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Structural decomposition analysis applied to energy and emissions: Frameworks for monthly data

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  • Su, Bin
  • Ang, B.W.

Abstract

Structural decomposition analysis (SDA) is a well-known approach to studying factors contributing to changes of an aggregate indicator in energy and emissions studies. Such studies normally rely on yearly data since input-output (I-O) tables are needed. With energy and economic transitions and seasonal factors, variations in renewable energy supply and in final demands of goods and services are becoming more prominent within a year in many countries. If monthly data are incorporated, some temporal dynamics within a year can be investigated in SDA application. In this paper, we propose an additive SDA framework and a multiplicative SDA framework that include monthly data to respectively reveal the drivers of temporal dynamics associated with energy/emissions embodiments and aggregate embodied intensity indicators. Based on China's 2018 and 2020 I-O tables, an empirical study is conducted using the proposed frameworks. The results obtained show that the increased granularity helps to reveal temporal dynamics mechanisms which will otherwise be overlooked. We discuss the findings and present areas for future research.

Suggested Citation

  • Su, Bin & Ang, B.W., 2023. "Structural decomposition analysis applied to energy and emissions: Frameworks for monthly data," Energy Economics, Elsevier, vol. 126(C).
    • Handle: RePEc:eee:eneeco:v:126:y:2023:i:c:s0140988323004759
    DOI: 10.1016/j.eneco.2023.106977
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    References listed on IDEAS

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    4. 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).
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    9. Wang, H. & Ang, B.W. & Su, Bin, 2017. "Assessing drivers of economy-wide energy use and emissions: IDA versus SDA," Energy Policy, Elsevier, vol. 107(C), pages 585-599.
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    Cited by:

    1. Li, Qiuping & Wu, Sanmang & Liu, Quanwen & Li, Shantong, 2024. "Role of global value chains in embodied domestic CO2 emissions of China's manufacturing exports: Normal and processing trade heterogeneity," Energy Economics, Elsevier, vol. 132(C).
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    4. Li, Yingzhu & Su, Bin, 2024. "Identification of the bias in embodied emissions flows and their sources," Energy Economics, Elsevier, vol. 136(C).

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    More about this item

    Keywords

    Input-output analysis; Temporal disaggregation; Structural decomposition analysis; Energy and emissions; Temporal dynamics;
    All these keywords.

    JEL classification:

    • C67 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Input-Output Models
    • P28 - Political Economy and Comparative Economic Systems - - Socialist and Transition Economies - - - Natural Resources; Environment
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • Q56 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth
    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods

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