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Gigatonnes Missing Biomass Energy Consumption in Rural China

Author

Listed:
  • Shimei Wu
  • Xiao Han
  • Chuan-Zhong Li
  • Andreas Löschel
  • Xi Lu
  • Limin Du
  • Xinye Zheng
  • Chu Wei

Abstract

To provide a more comprehensive reconstruction of China’s energy consumption, this paper built a machine-learning-based geospatial model that shows great accuracy in recovering historical biomass consumption data using the household survey dataset for China, combined with province-level characteristics and spatiotemporal information. Our study suggested that 6.9 ± 2.6 giga-tons of coal equivalent of biomass were uncounted in China’s statistics, representing 15.9 ± 6.0 percent for China and 2.5 ± 0.9 percent for global final energy consumption. This new estimate significantly reshaped our understanding of China’s energy composition, sectoral mix, indoor air pollutants, and the factors driving energy consumption. These findings provide a replicable template for developing countries hoping to uncover the biomass demand to better design public policy to achieve Sustainable Development Goals. JEL Classification: Q41, Q53, R12, C81, O13

Suggested Citation

  • Shimei Wu & Xiao Han & Chuan-Zhong Li & Andreas Löschel & Xi Lu & Limin Du & Xinye Zheng & Chu Wei, 2024. "Gigatonnes Missing Biomass Energy Consumption in Rural China," The Energy Journal, , vol. 45(5), pages 149-166, September.
    • Handle: RePEc:sae:enejou:v:45:y:2024:i:5:p:149-166
    DOI: 10.1177/01956574241266970
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    References listed on IDEAS

    as
    1. Ang, B. W., 2004. "Decomposition analysis for policymaking in energy:: which is the preferred method?," Energy Policy, Elsevier, vol. 32(9), pages 1131-1139, June.
    2. Niu, Shuwen & Zhang, Xin & Zhao, Chunsheng & Niu, Yunzhu, 2012. "Variations in energy consumption and survival status between rural and urban households: A case study of the Western Loess Plateau, China," Energy Policy, Elsevier, vol. 49(C), pages 515-527.
    3. Peng, Liqun & Zhang, Qiang & Yao, Zhiliang & Mauzerall, Denise L. & Kang, Sicong & Du, Zhenyu & Zheng, Yixuan & Xue, Tao & He, Kebin, 2019. "Underreported coal in statistics: A survey-based solid fuel consumption and emission inventory for the rural residential sector in China," Applied Energy, Elsevier, vol. 235(C), pages 1169-1182.
    4. Destek, Mehmet Akif & Sarkodie, Samuel Asumadu & Asamoah, Ernest Frimpong, 2020. "Does biomass energy drive environmental sustainability? An SDG perspective for top five biomass consuming countries," MPRA Paper 114218, University Library of Munich, Germany, revised 29 Mar 2021.
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    More about this item

    Keywords

    machine learning; biomass demand; rural energy statistics; China;
    All these keywords.

    JEL classification:

    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q53 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products

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