Study on the Factors Influencing on the Carbon Emissions of Shaanxi Province’s Transportation Industry in China

Abstract

Carbon peaking in the transportation industry is of great significance to achieving the goals of carbon peaks and carbon neutrality. By correcting the energy consumption data of Shaanxi province’s transportation industry in China, adding the energy consumption data of non-operating vehicles to the total energy consumptions and building a more accurate calculation model for the carbon emissions of provincial transportation industry, this paper studies the contribution of the population size, the economic growth, the energy intensity, the energy structure, and the industrial scale of transportation to the carbon emissions, respectively, through the construction of a Logarithmic Mean Divisia Index (LMDI) model. The result shows that the carbon emissions of Shaanxi province’s transportation industry in China increased from 15.730 million tons to 24.652 million tons from 2009 to 2019. Among them, the carbon emissions of the non-operating activities grew rapidly, increasing from 2.694 million tons to 10.334 million tons in the past 10 years. Within the research range, the economic growth effect is the most important factor driving the growth of the carbon emissions of Shaanxi province’s transportation industry in China, increasing total carbon emissions by 22.701 million tons. The energy intensity effect and the industrial scale effect of transportation play the main role in inhibiting the carbon emissions of Shaanxi province’s transportation industry in China, with a cumulative reduction in the total carbon emissions by 13.869 million tons.

1. Introduction

At the 75th Session of the United Nations General Assembly in September 2020, the government of China announced China’s aim to peak its carbon dioxide (CO₂) emissions by 2030 and achieve carbon neutrality by 2060. The implementation of their “double carbon” strategy is a prerequisite choice for China’s high-quality development. In China, the transportation industry ranks third in total CO₂ emissions and has a rapid growth rate. It is a key industry in need of controlling carbon dioxide emissions. Compared with developed countries and coastal provinces in China, Shaanxi province’s transportation industry has high energy consumptions per unit and high carbon emissions per unit. Therefore, reaching the carbon peak by 2030 for Shaanxi province’s transportation industry in China will be tough. In summary, by analyzing the influencing variables of carbon dioxide emissions in the transportation industry, it can provide some reference for relevant departments of the Shaanxi provincial government in formulating emission reduction policies and strategies.

At present, there have been some research findings on the transportation industry’s carbon dioxide emissions both domestically and internationally. At this stage, the transportation industry is an important industry that supports the development of the national economy and social development. It is also one of the industries with the most energy consumption and has the fastest growth rate of carbon emissions [1,2]. According to Li et al., there is still a significant difference in carbon dioxide emissions in the transportation industry between areas [3]. The total amount of carbon emissions in the Sanjiangyuan region was measured and calculated by Li Xiang et al., and the results showed that carbon dioxide emissions of transportation grew rapidly from 2008 to 2017 [4]. The carbon emissions of the passenger transport market were calculated and analyzed by Li Linna and Loo, who discovered that the highway is the mode of transportation with the largest carbon emissions [5]. The main methods used by scholars in empirical analysis are: IPAT model [6], Kaya identity [7,8,9], STRIPAT model [10,11,12], Logarithmic Mean Divisia Index (LMDI) decomposition method [13,14], Environmental Kuznets Curve [15,16], spatial measurement method [17,18], etc. In the research methods of carbon emissions factors, more scholars now tend to use the factor decomposition method. Among them, the LMDI factor decomposition method can accurately decompose the influencing factors. An LMDI model can not only solve the problem of residual error and zero value, but also meet other conditions of the “perfect decomposition method”, which has been widely recognized in the academic community [19]. Zeng Xiaoying et al. found that there is an obviously positive spatial correlation between the carbon dioxide emissions of transportation and the ownership of motor vehicles, the GDP, the freight turnover, and the passenger turnover [20]. According to Xu’s research, the main sources of increased carbon dioxide emissions from provincial transportation include the acceleration of urbanization, sustained economic growth, and the increase in private car ownership [21]. In the Belt and Road countries, per capita GDP, urbanization rate and the energy structure of transportation have a considerable promoting effect on the carbon dioxide emissions of transportation, according to Zhu’s research [22]. Kim believed that the addition of the carbon dioxide emissions in South Korea’s transportation industry was mainly affected by the economic growth effect during 1990–2013, while the suppression of the carbon emissions was mainly affected by the energy intensity effect of transportation [23]. Amin, Talbi, and Akram et al. [24,25,26], by using an environmental Kuznets curve (EKC) and vector autoregression (VAR) models, studied the factors influencing the transportation’s carbon dioxide emissions and discovered that increasing renewable energy consumption can effectively reduce carbon dioxide emissions. According to Yang et al., China’s economic development and improvement in residents’ income levels are the most important influencing factors for the sustained increase in transportation’s carbon dioxide emissions, while the development level of public transit has an obviously inhibitory effect [27]. Wang et al. found that urban traffic planning and transportation methods play significant roles in reducing carbon dioxide emissions [28].

To summarize, current research on the transportation industry’s carbon emissions has made progress to some extent; however, there are also the following two shortcomings: (1) The existing statistics of China’s current energy consumption is based on the management department, and the statistical data of the total energy consumption of transportation has not included that of the energy consumptions of social-private vehicles. Because of the wide range in statistics and large energy value, the data of the total energy consumptions in China’s transportation industry are much smaller than the total data under the international statistical caliber, which greatly influences the measurement result of carbon dioxide emissions in the transportation industry. (2) Most scholars analyze the influential factors of carbon dioxide emissions from a national or industrial perspective, and there are few studies focusing on the carbon dioxide emissions of the provincial transportation industry. This paper totals up the operating and non-operating vehicles’ energy consumptions by taking the social-private vehicles into account, building a more accurate calculation model for the provincial transportation industry’s carbon dioxide emissions. For the next step, this research uses the LMDI model to decompose and analyze the factors influencing carbon dioxide emissions in Shaanxi province’s transportation industry in China, then identifies the key influencing factors and provides a reference for the government departments to formulate low-carbon policies.

2. Materials and Methods

2.1. Calculation of Carbon Dioxide Emissions in Shaanxi Province’s Transportation Industry in China

In the international energy statistics, the energy consumption departments are generally divided into four categories: industry and transportation, civil/commercial/agricultural, and non-energy uses. The current statistics of China’s energy consumptions are somewhat different from the actual concepts of different sectors’ consumptions in terms of connotation. In which, China’s road transportation only calculates the oil used by the operating vehicles of transportation departments, not including those used by other departments, industries, and private vehicles, which has a great impact on the calculation of the transportation industry’s carbon dioxide emissions [29]. Therefore, in the process of calculating the transportation industry’s carbon dioxide emissions, this paper adds the data of the vehicles’ fuel consumptions of social-private sectors to the total energy consumption’s data of the transportation industry, so as to obtain a more accurate measurement result for the carbon emissions. At the same time, the proportion of carbon emissions generated by the transportation methods such as the subway and electric bicycles is extremely small, and it is difficult to calculated in relation to energy consumption. Therefore, this article ignores the data when calculating the total carbon emissions of the transportation industry.

The IPCC guideline provides two methods for calculating carbon dioxide emissions from the transportation industry, namely a “top-down” method and a “bottom-up” method. The “top-down” method, which is based on the volumes of energy consumption in the transportation industry and calculated according to the carbon emissions factor of each energy, can be employed to calculate the carbon dioxide emissions of transportation’s operating departments. The “bottom-up” method, which calculates carbon dioxide emissions based on vehicle mileage and can be used to calculate carbon emissions from non-operating transportation sectors while accounting for fuel combustion efficiency, details the data related to vehicle types, energy consumption per kilometer, and comprehensive mileage. In order to accurately estimate carbon dioxide emissions, this article uses a combination of the “top-down” and “bottom-up” methods to calculate the total carbon dioxide emissions of the Shaanxi province’s transportation industry in China.

2.1.1. Calculation Method of Carbon Dioxide Emissions in the Operating Departments of Transportation

When calculating carbon dioxide emissions in transportation operating departments, the consumption data of each energy is derived from the end energy consumption of transportation, storage, and postal services in the Shaanxi Statistical Yearbook 2010–2020, and the carbon emission coefficients of each energy are derived from the IPCC Guidelines for National Greenhouse Gas Inventory (

Table 1. Carbon emissions coefficient of energy in transportation industry.

Types of Energy	Average Low Calorific Value (KJ/(kg, m3))	Carbon Oxidation Rate	CO2 Emissions Coefficient (kg CO2/GJ)	Carbon Emissions Coefficient (kg C/GJ)
Raw coal	20,908	1	94.6	25.8
Gasoline	43,070	1	69.3	18.9
Kerosene	43,070	1	71.9	19.6
Diesel	42,652	1	74.1	20.2
Natural gas	38,931	1	56.1	15.3
Fuel oil	41,816	1	75.5	23.7

).

The specific calculation formula of carbon dioxide emissions of transportation operating departments in Shaanxi province of China is shown in Formula (1).

$$C_1={\displaystyle \sum _{\mathrm{i}}{C}_i}={\displaystyle \sum _i{E}_i}\times F_i\times V_i\times R_i\times \frac{44}{12},$$

(1)

In Formula (1), $C_1$ is the carbon dioxide emissions of the operating departments of Shaanxi province’s transportation industry in China, in the unit of ten thousand tons; $i$ represents the types of each energy; $E_i$ is the consumption of Energy $i$ in the transportation industry; $F_i$ is the average low calorific value of Energy $i$; $V_i$ is the carbon oxidation factor, namely, the carbon oxidation rate during energy combustion; $R_i$ is the carbon emission coefficient of Energy $i$; 44 and 12 are molecular weights of CO₂ and carbon, respectively.

2.1.2. Calculation Method of the Carbon Dioxide Emissions in the Non-Operating Departments of Transportation Industry

In terms of how to measure the CO₂ emissions of vehicles of the non-operating departments in the transportation industry, Wu Wenhua (2001), from the Comprehensive Transport Research Institute of the National Development and Reform Commission, proposed a method to calculate the total energy consumption based on the vehicles’ ownership [30]. When the basic data is relatively complete, the method can calculate the scale of motor vehicles’ energy consumptions. However, in the practical application, both the calculation accuracy and data availability should be considered. In most cases, it is difficult to obtain the vehicles’ different fuel types based on general public statistics [29]. In view of this, based on the principle of data availability, this paper finally adopts a method to measure the energy consumptions of social-private vehicles, which classifies vehicles by their types, rather than by gasoline and diesel [31], as shown in

Table 2. Vehicle classification.

Vehicle Types’ Classification	Instructions
Type 1 (Small and medium bus)	Passenger bus ≤ 19 seats
Type 2 (Large bus)	Passenger bus > 19 seats
Type 3 (Mini-truck)	Truck with load capacity ≤ 2t
Type 4 (Light truck)	Truck with 2t ≤ load capacity ≤ 7t
Type 5 (Medium truck)	Truck with 7t ≤ load capacity ≤ 14t
Type 6 (Heavy truck)	Truck with load capacity > 14t

.

According to the relevant statistics in the China Statistical Yearbook 2010–2020, it is possible to obtain the ownership of social-private vehicles with different types of passenger and cargo vehicles.

Different types of vehicles have different fuel consumptions at different driving speeds. According to the research results in The Optimal Utilization of Road Traffic Resources in China: Policy Suggestions and Planned Actions, the fuel consumptions per 100 km of vehicles have little change in the range of 30–80 km/h [32]. Therefore, the fuel consumptions in the calculation model of this paper are the average value of the fuel consumptions in the speed range of 30–80 km/h. The specific data is shown in

Table 3. Fuel consumptions of 100 km for different types of vehicles.

Vehicle Types	Small and Medium Passenger Bus	Large Bus	Mini-Truck	Light Truck	Medium Truck	Heavy Truck	Others
fuel consumptions per 100 km (L/100 km)	9.20	28.50	16.80	24.20	27.60	31.80	8.00

.

The mileage varies greatly for different types of vehicles. According to the research data cited in the Optimal Utilization of Road Traffic Resources in China: Policy Recommendations and Planned Actions, the average annual mileage of heavy trucks is 20,000–50,000 km, and that of medium and light trucks is 20,000–25,000 km. The average annual mileage of large buses is similar to that of trucks and higher than that of small and medium buses [32]. In recent years, the average annual mileage of urban private and commercial vehicles has decreased. According to Shaanxi Normal University and other research institutions’ surveys on the traffic travel, the average monthly mileage of small and medium passenger cars is 555.033 km. Therefore, this paper finally determines that the average annual mileage of small and medium-sized passenger cars in Shaanxi Province is thus 6700 km.

Table 4. Average annual mileage of different types of vehicles.

Vehicle Types	Small and Medium Passenger Car	Large Passenger Car	Mini-Truck	Light Truck	Medium Truck	Heavy Truck	Others
Average mileage (10 thousand km)	0.67	2.00	2.00	2.10	2.50	3.50	1.00

shows the recommended values of the average annual mileage of different types of vehicles.

This paper adopts the “bottom-up” method to establish a calculation model. The model of the carbon dioxide emissions of the vehicles in the non-operating departments of the transportation industry is shown in Formula (2).

$$C_2={\displaystyle \sum _{ij}{E}_{ij}}\times Q_{ij}\times L_{ij}\times m_i\times a_i\times \frac{44}{12}$$

(2)

In Formula (2), $C_2$ represents the carbon dioxide emissions of the vehicles in the non-operating departments in Shaanxi province of China (ten thousand tons); $i$ represents the type of the fuel consumed by vehicles; $j$ represents the types of vehicles (small–medium bus, large bus, microbus, small buses, medium bus, heavy bus, etc.); $E_{ij}$ represents the average fuel consumption of type of vehicle $j$ (ten thousand tons); $Q_{ij}$ represents the number of vehicle $j$ consuming fuel (ten thousand vehicles); $L_{ij}$ represents the average annual mileage of type of vehicle $j$ consuming fuel $i$ (ten thousand kilometers); $m$ represents fuel density (0.740 kg/L for gasoline and 0.839 kg/L for diesel); $a_i$ represents the carbon dioxide emission coefficient of type of fuel $i$ (kg CO₂/GJ).

2.2. Construction of the Decomposition Model for the Carbon Emissions Factors in Shaanxi Province’s Transportation Industry in China

The Kaya identity was proposed by Yoichi Kaya, a Japanese scholar, at the IPCC seminar. This identity connects carbon emissions with factors such as population, economy, and policy, etc., and analyzes the driving degree of each factor promoting the carbon emissions from a quantitative perspective [33]. The specific expression is shown in Formula (3).

$$C=P\times \frac{G}{P}\times \frac{E}{G}\times \frac{C}{E}$$

(3)

where $C$ represents the volume of carbon emissions (ten thousand tons); $P$ represents the population size (ten thousand people); $\frac{G}{P}$ represents the per capita GDP (ten thousand yuan/people); $\frac{E}{G}$ represents the energy intensity (tons standard coal/ten thousand yuan), and $\frac{C}{E}$ represents the carbon emission coefficient (kg CO₂/GJ).

This paper expands on the basis of Kaya identity and further subdivides the factors of influencing on the carbon emissions of Shaanxi province’s transportation industry in China, obtaining the improved Kaya identity, as shown in Formula (4).

$$C={\displaystyle \sum _i\frac{C_i}{E_i}}\times \frac{E_i}{E}\times \frac{E}{T}\times \frac{T}{G}\times \frac{G}{P}\times P$$

(4)

In Formula (4), $C$ is the total carbon emissions of Shaanxi province’s transportation industry in China(ten thousand tons); $C_i$ is the CO₂ emissions of energy $i$ (ten thousand tons); $E_i$ is the consumption of energy $i$ (ten thousand tons); $E$ is the total energy consumption of the transportation industry in Shaanxi province of China (ten thousand tons); $T$ is the added value of Shaanxi province’s transportation industry in China (100 million yuan); $G$ is the GDP of Shaanxi province of China (100 million yuan); P is the total population of Shaanxi province of China (ten thousand people).

Make $c_i=\frac{C_i}{E_i}$; $e_i=\frac{E_i}{E}$; $s=\frac{E}{T}$; $u=\frac{T}{G}$; $g=\frac{G}{P}$; $p=P$;

Then Formula (4) can be deformed as:

$$C=c_i\times e_i\times s\times u\times g\times p$$

(5)

In Formula (5), $c_i$ is the carbon emission coefficient; $e_i$ is the energy consumption structure; $s$ is the energy intensity; u is the industrial scale of transportation industry; $g$ is the per capita GDP; $p$ is the population size.

Ang optimized the Divisia Index decomposition method and established the Logarithmic Mean Divisia Index (LMDI) decomposition method, which effectively solved the problems of residual value and zero value [34]. In order to quantitatively analyze the contribution of each factor influencing carbon emissions, the LMDI decomposition model was chosen to focus on the influences of the carbon emission coefficient, the energy consumption structure, the energy intensity, the transportation industrial scale, the economic growth, and the population size on the carbon dioxide emissions of Shaanxi province’s transportation industry in China.

The result of the “multiplication” factor decomposition method is the same as that of the “addition” factor decomposition method in LMDI [35]. In this paper, the additional decomposition formula in the LMDI decomposition method is selected for the calculation. The expression of the factor decomposition model for the carbon dioxide emissions of Shaanxi province’s transportation industry in China is shown in Formula (6).

$$\mathrm{\Delta }C=C{O}_2{}_{}^T-C{O}_2{}_{}^0=\mathrm{\Delta }{C}_c+\mathrm{\Delta }{C}_e+\mathrm{\Delta }{C}_s+\mathrm{\Delta }{C}_u+\mathrm{\Delta }{C}_g+\mathrm{\Delta }{C}_p$$

(6)

In Formula (6), since the carbon emission coefficient of energy remains unchanged, there is $\mathrm{\Delta }{C}_c=0$.

Then, Formula (6) becomes

$$\mathrm{\Delta }C=C{O}_2{}_{}^T-C{O}_2{}_{}^0=\mathrm{\Delta }{C}_e+\mathrm{\Delta }{C}_s+\mathrm{\Delta }{C}_u+\mathrm{\Delta }{C}_g+\mathrm{\Delta }{C}_p$$

(7)

In Formula (7), $\mathrm{\Delta }C$ represents the change in the carbon dioxide emissions of Shaanxi province’s transportation industry in China; $\mathrm{\Delta }{C}_e$ represents the energy structure effect; $\mathrm{\Delta }{C}_s$ represents the energy intensity effect; $\mathrm{\Delta }{C}_u$ represents the scale effect of the transportation industry; $\mathrm{\Delta }{C}_g$ represents the economic growth effect; and $\mathrm{\Delta }{C}_p$ represents the population size effect.

The expression of the influence degree of each factor on the carbon emissions is shown in Formulas (8)–(12):

$$\mathrm{\Delta }{C}_e={\displaystyle \sum _i\frac{C_i^t-C_i^0}{\ln C_i^t-\ln C_0^t}}\times \ln (\frac{e(t)}{e(0)})$$

(8)

$$\mathrm{\Delta }{C}_s={\displaystyle \sum _i\frac{C_i^t-C_i^0}{\ln C_i^t-\ln C_0^t}}\times \ln (\frac{s(t)}{s(0)})$$

(9)

$$\mathrm{\Delta }{C}_u={\displaystyle \sum _i\frac{C_i^t-C_i^0}{\ln C_i^t-\ln C_0^t}}\times \ln (\frac{u(t)}{u(0)})$$

(10)

$$\mathrm{\Delta }{C}_g={\displaystyle \sum _i\frac{C_i^t-C_i^0}{\ln C_i^t-\ln C_0^t}}\times \ln (\frac{g(t)}{g(0)})$$

(11)

$$\mathrm{\Delta }{C}_p={\displaystyle \sum _i\frac{C_i^t-C_i^0}{\ln C_i^t-\ln C_0^t}}\times \ln (\frac{p(t)}{p(0)})$$

(12)

In Formula (8)–(12), $e(t),s(t),u(t),g(t),p(t)$ are the energy consumption structure, energy strength, industrial scale, total per capita, and population scale of the annual transportation industry; $e(0),s(0),u(0),g(0),p(0)$ are the energy consumption structure, energy intensity, industrial scale, total per capita product, population scale, and population scale of the benchmark transportation industry.

The specific contribution values of the energy structure effect, the energy intensity effect, the transportation industrial scale effect, the economic growth effect, and the population scale effect to the carbon dioxide emissions of Shaanxi province’s transportation industry in China can be calculated by studying the above five factors.

3. Results

3.1. Calculation Results of the Carbon Emissions in Shaanxi Province’s Transportation Industry in China

This study uses the structured calculation model for the carbon dioxide emissions of provincial transportation departments to calculate the carbon dioxide emissions of operating and non-operating activities of Shaanxi province’s transportation industry in China, respectively, and then adds them up, obtaining more precise data for carbon dioxide emissions in the industry based on the comprehensive assessment of the calculation method for carbon dioxide emissions in the industry, as shown in

Table 5. CO₂ emissions of Shaanxi province’s transportation industry in China (10,000 tons).

Years	CO2 Emissions of Operating Activities	CO2 Emissions of Non- Operating Activities	Total CO2 Emissions	Years	CO2 Emissions of Operating Activities	CO2 Emissions of Non- Operating Activities	Total CO2 Emissions
2009	1303.604	269.368	1572.973	2015	1524.409	766.946	2291.355
2010	1376.672	368.131	1744.803	2016	1323.372	827.918	2151.290
2011	1429.228	457.957	1887.186	2017	1326.178	901.190	2227.368
2012	1435.437	544.021	1979.458	2018	1464.722	994.475	2459.196
2013	1485.981	617.486	2103.467	2019	1431.799	1033.416	2465.214
2014	1557.097	689.830	2246.927

.

According to the analysis of carbon dioxide emissions in Shaanxi province’s transportation industry in China (Figure 1), during 2009–2019, the carbon dioxide emissions increased significantly and showed a steady upward trend, which increases by 0.57 times from 15.730 million tons to 24.652 million tons year by year. Carbon dioxide per capita increased from 0.422 to 0.636 tons per person, with an increase of 0.88 times as well as a 2.06% annual growth rate.

Within the research range, the carbon dioxide emissions in the operating activities of Shaanxi province’s transportation industry in China reached the peak of 15.57 million tons in 2014, and then the carbon emissions declined with fluctuations. From 2009 to 2019, the number of social-private vehicles’ ownership kept increasing, among which the number of small–medium buses with large energy consumptions increased from 878,100 in 2009 to 5,632,500 in 2019, with an increase of 5.41 times. Carbon dioxide emissions from non-operating activities increased rapidly from 2.694 million tons to 10.334 million tons, with an annual growth rate of 16.11 percent, due to the annual increase in the energy consumption of social-private cars.

3.2. Overall Analysis of the Factors Influencing the Carbon Emissions of Shaanxi Province’s Transportation Industry in China

The carbon dioxide emissions of Shaanxi province’s transportation in China sector are studied from 2009 to 2019, and the contribution value of the factors driving the carbon dioxide emissions of Shaanxi’s transportation industry in China is calculated using the LMDI model. Finally, this paper obtains the analyzed result of the total carbon dioxide emissions’ effect. Figure 2 and

Table 6. Decomposition analysis of CO₂ emissions’ factors in Shaanxi province’s transportation industry in China (in thousand tons).

Year	Energy Structure Effect	Energy Intensity Effect	Scale Effect of Transportation Industry	Economic Growth Effect	Population Size Effect	ΔC
2009–2010	0.885	−53.898	−119.529	340.819	3.553	171.830
2010–2011	−5.836	−147.476	−89.788	381.600	3.883	142.383
2011–2012	8.861	−151.703	−53.899	283.866	5.148	92.272
2012–2013	0.855	121.791	−238.376	233.766	5.973	124.009
2013–2014	−2.121	−96.382	46.375	189.243	6.345	143.460
2014–2015	−8.483	−93.611	82.838	52.910	10.773	44.428
2015–2016	−33.413	−306.071	61.891	125.883	11.645	−140.065
2016–2017	−13.322	−100.157	−72.522	249.512	12.568	76.078
2017–2018	−6.972	63.999	−79.631	236.817	17.615	231.829
2018–2019	−16.550	−39.205	−121.538	175.679	7.632	6.018
Cumulative contribution value	−76.095	−802.713	−584.180	2270.096	85.134	892.241

show the specific results.

By analyzing Figure 2, it can be seen that the energy structure effect and the population size effect are near the horizontal axis and have relatively weak influences on the total change of carbon dioxide emissions. The scale effect of the transportation industry and the energy intensity effect fluctuates to some extent; however, both have restraining effects on the carbon dioxide emissions. The economic growth effect is similar to the fluctuating trend of the total change of the carbon dioxide emissions, and it contributes the largest value to the change of carbon emissions, being positive for 10 years, with an obviously positive pulling effect on carbon dioxide emissions.

Under the comprehensive influences of the energy structure effect, the energy intensity effect, the scale effect of transportation industry, the economic growth effect and the population scale effect, and the general carbon dioxide emissions of Shaanxi province’s transportation industry in China showed a steady upward trend from 2009 to 2019, with a cumulative increase of 8.922 million tons.

3.3. Decomposition Analysis of the Factors Influencing the Carbon Dioxide Emissions in Shaanxi Province’s Transportation Industry in China

To further understand the degree of influence of each driving factor, this research analyzes it from five perspectives: energy structure effect, energy intensity effect, transportation industry scale effect, economic growth effect, and population size effect.

3.3.1. Economic Growth Effect

From Figure 3, among the five influence effects of the carbon dioxide emissions, economic growth effect is the most important driving factor for the carbon dioxide emissions’ growth. From 2009 to 2019, the cumulative value of the economic growth effect contributing to the carbon dioxide emissions reached 22.701 million tons. The main reason is that, in the research range, the GDP of the Shaanxi province grew rapidly, with the per capita GDP increasing from 21,460 yuan/person in 2009 to 66,550 yuan/person in 2019 and an average annual growth rate of 11.98%. The rapid growth of GDP promoted the development of the transportation industry to a certain extent, which, in turn, led to an increase in carbon dioxide emissions.

3.3.2. Population Size Effect

Population size effect plays a positive role in promoting the carbon dioxide emissions of the transportation industry in Shaanxi province of China; however, its influence is small. The population size effect contributed to a cumulative increase of 0.851 million tons of carbon dioxide emissions over the 10-year period. The main reason is that, in recent years, the population development of the Shaanxi province of China tends to be stable. Although the population keeps increasing, the growth rate is relatively slow, with an average annual growth rate of only 0.39%. Therefore, the population growth has a relatively small influence on the carbon dioxide emissions in Shaanxi province’s transportation industry in China.

3.3.3. The Scale Effect of Transportation Industry

The analysis of Figure 3 shows that the scale effect of the transportation industry has an overall inhibiting effect on the carbon dioxide emissions, with a cumulative reduction of 5.842 million tons of carbon dioxide emissions. The reason is that the growth rate of the transportation industry scale in the Shaanxi province of China is less than that of GDP, which has a certain inhibitory effect on the carbon dioxide emissions.

3.3.4. Energy Intensity Effect

The energy intensity effect is the main driving factor restraining the growth of the carbon dioxide emissions. In the research range, the energy intensity effect has reduced the carbon dioxide emissions by 8.027 million tons. This is mainly because, in recent years, the improvement degree of the transportation organization efficiency in the Shaanxi province of China has increased, but the improvement of poor road capacity and high vehicle no-load rate is still in a process of continuous promotion. At present, the ownership of private cars is increasing rapidly in the Shaanxi province of China, while the development of the urban public transportation is relatively slow. Low public transportation sharing rate, unreasonable network planning, imperfect infrastructure, and other factors also lead to the problem of low utilization rate of energy.

3.3.5. Energy Structure Effect

The energy structure effect has a small inhibitory influence on the carbon dioxide emissions of Shaanxi province’s transportation industry in China, which cumulatively reduced 760950 tons of carbon dioxide emissions in the research range. The main reason for the weak change in the energy structure’s effect is the slow growth of clean energy and new energy consumption of the Shaanxi province’s transportation industry in China. In 2019, clean energy such as natural gas and electricity accounted for only 11.24% of Shaanxi province’s transportation industry in China, therefore the energy consumption structure needs to be further improved.

4. Discussion

This paper constructs a more accurate calculation model for the carbon emissions of provincial transportation industry and analyzes the values of the main driving factors contributing to the carbon emissions by using the LMDI model, obtaining the following two discussions.

(1) Within the research range, the carbon dioxide emissions of Shaanxi province’s transportation industry in China showed a steady upward trend, increasing from 15.730 million tons to 24.652 million tons year by year. Among them, the carbon emissions from operating activities peaked in 2014, and those from non-operating activities maintained a high growth rate, with a cumulative increase of 7.640 million tons over the past 10 years. The significant increase in the carbon dioxide emissions of non-operating activities is mainly due to the rapid growth of social-private cars’ ownership. In particular, the ownership of small–medium buses and light trucks increased year by year from 878,100 and 91,200 in 2009 to 5,632,500 and 353,600 in 2019, respectively, which has obviously promoted the carbon dioxide emissions.

(2) The economic growth effect and the population size effect promoted the carbon dioxide emissions, during which the economic growth effect is the most important factor promoting the growth of carbon dioxide emissions, resulting in a cumulative increase of 22.701 million tons in the research range. The scale effect of the transportation industry, the energy structure effect, and the energy intensity effect all played a role in restraining the carbon emissions to some extent, and they cumulatively reduced 5.842 million tons, 0.761 million tons, and 8.027 million tons, respectively. It can be seen from this that the energy consumption structure and the energy intensity need to be optimized, and that the development level still needs to be further raised in the transportation industry of the Shaanxi province of China.

This study addressed the issue of the large error of energy consumption data caused by a different statistical caliber, made a more accurate calculation of the carbon emissions of the Shaanxi province’s transportation industry in China, and then analyzed the main driving factors influencing on the carbon dioxide emissions. However, due to the limitation of the statistical method of energy data in the industry, this paper failed to conduct a further study on the structure of the transportation mode and means of transportation. Therefore, in the next step, the author will focus on the factors impacting carbon dioxide emissions at the micro level and will then analyze the carbon peak situation of the transportation industry.

5. Conclusions and Suggestions

Finally, 2021 is the starting year of China’s transformation from a large to strong transportation country, and it is also the first year of the “14th Five-Year Plan”, in which the “double carbon” development strategy has been officially included. Shaanxi province, as one of the first low-carbon pilot provinces in China, should actively respond to the “double carbon” strategy and further promote the low-carbon emission reduction in the transportation industry. Based on the research conclusions, this paper puts forward the following three suggestions for controlling the carbon dioxide emissions in the transportation industry.

(1) Shaanxi province of China needs to decrease the transportation industry’s energy intensity even more. To begin, the province should gradually transfer the road freight to railway to rail freight, reducing the amount of high carbon emissions of road freight. In terms of passenger transportation, Shaanxi province of China should accelerate the construction of public transportation, rationally plan the station and supporting facilities, develop the rapid bus and rail transit with large volumes, and realize the improvement of the proportion of public transportation. Secondly, Shaanxi province of China can strengthen the comprehensive management of its urban traffic, achieve better the operation efficiency in its road networks, optimize and improve the travel information service of urban traffic, use big data analysis to achieve the accurate management of urban traffic, and reduce the empty vehicle rate and traffic congestion to achieve the goal of energy conservation and carbon dioxide emission reduction.

(2) Shaanxi province of China needs to speed up the adjustment of the energy consumption structure in the transportation industry. First of all, Shaanxi province of China should facilitate the transformation of the transportation industry from fossil fuels to clean energy, reduce the proportion of gasoline, diesel, and other high-emission fuels on an existing basis, and vigorously increase the proportion of electricity, natural gas, and other low-emission energy consumptions. Moreover, the province should accelerate the transition from traditional fuel vehicles to clean-and-new energy vehicles by increasing the consumptions of non-fuel such as natural gas, electricity, and methanol vehicles. Furthermore, Shaanxi province of China can increase the investment in non-fossil energy and further search for alternative energy by means of technological innovation, etc., for the transportation industry so as to control energy consumptions reasonably and further reduce the carbon emissions of the transportation industry.

(3) Shaanxi province of China should formulate the implementation plan of the carbon peak in the transportation industry to further realize the energy conservation and emission reduction. In order to prepare for the realization of the carbon peak as scheduled, Shaanxi province of China shall carry out the verification of carbon emissions’ data and the management training of carbon quota allocation for the key enterprises of the transportation industry. In addition, the province can implement the policies and requirements of clean energy substitution, clean transportation, and total energy consumption control, etc., in the transportation industry, and strive to form a replicable experience for the carbon emissions’ reduction, laying a working foundation for the realization of the “double carbon” goal.