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Improved granularity in input-output analysis of embodied energy and emissions: The use of monthly data

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  • Su, Bin
  • Ang, B.W.

Abstract

Input-output (I-O) analysis has been widely used in national energy and energy-related emission studies. These studies are generally conducted using annual data. In a growing number of countries, significant variations in renewable energy supply and in final demands of goods and services are observed over time within a year. These temporal variations cannot be captured in I-O analysis using annual data. To investigate such temporal dynamics, we propose an I-O analysis framework that uses monthly data. Further to that, the drivers in embodiments and aggregate embodied intensity (AEI) indicators are studied via Structural Decomposition Analysis (SDA). Additive SDA and multiplicative SDA are applied to reveal the temporal dynamics associated with energy and emission embodiments and AEI indicators, respectively. An application study using China's 2018 datasets show the importance of temporal dynamics in studying its embodiments and AEI indicators, with drivers of their changes show significant variations over months. It is shown that increased data granularity reveals useful information which would otherwise undetected if annual data are employed. Implications of the findings on future research are discussed.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:eneeco:v:113:y:2022:i:c:s0140988322003887
    DOI: 10.1016/j.eneco.2022.106245
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    References listed on IDEAS

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    Cited by:

    1. Sun, Ya-Fang & Yu, Shiwei & Zhang, Yue-Jun & Su, Bin, 2023. "How do imports change the energy consumption of China? An analysis of its role in intermediate inputs and final demands," Energy, Elsevier, vol. 270(C).
    2. Zhang, Guo-Xing & Yang, Yang & Su, Bin & Nie, Yan & Duan, Hong-Bo, 2023. "Electricity production, power generation structure, and air pollution: A monthly data analysis for 279 cities in China (2015–2019)," Energy Economics, Elsevier, vol. 120(C).
    3. Yan, Junna & Li, Yingzhu & Su, Bin & Ng, Tsan Sheng, 2022. "Contributors and drivers of Chinese energy use and intensity from regional and demand perspectives, 2012-2015-2017," Energy Economics, Elsevier, vol. 115(C).

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    More about this item

    Keywords

    Temporal disaggregation; Input-output analysis; Structural decomposition analysis; Embodied energy/emissions; Aggregate embodied intensity; China;
    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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