ORI GIN AL PA PER

The impact of economic growth, industrial structureand urbanization on carbon emission intensity in China

Yue-Jun Zhang • Zhao Liu • Huan Zhang • Tai-De Tan

Received: 29 November 2013 / Accepted: 11 February 2014� Springer Science+Business Media Dordrecht 2014

Abstract China’s macroeconomic policy framework has been determined to ensure

steady growth, adjust the industrial structure and advance the socioeconomic reforms in

recent years. And urbanization is supposed to be one of the most important socioeconomic

reform directions. Meanwhile, China also committed to reduce carbon emissions intensity

by 2020, then it should be noted that what kind of impact of these policy orientations on

carbon emission intensity. Therefore, based on the historical data from 1978 to 2011, this

paper quantitatively studies the impact of China’s economic growth, industrial structure

and urbanization on carbon emission intensity. The results indicate that, first, there is long-

term cointegrating relationship between carbon emission intensity and other factors. And

the increase in the share of tertiary industry [i.e., the ratio of tertiary industry value added

to gross domestic product (GDP)] and economic growth (here we use the real GDP per

capita) play significant roles in curbing carbon emission intensity, while the promotion of

population urbanization (i.e., the share of population living in the urban regions of total

population) may lead to carbon emission intensity growth. Second, there exists significant

one-way causality running from the urbanization rate and economic growth to carbon

emission intensity, respectively. Third, among the three drivers, economic growth proves

the main influencing factor of carbon emission intensity changes during the sample period.

Keywords Carbon emission intensity � Economic growth � Industrial

structure � Urbanization � ARDL

1 Introduction

Since the late twentieth century, global warming has become one of the most serious

problems facing the international community, while greenhouse gas especially carbon

emissions associated with human activities has been considered to be the main attributor to

Y.-J. Zhang � Z. Liu (&) � H. Zhang � T.-D. TanBusiness School, Hunan University, Changsha 410082, People’s Republic of Chinae-mail: liuzhao@hnu.edu.cn

123

Nat HazardsDOI 10.1007/s11069-014-1091-x

global warming (IPCC 2007). Therefore, a great deal of attention has been paid across the

world to controlling carbon emissions and developing a low-carbon economy; in particular,

some emerging countries such as China are facing great pressure to reduce carbon emis-

sions and its intensity from the international community (Yu et al. 2014). The data from the

US Laurence Berkeley National Laboratory shows that China’s total carbon dioxide

emissions have exceeded that of the USA since 2006, becoming the largest carbon emitter

in the world (Levine and Aden 2008). In order to be a responsible country and demonstrate

a pragmatic image in the international community, Chinese government promised to cut

carbon emissions per unit of gross domestic product (GDP) (i.e., carbon emission intensity)

by 40–45 % by 2020 compared with the level in 2005 (Yu et al. 2012). Meanwhile, a report

proposed by Chinese State Council, entitled Solutions for the Greenhouse Gas Emission

Control during the 12th Five-Year Plan Period, declares that China’s carbon emission

intensity should be reduced by 17 % during the 12th Five-Year Plan period (2011–2015),

which is also confirmed to be an obligatory target within the period.

Since the reform and opening up in 1978, China’s economy has been growing at a speed

of nearly 10 % per year, achieving the second largest economy in the world now.

According to the keynote report at the opening of the 18th National Congress of the

Communist Party of China in 2012, China’s GDP in total and income per capita for both

urban and rural residents may be doubled by 2020 compared with the 2010 level. Put

another way, China’s economy may still maintain a relatively high growth rate, which may

increase the wealth of citizens and then make them consume more energy and produce

more carbon emissions.

Meanwhile, China’s industrial structure has been adjusted continuously in the past

decades, and the proportion of the tertiary industry has been increased rapidly up from

23.9 % in 1978 to 44.6 % in 2012 (National Bureau of Statistics of China 2013). The

evolution of industrial structure reflects the changes in economic growth pattern of China,

which has significant impact on China’s ecological environment (Zhang and Deng 2010).

In the future, China is expected to further expand the proportion of the tertiary industry,

which may provide some impetus to achieve the carbon emissions intensity target due to its

relatively lower carbon emissions.

Besides, the pace of China’s urbanization has been accelerating in a row. For instance,

the percentage of urban population in total population in China has been increasing con-

stantly from 17.92 % in 1978 to 52.6 % in 2012. However, compared with the overall

urbanization rate of developed countries (more than 75 %), China’s urbanization level is

still relatively lower. The United Nations Development Program predicted that by the year

of 2030, China’s population urbanization rate would reach 70 % and the urban population

would reach one billion.1 Therefore, China’s urbanization will keep developing at a rela-

tively rapider rate for a long period in the future. In fact, since China’s new government took

office in 2012, the concept of urbanization has been attached immense attention and the

policy-making for urbanization has been raised on the agenda as the main driver of eco-

nomic reform and the new growth point of China in the future. Meanwhile, it should be

noted that China’s rapid urbanization may lead to the sharp increase in energy demand. For

one thing, the large-scaled construction of infrastructure and buildings in urban regions may

need to consume huge cement and steel and iron, which is hard to be completely imported

abroad, and has to be mainly produced at home. Eventually, it may pose great pressure on

environmental protection. For another, each urban citizen on average often consume more

energy and emit more carbon dioxide. According to Dhakal (2009), the urban contribution

1 http://news.xinhuanet.com/fortune/2013-08/27/c_117117424.htm.

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to China’s total commercial energy consumption reached 84 % in 2006 and the commercial

energy consumption per capita of urban regions is 6.8 times higher than that of rural regions;

and although the 35 largest cities in China only have 18 % of the total population, they are

consuming 40 % of the total energy and are producing 40 % of the total carbon emissions,

which has caused huge energy consumption and ecological environment pressure. Overall,

the process of urbanization in China makes the contradiction between economic and social

development and environmental protection increasingly prominent.

Under this circumstance, currently China has decided to make great efforts to ensure

steady economic growth, adjust industrial structure, and advance systematic reform

simultaneously.2 Then, what effect of economic growth, urbanization reform and industrial

structure adjustment may have on carbon intensity reduction in China? and what policies

should be adopted to coordinate the relationship among these factors and carbon emission

intensity in China? These questions are very important for China to achieve the target of

carbon intensity reduction by 2020 and develop a low-carbon economic system and society.

The rest paper is organized as follows. Section 2 reviews the related literature. Sec-

tion 3 presents the research methods and data definitions. Section 4 introduces the

empirical results and Sect. 5 concludes the paper.

2 Related literature review

Global warming has become the focus of worldwide concerns, and carbon dioxide is

considered to be one of the main sources of greenhouse effect (Paul and Bhattacharya

2004). Therefore, the study on carbon emissions has attracted huge attention of academics

across the world. And overall, current studies on carbon emissions are mainly concentrated

on the influencing factors of carbon emissions and the relationship between these factors

and carbon emissions, as well as the mitigation policies and technologies etc.

As for the influencing factors of carbon emissions, demographic factors especially the

population urbanization has aroused an amount of earlier attention of scholars (Al-mulali

et al. 2012; Martı́nez-Zarzoso and Maruotti 2011; Zhu et al. 2012; Zhang and Lin 2012).

And almost all studies show that population urbanization has significant effect on carbon

emissions, but scientists hold different views on the specific relationship between urbani-

zation and carbon emissions. Liu et al. (2011) hold that the population increase and the

expansion of urbanization all contribute to an increase of indirect carbon emissions. Zhang

and Lin (2012) argue that urbanization is positively related to carbon emissions in China.

Al-mulali et al. (2012) point out that there exists positive correlation between urbanization

and carbon emissions in 84 % of all the countries, while there is complex relationship

between them in 16 % countries. Specifically, there is negative relationship between

urbanization and carbon emissions in some countries, and no relationship has been iden-

tified between urbanization and carbon emissions in those low-income countries. While

Martı́nez-Zarzoso and Maruotti (2011) analyze the impact of urbanization on carbon

emissions in developing countries and find inverted U-shaped relationship between

urbanization and carbon emissions. Additionally, the environmental pressure equation

IPAT (Ehrlich and Holdren 1971) and the STIRPAT model (Dietz and Rosa 1997) are

widely used to investigate the relationship between urbanization and carbon emissions

(Poumanyvong and Kaneko 2010; Zhu et al. 2012). For instance, Poumanyvong and Kaneko

(2010) employ the STIRPAT model to analyze the panel data of 99 countries over the period

2 http://news.xinhuanet.com/2013-07/10/c_116470279.htm.

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1975–2005 and argue that the relationship between urbanization and energy consumption,

carbon emissions is influenced by the economic conditions and the affluence level.

Moreover, the relationship between economic growth and carbon emissions has been

frequently discussed in recent years, and a large number of studies show that economic

growth has significant effect on carbon emissions in China (Li 2010; Meng et al. 2011;

Zhang et al. 2012). But some studies also indicate that there is no significant correlation

between economic growth and carbon emissions (Lantz and Feng 2006). In particular,

since the environmental Kuznets curve (EKC) hypothesis was presented, which argues that

with the economic growth and the increase in income per capita, environmental quality

may go through a transformation process of an inverted U-shape where there first appears a

gradual deterioration and then a gradual improvement, a growing number of studies have

been focused on the existence test of the EKC. Some studies show that the relationship

between income per capita and carbon emissions conforms to the hypothesis of EKC (Jalil

and Mahmud 2009; Nasir and Rehman 2011; Ozturk and Acaravci 2013), while some

studies also prove that the EKC hypothesis should be rejected (Roca et al. 2001; Azomahou

et al. 2006). In our opinions, the differences mainly result from the different developing

stages of the research subjects and different empirical research methods.

In addition, a body of literature finds that there is close relationship between carbon

emissions and industrial structure, energy structure and energy intensity. By using the

logarithmic mean Divisia index (LMDI) decomposition method, Ang et al. (1998) examine

the relationship between energy consumption and carbon emissions of China’s industrial

sector during 1985–1990. The results indicate that industrial production is positively

related to carbon emissions, while the change in energy intensity is negatively related to

carbon emissions. Wang et al. (2005) analyze the change in aggregated carbon dioxide in

China from 1957 to 2000 based on the LMDI method and find that the improvement of

energy intensity is the main contributor to carbon emissions reduction, and fuel switching

and renewable energy penetration also play significant roles in curbing carbon emissions,

while economic development is negatively related to carbon emissions. Combined the

environmental literature with modern endogenous growth theories, Ang (2009) investigates

the factors affecting China’s carbon emissions from 1953 to 2006 and shows that energy

consumption and trade openness are positively related to carbon emissions; specifically,

more energy consumption and greater trade openness may cause more carbon emissions;

meanwhile, carbon emissions in China is negatively related to technology transfer and the

absorptive capacity of the economy to assimilate foreign technology. Moreover, Jalil and

Mahmud (2009) indicate that China’s economic growth indirectly causes the increase in

carbon emissions; in the long run, income and energy consumption are the main factors

influencing carbon emissions. Zhang et al. (2009) argue that since 1991, economic activity

effect has been the most important contributor to carbon emission increase, and energy

intensity effect is confirmed as the main contributor to the decline in carbon emission and

carbon emission intensity. Recently, Zhang and Da (2013) investigate the main drivers of

carbon emissions changes in China during the 11th Five-Year Plan period (2006–2010) and

find that economic growth and energy consumption are the two main contributors of

carbon emissions increase, while carbon abatement technology improvement and energy

intensity reduction play significant roles in mitigating carbon emissions.

Rather than carbon emissions, there is also an array of literature paying attention to carbon

emission intensity in attempt to reduce carbon emission intensity while maintaining normal

economic growth, and two main methods are applied, i.e., the factor decomposition method

and the time series econometric method. For example, the LMDI decomposition method is

used by Bhattacharyya and Ussanarassamee (2004) to analyze the changes in Thailand’s

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energy intensity and carbon emission intensity from 1981 to 2000, and the results indicate

that during the sample period, both energy intensity and carbon intensity have declined to

some extent and their relationship is different in each of the four phases because of the

changes in economic conditions, industrial structure and the structure of fuel. Ebohon and

Ikeme (2006) use the Laspeyres index method to investigate the carbon emission intensity

between oil-producing and non-oil-producing sub-Saharan African countries, and hold that

energy intensity, energy structure and economic growth are the main influencing factors of

the changes in carbon emission intensity. Moreover, Fan et al. (2007) use the adaptive

weighting Divisia (AWD) method to make an empirical analysis on China’s carbon emission

intensity from 1980 to 2003 and imply that the overwhelming contributor to the decline of

energy-related carbon intensity is the real energy intensity reduction, while the policies that

focus only on the decline in energy intensity are insufficient to further decrease carbon

intensity and the changes in primary energy mix may improve the decline of carbon intensity.

Zhang (2009) investigates the changes of carbon emission intensity in China from 1992 to

2006 with the factor decomposition approach and reveal that production pattern should be the

main contributor to carbon emission intensity decline, while demand pattern may push up the

carbon intensity. As for the econometrical approach, based on the EKC model, Auffhammer

and Carson (2008) analyze the influencing factors of carbon emission intensity of China’s 25

provincial regions from 1985 to 2004 and find that there exists an obvious inverted U-shaped

relationship between carbon emission intensity and GDP per capita in China, and there is

notable positive correlation between population density and carbon emission intensity in

every region. Giblin and McNabola (2009) investigate the feasibility of Ireland’s carbon-

based tax policy for new vehicles and confirm that the introduction of these new carbon-based

taxes in Ireland may lead to a reduction of 3.6–3.8 % in carbon emission intensity. Da-

vidsdottir and Fisher (2011) use the data of 48 states in the USA from 1980 to 2000 to

examine the relationship and the direction of causality between economic development and

carbon emission intensity. The results indicate that there is significant bidirectional causality

between state economic performance and carbon emission intensity, and it is feasible for the

USA to employ periodic special policies to reduce energy and carbon emission intensities

while maintaining steady economic growth.

To sum up, although the existing literature has investigated the factors influencing carbon

emission intensity in China, mainly including energy structure, energy intensity, economic

development and structure of the secondary industry etc., there is little literature studying the

impact of economic growth, the tertiary industrial structure and urbanization on carbon

emission intensity in China. Meanwhile, China now stays at a critical phase to advocate rapid

urbanization and upgrade industrial structure especially to enhance the tertiary industry.

Therefore, it is of vital significance to investigate the influence of economic growth, tertiary

industry and urbanization on carbon emission intensity in China. To this end, this paper

employs the autoregressive distributed lag (ARDL) model to empirically examine the impact

from 1978 to 2011, so as to provide some insights for decision making of Chinese

government.

3 Data definitions and methodologies

3.1 Data definitions

China’s macroeconomic policy framework has been determined to ensure steady growth,

adjust the industrial structure and advance the socioeconomic reforms in recent years. And

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urbanization has been one of the most important socioeconomic reform directions in China.

Therefore, according to China’s macroeconomic policy directions and the commitment to

carbon emissions intensity reduction, we try to assess the impact of China’s economic

growth, industrial structure and urbanization on carbon emissions intensity. The variables

and their data definitions are shown in Table 1.

It should be noted that this paper considers the carbon dioxide emissions caused by

energy consumption. And the carbon dioxide emission per year is calculated as Eq. (1):

Ct ¼X4

i¼1

Et � Sit � Fi � 44=12 ð1Þ

where Ct denotes the carbon dioxide emissions in the year t, and Et refers to the total

energy consumption, quoted in ton coal equivalent, Sit (i = 1, 2, 3, 4 and represents the

coal, oil, natural gas and non-fossil energy, respectively) refers to the share of the con-

sumption of the ith energy source in the total energy consumption in the year t. Fi means

the carbon emissions factor of the ith energy sources, which is adopted from China Sus-

tainable Energy and Carbon Emissions Scenario Analysis Comprehensive Report released

by Energy Research Institute, National Development and Reform Commission (NDRC) of

China (ERI 2003). Specifically, the carbon emission factors of coal, oil, natural gas and

non-fossil energy are 0.7476, 0.5825, 0.4435 and 0, respectively, and are quoted in ton

carbon per ton coal equivalent. And the ratio 44/12 indicates the conversion coefficient

from carbon to carbon dioxide.

It should also be noted that all the data are annual items from 1978 to 2011 and come

from the China Statistical Yearbook 2012 (National Bureau of Statistics of China 2012).

Meanwhile, all variables are expressed in natural logarithms for further investigation.

3.2 Methodologies

3.2.1 Autoregressive distributed lag model (ARDL)

In this paper, the model for the influence of economic growth, industrial structure and

urbanization on carbon emission intensity is developed as Eq. (2).

CI ¼ a0 þ a1Incomeþ a2Indusþ a3Urbanþ et ð2ÞThe ARDL, also known as the bounds test, was proposed by Charemza and Deadman

(1997). In recent years, a number of studies have used this model to investigate the

cointegration relationship between carbon emissions and various factors (Liu 2009; Ozturk

Table 1 Variable definitions

Variable Definition

CI It denotes the logarithm of carbon emission intensity, represented by the total carbon emissionsdivided by GDP measured at 2005 constant RMB

Income It denotes the logarithm of economic growth, represented by the real GDP per capita measured at2005 constant RMB

Indus It denotes the logarithm of industrial structure, represented by the ratio of the tertiary industryvalue added to GDP

Urban It denotes the logarithm of urbanization level, represented by the percentage of the urbanpopulation, who are living in urban regions, in total population

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and Acaravci 2010, 2013). Compared with other cointegration methods such as the two-

step approach by Engle and Granger (1987) and the Johansen test (Johansen 1988), the

ARDL cointegration approach has certain econometric advantages. It can avoid the ana-

lysis of singular integration, which is necessary in the two-step approach and the Johansen

cointegration test. Moreover, the ARDL approach can be applied regardless of whether the

series are I(0), I(1) or mutually cointegrated (Pesaran and Pesaran 1997) and has great

advantage to study the cointegration relationship among variables in small-scaled sample

(Pesaran and Shin 1999). Besides, the error correction model (ECM) of ARDL is able to

reflect both the short-term and long-term dynamic relationship among variables (Banerjee

et al. 1993). In this way, from Eq. (2), the ARDL model for the relationship between

China’s economic growth, industrial structure and urbanization carbon emission intensity

can be expressed as Eq. (3):

DCIt ¼ b0 þXm

i¼1

b1iDCIt�i þXn

i¼0

b2iDIncomet�i þXp

i¼0

b3iDIndust�i þXq

i¼0

b4iDUrbant�i

þ b5CIt�1 þ b6Incomet�1 þ b7Indust�1 þ b8Urbant�1 þ gt ð3Þ

where D is the first-difference operator; b0 is the drift item; b1, b2, b3, b4 represent the

short-term dynamic relationship and b5, b6, b7, b8 denote the long-term dynamic rela-

tionship; while gt is the white noise item. In the ARDL model, the bounds test is adopted

to determine whether there is long-term equilibrium among the variables, namely whe-

ther some cointegration relationship exists. The bounds test is based on the joint sig-

nificance of F statistic and the v2 statistic of Wald test. When the carbon emission

intensity (CI) acts as the explained variable and the real GDP per capita (Income), and

the ratio of tertiary industry value added in GDP (Indus) and the percentage of urban

population in total population (Urban) are assumed as the explanatory variables, the

hypothesis to examine whether there exists cointegration among them is expressed based

on Eq. (3) as follows:

The null of no cointegration hypothesis: H0 : b5 ¼ b6 ¼ b7 ¼ b8 ¼ 0

The alternative hypothesis: H1: b5 = 0, or b6 = 0, or b7 = 0, or b8 = 0

We can test the cointegration by examining the joint significance of the F statistic of b5,

b6, b7, b8. And if the F statistic falls above the upper critical value presented by Pesaran

et al. (2001), the null of no cointegration hypothesis is rejected. In other words, there is

cointegration among the variables. If the F statistic falls below the lower critical value, we

cannot reject the null of no cointegration hypothesis, which means that there is no coin-

tegration among the variables. And if the F statistic falls inside the upper and lower critical

bounds, it cannot be determined whether the cointegration exists among the variables.

If there is cointegration among the variables, an ECM can be developed as Eq. (4),

which presents the short-term impact of China’s economic growth, industrial structure and

urbanization on carbon emission intensity.

DCIt ¼ c0 þXm

i¼1

c1iDCIt�i þXn

i¼0

c2iDIncomet�i þXp

i¼0

c3iDIndust�i þXq

i¼0

c4iDUrbant�i

þ kEcmt�i þ vt ð4Þ

where D is the first-difference operator; Ecmt-i is the error correction term; and k denotes

the coefficient of error correction term, which shows how quickly the variables converge to

the equilibrium.

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3.2.2 Toda–Yamamoto causality test

The method of causality test proposed by Toda and Yamamoto (1995) can be applied

directly using the level variables, regardless of the order of integration of the series,

whether cointegrated or non-cointegrated of any arbitrary order. Thus, it can reduce testing

risk (Mavrotas and Kelly 2001) caused by mistakenly setting the order of integration of

variables. Provided that k C dmax (k represents optimum lag length of the vector auto-

regression (VAR) model, and dmax means the maximum order of integration of the vari-

ables), the VAR model of level variables can be set up directly to conduct the Toda–

Yamamoto causality test.

The basic idea of the Toda–Yamamoto causality test method is as follows. First, we

have to determine the optimum lag length k of the VAR model according to related

information criteria [such as AIC, Schwarz Bayesian Criterion (SBC)]; and then, we find

out the maximum order of integration dmax in all variables by testing the order of inte-

gration of each variable; Once this is done, a (k þ dmax)th order of VAR model is esti-

mated, and the coefficients of the last lagged dmax vectors are ignored (Caporale and Pittis

1999). For example, in order to conduct the causality test for a VAR model with 4 lags

(k and dmax equal 3 and 1, respectively), we estimate the following system of equations:

CI

Income

Indus

Urban

26664

37775 ¼ A0 þ A1

CIt�1

Incomet�1

Indust�1

Urbant�1

26664

37775þ A2

CIt�2

Incomet�2

Indust�2

Urbant�2

26664

37775þ A3

CIt�3

Incomet�3

Indust�3

Urbant�3

26664

37775

þ A4

CIt�4

Incomet�4

Indust�4

Urbant�4

26664

37775þ

e1

e2

e3

e4

26664

37775 ð5Þ

where A0 is a 4 9 1 vector of constant terms; A1;A2;A3 and A4 are 4 9 4 matrices of

coefficients; es (s = 1, 2, 3, 4) are the disturbance terms with zero mean and constant

variance. Then, we examine the causality from the results in Eq. (5) by employing the

Wald test. For example, we test the null hypothesis that the real GDP per capita (Income)

does not cause carbon emission intensity (CI) with H00: a121 = a12

2 = a123 = 0, where

a12’si are the coefficients of lagged Income in the first equation of the system in Eq. (5). If

the null hypothesis is rejected, then the real GDP per capita (Income) can be accepted as

the cause of carbon emission intensity (CI). The cases for industrial structure and urban-

ization are similar.

4 Empirical result analyses

4.1 Analyses of the long-term and short-term relationship

First of all, we conduct the augmented Dickey–Fuller (1981) (ADF) and Phillips–Perron

(1988) (PP) unit root tests for all variables in this paper to judge the stationariness of

variables. The results are shown in Table 2. We find that all the variables considered in this

paper are I(1) series at the 5 % significance level, which meets the requirements of the

ARDL method.

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Then, the ARDL bounds test is applied to analyze the cointegration relations. Based on

the SBC, we can verify the cointegration by testing the joint significance of the F statistic

of the coefficients of carbon emission intensity, the real GDP per capita, the ratio of tertiary

industry value added to GDP and the percentage of urban population in total population.

The results are shown in Table 3.

The results in Table 3 indicate that there is long-term cointegration relationship

between carbon emission intensity, the real GDP per capita, the ratio of tertiary industry

value added to GDP and the percentage of urban population in total population at the 1 %

significance level when carbon emission intensity is regarded as the dependent variable.

According to Eq. (3) and the principle of minimum SBC value, we single out the ARDL (2,

1, 0, 0) model, and the estimated results for the long-term relationship are shown in

Table 4.

Several overall tendencies are observed from Table 4. Briefly, urbanization is positively

related to carbon emission intensity during the sample period, whereas economic growth

and industrial structure are negatively related to carbon emission intensity. In other words,

in the long term, the increase in the percentage of urban population in total population may

result in the increase in carbon emission intensity, while the increase in real GDP per capita

and the ratio of tertiary industry value added to GDP may play significant roles in curbing

carbon emission intensity. The details are stated as below.

First, urbanization may have a positive impact on carbon emission intensity; specifi-

cally, a 1 % increase in the percentage of the urban population leads to 2.2195 % increase

in carbon emission intensity. Zhang and Lin (2012) and Poumanyvong and Kaneko (2010)

indicate that urbanization is positively correlated with carbon emissions, which is in

agreement with our finding here. In our opinions, there are four main reasons for this

positive relationship. (1) Urbanization may cause the movement of population force from

agricultural sector to industrial and service sectors, which may result in more construction

of infrastructure and buildings and the increase in energy consumption and carbon emis-

sions. (2) With the rapid development of urbanization and the improvement in residents’

life quality, the consumer demand and lifestyle in China have also undergone many

Table 2 Results of unit root tests

Variable Level First difference

ADF PP ADF PP

CI -1.2795 (0.626) -1.5156 (0.8037) -3.1559 (0.033) -2.8497 (0.0627)

Income -2.6025 (0.282) -1.9118 (0.6258) -3.5824 (0.013) -3.082 (0.0381)

Indus -1.5224 (0.801) -1.9205 (0.6214) -4.8398 (0.001) -6.0989 (0.0001)

Urban -2.6960 (0.245) -1.9465 (0.6079) -4.3418 (0.002) -4.5215 (0.0011)

P values are reported in parentheses

Table 3 Bounds F test forcointegration

P values are reported inparentheses

F statistics

F(CI | Urban, Indus, Income) 5.7803 (0.007)

F(Income | CI, Indus, Urban) 2.8610 (0.067)

F(Indus | CI, Urban, Income) 1.5221 (0.253)

F(Urban | CI, Indus, Income) 6.0103 (0.006)

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changes, which eventually bring fast increase in demand of energy-intensive products

including appliances, cars and the substantial increase in energy consumption per capita

and cause more carbon dioxide emissions. (3) As urbanization is associated with indus-

trialization and often calls for quantities of raw materials, which may have direct and

prominent influence on energy consumption (Liu and Xie 2013). (4) In the process of

population urbanization, there is a tendency of land urbanization in China’s local gov-

ernment, whose speed appears faster than that of population urbanization; meanwhile the

economic function of urban regions have been over-enlarged while their social function

and environmental conditions have not been attached enough attention. What is more,

owing to the restrictions of household registration system (hukou in Chinese) in China, the

process of population urbanization appears relatively slower and a large number of migrant

workers included in the urban population statistics do not own the real citizenship, who

often gather in some local area of cities. As a result, the environment can hardly afford the

large population in the urban regions, which made China go short of resources and face

traffic jam and environmental degradation. Overall, the misplacement of urban functions

and the urbanization mode at the cost of environmental destruction may result in an

increase in carbon emission intensity. Just as predicted by Zhang and Lin (2012), with

rising urbanization process, China’s carbon emissions may grow by an average rate of

2.6 % per year from 2008 to 2035, which may pose significant threats to the realization of

carbon emissions reduction targets.

Second, the growth of real GDP per capita plays a significant role in curbing carbon

emission intensity; specifically, an increase of 1 % of real GDP per capita may accompany

with 1.0894 % decrease of carbon emission intensity. These results are somewhat different

from previous literature. For instance, Jalil and Mahmud (2009) believe that economic

growth plays a significant role in the increase of carbon emissions; Auffhammer and

Carson (2008) indicate that there is an inverted U-shaped relationship between real GDP

per capita and carbon emission intensity. In our opinions, these results may be attributed to

the definition of carbon emission intensity. In this paper, we focus on the carbon emission

intensity instead of carbon emissions. Carbon emission intensity (i.e., carbon emissions per

unit of GDP) considers both carbon emissions and economic output (GDP). When the

growth rate of GDP is higher than that of energy consumption and carbon emissions,

carbon emission intensity will show a trend of decreasing. During the sample period, the

growth rate of China’s real GDP is indeed higher than that of carbon emissions, which

eventually causes the decrease of carbon emission intensity in a row.

Finally, the ratio of tertiary industry value added to GDP is negatively related to carbon

emission intensity; specifically, a 1 % increase in the ratio of tertiary industry value added

Table 4 Estimated long-term coefficients from the ARDL model

Coefficient Standard error t statistics

Constant 5.8246 0.6724 8.6625 (0.004)

Income -1.0894 0.4250 -2.5632 (0.017)

Indus -0.6737 0.3172 -2.1239 (0.044)

Urban 2.2195 1.2186 1.8213 (0.081)

The regression is based on Eq. (3). P values are reported in parentheses

Adjusted R2 = 0.9966, F statistic = 1,460.3 (0.000), D.W. = 1.9282

Serial correlation: F statistic = 0.0013 (0.971), Heteroscedasticity: F statistic = 1.5765 (0.219)

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to GDP may decrease carbon emission intensity by 0.67 %. For a long time, China’s

economic growth has relied too much on the secondary industry, which has become the

dominant industry of the national economy. While the development of the secondary

industry especially the manufacturing industry always consumes more resources and emits

more carbon dioxide than that of the tertiary industry. In 2010, the energy consumption of

the secondary industry was five times as that of the tertiary industry. Therefore, in order to

achieve the sustainable development of economy, China should transform the pattern of

economic development and raise the proportion of the tertiary industry in the national

economic system as soon as possible. In recent years, with the rapid development of the

tertiary industry in China, modern service industry such as finance, insurance, logistics,

e-commerce and IT has developed rapidly and has significantly restrain the carbon

emission intensity due to their relatively lower energy consumption intensity.

Next, according to Eq. (4), the results of ECM for the relationship between CI, Urban,

Indus and Income are shown in Table 5.

Table 5 shows that the estimated coefficient of the Ecm term appears negative and

statistically significant at the 1 % significance level, with the value -0.1941. It indicates

that the short-term disequilibrium (deviation) among carbon emission intensity, the per-

centage of urban population in total population, the ratio of tertiary industry value added to

GDP and the real GDP per capita may be corrected fast by their long-term cointegration

relationship in the previous period.

Also it should be noted that in the short term, the changes in urbanization may have

negative impact on carbon emissions intensity. In our opinions, this is mainly because the

impacting mechanisms of population urbanization on carbon emissions intensity men-

tioned above have not come into full effect. In fact, when more population enters into the

urban regions, they will increase the payment for consumption immediately, which may

prompt the GDP increase but not necessarily increase carbon emissions thus not neces-

sarily increase carbon emissions intensity.

Besides, the R2 value is 0.56, which means these independents can only explain 56 % of

the total information of carbon intensity changes in the short term and other factors may

explain 44 %. In fact, during the whole sample period, China’s carbon intensity overall

maintained a downward trend, but during 2002–2007, China’s carbon emission intensity

also presented an upward trend, while the three factors basically experienced upward

trends during the whole sample period. This has slowed down the explaining power of the

model here.

Table 5 Error correction representation of the selected ARDL model

Coefficient Standard error t statistics

Constant 1.1308 0.4629 2.4426 (0.022)

DCI(-1) 0.4032 0.1544 2.6121 (0.015)

DIncome -0.2115 0.0619 -3.4150 (0.002)

DIndus -0.1308 0.0763 -1.7124 (0.099)

DUrban -0.8311 0.4544 -1.8291 (0.079)

Ecm(-1) -0.1941 0.0647 -3.0026 (0.006)

D denotes the first-difference operator. P values are reported in parentheses

Adjusted R2 = 0.56, F statistic = 8.8202 (0.000), D.W. = 1.9282

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4.2 Analyses of the causality

This paper employs the Toda–Yamamoto (1995) approach to test the causality between

carbon emissions intensity and urbanization rate, industrial structure and economic growth,

respectively. First, the optimum lag length k is determined to be three based on the

criterions of minimum AIC and SC values, and then the maximum order of integration

dmax equals one based on the unit root test results. Therefore, according to Eq. (5), the

VAR(4) model is developed to estimate the causality among variables by employing the

Wald test. The results for the v2 statistics are shown in Table 6.

The results of the Toda–Yamamoto causality test reveal that there are unidirectional

long-term causality from economic growth and urbanization to carbon emission intensity at

the 5 % significance level, respectively. And several implications can be obtained from the

results.

On the one hand, the unidirectional causality running from the real GDP per capita to

carbon emission intensity reminds us to balance the relative speed of economic growth and

carbon emissions so as to realize the carbon emission intensity reduction target. Due to the

rapid economic growth in China, its residents’ income level has been constantly increased

in the past decades, which makes it easier for them to buy big ticket items such as

automobiles, houses, refrigerators, air conditioners, washing machines , which emit more

carbon dioxide. However, carbon emission intensity denotes carbon emissions per unit of

GDP, which is a relative indicator. When the growth rate of China’s real GDP is higher

than that of carbon emissions, carbon emission intensity may be curbed in the end. As

mentioned above, China’s GDP in total and income per capita may be doubled by 2020

compared with the level in 2010. Therefore, strict measures should be adopted to restrain

the growth rate of carbon emissions so as to achieve the carbon intensity reduction target

by 2020.

On the other hand, the unidirectional causality running from the percentage of urban

population in total population to carbon emission intensity and their positive correlation

during the sample period to some extent reflect the fact that the increase in urban popu-

lation and the changes in residents’ consuming behaviors in China in the past decades have

resulted in significant increase of energy consumption, carbon emission and its intensity,

which is in line with the results by Gu et al. (2011). This indicates that although urbani-

zation has been determined as the main economic policy in the future, if not appropriately

managed, it may become some obstacle to achieve carbon emission intensity target by

2020 and low-carbon socioeconomic development, which should be paid enough attention

by Chinese government.

Table 6 The causality test results

Null hypothesis v2 statistic Probability

Income does not cause CI 13.0608 0.0045

Indus does not cause CI 2.4620 0.4822

Urban does not cause CI 11.3556 0.0100

CI does not cause Income 1.7163 0.6333

CI does not cause Indus 2.0573 0.5606

CI does not cause Urban 0.3803 0.9443

The causality test approach used in the table is provided by Toda and Yamamoto (1995)

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4.3 Analyses of the impulse response and variance decomposition

In order to examine the impact of innovations in all variables in the system on carbon

emission intensity, the impulse response and variance decompositions analyses are con-

ducted based on the results of the VAR(4) model above. The impulse responses of carbon

emission intensity to one standard deviation innovations to economic growth, industrial

structure and urbanization are graphed in Fig. 1.

The impulse response results indicate that, first, the response of carbon emission

intensity (CI) to a shock on the percentage of urban population in total population (Urban)

appears positive in the first four periods and then turns to be negative and reaches the stable

level around -0.0074 in the end. The results indicate that the positive response of carbon

emission intensity to urbanization is fairly significant in a short time, and the urbanization

patterns of ‘‘high input and high carbon emissions’’ may lead to the increase in carbon

emissions, whereas in the wake of the continuous increase in urbanization rate, the gov-

ernment may gradually start to pay attention to environmental issues and transform the

extensive pattern of growth to promote the development of low-carbon city.

Second, the response of carbon emission intensity (CI) to a shock on the ratio of tertiary

industry value added to GDP (Indus) proves negative in the first thirteen periods then turns

positive and reaches the stable level about 0.014. The results tell us that in the earlier

periods, the increase of the ratio of tertiary industry may lead to the decrease of carbon

emission intensity. However, with the continuous scale increase of the tertiary industry, the

output efficiency will be gradually lowered, which eventually leads to the increase of

carbon emission intensity, because of the obstacles of technology upgrade and structural

adjustment.

Finally, the response of carbon emission intensity (CI) to a shock on the real GDP per

capita (Income) appears negative through all the period and in the end keeps stable at

-0.014.

Besides, in order to compare the contribution extent of the percentage of urban popu-

lation in total population (Urban), the ratio of tertiary industry value added in GDP (Indus)

-.04

-.03

-.02

-.01

.00

.01

.02

.03

.04

5 10 15 20 25 30 35 40 45 50 55 60

CIUrbanIndusIncome

Res

pons

e of

CI t

o C

hole

sky

O

ne S

.D. I

nnov

atio

ns

Period

Fig. 1 The results of the impulse response of carbon emissions intensity (CI). CI, Urban, Indus and Incomerepresent the log values of carbon emission intensity, the percentage of the urban population in the totalpopulation, the ratio of tertiary industry value added to GDP and the real GDP per capita, respectively

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and the real GDP per capita (Income) to the change in carbon emission intensity (CI), the

variance decomposition approach is adopted in this paper. And results are shown in Fig. 2.

Figure 2 indicates that as time goes by, the contribution extent of carbon emission

intensity itself to the change in carbon emission intensity has a downward trend, while that

of other variables rises steadily.

In addition, in spite of the contribution extent of carbon emission intensity itself, real

GDP per capita exerts the largest influence on carbon emissions changes, whose stable

contribution level approaches to 35 %; while the influence of the ratio of tertiary industry

value added to GDP and the percentage of urban population in total population follows,

with stable contribution levels 13 and 7 %, respectively. Therefore, we may say that

China’s economic growth should be the most crucial factor affecting carbon emission

intensity, which further validates the importance of low-carbon economic pattern in China.

5 Conclusions and policy recommendations

In this paper, the ARDL approach is employed to study the impact of economic growth,

industrial structure and urbanization on carbon emission intensity in China. We obtain

several conclusions as follows:

1. There is long-term cointegration relationship between economic growth, industrial

structure, urbanization and carbon emission intensity during the sample period.

Specifically, the increase in the ratio of tertiary industry value added to GDP and real

GDP per capita play significant roles in curbing carbon emission intensity, while the

increase in the percentage of urban population in total population may lead to carbon

emission intensity growth.

2. There exits significant one-way causality running from the real GDP per capita and the

percentage of urban population in total population to carbon emission intensity,

respectively. Therefore, it is urgent for Chinese government to take effective measures

to improve the quality of urbanization and avoid its concomitant high energy

consumption and carbon emission. In particular, some pragmatic policies should be

adopted to motivate the low-carbon consuming activities of urban residents and

restrain luxury consumption of energy-intensive products.

0

20

40

60

80

100

5 10 15 20 25 30 35 40 45 50 55 60

CIUrbanIndusIncome

Var

ianc

e D

ecom

posi

tion

of C

I (%

)

Period

Fig. 2 The results of thevariance decomposition ofcarbon emissions intensity (CI).CI, Urban, Indus and Incomerepresent the log values of carbonemission intensity, thepercentage of the urbanpopulation in the totalpopulation, the ratio of tertiaryindustry value added to GDP andthe real GDP per capita,respectively

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3. Economic growth proves the main factor influencing carbon emission intensity.

Therefore, the government must prudently balance carbon emissions reduction and

economic growth to reduce carbon emission intensity. The percentage of urban

population in total population and the ratio of tertiary industry value added to GDP are

also important factors contributing to the changes in carbon emission intensity.

Based on the results above, we also put forward several policy recommendations as

follows to help Chinese government achieve the goals of carbon emissions intensity

reduction and promote sustainable economic development.

1. The government has to make scientific planning for urban development. For instance,

they should assign preponderant weights to energy saving and environmental

protection components in the planning, such as encouraging energy saving

infrastructure and designing a series of developing indicators for energy saving and

environmental protection. Also they have to balance the development of urban land

and population and to prevent environment pollution and damage resulting from

overpopulation beyond environment capacity. In addition, it is necessary to carry out

the reform of household registration system and separate household registration system

from welfare system which involves education right, medical insurance and the

pension system etc. Besides, they should make policies to remove the barriers and

restrictions about labor migration in the process of urbanization and thereby realizing

the rational allocation of labor in urban and rural regions.

2. The government should pay close attention to the role of tertiary industry and develop

the modern service sectors with less resource consumption and higher value added,

such as finance and information sectors. The Chinese government should create a

favorable environment for the emerging of the tertiary industry and make relevant

polices to ensure its rapid and healthy development and ultimately help to achieve the

target of carbon emission intensity reduction.

3. The government has the responsibility to promote low-carbon consumption pattern in

society and make it integrated with every link of production and household living. As

mentioned by Liu et al. (2011), the direct and indirect CO2 emission from household

consumption accounted for more than 40 % of total carbon emissions from primary

energy utilization in China in 1992–2007. Therefore, the government has the

responsibility to adopt effective measures to help citizens (especially the young

citizens) to cultivate energy saving and environmental friendly consumption habits and

consume more low-carbon products.

This paper studies the influence of economic growth, industrial structure and urbani-

zation on carbon emission intensity, and there is still much work to be done in the future.

First, there is a tendency of land urbanization of China’s local government in the process of

urbanization and the speed of land urbanization is faster than that of population urbani-

zation, but the study does not analyze the influence of land urbanization, i.e., the evolution

of city size and structure, on carbon emission intensity. Second, this paper analyzes the

impact of the ratio of tertiary industry on carbon emission intensity, however it lacks

considering specific sectors of the tertiary industry (such as the transportation sector) due

to the data unavailability and complex contents. Third, we can consider the impact of the

secondary industry on carbon emissions intensity, such as manufacturing (e.g., construc-

tion materials industry) sector. Besides, the paper also does not consider the role of

technology advance in the process of urbanization, which may provide new impetus for

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carbon emissions intensity reduction in China. All of these may be possible valuable

directions on carbon emission intensity study in the future.

Acknowledgments We gratefully acknowledge the financial support from the National Natural ScienceFoundation of China (Nos. 71001008, 71273028, 71322103, 70903028, 71173207) and Basic ResearchFund of Beijing Institute of Technology (No. 20122142008).

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