Skip to main content

Advertisement

Log in

CO2 emissions in China’s power industry by using the LMDI method

  • Research Article
  • Published:
Environmental Science and Pollution Research Aims and scope Submit manuscript

Abstract

WIth the introduction of “carbon peak and neutrality” targets, China’s power industry is under enormous pressure to reduce carbon dioxide (CO2) emissions, as it produces more than 40% of emissions. In response, China’s power industry is actively reducing the investment in thermal energy and gradually shifting toward non-fossil energy sources. However, the CO2 reduction effect of these measures is still unknown. This study aims to analyze CO2 emissions from China’s power industry from 2009 to 2018 from an entire lifecycle perspective, considering that CO2 emissions also exist in non-fossil power generation. The logarithmic mean Divisia index (LMDI) method is employed to identify the factors influencing CO2 emissions. Then, the modified STochastic Impacts by Regression on Population, Affluence and Technology model is used for comparative validation. The results show that (1) CO2 emissions from China’s power industry increased significantly, from 276.5 million tons of CO2 equivalent (Mtce) in 2009 to 436.44 Mtce in 2018; (2) the investment intensity, investment structure, and emission intensity dampen CO2 emissions, with cumulative contribution rates of − 28.88%, − 11.89%, and − 3.16%, respectively. The investment efficiency, economic development level, and population size contribute to CO2 emissions, with cumulative contribution rates of 29.76, 24.68, and 1.07%, respectively; and (3) Investment into the hydropower contributes the least to CO2 emissions, followed by wind, nuclear, photovoltaic, and thermal power. These research findings suggest that the power industry should improve its investment decision-making capabilities and pay particular attention to the hydropower-led non-fossil energy sector.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data availability

Not applicable.

References

Download references

Acknowledgements

The authors are also grateful to the anonymous reviewer and editor for the careful scrutiny of the report and for the comments that helped improve this manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (grant number 71701069) and the Fundamental Research Funds for the Central Universities (grant number 2020MS129).

Author information

Authors and Affiliations

Authors

Contributions

Xin Zou: methodology, resources, writing — review and editing. Jiaxuan Li: conceptualization, software, validation, investigation, data curation, writing — original draft. Qian Zhang: resources, writing — review and editing.

Corresponding author

Correspondence to Jiaxuan Li.

Ethics declarations

Ethics approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Responsible Editor: Ilhan Ozturk

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zou, X., Li, J. & Zhang, Q. CO2 emissions in China’s power industry by using the LMDI method. Environ Sci Pollut Res 30, 31332–31347 (2023). https://doi.org/10.1007/s11356-022-24369-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11356-022-24369-8

Keywords

Navigation