Relationship between the development and CO2 emissions of transport sector in China

Transportation Research Part D 74 (2019) 1–14

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Relationship between the development and CO2 emissions of transport sector in China

T

Yi Li, Qiang Du , Xinran Lu, Jiao Wu, Xiao Han ⁎

School of Economics and Management, Chang’an University, Middle Section of South Second Ring Road, Xi’an 710064, Shaanxi, China

ARTICLE INFO

ABSTRACT

Keywords: CO2 emissions Transport sector Decoupling state LMDI Influencing factor

The transport sector imposes enormous challenges for energy consumption and CO2 emission reduction. Using data from 30 provinces in China, this paper adopted the Tapio decoupling index to examine the relationship between the development of the transport sector and its CO2 emissions from provincial perspective. Additionally, we employed the logarithmic mean divisia index method to explore the effect of several factors on the state of decoupling. The results showed that the under-developed provinces were more likely to present a weak decoupling state than the developed and coastal provinces. Income level was the major influential factor limiting the development of decoupling in the transport sector. The population scale had a very small negative role in the development of decoupling. Moreover, the effects of CO2 emissions efficiency, transport intensity and industry structure varied across provinces. By offering a provincial perspective on decoupling states and its driving factors, this study can provide a reference for governments in proposing carbon-reduction policies and promoting low carbon development of the transport sector.

1. Introduction Global climate change has become a serious challenge to the survival and advancement of human beings. In December 2015, the majority of countries adopted the Paris Agreement within the United Nations Framework Convention on Climate Change. As the largest developing and carbon-emitting country, China has taken responsibility for CO2 emissions reduction, committing to reducing its CO2 emissions intensity by 60–65% by 2030 compared with the level in 2005 (Mi et al., 2017; Zhao et al., 2017). The transport sector is a critical economic sector and an important source of CO2 emissions (Fan et al., 2018). Along with the urbanization process, China’s transport sector has recently experienced rapid development. Its development not only reduces the costs and time of transport, contributing to the development of the regional economy, but also imposes enormous challenges for the reduction of energy consumption and CO2 emissions (Jiang et al., 2017; Xie et al., 2017). Therefore, many empirical studies have examined the relationship between the development of the transport sector and its CO2 emissions (Guo et al., 2014; Li and Tang, 2017; Wang et al., 2017). Most studies approached the question from the perspective of the entire country or a single province (Dennehy and Gallachóir, 2018; Guo et al., 2014; Wang et al., 2011a, 2011b; Wang et al., 2017; Xu and Lin, 2015). However, these studies are unable to illustrate the variation within a country. China is a large-scale developing country, and the development of the transport sector is unbalanced across provinces in terms of factors such as infrastructure, natural resources and geographical factors. Almost all transport systems in China’s eastern and central provinces are reaching a mature state, while those in some western provinces are still ⁎

Corresponding author. E-mail address: [email protected] (Q. Du).

https://doi.org/10.1016/j.trd.2019.07.011

Available online 24 July 2019 1361-9209/ © 2019 Elsevier Ltd. All rights reserved.

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in a period of accelerating development. According to the National Bureau of Statistics of China (NBSC), the output of the transport sector in Guangdong Province is 4.11 times as many as that in Shaanxi Province, and its energy consumption is two times more than that in Shaanxi Province. Therefore, there are significant differences in the development and energy consumption of the transport sector in different provinces. Research from a national or a single provincial perspective cannot fully reveal the differing relationships between the development of the transport sector and CO2 emissions in China. To fill the gap, this paper explores the relationship using the data of 30 provinces in China. First, we present the current state of transport sector CO2 emissions in 30 provinces over the period 2000–2015. Then, we use the Tapio decoupling index to illustrate the relationship between the development of the transport sector and its CO2 emissions for each province. Finally, to explore what factors primarily affect the decoupling relationship between the development of the transport sector and its CO2 emissions, we adopt the logarithmic mean Divisia index (LMDI) method to decompose the decoupling index into five sub-indices and measure the contribution of each factor. Thus, this study can provide reference for government departments in formulating related policies. The rest of this paper is organized as follows. The next section presents the literature review. Section 3 describes the data sources and research methodology. In Section 4, we present the empirical results and provide a detailed discussion. Finally, we address the main conclusion and propose some policy suggestions. 2. Literature review The relationship between industrial development and environmental pollution has been a hot topic in environment economics and has been studied by many scholars using many methods. Some researchers have used the environmental Kuznets curve theory to explore the effects of economic development on the environment (Balado-Naves et al., 2018; Ertugrul et al., 2016), and a few have examined the causal relationship between economic growth and environmental pollution by panel vector autoregression (Acheampong, 2018; Shahbaz et al., 2014). The theory of decoupling is the method that is currently most widely used to research the relationship between industrial development and CO2 emissions (Dennehy and Gallachóir, 2018). The concept of decoupling was first proposed by the OECD in research concerning energy policy issues. The decoupling index is the ratio of the economic growth rate to the growth rate of energy consumption or CO2 emissions. Since 2005, when Tapio presented a theoretical framework defining eight states of decoupling in the European transport sector, the Tapio decoupling index has been used to research the relationship between industrial development and environmental pollution (Tapio, 2005). Ren et al. used the index to explore the trend of decoupling effects in China’s nonferrous metals industry (Ren et al., 2014). With regard to transport sector, Wang et al. researched the decoupling relationship between economic increase and CO2 emissions in Jiangsu province (Wang et al., 2017). Wang et al. used the Tapio decoupling index to calculate the state of decoupling of CO2 emissions in China and the U.S. and explored the major influencing factors (Wang et al., 2018). The existing research on decoupling state has mainly focused on the national or single provincial level, and few studies have researched the factors influencing the decoupling in the transport sector (Dennehy and Gallachóir, 2018). Compared to the method of index decomposition analysis, the LMDI, proposed by Wang (Sun, 1998) and Ang (2004), Ang and Liu (2001), has the advantages of not producing a residual and allowing the data to take zero and negative values (Lin and Long, 2016). It has been widely used to investigate the effect of factors influencing energy consumption issues. Wang et al. investigated the different factors how to effect the changes of CO2 emissions in transport sector (Wang et al., 2011a, 2011b). The researchers explored technoeconomic drivers of energy-related industrial CO2 emissions changes based on an extended LMDI model (Shao et al., 2016). The model was also used to investigate the relationship between natural gas consumption and its influencing factors (Chai et al., 2018). The existing studies concerning the transport sector have mainly explored the effect of various factors on CO2 emissions (Chai et al., 2020; Guo et al., 2014; Helgesen et al., 2018; Liu and Lin, 2018; Wang et al., 2011a, 2011b), and few have explored the relationship between transport sector development and CO2 emissions. Therefore, this study uses the Tapio decoupling index and the LMDI method to measure the effect of influencing factors on the state of decoupling in the transport sector. 3. Data and methods 3.1. Data We collected the available data from 30 provinces for the period 2000–2015 (Macao, Hong Kong, Taiwan and Tibet are excluded from the analysis due to lack of data). The 2000–2015 period witnessed the rapid development of the economy and the transport sector in China, the empirical results will be good references for China’s government to formulate scientific and realistic carbon reduction policies to transport sector. Simultaneously, during this period, the population owning vehicles increased rapidly in China, which has a meaningful impact on CO2 emissions of transport sector. In addition, statistics on the energy before 2000 is very limited in the China Statistical Yearbook. Therefore, we selected the 2000–2015 as the study period. The data used for this research are primarily from the China Statistical Yearbook, China Energy Statistical Yearbook and China Transport Statistical Yearbook. Output (GDP , TGDP ): GDP and TGDP denote the development of the province (in terms of gross domestic product, GDP) and the transport sector (in terms of added value), respectively. To avoid the effect of inflation, the provincial GDP and the transport sector added value are deflated to constant 2000 prices. Energy (E ): Within Energy Balance Table, the energy consumption in transport sector is divided into many categories. This study considers 11 types of energy consumption during the calculation of China’s transportation province-level CO2 emissions, including 2

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Table 1 Conversion coefficients from passenger to freight volume. Transport mode

Railway

Road

Waterway

Aviation

Conversion coefficient

1

0.1

0.33

0.072

coke, raw coal, crude oil, diesel oil, gasoline, fuel oil, LPG (liquefied petroleum gas), natural gas, electricity and heat. Transport service (TS ): The transport service can be measured by freight and passenger turnover volume. The calculation method in this study is consisted with the studies by Wang (2011) and Lin (2018a). As passenger and freight turnover volumes are not comparable across transport modes, they are converted to comparable values as follows:

Converted turnover volume (ton

km) = freight turnover volume + passenger turnover volume × conversion coefficient

The conversion coefficient is determined through comparing revenue and cost per person-km (moving one person one km) with revenue and cost of moving one ton of goods one km. The conversion coefficients for each transport mode are available from related studies (Liu and Lin, 2018; Wang et al., 2011a, 2011b) and are presented in Table 1. 3.2. Calculation of China’s transportation provincial-level CO2 emissions The calculation of the CO2 emissions of the transport sector involves more uncertainty than the calculations for other sectors because of the mobility of the CO2 emitters (Zhang et al., 2019). There are two main methods, top-down and bottom-up (Shan et al., 2016; Shao et al., 2018), are widely used to calculate CO2 emissions in different sectors. The bottom-up method requires more realtime data of transport systems, which may lead to errors in the results (Han et al., 2017). Calculation according to the top-down method is more accurate and convenient than that according to the bottom-down method. Therefore, we adopted the top-down method of the Intergovernmental Panel on Climate Change (IPCC) to calculate the CO2 emissions of the transport sector. The calculation of the CO2 emissions from the transport sector in year t can be expressed as follows:

Cit =

Ct = i

E tj × NCVj × CCj × Oj × j=1

44 12

(1)

and are the CO2 emissions from transport mode i and the consumption of fuel type j, respectively; NCVj denotes the net where calorific value of the jth fuel; CCj represents the carbon content of the jth fuel; Oj is the carbon oxidation rate of the jth fuel; and the conversion coefficient from carbon to CO2 is 44/12. In this paper, we utilized data specific to China instead of IPCC data to calculate the CO2 emissions of the transport sector. The specific variables are shown in Table 2.

E tj

Cit

3.3. Tapio decoupling model Based on the literature review, this paper constructs a model to explore the decoupling relationship between the development of the transport sector and its CO2 emissions from a provincial perspective. Eq. (2) shows the calculation of the decoupling index Dt :

C /C 0 C t C 0 TGDP t TGDP 0 = / TGDP /TGDP 0 C0 TGDP 0

Dt =

(2)

where C and TGDP are the change in the CO2 emissions and the output of the transport sector from the baseline year to the target year t. The states of decoupling can be classified into three categories and eight subcategories, which are presented in Tables 3. 3.4. Logarithmic mean Divisia index decomposition method The LMDI decomposition method has been widely used to analyze the effect of factors influencing CO2 emissions because of its theoretical foundation, adaptability, ease of use and not containing any unexplained residual terms in the decomposition result (Ang and Liu, 2007; Dai and Gao, 2016). Many studies, summarized in Table 4, have adopted the LMDI method to decompose the CO2 emissions of the transport sector. In view of the above studies, the current study decomposed the influencing factors into CO2 emissions efficiency, energy intensity, GDP, population, transport intensity, emission factor and transport structure. As a service sector, the development of the transport Table 2 The values of major fossil fuel variables. Source: The Guidelines to Provincial Lists of Greenhouse Gas Inventory. Variables 4

NCV (TJ/10 ) CC (t/TJ) O

Coal

Coke

Crude oil

Gasoline

kerosene

Diesel fuel

Fuel oil

liquefied petroleum gas

Natural gas

209.08 26.37 0.94

284.35 29.50 0.93

418.16 20.10 0.98

430.70 18.90 0.98

430.7 19.5 0.98

426.52 20.20 0.98

418.16 21.10 0.98

501.79 17.2 0.98

3893.10 15.30 0.99

3

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Table 3 Detailed explanations of the state of decoupling. Decoupling state

C

TGDP

Dt

Detail explanations

Negative decoupling

Expansive negative decoupling Strong negative decoupling Weak negative decoupling

>0 >0 <0

>0 <0 <0

> 1.2 <0 0 < Dt < 0.8

CO2 grows while output of transport sector increase CO2 grows while output of transport sector decrease The increase pace of CO2 is obviously smaller than output

Decoupling

Weak decoupling

>0

Coupling

Strong decoupling Recessive decoupling

<0 <0

>0

>0 <0

0 < Dt < 0.8 <0 > 1.2

The decrease pace of CO2 is obviously smaller than output

Expansive coupling Recessive coupling

>0 <0

>0 <0

0.8 < Dt < 1.2 0.8 < Dt < 1.2

The increase pace of CO2 is approximately equal to output The decrease pace of CO2 is approximately equal to output

CO2 decline while output of transport sector increase The decrease pace of CO2 is obviously bigger than output

Table 4 Summary of the current studies using the LMDI to decompose transport-related emissions. References

Sectors

Influencing factors

Wu and Xu (2014)

Cargo transport

Dai and Gao (2016) Wang et al. (2011a, 2011b)

Logistics industry Transport sector

Luo et al. (2016) Guo et al. (2014) Kharbach and Chfadi (2017) Huang et al. (2019)

Freighttransportation sector Transport sector Road transport sector

CO2 emissions efficiency, transport structure, gross domestic product, average cargo shipping distance energy intensity, transport intensity, added value, transport modes emission coefficient effect, transportation services share effect, transportation modal shifting effect, transportation intensity effect, per capita economic activity effect, population effect CO2 emission factor, fuel mix, modal split, freight intensity,GDP, population population effect, activity effect, intensity effect, structure effect, emission factor effect CO2 emission intensity, energy utilization, population

Chung et al. (2013)

Transport sector

Wu et al. (2019)

Aviation transport sector

Transport sector

carbon emission coefficient, energy structure, energy intensity, transport intensity, industrial structure activity effect, regional structural effect, regional energy intensity effect, regional energy mix effect number of flight frequency, number of flight routes, modal split, average travel distance, average seat capacity, comprehensive emission factor

sector depends on regional development. The population scale, income level and industry structure can better represent regional development than can the single factor of GDP, and in the absence of rich data including the energy consumption of different transport modes, CO2 emissions from the transport sector can be decomposed into the following five factors (See Eq. (3)):

Ct =

i, j

Cijt =

Cijt i, j TSj

×

TSj TGDP t

×

TGDP t GDP t

×

GDP t Pt

× Pt

= CEijt × TI tj × IS t × ILt × P t

(3)

where

Cijt CO2 emissions by mode j using energy type i in year t TSj transport service (measured by converted turnover volume) provided by mode j TGDP t economic output value of the transport sector in year t GDP t economic output value of the region in year t P t population in year t CEijt CO2 emissions efficiency: CO2 emissions per unit of transportation service TI tj Transport intensity: ton-km of freight movement per unit of economic output IS t Industry structure: output share of the transport sector in gross industrial output ILt Income level: per capita income of the province P t Population scale: permanent population of the province Then, based on the LMDI method proposed by Ang (2015), Ang and Liu (2001), the change in the CO2 emissions of the transport sector from the baseline year to the target year can be decomposed into five factors, as shown in Eq. (4).

Ctot = Ct

(4)

C0 = Cce + Cti + Cis + Cil + Cp

The respective effects are written as:

Cce = L (Ct , C0)ln(CEt / CE0)

(5)

Cti = L (Ct , C0)ln(TIt /TI0)

(6) 4

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Cis = L (Ct , C0)ln(ISt / IS0)

(7)

Cil = L (Ct , C0)ln(ILt / IL 0 )

(8)

Cp = L (Ct , C0)ln(Pt /P0)

(9)

L (Ct , C0) = (Ct

C0 )/(lnCt

(10)

lnC0)

This paper aims to explore the impact of the influencing factors on the state of decoupling; thus, Eq. (4) is applied to the Tapio decoupling model Eq. (2):

Dt =

Cce / C 0 TGDP /TGDP 0

= + =

C /C 0 TGDP /TGDP 0

=

+

Cil / C 0 + TGDP /TGDP 0 Dcet + Dtit + Dist

( Cce + Cti + Cis + Cil + Cp) / C 0 TGDP /TGDP 0 Cti / C 0 TGDP /TGDP 0

+

Cis / C 0 TGDP /TGDP 0

Cp / C 0 TGDP /TGDP 0 + Dilt + Dpt

(11)

The decoupling index (Dt) can be further decomposed into five sub-indices to explore what factors affect the relationship between the development of the transport sector and its CO2 emissions. Dcet represents the CO2 emissions efficiency decoupling index. Dtit represents transportation intensity decoupling index. Dist indicates the industry structure decoupling index. Dilt and Dpt indicate the income level decoupling index and the population scale decoupling index, respectively. 4. Empirical results and discussion Based on collected data and Eq. (1), transport sector’s CO2 emissions in each province is obtained. The trend of the transport sector CO2 emissions is consistent with the results of Guo et al. (2014) and Han et al. (2017). In addition, to conveniently compare the results, the 30 studied provinces can be divided into four regions by economic development level and geographic location. The detailed categorization is presented in Table 5. 4.1. Analysis of transport sector CO2 emissions from the provincial perspective Fig. 1 presents the changes in the CO2 emissions from the transport sector in different regions over the period 2000–2015. The CO2 emissions from the transport sector in all four regions exhibit an increasing trend. Especially from 2004 to 2012, CO2 emissions from the transport sector enter a period of rapid growth that is mainly due to China’s increased investment in building transport networks. Although CO2 emissions from the transport sector appear to decline in 2012–2013, they rebound in the following years. One of the main reasons for this phenomenon is the significant reduction in the transport infrastructure investment in 2012. In addition, it should be noted that the CO2 emissions of the transport sectors in the four regions are very similar in 2000. As the eastern region develops rapidly, its transport sector becomes the major source of CO2 emissions. The gap between the transport sector CO2 emissions of the eastern and other regions grows over the period. The CO2 emissions from the transport sector in the six provinces in the central region are similar to those from the eleven provinces in the western region from 2000 to 2015, but the average CO2 emissions of the transport sector in the western region are less than those in the central region. Moreover, the transport sector CO2 emissions in the northeast region continue to grow slowly but steadily. As shown in Fig. 2, there has been a large increase in the CO2 emissions from the transport sector in all studied provinces from 2000 to 2015. Most of the eastern provinces emit higher CO2 emissions than the provinces in other regions. In particular, the four provinces with the highest CO2 emissions in 2015 were Guangdong, Shandong, Shanghai and Jiangsu, all of which are in the eastern region. In Shandong Province, the transport sector CO2 emissions increased from 5.33 million tons (Mt) in 2000 to 47.47 Mt in 2015, a level approximately 12 times that of Qinghai, the province with the lowest CO2 emissions. In addition, the coastal provinces with waterway transport produced more CO2 emissions than the western and central inland provinces. This is mainly since waterway transport is a large subsystem that promotes the development of other transport subsystems. Regarding the average annual growth rate of emissions in each province, Shandong presented the fastest-growing trend (15.69%) during the research period, which made its transport sector the second-largest CO2 emitter among the surveyed provinces. The average annual growth rates in the northeast provinces are similar to each other and lower than the national average increase. In addition, some western provinces have high growth rates, and many provinces in the eastern region produce a great amount of CO2 Table 5 Categorization of researched regions (data from NBSC). Region

Province

West Centre East Northeast

Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Ningxia, Shaanxi, Gansu, Qinghai, Xinjiang Shanxi, Anhui, Jiangxi, Henan, Hubei, Hunan Beijing, Tianjin, Hebei, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Hainan Heilongjiang, Jilin, Liaoning

5

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Fig. 1. The trend of transport sector CO2 emissions in four regions during 2000–2015.

Fig. 2. Transport sector CO2 emissions in the studied provinces in 2000 and 2015.

emissions despite the low growth rate of their transport sectors. These results show that the CO2 emissions of the transport sector have increased in all provinces. This is not a good sign for the decoupling in the development of China’s transport sector. CO2 emissions are strongly linked to regional development. 4.2. Analysis of decoupling state in the transport sector According to the definition in Section 3.3, the state of decoupling between the development of the transport sector and its CO2 emissions in the studied provinces from 2000 to 2015 can be observed. To better understand the changes in each province, we estimated the state of decoupling in each year from the base period in 2000 to the year 2015. The results are is listed presented in Appendix Table A1. The statistical characteristics of the state of decoupling are as follows (see Table 6). Table 6 The statistical characteristics of the decoupling states in 30 provinces from 2001 to 2015 Decoupling state

Obs

Negative decoupling

Strong negative decoupling Weak negative decoupling Expansive negative decoupling

32 1 120

Coupling

Recessive coupling Expansive coupling

1 60

Decoupling

Recession decoupling Weak decoupling Strong decoupling

2 178 56

6

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Fig. 3. Trends of the state of decoupling in 30 provinces from 2001 to 2015.

According to Table 3 and Appendix Table A1, the three states of decoupling (weak negative decoupling, recessive decoupling, and recessive coupling) were seldom observed during the research period. The state of weak negative decoupling appeared in Fujian Province in 2005, and recessive coupling appeared in Shanxi Province in 2014. Gansu and Shaanxi Provinces presented a state of recessive decoupling in 2002 and 2015. These three states of decoupling suggest that both development of transport sector and its CO2 emissions appeared decline trend. This phenomenon indicates that the CO2 emissions are increasing as the transport sector develops. This could be attributed to the greater investment in infrastructure and energy consumption in each province during this period (Wang et al., 2015). Fig. 3 shows the decoupling trends in 30 provinces from 2001 to 2015. Most provinces were in the state of decoupling during the years studied, except in 2005. In addition, the state of weak decoupling was more common than strong decoupling before 2012. Many of the provinces exhibited the expansive negative state of decoupling. The least common state, coupling, also appeared in a few provinces, highlighting the differences among the 30 provinces. Due to the provinces’ different geographical locations and transport sector development trajectories, it is natural that the provinces would experience different states of decoupling. This paper presents the decoupling in China’s transport sector at the provincial level from the base period of 2000 to 2015 and the period of 2011–2015 to show the recent changes in the state of decoupling in Fig. 4. Fig. 4 shows that the changes in the state of decoupling differed considerably across provinces. During the period 2010–2013, almost all provinces presented the weak decoupling state, and some presented expansive negative decoupling and expansive coupling. A few provinces presented strong negative decoupling, recessive coupling (Shanxi in 2014) and recessive decoupling (Gansu in 2015). The studied provinces have presented various states of decoupling in recent years. In the western region, there were six provinces (Guangxi, Chongqing, Sichuan, Guizhou, Gansu and Ningxia) presented weak decoupling state, with CO2 increasing at a noticeably slower rate than output. Inner Mongolia, Shaanxi and Qinghai presented expansive coupling because of the remarkable achievements in the transport infrastructure development in these three provinces during the studied period. At the same time, Xinjiang and Yunnan experienced the expansive negative decoupling state, which may be due to their topography; in areas with complex terrain and landscape, transport modes inevitably consume more energy and cause more CO2 emissions. In the central region, only one province (Shanxi) presented weak decoupling state. Three provinces (Henan, Hubei and Hunan) presented an expansive coupling state, and two provinces (Anhui and Jiangxi) presented expansive negative decoupling. The provinces near the eastern region presented coupling and negative decoupling. The provinces along the eastern coast thus address the emissions associated with transport sector development more responsibly than do the provinces in the western region, which produce more CO2 emissions. Three states of decoupling were observed in the eastern provinces during the studied period. Tianjin, Hebei and Hainan presented weak decoupling state. In contrast, Beijing, Shanghai, Guangzhou and Zhejiang, the most developed provinces of China, presented the expansive coupling state from 2000 to 2015. This proves that the most developed areas are most likely to emit higher amounts of CO2 emissions. Meanwhile, Fujian, Shanghai and Guangdong, the eastern provinces with the most rapid development according to converted turnover volume, presented the state of weak decoupling in 2015. This may be because the waterway transport system provides more transport services in these regions. Among the three northeastern provinces, Liaoning and Jilin presented the expansive coupling state, and Heilongjiang presented expansive negative decoupling. However, Fig. 4 shows that Jilin presented expansive negative decoupling in the last two years of the period, while Liaoning and Heilongjiang experienced the superior state of weak decoupling. This might be because waterway transport is 7

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Fig. 4. Changes in the state of decoupling in 30 provinces from 2010 to 2015 (SND: Strong negative decoupling; END: Expansive negative decoupling; WND: Weak negative decoupling; WD: Weak decoupling; SD: Strong decoupling; RD: Recessive decoupling; EC: Expansive coupling; RC: Recessive coupling).

less significant in Jilin. Waterway transport has an obvious advantage over other forms of transport in terms of energy saving. According to the above analysis of the changes in the decoupling in the transport sector of each province, there are regional differences between the development of the transport sector and its CO2 emissions because of economic development, geographic distribution, population scale and other factors. In addition, the under-developed provinces were more likely to present weak decoupling than the developed and coastal provinces. This may be because the under-developed provinces have less transport activity than the developed provinces; therefore, the developed provinces could provide valuable experience for transport system construction in the under-developed provinces. 4.3. Decomposition analysis of the decoupling state The output and CO2 emissions of the transport sector increased from 2000 to 2015. Thus, there are only three states of decoupling: weak decoupling (0 < Dt < 0.8), expansive coupling (0.8 < Dt < 1.2) and expansive negative decoupling (Dt > 1.2) in this period. To explore the effects of the different influencing factors on the state of decoupling in China’s transport sector, this paper used Eqs. (3)–(11) to decompose the changes in the decoupling index Dt into five sub-indices: CO2 emissions efficiency (Dce ) , transport intensity (Dti ), industry structure (Dis ), income level (Dil ) and population scale (Dp ). The higher the value of the sub index is, the stronger the inhibiting effect on the state of decoupling. A negative value occurs where there is a promoting effect on the state of decoupling. The results of the decomposition analysis of the state of decoupling in the transport sector of China are presented in Appendix Table A2 and Fig. 5. Fig. 5 shows that the influencing factors in the different provinces vary significantly during the researched period. For almost all provinces, the dominant influencing factor is the income level, and the population scale has a minimal effect on the state of decoupling. The factors of CO2 emissions efficiency, transport intensity and industry structure vary widely across regions. In the western region, income level played a major negative role in the state of decoupling. Ningxia (66.8%), Sichuan (65.5%) and Gansu (64.9%) were the three provinces most affected by income level. The population scale also prohibited the development of decoupling in Qinghai, Ningxia and Xinjiang because the population increased significantly in these three provinces. In contrast, the factor of population scale had little impact on the state of decoupling in the rest of the western provinces. In Sichuan and Guizhou, the population scale played a slightly positive role in the state of decoupling because of population losses. Furthermore, the factor of industry structure had an inhibiting effect on the state of decoupling only in Guizhou and Ningxia because their output proportion of the transport sectors increased. The factor of CO2 emissions efficiency had an effect opposite that of the factor of transport intensity in almost all of the western 8

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100%

West

80%

Centre

East

Northeast

60% 40% 20% Jilin

Heilongjiang

Liaoning

Guangdong

Hainan

Shandong

Fujian

Jiangsu

Zhejiang

Shanghai

Heibei

Tianjin

Hunan

IL

Beijing

Henan

IS

Hubei

Jiangxi

TI

Anhui

Shanxi

Ningxia

CE

Xinjiang

Qinghai

Gansu

Shaanxi

Yunnan

Sichuan

Guizhou

-60%

Chongqing

-40%

Guangxi

-20%

Inner Mongolia

0%

P

Fig. 5. Decomposition results of decoupling state of transport sector in 30 provinces.

provinces, demonstrating that the development of the transport sector in the western region was accompanied by high CO2 emissions. In the central region, Hubei and Henan are the most developed provinces, and income level is the main influencing factor on the state of decoupling. In addition, Hubei and Hunan have the advantages of water transport, unlike other central provinces. Especially in Hubei, the freight turnover volume transported by waterway increased remarkably, from 26.4% in 2000 to 43.5% in 2015. The factors of CO2 emissions efficiency and transport intensity played a positive role in the development of the decoupling between transport and CO2 emissions in Hubei. Furthermore, the population scale factor played a slightly positive role in Henan, with some households leaving the province to seek jobs elsewhere. The proportion of transport sector output decreased in the central region; hence, industry structure promoted the development of decoupling development in this period. This indicates that the central region’s overall economy has grown faster than its transport sector. This is because the central provinces serve as a geographic link between the east and the west. In the eastern region, the changes in the decoupling index due to income level are not as obvious as they are in other regions. In Hebei, Shanghai, Jiangsu and Hainan, the factor of income level played the dominant negative role in the development of decoupling. Because of the provinces’ developed economy, many people immigrated, and the changes in the decoupling index due to population scale were the highest among the four regions. For example, because the population of Shanghai increased by 50.1% from 2000 to 2015, the effect of population scale on the state of decoupling was more significant than it was in the rest of the provinces. The influencing factors considered had similar impacts in Zhejiang and Fujian because these two provinces have highly similar transport systems structure, mainly depending on waterways (almost 80%) and roads (almost 15%). The factor of CO2 emissions efficiency promoted the development of decoupling in these two provinces. In addition, the factor of industry structure played a positive role in the decoupling in the eastern provinces, especially in Hebei. This means that the regional output has increased more than the output of the transport sector has. These changes may be largely related to the characteristics of local development. Hebei relied on the transport sector early in its development, while other industries have developed rapidly in recent years. The changes in the decoupling index are significantly affected by transport intensity in Beijing, Tianjin, Shandong, Zhejiang and Fujian. Notably, the factor of transport intensity played a positive role in the northern region and a negative role in the southern region. This indicates that the value of per unit transport service in the northern region increased, in contrast to the southern region. For the three northeastern provinces, the factor of population scale had almost no effect on the decoupling index compared to the other three regions. This may be because the northeastern provinces have grown very slowly for more than a decade and have a cold climate and therefore have attracted very few immigrants. The factor of income level had the most adverse effect on the decoupling index in Liaoning, which is the slowest-developing province in the northeastern region. As waterway transport increased from 31.6% in 2000 to 67.7% in 2015, the factor of CO2 emissions efficiency played a positive role in the development of decoupling in Liaoning. Moreover, the factor of transport intensity had a strong positive influence on the decoupling index in Jilin and Heilongjiang. This indicates that the transport service in these provinces developed faster than its output in this period. The effect of industry structure was positive but small in the three northeastern provinces compared to the provinces in the other regions, as the output at the provincial and transport sector levels did not increase significantly. Based on the above discussion, the factor of income level played the dominant negative role in the development of decoupling. The increase in income level leads the coordinated development of tourism, commerce, recreation and other sectors, which promotes transport activity and causes more CO2 emissions from the transport sector. With regard to the factor of population scale, the decoupling index was not appreciably affected, and only a few developed provinces were seriously influenced compared to the other provinces. The factor of industry structure significantly promoted the development of decoupling in almost all provinces. This may be because of the industrial characteristics of the transport sector, which provides services to other sectors and thereby speeds up local industrial and economic development. The transport sector is an energy-intensive industry and a principal source of CO2 emissions during this period (Liu et al., 2018). The decrease in the transport sector output as a proportion of the provincial output could mitigate the pressure for reduced CO2 emissions. 9

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The factor of CO2 emissions efficiency had an effect on the development of decoupling opposite that of the factor of transport intensity. There is a contradiction between the development of the transport sector and its CO2 emissions. The output of the transport sector relies heavily on the transport of a large amount of low-value shipments rather than on the improvement in transport efficiency. Transport efficiency must be developed to achieve the development of decoupling in the transport sector. 4.4. Limitations and future research directions This study adopted the top-down method provided by the IPCC to calculate transport sector CO2 emissions. The choice of emission factors is very important for estimating CO2 emissions (Mi et al., 2019, 2018). We utilized emission factors derived from the Guidelines to Provincial Lists of Greenhouse Gas Inventory that have characteristics particular to China instead of IPCC data to estimate the CO2 emissions in China. The calculation results in our paper are similar to those of previous studies. Because the aim of this paper was to explore the effect of different factors on the state of decoupling from a provincial perspective, the results are reliable and robust. This study did not consider the technological levels of energy utilization among the studied provinces, which may cause similar levels of energy consumption to lead to different levels of CO2 emissions. The biases due to different technological levels will be considered in future research. 5. Conclusions and policy proposals The transport sector is a pillar of China’s development and contributes a large amount of CO2 emissions. This study examined the relationship between the development of the transport sector and its CO2 emissions in China. We calculated the transport sector CO2 emissions and presented the state of decoupling according to the Tapio decoupling index in 30 provinces over the period 2000–2015. In addition, the LMDI method was used to measure the influence of the factors of CO2 emissions efficiency, transport intensity, income level, industry structure and population scale on the state of decoupling. The following conclusions can be drawn from the research results. First, the CO2 emissions from the transport sector appeared to continuously increase at an annual growth rate of 10.5% from 2000 to 2015. The transport sector of the eastern provinces accounted for a significant amount of CO2 emissions at the national level. The more provinces develop, the more CO2 emissions are produced by transport activity. Furthermore, there are five main states observed in the transport sector during 2000–2015: weak decoupling, expansive negative decoupling, expansive coupling, strong decoupling, and strong negative decoupling. This is because during the research period, the transport sector CO2 emissions increased annually along with the development of the transport sector in the studied provinces. The state of decoupling presented regional differences because of various factors including economic development, geographic distribution and population scale. Notably, the under-developed provinces were more likely to present weak decoupling than the developed and coastal provinces. Finally, although the extent to which the factor of income level influenced the state of decoupling varied across provinces, it played the dominant negative role in the development of decoupling, and its negative effect was more significant in western underdeveloped provinces than in eastern developed provinces. The factor of industry structure significantly promoted the development of decoupling in almost all provinces. In addition, the effect of CO2 emissions efficiency on the state of decoupling was partially offset by the changes in transport intensity in most provinces. The factor of population scale had the least influence on the state of decoupling. Based on the above conclusions, this paper offers some policy proposals and suggestions to promote the development of decoupling in the transport sector. The provinces in the eastern coastal regions produced significantly more CO2 emissions than the under-developed provinces in the western region. Therefore, the eastern and coastal provinces should take more responsibility to reduce the CO2 emissions from the transport sector. In the eastern developed provinces, local governments should provide more subsidies to the older enterprises to assist in the implementation of low-carbon technologies, such as cars with cleaner energy. In the central provinces, especially in Hunan and Hubei with their waterway transport, multimodal transport should be developed to improve transport capacity. In addition, the transport infrastructure system should be improved in the central and western provinces. Importantly, the low-carbon transport mode should be considered during the construction of the transport system. In addition, as the influence of various factors varied across provinces, local governments should consider the economic development level and unique provincial characteristics in formulating specific policies to control the CO2 emissions from the transport sector. The factor of income level played a major negative role in the development of decoupling, with higher income levels leading to more transport activity. Transport within and among urban areas is crucial and could provide new opportunities for policies that promote public transport and low-carbon development in the transport sector. Acknowledgements The authors gratefully acknowledge the financial support from the National Social Science Foundation of China (Grant No. 16CJY028) and The Fundamental Research Funds for the Central Universities (Grant No. 300102238303, 300102239617). Appendix A See Tables A1 and A2. 10

Expansive decoupling

Weak decoupling Weak decoupling

Weak decoupling

Weak decoupling Weak decoupling

Weak decoupling

Expansive decoupling

Inner Mongolia

Liaoning Jilin

Heilongjiang

Shanghai Jiangsu

Zhejiang

Anhui

11

Weak decoupling

Weak decoupling Weak decoupling

Weak decoupling

Expansive negative decoupling

Hunan

Guangdong Guangxi

Hainan

Chongqing

Hubei

Expansive negative decoupling Weak decoupling

Henan

Shandong

Expansive negative decoupling Weak decoupling

Weak decoupling

Shanxi

Jiangxi

Expansive decoupling

Hebei

Weak decoupling

Weak decoupling

Tianjin

Fujian

Weak decoupling

Beijing

2010

Weak decoupling

Weak decoupling

Weak decoupling Weak decoupling

Expansive negative decoupling Expansive negative decoupling Weak decoupling

Weak decoupling

Expansive decoupling

Expansive negative decoupling Expansive negative decoupling Expansive decoupling

Expansive negative decoupling Strong decoupling Weak decoupling

Weak decoupling Weak decoupling

Weak decoupling

Weak decoupling

Weak decoupling

Weak decoupling

Weak decoupling

2011

Table A1 Decoupling state of transport sector in 30 provinces.

Expansive negative decoupling

Weak decoupling Expansive negative decoupling Weak decoupling

Strong decoupling

Strong decoupling

Expansive negative decoupling Weak decoupling

Weak decoupling

Expansive negative decoupling Expansive negative decoupling Weak decoupling

Weak decoupling Expansive decoupling

Weak decoupling

Weak decoupling Weak decoupling

Expansive decoupling

Weak decoupling

Weak decoupling

Expansive negative decoupling Expansive decoupling

2012

Expansive decoupling

Strong decoupling

Expansive negative decoupling Strong decoupling Strong decoupling

Expansive negative decoupling Strong decoupling

Expansive negative decoupling Strong decoupling

Weak decoupling

Expansive decoupling

Strong decoupling

Expansive negative decoupling Weak decoupling Expansive decoupling

Strong decoupling Expansive decoupling

Expansive negative decoupling Strong decoupling

Weak decoupling

Strong decoupling

Strong decoupling

2013

Strong decoupling

Expansive negative decoupling Expansive decoupling Expansive negative decoupling Strong decoupling

Expansive decoupling

Strong negative decoupling Strong decoupling

Weak decoupling

Weak decoupling

Expansive decoupling

Strong decoupling Expansive negative decoupling Weak decoupling

Expansive negative decoupling Expansive decoupling Expansive negative decoupling Weak decoupling

Recessive decoupling

Strong negative decoupling Strong decoupling

Weak decoupling

2014

Expansive negative decoupling Expansive negative decoupling

Expansive negative decoupling Weak decoupling Weak decoupling

Expansive negative decoupling Weak decoupling

Expansive negative decoupling Weak decoupling

Expansive negative decoupling Weak decoupling

Expansive decoupling

Weak decoupling Expansive decoupling

Strong negative decoupling Weak decoupling Expansive negative decoupling Weak decoupling

Expansive negative decoupling Strong negative decoupling Weak decoupling

Weak decoupling

2015

(continued on next page)

Weak decoupling

Weak decoupling

Expansive coupling Weak decoupling

Expansive coupling

Expansive coupling

Expansive negative decoupling Expansive negative decoupling Expansive negative decoupling Expansive negative decoupling Expansive coupling

Expansive negative decoupling Expansive coupling Expansive negative decoupling Expansive coupling

Expansive coupling Expansive coupling

Expansive coupling

Weak decoupling

Weak decoupling

Weak decoupling

Expansive coupling

2000–2015

Y. Li, et al.

Transportation Research Part D 74 (2019) 1–14

Expansive negative decoupling Weak decoupling

Weak decoupling

Expansive decoupling

Gansu

Qinghai

Ningxia

Xinjiang

Shaanxi

Strong decoupling Weak decoupling Expansive negative decoupling Weak decoupling

Sichuan Guizhou Yunnan

2010

Table A1 (continued)

Weak decoupling

Expansive negative decoupling Weak decoupling

Weak decoupling

Weak decoupling

Strong decoupling Weak decoupling Weak decoupling

2011

Weak decoupling

Expansive negative decoupling Weak decoupling

Weak decoupling

Weak decoupling

Weak decoupling Expansive decoupling Weak decoupling

2012

Expansive negative decoupling

Expansive decoupling

Expansive negative decoupling Expansive decoupling

Strong decoupling

Strong decoupling Strong decoupling Strong decoupling

2013

Strong negative decoupling Weak decoupling

Expansive decoupling Expansive decoupling Expansive negative decoupling Expansive negative decoupling Strong negative decoupling Weak decoupling

2014

Expansive negative decoupling Expansive negative decoupling

Expansive decoupling

Recessive decoupling

Strong decoupling

Strong decoupling Weak decoupling Strong decoupling

2015

Expansive negative decoupling

Weak decoupling

Expansive coupling

Weak decoupling

Weak decoupling Weak decoupling Expansive negative decoupling Expansive coupling

2000–2015

Y. Li, et al.

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Table A2 Sub indices of the transport sector in 30 provinces. Region

Dt

DCE

DTI

DIS

DIL

DP

Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang

0.811619 0.43855 0.674927 0.770031 0.825543 0.817255 0.896085 1.272369 0.964766 1.260375 1.085372 1.422919 1.428017 1.263414 2.242305 1.17480 0.751826 0.99677 1.043885 0.760987 0.799976 0.786427 0.540951 0.25296 3.299729 1.197099 0.328993 1.161204 0.387237 1.277651

0.346309 0.763998 −0.06218 0.125876 0.450636 −0.19131 0.32021 0.750742 0.1289 0.162424 −0.47796 −0.29699 −0.64474 0.040484 1.478807 0.1666144 −0.11794 0.200507 −0.31277 −0.29252 −0.07693 0.22944 −0.25699 0.035108 −0.21106 −0.18759 −0.28405 0.055248 −0.11915 0.329607

−0.43677 −1.04699 −0.08553 −0.22888 −0.52332 0.101308 −0.37364 −0.5923 −0.14873 −0.02844 0.525344 0.525 0.895859 0.108059 −0.78469 −0.07568 −0.00353 −0.20216 0.338702 0.176867 −0.02934 −0.3173 0.040009 −0.23915 1.806471 0.290729 −0.00468 0.027855 −0.08456 −0.18145

−0.194203062 −0.455168132 0.616412352 −0.053621717 −0.184932025 −0.138996878 −0.306917619 −0.289855468 −0.30355087 −0.373614063 −0.470161226 −0.401453603 −0.644522557 −0.671530723 −0.525590186 −0.33203478 −0.301757208 −0.369793507 −0.646221915 −0.264575826 −0.368880791 −0.052867813 −0.213610624 0.056946261 −1.789476514 −0.564144876 −0.058755448 0.987501773 0.013004295 −0.338253358

0.841203 0.9533 0.155708 0.868507 1.058035 1.033301 1.248056 1.402606 0.975239 1.439488 1.406304 1.589695 1.709683 1.704201 1.981226 1.416501 1.155249 1.304152 1.467207 1.137681 1.180452 0.902736 0.973328 0.410746 3.289839 1.629255 0.664529 −0.2324 0.530898 1.282021

0.255083 0.223414 0.05051 0.058153 0.025119 0.012949 0.008376 0.001172 0.312907 0.060516 0.101846 0.006669 0.111735 0.082201 0.09255 −0.00059 0.019812 0.064061 0.196966 0.003536 0.094677 0.024421 −0.00179 −0.01069 0.203956 0.028846 0.011956 0.322996 0.047044 0.185725

Appendix B. Supplementary material Supplementary data to this article can be found online at https://doi.org/10.1016/j.trd.2019.07.011.

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