Skip to main content

Advertisement

Log in

Study on high energy-consuming industrial agglomeration, green finance, and carbon emission

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

Abstract

Based on the relationship between industrial agglomeration, green finance development, and carbon emissions, some relevant theoretical hypotheses are proposed, and this paper employs the combination of spatial Durbin model and panel threshold model to empirically test data from 30 provincial regions in China from 2006 to 2019. The results show that the agglomeration of high energy-consuming industries has an inverse U-curve relationship with carbon emission intensity, and the development of green finance will inhibit the growth of carbon emission intensity. There are significant spatial characteristics of high energy-consuming industrial agglomeration, green financial development, and carbon emissions. And the intensity of local carbon emissions will be influenced by the agglomeration of high energy-consuming industrial agglomeration and green financial development in local and neighboring areas. Moreover, green financial development plays a moderating role in the relationship between high energy-consuming industrial agglomeration and carbon emissions, and the role of high energy-consuming industrial agglomeration and green financial development on carbon emissions has a threshold effect due to the mismatch between the two developments. Under different levels of green financial development, the influence of high energy-consuming industrial agglomeration on carbon emissions varies widely, and green financial development helps to suppress the negative impact of high energy-consuming industrial agglomeration on carbon emissions. Accordingly, we argue that inter-regional joint prevention and control mechanism should be established for pollution control. And China should build more effective high energy-consuming industrial clusters to make them play an active role in reducing emissions. At the same time, China should accelerate the construction of green finance, strengthen the disclosure and transparency of green financial information, and establish a joint mechanism for the development of inter-regional green finance, so that it can contribute to regional industrial transformation and pollution control.

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Data Availability

The statistical yearbooks used for the data can be found at the following links: http://www.stats.gov.cn/. CSMAR Database: https://www.gtarsc.com/.

Notes

  1. Steps to calculate the level of green financial development by entropy method from the appendix.

References

Download references

Funding

This research was funded by “Liaoning Social Science foundation (L18BJL011).”

Author information

Authors and Affiliations

Authors

Contributions

H.H. was responsible for the methodology, software, analysis, writing, review, and editing; M.N. was responsible for the resources, investigation, data curation, and analysis. M.H. was responsible for software, data collection, analysis, and review.

Corresponding author

Correspondence to Minna Chen.

Ethics declarations

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent to publish

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Responsible Editor: Nicholas Apergis

Publisher's Note

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

Appendix

Appendix

Entropy weighting is an objective weighting method, and it determines the weight of an indicator by calculating the information entropy of the indicator, based on the degree of influence of the relative change of the indicator on the whole. The calculation steps are as follows:

  1. (1)

    Since there are differences in the magnitude and order of magnitude of each indicator, the standard treatment of each indicator Xij is required to eliminate the effects. The normalized value of 0 needs to be converted to 0.00001 to ensure the correctness of the subsequent calculation.

    Positive indicators \({Y}_{ij}=\frac{{X}_{ij}-\mathit{min}\{{X}_{j}\}}{\mathit{max}\{{X}_{j}\}-\mathit{min}\{{X}_{j}\}}\) (Positive indicators are those that are more favorable to the development of green finance when the value of individual indicators is higher.)

    Negative indicators \({Y}_{ij}=\frac{\mathit{max}\{{X}_{j}\}-{X}_{ij}}{\mathit{max}\{{X}_{j}\}-\mathit{min}\{{X}_{j}\}}\) (Negative indicator means that when the value of individual indicator is smaller the more favorable to green financial development).

  2. (2)

    Calculate the proportion of each indicator \({\omega }_{ij}=\frac{{Y}_{ij}}{{\sum }_{i=1}^{m}{X}_{ij}}\)

  3. (3)

    Calculate the information entropy of the index \({e}_{j}=-\frac{1}{\mathit{ln}m}{\sum }_{i=1}^{m}{\omega }_{ij}\times \mathit{ln}{\omega }_{ij}\)

  4. (4)

    Calculating information entropy redundancy \({d}_{j}=1-{e}_{j}\)

  5. (5)

    Calculate indicator weights \({\varphi }_{j}=\frac{{d}_{j}}{{\sum }_{i=1}^{m}{d}_{j}}\)

  6. (6)

    Using the weighting of multiple linear functions to find the composite score \({s}_{j}={\sum }_{j=1}^{n}{\varphi }_{j}\times {Y}_{ij}\)

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

Hou, H., Chen, M. & Zhang, M. Study on high energy-consuming industrial agglomeration, green finance, and carbon emission. Environ Sci Pollut Res 30, 29300–29320 (2023). https://doi.org/10.1007/s11356-022-24228-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11356-022-24228-6

Keywords

Navigation