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Promises and pitfalls in environmentally extended input-output analysis for China: a survey of the literature

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  • Hawkins, Jacob
  • Ma, Chunbo
  • Schilizzi, Steven
  • Zhang, Fan

Abstract

As the largest developing economy, China plays a key role in global climate change. Environmentally extended input-output analysis (EE-IOA) is an important and insightful tool seeing widespread use in studying large-scale environmental impacts in China: calculating and analyzing greenhouse gas emissions, carbon and water footprints, pollution, and embedded energy. Chinese EE-IOA are hindered, however, by unreliable data and limited resolution. This paper reviews the body of literature regarding EE-IOA for China in peer-reviewed journals and provides an overview of the articles, examining their methodologies, environmental issues addressed, and data utilized. This paper further identifies the shortcomings in using input-output analyses to gauge environmental impacts in China. Potentially fruitful areas of expansion in Chinese EE-IOA research are denoted, including under-researched environmental issues, underutilized methodologies, and techniques to disaggregate data to move beyond the limitations inherent in official Chinese input-output data.

Suggested Citation

  • Hawkins, Jacob & Ma, Chunbo & Schilizzi, Steven & Zhang, Fan, 2015. "Promises and pitfalls in environmentally extended input-output analysis for China: a survey of the literature," Working Papers 200175, University of Western Australia, School of Agricultural and Resource Economics.
    • Handle: RePEc:ags:uwauwp:200175
    DOI: 10.22004/ag.econ.200175
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    Cited by:

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    5. Wang, Ke & Wang, Jiayu & Wei, Yi-Ming & Zhang, Chi, 2018. "A novel dataset of emission abatement sector extended input-output table for environmental policy analysis," Applied Energy, Elsevier, vol. 231(C), pages 1259-1267.
    6. Zhang, B. & Qiao, H. & Chen, Z.M. & Chen, B., 2016. "Growth in embodied energy transfers via China’s domestic trade: Evidence from multi-regional input–output analysis," Applied Energy, Elsevier, vol. 184(C), pages 1093-1105.
    7. Su, Bin & Thomson, Elspeth, 2016. "China's carbon emissions embodied in (normal and processing) exports and their driving forces, 2006–2012," Energy Economics, Elsevier, vol. 59(C), pages 414-422.
    8. Minyoung Yang & Jinsoo Kim, 2022. "A Critical Review of the Definition and Estimation of Carbon Efficiency," Sustainability, MDPI, vol. 14(16), pages 1-18, August.
    9. Zhang, Bo & Yang, T.R. & Chen, B. & Sun, X.D., 2016. "China’s regional CH4 emissions: Characteristics, interregional transfer and mitigation policies," Applied Energy, Elsevier, vol. 184(C), pages 1184-1195.
    10. Wang, Yizhong & Hang, Ye & Jeong, Sujong & Wang, Qunwei, 2023. "Intersectoral transfers and drivers of net CO2 emissions in China incorporating sources and sinks," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    11. Sun, Xudong & Li, Jiashuo & Qiao, Han & Zhang, Bo, 2017. "Energy implications of China's regional development: New insights from multi-regional input-output analysis," Applied Energy, Elsevier, vol. 196(C), pages 118-131.
    12. Su, Bin & Ang, B.W., 2020. "Demand contributors and driving factors of Singapore’s aggregate carbon intensities," Energy Policy, Elsevier, vol. 146(C).
    13. Su, Bin & Ang, B.W., 2022. "Improved granularity in input-output analysis of embodied energy and emissions: The use of monthly data," Energy Economics, Elsevier, vol. 113(C).
    14. Jacob Hawkins & Chunbo Ma & Steven Schilizzi & Fan Zhang, 2018. "China's changing diet and its impacts on greenhouse gas emissions: an index decomposition analysis," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 62(1), pages 45-64, January.
    15. Zhu, Bangzhu & Su, Bin & Li, Yingzhu & Ng, Tsan Sheng, 2020. "Embodied energy and intensity in China’s (normal and processing) exports and their driving forces, 2005-2015," Energy Economics, Elsevier, vol. 91(C).
    16. Zhang, Xiaomei & Su, Bin & Yang, Jun & Cong, Jianhui, 2022. "Analysis of Shanxi Province's energy consumption and intensity using input-output framework (2002–2017)," Energy, Elsevier, vol. 250(C).
    17. Li, Yingzhu & Su, Bin & Dasgupta, Shyamasree, 2018. "Structural path analysis of India's carbon emissions using input-output and social accounting matrix frameworks," Energy Economics, Elsevier, vol. 76(C), pages 457-469.
    18. Lach, Łukasz, 2022. "Optimization based structural decomposition analysis as a tool for supporting environmental policymaking," Energy Economics, Elsevier, vol. 115(C).
    19. He, Peijun & Ng, Tsan Sheng & Su, Bin, 2017. "Energy-economic recovery resilience with Input-Output linear programming models," Energy Economics, Elsevier, vol. 68(C), pages 177-191.
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    21. Zhang, Zhonghua & Zhao, Yuhuan & Su, Bin & Zhang, Yongfeng & Wang, Song & Liu, Ya & Li, Hao, 2017. "Embodied carbon in China’s foreign trade: An online SCI-E and SSCI based literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 492-510.

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    More about this item

    Keywords

    Environmental Economics and Policy; Resource /Energy Economics and Policy;

    JEL classification:

    • C67 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Input-Output Models
    • D57 - Microeconomics - - General Equilibrium and Disequilibrium - - - Input-Output Tables and Analysis
    • F18 - International Economics - - Trade - - - Trade and Environment
    • O53 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Asia including Middle East
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • Q5 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics

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