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Identification of the bias in embodied emissions flows and their sources

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  • Li, Yingzhu
  • Su, Bin

Abstract

Input-output (IO) table has been widely used to investigate the relationship between the environment and the economy. As pre-analysis data treatment could give rise to information distortion in the adjusted tables, a large body of national IO-based analysis on environmental issues potentially suffer from imports data treatment. When national IO tables with different imports assumptions are available, it is important and possible to investigate the distortion and corresponding bias in findings. Via embodied carbon emissions, a set of IO-based techniques are utilized to analyze the source and transmission of the bias. The empirical study of China shows that relative indicator (embodied intensity) performs more robustly than absolute indicator (embodied emissions) at all transmission layers. Bias in embodied emissions accumulates to ±183.6 Mt. across the products. Supply chains of S27-Construction, S20-Electronic equipment and S18-Transport equipment are most distorted, with sectors such as chemicals, metals and non-metallic minerals as key nodes in transmitting the bias. The decomposition analysis also shows that the bias in S27 (137.9 Mt), S20 (34.7 Mt) and S18 (24 Mt) is primarily due to distorted intermediate use, while the bias in S14 (35.0 Mt) is mainly caused by distorted final demand. Implications on policies and other IO-based studies are discussed.

Suggested Citation

  • Li, Yingzhu & Su, Bin, 2024. "Identification of the bias in embodied emissions flows and their sources," Energy Economics, Elsevier, vol. 136(C).
    • Handle: RePEc:eee:eneeco:v:136:y:2024:i:c:s014098832400416x
    DOI: 10.1016/j.eneco.2024.107708
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