Treatise on the Relationship between Business ESG Performance and

Efficiency of Investment

YuChen Tian and Zhong Ma

Department of Accounting, BJTU, Shangyuancun, Beijing, China

Keywords: ESG Efficiency of Investment, Richardson Model, Social Responsibility, Corporation Management.

Abstract: This essay examines the relationship between corporate environmental, social, and corporate governance

(ESG) and corporate investment efficiency using data from Chinese A-share listed businesses from 2010 to

2020. The empirical findings indicate that solid environmental, social, and governance practices may help

businesses improve their non-investment efficiency. The empirical findings of this research, on the other hand,

indicate that ESG may help to minimize non-investment efficiency by reducing the agency problem. By

employing corporations from developing markets as research samples, this study contributes to the theoretical

literature on environmental, social, and corporate governance (ESG). At the same time, the findings of this

study have illuminating implications for the company's ESG management, which is to say, for the

management of stakeholders. At the same time, it supplies policymakers with valuable information on

resource allocation and other problems.

1 INTRODUCTION

With increasing public awareness of climate change

and societal challenges, environmental, social, and

governance (ESG) investment has progressively

become a popular subject. Against the backdrop of

the pandemic, ESG investment has been expanding

throughout the world unlike anything we’ve seen

before. ESG is a term that refers to an investing

strategy that takes into account three dimensions:

Environment, Social Responsibility, and corporate

governance. The ESG investment approach, as

opposed to the standard investment strategy, places a

greater emphasis on the overall enhancement of

company social value. Since the 1980s, the

divergences between firm owners and managers have

become more pronounced, and unfavorable events

such as financial fraud have been more prevalent,

resulting in increased interest in corporate

governance in both the business and academic

communities (Shleifer, Vishny 1997, Bebchuk, et al.,

2009, Bai, et al., 2005, Milosevic, et al., 2015)

Because of the fast expansion of China's economy

and culture, financial fraud events such as the

Kangmei and the kangdexin scandals have occurred

one after another, highlighting the need for deeper

study into corporate governance. Against a backdrop

of the steady expansion of the China's capital market

frameworks, the 2018 corporate governance

benchmarks for newly listed companies made it clear

that they need to actively learn from international

experience, encourage institutional investors to

participate in corporate governance, strengthen the

role played by the board of directors' audit committee,

and set up the fundamental framework of

environmental, social responsibility, and corporate

governance (ESG). In this context, the link between

ESG and firm investment efficiency, as well as the

particular effect mechanism, is investigated in this

research.

Enterprises' investment choices, as one of the

three primary decisions they make, are critical to their

long-term strategy and growth, and the index of

investment efficiency is the focus of both academics

and business communities. Increasing the

effectiveness of investment has emerged as a crucial

subject of concern for businesses and investors in

recent years, as a result of China's economic growth

and transition. The relationship between enterprise

ESG performance and investment efficiency is

currently dominated by two theories: on the one hand,

it is believed that better ESG performance can

enhance enterprise financing constraints and agency

costs, thereby improving enterprise investment

efficiency(Lambert, et al., 2007, Zhong, Gao, 2017,

Tian, Y. and Ma, Z.

Treatise on the Relationship between Business ESG Performance and Efﬁciency of Investment.

DOI: 10.5220/0011324900003440

In Proceedings of the International Conference on Big Data Economy and Digital Management (BDEDM 2022), pages 769-775

ISBN: 978-989-758-593-7

769

Anwar, Malik, 2020); On the other hand, it is claimed

that firms' attention to ESG would result in the waste

of company resources and the conduct of managers

pursuing their personal interests, resulting in a

reduction in the investment efficiency of enterprises

(Bé nabou, Tirole, 2010, Krüger, 2015).

According to previous researches, the current

relevant research is primarily based on international

data, whereas domestic research on enterprise ESG

performance and enterprise investment efficiency is

still in its infancy, and relevant research on China's

market environment is insufficient. This paper, in

contrast to previous research, is more concerned with

China's specific situation and makes use of the

relatively mature ESG rating data of SynTao Green

Finance to evaluate the ESG performance of A-share

listed companies; Simultaneously, this paper

investigates the internal influence mechanism of ESG

performance on enterprise investment efficiency,

thus provide useful advice and ideas to Chinese

businesses on how to enhance their ESG strategy and

investment efficiency, so increasing their value and

promoting economic growth.

2 MATERIALS AND METHODS

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Please remember that all the papers must be in

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For a best viewing experience the used font must

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named times, except on special occasions, such as

program code (Section 2.3.7).

2.1 Model Design and Variable

Definition

For the purpose of testing the aforementioned

hypotheses, this paper refers to Benlemlih and

Bitar(2018) in constructing the following basic

regression model：

𝑀𝑖𝑠𝑖𝑛𝑣𝑒𝑠𝑡

,

=𝛼



+𝛼



∗𝐸𝑆𝐺

,

∑

𝛼



𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠

,

+𝜇



+η



+ε

,

(1)

2.1.1 Explained Variable

𝐼𝑛𝑣𝑒𝑠𝑡



=𝛽



+𝛽



𝑇𝑜𝑏𝑖𝑛𝑄



+𝛽



𝐿𝑒𝑣



𝛽



𝐶𝑎𝑠ℎ



+𝛽



𝐴𝑔𝑒



+𝛽



𝑆𝑖𝑧𝑒



𝛽



𝑅𝑒𝑡𝑢𝑟𝑛𝑠



+𝛽



𝐼𝑛𝑣𝑒𝑠𝑡



+𝜀 (2)

Invest stands for newly investments, the amount

invested equals the product of (capital expenditure +

M&A expenditure - income from selling long-term

assets - depreciation) / total assets, where capital

expenditure is defined as "expenditure on purchasing

fixed assets, intangible assets, and other long-term

assets" in the cash flow statement (direct method);

Investment in mergers and acquisitions (M&A) is in

the cash flow statement (direct method) that

represents "net cash spent for acquiring subsidiaries

and other businesses." Specifically, income from the

sale of long-term assets is represented by "net cash

retrieved from the disposition of fixed assets,

intangible assets, and other long-term assets" in the

cash flow statement (direct method), and depreciation

is represented by "current depreciation expense" in

the cash flow statement (indirect method). When a

company's market value is divided by its book value,

the resulting ratio is called TobinQ. Total liabilities

divided by total assets is the asset liability ratio of the

corporation, which is abbreviated as Lev. Cash is the

sum of money and money equivalents divided by the

sum of all assets. Age stands for the natural logarithm

of the number of years it has been listed on the stock

exchange. Size is defined as the natural logarithm of

its total assets. A company's annual Return is

calculated by averaging the yearly return of

individual shares, taking into account the

reinvestment of cash dividends; Furthermore, the

yearly effect and the industry impact are also

included by model (1). To determine the investment

efficiency of a corporation, the absolute value

(AbsXinvest) of the Xinvest, as determined by model

(1), is used as an index. With increasing value, the

degree of inefficient investment increases, while the

degree of investment efficiency decreases.

2.1.2 Explaining Variables

As a result of the creation and promotion of the idea

of ESG, a plethora of environmental, social, and

governance (ESG) grading systems have evolved

both domestically and internationally, each with its

own set of assessment criteria, reference indicators,

and coverage. The Huazheng ESG rating index is

used to assess the environmental, social, and

governance (ESG) performance of businesses in this

study. Similar to Huazheng ESG rating, other ESG

assessment methods have shortcomings, such as

BDEDM 2022 - The International Conference on Big Data Economy and Digital Management

770

limited coverage and infrequent updates. For

example, CASVI rating and SynTao Green Finance

rating only cover a part of the constituent stocks and

are updated once every six months and once every

twelve months, respectively; Jiashi ESG is updated

more frequently than Huazheng ESG, but it has not

yet been launched in the WIND, CSMAR, or other

databases; Huazheng ESG system draws on the

mainstream ESG evaluation framework from abroad

and incorporates the realities of China's capital

market as well as the characteristics of various listed

companies, finally establishes 26 key indicators and

employs the industry weighted mean method for ESG

evaluation. It is updated quarterly and includes all

publicly traded companies. Huazheng ESG rating is

split into nine classes, ranging from low to high: C,

CC, CCC, B, BB, BBB, A, AA, and AAA. ESG is

built in accordance with the aforementioned rating by

using the assignment technique, in which the nine

grades from C to AAA are sequentially allocated as

1~9, that is to say, when the ratings are C, ESG = 1;

when the ratings are CC, ESG = 2; when the ratings

are CCC, ESG = 3, and so on.

2.1.3 Controlled Variables

According to Li Yanxi et al. (Li, et al, 2015), Lu Xin

et al. (Lu, 2017), and Cao Yue et al. (Cao, et al, 2020),

this paper primarily restricts other variables that may

impact the level of enterprise investment efficiency

from two aspects of the company's financial status

and internal governance level: organization (Size),

company debt ratio (Lev), profitability (ROA), and

growth (TobinQ). Internal governance variables such

as (INED), (Share Concentration), SOE, and Duality

are all important considerations. Additionally, in

order to better manage the unobservable

characteristics that do not vary with industry or time,

the time fixed effect (μt) and the industry fixed effect

(ηind) are included into the model.

Table 1 shows the specific definitions of

variables. TobinQ is the ratio of a company's market

value to its book value, with the formula being [(total

share capital - domestic listed foreign shares B

shares) × Current closing price of a shares + domestic

listed foreign shares B shares × Current closing price

of B shares (Shanghai Stock Exchange×CNY_ USD,

Shenzhen Stock Exchange / HKD_ CNY, converted

into RMB) + total liabilities at the end of the current

period] / total net worth

Table 1: Variable definition table.

Variable type Variable name Variable symbol Variable measurement

Explained variable Investment efficiency Misinvest

Model-estimated (1) absolute value of

residual

Over investment Overinv

Model 1: Regression-derived absolute value

of residual larger than zero

Insufficient investment Undinv

Model 1: Regression-derived absolute value

of residual less than zero

Explanatory variable ESG rating ESG Huazheng's ESG rating ranges from 1 to 9

control variable Enterprise scale Size Total assets natural logarithm

Profit level ROA Net profit to total assets ratio

Growth TobinQ

The ratio of a company's market value to its

ook value.

Ownership concentration Share_Concentration The greatest shareholder's shareholding ratio

Auditor BIG4

Dummy variable, 1 for the big 4 auditors,

otherwise 0

Nature of equity SOE

Dummy variable, state-owned enterprise is 1,

otherwise 0

Proportion of independent

directors

INED

Ratio of independent director to board of

directors

Duality Duality

Dummy variable, the chairman and general

manager are the same person, 1, otherwise 0

Corporate debt ratio

Lev Ratio of total liabilities to total assets

2.2 Data Sources and Sample

Selections

The study sample for this article is data from China's

A-share listed businesses from 2010 to 2020; the ESG

rating data is based from Huazheng ESG rating, and

other financial and governance data is sourced from

the CSMAR Guotai'an database. As a result, this

article (1) eliminates financial sector samples (2)

excludes ST company samples (3) excludes missing

values of regression variables. Furthermore, this

Treatise on the Relationship between Business ESG Performance and Efﬁciency of Investment

771

study winsorizes the variables to lessen the influence

of outliers on empirical analysis outcomes.

2.3 Descriptive Statistics

The descriptive statistical findings of all variables in

this research are shown in Table 2. Table 2 indicates

that the average ESG score for the firms in the sample

is 6.492, with a standard deviation of 1.070,

indicating that ESG performance in the sample ranges

from 5 to 7.49. The mean value of investment

efficiency is 0.159, and the standard deviation is

0.157, indicating that there are significant variances

in investment efficiency across the enterprises in the

sample. Table 3 shows the firms’ industry dispersion.

As can be seen, the sample includes listed

organizations from 18 different sectors.

Manufacturing enterprises made up a major chunk of

them, accounting for 65.44 percent. Companies in

other industries make up less than 10% of the total.

Table 2: Descriptive Statistics.

Variables

Sample

size

Mean

values

Minimum

value

Median

Maximum

value

Misinvest 9806 0.159 0.157 0.002 0.121 1.039

Overinv 4965 0.145 0.117 0.002 0.118 0.584

Undinv 4841 0.173 0.191 0.002 0.125 1.199

ESG 28247 6.492 1.070 4 6 9

Size 28681 22.125 1.300 19.764 21.936 26.157

ROA 28681 0.040 0.060 -0.251 0.039 0.195

TobinQ 28681 2.043 1.334 0.866 1.608 8.871

Share_Concentration 28681 34.802 14.903 8.773 32.810 74.824

BIG4 28681 0.058 0.234 0.000 0.000 1.000

SOE 28169 0.371 0.483 0.000 0.000 1.000

INED 28651 0.272 0.027 0.250 0.263 0.364

Duality 28315 0.281 0.449 0.000 0.000 1.000

Lev 28681 0.419 0.211 0.049 0.410 0.908

Table 3: Sample distribution statistics.

Indust

ies Sam

le sizes Percenta

oints

Agriculture, forestry, animal husbandry, and fisheries 369 1.29

Minin

674 2.35

Manufacturing 18,769 65.44

Electricit

, heat,

as, and water and distribution 935 3.26

Construction 761 2.65

Wholesale and retail 1,438 5.01

Transportation, storage, and mail services 872 3.04

Lod

and Caterin

88 0.31

Information transmission, software, and information

technolo

services

1,887 6.58

Real estate 1,179 4.11

Leasin

and commercial services 325 1.13

Scientific research and technology services 300 1.05

Water conservation, environmental protection, and public

utilities management

334 1.16

Home, re

air and other services 22 0.08

Education 28 0.1

Health, and social wor

630.22

Culture, sports, and entertainment 388 1.35

Total 249 0.87

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772

3 RESULTS & DISCUSSION

3.1 Basic Regression Results

Table 4 summarizes the main findings of this study's

basic regression analysis. First, the basic regression

without control variables is represented by the first

column, and the basic regression with control

variables added on the basis of the first column is

represented by the second column. The regression

coefficient indicates that environmental, social, and

governance factors have an inhibitory influence on

the company's non-investment efficiency ( β=

−0.00866, p<0.01). Therefore, every one unit rise in

environmental, social, and governance (ESG) boosts

by 0.00866 the non-investment efficiency level of the

organization. The third and fourth columns look at the

influence of ESG factors on corporate over-

investment. After controlling for other factors, the

regression coefficient of environmental, social, and

governance (ESG) is -0.00473, and the p value is less

than 0.05, indicating that ESG discourages excessive

investment by the corporation. The final two columns

of Table IV detail the consequences of ESG's under-

investment in the firm. The findings demonstrate that

environmental, social, and governance (ESG) may

hinder the company's under-investment ( β=

0.00951, p<0.01), which means that improving ESG

can mitigate the company's under-investment issue.

Table 4: Basic regression results.

(1) (2) (3) (4) (5) (6)

Variable

Mis

invest

Mis

invest

Overinv Overinv Undinv Undinv

ESG -0.00941*** -0.00866*** 0.00281 -0.00473** -0.0181*** -0.00951***

(

0.00153

)

(

0.00158

)

(

0.00185

)

(

0.00189

)

(

0.00207

)

(

0.00180

)

Controlled

variable

No Yes No Yes No Yes

Time effect Yes Yes Yes Yes Yes Yes

Industr

effect Yes Yes Yes Yes Yes Yes

Observed value 9,806 9,521 4,965 4,805 4,841 4,716

R2 0.684 0.684 0.684 0.684 0.684 0.684

Note: standard error in brackets

The significance level: *** p<0.01, ** p<0.05, * p<0.1

The controlled variables are: Sizes, ROA, TobinQ, Share Concentration, Auditors

(

BIG4

)

, SOE, INED, Dualit

, Lev

3.2 Robustness Test

This research used quantile regression to reassess

ESG in order to analyze the investment efficiency of

businesses at multiple quantile fractiles, in order to

further assess the reliability of the findings. Quantile

regression may be used to provide a more thorough

understanding of the relationship between

independent and dependent variables. Instead of

using an OLS linear model to estimate the model with

average effect, quantile regression creates various

effects at different points along the distribution

(quantile fractiles) of dependent variables. The

dependent variable is continuous, meaning that it

does not include any zeros or too many duplicates.

And the model is estimated once again in this

research, and the results of the quantile regression are

shown in Table 5. Table 5 contains the regression

findings for various quantiles, which are shown in

columns 1 through 5. The findings indicate that

environmental, social, and governance (ESG) factors

have an inhibitory effect on different quantiles of a

company's investment efficiency, that improving

ESG will decrease the company's non-investment

efficiency, which is consistent with the findings of the

basic regression in Table 4. As a consequence, the

findings of this paper are consistent and trustworthy.

Table 5: Robustness test (quantile regression).

(

)

(

)

(

)

(

)

(

)

Variables Misinvest Misinvest Misinvest Misinvest Misinvest

10quantile 25quantile 50quantile 75quantile 90quantile

ESG -0.00189** -0.00293** -0.00599*** -0.0104*** -0.0156***

(

0.000747

)

(

0.00117

)

(

0.00154

)

(

0.00223

)

(

0.00337

)

Controlled

variable

Yes Yes Yes Yes Yes

Treatise on the Relationship between Business ESG Performance and Efﬁciency of Investment

773

Time effect Yes Yes Yes Yes Yes

Industr

effect Yes Yes Yes Yes Yes

R2 0.0579 0.0699 0.0944 0.1361 0.2111

Observed value 9,521 9,521 9,521 9,521 9,521

Note: standard error in brackets

The significance level: *** p<0.01, ** p<0.05, * p<0.1

The controlled variables are: Company Sizes, ROA, TobinQ, Share Concentration, Auditors (BIG4), SOE,

INED, Duality, Lev

3.3 Impact Mechanism Analysis

This research also builds a model to evaluate whether

environmental, social, and governance (ESG) factors

may increase the investment efficiency of firms by

easing the agency issue in order to better understand

the mechanism of ESG's investment efficiency. In

accordance with previous research, the free cash flow

(FCF) of the firm is used as the measuring indicator

for agency cost in this study. Because when a firm

generates greater free cash flow, the management of

the organization is more driven to make investments

that will benefit the company. Whereupon, this

investment method that ultimately benefit the

management itself is frequently not in accordance

with the interests of the majority of shareholders in

the company, which impedes the development of the

enterprise, results in inefficient investment, and

ultimately results in agency problems in the

organization. According to Wen Zhonglin (2006), the

following is the model of mechanism analysis:

Median



=β



+β



∗ESG



+β





+μ



+η



+ε



Misinvest



=α



+α



∗ESG



+α



∗

Median



∑





+μ



+η



+ε



(3)

In which the median is the intermediate variable,

that is, the FCF. X



represents the control variable,



is the time-effect, η



, the industry effect, while



is the residual.

The findings of the study, which used enterprise

free cash flow as the mediator, are shown in Table 6.

The first column summarizes the influence of ESG on

company investment efficiency (Misinvest). The

second column provides the influence of ESG on the

mediator and enterprise free cash flow (FCF). The

data demonstrates that the regression coefficient of

ESG is -0.0141 *, passes the Statistical significance

test. This demonstrates that ESG may successfully

cut corporate agency expenditures while also

alleviating relevant difficulties. The last column

contains the regression findings obtained by

combining (ESG) factors with business free cash flow

(FCF). This analysis reveals that the regression

coefficient of enterprise free cash flow is positive and

significant at the 1% level of Statistical significance

( β = 0.00827, p<0.01), which indicates that the

greater a company's free cash flow, the higher its non-

investment efficiency. The regression coefficient of

ESG ( β = −0.00643 , p<0.01) was statistically

negative and less than the regression coefficient of

ESG in the first column (β = −0.00866, p<0.01)

This demonstrates that the agency cost serves as an

intermediate. In other words, ESG may help to ease

the agency issue, minimize non-efficient investment,

and ultimately enhance the investment efficiency of

businesses.

Table 6: Mechanism analysis.

(1) (2) (3)

Variable Misinvest Fcf Misinvest

ESG -0.00866*** -0.0141* -0.00643***

(0.00158) (0.00824) (0.00167)

Fcf 0.00827***

(0.00136)

Controlled

variable

Yes Yes Yes

Time effect Yes Yes Yes

Industry

effect

Yes Yes Yes

Observed

value

9,521 18,360 6,628

R2 0.329 0.329 0.329

Note: standard error in

rackets

The significance level: *** p<0.01, ** p<0.05, * p<0.1

The controlled variables are: Size, ROA, TobinQ, Share

Concentration, Auditors (BIG4), SOE, INED, Duality, Lev, the

natural logarithm of the company's free cash flow serves as the

intermediate variable (FCF)

4 CONCLUSIONS

With the rising attention being paid by all sectors of

society to the social responsibility of the environment

and other issues, businesses have begun to pay more

attention to their own ESG management practices.

This essay investigates the relationship between

environmental, social, and governance (ESG) and

BDEDM 2022 - The International Conference on Big Data Economy and Digital Management

774

company investment efficiency using data from

Chinese publicly traded firms from 2010 to 2020. The

empirical findings indicate that environmental,

social, and governance (ESG) factors may reduce a

company's non-investment efficiency, which is to say

that a strong ESG can increase an enterprise's

investment efficiency. ESG has the potential to

increase non-investment efficiency in both under-

investment and over-investment situations. The

empirical findings of the mechanism analysis reveal

that environmental, social, and governance (ESG)

factors have an influence on company investment

efficiency by easing the agency issue.

The following is the theoretical contribution made

by this research: First and foremost, this research

employs ESG as a measure of company performance

in areas such as the environment, social

responsibility, corporate governance, and so on.

Existing research on corporate social responsibility

tend to be narrowly focused on a single facet of the

issue. The ESG therefore more accurately portrays

the corporate social responsibility associated with

firms and their stakeholders. Second, the samples

included in this research represent a total of 18 sectors

of publicly traded businesses. As a result, the findings

of this research are more thorough and representative

than previous findings. Third, the research samples

for this study are publicly traded Chinese enterprises.

As a result, the findings of this research contribute to

the research on developing market economies.

Enterprise managers will also benefit from the

findings of this research. First and foremost, the

findings of this research demonstrate that effective

environmental, social, and governance (ESG)

practices may lower the non-investment efficiency of

businesses. As a result, business management should

develop proper ESG strategies that are tailored to

their specific scenario in order for the organization to

reap the benefits of sound ESG practices and policies.

Environmental management, environmental

protection, employee training, community social

responsibility and other practices should be

considered by businesses in order to enhance the

company's ESG performance and, ultimately, to

increase the company's investment efficiency.

Moreover, analysts should be involved in monitoring

and overseeing the ESG behavior of businesses. For

investors, the information disclosure of companies is

a key indicator of their performance. Thus, the

analyst's oversight function and their opinions are

critical in the operation of businesses.

REFERENCES

Anwar R, Malik J A. When does corporate social

responsibility disclosure affect investment efficiency?

a new answer to an old question[J]. SAGE Open, 2020,

10 (2).

Bai C, Liu Q, Lu Z, Song M, Zhang J. An empirical study

on the governance structure of Listed Companies in

China [J] Economic research, 2005, (2): 81-91

Bebchuk L, Cohen A, Ferrell A. What matters in corporate

governance? [J]. Review of Financial Studies, 2009,

22(2): 783-827.

Bénabou R, Tirole J. Individual and corporate social

responsibility[J]. Economica, 2010, 77(305): 1-19.

Benlemlih M, Bitar M. Corporate social responsibility and

investment efficiency[J]. Journal of Business Ethics,

2018, 148(3): 647-671.

Cao Y, Xin H, Zhang Z. The impact of the implementation

of the new environmental protection law on the

investment efficiency of heavy pollution industries [J]

China soft science, 2020, (8): 164-173

Chen R, Zheng M, Liu W. Intermediary effect analysis:

principle, procedure, bootstrap method and its

application [J], Journal of marketing science, 2017,9

(4): 120-135

Krüger P. Corporate goodness and shareholder wealth[J].

Journal of Financial Economics, 2015, 115(2): 304-

329.

Lambert R, Leuz C, Verrecchia R E. Accounting

information, disclosure, and the cost of capital[J].

Journal of Accounting Research, 2007, 45(2): 385-420.

Li Y, Zeng W, Ma Z, Chen K. External governance

environment, property right nature and investment

efficiency of Listed Companies [J] Nankai

management review, 2015, (1): 25-36

Lu X, Zhang L, Li H, Ding Y. Background characteristics

of executive team and investment efficiency -- A Study

on the regulatory effect of executive incentive [J] Audit

and economic research, 2017, (2): 66-77

Milosevic D, Andrei S, Vishny R W. A survey of corporate

governance[J]. Journal of Finance, 2015, 52: 737-783.

Richardson S. Over-investment of free cash flflow[J].

Review of Accounting Studies, 2006, 11(2-3): 159-189.

Shleifer A, Vishny R W. A survey of corporate

governance[J]. Journal of Finance, 1997, 52(2): 737-

783.

Wen Z, Zhang L, Hou J. Moderating variables with

mediation and moderating mediating variables [J]

Journal of psychology, 2006, (3): 448-452

Zhong M, Gao L. Does corporate social responsibility

disclosure improve firm investment efficiency? [J].

Review of Accounting and Finance, 2017.

Treatise on the Relationship between Business ESG Performance and Efﬁciency of Investment

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