How Green Finance Affects Green Total Factor Productivity—Evidence from China
Abstract
:1. Introduction
2. Literature Review
2.1. Related Research on Green Finance
2.2. Research on Green Total Factor Productivity
2.3. Research on Impact of Green Finance on GTFP
3. Mechanism Analysis and Hypothesis
3.1. Effect of Technological Market Activity
3.2. The Effect of Financial Development Level
4. Model Specification and Variable Description
4.1. Model Setting
- (1)
- Calculation and decomposition of GTFP.
- (2)
- The impact model of green finance on GTFP.
4.2. Variable Description and Data Explanation
- (1)
- The explained variable: green total factor productivity.
- (2)
- Core explanatory variable—green finance.
- (3)
- Mediating variables.
- ①
- Technology Market Intensity (TMI): Technology trading, focused on providing innovation and technology services for social development, plays a crucial role that cannot be replaced by other market factors. It is centered on innovation and technology services, with the goal of diffusing knowledge and transferring technology to truly transform technological advantages into economic advantages. Enhancing the intensity of the technology market can promote sustainable economic and social development and improve the quality of economic development. This article measures the level of local technology market activity by selecting the proportion of the annual technology market turnover to the local GDP in each province of China.
- ②
- Level of financial development (LFD): Financial development focuses on improving the efficiency of fund allocation in the operation of the social economy, providing financial support for the development of enterprises and industries, promoting investment and innovation, and resisting uncertainty in the process of economic development. A sound financial system can promote economic stability and sustainable growth. Green finance is an innovative service in the process of financial development, and the stronger the financial development, the greater the support provided for green finance. This article selects the proportion of loan balance to local GDP in each province of China to measure financial development.
- (4)
- Control variables.
- ①
- Population size (POP): The size of the population has direct and indirect impacts on economic development. A larger population can provide more labor resources, promote economic growth, and expand the output scale. However, rapid population growth can also lead to excessive resource consumption and exacerbate environmental pollution. In this article, the annual average population of each province is used to represent the population size.
- ②
- Dependency on foreign trade (DFT): The increase in the degree of dependence on foreign trade can bring benefits such as foreign exchange income, technology transfer, and knowledge renewal, which can maintain a good international trade environment and competitiveness, thereby improving GTFP. However, excessive reliance on the export of certain resources or industries may lead to excessive resource consumption and increased environmental pressure, which can affect GTFP. In this article, the degree of trade dependence of each province in a given year is represented by the ratio of the number of exports to the local GDP.
- ③
- Research and development (R&D): Research and development (R&D) investments can directly enhance innovation and technological progress, thereby affecting gross total factor productivity (GTFP). However, due to the phenomenon of carbon lock-in, R&D activities centered around fossil fuels may crowd out the development of green technologies, thereby affecting the growth of GTFP. In this paper, the proportion of local fiscal scientific expenditure to local GDP in each province is used to represent R&D.
- ④
- Total retail sales of social consumer goods (TRSCG): The total retail sales of consumer goods in a society reflect the local economic development status and the level of consumption activity of individuals and households. A higher total retail sales of consumer goods can stimulate economic growth, but it can also lead to excessive resource consumption and increased environmental pressure, thereby affecting GTFP. In this study, the total retail sales of social consumer goods in each province in the given year are used to represent this indicator.
- ⑤
- Foreign direct investment (FDI): FDI typically brings advantages such as advanced production technology, capital, management expertise, and market access. However, it can also lead to some multinational corporations pursuing short-term economic gains while neglecting the importance of environmental protection. This may result in excessive resource exploitation and the transfer of pollution to the host country. The active guidance and proper regulation of foreign investment play a crucial role in enhancing GTFP. In this study, the amount of foreign direct investment in each province is used to represent this indicator.
- (5)
- Data sources and descriptive statistics.
5. Analysis of Empirical Results
5.1. Regression of the Impact Mechanism of Green Finance on GTFP
5.2. Testing the Mediating Effect of Green Finance on GTFP and TC
5.3. Robustness Test
6. Conclusions and Policy Recommendations
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Category of Indicators | Indicator Name | Indicator Content |
---|---|---|
Input indicators. | Capital input | The fixed-asset capital stock of each province calculated using the perpetual inventory method. |
Labor input | End-of-year employed population in each province. | |
Energy input | Energy consumption (in 10,000 tons of standard coal) in each province. | |
Expected output indicators. | Economic output | The local gross domestic product calculated at constant prices in 2005. |
Unexpected output indicators. | Environmental Pollution Index | The comprehensive calculation of industrial wastewater, carbon dioxide, and industrial sulfur dioxide emissions for each province, conducted using the entropy weight method. |
Primary Indicator | Indicator | Indicator Description |
---|---|---|
Green credit | Proportion of interest expenditure in high-energy-consuming industries | Interest expenditure on the six major high-energy-consuming industrial sectors as a proportion of total industrial interest expenditure Green investment |
Green investment. | Proportion of environmental pollution control investment to GDP | Environmental pollution-control investment as a percentage of GDP |
Green insurance | Agricultural insurance intensity | Agricultural insurance income as a proportion of total agricultural output value |
Government support | Proportion of fiscal expenditure on environmental protection | Expenditure on fiscal environmental protection as a proportion of total fiscal budget expenditure |
Variable | Obs | Mean | Std. dev. | Min | Max | |
---|---|---|---|---|---|---|
Dependent variable. | GTFP | 450 | 0.924 | 0.239 | 0.475 | 2.178 |
EC | 450 | 0.682 | 0.106 | 0.174 | 1.137 | |
TC | 450 | 0.936 | 0.192 | 0.644 | 2.437 | |
Core variable. | GF | 450 | 0.149 | 0.081 | 0.049 | 0.609 |
Mediating variable. | TMI | 450 | 0.013 | 0.024 | 0.000 | 0.175 |
LFD | 450 | 2.996 | 1.143 | 1.288 | 8.131 | |
Control variables. | POP | 450 | 6.003 | 0.763 | 3.789 | 7.667 |
DFT | 450 | 0.284 | 0.319 | 0.007 | 1.708 | |
RD | 450 | 0.010 | 0.006 | 0.000 | 0.032 | |
TRSCG | 450 | 8.524 | 1.066 | 5.213 | 10.668 | |
FDI | 450 | 14.493 | 1.676 | 7.990 | 16.932 |
Variables | GTFP | GTFP | GTFP | GTFP |
---|---|---|---|---|
GF | 1.360 *** | 2.431 *** | 0.692 *** | 1.683 *** |
(0.253) | (0.255) | (0.117) | (0.264) | |
POP | −0.191 *** | −0.238 *** | −0.031 | |
(0.062) | (0.041) | (0.069) | ||
DFT | 0.042 | 0.011 | 0.109 * | |
(0.064) | (0.030) | (0.065) | ||
R&D | −13.468 *** | 6.856 *** | −11.808 *** | |
(3.088) | (2.003) | (2.939) | ||
TRSCG | 0.167 *** | 0.138 *** | −0.032 | |
(0.023) | (0.035) | (0.039) | ||
FDI | −0.021 *** | −0.007 | 0.000 | |
(0.004) | (0.009) | (0.006) | ||
Constant | 0.205 | 1.017 *** | 0.691 | |
(0.320) | (0.155) | (0.423) | ||
Observations | 450 | 450 | 450 | 450 |
Number of ids | 30 | 30 | 30 | 30 |
R-squared | 0.803 | 0.785 | 0.589 | 0.817 |
Province FE | YES | YES | NO | YES |
Year FE | YES | No | YES | YES |
Variables | EC | TC |
---|---|---|
GF | 0.261 | 0.719 *** |
(0.166) | (0.193) | |
POP | 0.072 * | −0.262 *** |
(0.044) | (0.050) | |
DFT | 0.172 *** | −0.143 *** |
(0.041) | (0.048) | |
R&D | −3.009 | −5.382 ** |
(1.850) | (2.144) | |
TRSCG | −0.016 | 0.042 |
(0.024) | (0.028) | |
FDI | 0.004 | −0.004 |
(0.004) | (0.005) | |
Constant | 0.108 | 2.052 *** |
(0.266) | (0.309) | |
Observations | 450 | 450 |
Number of id | 30 | 30 |
R-squared | 0.628 | 0.849 |
Province FE | YES | YES |
Year FE | YES | YES |
Variables | TMI | GTFP | TC | LFD | GTFP | TC |
---|---|---|---|---|---|---|
GF | 0.278 *** | 0.262 | 0.348 ** | 9.689 *** | 0.601 *** | 0.218 * |
(0.011) | (0.182) | (0.141) | (0.586) | (0.148) | (0.111) | |
TMI | 1.891 *** | 0.721 * | ||||
(0.507) | (0.393) | |||||
LFD | 0.019 ** | 0.034 *** | ||||
(0.009) | (0.007) | |||||
POP | 0.008 *** | −0.367 *** | −0.265 *** | −0.677 *** | −0.338 *** | −0.236 *** |
(0.003) | (0.028) | (0.022) | (0.143) | (0.029) | (0.022) | |
DFT | 0.006 ** | −0.017 | 0.021 | 1.345 *** | −0.031 | −0.020 |
(0.003) | (0.029) | (0.023) | (0.148) | (0.032) | (0.024) | |
R&D | −0.708 *** | 7.178 *** | −0.715 | −57.750 *** | 6.948 *** | 0.744 |
(0.187) | (2.028) | (1.571) | (10.167) | (2.090) | (1.567) | |
TRSCG | −0.011 *** | 0.256 *** | 0.200 *** | 0.228 ** | 0.231 *** | 0.184 *** |
(0.002) | (0.023) | (0.018) | (0.116) | (0.0230) | (0.017) | |
FDI | −0.001 * | −0.030 *** | −0.039 *** | −0.053 * | −0.031 *** | −0.038 *** |
(0.000) | (0.005) | (0.004) | (0.027) | (0.005) | (0.004) | |
Constant | 0.062 *** | 1.311 *** | 1.395 *** | 3.072 *** | 1.370 *** | 1.335 *** |
(0.011) | (0.121) | (0.093) | (0.593) | (0.121) | (0.091) | |
Observations | 450 | 450 | 450 | 450 | 450 | 450 |
Number of ids | 30 | 30 | 30 | 30 | 30 | 30 |
R-squared | 0.715 | 0.571 | 0.600 | 0.512 | 0.561 | 0.617 |
Sobel test | 0.526 *** (0.142) | 0.200 ** (0.11) | 0.186 ** (0.092) | 0.200 ** (0.11) |
Inland Provinces | Coastal Provinces | Eastern Provinces | Central Provinces | Western Provinces | |
---|---|---|---|---|---|
GF | 1.699 *** | 1.652 ** | 1.336 *** | 3.367 *** | −0.595 |
(0.316) | (0.660) | (0.400) | (1.081) | (0.790) | |
Control variables | Control | Control | Control | Control | Control |
Constant | −0.219 | 3.798 *** | 3.892 *** | −0.533 | 0.129 |
(0.486) | (0.815) | (1.057) | (0.784) | (0.704) | |
Observations | 450 | 450 | 450 | 450 | 450 |
Number of ids | 30 | 30 | 30 | 30 | 30 |
R-squared | 0.862 | 0.697 | 0.780 | 0.874 | 0.910 |
Province FE | YES | YES | YES | YES | YES |
Year FE | YES | YES | YES | YES | YES |
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Zhang, M.; Li, C.; Zhang, J.; Chen, H. How Green Finance Affects Green Total Factor Productivity—Evidence from China. Sustainability 2024, 16, 270. https://doi.org/10.3390/su16010270
Zhang M, Li C, Zhang J, Chen H. How Green Finance Affects Green Total Factor Productivity—Evidence from China. Sustainability. 2024; 16(1):270. https://doi.org/10.3390/su16010270
Chicago/Turabian StyleZhang, Min, Chengrong Li, Jinshan Zhang, and Hongwei Chen. 2024. "How Green Finance Affects Green Total Factor Productivity—Evidence from China" Sustainability 16, no. 1: 270. https://doi.org/10.3390/su16010270
APA StyleZhang, M., Li, C., Zhang, J., & Chen, H. (2024). How Green Finance Affects Green Total Factor Productivity—Evidence from China. Sustainability, 16(1), 270. https://doi.org/10.3390/su16010270