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    16. Zhou, Qiang & Liu, Yong & Qu, Shen, 2022. "Emission effects of China's rural revitalization: The nexus of infrastructure investment, household income, and direct residential CO2 emissions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    17. Sager, Lutz, 2019. "Income inequality and carbon consumption: Evidence from Environmental Engel curves," Energy Economics, Elsevier, vol. 84(S1).
    18. Allinson, David & Irvine, Katherine N. & Edmondson, Jill L. & Tiwary, Abhishek & Hill, Graeme & Morris, Jonathan & Bell, Margaret & Davies, Zoe G. & Firth, Steven K. & Fisher, Jill & Gaston, Kevin J. , 2016. "Measurement and analysis of household carbon: The case of a UK city," Applied Energy, Elsevier, vol. 164(C), pages 871-881.
    19. Volker Krey, 2014. "Global energy-climate scenarios and models: a review," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 3(4), pages 363-383, July.
    20. Li, Jianglong & Chen, Chang & Liu, Hongxun, 2019. "Transition from non-commercial to commercial energy in rural China: Insights from the accessibility and affordability," Energy Policy, Elsevier, vol. 127(C), pages 392-403.

    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:7:y:2015:i:12:p:15789-15846:d:59542. 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.