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Green finance, environmental technology progress bias and cleaner industrial structure

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Abstract

In this paper, green finance is incorporated into the theoretical framework of technological progress bias to investigate the internal mechanism of green finance influencing environmental technological progress bias and the cleaning of industrial structure. Quantitative analysis is carried out by parameter calibration and numerical simulation. The results show that the environmental technology progress bias has obvious path-dependent characteristics. By means of financing scale and financing cost, green finance influences the relative profit, R&D behavior choice and environmental technology progress preference of the two types of R&D firms and then influences the cleaning of industrial structure through direct productivity effect, indirect price effect and market size effect. Only when the proportion of financing scale of clean R&D departments or the interest rate subsidy exceeds a certain critical value, can green finance successfully induce the occurrence of clean technological progress and promote the cleanliness of industrial structure. However, when the proportion of financing scale or interest rate subsidy of clean R&D departments is lower than a certain critical value, the improvement effect of green finance on the output value of clean industry is offset by the effect of polluting technological progress, which is not conducive to the clean industrial structure.

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Data availability

The datasets generated during the current study are available from the corresponding author on reasonable request.

Notes

  1. Note Data are from "China Ecological and Environmental Status Bulletin 2017".

  2. Data from Environmental Performance Index 2018.

  3. In this paper, the pollution emission intensity is taken as the classification standard, and the industries with less than the median pollution emission intensity are classified as clean industries, while the industries with more than the median pollution emission intensity are classified as polluting industries. Pollution emission intensity is calculated by calculating pollutant emissions (including industrial waste water, industrial sulfur dioxide and industrial solid waste) per unit of industrial output value of various industries over the years, and is weighted by equal weight after standardization.

  4. Consider two kinds of situations: \(\varepsilon = 5\) and \(\varepsilon = 10\).

  5. The greater the elasticity \(\varepsilon\) of substitution of clean and polluting intermediate products, the smaller the critical value will be. When \(\varepsilon = 5\), the critical value is 0.7789; When \(\varepsilon = 10\), the critical value is 0.7126.

  6. Consider two kinds of situations: \(\varepsilon = 5\) and \(\varepsilon = 10\).

  7. The greater the elasticity \(\varepsilon\) of substitution of clean and polluting intermediate products, the smaller the critical value will be. When \(\varepsilon = 5\), the critical value is 0.0278; When \(\varepsilon = 10\), the critical value is 0.0216.

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Funding

The study is supported by Natural Science Foundation of China (Grants No. 71673136).

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Authors and Affiliations

  1. Business School, Jinling Institute of Technology, Nanjing, 211169, Jiangsu, People’s Republic of China

    Xiaomeng Liu & Yingquan Zhang

  2. Business School, Hohai University, Nanjing, 211100, Jiangsu, People’s Republic of China

    Xiaomeng Liu

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  1. Xiaomeng Liu
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  2. Yingquan Zhang
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Contributions

XLIU contributed to conceptualization, data curation, methodology, and writing—original draft. YZ contributed to writing—review and editing—and software.

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Correspondence to Xiaomeng Liu.

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Liu, X., Zhang, Y. Green finance, environmental technology progress bias and cleaner industrial structure. Environ Dev Sustain 26, 8643–8660 (2024). https://doi.org/10.1007/s10668-023-03062-x

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