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Structural Evolution of Household Energy Consumption: A China Study

Author

Listed:
  • Qingsong Wang

    (School of Energy and Power Engineering, Shandong University, 17923 Jingshi Road, Jinan 250061, China
    These authors contributed equally to this work.)

  • Ping Liu

    (School of Management, Shandong University, 27 Shanda Road, Jinan 250100, China)

  • Xueliang Yuan

    (School of Energy and Power Engineering, Shandong University, 17923 Jingshi Road, Jinan 250061, China
    These authors contributed equally to this work.)

  • Xingxing Cheng

    (School of Energy and Power Engineering, Shandong University, 17923 Jingshi Road, Jinan 250061, China)

  • Rujian Ma

    (School of Energy and Power Engineering, Shandong University, 17923 Jingshi Road, Jinan 250061, China)

  • Ruimin Mu

    (School of Municipal and Environmental Engineering, Shandong Jianzhu University, Jinan 250101, China)

  • Jian Zuo

    (School of Natural and Built Environments, University of South Australia, Adelaide 5000, Australia)

Abstract

Sustainable energy production and consumption is one of the issues for the sustainable development strategy in China. As China’s economic development paradigm shifts, household energy consumption (HEC) has become a focus of achieving national goals of energy efficiency and greenhouse gas reduction. The information entropy model and LMDI model were employed in this study in order to analyse the structural evolution of HEC, as well as its associated critical factors. The results indicate that the information entropy of HEC increased gradually, and coal will be reduced by clean energies, such as natural gas and liquefied petroleum gas. The information entropy tends to stabilize and converge due to rapid urbanization. Therefore, from the perspective of environmental protection and natural resource conservation, the structure of household energy consumption will be optimized. This study revealed that residents’ income level is one of the most critical factors for the increase of energy consumption, while the energy intensity is the only driving force for the reduction of HEC. The accumulated contribution of these two factors to the HEC is 240.53% and −161.75%, respectively. It is imperative to improve the energy efficiency in the residential sector. Recommendations are provided to improve the energy efficiency-related technologies, as well as the standards for the sustainable energy strategy.

Suggested Citation

  • Qingsong Wang & Ping Liu & Xueliang Yuan & Xingxing Cheng & Rujian Ma & Ruimin Mu & Jian Zuo, 2015. "Structural Evolution of Household Energy Consumption: A China Study," Sustainability, MDPI, vol. 7(4), pages 1-14, April.
    • Handle: RePEc:gam:jsusta:v:7:y:2015:i:4:p:3919-3932:d:47675
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    1. Qingsong Wang & Xueliang Yuan & Jian Zhang & Yun Gao & Jinglan Hong & Jian Zuo & Wei Liu, 2015. "Assessment of the Sustainable Development Capacity with the Entropy Weight Coefficient Method," Sustainability, MDPI, vol. 7(10), pages 1-22, October.
    2. Qingsong Wang & Zhenlei Gao & Hongrui Tang & Xueliang Yuan & Jian Zuo, 2018. "Exploring the Direct Rebound Effect of Energy Consumption: A Case Study," Sustainability, MDPI, vol. 10(1), pages 1-21, January.
    3. Fei Wang & Changjian Wang & Yongxian Su & Lixia Jin & Yang Wang & Xinlin Zhang, 2017. "Decomposition Analysis of Carbon Emission Factors from Energy Consumption in Guangdong Province from 1990 to 2014," Sustainability, MDPI, vol. 9(2), pages 1-15, February.
    4. Qingsong Wang & Hongrui Tang & Xueliang Yuan & Mansen Wang & Hongkun Xiao & Zhi Ma, 2018. "An Early Warning System for Oil Security in China," Sustainability, MDPI, vol. 10(1), pages 1-17, January.
    5. Zhang, Junyi & Teng, Fei & Zhou, Shaojie, 2020. "The structural changes and determinants of household energy choices and energy consumption in urban China: Addressing the role of building type," Energy Policy, Elsevier, vol. 139(C).
    6. Huang, Yun-Hsun, 2020. "Examining impact factors of residential electricity consumption in Taiwan using index decomposition analysis based on end-use level data," Energy, Elsevier, vol. 213(C).

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