Microsoft Word - 001.docx


 CHEMICAL ENGINEERING TRANSACTIONS  
 

VOL. 66, 2018 

A publication of 

 

The Italian Association 
of Chemical Engineering 
Online at www.aidic.it/cet 

Guest Editors: Songying Zhao, Yougang Sun, Ye Zhou 
Copyright © 2018, AIDIC Servizi S.r.l. 

ISBN 978-88-95608-63-1; ISSN 2283-9216 

Analysis on Carbon Emission Decoupling Effect and Driving 

Factors of Environmental Pollution in China’s Transportation 

Industry 

Wenlong Zheng*, Jianwei Wang, Shiqing Zhang 

 School of Economics and Management, Chang’an University, Xi’an 710064, China 

 534236243@qq.com  

In order to study the relationship between carbon emission and economic growth in China’s transportation 

industry and the driving factors of carbon emission, the Logarithmic Mean Divisia Index (LMDI) decomposition 

model of carbon emission elastic decoupling and carbon emission driving factors is established. The results 

show that the carbon emission decoupling value of China’s transportation industry has obvious spatial 

disequilibrium characteristics, and the decoupling state of “weak decoupling-expansion negative decoupling-

growth linkage” appears from coastal areas to inland are as a whole. From 2009 to 2016, there are five types 

of carbon emission Kuznets curves of China’s transportation industry, namely linear type, U type, inverted-U 

type, inverted-N type and wave type. The carbon emission intensity of transportation industry in many 

provinces still tends to increase with the increase of economy. The energy structure effect and the energy 

intensity effect have a relatively weak negative impact on the carbon emission in the transportation industry. 

However, the industry scale effect has a significant positive impact, which is the main factor to promote the 

growth of carbon emission. 

1. Introduction 

Since the industrial revolution, the consumption of a large number of fossil energy has made the global carbon 

dioxide emission increase rapidly with the rapid development of economy, which severely restricts the 

sustainable development of human society. In 2006, China’s CO2 emission China made up 20% of the world’s 

total carbon emission, surpassing the United States to become the world’s largest greenhouse gas emission 

country. Therefore, there is still a long way to go for the realization of China’s economic and social low-carbon 

development (Hu and Tang, 2015; Lu et al., 2012; Liu and Yang, 2012). Transportation industry is one of the 

three major industrial systems characterized by low carbon emission, which is determined in the national plan 

for dealing with climate change. The establishment of low carbon transportation system plays an important 

role in coping with climate change and achieving carbon emission reduction target in China (Lin and Huang, 

2011).  

Decoupling theory is used to measure whether there is a synchronous relationship between material 

consumption and economic growth in the course of industrialization development or not. Tapio put forward the 

elastic decoupling theory on the basis of decoupling theory to analyze the decoupling between carbon 

emission and economic growth of transportation industry. On this basis, Andreoni conducted a decoupling 

analysis of the relationship between carbon emission and economic growth in Italy’s agriculture, industry, and 

transportation industry from the perspective of carbon emission intensity, energy intensity, energy structure, 

and economic growth. Domestic scholars started to study the decoupling theory late. used OECD decoupling 

index and Tapio elastic decoupling model to study the relationship between industrial carbon emission, energy 

consumption and industrial added value, providing reference for similar analysis in other provinces. Tapio 

elastic decoupling model lays particular emphasis on the analysis of individual years. It is easy to observe the 

difference between carbon emission and economic growth in each year, which is helpful to clarify the change 

of performance before and after implementation of policy, and to refine decoupling indexes. The OECD model 

focuses on the analysis of the whole year. The result of the decoupling analysis is different for the different 

                                

 
 

 

 
   

                                                  
DOI: 10.3303/CET1866107 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Please cite this article as: Zheng W., Wang J., Zhang S., 2018, Analysis on carbon emission decoupling effect and driving factors of 
environmental pollution in china’s transportation industry, Chemical Engineering Transactions, 66, 637-642  DOI:10.3303/CET1866107   

637



selection of the base period. In order to understand the decoupling of carbon emission in transportation 

industry more comprehensively, it is necessary to conduct decoupling analysis of carbon emission in each 

year. Thus, Tapio decoupling model is selected to analyze the carbon emission in transportation industry. 

Structural decomposition approach (SDA) and index decomposition approach (IDA) are widely used in the 

study of driving factor decomposition of carbon emission. Compared with SDA, IDA, especially LMDI is simple 

and easy to operate, which can not only eliminate the residual term and solve the zero problem in data 

processing, but also facilitate the time series analysis and the comparison between provinces. 

To sum up, the related researches on carbon emission in transportation industry are at the national level or 

single provincial level and haven’t calculated the carbon emission in transportation industry or analyzed 

decoupling effect and driving factors from 30 provinces in China. Therefore, an empirical analysis of the 

decoupling effect and driving factors of carbon emission in transportation industry in 30 provinces of China is 

of great significance to the decomposition of emission reduction indexes and the targeted guidance of energy 

conservation and emission reduction in each province. 

2. Research Methods and Model Building 

2.1 Calculation method of carbon emission in provincial transportation industry 

In order to ensure the accuracy of the calculation of carbon emission in transportation industry in each 

province, this analysis takes 21 kinds of energy sources as statistical object, which is different from that in the 

past. The carbon emission amount (D) of transportation industry is calculated, as shown in Formula (1). 

44

12
i i i i

D A B C N    
                                                                                                                            (1) 

Where, D is carbon emission from energy consumed by construction industry; 𝐴𝑖 is i fuel consumption; 𝐵𝑖  is 

conversion standard coal coefficient of i fuel; 𝐶𝑖 is a carbon emission coefficient of i fuel; 𝑁𝑖  is oxidation rate of 

i fuel. 𝐵𝑖 comes from the conversion coefficient of “General Principle of Comprehensive Energy Consumption 

Calculation” (GB / T 2589-2008); 𝐶𝑖  derives from default value of IPCC carbon emission calculation guidelines. 

2.2 Elastic decoupling model of carbon emission and economic growth in transportation industry 

Decoupling between carbon emission and economic growth means that carbon emission doesn’t increase with 

economic growth, but tends to decline, and a complete decoupling between the two is ideal. In this analysis, 

decoupling elasticity mainly refers to the change ratio of the carbon emission in transportation industry to the 

GDP of the industry. Based on Tapio’s elastic decoupling model, this analysis proposes an elastic decoupling 

model of the relationship between transportation and economic growth in China’s provinces, as shown in 

Formula (2). 

2, 2 2
( / ) / ( / )

CO IGDP
t CO CO IGDP IGDP  

                                                                                                    (2) 

Where, tCO2 , IGDP  indicates the decoupling elasticity of the relationship between carbon emission in 

transportation industry and GDP of transportation industry; CO2 is the carbon emission in transportation 

industry in each province (10,000 t); IGDP is the annual GDP of transportation industry (billion yuan). In order 

to eliminate the factor of price change and ensure the comparability of indexes between different periods and 

regions, the GDP of transportation industry is calculated with a constant price. 

2.3 Decomposition model of influence factors of carbon emission 

In this analysis, the influence factors of carbon emission are decomposed into four factors: carbon emission 

intensity effect, energy structure effect, energy intensity effect and industry scale effect by using extended 

Kaya identical equation and LMDI. According to the research results of Ang B.W et al. and the characteristics 

of carbon emission in transportation industry, a decomposition model is established. 

i i
i i

i

C E E
C IGDP f s n g

E E IGDP
        

                                                                                    (3) 

Where, 𝐶𝑖 represents the carbon emission (10,000 t) from the consumption of 𝑖th energy source; 𝐸𝑖  represents 

the consumption of 𝑖𝑡ℎ energy source (10,000 t of standard coal); 𝐸 indicates the total energy consumption 

(10,000 t of standard coal); IGDP expresses the GDP of transportation industry (billion yuan); 𝑓𝑖 = 𝐶𝑖 /𝐸𝑖 

represents the carbon emission intensity effect of 𝑖𝑡ℎ  energy source; 𝑠𝑖 = 𝐸𝑖 /𝐸 represents the consumption 

ratio of 𝑖𝑡ℎ  energy source in the total energy, which is used to explain the energy consumption structure; 

638



n=E/IGDP is the energy consumption intensity; g indicates the impact of the industry scale effect on the 

carbon emission in the transportation industry. 

According to the decomposition analysis of Formula (3) by LMDI, the change ∆C of carbon emission from t to t 

+ 1 in transportation industry in each province can be expressed as Formula (4). 

1t t f s n g
C C C C C C C


       

                                                                                                 (4) 

 

 

1

1

1
ln

ln ln

t t

i i
f t t

i i

f tC C
C

C C f t





 
       


                                                                                              (5) 

 

 

1

1

1
ln

ln ln

t t

i i
s t t

i i

s tC C
C

C C s t





 
       


                                                                                             (6) 

 

 

1

1

1
ln

ln ln

t t

i i
n t t

i i

n tC C
C

C C n t





 
       


                                                                                              (7) 

 

 

 

1

1

1
ln

ln ln

t t

i i
g t t

i i

g tC C
C

C C g t





 
       


                                                                                              (8) 

Where, ∆C𝑓 , ∆C𝑠, ∆C𝑛, and ∆C𝑔 are the contribution values of carbon emission intensity effect, energy structure 

effect, energy consumption intensity effect and industry scale effect to carbon emission change in 

transportation industry respectively. The carbon emission coefficients of various energy sources are kept 

constant, so ∆C𝑓 = 0 in Formula (4). 

3. Empirical Analysis 

3.1 Decoupling analysis of carbon emission and economy in transportation industry 

 

 

Figure 1: EKC of carbon emission in China’s transportation industry over the years 

The carbon emission in transportation industry in 30 provinces of China from 2009 to 2016 is calculated by 

formula (1), and the mean GDP, carbon emission and the average rate in transportation industry from 2009 to 

2016 are calculated. Based on the elastic decoupling model of carbon emission in transportation industry, the 

639



elastic decoupling value of carbon emission in transportation industry is calculated by Formula (2). The results 

show that the change of carbon emission in transportation industry and the change of GDP in transportation 

industry are both positive from 2009 to 2016, indicating that the carbon emission and GDP of transportation 

industry in China show an increasing trend. 

In order to more intuitively explain the dynamic relationship between carbon emission intensity and economic 

growth in transportation industry in each province, the environmental Kuznets curve (EKC) analysis is carried 

out for each province. The EKC curve of carbon emission is an empirical curve obtained by statistical actual 

data and reflects the relationship between carbon dioxide emission and economic growth. Based on the 

indexes of carbon emission, mean value of GDP and average change rate of transportation industry, 30 

provinces are classified into 6 categories by K-means cluster analysis. GDP of transportation industry of each 

province (price is comparable and 2009 is base period) is taken as abscissa and unit GDP carbon emission of 

transportation industry is taken as ordinate, we can obtain EKC of carbon emission in transportation industry 

in each province from 2009 to 2016, as shown in Figure 1. 

As can be seen from Figure 1, the relationship between carbon emission and economic growth in China’s 

transportation industry from 2009 to 2016 has five types of curves, namely, linear type, U type, inverted-U 

type, inverted-N type and wave type. The EKC of Shandong, Guangdong, and other 9 provinces is declivitous 

to be approximate straight line, which shows that the carbon emission intensity decreases gradually with the 

increase of GDP, and the effect of energy conservation and emission reduction in transportation industry is 

obvious. The EKC of Sichuan, Guizhou, Henan, Chongqing, Guangxi and Inner Mongolia is wave type. The 

EKC of Chongqing and Inner Mongolia is relatively stable while that of Guangxi is declivitous wave type. The 

carbon emission intensity in Sichuan, Guizhou and Henan increases with the economic growth. It shows that 

there is a strong coupling relationship between carbon emission and economic growth in these provinces, and 

the task of energy conservation and emission reduction is arduous. The EKC of Heilongjiang and Anhui is U 

type while that of Jilin, Shaanxi, Shanghai, Qinghai and Beijing is inverted-U type, which shows that the 

carbon emission intensity decreases with the economic growth in recent years. The EKC of Hainan, Yunnan, 

Gansu, Shanxi, Fujian and Jiangxi is inverted-N type, which indicates that during the study period, the carbon 

emission intensity decreases first, then increases and decreases with the economic growth. Although it shows 

a downward trend recently, the carbon emission intensity fluctuates greatly. The situation of energy 

conservation and emission reduction in transportation industry is not optimistic. 

3.2 Decomposition model of influence factors of carbon emission in transportation industry 

Based on the energy consumption of transportation industry in 30 provinces of China from 2009 to 2016, the 

dynamic change of the energy consumption of 9 energy sources of transportation industry, including diesel, 

gasoline, kerosene, natural gas, heating power and electric power are calculated, as shown in Figure 2. From 

2009 to 2016, the total energy consumption of the national transportation industry increases from 176 × 106 t 

in 2009 to 333 × 106 t in 2016 with an average annual growth rate of 12.7%. It can be seen from the dynamic 

change of various energy consumption that the dynamic change of diesel consumption is the same as that of 

total energy consumption. This indicates that the transportation energy consumption is excessively dependent 

on diesel, and the proportion of alternative energy and renewable energy needs to be increased. The 

consumption of clean energy such as natural gas, electric power and thermal power is slow because of the 

promotion of the application of new clean energy in passenger service vehicles in recent years. However, the 

proportion of natural gas, thermal power and electric power in the energy structure is still low, which indicates 

that the energy conservation and emission reduction of transportation industry is still urgent and has a long 

way to go. 

 

Figure 2: Dynamic change of energy consumption in China from 2009 to 2016 

640



According to the calculation result of carbon emission in transportation industry in China, the annual added 

value of carbon emission in transportation industry in China from 2009 to 2016 is calculated, and the 

contribution and contribution rate of energy structure effect, energy intensity effect and output scale effect to 

carbon emission in transportation industry in China are analyzed respectively. The calculation results are 

shown in Figure 3. 

 

Figure 3: Contribution rates of factors of carbon emission in China’s transportation industry 

The results shown that, the carbon emission of China’s transportation industry shows a fluctuating growth 

trend from 2009 to 2016, and the growth is relatively stable from 2015 to 2016. From 2012 to 2013, 

transportation industry develops slowly for economic restructuring and carbon emission declines year after 

year. Subsequently, Chinese government introduces a series of supply-side structural reform measures so 

that the transportation industry shows a steady growth momentum from 2015 to 2016.  

As can be seen from Figure 3, the contribution rate of industry scale effect to the annual carbon emission in 

the national transportation industry is over 100%, which is far higher than other factors, indicating that scale 

effect is the main factor of the growth of carbon emission in transportation industry in China during the 

analysis period. The energy structure effect and energy intensity effect have relatively weak negative influence 

on the carbon emission in the national transportation industry year by year, which indicates that the energy 

consumption structure of China’s transportation industry is not adjusted and optimized enough and the energy 

consumption efficiency is not high. The results show that it is urgent for China’s transportation industry to 

speed up the transformation of development mode so as to realize the remarkable improvement of energy and 

resource use efficiency, as well as the sustainable improvement of ecological environment. 

4. Conclusions 

In this analysis, the carbon emission data of transportation industry in 30 provinces of China from 2009 to 

2016 are calculated according to the IPCC carbon emission calculating method. The decoupling relationship 

between carbon emission and economic growth in transportation industry is analyzed by using Tapio 

decoupling model. Besides, the carbon emission intensity effect, energy structure effect, energy intensity 

effect and industry scale effect on carbon emission in transportation industry are analyzed by using extended 

Kaya equation and LMDI model respectively. This plays an important role in introducing differentiated 

development policies for transportation industry. 

It can be seen from the analysis result of the decoupling model that the carbon emission decoupling value of 

China’s transportation industry has obvious spatial disequilibrium characteristics, and the decoupling state of 

“weak decoupling-expansion negative decoupling-growth linkage” appears from coastal areas to inland areas 

as a whole. 40% of provinces are in weak decoupling state, which shows that the energy conservation and 

emission reduction of transportation industry in these provinces are relatively good, and it is expected that the 

construction of low-carbon transportation system will achieve full decoupling between carbon emission and 

economic growth in the future. 26.7% and 33.3% of provinces are in the state of growth linkage and expansion 

negative decoupling respectively. There hasn’t been a province with strong decoupling yet. It shows that these 

provinces will be the focus of building a low-carbon transportation system. Relevant departments can 

formulate targeted low-carbon objectives for development of transportation industry and introduce 

differentiated development policies for transportation industry. 

The analysis from two level shows that the energy structure effect and the energy intensity effect have a 

relatively weak negative impact on the carbon emission in the national transportation industry and shows that 

641



the energy consumption structure of the transportation industry in China is not sufficiently adjusted and 

optimized, and the energy consumption efficiency is not high. The industry scale effect has a significant 

positive impact and is the main factor to promote the growth of carbon emission. Therefore, properly solving 

the contradiction between economic growth and carbon emission is the key for China’s transportation industry 

to realize low-carbon development. We should accelerate the research and development and promotion of 

energy conservation and emission reduction technologies of transportation infrastructure, improve energy 

structure, increase the proportion of new energy and clean energy to total energy, as well as increase the 

inhibiting effect of energy structure effect and energy intensity effect on carbon emission in transportation 

industry. What’s more, we shall further improve the comprehensive transportation network and build an 

efficient transportation organization system so as to reduce promoting function of industry scale effect on 

carbon emissions in transportation industry. 

References 

Andreoni V., Galmarini S., 2012, Decoupling economic growth from carbon dioxide emissions: A 

decomposition analysis of Italian energy consumption, Energy, 44(1), 682-691. 

Hu Z.Y., Tang L.W., Su J., 2015, Convergence of provincial carbon emission intensity and dynamic 

processes, Resources Science, 37(1), 142-151. 

Lin B.Q., Huang G.X., 2011, Evolution trend of regional carbon emissions in China under the gradient 

development model -- from the perspective of spatial analysis, Financial research, (12), 35-46. 

Liu D.H., Yang Y.C., 2012, Factor Decomposition and Demonstration Analysis of Carbon Emission Variation in 

Gansu Province, Arid zone research, 29(3), 510-516. 

Liu H.J., Bao Z., Yang, Q., Distributional Dynamic and Evolution of China’s CO2 Emissions, Resource science, 

35(10), 1925-1932. 

Lu J.Y. Huang X.J., Dai L., Chen Z.G., Li Y., 2017, Spatio-temporal Scale Analysis on the Equality of Energy 

Consumption Carbon Emission Distribution in China, Journal of natural resources, 27(12), 2006-2017. 

Ruefing K., 2002, Indicators to measure decoupling of environmental pressure from economic growth, [R/OL]. 

http: //www. olis. oecd. org/olis/2002doc. nsf/LinkTo/ sg-sd (2002) 1-final. 2008-09-26. 

Tapio P., 2005, Towards a theory of decoupling: degrees of decoupling in the EU and the case of road traffic 

in Finland between 1970 and 2001, Transport Policy, 12(2), 137-151, DOI: 10.1016/j.tranpol.2005.01.001  

642

http://dx.doi.org/10.1016/j.tranpol.2005.01.001