1

Corporate sustainability and financial performance of

Chinese banks

Olaf Weber, University of Waterloo, oweber@uwaterloo.ca

Weber, O. (2017). Corporate sustainability and financial performance of Chinese banks. Sustainability Accounting, Management and Policy Journal, 8(3).

Abstract

Purpose – This paper analyses the connection between the sustainability performance of Chinese

banks and their financial indicators to explore whether sustainability regulations can be

implemented without decreasing the financial performance of the banking sector.

Design/methodology/approach – The study examined reports and websites of Chinese banks,

categorized different corporate sustainability aspects, and conducted panel regression and

Granger causality to analyse cause and effect variables.

Findings –The environmental and social performance of Chinese banks increased significantly

between 2009 and 2013. Furthermore, a bi-directional causality between financial performance

and sustainability performance of Chinese banks has been found. Based on institutional theory,

this interaction may be influenced by the Chinese Green Credit Policy.

Research limitations/implications – The findings suggest that corporate sustainability

performance and financial performance are not a trade-off but correlate positively. Further

research is needed to analyse the effect of financial regulations, such as the Chinese Green Credit

Policy.

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Practical implications – According to the good management theory by Waddock and Graves

(1997) that claims a positive impact of corporate social performance on financial performance,

Chinese banks can invest in corporate sustainability to increase their financial success and re-

invest parts of the additional returns - also called slack resources - in sustainability activities.

Social implications – Chinese banks are able to influence the economy to become greener and

less polluting without sacrificing financial returns.

Originality/value – To the best of our knowledge, this is the first study to explore the

sustainability performance of Chinese banks, including their products and services.

Keywords Banks; China; credit; green economy; good management theory; regulation;

corporate sustainability

Paper type Research paper

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Introduction

The Chinese economy is growing significantly despite the fact that China’s strong economic

growth has been cooling down since 2012. A drawback of China’s economic success is the

negative environmental impact of many of its economic activities. Currently, China is the

world’s largest emitter of greenhouse gas emissions with more than 23 percent of the global

emissions (Vaughan & Branigan, 2014). Furthermore, local emissions have significant impacts

on air and water quality. Air pollution has become one of the major environmental concerns in

China (Chan & Yao, 2008), and a significant amount of China’s groundwater and surface water

is polluted (Ding, Yu, & Liu, 2016).

Consequently, China initiated a Green Credit Policy that has achieved international recognition

(Jun & Zadek, 2015; Zadek & Robins, 2015). The policy has introduced guidelines and

regulations for integrating environmental issues into financial decision-making (Bai, Faure, &

Liu, 2013) in a standardized way (Bendell, Miller, & Wortmann, 2011) to enable banks and

financial markets to support the transformation to a greener economy (Busch, Bauer, & Orlitzky,

2015; Oyegunle & Weber, 2015; Zhao, 2015). Its goal is to initiate green innovation (Aguilera-

Caracuel & Ortiz-de-Mandojana, 2013) in the financial sector as well as in other sectors. In

contrast to other programs, such as the Environmental Response, Compensation, and Liability

Act of 1980 (CERCLA) in the United States of America and to environmental regulations in

Europe (Gemmell & Scott, 2013; Weber, Fenchel, & Scholz, 2008a), the Chinese initiative

focuses directly on banks and other lenders.

The Green Credit Policy, implemented in 2006, is overseen by three agencies, the Ministry of

Environmental Protection, the Peoples’ Bank of China, and the China Banking Regulatory

Commission (Aizawa & Chaofei, 2010). A central part of the program demands that banks

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restrict loans to polluting industries and offer adjusted interest rates depending on the

environmental performance of the borrowers’ industries. Having said that, pollution control

facilities as well as borrowers involved in environmental protection and infrastructure, renewable

energy, circular economics, and environmentally friendly agriculture qualify for loans with

reduced interest rates (He & Zhang, 2007; Zhao & Xu, 2012).

The policy even asks lenders to limit loans to polluting industries, and to withdraw loans that

have been already provided should environmental controversies or instances of non-compliance

occur (Jin & Mengqi, 2011). Furthermore, interest rates for polluting industries have to be higher

than for non-polluting borrowers.

The regulations are compulsory for all Chinese banks, regardless of whether they are government

owned, joint-stock banks, or credit unions (China Banking Regulatory Commission, 2012).

Consequently, Chinese banks introduced environmental policies, strategies, and assessment

systems to evaluate credit clients (Chan-Fishel, 2007). The question remains, however, whether

the introduction of corporate sustainability has positive or negative impacts on the financial

performance of Chinese banks and whether the size and financial performance of the banks have

an impact on their corporate sustainability performance.

Furthermore, whether the program has been implemented successfully, and has been contributing

to environmental improvements as well as to a low carbon economy remains a controversial

discussion (Hill, 2014; Jiguang & Zhiqun, 2011; Zhang, Yang, & Bi, 2011; Zhao & Xu, 2012).

Hence, to help banks to implement the program, and to overcome difficulties in assessing

environmental information from clients, the China Banking Regulatory Commission (CBRC)

issued the Green Credit Guidelines in 2012 (Zhao & Xu, 2012).

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It has to be emphasized that not only the Chinese Central Bank and Central Regulators

contributed to greener and sustainable banking. Many local and regional governments also

introduced regulations to support green banking (Jiguang & Zhiqun, 2011). Nevertheless, some

local governments still support polluting industries because of economic benefits (Jin & Mengqi,

2011), and have an ambivalent role with regard to the enforcement of environmental regulations

(Lo, Fryxell, van Rooij, Wang, & Honying Li, 2012).

The Green Credit Policy, as well as other initiatives integrating the financial sector into social

and environmental sustainability financing (Park & Ren, 2001; Song, Xue, & Zhong, 2010;

Wang & Juslin, 2009), may have a positive influence on sustainable development (Scholtens,

Cerin, & Hassel, 2008; Stephens & Skinner, 2013). The open question is, however, whether

sustainability integration goes hand-in-hand with financial benefits for lenders, or whether it is a

trade-off (Weber, 2014c). Jin and Mengqi (2011) as well as Mengze and Wei (2015) suggest that

environmental risk management practices have to be improved in Chinese banks to meet the

requirements of the Green Credit Guidelines and to create a win-win situation. Only if banks

have the resources and capabilities to assess environmental and social risks as well as

opportunities will they be able to achieve a positive impact on sustainable development, a

reduction of financial risks (Weber et al., 2008a), and to develop innovative sustainable financial

products (Chang & Sam, 2015). Therefore, this study analyses whether sustainability

performance and financial performance of Chinese banks correlate positively or negatively.

Sustainability in the banking sector

Globally, the banking sector began to integrate environmental and social aspects into their

business during the 1980s. The first activities concentrated on internal environmental

management (Jeucken & Bouma, 1999), resulting in environmental resource savings, lower

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emission, and increased reputation (Babiak & Trendafilova, 2011). As the second step, banks

integrated environmental issues into lending, investing, asset management, and project finance

(Schmidheiny & Zorraquin, 1996; Scholtens, 2008a). Environmental risks, such as those caused

by climate change, can have a significant influence on the financial risk of credit and investment

portfolios. Consequently, they have to be managed thoroughly (Weber, Hoque, & Islam, 2015;

Weber, Scholz, & Michalik, 2010; Zeidan, Boechat, & Fleury, 2015). Therefore, many banks

have implemented environmental credit risk assessment procedures (Weber, 2012). Finally, the

financial industry adopted socially responsible investment (SRI) processes to mange investment

risks and to seize SRI opportunities (Cerin & Scholtens, 2011).

Though heavily regulated, compared with other industrial sectors, the financial sector is not

much exposed to institutional pressure (Darnall & Carmin, 2005) or to regulations regarding

community, labor issues or the environment (Helleiner & Thistlethwaite, 2009; Rodriguez-

Dominguez, Gallego-Alvarez, & Garcia-Sanchez, 2009). Financial sector regulations focus

mainly on risk adjusted financial capital provision and on financial risks to guarantee the stability

of the financial industry. Consequently, for a long time, the pressure on the financial sector to

perform well with respect to the sustainability impact of their main products and services, such

as lending and investing, has been lower than in many other industries (Weber, Diaz, &

Schwegler, 2014). Studies have found, however, that environmental and sustainability reporting

is positively correlated with the size and the profitability of financial institutions (Alberici &

Querci, 2015; Chih, Chih, & Chen, 2010), and that the integration of environmental and

sustainability issues into financial sector products and services has been increasing over time

(Scholtens, 2008a).

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Consequently, findings about the positive correlation between sustainability performance and

financial performance in the banking sector found entrance into financial sector sustainability

regulations, such as the Chinese Green Credit Guidelines. Other countries such as Brazil,

Bangladesh, and Nigeria have also introduced regulations and guidelines focusing on the impact

of the banking sector on environment and sustainable development (Zadek & Robins, 2015).

These regulations assume that sustainability performance and financial performance go hand-in-

hand and that sustainability and financial stability correlate.

Corporate sustainability in China

The comparatively low corporate sustainability performance in emerging countries in the Asia

Pacific region (Forbes & McIntosh, 2011) may be explained by the relatively recent

establishment of corporate sustainability and social responsibility in these countries (Cheung,

Jiang, Mak, & Tan, 2013; Fang, Côté, & Qin, 2007). This, however, may change in light of new

regulations regarding banking and sustainability, as discussed above. With regard to the Chinese

financial sector, Hu and Scholtens (2012) even found that two of China’s major banks, Bank of

China and Industrial Bank of China, ranked upon the highest with regard to corporate social

responsibility in emerging countries. Also in the academic international business and

management literature, corporate sustainability has been analyzed only for a relatively short time

(Kolk, 2016).

Though Chinese companies have a significant environmental impact, the perception of the

financial benefits of corporate sustainability and the level of sustainability management and

reporting are still low (Wong, Long, & Elankumaran, 2010). Until recently, the majority of

Chinese firms did not disclose any environmental information or published environmental

reports (Liu & Anbumozhi, 2009). Furthermore research on ESG, CSR and environmental

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reporting in China is relatively new (Fifka, 2011). Newer findings, however, suggest an

institutionalisation of sustainability reporting (Shabana, Buchholtz, & Carroll, 2016) and a non-

linear relationship between corporate environmental disclosure and environmental performance

of Chinese firms (Meng, Zeng, Shi, Qi, & Zhang, 2014).

The advantages of connecting corporate sustainability issues with business success have been

acknowledged by Chinese companies just currently (Liu et al., 2010). Like in other regions,

sustainability and business success is still seen as a trade-off rather than a win-win situation

(Hahn, Figge, Pinkse, & Preuss, 2010; Winn, Pinkse, & Illge, 2012), or is perceived as non-

correlated. Therefore, this study addresses the following research question: Is there a connection

between the sustainability performance and the financial performance of Chinese banks?

Theoretical background

The relationship between corporate sustainability and financial indicators is discussed in many

studies (McGuire, Sundgren, & Schneeweis, 1988; Pava & Krausz, 1996; Simpson & Kohers,

2002) and in meta studies (Friede, Busch, & Bassen, 2015; Griffin & Mahon, 1997; Horváthová,

2010; Margolis & Walsh, 2001; Orlitzky, Schmidt, & Rynes, 2003). The majority of the studies

suggests a positive relationship between sustainability performance and financial performance.

What is still unclear, however, is the direction of causality that can be explained by two theories,

the slack resources theory and the good management theory (Waddock & Graves, 1997).

Furthermore, institutional theory (DiMaggio & Powell, 1983) has been used to explain the bi-

directional causality between corporate sustainability and financial performance.

On the one hand, good management theory is closely linked to the resource based view of a firm

(Wernerfelt, 1984) and claims that corporate sustainability may have an impact on financial

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performance because it helps a firm to reduce costs, to increase its reputation (Deephouse,

Newburry, & Soleimani, 2016), and to address stakeholders that are interested in the firm’s

social and environmental responsibility (Chan, 2010; Lankoski, 2008; Liu, Tang, Lo, & Zhan,

2016; Park & Ghauri, 2015; Qi et al., 2014; Waddock & Graves, 1997). Thus, corporate

sustainability leaders practice good management and thereby achieve a competitive advantage

that helps them to outperform their competitors financially (Lin, Chang, & Dang, 2015; Sharma

& Vredenburg, 1998). Good management theory defines sustainability performance as a sub-

category of general management performance. The theory’s representatives assume that general

management performance consists of different aspects and that sustainability management is a

part of it. Consequently, corporate sustainability and general sustainability correlate.

On the other hand, good financial performance may influence corporate sustainability because it

provides the financial resources - often called slack resources - that are needed to invest in

corporate sustainability (Scholtens, 2008b). Particularly, accounting based measures for financial

performance that are independent from financial market influences, such as ROA, are able to

predict corporate sustainability because corporate sustainability performance is firm specific

(McGuire et al., 1988). The reasons is that firms with high financial performance and low risk

can afford to act more responsibly than competitors with lower returns and higher risk.

Consequently, increased corporate sustainability performance appears after the accumulation of

slack resources.

The third explanation for a connection between corporate sustainability and financial

performance is that both influence each other, also called bi-directional causality or virtuous

circle (Waddock & Graves, 1997). Slack resources or assets lead to improved corporate

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sustainability that, in turn, leads to positive reputation effects, cost savings, and increased profit.

This, in turn, has a positive effect on the financial performance of a firm as explained above.

Corporate sustainability and financial performance may also be influenced by a third variable

that has an impact on both (Ameer & Othman, 2012). As described above, and in-line with

institutional theory, the Chinese Green Credit Guidelines may influence both corporate

sustainability performance and financial performance of Chinese banks by exposing them to

coercive pressure (Phan & Baird, 2015). Formal and informal pressure can lead to coercive

isomorphism. On the one hand, banks react to the Chinese Green Credit Policy as a form of

formal pressure issued by the central regulator. On the other hand, banks respond to societal

pressure that asks them to be more sustainable and to invest the green economy.

Consequently, our research question is whether sustainability performance and financial

performance of Chinese banks correlate, and, if so, which direction this connection may take.

Following the theories described above, our two hypotheses are:

Hypothesis 1: Better financial performance leads to better corporate sustainability performance

of Chinese banks.

Hypothesis 2: The sustainability performance of Chinese banks has a positive effect on their

financial performance.

Because we used government influence and region as control variables, the models for the

hypotheses are:

(1) Sustainability Performance = f(financial performance, government influence, region)

(2) Financial performance = f(sustainability performance, government influence, region)

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Material and methods

We assessed all members of the China Banking Association (http://www.china-cba.net/) with

respect to their environmental, social, and economic sustainability performance by analyzing

data from annual financial and non-financial reports, and from websites of the respective banks

and financial institutions. The study selected Chinese organizations, because of the introduction

of the Green Credit Policy that is unique and puts a pressure on banks to conduct their business

in line with sustainable development. Further reasons for the focus on China are the fast and

significant development of corporate social responsibility, and corporate sustainability

performance of Chinese banks being under-researched compared to Western industrialized

countries.

46 of the 70 members of the China Banking Association, such as banks and credit unions,

published information related to financial, environmental, social, and sustainability aspects.

Particularly, many rural credit cooperatives did not disclose any financial or non-financial

information. Additionally, we analysed the members’ total assets, net profit, ROA, ROE, and the

non-performing loan ratio as key financial accounting indicators. Financial data was gathered

from the banks’ annual reports. Data was assessed for the years 2009 to 2013 to conduct a 5-year

analysis. We used the natural logarithm of the financial indicators to normalize their distribution.

The main approach for assessing the sustainability performance has been to analyze how and

whether the banks’ financial products, services and processes address sustainability issues. To

assess these products, services and processes we applied the criteria presented in Table 1. Similar

to corporate sustainability reporting and rating systems, such as the Global Reporting Initiative

(Global Reporting Initiative, 2013) and Thomson Reuters’ ESG Rating Asset4

(http://www.trcri.com), the research centered on products and services as well as on policies and

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management systems to avoid focusing too much on general green visions and policies that tend

to bear the risk of greenwashing (Bowen & Aragon-Correa, 2014). The method also takes into

account that the ‘green part’ of the banks’ portfolios is probably still too small to have an effect

on their financial performance. Other studies used similar combinations of indicators to assess

the effect of corporate sustainability performance on financial performance (Scholtens, 2008a;

Waddock & Graves, 1997; Weber et al., 2014).

As the highest level of analysis, we used indicators, such as social and environmental policies,

social and environmental management systems, and internal environmental and social

management processes. Common banking products and services that have been analysed were

loans, mortgages, funds, indices, asset management, bonds, microfinance, project finance,

savings, and investment banking. Table 1 presents examples for products and services as well as

for policies and management systems. We assessed whether the policies, processes, products and

services addressed the environmental, social or economic component of the triple-bottom line

approach of sustainability (Elkington, 1998) using two categories (yes, no). Our analysis, based

on the exploration of all reports and website in the sample, suggests that there were no other

additional products and services offered by the banks and credit unions in the sample.

Table 1 about here

In order to calculate the environmental and social scores for the banks, we assigned ‘1’ if the

particular environmental or social product, service, policy, or management system was

implemented. Otherwise, we assigned the value 0. For instance, we assigned ‘1’ if a bank

reported that they conduct environmental credit risk assessment or if they reported about their

green lending business (see Table 1). Then, we calculated the sum of the environmental and

social indicators. The maximum value was 30, with a maximum of 15 for environment and social

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performance, respectively. As the next step, we divided the banks’ results by the maximum

achievable points for the social and the environmental indicators to standardize the values for the

banks’ environmental and social performance. Finally, we calculated the total sustainability

score using the average of the environmental and the social score. Consequently, the

sustainability score is an equally weighted combination of the environmental and social scores.

Using 1 and 0 scoring has the advantage of being independent from subjective performance

scaling, thereby increasing the reliability of the assessment. The study mitigated the risk of this

method to be too simplistic through combining 30 indicators by summation and calculating

sustainability scores. Approaches based on 0 / 1 categorization are also applied by well-known

ESG rating systems, such as MSCI-KLD Score and Thomson Reuters Asset4 ESG - Rating that

are used in many academic studies (Gregory & Whittaker, 2012; Griffin & Mahon, 1997; Kempf

& Osthoff, 2007; Scholtens, 2008a; Weber, Koellner, Habegger, Steffensen, & Ohnemus,

2008b).

To analyse the connection between sustainability performance and financial performance, we

used panel regressions without time lags (Petersen, 2009) as well as panel regressions with one-

and two-year time lags to analyse Granger causality (Granger, 1969). Panel regression can be

used for variables X that are assessed at different points in time t (t = 1,…, T) for different

subjects i (i = 1,…, n). One advantage of using panel regressions with time lags compared to

simple regressions with time lags is that panel regressions analyze one- and two-year lags during

a longer time-period, in our case, five years.

Panel regressions with random effects analyse two sources of variance, the variance between

subjects (the banks in the sample) and the variance within the subject over time (Kahane, 2007),

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assuming that the variables within the subjects correlate over time (Liang & Zeger, 1986). This

study assessed financial data and sustainability performance data for the period between 2009

and 2013.

We conducted a Breusch and Pagan Lagrangian multiplier test for random effects to test whether

to use panel regressions instead of simple OLS regression. A significant test result suggests

differences between the banks over time, and, therefore, recommends the use of panel

regressions (Breusch & Pagan, 1979).

In addition to analysing the regression between financial data and sustainability performance in

the same year, we used both one and two-year lags between financial variables and the benks’

sustainability performance for a period of five years to analyse Granger causality (Granger,

1969). The impact of the independent variable on the dependent variable was calculated by using

data in year x for the independent variable while the date for the dependent variable was taken

from year x+1 and x+2, with x being the period between 2009 and 2013. This method was used

because we expected that the effect of the independent variable would appear with a certain

delay. Consequently, we calculated panel regression functions with financial indicators as

independent variables in year x and sustainability indicators in year x + 1 and year x+2

(regressionfin), and vice versa (regressionsust) and compared the coefficients of determination (r2)

for the regressions with sustainability performance as dependent variable versus those with

financial indicators as dependent variable. Uni-directional causality appears if only one of the

two regressions is significant. If both, regressionfin and regressionsust are significant, they indicate

bi-directional causality (Waddock & Graves, 1997).

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Finally, we conducted analyses of variance (ANOVA) to analyse differences with regard to

financial indicators between types of banks, levels of government influence, and regions.

Sample

The financial institutions in our sample were city commercial banks, national joint-stock

commercial banks, policy banks, postal savings banks, rural cooperative banks, rural credit

unions, and state-owned commercial banks listed by the China Banking Association

(http://www.china-cba.net/). The sample consists of institutions that offered publicly available

annual reports, such as environmental, CSR, and sustainability reports, or disclosed any

sustainability and financial information on their websites. In-line with Bi (1993), we categorized

the banks into governmental banks (state-owned commercial banks and policy banks), banks

with government majority (rural and city commercial banks), and joint-stock banks (publicly-

traded banks) to take a possible governance effect into account.

Because of the different levels of regional economic development in China, we used the regions

in which the financial institutions are active as control variables. Other studies suggest that

regional differences, for instance with regard to economic development, have an impact on the

sustainability of banks (Zhang et al., 2011). While both, the east and northeast of China are

highly industrialized and are comparable to industrialized countries, the middle and the west of

China are less developed. Furthermore, we categorized financial institutions as national if they

conduct business activities on a national level. Dummy variables in the regression analyses were

the type of financial institution and their region of origin. Table 2 presents the financial

institutions, their categories, and regions.

Table 2 about here

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Results

As the first step, we present the results of the descriptive statistics for the sample. Secondly, we

describe the results of the analysis of variance (ANOVA), conducted to analyse differences

between the types of banks and regions. Thirdly, we present the results of the panel regression

analyses with and without time lags.

Descriptive analyses

We analysed eight types of banks according to the categories of the Chinese Bankers

Association. In total, our sample consisted of those 46 banks and credit unions (see Table 2) that

disclosed any financial and sustainability information. Data was collected for the years 2009 to

2013. Of these banks, City Commercial Banks was the biggest group with 18 banks, followed by

12 National Joint-Stock Commercial Banks. Furthermore, five banks in the sample were Rural

Commercial Banks and State-owned Commercial Banks, respectively. Of the banks in the

sample, five were Policy Banks, meaning that they are state-owned banks responsible for

agricultural, export-import, and development financing. The other banks in the sample were one

Postal Savings Bank, one Rural Cooperative Bank, and one Rural Credit Union. Hence, the

sample consists of nine government owned institutions, 23 institutions with government

majority, and 14 joint-stock commercial banks.

Nearly half of the banks (N = 22) come from China’s industrialized east and northeast.

Furthermore, 13 of the banks are national banks. Nine banks are active in China’s west, while

two banks are located in the middle of China.

Table 3 presents the descriptive statistics for the financial indicators in total, and split by the type

of bank. At the time of the study, the dollar value of 1 RMB was $0.16.

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Table 3 about here

Table 4 presents the correlation between the log-transformed financial indicators. It demonstrates

that assets and net profits as well as ROE and ROA are highly correlated. This was expected and

has to be taken into account if these indicators are integrated as variables into multivariate

regression analyses. Because of this correlation, we did not use multiple financial indicators in

the regression analyses as independent variables in one function.

Table 4 about here

Differences between types of financial institutions

We used ANOVAs to analyse differences between the types of banks with respect to their

financial figures. Furthermore, we controlled for the year as an additional factor. The analyses

suggest significant differences between bank types with regard to total assets (df = 212, F =

10.87, p < .00001), net profit (df = 210, F =9.75, p < .00001), ROA (df = 165, F =3.54, p <

.00001), ROE (df = 194, F =2.23, p = .0007), and non-performing loan ratio (df = 195, F =2.19,

p < .0009) but no significant difference between the years. Depending on the type of bank, some

of the financial indicators were different. Postal savings banks had the highest value for total

assets, state-owned banks had the highest net profit, ROA and ROE, while rural commercial

banks had the highest non-performing loan ratio.

ANOVAs were also conducted to test differences between banks with different government

influences. The analyses suggest significant differences between the levels of government

influence with regard to total assets (df = 212, F = 28.1, p < .00001), net profit (df = 210, F

=20.7, p < .00001), ROA (df = 165, F =7.61, p < .00001), and non-performing loan ratio (df =

195, F =3.80, p < .0009). Post-hoc Scheffé tests suggest that government banks have

significantly higher assets than banks with government majority and joint-stock banks (p <

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.0001). The same is valid for net profits (p < .0001). With regard to the non-performing loan

ratio, joint-stock banks have a significantly lower ratio than government majority banks (p =

.001) and government banks (p = .036).

Sustainability performance

The descriptive statistics of the environmental and social scores (mean, standard deviation,

skewness, and kurtosis) of the banks in the sample are presented in Table 5. The descriptive data

indicates an increase of the environmental and social performance scores over time.

Table 5 about here

Figure 1 presents the development of the social, the environmental, and the sustainability score

over time. To test for significant changes with respect to the sustainability performance of the

banks over time, we used a multi-factor ANOVA with the sustainability score as the dependent

variable, and year and sustainability fields (social, environment) as independent factors. The

ANOVA suggests a significant difference for the scores over time (p = .003, df = 4, F = 4.17),

between the sustainability fields (p < .00001, F = 72.6, df = 1) and for the model (p < .00001, F =

10.4, df = 9, r2 = .17). The interaction between the fields over time was not significant (p = 0.39,

F = 1.03, df = 4), demonstrating that the environmental and social performance of the banks

developed in parallel.

Figure 1 about here

Regression analyses for sustainability and financial indicators

We conducted a Breusch and Pagan Lagrangian multiplier test for random effects that resulted in

significant differences between the banks over time with regard to their environmental, social

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and sustainability performance. The result was significant (p < .00001), suggesting a panel effect

that justifies the use of a random effects panel regression instead of OLS regression.

First, the panel regression used the sustainability score (average of the social and environmental

scores) as dependent variable and the natural logarithm of total assets, net profit, ROE, ROE, and

the ratio of non-performing loans respectively as independent variables over five years between

2009 and 2013. Government influence and region of banks were integrated into the regression

function as dummy variables. The results are presented in Table 6

Table 6 about here

Table 6 indicates that the regression functions and the regression coefficients for all financial

indicators were significant, suggesting a connection between the banks’ financial indicators and

their corporate sustainability performance. The results demonstrate that the sustainability

performance is higher for bigger banks (total assets), for those that have higher net profits, higher

ROE, and ROA. Consequently, sustainability performance and financial performance correlate.

In addition to using the sustainability score as the dependent variable, to analyze the impact of

the financial performance on corporate sustainability, the following analyses explore whether

sustainability performance – the combined environmental and social performance - has a positive

impact on financial indicators, such as total assets, net profit, ROA, ROE, and non-performing

loan ratio (see Table 7). The results suggest a significant impact of the sustainability

performance indicator on all financial indicators with the exception of non-performing loans.

Table 7 about here

Finally, to test causality, we used the sustainability performance and the financial performance as

both, dependent and independent variables, in panel regression analyses. Granger causation

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(Granger, 1969) has been applied to take cause and effect into account, considering one- and

two-year lags for the years between 2009 and 2013 (see Methods section above). Consequently,

we calculated panel regression functions with financial indicators as independent variables in

year x and sustainability indicators in year x + 1 as well as in year + 2 and vice versa for four and

three years respectively. After having calculated the regressions, we compared r2 as well as the

significance level of the regressions with the sustainability performance as dependent variable

versus those with the financial indicators as dependent variable. If the independent variable is

able to predict the time-lagged dependent variable we can assume a cause-effect relation

(Granger, 1969). Table 8 presents the results of the time-lagged panel regression analyses.

Table 8 about here

For both, total assets and net-profits, r2 is similar for the sustainability score as independent and

dependent variable. This is valid for regressions with one-year lags and two-year lags.

Furthermore, all regressions for the sustainability score and total assets are significant (p <

.00001). In addition, the one- and two- year lagged regressions for the sustainability score and

net profits are significant in both directions (p < .00001). Furthermore, their r2 is similar. In these

cases, a high sustainability score has a positive impact on assets and on profits in the following

years and vice versa.

The results for ROA and ROE, however, are different. The explained variance (r2) for these

regressions is much lower than for total assets and net profits in both directions. Furthermore, the

significance level for the one-year lag is higher than for the two-year lag. Although the

regressions for the sustainability score, ROA, and ROE are significant, their r2 is relatively low.

The results of the regression analyses for ROA and ROE, however, suggest a positive impact of

the sustainability performance on the selected financial figures, and vice versa a causality

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between financial indicators and sustainability performance for a one-year lag. The only

financial indicator with mixed results with regard to the connection to the sustainability score is

‘non-performing loans’ with only one significant regression for the two-year lag with the

sustainability score as the independent variable (p = .0018).

Overall, the results of the regression analyses with time lags indicate bi-directional causation

between the sustainability score on the one hand and total assets as well as net profits on the

other hand. For ROA, we found a uni-directional causation between ROA as cause and the

sustainability score as the effect. We found similar results for ROE, though not all regressions

were significant. The correlation between the sustainability score and non-performing loans,

however, was rather weak. Consequently, our results suggest a bi-directional causation between

corporate sustainability performance and financial performance of the banks in the sample.

Discussion

Following Hoffman (2001), this study focuses on the interface between sustainability

performance of Chinese banks and their financial performance. Our results suggest that the

environmental and social performance of Chinese banks increased significantly between 2009

and 2013. The increase could have been expected because other studies found a general increase

in corporate sustainability in Chinese firms during a relatively short period (Weber, 2014a; Xun,

2012). Furthermore, the increase could be triggered by the Green Credit that expect banks to

become active with regard to integrating environmental risks into their credit risk assessment

procedures (Aizawa & Chaofei, 2010; Zhang et al., 2011); therefore, initiating a culture change

in Chinese banks toward a more sustainable direction (Linnenluecke & Griffiths, 2010).

22

In-line with the results of Xun (2012), our study demonstrates that the integration of

environmental and social issues into business strategies, products and services of Chinese banks

correlates with increased total assets and net profits and is not a trade-off. This result suggests

that, on the one hand, institutional pressure caused by the Green Credit Guidelines (Bai et al.,

2013) may increase both, assets and profits, as well as the sustainability performance of Chinese

banks. On the other hand, the results support the good management theory (Waddock & Graves,

1997) claiming that corporate social performance influences financial performance positively

(Friede et al., 2015).

We found the strongest correlation between financial indicators and sustainability performance

for total assets and net profits assessed at the same year as well as for one-year and two-year

lags. Alberici and Querci (2015) as well as Chih et al. (2010) reported similar results with regard

to net-profits in their studies that focused on environmental disclosures and sustainability

performance of financial intermediaries. Also, Weber (2014b) found a correlation between the

size of financial institutions, assessed by their total assets, and the quality of their sustainability

reporting.

Furthermore, Dam and Scholtens (2015) suggested a connection between corporate sustainability

and return on assets as an indicator that is not dependent on financial market assessments. Our

study confirmed the results for Chinese banks for a one-year lag. In this case, the impact of ROA

on the sustainability performance was stronger than the other direction. The present study,

however, broadened the literature by demonstrating that even in a highly regulated sector, such

as the Chinese financial sector, size and financial returns positively influence the integration of

sustainability aspects into financial business strategies, processes, products and services.

23

Having explored the correlation between financial indicators and sustainability performance

without time lags, we analysed the cause and effect between financial performance and

sustainability performance using Granger causality. The results suggest a bi-directional causality

(Fischer & Sawczyn, 2013; Jaccard & Turrisi, 2003) between sustainability performance and

financial indicators, such as total assets, net profit, ROA, and ROE.

The bi-directional causality is in contrast to a study by Fischer and Sawczyn (2013) who

suggested that corporate financial performance influences corporate sustainability performance,

but is in-line with Waddock and Graves (1997). It seems that both, corporate sustainability and

financial performance interact and that the same driver might influence both. This interpretation

corresponds with institutional theory (DiMaggio & Powell, 1983). It seems that on the one hand,

institutional pressure for higher sustainability performance does not affect the financial

performance of Chinese banks negatively; a finding that is in-line with the good management

theory (Waddock & Graves, 1997). On the other hand, Chinese banks invest slack resources into

green and sustainable products, services, and strategies. Both, higher sustainability performance

and higher financial performance, is intended by the Green Credit Policy that focuses on market-

based mechanisms to address both, green growth and environmental risk management in the

financial sector (Aizawa & Chaofei, 2010). Hence, the financial sector seems to become more

sustainable, is able to create higher financial returns, and increases its assets, ROA, and ROE.

Finally, an explanation for the non-significant relation between non-performing loans ratio and

sustainability performance of Chinese banks could be the politicization of non-performing loans.

Studies suggest that Chinese financial policies influence the non-performing loan ratio (Shih,

2004). Consequently, political interventions may have biased the ratio.

24

Conclusions

We conclude that integrating sustainability into the financial sector does not harm financial

performance but rather increases it. Therefore, green credit policies, such as the Chinese Green

Credit Policy, may produce two effects: increasing banks’ corporate sustainability and creating a

more stable and successful financial sector. Hence, because our results suggest a bi-directional

effect, Chinese banks should invest in corporate sustainability to increase their financial success,

and re-invest slack resources in sustainability activities.

The bi-directional causation between corporate sustainability performance and financial

performance may be explained through institutional theory, and through international

stakeholder pressure (Yin, 2015), though empirical research on the impact of the Green Credit

Policy and international pressure is still missing. Institutional theory emphasises the influence of

impacts outside the organization, such as societal norms and public policies, on business

strategies and activities (DiMaggio & Powell, 1983). Rules, laws, regulations, norms, or cultures

determine the behaviour of firms in addition to other influences, such as competitive factors and

markets (Zhilong, Hafsi, & Wei, 2009). Using concepts, such as coercive, normative and

mimetic mechanisms (Amran & Haniffa, 2011; Li & Parboteeah, 2015), institutional theory

explains both, why firms act similarly and why strategic reactions to institutional pressure can be

manifold, such as organizational resistance, passive conformity, or proactive manipulation

(Oliver, 1991). Many institutional theorists emphasise the benefit of regulations and the

influence of governments on corporate sustainability (Cheung, Welford, & Hills, 2009; Dobers

& Halme, 2009; Dutta, Lawson, & Marcinko, 2012; Xun, 2012) and suggest that organizations

respond to institutional pressures toward corporate social responsibility (CSR) by increasing

their CSR performance (Oliver, 1991; Shrivastava, 1995).

25

Though some scholars argue that institutional pressure for demonstrating CSR is rather low in

China (Liu & Anbumozhi, 2009), the implementation of the Green Credit Guidelines may have

created institutional pressure on the Chinese financial sector. Other studies found that the

Chinese government urges companies, such as banks, to take CSR seriously (Marquis & Qian,

2014), and that firms respond to this pressure through social and environmental reporting (Zhao

& Patten, 2016).

Furthermore, there is a need for Chinese banks that are involved in international project finance

and lending to adopt voluntary sustainability codes of conducts such as the Equator Principles

(Weber & Acheta, 2014) and, consequently, to manage their sustainability performance. Hence,

the Green Credit Guidelines as well as factors, such as the participation in multinational projects

and collaboration with transnational financial institutions, such as IFC and World Bank, may

influence the sustainability performance of Chinese banks (Christmann & Taylor, 2001).

Obviously, sustainability activities are not always free of costs. Sustainability activities require

significant resources (Orlitzky, Siegel, & Waldman, 2011). Chinese banks are not an exception

to this rule. They have to implement management systems and build expertise in the relatively

current field of sustainable banking (Jin & Mengqi, 2011; Zeng, Xu, Dong, & Tam, 2010). Thus,

banks will only conduct sustainability activities if they assume that there will be a financial

benefit, if they have the necessary resources, and as a reaction to institutional pressure.

Because our results suggest a correlation between the size of banks assessed by their assets and

their sustainability performance, we conclude that regulators should develop implementation

guidelines for banks of different sizes. Usually, smaller banks have fewer resources to implement

sustainability activities and therefore need more support to be successful. After having

26

introduced the Green Credit Policy in China, the regulator developed implementation guidelines

to enable banks to integrate the policy into their decision-making processes and strategies. In-line

with Zhang et al. (2011), we propose implementation guidelines that take regional differences,

different business models, the capacity, and the size of the financial institution into account to

guarantee an effective application of sustainability regulations in the financial sector.

Further research is necessary to analyse the effect of the Green Credit Policy and similar policies

in China and in other countries on both, financial sector stability and sustainability, and to

analyse the environmental impacts of green finance in China. Because of the lack of public

reporting of Chinese banks, the current study relies on a relatively small sample. Future research

may be conducted by using big data approaches (Etzion & Aragon-Correa, 2016) that connect

environmental, financial, and economic data. This type of data may be gathered form databases

of financial regulators, general economic performance databases, and internet-based data on

corporate reputation.

Another future research direction might connect the ratio of green economy investments and

loans compared to conventional financial businesses. Such studies would be able to analyze

whether financial sector sustainability policies really cause an increase in green economy finance

in comparison with conventional finance and whether the policies cause a reduction of the

carbon shadow (Ritchie & Dowlatabadi, 2014) of portfolios, also called financed emissions.

Finally, future research should analyse the impact and the efficiency of financial sector

sustainability regulations. This research should not only focus on reports and data from banks

and other financial sector institutions, but should also analyse the effect of the financial sector

sustainability performance on the environment and sustainable development of the countries that

27

have implemented such regulations. This research will manly be conducted form an international

business research perspective.

28

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Figure 1: Development of the social, the environmental, and the sustainability score between

2009 and 2013

44

Table 1: Environmental and Social Banking Indicators for 2009 to 2013 and total (N = 46)

Indicator Examples 2009 2010 2011 2012 2013 Total Social Social Policy Policies addressing societal

issues, employees, and social finance products and services

26 26 26 26 28 132

Social Management System

Balanced scorecard, six sigma 0 0 1 2 2 5

Internal Social Management

Compliance management, social procurement, employees, management of benefits and incentives, career management

11 13 17 19 17 77

Social Credit Risk Assessment

Integration of social indicators into credit risk assessment

1 1 1 1 1 5

Social Loans SME loans, student loans, Sannong loans (villages, agriculture, farming), SME group lending, sanitary loans, reconstruction loans

23 28 32 37 44 164

Social Mortgages Reconstruction mortgages, social housing,

6 8 11 18 21 64

Social Funds Funds for developing countries, rural areas, cultural sector, domestic development funds

3 1 3 2 1 10

Social Indices Indices including social sustainability criteria, such as China well-off family index

1 0 1 0 1 3

Social Asset Management Services

Socially responsible investment, impact investing

0 0 0 0 0 0

Social Bonds Bonds for social projects 2 3 3 4 2 14 Social Microfinance

Start-up microfinance, microfinance for laid-off workers, farmers, and rural women

8 8 11 12 15 54

Social Project Finance Assessment

Assessment of social project risk, application of Equator Principles, project finance for municipal facilities

6 10 8 8 6 38

Social Savings Products

Savings products that are invested in social loans

0 0 0 0 0 0

Social Investment Banking

Industrial zone development project

2 3 5 5 5 20

45

Other Social Products and Services

Other products and services addressing social issues

10 15 18 22 25 90

Environment Environmental Policy

Policies addressing environmental issues, such as green products and services, supporting the development of environment protection

1 1 1 1 1 5

Environmental Management System

ISO140001 0 1 1 2 2 6

Internal Environmental Management

Green office and green building management, green procurement, green operations, waste management, paperless banking

14 16 20 21 19 90

Environmental Credit Risk Assessment

Integration of environmental credit risk indicators into credit risk assessment

1 4 4 4 4 17

Green Loans Green industry loans 8 13 13 16 16 66 Green Mortgages Green housing mortgages 0 0 0 0 0 0 Green Funds Green industry investment funds 0 0 1 0 0 1 Green Indices Indices using environmental

criteria 0 0 0 0 0 0

Green Asset Management Services

Green mutual funds, environmental asset management products and services

0 0 0 0 0 0

Green Bonds Green bonds issued 0 0 1 0 0 1 Green Microfinance Microfinance for

environmentally friendly businesses

0 0 0 0 0 0

Green Project Finance Assessment

Environmental risk assessment, financing of green projects (energy, water, infrastructure, waste management, restoration)

13 12 6 13 6 50

Green Savings Products

Saving products that are invested in green loans

0 0 0 0 0 0

Green Investment Banking

Emissions trading, investment in clean development mechanism projects

3 5 9 9 9 35

Other Green Products and Services

Management of low-carbon fund 7 7 10 11 11 46

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Table 2: List of banks in the sample (data was assessed for the years 2009 to 2013)

Name Type Governmental influence

Region

Agricultural Bank of China State-owned Commercial Bank

Government National

Agricultural Development Bank of China

Policy Bank Government National

Bank of Beijing City Commercial Bank Majority government East Bank of Chengdu City Commercial Bank Majority government West Bank of China Limited State-owned Commercial

Bank Government National

Bank of Chongqing City Commercial Bank Majority government West Bank of Communications State-owned Commercial

Bank Government National

Bank Of Dalian City Commercial Bank Majority government Northeast Bank of Guangzhou City Commercial Bank Majority government East Bank of Hangzhou City Commercial Bank Majority government East Bank of Jiangsu City Commercial Bank Majority government East Bank of Nanjing City Commercial Bank Majority government East Bank of Shanghai City Commercial Bank Majority government East Bank of Xi'an City Commercial Bank Majority government West Baoshang Bank City Commercial Bank Majority government West Beijing Rural Commercial Bank

Rural Commercial Bank Majority government East

China Bohai Bank National Joint-stock Commercial Bank

Joint-stock / cooperative

East

China Citic Bank National Joint-stock Commercial Bank

Joint-stock / cooperative

National

China Construction Bank State-owned Commercial Bank

Government National

China Development Bank Policy Bank Government National China Everbright Bank National Joint-stock

Commercial Bank Joint-stock / cooperative

National

China Guangfa Bank National Joint-stock Commercial Bank

Joint-stock / cooperative

East

China Merchants Bank National Joint-stock Commercial Bank

Joint-stock / cooperative

East

China Minsheng Banking National Joint-stock Commercial Bank

Joint-stock / cooperative

National

China Zheshang Bank National Joint-stock Commercial Bank

Joint-stock / cooperative

East

Chongqing Rural Cooperative Bank

Rural Commercial Bank Majority government West

Evergrowing Bank National Joint-stock Commercial Bank

Joint-stock / cooperative

East

47

Fudian Bank City Commercial Bank Majority government West Hua Xia Bank National Joint-stock

Commercial Bank Joint-stock / cooperative

National

Huarong Xiangjiang Bank City Commercial Bank Majority government Middle Huishang Bank City Commercial Bank Majority government Middle Industrial and Commercial Bank of China

State-owned Commercial Bank

Government National

Industrial Bank National Joint-stock Commercial Bank

Joint-stock / cooperative

East

Lanzhou City Commercial City

City Commercial Bank Majority government West

Ping An Bank National Joint-stock Commercial Bank

Joint-stock / cooperative

East

Postal Savings Bank of China

Postal Savings Bank of China Government National

Rural Credit Cooperative of Hebei

Rural Credit Union Joint-stock / cooperative

East

Shanghai Pudong Development Bank

National Joint-stock Commercial Bank

Joint-stock / cooperative

East

Shanghai Rural Commercial Bank

Rural Commercial Bank Majority government East

Tangshan City Commercial Bank

City Commercial Bank Majority government East

The Export-Import Bank of China

Policy Bank Government National

Tianjin City Commercial Bank

City Commercial Bank Majority government East

Tianjin Rural Cooperative Bank

Rural Commercial Bank Majority government East

Urumqi City Commercial Bank

City Commercial Bank Majority government West

Yellow River Rural Commercial Bank

Rural Commercial Bank Majority government West

Yinzhou Bank Rural Cooperative Bank Joint-stock / cooperative

East

48

Table 3: Descriptive statistics for the financial indicators

Type of Bank Total Assets in RMB million

Net Profit in RMB million

ROA ROE Non-performing loan ratio

City Commercial Banks

Mean 259747.8 2716.7 1.10% 18.80% .80% SD 269800.1 2695.7 .20% 4.70% .50% Skewness 1.8 1.8 -0.4 .04 0.28 Kurtosis 6.5 6.4 3.89 4.65 3.41

National Joint-stock Commercial Bank

Mean 1461895 16557.8 1.00% 19.90% .70% SD 1056889 13572 .20% 4.30% .40% Skewness .5 .8 -0.7 -.97 1.44 Kurtosis 2.2 2.5 3.82 4.32 7.12

Policy Bank Mean 3214197 19307.3 .90% 11.50% 1.20% SD 2465173 25873.6 .10% 2.70% 1.10% Skewness .9 1.2 .64 .35 1.28 Kurtosis 2.4 3.2 1.78 1.55 3.21

Postal Savings Bank Mean 5238777 29019.5 .60% 25.50% .40% SD 474714.7 917.1 .00% 3.30% .10% Skewness 0 0 0 0 0 Kurtosis 1 1 1 1 1

Rural Commercial Bank

Mean 300938.1 2613 .90% 14.40% 1.90% SD 115390.6 1620.2 .30% 3.50% .90% Skewness 0 .4 -1.04 -.59 1 Kurtosis 1.9 2.3 3.21 3.19 3

Rural Cooperative Bank

Mean 57162.4 909.2 1.70% 24.10% 1.00% SD 13639.9 307.2 .30% 4.00% .40% Skewness -.1 -.4 -0.22 -.62 .16 Kurtosis 1.7 1.9 1.95 1.71 1.67

Rural Credit Union Mean 655930 6246 SD 133422.5 2602.5 Skewness .2 .4 Kurtosis 1.7 1.8

State-owned Commercial Bank

Mean 11100000 130734.5 1.20% 20.40% 1.20% SD 4139119 63141.8 .20% 2.10% .40% Skewness -.3 .3 -.09 -.56 2.35 Kurtosis 2.4 2.3 2.65 2.65 8.98

Total Mean 2137422 23280.3 1.10% 19.00% .90% SD 3791895 46503.8 .30% 4.70% .60% Skewness 2.5 3 -.11 -.35 1.83 Kurtosis 8.4 11.8 4.53 3.79 8.51

49

Table 4: Correlation between the financial indicators (natural logarithm)

Net Profits

ROA ROE Non-performing loans

Assets .91*** .12 .17* .14 Net Profits .35*** .30*** .09 ROA .70*** .04 ROE -.06

***: p < .001; *: p < .05

Table 5: Descriptive statistics for the environmental, social, and sustainability scores

Year Score Mean SD Skewness Kurtosis 2009 Social 0.143 0.134 0.851 2.494 2009 Environment 0.068 0.104 1.466 3.964 2009 Sustainability 0.106 0.111 1.172 3.414 2010 Social 0.168 0.151 0.907 2.788 2010 Environment 0.086 0.116 1.254 3.431 2010 Sustainability 0.127 0.126 1.131 3.436 2011 Social 0.199 0.154 0.516 1.931 2011 Environment 0.096 0.127 1.583 5.054 2011 Sustainability 0.147 0.127 0.882 3.075 2012 Social 0.226 0.153 0.303 1.886 2012 Environment 0.112 0.117 1.060 3.360 2012 Sustainability 0.169 0.118 0.403 2.312 2013 Social 0.243 0.131 0.371 1.778 2013 Environment 0.099 0.110 1.136 3.302 2013 Sustainability 0.171 0.107 0.657 2.465

2009-2013 Social 0.196 0.148 0.536 2.084 2009-2013 Environment 0.092 0.115 1.315 3.984 2009-2013 Sustainability 0.144 0.120 0.796 2.803

50

Table 6: Results of the panel regression analysis with the sustainability score as dependent

variable and the natural logarithms of the financial indicators

Coefficient P>z R2 Sig. Sustainability score .46 <.00001 Total Assets .03 <.0001 Government influence Joint-stock -.047 .357 Government majority -.057 .368 Region Middle -.016 .807 National .007 .883 Northeast .031 .721 West .004 .907 Constant -.222 .03 Sustainability Score .55 <.00001 Net Profit .047 <.0001 Government influence Joint-stock -.004 .349 Government majority -.029 .615 Region Middle -.001 .980 National -.002 .959 Northeast .040 .620 West .016 .669 Constant -.222 .002 Sustainability Score 0.47 <.00001 ROA .131 <.0001 Government influence Joint-stock -.148 .013 Majority government -.163 .023 Region Middle -.112 .220 National .013 .818 Northeast .010 .912 West -.055 0.227 Constant .908 <.0001 Sustainability Score .48 <.00001 ROE .077 <.0001 Government influence Joint-stock -.134 .01 Government majority -.166 .01 Region

51

Middle -.071 .272 National .033 .505 Northeast .011 .899 West -.045 .249 Constant .417 <.0001 Sustainability Score .35 .0002 Non-performing loan ratio -.016 .132 Government influence Joint-stock -.099 .083 Government majority -.133 .064 Region Middle -.078 .286 National .040 .481 Northeast .021 .827 West -.042 .342 Constant .173 .041

52

Table 7: Panel regression analysis using financial indicators as dependent variable and

sustainability score as independent variable

Coef. P>z R2 Significance Total Assets 0.72 <.00001 Sustainability Score 3.872 <.0001 Government influence Joint-Stock -.826 .147 Government majority -1.139 .190 Region Middle -1.272 .082 National 1.083 .052 Northeast -.385 .696 West -1.204 .006 Constant 13.454 <.0001 Net Profit 0.69 <.00001 Sustainability Score 6.534 <.0001 Government influence Joint-Stock -.293 .651 Government majority -1.096 .171 Region Middle -.903 .276 National .720 .256 Northeast -.516 .644 West -.878 .079 Constant 8.082 <.0001 ROA 0.11 <.00001 Sustainability Score 1.589 <.0001 Government influence Joint-Stock .286 .106 Government majority .286 0.182 Region Middle .466 .078 National .064 0.700 Northeast -.099 .709 West .181 .176 Constant -5.136 <.0001 ROE 0.05 .0103 Sustainability Score 1.075 <.0001 Government influence Joint-Stock .166 .252 Government majority .054 .765

53

Region Middle .156 .378 National -.042 .757 Northeast .100 .668 West .121 .252 Constant -1.964 <.0001 Nonperforming loans ratio 0.10 .2651 Sustainability Score -.858 .078 Government influence Joint-Stock Commercial -.232 .640 Government majority -.136 .827 Region Middle -.787 .212 National .438 .365 Northeast .611 .469 West .331 .389 Constant -4.818 <.0001

54

Table 8: Regression coefficients for the sustainability score and financial figures with the

dependent variable delayed for one year and for two years

Delay (lag)

Dependent variable Independent variable

Coefficient R square Significance

1 Sustainability score Total assets .038 .46 < .00001 1 Total assets Sustainability score 4.093 .45 < .00001 2 Sustainability score Total assets .035 .45 < .00001 2 Total assets Sustainability score 3.987 .43 < .00001 1 Sustainability score Net profits .044 .50 < .00001 1 Net profits Sustainability score 5.569 .52 < .00001 2 Sustainability score Net profits .036 .45 < .00001 2 Net profits Sustainability score 5.399 .52 < .00001 1 Sustainability score ROA .122 .03 < .00001 1 ROA Sustainability score .639 .02 .0009 2 Sustainability score ROA .054 .007 .0125 2 ROA Sustainability score .150 .02 .4353 1 Sustainability score ROE .072 .01 < .00001 1 ROE Sustainability score .535 .02 .0069 2 Sustainability score ROE .022 .0004 .2189 2 ROE Sustainability score .054 .005 .7773 1 Sustainability score Non-performing

loans -.016 .0002 .2023

1 Non-performing loans

Sustainability score .451 .014 .3117

2 Sustainability score Non-performing loans

-.010 .0003 .412

2 Non-performing loans

Sustainability score 1.792 .031 .0018