Can the reform of green credit policy promote enterprise eco-innovation? A theoretical analysis

Sheng Wu; Liangpeng Wu; Xianglian Zhao

doi:10.3934/jimo.2021028

Article Contents

2022, Volume 18, Issue 2: 1451-1485. Doi: 10.3934/jimo.2021028

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Can the reform of green credit policy promote enterprise eco-innovation? A theoretical analysis

a.
College of Economic and Management, Nanjing University of Aeronautics, and Astronautics, Nanjing 211106, China
b.
College of Intelligent Manufacturing, TaiZhou Institute of Sci.& Tech., Njust, Taizhou 225300, China
c.
College of Management Engineering, Nanjing University of Information, Science & Technology, Nanjing, 210044, China

^* Corresponding author: 1017379408@qq.com
^* Corresponding author: 1017379408@qq.com

Received: July 31, 2020

Revised: October 31, 2020

Early access: February 2021

Published: March 2022

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Abstract

The weakness that China's traditional credit fails to effectively limit enterprise emissions has become increasingly evident. Although the industry-oriented green credit policy has achieved certain effects on environmental performance through the differentiated resource allocation of the industries, banking financial institutions have the ambiguity in the definition of the credit object and the characteristics of profit maximization, which cannot achieve the essential purpose of green credit sustainably. Hence, we propose a new eco-innovation-oriented green credit policy. We prove theoretically that the new green credit is feasible and can be used as an exogenous driver for improving enterprises' eco-innovation. Contrasting with traditional credit, the newly proposed credit policy is an expansionary monetary policy, which has the characteristics of expanding credit lines and differential interest rates. Utilizing evolutionary game theory, we calculate the evolution stability conditions of green credit and eco-innovation. The results show that the key to green credit to maintaining sustainable development is the return on investment due to eco-innovation. Our theoretical analysis also reveals that environmental benefit-cost ratios and adjustment cost parameters of different assets are the important factors for green credit.

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Mathematics Subject Classification: 91B03.

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Figure 1. Path of credits to provide benefits

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Figure 2. Game matrix

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Figure 3. Impacts of $ \alpha $ and $ \beta $ on $ X_{2} $ when $ \varepsilon = J $. Evolution stability constraint $ X_{2} $ increases with the increase of the bank's environmental preference $ \alpha $ and the enterprise's environmental preference $ \beta $. The figure shows that some space remains where $ X_{2}>0 $ if $ \varepsilon>J $ with the increase in $ \alpha $ and $ \beta $

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Figure 4. Impacts of $ \alpha $ and $ \beta $ on $ r. $ The green surface denotes the optimal interest rate of EOGC, and the red surface denotes the optimal interest rate of the traditional credit. The figure shows the change in the optimal interest rate of different credits with respect to environmental preferences

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Figure 5. Impacts of $ \alpha $ and $ \beta $ on $ \frac{I}{K} . $ The green surface denotes the ratio of the optimal credit line to the enterprise capital under EOGC, $ \frac{I_{g}^{*}}{K}, $ and the red surface denotes the ratio of the optimal credit line to the enterprise capital under the traditional credit, $ \frac{I_{o}^{*}}{K} . $ The figure shows that the credit line of EOGC changes with the environmental preference, and the credit line of the traditional credit does not

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Figure 6. Impacts of $ \alpha $ and $ \beta $ on $ \frac{I_{ {ina}}^{*}}{I_{ {fia}}^{*}} . $ The green surface denotes the optimal eco-innovation investment ratio of intangible assets to fixed assets when $ \eta_{i n a} = 0.1, $ the red surface is the optimal ratio when $ \eta_{i n a} = 0.5, $ and the yellow surface is the optimal ratio when $ \eta_{i n a} = 0.8, $ with the condition of $ \eta_{fi a} = 0.3 . $ The figure shows that the impacts of $ \alpha $ and $ \beta $ on $ \frac{I_{ {ina}}^{*}}{I_{ {fia}}^{*}} $ change with the environmental benefit-cost ratio of intangible assets

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Figure 7. Impacts of $ \eta_{ {ina}} $ and $ \eta_{ {fia}} $ on $ X_{2} $ when $ \varepsilon = J $. The green surface denotes the evolution stability constraint $ X_{2} $ when $ \beta = 0.7, $ the red surface denotes $ X_{2} $ when $ \beta = 0.5, $ and the yellow surface denotes $ X_{2} $ when $ \beta = 0.2, $ with the condition of $ \alpha = 0.3 . $ The figure shows that some space remains where $ X_{2}>0 $ if $ \varepsilon>J $ with the increase in $ \eta_{i n a} $ and $ \eta_{f i a}, $ and the impact of $ \eta_{ {ina}} $ is greater than that of $ \eta_{ {fia}} $

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Figure 8. Impacts of $ \eta_{i n a} $ and $ \eta_{fi a} $ on $ \frac{I_{i n a}^{*}}{I_{f i a}^{*}} . $ The green surface denotes the optimal eco-innovation investment ratio of intangible assets to fixed assets when $ \beta = 0.7, $ the red surface denotes the optimal investment ratio when $ \beta = 0.5, $ and the yellow surface denotes the optimal investment ratio when $ \beta = 0.2, $ with the condition of $ \alpha = 0.3 . $ The figure shows that the impacts of the environmental benefit-cost ratios on the optimal eco-innovation investment ratio of intangible assets to fixed assets change with the enterprise's environmental preference

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Figure 9. Impacts of EOGC on the bank's economic benefits. The green surface denotes the bank's economic benefits under EOGC, and the red surface denotes the bank's economic benefits under the traditional credit. The values of the bank's environmental preference $ \alpha $ and the enterprise's environmental preference $ \beta $ in the figure are 0.1, 0.3, and 0.6, respectively. The figure shows that the bank's economic benefits under EOGC change with the environmental preferences and differ from that of traditional credit

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Figure 10. Impacts of EOGC on the enterprise's economic benefits. The green surface denotes the enterprise's economic benefits growth caused by eco-innovation under EOGC, the red surface denotes the enterprise's economic benefits growth caused by the other activities under the traditional credit, and the yellow surface denotes the enterprise's economic benefits growth caused by eco-innovation under the traditional credit. The values of the bank's environmental preference $ \alpha $ and the enterprise's environmental preference $ \beta $ in the figure are 0.1, 0.3, and 0.6, respectively. The figure shows the change of the enterprise's economic benefits with the environmental preferences under EOGC

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Table 1. Comparison oftypes of credit

types		traditional credit		EOGC
characteristics		ordinary credit	IOGC	EOGC
credit object	pollution industries	√	×	√
	general industries	√	×	√
	green industries	√	√	√
guidance	market means	√	×	√
guidance	administrative means	×	√	×
credit standards	maximum economic benefits	√	√	×
credit standards	maximum comprehensive benefits	×	×	√
loans usage	activity with the greatest return on investment	√	√	×
loans usage	eco-innovation	×	×	√

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Table 2. Variables and parameters

Notation		Definition
Variables	$ B $	comprehensive benefits of the bank
	$ R $	economic benefits of the bank
	$ F $	comprehensive benefits of the enterprise
	$ V $	economic benefits of the enterprise
	$ M $	environmental benefits
	$ I $	credit line
	$ r $	interest rate
Parameters	$ \alpha $	environmental preferences of the bank
	$ \beta $	environmental preferences of the enterprise
	$ \eta $	environmental benefit-cost ratio
	$ q $	Tobin's Q
	$ d $	credit risk
	$ \rho $	adjustment cost parameter
	$ J $	return on capital of eco-innovation
	$ \varepsilon $	return on capital of the other activities
	$ K $	enterprise capital
	$ \tau $	discount rate
Indexes	$ fna $	intangible assets of the enterprise
	$ fia $	fixed assets of the enterprise
	$ o $	traditional credits
	$ g $	EOGC
	$ t $	time

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Table 3. Evolutionary stability conditions

Scenarios	Points	$ \operatorname{det} T $	$ \operatorname{tr} T $	Equilibrium results	Stability conditions
I	(0, 0)	$ + $	$ + $	instability point
	(0, 1)	$ - $	$ \pm $	saddle point	$ B_{1}>B_{2} $
	(1, 0)	$ - $	$ \pm $	saddle point	$ F_{1}>F_{3} $
	(1, 1)	$ + $	$ - $	ESS
II	(0, 0)	$ - $	$ \pm $	saddle point
	(0, 1)	$ + $	$ - $	ESS	$ B_{2}>B_{1} $
	(1, 0)	$ + $	$ + $	instability point	$ F_{2}>F_{4} $
	(1, 1)	$ - $	$ \pm $	saddle point
III	(0, 0)	$ - $	$ \pm $	saddle point
	(0, 1)	$ + $	$ + $	instability point	$ B_{3}>B_{4} $
	(1, 0)	$ + $	$ - $	ESS	$ F_{3}>F_{1} $
	(1, 1)	$ - $	$ \pm $	saddle point
IV	(0, 0)	$ + $	$ - $	ESS
	(0, 1)	$ - $	$ \pm $	saddle point	$ B_{4}>B_{3} $
	(1, 0)	$ - $	$ \pm $	saddle point	$ F_{4}>F_{2} $
	(1, 1)	$ + $	$ + $	instability point

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Table 4. Optimal interest rates and credit lines

Scenarios	Optimal interest rates	Optimal credit lines
I	$ \frac{1}{2}(q+d)+\frac{\tau\left(\eta_{{ina}} \rho_{\text {fia}}+\eta_{{fia}} \rho_{{ina}}\right)}{2\left(\rho_{{ina}}+\rho_{{fia}}\right)}\left(\frac{\beta}{1-\beta}-\frac{\alpha}{1-\alpha}\right) $	$ \frac{K\left(\rho_{{ina}}+\rho_{{fia}}\right)}{2 \rho_{{ina}} \rho_{ {fia}}}(q-d)+\frac{\tau K\left(\eta_{ {ina}} \rho_{{fia}}+\eta_{ {fia}} \rho_{ {ina}}\right)}{2 \rho_{ {ina}} \rho_{ {fia}}}\left(\frac{\alpha}{1-\alpha}+\frac{\beta}{1-\beta}\right) $
II	$ \frac{1}{2}(q+d)+\frac{\tau\left(\eta_{{ina}} \rho_{{fia}}+\eta_{{fia}} \rho_{{ina}}\right) \beta}{2\left(\rho_{{ina}}+\rho_{{fia}}\right)(1-\beta)} $	$ \frac{K\left(\rho_{{ina}}+\rho_{{fia}}\right)}{2 \rho_{{ina}} \rho_{ {fia}}}(q-d)+\frac{\tau K\left(\eta_{ {ina}} \rho_{{fia}}+\eta_{{fia}} \rho_{ {ina}}\right) \beta}{2 \rho_{{ina}} \rho_{{fia}}(1-\beta)} $
III	$ \frac{1}{2}(q+d)-\frac{\tau\left(\eta_{{ina}} \rho_{{fia}}+\eta_{{fia}} \rho_{{ina}}\right) \alpha}{2\left(\rho_{{ina}}+\rho_{{fia}}\right)(1-\alpha)} $	$ \frac{K\left(\rho_{{ina}}+\rho_{ {fia}}\right)}{2 \rho_{ {ina}} \rho_{ {fia}}}(q-d)+\frac{\tau K\left(\eta_{ {ina}} \rho_{ {fia}}+\eta_{ {fia}} \rho_{ {ina}}\right) \alpha}{2 \rho_{ {ina}} \rho_{ {fia}}(1-\alpha)} $
IV	$ \frac{1}{2}(q+d) $	$ \frac{K\left(\rho_{ {ina}}+\rho_{ {fia}}\right)}{2 \rho_{ {ina}} \rho_{ {fia}}}(q-d) $

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