The Impact of Green Investment on the Upgrading of Industrial

Structure in China: Analysis Based on Panel Data Techniques

Heyuan Wang

1,* a

and Ran Jin

Wuhan University of Technology, Wuhan, China

Changchun Normal University, Changchun, China

Keywords: Green Investment, Upgrading of Industrial Structure, China, Panel Data Techniques.

Abstract: The report of the 19th National Congress of the CPC have proposed to establish and improve a sound

economic system for green, low-carbon and circular development. Developing green economy has become

the national strategy, and green investment (GIP) will play an important role in developing the green economy.

From a global, long-term and strategic perspective, the green economy and the digital economy are mutually

reinforcing. The continuous development of the digital economy in recent years has also led to further

optimization and upgrading of the industrial structure (ISU). The important role of GIP for ISU in the context

of the mutual integration of greening and digitalization has thus emerged. This paper makes full use of panel

data techniques and combines with the computer software STATA to empirically test the influence mechanism

of GIP on ISU, using 30 provincial panel data of China from 2003 to 2020.The results reflect that GIP has

obvious inhibitory effect on ISU. At the meantime, after the 2008 financial crisis, GIP has a more significant

inhibitory effect on the ISU. The GIP mainly restrains ISU in the eastern region. In addition, the GIP

significantly inhibits the development of primary and tertiary industries but promotes the development of

secondary industry.

1 INTRODUCTION

In recent years, the global ecological and

environmental problems has become increasingly

prominent. Environmental pollution, ecological

deterioration and other problems have brought great

challenges and threats to human survival and

development. Therefore, it is urgent to protect the

ecological environment and promote green

transformation. On a global scale, developing the

green economy is an important part of sustainable

development. The data show that GIP has developed

rapidly, showing great growth potential in the deep

adjustment of the global economy. The data from the

United Nations Trade Conference show that global

GIP reached $5.2 trillion in 2021, up 63%. The vision

of green development has become a goal-oriented and

path mode to guide the ISU and achieve high-quality

development. Since the 18th National Congress of the

CPC, China has incorporated "ecological civilization"

https://orcid.org/0000-0003-0341-4092

https://orcid.org/0000-0001-6100-9242

into the "five-in-one" overall layout of the socialist

cause with Chinese characteristics. As China's

economy has shifted from rapid growth to high-

quality development, showing leaps and bounds,

problems such as irrational industrial structure and

inefficient allocation of industrial factors have begun

to plague us. The emergence and development of the

digital economy has become a new breakthrough in

economic development to promote ISU and improve

resource allocation efficiency. The digital economy,

supported by the development of digital technology

and data as an important production factor, has

become a new economic form that promotes the

development of high-tech industries, the renewal of

business models, the comprehensive flow of

production factors and the realization of high-quality

development. At the same time, technologies such as

cloud computing and artificial intelligence in the

digital economy can transform the production and

operation methods of traditional industries, making

energy and electricity, transportation, industrial

160

Wang, H. and Jin, R.

The Impact of Green Investment on the Upgrading of Industrial Structure in China: Analysis Based on Panel Data Techniques.

DOI: 10.5220/0012027300003620

In Proceedings of the 4th International Conference on Economic Management and Model Engineering (ICEMME 2022), pages 160-167

ISBN: 978-989-758-636-1

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

production and other fields The operational efficiency

and energy efficiency of these industries have been

greatly improved, reducing energy and resource

consumption, promoting industrial structure

optimization and upgrading, and promoting green

economic development. The digital economy not only

plays a positive role in promoting the improvement of

production efficiency and optimizing industrial

structure, but also is becoming a "major carbon

emitter" due to factors such as the construction of new

infrastructure based on digital technology and the

upgrading of data centers, which results in the

elimination of old equipment. According to the Index

Climate Action Roadmap released by the 2020 Global

Climate Action Summit, digital technology solutions

in the fields of energy, manufacturing, agriculture,

land, construction, services, transportation and traffic

management can help reduce global carbon emissions

by 15%. Green and low-carbon development is the

choice of industrial development direction, and it is

also a fundamental issue related to the development of

industrial clusters. As an important driving force for

energy conservation, emission reduction and green

development, GIP will inevitably affect the

adjustment of industrial structure in the era of digital

economy,. From this point of view, there is a close

relationship between ISU and green development.

This paper further reveals the relationship between

ISU and GIP, and provides new ideas for the green and

high-quality economic development, which has

important practical significance.

2 LITERATURE REVIEW

The literature closely bound to this paper mainly

includes the following two aspects. One is the factors

affecting the ISU; the other is the research on green

finance and green credit, which are closely related to

GIP. Since the reform and opening up, China has

comprehensively deepened reform, the innovation-

driven strategy has been effectively implemented, the

industrial structure has been continuously optimized,

and the resilience and advantages of the industrial

chain have been improved. As an important way to

improve productivity, the adjustment and ISU is of

great practical significance to promote economic

development (Lv and Zhou, 1999). Therefore, many

scholars study how to promote the ISU. Lan and Chen

(2013) found that new urbanization has a strong

spatial impact on the ISU and can significantly

improve the level of industrial development.

Moreover, Wang and Zhao (2015) believed that the

scale and rationalization of the financial development

also promote ISU so it is necessary to build a

diversified financial service system, accelerate the

reform of the financial system and give play to the

guidance of policy-based finance. From the

perspective of geography and economy, Guo and

Wang (2021) used spatial Dubin model to conclude

that the influence of financial agglomeration and

housing price on ISU has industry heterogeneity and

spatial difference. Liu (2021) discussed the impact

mechanism of population aging on the industrial

structure from the perspective of the upgrading and

rationalization of industrial structure, and concluded

that the impact effect of population aging on the ISU

and rationalization of industrial structure was

significantly positive.

With the aggravation of the world's environmental

pollution and energy crisis, energy conservation,

emission reduction and the green economic

development have become the focus of global

attention, and the research on GIP and green finance

has become increasingly rich. Zhou et al. (2021)

empirically found that green finance improved the

comprehensive level of high-quality economic

development, promoted the optimization of economic

structure and the innovative development of economy,

but inhibited the stable development of economy. In

addition, other scholars have found that green finance

does not simply promote economic development. Shi

and Shi (2022) used green total factor productivity to

measure the quality of economic development, and

concluded that green finance has a threshold effect on

green total factor productivity rather than a linear

relationship. Only when the development level of

green finance was higher than the threshold value

could GIP play a significant role in promoting

economic development. Zhao and Wang (2022)

empirically analyzed the data of listed enterprises in

heavy pollution industries in China and concluded

that the influence of GIP on business performance of

enterprises showed a U-shaped relationship, and the

increase of green expenses would reduce business

performance of enterprises. On the theme of green

development, the influencing factors of GIP are also

a hot research topic. Yang (2022) concluded based on

the panel data of Shenzhen listed enterprises that after

the launch of the carbon trading system, enterprises

would enhance their awareness of environmental

protection and thus increase their GIP. Xie and Zou

(2021) believed that government environmental

protection subsidies played an incentive effect on

enterprises' GIP, and the promoting effect of market-

oriented environmental regulation represented by

government environmental subsidies was more

obvious in non-state-owned enterprises. Wang and

The Impact of Green Investment on the Upgrading of Industrial Structure in China: Analysis Based on Panel Data Techniques

161

Hou (2021) made further empirical research and

concluded that command-and-control, market-

incentive and voluntary environmental regulation

tools can all promote GIP of industrial enterprises,

and command-and-control environmental regulation

tools have the best effect at present.

In summary, there are abundant studies on the two

single directions of ISU and GIP, but few studies on

the relationship between them. Compared with

previous studies, the contributions of this paper are

mainly showed in the following aspects. Firstly, the

existing literature mainly focuses on the impact of

GIP on energy conservation and emission reduction,

ignoring the negative impact of GIP on the ISU. The

combination of GIP and ISU in this paper is

conducive to a more scientific understanding of the

impact mechanism of GIP. Secondly, this paper not

only studies the direct impact of GIP on ISU from the

macro level, but also analyzes the heterogeneity and

discusses the impact mechanism, which provides a

rich reference for subsequent research. Thirdly, it

provides some policy enlightenment for GIP and

green development. It is of immediate significance to

promote high-quality development of GIP.

3 DATA AND EMPIRICAL

MODELS

3.1 Models

In order to study the impact of GIP on ISU, this paper

first constructs a panel fixed effect model as shown in

Eq. (1).

ititit

itit

cityyearTFPD

RMTRADEGIGIPISU

εββ

++++

+++++=

43it2it10it

(1)

Where, 𝐼𝑆𝑈 is the explained variable, namely

the industrial structure upgrading index in this paper.

𝛽0 is a constant term, and 𝐺𝐼𝑃 is the core

explanatory variable, namely GIP.

𝐺𝐼

、

𝑇𝑅𝐴𝐷𝐸

、

𝑅𝑀

、

𝑃𝐷

、

𝑇𝐹 indicate that control

variables affecting the ISU are added into the model,

which respectively represent government

intervention, trade openness, highway mileage,

population density and R&D input intensity. year

and city represent the characteristics of time trend

and individual differences, respectively. 𝜀 is the

random disturbance term.

3.2 Variables

The level of industrial structure upgrading. This paper

draws on the calculation method of Wang (2015) to

measure the ISU. The specific formula is as follows.

ixi ×=



ISU

Where, xi represents the proportion of added

value of the ith industry in GDP.

Since the research on GIP theory is still in its

infancy, the theoretical interpretation of GIP is

diverse and not unified, and there is a lack of relevant

research and data. In this paper, the sum of urban

environmental infrastructure investment, industrial

governance investment, forestry investment and

water conservancy construction investment is

selected to measure GIP.

In order to control the endogeneity problem

caused by omitted variables, government intervention,

trade factors, highway mileage, demographic factors

and technological factors are introduced into the

model as control variables. Government intervention

(GI), TRADE (TRADE) and technical factor (TF) are

the proportions of fiscal expenditure, total import and

export TRADE and R&D internal expenditure in total

GDP, highway mileage (RM) is the total mileage of

highway construction, and population factor (PD) is

calculated by population density.

3.3 Data

Considering the availability of data, the sample in this

paper is the panel data from 30 provinces in China

from 2003 to 2020, with a total of 540 observation

samples. Tibet, Hong Kong, Macao and Taiwan are

not included due to the lack of data. In order to reduce

the influence of heteroscedasticity, all variables are in

logarithmic form. The data involved in the following

variables are from China Statistical Yearbook and

China Environmental Statistical Yearbook. The

descriptive statistics of each variable are shown in

Table 1, and the data processing is implemented in

STATA 11 software.

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162

Table 1: Descriptive statistics of variables.

Obs Mean Std.dev Min Max

GIP 540 897.2292 776.4135 67.9708 7996.259

GI 540 0.2189 0.0977 0.07678 0.6430

TRADE 540 0.3097 0.3815 0.0076 1.8432

RM 540 3373.079 2087.735 200 14190

PD 540 449.0474 665.6252 7.3931 3949.206

TF 540 0.0147 0.0113 0.0018 0.0741

4 EMPIRICAL RESULTS AND

DISCUSSION

4.1 Full Sample Analysis

To study the impact of GIP on ISU, this paper

estimates the regression model shown in Equation (1),

and the regression results are reflected in Table 2.

From columns (1), (2) to (3), we gradually add control

variables, and the regression results show that the

coefficient of GIP is always significantly negative at

the 1% level, which indicates that GIP significantly

inhibits the ISU. In conclusion, GIP has a

comprehensive inhibitory effect on ISU of China.

In-depth investigation of its reasons, we

summarize the following two points. First, although

the input of GIP has been continuously increased, the

growth of GIP in some regions has been slow or even

fallen after 2008. Overall, the total amount of GIP in

China is small, so it has not played a vital role in

promoting the ISU. Second, with the economic

development, some regions put economic benefits

first and put most GIP into high energy consumption

and high pollution enterprises, which will bring GDP

and tax revenue in the short term. This will not only

inhibit the ISU, but also may cause environmental

pollution.

Table 2: Full sample regression.

(1) (2) (3)

GIP

-0.005

***

(-2.60)

-0.006

***

(-3.27)

-0.006

***

(-3.18)

0.002

(0.26)

0.002

(-0.39)

-0.003

(-0.51)

TRADE

0.006

***

(3.08)

0.006

***

(2.75)

0.022

***

(4.83)

0.020

***

(3.97)

0.0009

(0.07)

0.004

(1.07)

0.8631 0.8713 0.8716

N 540 540 540

cons

0.831

***

(56.22)

0.672

***

17.73

0.701

***

8.85

Year effects yes yes yes

Province effects yes yes yes

Notes: *** p < 0.01, ** p < 0.05, and * p < 0.1.

The Impact of Green Investment on the Upgrading of Industrial Structure in China: Analysis Based on Panel Data Techniques

163

4.2 Heterogeneity Test

In order to test the impact of heterogeneity, we

conducted the test again from the two aspects of time

and space, and the regression results are shown in

Table 3. Considering the slowdown of economic

growth in China's economic development after the

2008 financial crisis, we divided the full sample into

two sub-samples from 2003-2008 and 2009-2020 for

regression based on the 2008 financial crisis. Column

(1) lists the regression results of the data from 2003

to 2008. Column (2) shows the regression data results

from 2009 to 2020. The results show that the GIP

coefficient from 2003 to 2008 is not significant, and

the GIP coefficient from 2009 to 2020 is negative at

the significance level of 0.5%. This reveals that GIP

has a significant negative impact on the ISU after the

financial crisis. After the financial crisis, in order to

deal with the negative impact of the financial crisis

and change the situation of economic downturn, the

Chinese government put GIP into low-end industries

to satisfy the demand of economic development and

improve economic benefits, thus showing a

significant inhibitory effect.

In order to study the impact of GIP on industrial

structure in different regions, we divided the whole

sample into three sub-samples: eastern, central and

western region. Column (3), (4) and (5) in Table 3

show the impact of GIP on ISU in eastern region,

central region and western region, respectively. The

results reflect that the coefficient of GIP in the eastern

region is significantly negative, the coefficient of GIP

in the central region is insignificant, and the

coefficient of GIP in the western region is

significantly positive. In general, GIP mainly inhibits

the ISU in the eastern region, has no significant

impact on the central region, and significantly

promotes the ISU in the western region. This is

because the level of ISU in the eastern region is

relatively high, whereas the total amount of GIP is

insufficient and the investment efficiency is not high,

which cannot satisfy the needs of industrial

optimization in the eastern region. However, the

industrial structure of western China is relatively

backward and the level of economic development is

not high, so the driving effect of GIP on the ISU is

more obvious.

Table 3: Heterogeneity analysis.

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

GIP

0.005

(0.96)

-0.005

(-2.18)

-0.004

(-1.66)

0.002

(0.50)

0.016

***

(-4.12)

-0.046

***

(-3.50)

0.002

(0.28)

0.016

(1.56)

-0.036

(-2.34)

-0.005

(-0.48)

TRADE

0.006

(0.83)

0.006

(2.38)

0.016

(2.35)

0.011

(2.11)

-0.001

(-0.19)

0.008

(0.90)

0.020

(2.52)

0.035

***

(5.49)

0.033

(-2.08)

-0.010

(-1.02)

-0.020

(-0.53)

-0.031

(-1.37)

0.029

(1.58)

0.182

***

(3.65)

-0.111

***

(-3.49)

-0.006

(-0.80)

0.002

(0.35)

-0.009

(-1.15)

0.015

(-1.98)

0.009

(1.50)

0.5205 0.8470 0.9143 0.9275 0.8682

N 180 360 198 144 198

cons

0.724

***

(3.43)

0.898

***

(6.62)

0.423

***

(3.33)

-0.848

(-0.34)

1.464

***

(9.15)

Year

effects

yes yes yes yes yes

Province

effects

yes yes yes yes yes

Notes: *** p < 0.01, ** p < 0.05, and * p < 0.1.

We further test the internal impact of GIP on

industrial structure and examine the different impacts

of GIP on primary industry, secondary industry and

tertiary industry. Column (1), column (2) and column

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(3) in Table 4 respectively show the impact of GIP on

primary, secondary and tertiary industries. As can be

seen in Table 4, GIP has a significantly positive

impact on the secondary industry, whereas it has a

significant inhibitory effect on the primary and

tertiary industries. The secondary industry is mostly

low-end industry with high energy consumption and

emission, while the tertiary industry is mostly high-

tech industry and service industry. Local governments

choose to invest GIP in the secondary industry in the

cause of obtaining short-term economic benefits. This

proves once again that GIP can inhibit the ISU

because it hinders the development of the tertiary

industry.

Table 4: Industrial heterogeneity.

(1) (2) (3)

GIP

-0.034

(-1.91)

0.076

***

(7.16)

-0.062

***

(-6.99)

0.506

***

(8.20)

-0.163

***

(-4.47)

0.064

(2.12)

TRADE

-0.077

***

(-3.61)

0.048

***

(3.79)

0.00004

(0.00)

0.134

***

(2.64)

0.193

***

(6.43)

-0.013

(0.52)

-1.600

***

(-12.70)

-0.350

***

(-4.69)

-0.043

(-0.70)

-0.010

(-0.25)

0.072

***

(3.16)

0.010

(0.55)

0.7799 0.7075 0.8222

N 540 540 540

cons

6.501

***

(8.17)

-0.651

(-1.39)

-0.133

(-0.34)

Year effects yes yes yes

Province effects yes yes yes

Notes: *** p < 0.01, ** p < 0.05, and * p < 0.1.

4.3 Robustness Test

In order to verify the robustness of the above

conclusions, this paper chooses to test the explanatory

variables with one-period lag and two-period lag

respectively. Column (1) reveals the results of one

period lag, and column (2) reveals the results of two

periods lag. The regression results are reflected in

Table 5, and the coefficients of the core variables are

all significantly negative, consistent with the previous

results, indicating that the inhibitory effect of GIP on

the ISU is sustainable. At the same time, endogeneity

problems may arise due to omitted variables and

reverse causality. For example, there may be some

factors that are difficult to quantify that affect the ISU.

Therefore, this paper adopts the instrumental variable

method for further testing. We choose the lagged one

period of GIP as its own instrumental variable. The

regression results in column (3) of Table 5 reveal that

the coefficient of the core explanatory variable is still

significantly negative, indicating that GIP still has an

inhibitory effect on ISU. Therefore, after overcoming

the endogeneity problem, the core results are robust.

Combining the robustness analysis results of the

above two different methods shows that the core

conclusion of this paper is robust.

The Impact of Green Investment on the Upgrading of Industrial Structure in China: Analysis Based on Panel Data Techniques

165

Table 5: Robustness test.

(1) (2) (3)

GIP

-0.004

(-2.39)

-0.006

***

(-3.01)

-0.006

(-1.97)

-0.002

(-0.27)

0.001

(0.15)

0.0006

(0.08)

TRADE

0.005

(2.10)

0.005

(2.17)

0.007

***

(3.42)

0.016

***

(3.03)

0.011

(2.21)

0.020

***

(3.17)

0.006

(-0.49)

-0.015

(-1.15)

-0.0003

(-0.02)

0.010

(2.33)

0.014

***

(3.52)

0.004

(0.89)

0.8729 0.8702 0.9590

N 510 480 510

cons

0.783

***

(9.59)

0.918

***

(10.86)

0.881

***

(5.83)

Year effects yes yes yes

Province effects yes yes yes

Notes: *** p < 0.01, ** p < 0.05, and * p < 0.1.

5 CONCLUSION AND

ENLIGHTENMENT

Based on China's provincial panel data from 2003 to

2020, this paper empirically tests the influence of GIP

on ISU, and the main conclusions are as follows. First,

GIP has a inhibitory effects on ISU, which still

supports this result after robustness check. Second,

the impact of GIP on ISU has the characteristics of

spatial and temporal heterogeneity. The regional

results show that GIP has a different impact on the

ISU in different regions of China. Specifically, GIP in

the eastern region has a significant inhibiting effect,

and the positive effect of GIP in the western region is

significant, while the positive effect of GIP in the

central region is not obvious. Secondly, taking the

2008 financial crisis as the time demarcating point,

GIP has no significant influence on the industrial

structure adjustment before 2008, but has a

significant inhibition effect on the ISU after the

financial crisis. Third, GIP will inhibit the ISU by

hindering the development of tertiary industry.

On the basis of the foregoing, the policy

implications of this paper are shown below. Firstly,

the government should increase investment in GIP to

meet the needs of ISU and encourage high-quality

economic development. Secondly, I attach

importance to giving full play to the leading role of

the government to clarify the direction of GIP and not

simply pursue short-term economic benefits while

ignoring the long-term ecological benefits. It is

recommended to guide GIP from the root to the

inflow of energy-saving and environmental

protection industries, to promote the development of

green industries and to a certain extent to curb the

development of high energy-consuming and high-

polluting traditional enterprises. Meanwhile,

government should encourage and support policies to

force enterprises to "green" transformation and

achieve industrial structure optimization. Thirdly, the

economic level of each region in China varies and the

regional development is unbalanced, so policy

makers should adopt differentiated policies for

different regions. Specifically, the eastern region has

a higher level of economic development and the

inhibitory effect of GIP on ISU is significant, so it is

crucial to increase the investment in GIP to make it

compatible with the higher level of industrial

upgrading. Although the western region of China has

improved its economic level since the

implementation of the western development strategy,

its economic development level still is much lower

compared with that of the east and central regions due

to its weak economic foundation. The western region

should improve the endogenous power to pull the GIP

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into the development of tertiary industry so as to

promote the transformation and optimization of

industrial structure.

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