Abstract: The problem of overcapacity has become one of the most important economic issues for Chinese government departments. Promoting green credit and developing green finance are of great significance to resolve overcapacity. From the perspectives of practical guidance and theoretical exploration, this study attempts to combine green credit policies with enterprises' capacity utilization rate. Based on highly detailed data of micro-listed non-financial enterprises in China, and using Opinions on Implementing Environmental Protection Policies and Regulations to Prevent Credit Risks jointly issued by the former Ministry of Environmental Protection, the People's Bank of China, and the former Banking Regulatory Commission on July 30, 2007 as a quasi-natural experiment, this paper conducts a study on the impact of the green credit policy on enterprises' capacity utilization rate and its channel mechanism.
The main findings are as follows. First, this study makes a benchmark regression analysis to investigate the effect of green credit policies on firms' capacity utilization rate by using the multiplicative difference method based on the database of listed non-financial firms in China from 2004 to 2020 and it is found that green credit policies significantly suppressed the capacity utilization rate of highly polluting firms. Second, drawing on the research ideas of He et al. (2020), this study constructs the logarithms of firms' marginal productivity of labor(lnMPL)and capital productivity(lnMPK)as a means of assessing the environmental costs borne by firms as a result of green credit policies. The total factor productivity(lnTFP)of non-financial firms from 2004 to 2020 are measured with the help of research methods out forward by Olley and Pakes(1996)and Levinsohn and Petrin(2003), respectively. A mediating effect model is constructed from the cost and productivity perspectives to test the impact channels. The results of the study indicate that the cost effect and the productivity effect mechanisms are the main influence channels affecting the capacity utilization rate of enterprises. Third, the green credit policy, as a kind of environmental regulation, can have asymmetric effects on resources allocation. When faced with credit policy changes, firms may exhibit different resilience depending on significant differences in development environment, ownership, input and output levels. This study also find that green credit policies exhibit asymmetric effects on capacity utilization rates of highly polluting firms with different characteristics in terms of factor intensity, degree of financial marketization, firm size, ownership, and domestic and foreign capital differences. The empirical results show that the negative impact of the green credit policy is particularly pronounced for high polluting enterprises with factor intensity greater than 50% quantile, high degree of financial marketization in the province where they are located, the size less than 50% quantile, and state-owned and domestic capital.
Compared with the existing literature, the possible contributions of this study are as follows. (1)As for the research perspective, most of the previous studies focus on the factors influencing enterprises' capacity utilization rate, but this study focuses on the implementation effect of the green credit policy from the perspective of enterprises' capacity utilization rate, which provides new micro-level evidence to objectively assess the effect of the green finance policy and has important policy implications for promoting green finance development, resolving overcapacity, solving the problem of resources and environmental constraints, and promoting high-quality enterprise development. (2)As for the impact mechanism, this study explores the mechanism of green credit policy afftecting enterprises' capacity utilization rate through two channels, the productivity effect and the cost effect, which provides a useful supplement to the research on the impact of green finance on enterprises' capacity utilization rate. (3)As for the research method, by using the multiplicative difference method(DID), the difference in the distribution of capacity utilization rate between the treatment and control groups before and after the introduction of the policy is examined in order to assess the policy effect of green credit, so as to investigate the causal relationship between the green credit policy and the capacity utilization rate of enterprises. In addition, in this study, the validity of the DID model estimation is further ensured through a series of testing strategies such as the parallel trend hypothesis testing and the placebo testing to make the estimation results more accurate.
The research findings of this study have important policy implications. First, we should strengthen top-level design, improve supporting policies and create a good policy environment. The government should strengthen the improvement and establishment of market mechanisms and regulatory systems related to green finance in terms of green finance standard design, green finance incentive mechanisms, environmental information disclosure and risk management. At the same time, the regulatory authorities should also implement differentiated regulatory policies for green credit, encourage differentiated development of banks, optimize the investment structure of green credit funds, increase incentives for green credit, expand the coverage of green finance, and actively guide green credit to play a more active role in the green transformation of high-polluting enterprises and the resolution of overcapacity. Second, we should pay attention to the productivity and cost mechanism effects of green credit policies on enterprises to resolve overcapacity, and strengthen the synergy between rigid constraints and flexible market regulation. Government departments should create a favorable atmosphere and environment, increase investment in green financial infrastructure, and guide enterprises to improve productivity and reduce costs through financial support, R&D subsidies, tax and fee reductions and other incentives. Banks and other financial institutions should improve green financing channels, strengthen the supervision of the use of credit funds, improve the efficiency of the use of green credit funds, provide certain credit support for enterprises to enhance productivity, support enterprises to achieve productivity improvement and production and operation cost reduction through green development, and fundamentally solve the contradiction of overcapacity. Third, we should implement differentiated green financial service strategies, further enrich green credit products, and clarify key support areas, fields and industries. For enterprises located in different regions and with different characteristics, banks and other financial institutions should, under the principle of adapting to local conditions and cities, explore the characteristic ways of green finance to support enterprises in less developed areas of economic development to enhance capacity utilization, appropriately adjust and refine green finance standards, formulate green credit policies with differentiated and regional characteristics, refine and develop green credit products, and create the green finance program of “one product for each region and one product for each enterprise”, so as to solve the “last mile” problem of credit financing for enterprises with different characteristics, and precisely and effectively match and support the credit fund demand of enterprises with different characteristics in improving capacity utilization rate.

Key words: Green Credit Policy, Capacity Utilization Rate, DID, High Polluting Enterprises

摘要： 本文基于2004—2020年中国非金融类上市企业数据,以2007年出台的绿色信贷政策作为准自然实验,系统考察绿色信贷政策对企业产能利用率的影响效应和渠道机制。研究结果发现,绿色信贷政策对高污染企业的产能利用率提升起到明显的抑制作用,并通过成本效应和生产率效应对企业产能利用率产生负面影响。此外,绿色信贷政策对不同的企业产能利用率存在异质性影响。本文的研究为不断完善绿色金融政策体系,制定具有差异化、区域性特征的绿色信贷政策,以精准有效对接、支持不同特征企业的产能利用率改善提供了决策参考。

关键词: 绿色信贷政策, 产能利用率, 倍差法, 高污染企业