Examining the Factors Influencing Transport Sector CO₂ Emissions and Their Efficiency in Central China

Abstract

The fast development of the transport sector has resulted in high energy consumption and carbon dioxide (CO₂) emissions in China. Though existing studies are concerned with the factors influencing transport sector CO₂ emissions at the national level (or in megacities), little attention has been paid to the comprehensive impact of socio-economic, urban form, and transportation development on transport sector carbon emissions and emissions efficiency in central China. This paper examines the comprehensive impact of the transport sector’s carbon emissions from six provinces in central China, during the period from 2005 to 2016, based on the panel data model. The dynamic change of CO₂ emissions efficiency is then analyzed using the Global Malmquist Luenberger Index. The results indicate that, firstly, economic growth, road density, the number of private vehicles, and the number of public vehicles have caused greater CO₂ emissions during the study period, while the freight turnover, urbanization level, and urban population density had repressing effects on CO₂ emissions. Secondly, an uneven distribution of CO₂ emissions and CO₂ emissions efficiency was found among different provinces in central China. Thirdly, changes in CO₂ emissions efficiency were mainly due to technical changes. Finally, we present some policy suggestions to mitigate transport sector CO₂ emissions in central China.

1. Introduction

The main culprit in global warming is carbon dioxide (CO₂), much of which is produced by the combustion of fuel [1]. On a global scale, the transport sector emitted around 8000 million tons of CO₂, which is about one-quarter of the grand total in 2016. More and more countries and regions developing their transport sectors are trying to cut down on energy consumption and CO₂ emissions. America has historically had the highest transport sector CO₂ emissions levels of all regions, and this value has persisted in recent years. However, China is quickly closing the gap, with annual growth rates five times larger than America since 2000. China is also the country with the largest increase in transport sector CO₂ emissions. Thus, exploring the influencing factors and efficiency of CO₂ emissions in the transport sector is the basis of reducing transportation CO₂ emissions in China.

Extensive analysis of the influencing factors of Chinese transport sector CO₂ emissions has been carried out [2]. The earliest literature studied the influence of socio-economic factors on transport sector carbon emissions such as per capita GDP and GDP growth [3,4]. Later, transportation development factors, such as passenger turnover and freight turnover, were determined to affect the change of CO₂ emissions in the transport sector [5,6]. With the development of urbanization, some scholars began to explore the impact of urban form and urban land on traffic carbon emissions [7,8]. Most existing studies concentrate on the transport sector’s CO₂ emissions at the national level [9,10], while others focus on megacities or east and south developed regions in China [8,11,12]. These studies ignore transport sector CO₂ emissions and the mitigation of such emissions in central China, despite central China being a transportation hub connecting the east and west. The present study investigated the effect of socio-economic urban form and transportation development on transport sector carbon emissions in central China, which can play a pivotal role in effective emissions reduction.

Improving the efficiency of CO₂ emissions has been recognized as the most effective way to reduce the greenhouse effect and achieve sustainable development, especially in manufacturing industries with high energy consumption [13,14]. Nevertheless, little literature has focused on the transport sector, and the performance of transport sector CO₂ emissions has mainly been measured via data envelopment analysis (DEA) [15,16,17,18]. Nevertheless, these studies used a relatively static carbon performance measure within a cross-sectional framework without considering dynamic performance changes. The Global Malmquist Luenberger (GML) index integrates the cross-sectional and time-series performances and has some advantages in calculating dynamic changes in efficiency. Some literature discusses panel data using the GML index in many other sectors, including examinations of the industrial sector [19,20], the light industry [21], the water industry [22,23], and the iron and steel industry [24]. Zhang et al. [25] measured the dynamics of the transport sector’s total CO₂ emissions over time via a non-radial Malmquist CO₂ emissions performance index. However, there are few studies that use GML to measure CO₂ emissions efficiency in the Chinese transport sector.

The objective of this study is to comprehensively explore the impacts of socio-economic factors, urban forms, and transportation developments on the transport sector’s carbon emissions in central China using panel data from six provinces from 2005 to 2016. In addition, to improve CO₂ emissions efficiency, this paper measures the dynamics of CO₂ emissions efficiency in the transport sector using panel data based on the Global Malmquist Luenberger index and comprehensively analyzes the possible reasons for the fluctuation of transport sector CO₂ emissions efficiency in each province. The remainder of this paper is organized as follows: Section 2 briefly reviews the related literature; section 3 describes the impact of urban form and transportation development on transportation CO₂ emissions using the panel data model; section 4 evaluates dynamic CO₂ emissions efficiency changes using the Global Malmquist Luenberger index; lastly, conclusions and policy suggestions to mitigate transportation CO₂ emissions are provided.

2. Literature Review

Many existing studies in various countries have been concerned with CO₂ emissions in the transport sector. For the most part, these studies separately focus on the impacts of socio-economic, transportation development, and urban form factors on CO₂ emissions. Most studies explore the influence of CO₂ emissions and socio-economic factors such as GDP, per capita GDP, energy intensity, and population size [26,27,28,29,30,31]. With the increase of urban populations in New Zealand, CO₂ emissions from the transport sector have increased [32]. Andreoni and Galmarini [33] found that economic growth was the main factor behind CO₂ emissions based on the water and aviation transport sectors in Europe. Saboori et al. [34] explored the bi-directional long-run relationship between CO₂ emissions from the road transport sector and economic growth in all the countries belonging to the Organization for Economic Co-operation and Development over the period from 1960 to 2008. Fan and Lei [35] found that economic growth is the dominant factor behind CO₂ emissions in Beijing, but influence from population size was limited. In addition to the various socio-economic factors considered by scholars, an increasing number of studies suggest that transportation development exerts an extensive and lasting influence on the level of CO₂ emissions. Taking Tunisia for example, road freight transport intensity is second only to economic growth in terms of CO₂ emissions [36]. A similar study was also undertaken in European countries [37]. For China, passenger turnover, freight turnover, and private vehicle inventories are the three most frequently used transportation development factors impacting CO₂ emissions [2,5]. Some scholars have concluded that passenger transport plays a more critical role than freight transport in mitigating CO₂ emissions [5]. Others have argued that the effect caused by passenger transport is as little as one-eighth that of freight transport [6]. In the wake of rapid economic and technological developments from 1995 to 2016, the number of private cars in China has climbed from 2.49 million to 160.30 million, an increase of 64 times. The rapid development of public transportation has also played an important role in the overall development of transportation during the same period. However, the quantity of public transportation is neglected as an impacting factor for CO₂ emissions in existing research.

Existing studies considered socio-economic factors and transportation development factors but ignored the impact of urban form. Urban cities are not only the center of human production and activity but also gather traffic elements and represent the pivot point of a transportation network [38,39]. Urban areas generally have a more intensive transport infrastructure, also highlighting the regional imbalance between the supply and demand of traffic. Reckien et al. [40] argued that the total built area and the total traffic area are positively related to road CO₂ emissions in Berlin’s urban area. The impacts of urban form on CO₂ emissions in Chinese megacities were also explored by Ou et al. [41]. The number of patches and edge density of urban areas are factors that help quantify the urban form. Wang et al. [8] found that the compact size of urban land helps decrease CO₂ emissions. However, the factors involved did not consider urbanization, urban road density, and urban population level. Urban planning has an important effect on the process of building a low-carbon transport system. Further understanding of the relationship between urban forms (like urban road density, urbanization, as well as urban population level) and CO₂ emissions may facilitate further research. On the other hand, due to China's vast territory, significant regional differences, economic classifications, and population distribution, other studies have explored the mitigation of carbon emissions in east and south coastal China, which are areas with developed economies and dense populations [12,42]. Moreover, much scholarly attention has been drawn towards the mitigation of CO₂ emissions in China’s megacities. Taking Beijing as an example, Wang et al. [7] indicated that urban form is a major factor for transport sector CO₂ emissions. The study’s results on China's four megacities (Beijing, Shanghai, Guangzhou, and Tianjin) also showed that urban road density had significant negative effects on the level of CO₂ emissions [8].

Although the influential factors behind carbon emissions in the transport sector have been widely discussed in previous studies, few studies have evaluated the efficiency of the transport sector’s CO₂ emissions. Cui and Li [43] employed a virtual frontier Data Envelopment Analysis (DEA model to estimate transportation’s carbon efficiency using cases from 15 countries. Zhou et al. [44] analyzed the CO₂ performance of China’s transport sector using undesirable DEA models, which only adopt energy and labor as the inputs. Zhang et al. [25] first proposed a non-radial Malmquist index to conduct a dynamic CO₂ emissions performance change analysis for the Chinese transport industry. Total fixed assets, employees in the transport sector, and energy consumption were used as inputs in their study. Generally, CO₂ emissions are an undesirable output of the production process for marketable or desirable outputs.

As mentioned above, there remain some research gaps that merit closer study. Firstly, previous studies focused on the national or megacity level, where economic growth has promoted global economic development. CO₂ emissions have significantly affected global warming in the Organization for Economic Co-operation and Development (OECD) countries, New Zealand, coastal regions of China, and Chinese megacities. Central China is an ignored study area, where economic growth and transportation have been developing rapidly in recent years. Secondly, it is clear that the impact of socio-economic, urban form, or transportation development on CO₂ emissions is not enough to illustrate the whole picture in the transport sector. Comprehensive systematic studies of the transport sector’s CO₂ emissions and their efficiency in central China, incorporating socio-economic factors, urban forms, and transportation developments, are relatively less common. Finally, investigating CO₂ emissions efficiency plays an important role in developing reduction policies for CO₂ emissions. In addition, the DEA method has gained popularity in the field evaluation of energy and CO₂ emissions efficiencies, such as in the industrial, iron, and steel sectors. There are few studies about transport sector CO₂ emissions efficiency, and even fewer studies employ Global Malmquist Luenberger to estimate CO₂ emissions efficiency in the transport sector dynamically.

As the geographical heart of China, central China is an important raw-material base with abundant coal and non-ferrous metals. Central China is, therefore, the economic development and transportation hub connecting east and west China. China has a vast territory, and because of its differences in geographical locations, economic foundations, regional policies, and transportation developments, the country’s ability to mitigate regional emissions is not balanced. With the implementation of the strategy called "the rise of central China", the development of transportation infrastructure has been accelerated, effectively driving the development of transportation in the central region. For this reason, six provinces (Anhui, Shanxi, Jiangxi, Hubei, Hunan, and Henan) in central China were selected as the related areas in this study. The aim of this study is to explore and improve the transport impact on CO₂ emissions efficiency. The present study first examines the impacts of socio-economic factors, urban forms, and transportation developments on CO₂ emissions in central China using panel data for six provinces from the National Bureau of Statistics of China (NBSC). The differences in CO₂ emissions efficiency for the transport sector were then dynamically analyzed using the Global Malmquist Luenberger Index. Finally, some suggestions for improving CO₂ emissions efficiency and reducing CO₂ emissions from transportation in central China are proposed.

3. Influencing Factors on Transport Sector CO₂ Emissions

3.1. Transportation Carbon-emissions Estimation

Inspired by Xu et al. [2], calculation of transport sector CO₂ emissions for the six provinces in central China from 2005 to 2016 was based on the quantity of the various types of fossil fuels consumed, as well as their CO₂ emissions factors, which were taken from the 2006 Intergovernmental Panel on Climate Change (IPCC) reports and China’s National Development and Reform Commission [45]. The model is described by the following equation:

$$C{O}_2={\displaystyle \sum _{i=1}^{5}C{O}_{2i}}={\displaystyle \sum _{i=1}^{5}Ene{r}_i\times Con{f}_i}$$

(1)

where CO₂ means the amount of CO₂ emissions in the transport sector, i represents the variety of fossil fuel (gasoline, kerosene, diesel, fuel oil, and natural gas); Ener_i is the total consumption of fossil fuel i in the whole transport sector; and Conf_i means the CO₂ emissions coefficient for i type of fossil fuel. The carbon emissions coefficients for fossil fuels are shown in

Table 1. Different Fossil Fuels’ Carbon Emissions Coefficients.

Fuel	Gasoline	Kerosene	Diesel	Fuel Oil	Natural Gas
Emissions coefficient	0.5538	0.5714	0.5921	0.6185	0.4483

. All data are collected from China Statistical Yearbook (2006–2017) and the provincial statistical yearbooks (2006–2017).

As a result, Figure 1 presents the dynamic changes in the transport sector’s CO₂ emissions for six provinces in central China. It was found that the CO₂ emissions of these provinces maintained an increase between 2005 and 2016. Both Henan and Jiangxi province had a sharp increase in 2011. Hubei province was exposed to be the largest emitter. Between 2005 and 2016, the emissions of Hubei province increased from 2442.18 million tons to 5323.20 million tons. Before 2006, Jiangxi Province had lower CO₂ emissions than other provinces (Hubei, Henan, Hunan, Shanxi, and Anhui), but close to those of Shanxi province since 2014. In addition, the minimum emission level (Anhui, at 2173.00 million tons) is two-fifths that of the maximum (Hubei, at 5323.20 million tons) in 2016. This result implies that provincial differences exist for the CO₂ emissions in the transport sector in central China.

3.2. Influencing Factors

3.2.1. Socio-economic Factors

The economy in central China has grown rapidly since the policies of “the Rise of Central China” were issued. The income of the region’s residents has gradually increased, which was followed by private car ownership, which caused an increase in the transport sector’s CO₂ emissions. In this study, per capita GDP (pGDP) was selected as the variable for the socio-economic development level. Figure 2 describes the trend of per capita GDP for provinces in central China. This trend shows steady growth, except in Shanxi province. In particular, Hubei province has the highest per capita GDP among the six provinces in central China. Hubei is also the largest emitter of CO₂ emissions from the transport sector in central China. The per capita GDP growth rates of the other provinces (Hunan, Henan, Jiangxi, and Anhui) are similar to each other. This similarity means that the overall economic growth in central China is balanced.

3.2.2. Transportation Development Factors

In order to better understand the impacts of transportation development on CO₂ emissions, we selected three variables according to existing researches, comprising the number of private vehicles per 10,000 people (PRV), the number of public vehicles per 10,000 people (PUV), and freight turnover (FT) [9]. As residents' living standards have improved, and the number of private vehicles per 10,000 people in the central region has grown from 515 in 2005 to 5582 in 2016. These results are shown in Figure 3a. An increasing number of both energy consumption and CO₂ emissions occurred because, before 2016, private cars could not function without consuming gasoline and diesel. At the same time, the structure of mobile vehicles in the central region is unbalanced, and the proportion of private cars is increasing by the year, but the proportion using public transport seldom fluctuates (Figure 3b). In China, emissions from moving freight (tkm) is growing faster than that of moving passengers (person·km) [6]. In this way, the trend in central China is the same as the trend in the entire country. By the end of 2016, the freight transportation service turnover consisted of 3.57 trillion tkm in the central region. Since 2007, the freight turnover in these provinces has been growing rapidly (Figure 4). This growth unavoidably results in high growth in energy consumption and CO₂ emissions.

3.2.3. Urban Form Factors

Between 2000 and 2015, the proportion of people living in urban areas in China increased rapidly from 35.87% to 55.61% and has exceeded the world average since 2013 [46]. With this rapid urban expansion, many urban dwellers have begun to drive cars that consume biofuels, which has precipitated a climbing increase in CO₂ emissions generated by cities. We chose three indicators to quantify the urban form: road density per 100 square meters (RD), urban population density (UPD), and urbanization level (UL). As shown in Figure 5, the proportion of the urban population showed a steady increase.

Table 2. Variable description.

	Variable	Definition	Mean	SD	Min	Max	Observation
Dependent variable	C	CO2 emissions in transport sector (104 ton)	2603.02	1141.28	824.87	5323.20	72
Socio-economic factor	pGDP	Per capita GDP (CNY/person)	26,574.22	11477.23	8670	55665	72
Urban form factors	RD	The average road length per 100 km2 (km)	88.14	36.62	31.30	160.12	72
	UL	Population living in urban areas divided by total population	0.42	0.09	0.21	0.58	72
	UPD	Urban population divided by total urban size	2896.69	1465.92	466.00	5967.00	72
Transportation development factors	PRV	Vehicles number of private cars per 10,000 population	313.41	302.02	19.16	1284.47	72
	PUV	Number of public vehicles per 10,000 population	8.82	2.17	3.80	15.13	72
	FT	Traffic volume multiplied by the transport distance	3520.34	2767.70	653.60	13,500.60	72

shows a statistical description of all the variables in this study.

3.3. Panel Data Models and Results

The Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model was proposed by Dietz and Rosa [47] to analyze the influences of impacting factors on the environment, as follows:

$$I_i=a{P}_i^b{A}_i^c{T}_i^d{e}_i$$

(2)

where P is the population size, A means the average affluence, and T denotes the technology index; a represents the constant term, b, c, and d are the parameters for the environmental impacts as they relate to P, A, and T, respectively, and e is a random error. In empirical research, this model is often used in its logarithmic form. Based on the above analysis, the established model is as follows:

$$\ln C_{it}=c_{\mathrm{i}}+{\beta }_1\ln U{L}_{it}+{\beta }_2\ln UP{D}_{it}+{\beta }_3\ln R{D}_{it}+{\beta }_4\ln PU{V}_{it}+{\beta }_5\ln PR{V}_{it}+{\beta }_6\ln F{T}_{it}+{\beta }_7\ln pGD{P}_{it}+{\epsilon }_{it}$$

(3)

where C is the amount of CO₂ emissions in the transport sector, UL is the urbanization level, UPD means urban population density, RD represents the urban road density, PUV denotes the number of public vehicles per 10,000 people, PRV represents the number of private vehicles per 10,000 people, FT describes turnover of freight traffic, pGDP is per capita GDP, ε is random error, and i and t represent province and year, respectively. All variables are expressed in their logarithmic forms to facilitate the estimation.

Before estimating the regression models for the panel data, it is necessary to ensure that the variables are stationary. The results could show spurious relationships if they do not meet this condition. The most common stationary test is the unit root test. We employed the widely used Levin-Lin-Chu (LLC) and Phillips-Perron (PP-Fisher) unit root tests. In the unit root test, the optimal lag order was determined according to the Akaike Information Criterion (AIC). The unit root test results are shown in

Table 3. Results of the unit root test.

Variable		Unit Root Test
		LLC	PP-Fisher
		p-Value	p-Value
Ln C	level	0.0656	0.9487
	(D)	0.0002 ***	0.0003 ***
Ln UL	level	0.0004 ***	0.0049 **
	(D)	0.0000 ***	0.0001 ***
Ln UPD	level	0.9958	0.0000 ***
	(D)	0.0031 **	0.0000 ***
Ln RD	level	1.0000	0.0000 ***
	(D)	0.0000 ***	0.0000 ***
Ln PUV	level	0.0015 **	0.2788
	(D)	0.0000 ***	0.0000 ***
Ln PRV	level	0.7693	0.2311
	(D)	0.0023 **	0.0207 **
Ln FT	level	0.9423	0.8942
	(D)	0.0000 ***	0.0000 ***
Ln pGDP	level	0.1835	0.0458 **
	(D)	0.0006 ***	0.0015 **

** for p < 0.05, *** for p < 0.01.

, which means that all these variables except UL are not stationary at the level and contain a panel unit root at the 5% significance level. When assessing the first-order differences, all the variables reject the null hypothesis of being non-stationary. This result indicates that all the variables are stationary after the first-order difference.

Models for panel data often allow for autocorrelation and heteroskedasticity (as well as being cross-sectional), which result in an estimated parameters bias. In this paper, a modified Wald test for groupwise heteroskedasticity, a Breusch–Pagan test for cross-sectional independence, and a Wooldridge test for serial correlation for the residuals of a fixed effect regression model are employed. The results show that there are autocorrelation (F value = 48.05, p-value = 0.0010) and heteroscedasticity (R-square value = 0.8106, p-value = 0.0000) problems without cross-sectional dependency, as shown in

Table 4. Correlation matrix of residuals.

	Shanxi	Henan	Hubei	Hunan	Jiangxi	Anhui
Shanxi	1.0000
Henan	−0.2078	1.0000
Hubei	−0.5433	0.2209	1.0000
Hunan	0.1243	0.1980	0.1085	1.0000
Jiangxi	−0.5187	−0.2069	0.4124	−0.1185	1.0000
Anhui	−0.2718	0.3634	0.3716	−0.5988	−0.0022	1.0000
Chi2 (15) = 19.826, Pr = 0.1787

. The panel corrected standard error (PCSE) estimation method introduced by Beck and Katz [48] is an innovation of the panel data model estimation method. This method can effectively deal with complex panel error structures, such as autocorrelation, heteroscedasticity, sequence correlation, etc. It is especially useful when the sample size is not large enough for other methods. In existing empirical applications, especially when estimating the panel data of national and provincial types, the PCSE method is widely used to deal with complex panel error structures [5,49,50].

The estimation results for the PCSE model are shown in

Table 5. Results of the variable intercept model of panel corrected standard error (PCSE).

	Coef.	Std. err.	t	p
pGDP	1.044 ***	0.262	(3.82)	0.000
RD	0.470 ***	0.134	(4.09)	0.000
UL	−3.454 ***	0.558	(−6.37)	0.000
UPD	−0.620 ***	0.087	(−7.58)	0.000
PRV	0.445 ***	0.111	(7.26)	0.000
PUV	0.717 ***	0.205	(4.58)	0.000
FT	−0.444 **	0.080	(−4.06)	0.000
_cons	−3.195	1.793	(−1.80)	0.072
R-squared		0.8106

** for p < 0.01, and *** p < 0.001.

. The significance test for the regression equation (Chi-square value = 308.09, p-value = 0.0000) indicates that the comprehensive influence of the independent variables on the dependent variable has statistical significance. All independent variables are significant at the 1% significance level. Based the empirical results, the per capita GDP had the most positive effects on the dependent variable, which shows that a 1% GDP increase would cause a 1.04% increase of CO₂ emissions in the transport sector. Among transportation development factors, the number of private vehicles (0.445) and public vehicles (0.717) had positive effects on transportation CO₂ emissions, while the quantitative coefficient of the freight turnover is −0.444. The number of private vehicles and public vehicles is the main contributor to CO₂ emissions, while freight turnover is negatively related to CO₂ emissions in the transport sector. Road density (0.470) also had positive effects on transportation CO₂ emissions. The elasticity of the urbanization level and urban population density are −3.454 and −0.620, respectively. To a certain extent, urban development and the improvement of road capacity promote CO₂ emissions from transportation. The increase in urbanization level leads to an increase in built-up urban areas and promotes the convenience of urban transportation, which could curb CO₂ emissions from the transport sector. Though public transportation development is low-carbon and environmentally friendly to a certain extent, excessive allocation of public transportation will also lead to a rise in carbon emissions. Growing vehicle ownership, accompanied by rapid economic development, has enhanced CO₂ emissions. Freight turnover is a comprehensive reflection of the need for freight transport and the total amount of freight transport work provided and has a negative effect on CO₂ emissions in the transport sector.

4. CO₂ Emissions Efficiency of the Transport Sector

To measure the efficiency of CO₂ emissions with the development of the transportation and develop detailed CO₂ emissions reduction policies, a Global Malmquist Luenberger (GML) index, based on DEA, is employed to estimate the CO₂ emissions efficiency in central China’s transport sector as an undesirable factor and explores the key factors contributing to efficiency (from the standpoints of technological progress and scale efficiency).

We chose five inputs, three desirable outputs, and CO₂ emissions as the undesirable output. Labor input (L) is represented by employees in the transport sector; this information is collected directly from the China Statistical Yearbook. Here, the amount of capital input (𝐾) is represented by the number of private vehicles per 10,000 people, the number of public vehicles per 10,000 people, and the road density. The rest input is represented by energy consumption (E). Three desirable outputs are passenger turnover (P), freight turnover (F), and value-added from the transport sector (V).

4.1. Global Malmquist Luenberger Model

Regarding each province as a decision-making unit (DMU), there are six provinces in the Central region: $i=1,\cdots ,K(K=6)$. Each province uses N (N = 5) inputs to produce M (M = 3) desirable outputs and L (L = 1) undesirable outputs in T time periods ($t=1,\cdots ,T$) defined, respectively, as: $X=(x_1,\cdots ,x_N)\in R_{+}^N$,$Y=(y_1,\cdots ,y_M)\in R_{+}^M$, and $Yu=(u_1,\cdots u_L)\in R_{+}^L$. Hence, the environmental production technology set can be expressed as: $P(X)=\left\{(x,y,u)|x\mathrm{can}\mathrm{produce}(y,u)\right\}$. A global benchmark technology is defined as $P^G=P^1\cup P^2\cup \cdots \cup P^T$. The GML index, proposed in this paper, is defined as follows:

$$GM{L}^{t,t+1}(x^t,y^t,u^t,x^{t+1},y^{t+1},u^{t+1})=\frac{1+D^G(x^t,y^t,u^t)}{1+D^G(x^{t+1},y^{t+1},u^{t+1})}$$

(4)

where the directional function, $D^G(x,y,u)=\max \{\beta |(y+\beta y,b-\beta b)\in P^G(x)\}$, is defined based on the global technology set $P^G$. If the $GM{L}^{t,t+1}$>1, CO₂ emissions efficiency increases, and the evaluated unit is capable of producing more of the desired output with less of the undesired output. However, if $GM{L}^{t,t+1}$ = 1, then performance remains unchanged, and $GM{L}^{t,t+1}$ < 1 signals a performance decline.

The GML index can also be decomposed into efficiency change (EC) and best practice gap change (BPC), as follows:

$$\begin{array}{l}GM{L}^{t,t+1}(x^t,y^t,u^t,x^{t+1},y^{t+1},u^{t+1})=E{C}^{t,t+1}\times BP{C}^{t,t+1}\\ =\frac{1+D^t(x^t,y^t,u^t)}{1+D^{t+1}(x^{t+1},y^{t+1},u^{t+1})}\times \left[\frac{(1+D^G(x^t,y^t,u^t))/(1+D^t(x^t,y^t,u^t)}{(1+D^G(x^{t+1},y^{t+1},u^{t+1}))/(1+D^{t+1}(x^{t+1},y^{t+1},u^{t+1}))}\right]\end{array}$$

(5)

where $E{C}^{t,t+1}$ means a change in the efficiency between the time period t and t + 1.$BP{C}^{t,t+1}$ denotes the best practice gap change and measures technical change during the two time periods. The improvement in EC suggests progress in management skills. Unlike the change in efficiency, technological change can be achieved by adopting new technologies to reduce the amount of bad output under the premise of a quantitative input.

4.2. The Results of GML and Discussion

Based on the GML model, the results of energy and CO₂ emissions efficiency in the transport sector of central China are shown in

Table 6. CO₂ emissions efficiency in the provincial transport sector, 2006–2016.

Global Malmquist Luenberger Index
	Henan	Shanxi	Hubei	Hunan	Jiangxi	Anhui	Central
2005–2006	1.000	0.998	1.000	0.940	0.980	0.981	0.983
2006–2007	1.000	1.080	1.000	0.990	0.972	0.982	1.004
2007–2008	1.000	1.016	1.000	1.074	1.021	1.038	1.025
2008–2009	1.000	0.838	0.941	0.995	0.977	1.000	0.959
2009–2010	1.000	1.208	1.063	1.005	0.967	1.000	1.040
2010–2011	1.000	1.000	1.000	1.000	1.022	1.000	1.004
2011–2012	1.000	0.967	0.908	1.000	1.065	1.000	0.990
2012–2013	1.010	0.919	1.101	1.000	0.985	1.000	1.002
2013–2014	0.990	1.016	0.882	1.000	0.969	1.000	0.976
2014–2015	1.000	1.063	0.986	1.000	0.966	0.917	0.989
2015–2016	1.000	1.016	0.978	1.000	1.018	1.005	1.003
Mean	1.000	1.011	0.987	1.000	0.995	0.993	0.998

. Only Shanxi province was observed to experience a positive efficiency growth (1.1%), while half of the provinces (Hubei = −1.3%, Jiangxi = −0.5%, and Anhui = −0.7%) showed negative growth. This result shows that Shanxi province has actively responded to the low-carbon development policies for the transport sector. Other provinces in central China have made remarkable progress in the transport sector, but have ignored the importance of low-carbon transportation.

Under the inclination for green transportation outputs in this study, when the number of expected outputs (i.e.; passenger volume, freight volume, and value-added from the transport sector) increases based on a given set of inputs, efficiency will increase. The trends of the GML index and its decomposition in the transport sector are shown in Figure 6. As indicated by GML, the average CO₂ emissions efficiency shows a decline of –0.2% during the study period. It was found that the fluctuation of the BPC index is similar to that of the GML index, while the EC index seldom fluctuated, indicating that a change in CO₂ emissions efficiency is primarily caused by technological change. It is recommended that the government invest in green technologies for the transport sector, such as buses and taxis with renewable fuels in Shanxi province, road construction with renewable material in Henan province, and the installation of an Intelligent Transportation System (IST) in Hunan province.

The EC and BPC indexes of energy and CO₂ emissions efficiency among the six provinces are shown in

Table 7. The EC and BPC of the provincial transport sector, 2005–2016.

DMUs	Henan		Shanxi		Hubei		Hunan		Jiangxi		Anhui
	EC	BPC	EC	BPC	EC	BPC	EC	BPC	EC	BPC	EC	BPC
2005–2006	1.000	1.000	1.000	0.998	1.000	1.000	1.000	0.940	1.000	0.980	1.000	0.981
2006–2007	1.000	1.000	1.000	1.080	1.000	1.000	1.000	0.990	0.960	1.013	1.000	0.982
2007–2008	1.000	1.000	1.000	1.016	1.000	1.000	1.000	1.074	1.042	0.980	1.000	1.038
2008–2009	1.000	1.000	1.000	0.838	1.000	0.941	1.000	0.995	1.000	0.977	1.000	1.000
2009–2010	1.000	1.000	1.000	1.208	1.000	1.063	1.000	1.005	0.991	0.975	1.000	1.000
2010–2011	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.009	1.013	1.000	1.000
2011–2012	1.000	1.000	1.000	0.967	1.000	0.908	1.000	1.000	1.000	1.065	1.000	1.000
2012–2013	1.000	1.010	0.914	1.004	1.000	1.101	1.000	1.000	1.000	0.985	1.000	1.000
2013–2014	1.000	0.990	1.029	0.987	1.000	0.882	1.000	1.000	0.984	0.985	1.000	1.000
2014–2015	1.000	1.000	1.062	1.000	1.000	0.986	1.000	1.000	0.985	0.981	1.000	0.917
2015–2016	1.000	1.000	1.000	1.016	1.000	0.978	1.000	1.000	1.019	1.000	1.000	1.005
Mean	1.000	1.000	1.001	1.010	1.000	0.987	1.000	1.000	0.999	0.996	1.000	0.993

. Shanxi province is rich in coal resources, so its freight transport demand is particularly large. However, the transportation CO₂ emissions of Shanxi province have barely increased since 2009. According to the GML index, only Shanxi had an average increase in CO₂ emissions efficiency (of 1.1%). In other words, Shanxi performed well in reducing its transportation CO₂ during the study period. As seen in Table 6, both the EC and BPC indexes are greater than 1, which indicates that Shanxi has adopted new technology and management skills to achieve their CO₂ emissions mitigation goals. Over the last decade, the capacity for scientific and technological innovation in the transport sector has been enhanced. Traditional buses have been gradually replaced by hybrid or pure electric buses. There are many projects that demonstrate CO₂ reduction goals, including key transport process monitoring and management services in 2013 and the application of renewable energy in the construction and operation of the “Gaoqin expressway” in 2014.

Among the six provinces in central China, Hubei province produced the highest CO₂ emissions in the transport sector during the study period. The average GML index is measured as −1.3%, which indicates a declining trend of CO₂ emissions efficiency. The main reason for this result is that the BPC index decreased, especially after 2013, while Hubei was deteriorating from an efficient province to an inefficient one. From 2013 to 2016, the BPC index experienced a yearly decline of 11.8%, 1.4%, and 2.2%, respectively. During the research period, massive investment and fast construction allowed Hubei to form a comprehensive transportation hub, which provided a skeleton network of “four vertical, four horizontal, and one ring” highways. These results indicate that low-carbon technological innovation for the transport sector in Hubei has been neglected during the process of transportation development.

For Henan and Hunan province, GML = 1—indicating no improvement in CO₂ emissions efficiency. A possible cause for this might be the stabilization of management style and technological innovation. The remaining provinces (Jiangxi and Anhui) had a CO₂ emissions efficiency index less than 1 in most of the time periods, and both improvements and declines occurred during these 12 years. However, during 2015–2016, the GML index was 1.018 in Jiangxi and 1.005 in Anhui, indicating that these provinces were increasing their efforts to improve their efficiency. For example, by the end of 2016, public transport in Anhui province accounted for 40.66% of motor vehicle trips, gradually realizing full coverage of public transport star services. The “Changzhang expressway reconstruction and expansion project” in Jiangxi province actively applied new technology for green recycling, which reduced transport sector CO₂ emissions by more than 30,000 tons in 2016.

5. Conclusions

China is currently facing environmental pressures, which are the result of the rapidly increasing pace of energy consumption and CO₂ emissions in the transport sector. Issues of CO₂ emissions and mitigation in the transport sector have attracted intense attention from both governments and academics. This paper explores the factors driving transport CO₂ emission and the differences in CO₂ efficiency in the central region of China and provides some policy suggestions for the Chinese government.

On the base of the provincial panel data of six provinces in central China, this paper constructed an FGLS model that was used to investigate the impact of urban form and transportation development on the CO₂ emissions of the transport sector. Furthermore, the Global Malmquist Luenberger index was used to quantify CO₂ emissions efficiency in the transport sector, and possible reasons for the fluctuation of transportation carbon emissions efficiency in each province were comprehensively analyzed.

Transportation CO₂ emissions in central China increased continuously from 2005 to 2016. The overall efficiency of CO₂ emissions in the central region of China fluctuated during this period. BPC was the main driver of GML growth, which indicates that the technical efficiency needed to accelerate transport development must be further improved.

Some policy suggestions have been generated based on the above explorations. Firstly, there are provincial differences in the CO₂ emissions efficiency in the transport sector of central China. Hubei should strengthen the construction of its talented team in the transport sector and support the research and development of key technologies and core equipment for transportation to improve CO₂ emissions efficiency. Hunan and Henan should optimize their transportation systems to improve their CO₂ emissions efficiency. Jiangxi and Anhui could learn advanced management skills and introduce advanced technologies from other provinces with higher CO₂ emissions efficiency such as Shanxi. Secondly, there is a positive correlativity between the number of public vehicles and CO₂ emissions during the study period. The government should improve public transport organization and reduce the energy consumption of public transport. On the other hand, developing urban light rail transit with the potential to mitigate CO₂ and expanding the utilization of fuel-cell-driven and power-driven vehicles are critical to controlling emissions in urban public transport. Thirdly, policies aimed at the ownership of private vehicles should be strengthened. Due to rapid economic growth and low energy efficiency, private vehicles have become the main contributors to CO₂ emissions. Moreover, hybrid and battery electric vehicles with renewable electricity can significantly contribute to CO₂ mitigation in car transport [51]. Accordingly, the government ought to tighten traditional energy-intensive vehicle purchase standards and advocate and subsidize the purchase and utilization of hybrid and electric-powered vehicles. The government must also improve the R&D of green vehicles and renewable electricity technology using fiscal instruments. Fourthly, road transport is still an important part of freight transport but relies on an unreasonable freight structure. Pollution-free road transport and low-energy rail transport should be further developed for freight transport. In addition, improving intelligent traffic systems may also help reduce freights’ empty-load rates, which may also help mitigate CO₂. Finally, urban planning and transportation organization play an increasingly important role in the mitigation of CO₂ emissions in central China. This suggests that urban planners should work to improve the connection between the pace of urbanization and road programs to reduce CO₂ emissions. Furthermore, technical methods could be used to strengthen the recycling of renewable materials to improve CO₂ emissions efficiency.