Green credit policy, credit allocation efficiency and upgrade of energy-intensive enterprises
Introduction
With the increasing environmental awareness of residents and the appeal for a better ecological environment, China is attaching an increasing amount of importance to the role of enterprises in the environmental protection. Various instruments of environmental regulation, including command-and-control policies and market-oriented instruments, have been used to encourage enterprises to update green production technology, reduce emissions, and improve environmental performance (Wen and Lee, 2020). In recent years, financial instruments, especially green credit, have been playing an increasingly important role in governing enterprise environmental behavior and limiting the blind expansion of polluting industries (Hao and Wu, 2020; Lee et al., 2020; Xu and Li, 2020). In 2012, China implemented a policy called the Green Credit Guidelines (GCG2012) to curb industrial pollution by financially penalizing polluters. It requires commercial banks to restrict lending to non-green firms and provide financial supports for environmentally friendly enterprises. To meet these requirements, banks may decline loan applications in regulated industries or non-green industries and even refuse to provide investment financing for enterprises that are seeking to upgrade. Thus, enacting the green credit policy has become a major challenge, as credit intervention may hinder industrial upgrading.
In China, energy-intensive industries consist of six industrial sectors that consume a large amount of energy in the production process, with energy expenditure accounting for a high proportion of output (Wang et al., 2019; Yuan et al., 2020). These energy-intensive industries also have other related problems that affect sustainable economic development, such as heavy pollutant emissions, the blind expansion of production scale, and low profitability (Tan and Lin, 2018; Liu and Lee, 2020). According to the National Bureau of Statistics of China, the air pollutants emitted by energy-intensive industries account for more than 80% of industrial emissions in 2012 (See more detail in the Table A1), and even these industries have a serious crisis of overcapacity. China also refers to these industries as “two high and one surplus” industries. The country has carried out special statistics and has also called for the transformation of these industries in various official documents. Therefore, energy-intensive industries must be given attention when exploring measures to promote industrial green upgrading (Bai et al., 2019; Bongers, 2020; Liu et al., 2020).
Green finance is regarded as an important instrument to promote the green transformation of the economy by providing financial supports for green enterprises (Zhang and Wang, 2019). However, some policies of green finance may go against their goals of providing financing support for industrial upgrading. The GCG, which has been designed as a helping hand for promoting the development of environmentally friendly enterprises, imposes credit restrictions on regulated industries. These restrictions may also lead to a further reduction in the allocation efficiency of bank credit due to information asymmetry. In addition, firms with liquidity shortage disturb the normal pricing market mechanism. Financing enables enterprises to access more capital, thus promoting enterprises to increase investment in R&D activities (Shi et al., 2019). Bank credit is the most extensive external financing channel, especially in transition countries with imperfect capital markets, and plays a vital role in enterprise upgrading investment or innovation activities (Hall, 2002; Atanassov, 2016; Chiu and Lee, 2020). It has shown that long-term loans with low interest encourage enterprises to invest in innovation activities (Huergo and Moreno, 2017). In contrast, credit constraints reduce enterprise upgrading investment, and firms without access to bank credit are always less productive (Cao and Leung, 2019; Mannasoo and Merikull, 2020). Therefore, the extent to which bank credit constraints due to the intervention of green credit contribute to misallocation and reduce the productivity of energy-intensive enterprises has important policy implications.
Inspired by the growth miracle of East Asian countries, a large body of literature has supported the notion that incentive industrial policies are important for enterprises to invest in long-term assets and innovation activities, as described in infant industry theory or Marshallian externalities theory (Wade, 1990; Chang, 1994; Rodrik, 2006; Chen and Lee, 2020; Wen and Zhao, 2020). Industrial policy is also criticized for bringing in market inefficiency and resource misallocation (Lazzarini, 2015). Although such policy remains a hot topic, few studies have focused on industrial policies in restricted industries, and our understanding of the mechanisms for restrictive industrial policies remains limited. Intervention policies for credit allocation are always used as a conventional instrument to limit the blind expansion of traditional industries. Referring to Hao et al. (2019) and Wang et al. (2018), who investigated the effects of the capacity-reduction initiative of China, this study treats the GCG2012 as a specific restrictive industrial policy and provides empirical evidence to understand the effectiveness of such policy.
Some studies have shown the impact of GCG policy on the debt financing of non-green firms. Liu et al. (2019) showed that the GCG2012 has significantly dropped the debt financing capacity, Xu and Li (2020) demonstrated the asymmetric effect of the green credit policy on the financing between non-green firms and green firms. This study holds that the policy and its potential impact must be analyzed further, not only as the object of analysis, but also as aspects of policy effects. On the one hand, energy-intensive industries should be taken as the objects of analysis not only because of their important role in industrial green transformation (as detailed in the discussion in the second paragraph of this part) but also because of the realities of policy practice. Banks always lend to firms by sector because these banks do not own detailed information on pollution, and no official document has disclosed which industries are heavily polluting. According to the annual reports of listing banks in China, banks mainly reduced loans to enterprises in energy-intensive industries after the promulgation of the GCG2012 (see more details in Table A2). On the other hand, the effects of policy interventions on enterprise upgrading should be investigated. The GCG2012 policy is aimed at promoting industrial upgrading through credit intervention, and the effectiveness of the GCG2012 should be assessed against its ultimate goal. In addition, this study analyzes the impact of this policy on the efficiency of credit allocation, as it plays an important role in encouraging enterprises to upgrade under the constraint of total credit supply.
This study uses a series micro-data of Chinese A-share listed firms from 2009 to 2017. It explores the impact of green credit policy on the upgrade of energy-intensive firms from the perspective of credit allocation efficiency. Specifically, three main empirical works provide an analytical framework for this topic. First, this study follows the analysis framework of the existing literature. Moreover, it uses the difference-in-differences (DID) method to evaluate the impact of GCG2012 on the financing and the upgrading of energy-intensive enterprises. Second, this study treats GCG2012 as a quasi experiment of credit intervention to explore its effects on the allocation efficiency empirically and examine the substitution hypothesis of trade credit. Third, this study introduces an improved green credit policy, the Energy Efficiency Credit Guidelines (EECG2015), to investigate whether the incentive policy helps achieve industrial upgrading.
The study extends the existing literature primarily in two aspects. First, we discover that the policy is inefficient in credit allocation and contradicts the goal of financing support for firm upgrading. Second, our study focuses on the effects of credit intervention policies on restrictive industries in the transition economies and enriches the theoretical literature on industrial policy. Third, this study shows that trade credit allocated by the market can replace bank credit to a certain extent, and the decline in the allocation efficiency of bank credit may be the result of government intervention. The rest of our study proceeds as follows. The next section provides a brief overview of green credit policies in China and their theoretical effects. Section 3 introduces the quasi-experimental method and the data. Then, Section 4 discusses the empirical results. Section 5 provides the extended analysis based on the EECG2015. Lastly, the final section presents the conclusions of this study.
Section snippets
Green credit policy in China
Green credit consists of a series of policies, institutions, and practices to promote pollution reduction and energy efficiency improvement through credit intervention. Specifically, green credit policies influence the environmental behavior of enterprises by the tools of loan products, loan maturity, loan interest rate, and credit quota. A number of highly certified international conventions, such as the Equator Principles, the UNEP Finance Initiative, and the IFC Framework, require commercial
Model specification
The difference-in-differences (DID) design is effective in identifying causal relationships and is therefore widely used for policy evaluation (Wen and Zhao, 2020). Referring to Wen and Lee (2020), who studied the treatment effect of policy shock on firm performance, this study uses the micro-level DID method to evaluate the treatment effects of policy intervention on credit allocation and enterprise upgrading. The micro-level DID model applied in our empirical analysis can be expressed as
Green credit policy and credit allocation
This study adopts the DID method and mainly focuses on the net effect of the credit allocation of energy-intensive enterprises relative to other enterprises before and after the issuance of guidelines, namely, the coefficients of interaction term (Treat×After) in the DID model. Table 2 shows the impacts of the GCG2012 on bank loans and trade credit. Column (1) to Column (4) in Table 2 regard all enterprises in non-energy-intensive industries as the control group. In contrast, Column (5) to
Energy efficiency credit policy and credit allocation
To investigate whether the EECG2015 can ameliorate the negative effects of the green credit policy, we examine the treatment effects of the EECG2015 on bank credit and trade credit within energy-intensive industries. This study classifies firms according to their investments in the improvements of energy efficiency. The variable of Intensity represents the degree of the attention given by an enterprise to energy efficiency investment. Treat refers to the corresponding dummy variable. The
Conclusion and implication
The sustainable development of energy-intensive industries is severely restricted because of the environmental problems from energy use and the finiteness of fossil energy. Methods for the effective promotion of green, low-carbon and sustainable development of energy-intensive industries have been extensively discussed in recent years. To promote the green transformation of industrial production, China has implemented the policy of GCG2012. This study uses the quasi-experimental design, and
CRediT author statement
Three authors provided critical feedback and helped shape the research, analysis and manuscript. They contributed equally to this study and share first authorship.
Funding
We acknowledge the financial support from the Jiangxi Humanities and Social Sciences Key Research Base Project of University (JD18016) and the Natural Science Foundation of Jiangxi Province of China through Grant No: 20202BAB201006, Jiangxi Humanities and Social Sciences Project of University (NO. JJ20125).
Data availability statement
Data are available from the authors upon request.
Declaration of Competing Interest
The authors declare that they have no conflict of interest. This article does not contain any experiments with human participants or animals performed by any of the authors.
Acknowledgements
The authors are grateful to the Editor and the anonymous referees for helpful comments and suggestions.
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