基于DSGE模型的绿色信贷激励政策研究 (China’s Incentive Policies for Green Loan: A DSGE Approach)

18 Pages Posted: 26 Nov 2019

See all articles by Yao Wang

Yao Wang

Central University of Finance and Economics (CUFE)

Dongyang Pan

University College London; Central University of Finance and Economics (CUFE)

Yuchao Peng

Central University of Finance and Economics (CUFE) - School of Finance

Liang Xi

University of Edinburgh - Edinburgh Business School

Date Written: November 10, 2019

Abstract

The English version of this paper can be found at http://ssrn.com/abstract=3486211.

Chinese Abstract: 在绿色金融政策实践与有关学术理论快速发展的背景下,本文以绿色信贷的激励政策为切入点,提供一种分析绿色金融政策的理论模型分析框架,并基于模型开展量化的政策效果分析。本文在真实商业周期框架的基础上引入银行部门,通过拆分厂商部门为“绿色”与“其它”两部分,并设置中央银行与财政部门的相关政策,纳入了绿色信贷激励政策。研究发现,针对绿色信贷的贴息、定向降准、再贷款(调整再贷款利率与质押率)均是有效且合意的激励政策,一定强度的政策,不仅能够提高绿色信贷量、在绿色意义上优化经济的结构,而且对总产出、总就业不会造成显著的负面影响,带来“经济”与“环境”双赢的结果。

English Abstract: In the context of "constructing an ecological civilization" in China, "green finance" and "green loan" policies that promote capital resource to support sustainable development have developed rapidly in the recent years. Meanwhile, relevant academic research has started to thrive. However, theories and models for green finance and policy analysis based on them are still not enough. This study attempts to develop a theoretical and quantitative model for analysing China’s incentive policies for green loan, and applies this model to find the potential effects of such policies. This will provide a prototype for the modelling work on green financial policy in the academia and help the government better design such policies in the real world.

In China, green financial policy normally means governmental and regulatory measures aiming to make financial services supportive of environment improvement, climate change mitigation and adoption and more efficient resource utilization. Narrowly, it also refers to financial policy tools and inventions that can incentivize green financing activities, such as interest subsidy, central bank relending, government guarantee, lowering risk weight and capital requirement for green loan (i.e. incentive policies for green loan). These policy tools have been proposed or implemented mainly after the release of the Integrated Reform Plan for Promoting Ecological Progress in 2015 and the Guidelines for Establishing the Green Financial System in 2016 by the central government.

While the rapid development of green financial policy in practice, relevant academic research lags behind. Previous research on the issue of “green financial policy” are mostly qualitative policy recommendations. Quantitative research on green finance has started in recent years, however, few studies focused on the economic and environmental effects of green financial policy. People can hardly know if this kind of policy is effective and what it will bring to the macro-economy.

Given the above background, this research aims to provide a theoretical model suitable for the quantitative analysis on incentive policies for green loan, and to theoretical show their economic and environmental effects. The specific policy tools we study include interest subsidy, directional reduction for reserve ratio requirement, and central bank relending for green loan.

To do this, we build a Dynamic Stochastic General Equilibrium (DSGE) model based on the Real Business Cycle (RBC) framework. The model has two major extensions compared with RBC framework: (1) Banking sector conducting green lending is added. Firm sector must use loan as “working capital” to pay for all costs. Bank provide green and traditional loans to different firms. Household sector can deposit their savings to bank. (2) The firm sector is divided into two sub-sectors: green and other firms. The pollution from production process is introduced and the green firm pollutes less than the other. Green firm is financed by green loan, while the other by traditional loan. These two extensions allow us to analyse financing activities and to distinguish green loan from traditional loan. Incentive policies for green loan can then be included after introducing central bank and government sector. Parametric data is calibrated from China.

According to this model, we find: (1) All three policies including interest subsidy, directional reduction for reserve ratio requirement and central bank relending, can increase the amount of green loan. Policy strength has a certain order. This shows the direct effect of such policies. (2) Temporary policy changes (incentive) can increase the output and employment of green firm, while decrease the output and employment of the other firm. The total output and total employment will also be slightly affected negatively, and so does pollution emission. The positive impacts of policy are more significant than the negative impact. This shows the indirect effects including benefit and cost of such policy on the entire economy and environment. (3) If the three policies are set as peg-type rules endogenously in the economy, they can also enhance the share of green related variables in the economy. However, only if a certain level of strength is reached, can the peg-type policies bring a green transformation for the economy when facing productivity shock.

The conclusion is that interest subsidy, directional reduction for reserve ratio requirement and central bank relending are all effective for incentivizing green loan and have positive effects on the green side of the economy. The policy cost is not high. This implies that the deepening of green financial policy is desirable.

Note: Downloadable document is in Chinese.

Keywords: 绿色信贷, 激励政策, DSGE模型 (Green Loan, Incentive Policy, DSGE Model)

JEL Classification: E10, E58, G28

Suggested Citation

Wang, Yao and Pan, Dongyang and Peng, Yuchao and Xi, Liang, 基于DSGE模型的绿色信贷激励政策研究 (China’s Incentive Policies for Green Loan: A DSGE Approach) (November 10, 2019). Available at SSRN: https://ssrn.com/abstract=3484817 or http://dx.doi.org/10.2139/ssrn.3484817

Yao Wang

Central University of Finance and Economics (CUFE) ( email )

39 South College Road
Haidian District
Beijing, Beijing 100081
China

Dongyang Pan (Contact Author)

University College London ( email )

Gower Street
London, WC1E 6BT
United Kingdom

Central University of Finance and Economics (CUFE)

39 South College Road
Haidian District
Beijing, Beijing 100081
China

Yuchao Peng

Central University of Finance and Economics (CUFE) - School of Finance ( email )

Beijing
China

Liang Xi

University of Edinburgh - Edinburgh Business School

29 Buccleuch Pl
Edinburgh, Scotland EH8 9JS
United Kingdom

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