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.
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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
Steps to calculate the level of green financial development by entropy method from the appendix.
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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.
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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:
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(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).
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(2)
Calculate the proportion of each indicator \({\omega }_{ij}=\frac{{Y}_{ij}}{{\sum }_{i=1}^{m}{X}_{ij}}\)
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(3)
Calculate the information entropy of the index \({e}_{j}=-\frac{1}{\mathit{ln}m}{\sum }_{i=1}^{m}{\omega }_{ij}\times \mathit{ln}{\omega }_{ij}\)
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(4)
Calculating information entropy redundancy \({d}_{j}=1-{e}_{j}\)
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(5)
Calculate indicator weights \({\varphi }_{j}=\frac{{d}_{j}}{{\sum }_{i=1}^{m}{d}_{j}}\)
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(6)
Using the weighting of multiple linear functions to find the composite score \({s}_{j}={\sum }_{j=1}^{n}{\varphi }_{j}\times {Y}_{ij}\)
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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
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DOI: https://doi.org/10.1007/s11356-022-24228-6