Melco Management Accounting Research
Discussion Paper Series
No.MDP2023-003

The Impact of ESG Performance on Bond Financing Costs:
Evidence from China’s Corporate Bond Market
November 2023
Qixin Wu
Shanghai Huanqing Technology Co., Ltd.
Wan Zhang*
School of Finance, Southwestern University of Finance and Economics,
Chengdu, 611130, Chinazhangwan@smail.swufe.edu.cn
Qizhi Tao
Professor, Institute of Chinese Financial Studies,Southwestern University of Finance and Economics
Yang Xu
Professor, Faculty of Economics, Nagasaki University*Corresponding author

Maki Makoto Foundation
Nagoya, Japan
Discussion Paper Series of the Melco Management Accounting Research, in order to promote
the study of management accounting, has published this unfinished paper onto the Web. Pleaseobtain the permission of the author when citing this paper.

The Impact of ESG Performance on Bond Financing Costs: Evidence
from China’s Corporate Bond Market

Qixin Wu
Shanghai Huanqing Technology Co., Ltd., Shanghai, 200120, China. E-mail:qixin_wu@163.com

Wan Zhang*
School of Finance, Southwestern University of Finance and Economics, Chengdu,
611130, China. Email: zhangwan@smail.swufe.edu.cn (corresponding author)

Qizhi Tao
Professor, Institute of Chinese Financial Studies, Southwestern University of Financeand Economics, Chengdu, 611130, China. Email: taoqizhi@swufe.edu.cn

Yang Xu
Professor, Faculty of Economics, Nagasaki University, 4-2-1, Katafuchi, Nagasaki
850-8506, Japan. TEL:0958206373. E-mail: jxuyang@nagasaki-u.ac.jp1

The Impact of ESG Performance on Bond Financing Costs: Evidence
from China’s Corporate Bond Market

Qixin Wu1
Wan Zhang2*
Qizhi Tao3
Yang Xu4
Abstract
This study investigates the impact of environmental, social, and governance (ESG)
performance on firms’ bond financing costs in China, based on stakeholder, signaling,
and agency theories. While research exists on ESG performance and bond financing
costs in mature markets, little research has been conducted on emerging markets andunlisted firms. To address this gap, our analysis incorporates bond financing costs ofboth listed and unlisted Chinese companies and employs a unique regression model.
The empirical findings reveal that firms with better ESG performance have lower
bond financing costs. This effect is more pronounced in small firms. Furthermore, wefind that improved ESG performance reduces operational and default risk while
enhancing cash flow, which serves as an underlying mechanism. The results
demonstrate robustness through the use of alternative measurements, different samples,
and the inclusion of endogeneity tests.

Keywords: ESG performance; Bond financing costs; Stakeholder theory; Signaling
theoryQixin WU, Shanghai Huanqing Technology Co., Ltd., Shanghai, 200120, China. E-mail:qixin_wu@163.com
2* Wan ZHANG (corresponding author), School of Finance, Southwestern University of
Finance and Economics, Chengdu, 611130, China. Email: zhangwan@smail.swufe.edu.cn
3
Qizhi TAO, Professor, Institute of Chinese Financial Studies, Southwestern University of
Finance and Economics, Chengdu, 611130, China. Email: taoqizhi@swufe.edu.cn
4Yang Xu, Professor, Faculty of Economics, Nagasaki University, 4-2-1, Katafuchi, Nagasaki
850-8506, Japan. TEL:0958206373. E-mail: jxuyang@nagasaki-u.ac.jp
12

1. Introduction
Environmental, social, and governance (ESG) is used to assess the sustainable
development performance and responsibility of companies (Cheung, 2016; Mishra andModi, 2012). In light of global challenges such as climate change, resource scarcity, andthe COVID-19 pandemic, ESG has gained widespread attention from governments,
companies, and investors.
Companies are facing the challenge of balancing short-term and long-term interests,
with increasing pressure to "do good" (Huang, 2021). Scandals involving Chinese listedcompanies, such as the Changsheng Biological Vaccine Fraud (2018), Luckin Coffee
Financial Data Fraud (2020), and the Evergrande Debt Crisis (2021), have damaged theimage and reputation of these companies, resulting in negative consequences. However,listed companies with better information disclosure and stricter adherence to regulations
still underperform in ESG, and unlisted companies face greater information asymmetryissues. Therefore, this study aims to examine the ESG practices of Chinese companies,
including unlisted ones.
While research exists on ESG performance and bond financing costs in mature
markets, little research has been conducted on emerging markets and unlisted firms. This
study fills this gap by examining the impact of ESG performance on the bond financing
costs of Chinese companies, including those of unlisted companies.
First, we investigate the relationship between firms’ ESG performance and bondfinancing costs. Our findings show that the better a company's ESG performance, thelower its bond financing costs. Specifically, for every one percent increase in a company's
ESG score, its bond spread decreases by an average of 0.149 percent when other variables
are held constant. Second, the negative influence of ESG performance on bond financing
is stronger for small companies, indicating that ESG performance effectively mitigatescredit risk for smaller firms. Third, we examined two potential mechanisms throughwhich ESG practices influence bond financing costs. One mechanism is to reduce firms’
principal risk; our results show that ESG scores have a significant positive effect onmanagement discipline and higher credit ratings, which leads to lower operational anddefault risks. The other mechanism is that a firm's ESG performance can impact its cash
flow from financing activities (CFF) and, consequently, affects bond financing costs; theempirical results show that ESG scores have a significant positive effect on CFF. Fourth,
we used an instrumental variable approach to alleviate endogeneity concerns. Finally, we
3use alternative measurements and different samples to repeat our baseline regression, andthe results hold.
This study contributes to the literature on corporate bonds and ESG. First, unlike
previous studies that focused on the equity market, this study focuses on the bond market.
Bond issuers often need refinancing compared with equity issuers, which may result in agreater need to meet the needs of various stakeholders to avoid paying higher financing
costs (Jang et al., 2020).
Second, compared to previous research, this study goes beyond the study of debt
ratings by examining the channels of operational and default risks and the role of cash
flow from firm financing activities. Our study provides a more detailed analysis of therelationship between ESG performance and bond financing costs for Chinese firms, inalignment with stakeholder and signaling theories.
Finally, this study extends the scope of analysis beyond Chinese listed companies toinclude unlisted companies, enhancing the market guidance of the findings. A morecomprehensive understanding of the relationship between ESG performance and bondfinancing costs can be achieved by considering both listed and unlisted companies.
The remainder of this paper is structured as follows: the second section comprises aliterature review and hypothesis development; the third section covers the data, model,
and variables; the fourth section presents the empirical results; and the fifth section
concludes the study.

2. Literature review and hypothesis development.
2.1 ESG literature
ESG investing is an investment concept and corporate evaluation criterion
introduced by the United Nations Environment Program in 2004. It encompasses a
company's ESG performance, going beyond traditional financial performance. Thirdparty rating agencies rely primarily on ESG ratings or scores to quantify and assess acompany's ESG performance and alignment with sustainability goals. Numerous studies
have examined the impact of ESG on corporate performance/value (Cheung, 2016;
Mishra and Modi, 2012), but conclusive findings are lacking. Overall, most of theliterature suggests that ESG performance has a positive impact on firms (Friede et al.,2015).
4

The impact of ESG on firm performance can be summarized as "doing well by doinggood" (Huang, 2021). Studies have found that ESG is an effective way to improve firm
value and M&A performance (Benabou and Tirole, 2010; Ferrell et al., 2016; EstebanSanchez et al., 2017; Deng et al., 2013). However, there is also the argument that ESG
results in inefficient allocation of resources, leading to a reduction in shareholder value
and damage to stakeholder relationships (Barnett, 2007; Jensen, 1993). Additionally,
research suggests that the positive relationship between ESG and firm value is moresignificant in the long term than in the short term, because ESG activities take time tohave an effect (McWilliams et al., 2006; Dhaliwal et al., 2011; Lu and Taylor, 2015; Sunet al., 2019). Most studies align with the notion that ESG positively impacts firm
performance and value, although a few hold the opposite view.
In addition to the literature that explores the relationship between ESG performanceand firm performance/value, mainstream studies have also investigated the connection
between ESG performance and the cost of corporate capital, with a focus on equity market
research. ElGhoul et al. (2011) found a negative relationship between the issuance ofsustainability reports and the cost of capital. Goss and Roberts (2011) presented robust
evidence that socially irresponsible companies pay 7 to 18 basis points higher interest
rates on loans than socially responsible companies. In China, Zhou et al. (2016)
documented a negative relationship based on their study of listed companies issuing debt
in Shanghai and Shenzhen.
In short, the literature offers various perspectives on the relationship between ESG
and firm performance and value. Most studies support the idea that “doing good can bebetter,” and emphasize the long-term effects. However, few view ESG as an agency
problem or as being responsible for the misallocation of resources. Additionally, theliterature has examined the relationship between ESG and firms’ cost of capital, primarily
in equity markets. These studies find that ESG practices reduce information asymmetry,
resulting in a lower cost of equity. Some studies have also investigated the cost of debt
capital and observed a negative correlation between ESG and loan rates.
2.2 Literature related with bond financing costs
Bond financing costs are crucial in the financial market and play a significant rolein promoting industrial growth. However, China's bond market lags behind mature
markets in Europe and the US by a substantial margin. Therefore, studying the factors

5that affect bond financing costs is important for companies' risk management andinvestors' investment decisions.
The study of bond spreads originated with Fisher (1959), who argued that bondspreads depend on the default risk and marketability of corporate bonds. Later, Merton(1974) applied an option pricing model to demonstrate that bond spreads are influenced
by the risk-free rate, a firm's gearing, and bond maturity. Additionally, researchers
examined three perspectives: macro factors, firm characteristics, and bond characteristics.
At the macro level, factors such as the economic growth rate, inflation expectations, riskfree interest rate, and economic volatility (Tang and Yan, 2006) impact bond financing
costs. At the micro level, factors such as firm size (Longstaff and Schwartz, 1995),
gearing ratio (Chen and Fanara, 1992), operating conditions, and the nature of property
rights (Fang Hongxing et al., 2013) affect bond financing costs. Regarding bondcharacteristics, factors such as credit rating, issue size, and maturity influence bondfinancing costs.
In addition to these factors, qualitative factors can influence the "soft" aspects of acompany's solvency, such as its willingness to pay interest, information transparency, andreputation. Companies are recognizing the significance of ESG performance in improving
these "soft" strengths. Some studies have found that strong ESG performance indeveloped countries such as the US is associated with lower debt costs. Therefore, in atransitioning economy such as China, it is worth exploring whether the ESG performanceof debt issuers also affects bond financing costs.
2.3 Hypothesis development
ESG factors are believed to increase company costs and weaken competition.
However, the concept of sustainable ESG has attracted increasing public attention inrecent years. Consequently, many researchers have challenged the view that ESG is a"waste of resources," arguing that improved ESG performance can lead to better
economic or financial performance (Edmans, 2011; Guiso et al., 2015), without
necessarily increasing costs (Porter and van der Linde, 1995; Zhu et al., 2021; Ge and Liu,
2015). However, there is no consistent conclusion regarding the relationship between
ESG performance and bond financing costs.
On one hand, firms’ strong ESG performance can help reduce issues such as
information asymmetry (Cui et al., 2016), future adverse events (Ng and Rezaee, 2015),
business risk (Wen et al., 2021), and monitoring costs (Healy and Palepu, 2001), which6can lower bond financing costs. Additionally, ESG performance can provide companies
with reputational insurance (Benlemlih et al., 2016), enhancing stakeholder trust andsupport. On the other hand, companies' involvement in ESG activities requires significant
investments that may lead to wasteful expenditures, inefficiencies, credit risks, and otherfactors that increase bond financing costs.
This study assumes that ESG performance reduces bond financing costs. On one
hand, a sustainable economy is increasingly valuable in the context of China's dual carbon
goals and the need for a green economic transition. Thus, ESG-performing companies
can adapt to future market demands and policy orientations to improve theircompetitiveness and profitability. On the other hand, ESG is voluntary for mostcompanies, and ESG disclosure is not strictly required by governments and regulators.
However, companies with more robust ESG disclosure may gain higher visibility to thegovernment and market participants. This suggests that companies with good ESG
performance can reduce their bond default and reputation risks by establishing strong
stakeholder relationships. Accordingly, we propose the following hypothesis:
H1: The better the ESG performance of the bond-issuing company, the lower thefinancing cost of the bond.

Prior research found that companies with high corporate social responsibility (CSR)
have lower risks and returns (Lee and Faff, 2009). Attig et al. (2013) demonstrated thatcredit rating agencies consider CSR and assign higher ratings to companies with strongsocial performance. This implies that companies with solid ESG performance can earn
recognition and trust from credit rating agencies, enhancing their credibility and credit
quality, while lowering bond financing costs. Companies with high ESG scores are
expected to experience fewer and less severe negative events and defaults, which
influences the credit quality and financing costs of bonds. Consequently, we propose thefollowing hypothesis:
H2: A company’s strong ESG performance can reduce bond financing costs bydecreasing the company's operational and default risks.

The stakeholder theory posits that a firm's social capital fosters stakeholder cooperationand enhances economic efficiency (Amiraslani et al., 2017). This implies that companies
with strong ESG performance are more adept at addressing the needs and expectations of
all parties and boosting their satisfaction and loyalty, which in turn leads to increased
7

revenues and profits. Moreover, highly qualified companies can lower their refinancing
costs by frequently issuing new debt to repay old debt. For companies with substantial
CFF, there is also reduced pressure to repay debt, and consequently, a lower risk of default.
Consequently, the bond financing costs are likely to be lower. We hypothesize that ESG
performance negatively affects bond financing costs, partially through the influence of
ESG on CFF.
H3: A company’s strong ESG performance can reduce bond financing costs byincreasing the cash flow from the company's financing activities.

3. Data, model and variables
3.1. Data sources
Our sample includes all bond-issuing firms, both listed and unlisted, from the
beginning of 2010 to the end of 2022 to ensure a comprehensive representation of theresearch population. ESG data were obtained from China Bond Valuation sourced fromthe China Bond ESG Database, whereas the remaining variables were obtained from theWind database, both of which are reputable and professional financial data providers in
China. To ensure the validity of the analysis results, the original data underwent several
processing steps: (1) exclusion of companies in the financial sector; (2) exclusion ofsamples with missing data, such as ESG scores and bond credit ratings; (3) retention of
only the earliest bond issued by the same issuer in a year, in cases where multiple bonds
were issued; and (4) application of a 1% tail shrinkage to all continuous variables tomitigate the influence of outliers. These processing steps were undertaken with the aimof enhancing the quality and robustness of the data. Following the initial sample
processing, a final sample of 3485 corporate bonds was obtained, covering diverse
industries and regions within the Chinese bond market, thus ensuring high representationand diversity.
3.2. Models and variables
To test the first hypothesis—that is, higher the ESG performance of the bondissuing company, the lower the bond financing costs—we constructed Model (1). To
avoid the problem of error correlation among different bonds of the same issuer in thedata, while the errors among different issuers are independent of each other, we use
8clustering-robust standard errors with issuers as clustering variables. Consistent with theprevious literature on the relationship between CSR/ESG and firm value/cost of capital,
the firm-valued independent variables in all models are lagged by one period. This is done
for the following reasons. First, it helps avoid potential endogeneity issues and clarifies
the direction of impact (Godfrey et al., 2009). Second, this helps capture the lagged effectof ESG performance on bond financing costs (Chen et al., 2014). Finally, it helps reduce
measurement errors and missing data issues (Cheng et al., 2014).
𝑆𝑝𝑟𝑒𝑎𝑑𝑖,𝑡 = 𝛼 + 𝛽1 𝐸𝑆𝐺𝑖,𝑡−1 + 𝛽2 𝐶𝑟𝑒𝑑𝑖𝑡𝑆𝑐𝑜𝑟𝑒𝑖,𝑡 + 𝛽3 𝑆𝑖𝑧𝑒𝑖,𝑡 + 𝛽4 𝑀𝑎𝑡𝑢𝑟𝑖𝑡𝑦𝑖,𝑡+𝛽5 𝑀𝑎𝑡𝑢𝑟𝑖𝑡𝑦2 𝑖,𝑡 + 𝛽6 𝐴𝑔𝑒𝑖,𝑡 + 𝛽7 𝑙𝑛𝑇𝐴𝑖,𝑡−1 + 𝛽8 𝑁𝑒𝑡𝑃𝑟𝑓𝑖,𝑡−1+𝛽9 𝑅𝑂𝐴𝑖,𝑡−1 + 𝛽10 𝐴𝑠𝑠𝐿𝑖𝑎𝑅𝑎𝑡𝑖𝑜𝑖,𝑡−1 + 𝛽11 𝐶𝑎𝑠ℎ𝑅𝑎𝑡𝑖𝑜𝑖,𝑡−1+𝛽12 𝐼𝑛𝑡𝐶𝑜𝑣𝑖,𝑡−1 + 𝛽13 𝑑𝑢𝑚_𝐿𝑖𝑠𝑡𝑖,𝑡 + 𝛽14 𝑑𝑢𝑚_𝑁𝑎𝑡𝑖,𝑡+𝛽15 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦𝑖,𝑡 + 𝛽16 𝑌𝑒𝑎𝑟𝑖,𝑡 + 𝜀𝑖,𝑡(1)

To test the second hypothesis—that is, one of the mechanisms by which a firm’s
ESG performance reduces bond financing costs is that good ESG performance reduces afirm’s risk (both operational and default risks)—we developed Model (2), where risk isreplaced by management specification (MgtStd) and firm credit rating (IsserScore):
𝑅𝑖𝑠𝑘𝑖,𝑡 = 𝛼 + 𝛽1 𝐸𝑆𝐺𝑖,𝑡−1 + 𝛽2 𝐶𝑟𝑒𝑑𝑖𝑡𝑆𝑐𝑜𝑟𝑒𝑖,𝑡 + 𝛽3 𝑆𝑖𝑧𝑒𝑖,𝑡 + 𝛽4 𝑀𝑎𝑡𝑢𝑟𝑖𝑡𝑦𝑖,𝑡+𝛽5 𝑀𝑎𝑡𝑢𝑟𝑖𝑡𝑦2 𝑖,𝑡 + 𝛽6 𝐴𝑔𝑒𝑖,𝑡 + 𝛽7 𝑙𝑛𝑇𝐴𝑖,𝑡−1 + 𝛽8 𝑁𝑒𝑡𝑃𝑟𝑓𝑖,𝑡−1+𝛽9 𝑅𝑂𝐴𝑖,𝑡−1 + 𝛽10 𝐴𝑠𝑠𝐿𝑖𝑎𝑅𝑎𝑡𝑖𝑜𝑖,𝑡−1 + 𝛽11 𝐶𝑎𝑠ℎ𝑅𝑎𝑡𝑖𝑜𝑖,𝑡−1+𝛽12 𝐼𝑛𝑡𝐶𝑜𝑣𝑖,𝑡−1 + 𝛽13 𝑑𝑢𝑚_𝐿𝑖𝑠𝑡𝑖,𝑡 + 𝛽14 𝑑𝑢𝑚_𝑁𝑎𝑡𝑖,𝑡+𝛽15 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦𝑖,𝑡 + 𝛽16 𝑌𝑒𝑎𝑟𝑖,𝑡 + 𝜀𝑖,𝑡(2)

To test the third hypothesis—that is, one of the mechanisms by which a firm’s ESG
performance reduces bond financing costs is that good ESG performance increases afirm’s CFF—Model (3) was developed:
𝐶𝐹𝐹𝑖,𝑡 = 𝛼 + 𝛽1 𝐸𝑆𝐺𝑖,𝑡−1 + 𝛽2 𝐶𝑟𝑒𝑑𝑖𝑡𝑆𝑐𝑜𝑟𝑒𝑖,𝑡 + 𝛽3 𝑆𝑖𝑧𝑒𝑖,𝑡 + 𝛽4 𝑀𝑎𝑡𝑢𝑟𝑖𝑡𝑦𝑖,𝑡+𝛽5 𝑀𝑎𝑡𝑢𝑟𝑖𝑡𝑦2 𝑖,𝑡 + 𝛽6 𝐴𝑔𝑒𝑖,𝑡 + 𝛽7 𝑙𝑛𝑇𝐴𝑖,𝑡−1 + 𝛽8 𝑁𝑒𝑡𝑃𝑟𝑓𝑖,𝑡−1+𝛽9 𝑅𝑂𝐴𝑖,𝑡−1 + 𝛽10 𝐴𝑠𝑠𝐿𝑖𝑎𝑅𝑎𝑡𝑖𝑜𝑖,𝑡−1 + 𝛽11 𝐶𝑎𝑠ℎ𝑅𝑎𝑡𝑖𝑜𝑖,𝑡−1+𝛽12 𝐼𝑛𝑡𝐶𝑜𝑣𝑖,𝑡−1 + 𝛽13 𝑑𝑢𝑚_𝐿𝑖𝑠𝑡𝑖,𝑡 + 𝛽14 𝑑𝑢𝑚_𝑁𝑎𝑡𝑖,𝑡+𝛽15 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦𝑖,𝑡 + 𝛽16 𝑌𝑒𝑎𝑟𝑖,𝑡 + 𝜀𝑖,𝑡(3)

Dependent variable: The dependent variable in Model (1) is the financing cost ofbonds (Spread), and we adopt the common academic approach of using the difference
between the bond yield to maturity and the yield of a treasury bond of the same maturity
issued at the same time as the credit spread (Campbell and Taksler, 2003; Chen et al.,2007; Huang and Huang, 2012). Because the practice in the Chinese bond market is to9issue bonds at par, we use the coupon rate instead of the yield to maturity to simplify thecalculation. If comparable treasury bonds cannot be found, we complement them using
linear interpolation. In the robustness test, we use the difference between the coupon rate
and annual risk-free rate of return as bond financing costs, where the risk-free rate of
return is obtained from banks’ one-year fixed interest from the Cathay Capital database.
Independent variable: The main independent variable is the ESG performance ofdebt-issuing companies using data from the China Bond ESG Valuation Center. For therobustness test, we adopted data from the SynTao Green Finance ESG evaluation
database.
Control variables: Based on the literature on domestic corporate bond pricing(Huang et al, 2018), we constructed a regression model to analyze the factors influencing
bond financing costs. In this study, non-financial information (e.g., age, size, whether
private enterprise, listed, etc.), financial information (e.g., return on total assets, net profit,etc.), and debt characteristics (e.g., bond size, maturity, etc.) of corporate bond issuers
were selected as control variables.
Intermediate variables: Company credit rating (IsserScore) reflects the size of thecompany’s default risk. The data, obtained from the Wind database, is presented in text
type. To facilitate model analysis, we convert the IsserScore into a numerical variable,
rating from low to high, given a value from small to large. CFF represents the cash flow
generated from the company’s financing activities. Data for CFF was sourced from Wind.
Appendix Table 1 presents the definitions and calculations of each variable in the
regression model.

Appendix Table 1. Definition of Variables
Variable Name

Variable
Symbols

Variable Description

ESG Score
Issuer’s main rating
Debt Ratings
Bond Issue Size
Remaining maturityof bonds
Company Age

ESG
IsserScore
CreditScore
Size

The coupon rate of the bond minus the yield of thegovernment bond in the same period, and if the two
cannot be matched one by one, the linear interpolation
method is used to make up the difference
From China Debt Valuation Database
From Wind database, higher ratings give higher values
From Wind database, higher ratings give higher values
Data from Wind database, in billion yuan

Maturity

Data from Wind database, in years

Age

Company Nature

dum_Nat2022 minus the year the company was founded plus 1
Dummy variable, take 1 if the company is a private
enterprise, otherwise take 0, data from Wind database

Bond Credit
Spreads

Spread10

Whether thecompany is listed ornotdum_List

The area where thecompany is locatedRegionTotal Assets

lnTA

Net Profit
Return on Total
AssetsGearing ratio

NetPrfDummy variables. According to the province where
the debt issuing company is located, it is divided into
three regions, East, West and Central
Logarithm of total assets, total assets data from Wind
database
Data from Wind database

ROA

Net income divided by average balance of total assets

AssLiaRatio

Cash Ratio

CashRatio

Total liabilities / total assets
Ratio of company’s cash flow assets to current
liabilities

Cash flows fromfinancing activities
Management
standardization
degreeIndustryYear

Dummy variable based on whether the company islisted or not, listed takes 1, otherwise takes 0

CFF

Data from Wind database

MgtStdData from China Debt Valuation Database

Ind
Year

Industry dummy variables
Year dummy variables

3.3. Descriptive Statistics
Table 1 presents the descriptive statistics for the key variables. The mean spread(versus same-period treasury bonds) is 1.31% with a standard deviation of 1.03, showing
a small difference between corporate bond financing costs and treasury yields. Spread2's
mean is 2.55, with a standard deviation of 1.12. The average ESG score is 5.05 (out of10), with a standard deviation of 0.94, indicating poor ESG performance and room for
improvement among Chinese bond issuers. The mean bond issuance size is 1.181 billion
yuan, with a standard deviation of 7.6, indicating variations in issuance size. The averageremaining bond maturity is 4.67 years, with a standard deviation of 1.87, suggesting
medium-term maturities. The mean firm age was 22.93 years, with a standard deviationof 8.36, indicating established firms of varying ages. Finally, the mean logarithm of total
assets (lnTA) is 7.32, with a standard deviation of 1.28, indicating little difference incompany size.
Table 1. Descriptive Statistics
Variables

N

AveragevalueStandard
deviation

25%
quantile

Medianvalue

75%
percentile

Minimumvalue

Maximum
value

SpreadSpread2
ESG
Size

4338.00
4339.00
4339.00
4339.00

1.31
2.55
5.05
11.81

1.03
1.12
0.94
7.60

0.67
1.81
4.00
6.00

0.99
2.27
5.00
10.00

1.52
2.97
6.00
15.00

-0.23
0.31
3.00
2.00

4.90
8.51
7.00
40.00

11

Maturity
Maturity_2
AgelnTA
NetPrf
CFF
ROA
AssLiaRatio
CashRatio
IntCov

4339.00
4339.00
4313.00
4191.00
4191.00
4190.00
4173.00
4184.00
4157.00
3859.00

4.67
25.27
22.93
7.32
53.68
90.90
3.44
65.16
0.34
7.14

1.87
22.51
8.36
1.28
96.42
213.04
2.69
11.80
0.28
14.74

3.00
9.00
16.00
6.37
4.31
-4.33
1.51
58.44
0.18
1.74

5.00
25.00
23.00
7.26
17.34
27.62
2.88
66.38
0.27
3.04

5.00
25.00
29.00
8.28
54.42
111.83
4.58
73.49
0.40
5.99

2.00
4.00
6.00
4.47
-20.96-333.38
-1.24
30.08
0.04
-2.04

10.00
100.0042.00
10.42567.35950.87
14.51
89.80
1.78
112.38The Pearson correlation coefficient method was then used to analyze the variablecorrelations, and the results are shown in Table 2. The results reveal a significant negative
correlation (at the 1% level) between a company's spread and ESG performance,suggesting that good ESG performance reduces bond financing costs, supporting the core
hypothesis. The control variables, including bond rating (credit score), bond size (size),remaining bond maturity, firm age, firm size (lnTA), firm net profit (NetPrf), return on
total assets (ROA), cash ratio (CashRatio), and interest coverage multiple (IntCov) show
a negative correlation with bond spread. However, the square of the bond maturity(Maturity_2) and the company's gearing ratio (AssLiaRatio) are positively correlated.
These correlations align with real-world scenarios and explain economic phenomena.

4. Empirical results
4.1 Impact of ESG performance on bond financing costs
This study investigates the relationship between a firm’s ESG performance and its bondfinancing costs. A regression analysis was conducted using Model (1), and the results are12presented in Table 3. Specifically, Columns (1) and (4) show the impact of ESG score as
the only explanatory variable after controlling for other influences on bond spread. As
shown in Table 3, the regression coefficient of the ESG score is negative in both
univariate and multivariate regressions, passing the statistical test at the 1% level ofstatistical significance. This finding supports our hypothesis that the better a company's
ESG performance, the lower its bond financing costs. Specifically, according to theresults in Column (4), for every 1-point increase in a company's ESG score, its bondspread decreases by an average of 0.149 percentage points when the other variables areheld constant.
Regarding the control variables, higher values for CreditScore, Size, lnTA, NetPrf,
CashRatio, and IntCov are associated with lower bond financing costs, supporting ourhypothesis. Maturity has a negative coefficient, whereas its squared term has a positivecoefficient, implying a nonlinear relationship with bond financing costs. Age has a small
negative coefficient, suggesting some influence on financing costs. AssLiaRatio anddum_Nat have positive coefficients, indicating higher debt ratios and private companies
facing higher financing costs. These findings provide insights into the relationshipbetween ESG performance and bond financing costs, as well as the impact of the control
variables.

Table 3: Impact of Corporate ESG Performance on Bond Financing Costs
Variables

(1)

(2)

(3)

(4)

ESG

-0.201***(-12.17)-0.104***(-6.95)-0.990***(-37.34)-0.007***(-3.71)-0.307***(-9.44)0.023***(8.54)-0.113***(-7.15)-1.019***(-33.80)-0.009***(-4.10)-0.272***(-7.80)0.020***(7.05)-0.005***(-2.76)-0.048**(2.57)-0.001***(-4.87)-0.026***(3.85)
0.009***(5.63)-0.149***(-7.98)-0.853***(-29.70)-0.008***(-4.01)-0.264***(-8.20)0.021***(7.82)-0.006***(-3.62)-0.052***(2.90)-0.001***(-6.09)-0.003(-0.50)0.005***(3.43)

CreditScore
Size
Maturity
Maturity_2
AgelnTA
NetPrfROA
AssLiaRatio

13

CashRatio-0.295***(-4.58)-0.004***(-3.63)-0.276***(-4.63)
IntCov-0.003***(-3.14)
dum_List
0.205***(4.83)
dum_Nat1.282***(23.33)
Constant
2.321***
17.377***17.080***14.971***(27.41)(43.23)(38.41)(35.22)
Year
Yes
Yes
Yes
Yes
Ind
Yes
Yes
Yes
Yes
F
148.097353.548145.212188.813
N
4338
3922
3485
3485
0.033
0.311
0.334
0.432
𝑅2
Note: t-values are in parentheses. ***, **, and * indicate significance at 1%, 5%, and
10% statistical levels, respectively. Columns (1) to (4) of the model use mixed
regression models with clustering robust standard errors selected with the issuing entity
as the clustering variable.

4.2 The effect of company size on the relationship between ESG and bond financing
costs
Bond financing costs are influenced by a combination of bond and firm
characteristics, and information asymmetry plays a crucial role. Smaller firms typicallyexperience greater information asymmetry than larger ones, resulting in increased credit
risk and limited access to financing. Consequently, bond financing is more appealing tosmaller firms, although it requires additional credit signals to mitigate information
asymmetry. ESG performance serves as an effective credit signal that reflects a firm's
ESG responsibilities and achievements. We hypothesize that the impact of ESG
performance on reducing bond financing costs is more pronounced for smaller companies.
To test this hypothesis, the methodology of Bharath et al. (2008) was employed, ranking
all sample companies by size from smallest to largest. The smallest 33% of the companies
were designated as the small-company group, and the largest 33% were designated as thelarge-company group for basic regressions. The regression results are presented in Table
4.
The regression results in Table 4 reveal that ESG performance significantly reduces
bond financing costs for both the large and small groups, with a more substantial effectobserved for the small group. This finding indicates that ESG performance mitigatescredit risk more effectively in smaller firms. Specifically, for small firms, each one-point
increase in ESG performance, controlling for other variables, corresponds to an average
14relative reduction of 0.171% in bond financing costs. For large firms, a one-point increasein ESG performance results in a 0.128% reduction in bond financing costs, after
controlling for other variables. Furthermore, this study identifies a significant negative
relationship between cash ratio and bond financing costs for the small group, suggestingthat cash ratio is another vital credit signal for smaller firms. In conclusion, the results inTable 4 confirm that ESG performance has a heterogeneous impact on bond financing
costs for companies of various sizes.

Table 4. Subsample Regressions Based on Different Company Sizes
Variables
ESG
CreditScore
Size
Maturity
Maturity_2
AgelnTA
NetPrfROA
AssLiaRatio
CashRatio
IntCov
dum_List
dum_Nat
Constant
Year
Indu
F
N

(1)

(2)

-0.171***(-3.15)-0.419***(-13.06)-0.012*(-1.96)-0.176***(-2.95)0.013***(2.69)-0.007*(-1.86)-0.027*(-0.40)-0.010**(-2.09)-0.016(-0.76)0.009***(2.95)-0.281***(-2.72)-0.002(-1.21)0.255***(2.70)
1.476***(12.23)8.725***(13.21)
Yes
Yes
61.5781143-0.128***(-5.16)-0.168***(-9.46)-0.012***(-5.29)-0.061***(-3.46)0.003***(2.88)-0.004*(-1.72)-0.203***(3.60)-0.003***(-7.45)-0.039***(2.93)
0.006***(2.76)
0.142(1.07)-0.001(-1.23)0.300***(5.32)
1.286***(15.06)2.535***(4.17)
Yes
Yes
54.908
1155

15

𝑅2

0.340
0.407
Note: t-values are in parentheses. ***, **, and * indicate significance at
1%, 5%, and 10% statistical levels, respectively. Models (1) and (2) show
the regression results for the group of small-sized firms and the group oflarge-sized firms, respectively. Both columns (1) and (2) of the model use
mixed regression models, and cluster robust standard errors with theissuing entity as the clustering variable are chosen.

4.3 Mechanism test: corporate risk
One argument in favor of ESG/CSR investing is its ability to reduce corporate risk,
known as the risk-mitigation view. This argument suggests that ESG/CSR investing helps
mitigate the environmental, social, and governmental risks faced by companies. Theserisks can negatively affect a company's reputation and credit rating, leading to lower bondprices and returns. Therefore, according to the risk mitigation theory, we anticipate thatcompanies with strong ESG/CSR performance will have lower credit risk, higher credit
ratings, and ultimately lower bond financing costs. Several empirical studies support thistheory, including those of Attig et al. (2013).
This section focuses on one potential mechanism by which ESG practices influencebond financing costs by reducing a firm's principal risk. Principal risk refers to theuncertainties a company faces in its operations and debt repayment, which can affect bondinvestors' confidence and expectations of the company, thereby influencing the demandand price of bonds. Principal risk can be divided into operational and default risks.
Operational risk encompasses the negative events a company may encounter during its
operations, such as litigation, arbitration, and regulatory penalties. These events can harmthe company's reputation, profits, and cash flow, thereby increasing its financial stressand instability. On the other hand, default risk refers to the possibility that a company will
be unable to repay its debts on time and is dependent on factors such as the company's
financial position and solvency.
To measure these two risks, this study utilizes the management normative scoreprovided by China’s Debt Valuation Center as an indicator of operational risk, and thecompany's credit rating provided by Wind as an indicator of default risk. Subsequently,
we examine the impact of ESG scores as explanatory variables on normative managementscores and credit ratings through regression analysis. The objective of this study was toinvestigate whether ESG practices effectively reduce risk. Table 5 presents the regression
analysis results. It is apparent that ESG scores have a significant positive effect on bothrisk indicators, implying that companies with better ESG performance exhibit stronger
16management discipline and higher credit ratings, indicating lower operational and default
risks. These findings validate the regression model constructed in this study. Figure 1
shows that debt-issuing companies’ credit ratings are positively correlated with ESG
performance, supporting the hypothesis of a corporate risk channel that ESG performancecan influence bond financing costs by reducing corporate risk.

Table 5: Mechanism Test: Corporate Risk
Variables

MgtStdESG

IsserScore

0.188***
0.059***(6.40)(5.59)
CreditScore-0.0030.123***(-0.06)(15.51)Size-0.006**0.009***(-2.12)(7.54)
Maturity
0.171***
0.008(3.38)(0.83)
Maturity_2-0.013***-0.001(-3.21)(-0.50)Age-0.009***-0.001(-3.23)(-0.54)lnTA-0.281***0.357***(-9.99)(35.70)NetPrf-0.001***-0.002***(-3.65)(-12.09)
ROA
0.019*
0.054***(1.91)(13.82)
AssLiaRatio-0.010***-0.005***(-4.12)(-5.56)
CashRatio
0.201**
0.092***(2.14)(2.78)
IntCov-0.006***0.001**(-4.11)(2.54)
dum_List-0.574***-0.070***(-8.57)(-2.90)dum_Nat
0.158*-0.161***(1.82)(-5.14)
Constant
10.790***-0.023(16.11)(-0.16)
Year
Yes
Yes
Indu
Yes
Yes
F
58.38
370.090N
3485
3242
0.19060.529
𝑅2
Note: t-values are in parentheses. ***, **, and * indicate
significance at 1%, 5%, and 10% statistical levels,
respectively.

17

ESG
6.20

6.10

6.10
5.98

6.00
5.90
5.74

5.80
5.70

5.67

5.60
5.50
5.40
AA-

AA

AA+

AAA

Figure 1 Debt Issuer Ratings and ESG Performance
Source: China Debt Valuation Center

4.4 Mechanism test: cash flow
Another hypothesis in this study suggests that a firm's ESG performance can impact
its CFF, and consequently, its bond financing costs. CFF refers to the net cash inflow thata firm receives from both creditors and shareholders, reflecting its financing capacity.
The literature suggests that a firm's social capital can foster stakeholder cooperation,
leading to economic benefits in the form of higher cash flows (Amiraslani et al., 2017;
Edmans, 2011; Guiso et al., 2015). When a company encounters financial difficulties,
stakeholders are more likely to provide additional support to facilitate recovery, such as
extending repayment terms. These behaviors increase a company's CFF. We argue that acompany's strong ESG performance enhances its stakeholders' willingness and ability tocooperate, resulting in increased CFF, more secure financing sources, reduced debt
service pressure, and lower bond financing costs.
To test the hypothesis regarding the CFF channel, we conducted a regression
analysis between ESG performance and CFF. The results are presented in Table 6. They
indicate that ESG scores have a significant positive effect on CFF, implying thatcompanies can secure more financing from the external market. This finding is consistent
with previous studies (Wang et al., 2022; Gregory, 2022).

Table 6: Mechanism Test: Cash Flow
Variables
ESG

CFF
48.797***(10.88)CFF
18

Spread
-0.161***(-7.91)-0.001**

(-2.34)
CreditScore-6.201-0.256***(-17.92)(-0.90)Size
0.536-0.013***(-5.62)(1.16)
Maturity-1.180-0.058***(-0.15)(-3.33)0.003**
Maturity_2-0.287(-0.45)(2.37)-0.008***Age-2.997***(-7.35)(-4.48)lnTA80.801***-0.034*(18.83)(-1.69)NetPrf-0.260***-0.001***(-4.16)(-4.97)ROA-2.464-0.019**(-1.64)(-2.57)
AssLiaRatio-0.5220.006***(-1.46)(3.50)-0.299***CashRatio
67.639***(4.71)(-4.72)IntCov
0.424*-0.002***(1.87)(-3.01)
dum_List-83.869***
0.177***(3.82)(-8.20)dum_Nat-43.235***
1.353***(22.48)(-3.27)
Constant-528.151***6.479***(-5.17)(23.96)
Year
Yes
Yes
Indu
Yes
Yes
177.605F
209.548
N
3485
3,485
0.349
0.283
𝑅2
Note: t-values are in parentheses. ***, **, and * indicate significance
at 1%, 5%, and 10% statistical levels, respectively.

4.5 Endogeneity test
To further alleviate endogeneity concerns, this study adopted the instrumental
variables approach employed by Chen et al. (2022) and Gao et al. (2021), selecting theannual industry ESG score as the instrumental variable for a company's ESG performance.
The main regression analysis was conducted, and the results are presented in Table 7. As
shown in the table, there is a significant negative effect at the 1% level between the
instrumental variables and the bond financing cost spread. The significance and signs of
the coefficients for the other control variables are largely consistent with their economic
implications, indicating that the endogeneity test is satisfied and the model's endogeneity
19

problem is effectively mitigated. Specifically, the coefficient of the instrumental variable
is -0.203, which is larger in absolute value than the ESG coefficient of the main regression.
This result suggests that, on average, a one-unit increase in the instrumental variable leads
to a 0.203% decrease in bond spread, after controlling for other variables.

Table 7. Endogeneity Test
VariablesIV

Spread-0.203***(-2.93)CreditScore-0.867***(-30.01)
Size-0.006***(-3.36)
Maturity-0.269***(-8.27)
Maturity_2
0.021***(7.86)
Age-0.007***(-4.21)lnTA
0.027(1.52)
NetPrf-0.001***(-5.33)
ROA-0.003(-0.43)
AssLiaRatio
0.006***(4.27)CashRatio-0.294***(-4.89)IntCov-0.003***(-2.71)
dum_List
0.006(0.16)
dum_Nat1.323***(24.02)
Constant
15.613***(28.38)
Year
Yes
Indu
Yes
F
182.011
N
3485
0.423
𝑅2
Note: t-values are in parentheses. *** , **, and* indicate significance at 1%, 5%, and 10%
statistical levels, respectively.

20

4.6 Other robustness tests
We also conducted several robustness tests to ensure the validity of our results. We
employed the SynTao Green Finance ESG indicator as an alternative measure of ESG
performance by repeating the main regression model to evaluate the robustness of theregression results. The ESG_sd variable in the model is selected from the SynTao Green
Finance ESG rating database because SynTao Green Finance provides ESG ratings rather
than scores. We numericize the ESG ratings, and the higher the rating the higher theassigned value. Columns (1) and (2) of Table 8 show the results of the final regression.
As the table shows, the coefficient of ESG is negative and significant in both theunivariate and multivariate regressions. Specifically, for the multivariate regression
model in the second column, all other things being equal, each unit increase in ESG
performance is associated with a 0.186% decrease in bond financing costs on average.The
shortcoming of using SynTao Green Finance ESG evaluation data is that the companies
evaluated by SynTao Green Finance ESG data are listed companies, and the ESG
evaluation of non-listed companies is lacking. This study examines the corporate bondmarket, which includes more non-listed issuers, and the final sample size obtained using
SynTao Green Finance ESG data is smaller (81); however, the regression results canprovide stronger support for the conclusions of this study.
Second, we use an alternative measure of bond financing costs to repeat our baseline
regression. Specifically, the risk-free rate provided by the CSMAR database is used as
the benchmark, and the difference between the coupon rate of the bond and the risk-freerate is used to obtain Spread2 as an alternative expression of bond financing costs.
Spread2 is brought into the main regression model to re-evaluate our regression results,
as shown in columns (3) and (4) in Table 8. The dependent variable in the first column is
Spread2 and the dependent variable in the second column is the original Spread. From
Table 8, we can observe that the variable after differencing bond financing costs from therisk-free rate of return as the dependent variable has the effect of having a greater degreeof impact than the original model. On average, the bond spread (Spread2) decreases by
0.192% for each unit increase in ESG, after controlling for other variables.

Table 8. Robustness Test1: Alternative Measurements
Dependent Variables
ESG_sdSpread1(1)-0.319***Spread1(2)-0.186**21

Spread2

Spread2

(-3.76)(-2.11)ESG-0.192***-0.169***(-8.88)(-8.43)CreditScore-1.138*-0.267***-0.259***(-1.61)(-17.35)(-18.16)Size-0.002-0.013***-0.013***(-0.19)(-5.28)(-5.61)Maturity-0.3240.055***-0.057***(-1.27)(2.94)(-3.26)
Maturity_2
0.025
0.000
0.003**(1.03)(0.28)(2.34)
Age-0.002-0.007***-0.008***(-0.13)(-3.49)(-4.23)lnTA-0.099-0.065***-0.045**(-0.59)(-3.12)(-2.35)NetPrf0.002-0.001***-0.001***(0.89)(-3.47)(-4.06)
ROA-0.033-0.009-0.018**(-0.72)(-1.16)(-2.47)
AssLiaRatio
0.000
0.007***
0.006***(-0.00)(3.79)(3.54)CashRatio-0.056-0.304***-0.310***(-0.10)(-4.45)(-4.90)IntCov-0.014-0.003***-0.002***(-0.97)(-3.67)(-3.06)
dum_List
0.197***
0.191***(3.95)(4.14)
dum_Nat1.229***
1.378***
1.359***(5.89)(21.20)(22.61)
Constant
3.080***
20.764*7.686***
6.604***(6.58)(1.98)(26.81)(24.90)
Year
Yes
Yes
Yes
Yes
Indu
Yes
Yes
Yes
Yes
F
14.122
6.462
167.310188.813
N
85
81
3485
3485
0.330
0.348
0.145
0.556
𝑅2
Note: t-values are in parentheses. ***, **, and * indicate significance at 1%, 5%, and
10% statistical levels, respectively.

Third, to further demonstrate the robustness of the model, all listed debt issuers are
selected for validation analysis; the results are shown in Table 9. The results show thatfor listed debt issuers, there is a significant negative relationship between ESG
performance and bond spreads, and for every 1 unit increase in ESG performance, bondspreads fall by 0.199% on average.

Table 9. Robustness Test1: Small Sample Regression Results
Variables

(1)
22

(2)

ESG

-0.214***(-5.00)-0.199***(-5.58)CreditScore-0.867***(-15.74)Size-0.004(-1.06)
Maturity-0.265***(-4.14)
Maturity_2
0.022***(4.10)
Age-0.004(-1.00)lnTA-0.050(-1.34)
NetPrf-0.001(-1.55)
ROA-0.010(-0.88)AssLiaRatio
0.014***(4.63)CashRatio
0.059(0.40)
IntCov-0.002(-0.81)
dum_Nat1.305***(14.61)
Constant
2.575***
15.555***(9.85)(17.28)
Year
Yes
Yes
Indu
Yes
Yes
F
24.97369.847
N
948
857
0.026
0.519
𝑅2
Note: t-values are in parentheses. *** , **, and * indicate
significance at 1%, 5%, and 10% statistical levels,
respectively.

5. Conclusion
This study examined the relationship between a firm's ESG performance and its bondfinancing costs. Our findings revealed that improved ESG performance is associated withlower bond financing costs. Specifically, for every one percent increase in a company's
ESG score, the bond spread decreases by an average of 0.149 percent. Furthermore, we
observed that the negative influence of ESG performance on bond financing costs is more
pronounced for smaller firms, suggesting that ESG performance effectively mitigatescredit risk for these companies.
23

We also investigated two potential mechanisms through which ESG practices
influence bond financing costs. The first reduces a firm's principal risks. Our results
indicate that ESG scores have a significant positive effect on management discipline andcredit ratings, leading to decreased operational and default risks. The second mechanism
posits that a firm's ESG performance can affect its CFF, subsequently affecting bondfinancing costs. Empirical evidence supports this notion as ESG scores demonstrate asignificant positive effect on CFF.
Finally, we employed an instrumental variable approach to address endogeneity
concerns. We also used alternative measurements and different samples to replicate ourbaseline regression, and the results remained robust.
Our study contributes to the literature in three ways. First, it advances the ESG andcorporate finance literature by focusing on bond markets, where issuers face greaterrefinancing needs and stakeholder pressures than in equity markets. Second, this study
investigates corporate operational risk, default risk, and CFF providing a thoroughanalysis in line with stakeholder and signaling theories. Third, this study encompasses
both listed and unlisted Chinese companies, offering a more comprehensive
understanding of the relationship between ESG bond financing costs.
This study acknowledges certain limitations that warrant consideration. Firstly, theinclusion of both listed and unlisted Chinese firms offers a nuanced perspective specificto the Chinese context; however, this focus may limit the extrapolation of our findings tofirms operating in disparate cultural, regulatory, and economic environments. The
distinctive attributes of the Chinese bond market, which are not universally applicable,
might also shape the dynamics between ESG practices and bond financing costs in ways
not representative of other markets. Secondly, the methodological approach, reliant on
statistical models, inherently poses constraints on our ability to discern causality fromcorrelation. Given the intricate nature of ESG considerations and their assimilation into
the fabric of corporate finance, it is plausible that certain influential variables remain
undetected and, thus, unaccounted for within the scope of our analysis.

24

Acknowledgments: This study was supported by the Maki Makoto Foundation(Research No. 2020007). We gratefully acknowledge. We would also like toacknowledge Editage for providing us with their English language editing services.

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