1. Introduction
In recent decades, China has tried to establish a high-quality legal framework for pursuing sustainable development and environmental progress. For example, some modern environmental legislation has been introduced in order to encourage water conservation (see, Appendix). Nevertheless, the destruction of the environment is still one of the serious challenges China faces at present because of its rapid urbanization and economic growth. The air pollution levels in some cities are among the worst in the world; about a third of China's total river length is highly polluted (OECD, 2007). Pollution has caused huge damage to the economy of China (Chang et al., Reference Chang, Hans and Haakon2001); the significant regional differences in pollution intensities (see, figures 1 and 2) have made the enforcement of environmental regulations quite difficult. More effective environmental policies, therefore, are needed to improve the environmental performance of China.
Figure 1. Regional differences in the intensity of industrial SO2 emissions in 2006
Figure 2. Regional differences in the intensity of industrial waste water discharges in 2006
To formulate effective policies, the factors that influence environmental performance should be well studied. The previous literature has emphasized the importance of income and growth, technology, foreign direct investment, government expenditure, corruption, and informal regulations (Grossman and Krueger, Reference Grossman and Krueger1995; Pargal and Wheeler, Reference Pargal and Wheeler1996; Lopez and Mitra, Reference Lopez and Mitra2000; Eskeland and Harrison, Reference Eskeland and Harrison2003; He, Reference He2006; Karen et al., Reference Karen, Gary, Ma and Xu2006; Cole, Reference Cole2007; Tsuzuki, Reference Tsuzuki2008; Yuxiang and Chen, Reference Yuxiang and Chen2010a). Almost all the previous studies have the implicit premise that enterprises can get enough financial services to conduct environment-friendly production. Unfortunately, this assumption is challenged by the evidence from developing countries, in which there are significant financing constraints on small and medium-size enterprises, and even some of the large ones have limited access to formal sources of external finance (McKinnon, Reference McKinnon1973, Reference McKinnon1994; Shaw, Reference Shaw1973; Perotti, Reference Perotti1993; Athey and Laumas, Reference Athey and Laumas1994; Chow and Fung, Reference Chow and Fung2000; Perotti and Vesnaver, Reference Perotti and Vesnaver2004; Beck and Demirgüc-Kunt, Reference Beck and Demirgüc-Kunt2006). In such circumstances, financial development can profoundly change the countries’ economic performance.
As the largest developing country, China also experienced substantial changes in its financial system in recent decades. Before the 1970s, the state-owned enterprises (SOEs)’ investment was financed mainly from interest-free budgetary grants and retained profits. During the 1980s, the SOEs came to rely on banks and the share of their total investment financed by bank loans increased from 14.3% in 1983 to 30.4% in 1992 (Cull and Xu, Reference Cull and Xu2003). The banks’ lending decisions were much less susceptible to political influence than was the allocation of government grants (Cull and Xu, Reference Cull and Xu2000).
Subsequently, the financial reform that began in 1993 significantly improved the performance of financial institutions. For example, in 1994, three policy banks were established to provide noncommercial financial services; in 1995, the Commercial Bank Law was introduced. As a result, the state-owned commercial banks could be free to pursue commercial objectives. Owing to the series of reforms, financial development was stimulated, and the ratio of the bank loans to gross domestic product (GDP) increased from 0.83 in 1995 to 1.44 in 2007 (China Finance Association, 2008). A survey of enterprises for the period between 2000 and 2002 indicated that the bank loans were supporting valid investment, and the better performing private enterprises were more likely to receive loans (Cull and Xu, Reference Cull and Xu2005).
To what extent does such financial development influence the enterprises’ environmental performance? This question has not been well answered by the previous literature. An exception is the recent studies of Tamazian et al. (Reference Tamazian, Chousa and Vadlamannati2009), who found that financial development was one of the key factors in the carbon dioxide (CO2) reduction of the Brazil, Russia, India, and China (BRIC) economies. To get improved insights into this topic, further analysis is still needed. With the aim of making a contribution, we compile detailed provincial panel data of China to analyze the relationship between financial development and industrial pollution discharges. As shown in figure 3, while the ratio of total loans to GDP increased steadily in recent years, the intensities of pollution discharges decreased gradually. This inverse relationship will be analyzed in the following sections.
Figure 3. Financial development and the evolution of the intensities of industrial pollution discharges in China
The rest of the paper is organized as follows: Section 2 provides the theoretical analysis. The empirical results are reported in section 3, and section 4 concludes the paper.
2. Theoretical analysis
Financial development, in this paper, refers to the increased quantity and improved quality of the provision of financial services. During the process, enterprises, especially those of relatively high productivity, get access to external finance more easily or at lower cost than they did before. The effects of financial development on the environmental performance of developing countries can be classified into the following four aspects.
First, financial development facilitates the investment of enterprises and thus has capitalization effects on their environmental performance. On one hand, some of the effects are positive, because the investment in abatement equipment may be facilitated. Such investment is beneficial to enterprises because of environmental regulations and the consumer preference for green products (Lundgren, Reference Lundgren2003). Thus, the introduction of environment-friendly technologies will be encouraged if enterprises get access to external finance more easily or at lower cost. Furthermore, financial development tends to encourage the growth of large and medium-size enterprises, since it reduces financing constraints on them. As a result, their environmental performance may be improved, because such enterprises have the benefits of economies of scale in both resource use and pollution abatement (Cole et al., Reference Cole, Elliott and Shimamoto2005).
On the other hand, some of the capitalization effects may be detrimental to the environment, as the growth of capital-intensive sectors may be encouraged by financial development. When the provision of financial services increases, some enterprises may become reliant on industrial machinery. Consequently, such upward movement in the capital intensity tends to increase pollution intensities, since there is a positive correlation between them (see Cole et al., Reference Cole, Elliott and Shimamoto2005). Moreover, financial development may also stimulate the entry or growth of small enterprises, which have few benefits of economies of scale in resource use and pollution abatement. As a result, such entry or growth may increase the pollution intensities, though this detrimental effect is trifling because of the minor role played by such enterprises in changing the environmental performance of the whole industry. Therefore, the capitalization effects of financial development are mixed according to the theoretical predictions.
Second, financial development has technology effects on environmental performance, because it can stimulate R&D activities and technological improvements. The significant linkage between R&D activities and external finance has long been recognized by Switzer (Reference Switzer1984) and Hansen (Reference Hansen1999). This relationship is important, because technology is regarded as a crucial factor in determining the environmental performance of enterprises (Cole et al., Reference Cole, Elliott and Shimamoto2005; Karen et al., Reference Karen, Gary, Ma and Xu2006; Ma and Stern, Reference Ma and Stern2008). Theoretically, technological improvements have two different effects on the environment. On one hand, such improvements may encourage the introduction of new environment-friendly technologies, which are beneficial to the environment. On the other hand, technological improvements may also increase the demand for natural resources such as energy. This reverse effect is named as the rebound effect by some previous studies (Sanstad et al., Reference Sanstad, Roy and Sathaye2006; Brännlund et al., Reference Brännlund, Ghalwash and Nordström2007). Therefore, the technology effects of financial development are also mixed according to the theory.
Third, financial development has income effects on the environmental performance, as it constitutes an important mechanism for long-run growth (McKinnon, Reference McKinnon1973; King and Levine, Reference King and Levine1993a, Reference King and Levineb; Odedokun, Reference Odedokun1996; Levine and Zervos, Reference Levine and Zervos1998; Christopoulos and Tsionas, Reference Christopoulos and Tsionas2004; Yuxiang and Chen, Reference Yuxiang and Chen2010b). According to the Environmental Kuznets Curve (Grossman and Krueger, Reference Grossman and Krueger1995; Suri and Chapman, Reference Suri and Chapman1998), the income effects can be either positive or negative.
Finally, if some environmental regulations are incorporated in the provision of financial services, financial development, which makes enterprises dependent on external finance, tends to strengthen the effects of environmental policies. For example, the People's Bank of China issued The Ordinance on the Implementation of Credit Policy to strengthen Environmental Protection in 1995. The banks were required to support environmental policies when assessing loans to enterprises. Specifically, the ordinance encouraged banks to assign high priority to the projects that were aiming at abating industrial pollution, or had authorized reports on environmental impact assessment. Subsequently, environmental performance rating and information disclosure schemes have been used to publicize the environmental performance of enterprises since 2002. These schemes are taken into account by banks when making decisions (OECD, 2007).
The banks have incentives to assess the environmental risks, since they do not want to run the risk of losing their money. Such incentives are influenced by the legal framework for the protection of the environment. Thus, this regulation effect of financial development will become stronger if a sound framework of environmental protection comes into operation. In this regard, such effects may be small at present, but they have the potential to play an important role in the future.
In summary, financial development has capitalization, technology, income, and regulation effects on the environmental performance of developing countries. Its real effects can be either positive or negative according to the theoretical analysis. This reveals the necessity of conducting empirical analysis to examine the effects in specific situations.
3. Econometric analysis
3.1. Data description and summary statistics
The analysis uses the provincial panel data of China from 1999 to 2006. The time span is decided by the availability of the data of private loans. Because the data of several provinces are unavailable, the sample includes twenty-nine provinces. All the data come from China Statistical Yearbook, China Environment Yearbook, China Finance Yearbook, and China Science and Technology Statistical Yearbook.
The variables of water and air pollution
The intensities of industrial waste water and chemical oxygen demand (COD)-related discharges are selected as the variables of water pollution. The following equations are used to estimate the two variables:
![\begin{equation}
\begin{array}{*{20}rcl}
{\rm IWWI}_{\it it} &=& \displaystyle \frac{{{\rm WWD}_{\it it}}}{{{\rm VAI}_{\it it}}}, \\[6pt]
{\rm CODI}_{\it it} &=& \displaystyle \frac{{{\rm CODD}_{\it it}}}{{{\rm VAI}_{\it it}}}, \\
\end{array}\end{equation}](https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20160920233705263-0355:S1355770X10000422:S1355770X10000422_eqnU1.gif?pub-status=live)
where IWWI is the intensity of industrial waste water discharges and WWD the volume of such discharges. VAI denotes the value added of the industry at the 1995 price. Similarly, CODI is the intensity of industrial COD-related discharges, and CODD the volume of such discharges.
In addition, the changes in sulfur dioxide (SO2) emissions are also estimated as another pollution variable. The amount of industrial SO2 emissions divided by the value added of the industry at the 1995 price is estimated as the variable of air pollution intensity.
The pollution data are collected by the State Environmental Protection Administration (SEPA), which surveys 85% of the pollution-intensive enterprises and estimates the other ones’ pollution discharges according to their production. As the data come from the authoritative yearbook on this subject, these variables can soundly indicate the changes in the environmental performance of the industry.
The variables of financial development
In China, the main access of enterprises to external finance is provided by banks. Because of this bank-dominated characteristic of the financial system, this variable should be an indicator relevant to banks. According to the China Statistical Yearbook, there is a substantial difference between the deposits and loans of financial institutions, and more than 30 per cent of the deposits have been sleeping in banks in the past decade. Thus, the ratio of the deposits to GDP can hardly indicate the financial development in China.
Since loans are more relevant to the activities of enterprises, the ratio of bank loans to GDP is estimated as the proxy for financial development. This ratio indicates how many financial services are needed in order to produce a unit of GDP and is the commonly used indicator in the previous studies (Liang and Teng, Reference Liang and Teng2006; Baltagi et al., Reference Baltagi, Demetriades and Law2009; Giuliano and Ruiz-Arranz, Reference Giuliano and Ruiz-Arranz2009; Hasan et al., Reference Hasan, Wachtel and Zhou2009; Lu and Yao, Reference Lu and Yao2009; Yuxiang and Chen, Reference Yuxiang and Chen2010b).
In addition, two other financial variables are also introduced to provide alternative proxies for assessing the pace of financial development. The first one is the ratio of the private loans to GDP. In the yearbooks of China, private loans include short-term loans to agriculture, township enterprises, domestic private firms, and foreign-funded enterprises. Agriculture is privately operated, because it is household based in China. The township enterprises are legally owned by town or village collectives, but they are treated differently from the SOEs in the access to bank loans (Lu and Yao, Reference Lu and Yao2009. In the official statistics, the domestic private firms include only those owned by a single private owner. As a result, the private loans in our estimation cover only a portion of the loans to the private sector, but this indicator is the best proxy that is available.
The second variable is the value of the nonprivate loans (total loans minus private loans) divided by GDP. In our analysis it is as important as the variable of private loans, because some if not all of the loans to SOEs also reflect the pace of financial development.
The control variables
To control the impacts of other factors, four variables are also included in our models.
First, the impacts of R&D activities should be controlled, since they are important and not fully decided by financial development. The annual R&D expenditure of industrial enterprises divided by gross industrial output value is estimated as the proxy for R&D intensity. In the estimation, the expenditure on Science and Technology Activities (S&TA) is used instead of standard R&D expenditure, because the latter's time-series data are unavailable in the official statistics. The S&TA includes the research activities and the purchase and construction of fixed assets for scientific research. Thus, the expenditure on S&TA can be a good proxy for R&D activities.
Second, the value of annual foreign direct investment (FDI) divided by the gross investment in fixed assets is estimated as the variable of FDI, since it is the commonly used proxy in the previous Chinese studies (see, e.g., Zhang and Song, Reference Zhang and Song2000). The foreign direct investment refers to the investment inside China by foreign enterprises, organizations, or individuals (including overseas Chinese, compatriots from Hong Kong, Macao and Taiwan, and some Chinese enterprises registered abroad) for the establishment of ventures exclusively with foreign own investment, Sino-foreign joint ventures and cooperative enterprises, or for cooperative exploration of resources with enterprises in China. This variable is used to control the environmental impacts of FDI.
To explain the FDI-environment nexus in developing countries, two conflicting theoretical hypotheses have been formulated by some previous literature. The pollution haven hypothesis emphasizes the importance of the relatively lax environmental regulations in developing countries, and thus predicts a positive correlation between FDI and environmental pollution. By comparison, the pollution halo hypothesis stresses the importance of advanced technologies and environmental management systems that are utilized by the foreign-funded enterprises (see Cole et al., Reference Cole, Elliott and Strobl2008). It, therefore, points out that the inflow of foreign capital can yield environmental benefits to developing countries. Since this nexus attracts special attention, using this variable helps compare the environmental impacts of FDI and financial development.
Third, provincial GDP per capita at the 1995 price is included to control the effects of income growth according to the Environmental Kuznets Curve.
Finally, a dummy variable is included to control the impacts of environmental performance rating and information disclosure schemes, which have been used in China since 2002.
Summary statistics
Table 1 presents summary statistics of the above variables.
Table 1. Summary statistics, 1999–2006
Note: All the variables have 232 observations except the FDI-investment ratio, which has 224 observations because the data of a province are unavailable.
3.2. Econometric approach
In order to control the habitual inertia and adjustment cost, the lagged dependent variable is included in the models. Thus, the optimal lag period of the dependent variable needs to be selected. Following Arellano and Bond (Reference Arellano and Bond1991), the autocorrelation test is employed to identify the most appropriate lag. That is to say, the selection is based on the absence of significant second-order serial correlation in the panel vector auto-regression (VAR) residuals (Huang et al., Reference Huang, Hwang and Yang2008).
In addition, province dummies are included to capture the time-invariant specific effects. Time dummies are also introduced to control the effects of other exogenous factors. Specifically, the basic equation is
where y denotes a dependent variable and X a vector of independent variables. α is a parameter, and β a vector of parameters. η denotes province dummies and λ time dummies. ν is the usual error term.
To deal with the time-invariant fixed effects, the first-difference transformation is suggested by Arellano and Bond (Reference Arellano and Bond1991). Thus, the equation becomes
where Δ is the first-difference operator.
According to the simulation results of Blundell and Bond (Reference Blundell and Bond1998), when the coefficient of the lagged dependent variable is close to one, the efficiency of using the system generalized method of moments (GMM) estimator is improved substantially. Moreover, the one-step estimator is selected according to the studies of Bond (Reference Bond2002). Therefore, the robust one-step system GMM estimator is employed in our empirical analysis.
3.3. Results
Financial development and industrial waste water discharges
Equation (1) in table 2 shows that the variable of financial development significantly and negatively correlates with the intensity of waste water discharges. In equation (2) of this table, this correlation remains statistically significant, implying a negative impact of financial development on waste water discharges. Such negative relationships can also be found in equations (3) and (4) of table 2, but the negative coefficient of nonprivate loans is more significant than that of private loans. This suggests that the loans to SOEs play an important part in the negative relationship between financial development and waste water discharges.
Table 2. Financial development and waste water discharges: equations (1)–(4)
Notes: c, b, and a denote the significance at the 10 per cent, 5 per cent, and 1 per cent level, respectively. Robust standard errors are in parentheses. AR (1) and AR (2) Tests denote Arellano-Bond first- and second-order autocorrelation test, respectively; Hansen Test is the Hansen χ2 test of over-identifying restrictions (Hansen, Reference Hansen1982). Their p values are shown in square parentheses.
In equations (2) and (4) of table 2, the income variable has both negative and positive coefficients, which indicate two different impacts of income growth. The fact that its positive coefficient is smaller than the negative one implies the income growth tends to reduce the intensity of waste water discharges. According to the negative coefficient of the 2002 dummy variable in table 2, the environmental protection schemes that have been used since 2002 also help reduce waste water discharges.
Financial development and COD-related discharges
In equations (5) and (6) of table 3, the negative coefficient of financial development is statistically significant and confirms the above findings. But its positive coefficient is significant too, implying some positive impacts of financial development on the intensity of COD-related discharges. Such impacts may be largely due to the capitalization effects of financial development. If enterprises get access to external finance more easily or at lower cost, the growth of capital-intensive sectors will be stimulated, and thus the intensity of pollution discharges will increase. These positive impacts are large in comparison with those on waste water discharges. A possible explanation is that waste water pollution is more strictly regulated than COD-related discharges, and the pollution charge has been extended to all waste water discharges instead of those exceeding the effluent standards since 1993. This suggests that more effective environmental regulations be incorporated in the provision of financial services to reduce the detrimental effects of financial development.
Table 3. Financial development and COD-related discharges: equations (5)–(8)
Notes: c, b, and a denote the significance at the 10 per cent, 5 per cent, and 1 per cent level, respectively. Robust standard errors are in parentheses. AR (1) and AR (2) Tests denote Arellano-Bond first- and second-order autocorrelation test, respectively; Hansen Test is the Hansen χ2 test of over-identifying restrictions (Hansen, Reference Hansen1982). Their p values are shown in square parentheses.
These positive impacts come later than the negative ones, because a substantial change in capital formation usually requires more time than does the introduction of environment-friendly manufacturing methods. More importantly, the negative coefficient is larger than the positive one; this indicates that the main impact of financial development on COD-related discharges is negative.
The above findings are confirmed by equations (7) and (8) in table 3. The significant coefficient of private loans is negative, implying that the loans to the private sector help reduce COD-related discharges. The coefficients of nonprivate loans are similar to those of total loans, suggesting that the positive impacts of financial development on COD-related discharges are largely connected with the loans to SOEs. As many SOEs have acquired interest-free budgetary grants and become relatively capital-intensive before the financial reform, financial development may further increase their capital intensity, natural resources use, and hence COD-related discharges.
As regards the control variables, the negative coefficient of FDI is also significant in equation (8) of table 3. It suggests that the growth of FDI is associated with the reduction in COD-related discharges. This relationship offers some support for the pollution halo hypothesis, which predicts that the multinationals’ advanced technologies and environmental management systems will yield benefits to the environment of developing countries. Similarly, the coefficient of the 2002 dummy variable is also negative and indicates a significant impact of the environmental protection schemes on COD-related discharges.
Financial development and SO2 emissions
In equations (9) and (10) of table 4, the variable of financial development has two significant coefficients, and the negative one is larger than the positive. The results are similar to those in table 3 and indicate some significant impacts of financial development on industrial SO2 emissions. According to these coefficients, the main effect of financial development is negative, though there are also some positive impacts. This negative relationship is confirmed by equations (11) and (12) in the same table. The growth of nonprivate loans tends to reduce the intensity of industrial SO2 emissions. However, the coefficients of private loans are statistically insignificant. The most likely explanation is that the main source of industrial SO2 emissions is the sector of Electric Power and Heat Supply, in which the SOEs have dominant positions.
Table 4. Financial development and SO2 emissions: equations (9)–(12)
Notes: a, b, and c denote the significance at the 10 per cent, 5 per cent, and 1 per cent level, respectively. Robust standard errors are in parentheses. AR (1) and AR (2) Tests denote Arellano-Bond first- and second-order autocorrelation test, respectively; Hansen Test is the Hansen χ2 test of over-identifying restrictions (Hansen, Reference Hansen1982). Their p values are shown in square parentheses.
As to the control variables, the R&D intensity has a significant and positive coefficient in equation (10) of table 4. This is consistent with the results of Ma et al. (Reference Ma, Oxley, Gibson and Kim2008), who found that the increased use of energy-intensive technologies had driven the energy intensity up in China since 1995. In this table, the significant coefficient of FDI variable is negative, implying the FDI is beneficial to air pollution control. Therefore, some support for the pollution halo hypothesis is provided by this table. In addition, the 2002 dummy variable has a significant and negative coefficient in most of the equations in table 4. This indicates that the environmental protection schemes bring significant impacts on industrial SO2 emissions.
4. Conclusions
Theoretically, financial development can facilitate enterprises’ investment, stimulate their R&D activities, constitute a mechanism for long-run growth, and strengthen the effects of environmental policies. It, therefore, has capitalization, technology, income, and regulation effects on the environmental performance of developing countries. Some of the effects are mixed according to theoretical analysis, and thus the real effects of financial development can be either positive or negative.
Using the provincial panel data of China, this paper examines the impacts of financial development on industrial water and air pollution discharges. The empirical results reveal that financial development tends to reduce the pollution intensities, though some of its capitalization effects are detrimental to the environment. In recent years, financial development has played an important role in improving the environmental performance of China.
According to our analysis, environmental regulations are important as well, because they are also found to play a crucial rule in pollution control. If a sound framework of environmental protection comes into operation, the beneficial impacts of financial development on the environment can be strengthened considerably.
These results offer new implications for policy makers in developing countries. On one hand, there may be good news for them, because FDI and income growth can provide some stimuli for improvement in environmental performance. On the other hand, the gains will be limited if the financing constraints on enterprises are not reduced substantially. Financial development not only constitutes an important mechanism for long-run growth, but also helps improve the enterprises’ environmental performance; it seems necessary for developing countries to stimulate the development of their financial systems.
Our analytical framework can be extended into the analysis of international differences in environmental performance, as the developing countries have diverse financial systems. More importantly, this finance-environment relationship may be influenced by their different economic situations and environmental regulations. Further analysis, therefore, is called for to verify this relationship and to examine the factors in determining its presence.
Appendix: Selected water-related rules and regulations in China
Source: OECD Environmental Performance Review: China.
