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Demand contributors and driving factors of Singapore’s aggregate carbon intensities

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  • Su, Bin
  • Ang, B.W.

Abstract

Singapore is an island city state. It lacks conventional energy resources and is alternative energy disadvantaged. Yet its aggregate carbon intensity, given by the ratio of carbon emissions to GDP, dropped by 30% from 2005 to 2014. Currently it has a relatively low carbon intensity. Using I–O analysis, we study Singapore’s aggregate carbon intensity and the factors contributing to its changes at different levels, i.e. final demand, sector, and transmission levels. It is found that domestic exports accounted for nearly two-thirds of Singapore’s aggregate carbon intensity, followed by private consumption and investment. At the sectoral level, the top three contributors were the petroleum, petrochemical, and land transport sectors. Improvements in emission intensity and final demand structure were major factors contributing to reductions in carbon intensity. Our study contributes to the literature in two aspects. Methodologically, it deals with embodied emission intensity and analyzes its changes from the demand perspective. This differs from most studies in the literature from the production perspective. Empirically, the findings and policy implications are unique as they are applicable to a major city. With urbanization taking place in many developing countries, cites’ role in emissions and environmental sustainability are growing in importance but not many in-depth analyses similar to our study have been reported.

Suggested Citation

  • Su, Bin & Ang, B.W., 2020. "Demand contributors and driving factors of Singapore’s aggregate carbon intensities," Energy Policy, Elsevier, vol. 146(C).
    • Handle: RePEc:eee:enepol:v:146:y:2020:i:c:s0301421520305358
    DOI: 10.1016/j.enpol.2020.111817
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    References listed on IDEAS

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    Cited by:

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    2. Wang, Enci & Su, Bin & Zhong, Sheng & Guo, Qinxin, 2022. "China's Embodied SO2 Emissions and Aggregate Embodied SO2 Intensities in Interprovincial and International Trade," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    3. 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.
    4. Xu, Renjing & Xu, Bin, 2022. "Exploring the effective way of reducing carbon intensity in the heavy industry using a semiparametric econometric approach," Energy, Elsevier, vol. 243(C).
    5. Juanjuan Tian & Xiaoqian Song & Jinsuo Zhang, 2022. "Spatial-Temporal Pattern and Driving Factors of Carbon Efficiency in China: Evidence from Panel Data of Urban Governance," Energies, MDPI, vol. 15(7), pages 1-24, March.
    6. 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).
    7. Loh, Jiong Rui & Bellam, Sreenivasulu, 2024. "Towards net zero: Evaluating energy security in Singapore using system dynamics modelling," Applied Energy, Elsevier, vol. 358(C).
    8. Wang, Zhen & Yan, Haoben & Gao, Xue & Liang, Qiaomei & Mi, Zhifu & Liu, Lancui, 2024. "Have consumption-based CO2 emissions in developed countries peaked?," Energy Policy, Elsevier, vol. 184(C).
    9. Sun, Ya-Fang & Zhang, Yue-Jun & Su, Bin, 2022. "How does global transport sector improve the emissions reduction performance? A demand-side analysis," Applied Energy, Elsevier, vol. 311(C).
    10. Yan Gao & Xin Wang & Liyan Zhang, 2023. "Serial Dynamics, Spatial Spillover and Common Factors of Carbon Emission Intensity in China’s Bohai Economic Rim," Sustainability, MDPI, vol. 15(9), pages 1-17, April.
    11. Duan, Yuwan & Yan, Bingqian, 2021. "Has processing trade made China's exports cleaner? A regional level analysis," Energy Economics, Elsevier, vol. 96(C).

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    More about this item

    Keywords

    Input-output analysis; Structural decomposition analysis; Attribution analysis; Aggregate carbon intensity; Aggregate embodied intensity; Singapore;
    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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