CO₂ Emission Reduction Potential of Road Transport to Achieve Carbon Neutrality in China

Abstract

Under the targets of peaking CO₂ emissions and carbon neutrality in China, it is a matter of urgency to reduce the CO₂ emissions of road transport. To explore the CO₂ emission reduction potential of road transport, this study proposes eight policy scenarios: the business-as-usual (BAU), clean electricity (CE), fuel economy improvement (FEI), shared autonomous vehicles (SAV), CO₂ emission trading (CET) (with low, medium, and high carbon prices), and comprehensive (CS) scenarios. The road transport CO₂ emissions from 2020 to 2060 in these scenarios are calculated based on the bottom-up method and are evaluated in the Low Emissions Analysis Platform (LEAP). The Log-Mean Divisia Index (LMDI) method is employed to analyze the contribution of each factor to road transport CO₂ emission reduction in each scenario. The results show that CO₂ emissions of road transport will peak at 1419.5 million tonnes in 2033 under the BAU scenario. In contrast, the peaks of road transport CO₂ emissions in the CE, SAV, FEI, CET-LCP, CET-MCP, CET-HCP, and CS scenarios are decreasing and occur progressively earlier. Under the CS scenario with the greatest CO₂ emission reduction potential, CO₂ emissions of road transport will peak at 1200.37 million tonnes in 2023 and decrease to 217.73 million tonnes by 2060. Fuel structure and fuel economy contribute most to the emission reduction in all scenarios. This study provides possible pathways toward low-carbon road transport for the goal of carbon neutrality in China.

1. Introduction

With improvements in people’s living standards, China’s vehicle population is increasing yearly. Consequently, CO₂ emissions from road transport are showing an increasing trend. According to data from the International Energy Agency, in 2019, CO₂ emissions generated by the global transport sector accounted for approximately 1/4 (24.2%) of total global CO₂ emissions, with emissions of approximately 8 million tonnes, of which emissions in the road transportation sector were approximately 6.5 billion tonnes, accounting for 81% of the whole transportation sector [1]. In addition, CO₂ emissions from the transport sector have increased at the fastest rate in recent decades. This proportion is expected to increase to 41% by 2030. To address global climate change, China proposed to achieve the goals of CO₂ emission peaking by 2030 and carbon neutrality by 2060. Therefore, reducing transport CO₂ emissions is essential for achieving the goal of carbon neutrality [2,3], especially for the road transport sector.

Due to the uncertainty in policies, the future trend of road transport CO₂ emissions is ambiguous. Therefore, it is a matter of urgency to make reliable predictions on road transport CO₂ emissions and propose effective ways toward low-carbon road transport. Presently, most existing studies focus on the CO₂ emissions of transportation companies, disregarding private transportation. In addition, certain emerging scenarios, such as electric vehicles with cleaner power, shared autonomous vehicles, and CO₂ emission trading markets, are rarely considered. Therefore, research on road transport CO₂ emissions under the target of carbon neutrality is still insufficient.

To fill these gaps, this study aims to explore the CO₂ emission reduction potential of road transport to achieve carbon neutrality in China. First, we propose eight policy scenarios: the business-as-usual (BAU), clean electricity (CE), fuel economy improvement (FEI), shared autonomous vehicles (SAV), CO₂ emission trading (CET) (with low, medium, and high carbon prices), and comprehensive (CS) scenarios. Second, we analyze the CO₂ emission reduction of road transport in each scenario and identify the key factors that contribute to emission reduction. Last, we make several policy suggestions to reduce CO₂ emissions of road transport for the goal of carbon neutrality in China. The main contributions of this study are as follows:

• First, taking into account private road transport, the total CO₂ emissions of China’s road transport can be calculated and predicted more comprehensively and accurately.
• Second, future policy scenarios with emerging technologies and markets that are aimed at significantly enhancing the CO₂ emission reduction potential of road transport are introduced.
• Third, the contribution of key factors that influence road transport CO₂ in different scenarios are decomposed to support policy design and decision-making.

The remainder of this study is organized as follows: Section 2 reviews existing research on road transport CO₂ emissions. Section 3 elaborates on the method and model for the analysis of road transport CO₂ emissions. Section 4 presents several policy scenarios and related parameters. Section 5 analyzes and discusses the research results of this study. Section 6 summarizes the main findings and makes policy suggestions.

2. Literature Review

2.1. Calculation of Road Transport CO₂ Emissions

Given the complexity and dispersion of transportation CO₂ emissions, there are still great challenges in the quantitative assessment of road transport CO₂ emissions. Referring to the national greenhouse gas inventory guidelines of the Intergovernmental Panel on Climate Change (IPCC), the current calculation methods of CO₂ emissions from mobile sources mainly include the “top-down” and “bottom-up” approaches [4]. The “top-down” method calculates the road transport CO₂ emissions by multiplying the total consumption of various fuels by their corresponding CO₂ emission factors [5]. For the “bottom-up” method, the vehicle mileages, fuel structures, and fuel economy of various types of vehicles should be collected to calculate the total CO₂ emissions of road transport [6]. For comparison, the “top-down” method has advantages in data collection and calculation, while the “bottom-up” method provides more details about the contribution of CO₂ emissions. In practice, researchers usually combine both approaches to calculate CO₂ emissions from road transport [7]. For a life cycle assessment of road transport CO₂ emissions, the “life cycle” method should be used to calculate the total CO₂ emissions of upstream, midstream, and downstream industries associated with road transport, including the production, transportation, use, disposal of vehicle, and fuels [8]. Due to the lack of data on private transport, most of the existing empirical studies of road transport CO₂ emissions focus on operational transportation, such as buses, taxis, intercity passenger transport, intercity freight transport, etc. To fill this gap, this study accounts for both operational and private transport and analyzes the CO₂ emission reduction potential of different road transport subsectors.

2.2. Influencing Factors of Road Transport CO₂ Emissions

Presently, many scholars are committed to studying the influencing factors of road transport CO₂ emissions and have produced more systematic research results in this field. For example, Zhang et al. [9] divided the main factors affecting transport carbon dioxide emissions into three categories: demand-side factors, supply-side factors, and environmental measurement factors. To further analyze the impact of different factors on road transport CO₂ emissions, the CO₂ emission contribution of each factor can be obtained by the Log-Mean Divisia Index (LMDI) method, and targeted policy suggestions can be presented. Based on this method, Liu et al. [10] investigated the main factors of CO₂ emissions in the field of transportation and formulated corresponding energy-saving policies in China. Quan et al. [11] analyzed the influences of the CO₂ emission factor, energy intensity, energy structure, economic level, and population on CO₂ emissions from China’s logistics industry. However, most of the existing studies neglect the influence of power generation. With the substantial increase in the market share of electric vehicles in the future, the emission factor of power generation will also become a key factor affecting the CO₂ emissions of road transportation. Therefore, this study will explore the contribution of clean electricity to the CO₂ emission reduction of road transport.

2.3. Emission Reduction Potential of Low-Carbon Transport Policy

The formulation and implementation of low-carbon transport policies are the main ways to promote the low-carbon transition of transport. Many studies analyze the road transport emission reduction potential under different low-carbon transport scenarios. In these studies, the impacts of various policies and combinations, such as fuel tax, fuel labeling, new car purchase tax reduction, high emission vehicle purchase penalty tax, vehicle scrapping incentive, and vehicle transport restriction, on transport CO₂ emissions have been widely analyzed and discussed [12,13,14,15,16,17,18,19,20,21]. The bottom-up models, such as the Transport and Mobility Leuven (TREMOVE), Integrated Land Use and Transport Modeling System (TRANUS), Integrated MARKAL-EFOM System (TIMES), and Low Emissions Analysis Platform (LEAP), can be used to evaluate the emission reduction potential of low-carbon transport policies. Local parameters such as vehicle population, vehicle structure, fuel economy, and emission factors are usually inputted into the model [22,23,24]. Among them, the LEAP is one of the most popular analytical methods because it requires less data and can provide a comprehensive scenario analysis. For example, based on the LEAP, Feng et al. [25] analyzed the trend of energy demand, pollutants, and carbon emissions in China’s transportation sector under three policy scenarios: pollution reduction (PR), low carbon (LC), and the deep-seated low carbon (DLC) scenarios. Pang et al. [26] analyzed the impact of three scenarios with different policies and measures on greenhouse gas emissions from road transport in Lanzhou, China, from 2015 to 2040 based on the LEAP. However, most scenarios in the existing studies did not include certain future policy scenarios, such as electric vehicles with cleaner power, shared autonomous vehicles, CO₂ emission trading markets, etc. Therefore, the emission reduction potential of road transport may be underestimated [27]. To help reach the goal of carbon neutrality in China, this study will propose more emerging and comprehensive policy scenarios and assess the CO₂ emission reduction potential of road transport under these scenarios.

3. Methods

3.1. Research Framework

To evaluate the CO₂ emission reduction potential of China’s road transport sector and the contribution of various influencing factors, this study integrates the LEAP with the LMDI method. The research framework is shown in Figure 1. First, the future population of China is predicted based on the death rate and birth rate of the historical population. Meanwhile, the future vehicle population is predicted based on the Gompertz model. Second, we set eight policy scenarios with different parameters, such as vehicle structure, vehicle mileage, fuel economy, and emission factors. Third, the LEAP is used to predict CO₂ emissions of road transport in China from 2020 to 2060 and analyze the CO₂ emission reduction potential of different scenarios. Last, the LMDI method is applied to calculate the contribution of various factors to CO₂ emission reduction in each scenario.

3.2. Road Transport CO₂ Emission Calculation

In this study, the CO₂ emissions of road transport are calculated by the bottom-up method. This method calculates road transport CO₂ emissions based on the “ASIF” framework [2,3], where “A” represents the travel activity, “S” represents the mode share, “I” represents the energy intensity of each mode, fuel, and vehicle type, and “F” represents and carbon content of each fuel to total emissions. The road transport CO₂ emissions include emissions from both passenger and freight transport. The equation is expressed as follows:

$$C=C_P+C_T$$

(1)

where C_P is the total CO₂ emissions of road passenger transport, C_T is the total CO₂ emissions of road freight transport, and C is the total CO₂ emissions of road transport.

The equation for calculating passenger transport CO₂ emissions is expressed as follows:

$$C_P={\displaystyle \sum _{i=1}^4{\displaystyle \sum _{k=1}^3{N}_P{S}_i{M}_i{Q}_{ik}{F}_{ik}{E}_k}}$$

(2)

where i = 1, 2, 3, and 4 refer to mini, small, medium, and large passenger vehicle types, respectively; k = 1, 2, and 3 represent fuel types of gasoline, electricity, and hybrid, respectively; N_P is the total number of passenger vehicles; S_i is the proportion of passenger vehicles of type i; M_i is the average annual VKT of vehicles of type i; Q_ik is the proportion of fuels of type k for vehicles of type i; F_ik is the fuel economy of vehicles of type i with fuel type k; and E_k is the CO₂ emission factor of fuel of type k.

The equation for calculating freight transport CO₂ emissions is expressed as follows:

$$C_T={\displaystyle \sum _{j=1}^4{\displaystyle \sum _{h=1}^4{N}_T{S}_j{M}_j{Q}_{jh}{F}_{jh}{E}_h}}$$

(3)

where j = 1, 2, 3, 4 refer to mini, light, medium, and heavy freight vehicles, respectively; h = 1, 2, 3, 4 represent fuel types of diesel, gasoline, electricity, and natural gas, respectively; N_t is the total number of freight vehicles; S_j is the proportion of freight vehicles of type j; M_j is the average annual VKT of trucks of type j; Q_jh is the proportion of fuels of type h for freight vehicles of type j; F_jh is the fuel economy of trucks of type j with fuel type h; and E_h is the CO₂ emission factor of fuel of type h.

According to Equations (1)–(3), Equation (1) is equivalent to:

$$C={\displaystyle \sum _{i=1}^4{\displaystyle \sum _{k=1}^3{N}_P{S}_i{M}_i{Q}_{ik}{F}_{ik}{E}_k}}+{\displaystyle \sum _{j=1}^4{\displaystyle \sum _{h=1}^4{N}_T{S}_j{M}_j}{Q}_{jh}{F}_{jh}{E}_h}$$

(4)

3.3. Low Emissions Analysis Platform (LEAP)

The LEAP is mainly aimed at the whole process of terminal energy consumption and comprehensively evaluates the influence of various technologies and policies on energy conservation and emission reduction from the aspects of energy supply structure, energy technology level, energy demand, etc., which is more consistent with the content and goal of research on the low-carbon development path of urban transportation [28,29,30]. The LEAP is a medium- and long-term modeling tool. In most studies that use the LEAP, the prediction period is generally 20 to 50 years. Therefore, this study uses the LEAP to predict the changing trend of China’s road transport CO₂ emissions in different policy scenarios from 2020 to 2060. The parameter settings of these scenarios are introduced in the next section. We can then analyze the characteristics of peak CO₂ emissions years and trends in different scenarios and compares them with the BAU scenario.

3.4. Log-Mean Divisia Index (LMDI) Method

The LMDI method is a complete decomposition analysis method without residual error [31]. Using the LMDI method, the contribution of different factors to CO₂ emission reduction can be examined. This study decomposes the change in road transport CO₂ emissions from base year 0 to target year t into six parts of contribution, as shown in Equation (5).

$$\Delta C=C^t-C^0=\Delta C_N+\Delta C_S+\Delta C_Q+\Delta C_F+\Delta C_M+\Delta C_E$$

(5)

where C^t represents the CO₂ emissions of road transport in the target year; C⁰ represents the CO₂ emissions of road transport in the base year; ΔC_N represents the contribution of vehicle population; ΔC_S represents the contribution of vehicle structure; ΔC_Q represents the contribution of fuel structure; ΔC_F represents the contribution of fuel economy; ΔC_M represents the contribution of average annual VKT, and ΔC_E represents the contribution of fuel CO₂ emission factors.

According to the decomposition method of the LMDI, each item on the right side of Equation (5) can be expressed as follows:

$$\Delta C_N={\displaystyle \sum _{i=1}^4\frac{C_i^t-C_i^0}{\ln C_i^t-\ln C_i^0}}\ln (\frac{N_P^t}{N_P^0})+{\displaystyle \sum _{j=1}^4\frac{C_j^t-C_j^0}{\ln C_j^t-\ln C_j^0}}\ln (\frac{N_T^t}{N_T^0})$$

(6)

$$\Delta C_S={\displaystyle \sum _{i=1}^4\frac{C_i^t-C_i^0}{\ln C_i^t-\ln C_i^0}}\ln (\frac{S_i^t}{S_i^0})+{\displaystyle \sum _{j=1}^4\frac{C_j^t-C_j^0}{\ln C_j^t-\ln C_j^0}}\ln (\frac{S_j^t}{S_j^0})$$

(7)

$$\Delta C_Q={\displaystyle \sum _{i=1}^4{\displaystyle \sum _{k=1}^3\frac{C_{ik}^t-C_{ik}^0}{\ln C_{ik}^t-\ln C_{ik}^0}}}\ln (\frac{Q_{ik}^t}{Q_{ik}^0})+{\displaystyle \sum _{j=1}^4{\displaystyle \sum _{h=1}^4\frac{C_{jh}^t-C_{jh}^0}{\ln C_{jh}^t-\ln C_{jh}^0}}}\ln (\frac{Q_{jh}^t}{Q_{jh}^0})$$

(8)

$$\Delta C_F={\displaystyle \sum _{i=1}^4{\displaystyle \sum _{k=1}^3\frac{C_{ik}^t-C_{ik}^0}{\ln C_{ik}^t-\ln C_{ik}^0}}}\ln (\frac{F_{ik}^t}{F_{ik}^0})+{\displaystyle \sum _{j=1}^4{\displaystyle \sum _{h=1}^4\frac{C_{jh}^t-C_{jh}^0}{\ln C_{jh}^t-\ln C_{jh}^0}}}\ln (\frac{F_{jh}^t}{F_{jh}^0})$$

(9)

$$\Delta C_M={\displaystyle \sum _{i=1}^4\frac{C_i^t-C_i^0}{\ln C_i^t-\ln C_i^0}}\ln (\frac{M_i^t}{M_i^0})+{\displaystyle \sum _{j=1}^4\frac{C_j^t-C_j^0}{\ln C_j^t-\ln C_j^0}}\ln (\frac{M_j^t}{M_j^0})$$

(10)

$$\Delta C_E={\displaystyle \sum _{i=1}^4{\displaystyle \sum _{k=1}^3\frac{C_{ik}^t-C_{ik}^0}{\ln C_{ik}^t-\ln C_{ik}^0}}}\ln (\frac{E_k^t}{E_k^0})+{\displaystyle \sum _{j=1}^4{\displaystyle \sum _{h=1}^4\frac{C_{jh}^t-C_{jh}^0}{\ln C_{jh}^t-\ln C_{jh}^0}}}\ln (\frac{E_h^t}{E_h^0})$$

(11)

To further compare and analyze the relative contribution of each factor under different scenarios, this study calculates the contribution rate of each factor to the total change of road transport CO₂ emissions as follows:

$$CR(l)=\Delta C_l/\Delta C$$

(12)

where $CR(l)$ is the contribution rate of factor l and ΔC_l is the CO₂ emission contribution of factor l. If $CR(l)$ > 0, then the influencing factor l may drive the increase of CO₂ emissions. Otherwise, it contributes to reducing CO₂ emissions.

4. Scenario Setting

This study aims to analyze the CO₂ emission reduction potential of road transport from 2020 to 2060. Therefore, different policy scenarios, such as the business-as-usual (BAU), clean electricity (CE), fuel economy improvement (FEI), shared autonomous vehicles (SAV), CO₂ emission trading (CET) (with low, medium and high carbon prices), and comprehensive (CS) scenarios, are established based on the key influencing factors of road transport CO₂ emissions. The relevant scenario parameters involved in this study include vehicle structure, fuel economy, fuel emission factor, average annual VKT of vehicles, etc. The settings of these scenarios are presented hereafter.

4.1. Business-as-Usual (BAU) Scenario

In the BAU scenario, we assume the CO₂ emission of road transport keeps the historical development trend with no additional policy implemented in the future. According to the national statistical yearbook, China’s total population in 2020 was 1.412 billion, with a birth rate of 8.52‰ and a death rate of 7.09‰. This research set the initial death rate DR(0) = 7.09‰, which increases yearly with an increment of 0.17‰, and the initial birth rate BR(0) = 8.52‰, which decreases yearly with a decrease of 0.2‰. The projection of the population of China from 2010 to 2060 in the BAU scenario is shown in Figure 2.

Based on the forecast of the International Energy Agency [32,33,34], the car ownership per thousand people in China will be approximately 494 in 2050. Therefore, the number of vehicles per 1000 people in China in the BAU scenario in 2060 is set to 500 cars per 1000 people [35]. The Gompertz model is then used to predict the vehicle population in China from 2020 to 2060 based on historical data from 2010 to 2020. The projections of China’s vehicle population, freight vehicle population, and passenger vehicle population from 2010 to 2060 are shown in Figure 3.

Based on historical data and related studies [36,37,38,39,40,41], we predict the vehicle structure, fuel structure, fuel economy, average annual VKT, and fuel emission factor of road transport from 2020 to 2060 for the BAU scenario. The settings of these parameters for passenger transport and freight transport in BAU scenarios are summarized in

Table 1. Parameter settings of passenger transport in the BAU scenario.

Influencing Factors of CO2 Emission	Vehicle Type	Fuel Type	2020	2030	2040	2050	2060
Passenger vehicle population (ten thousand)	All	All	24,166.2	38,958.2	45,927.4	48,574.4	49,510.0
Vehicle structure	Mini	All	0.65%	0.10%	0.01%	0.00%	0.00%
	Small	All	98.40%	99.13%	99.22%	99.23%	99.23%
	Medium	All	0.28%	0.38%	0.52%	0.58%	0.62%
	Large	All	0.67%	0.38%	0.26%	0.19%	0.15%
Fuel structure	Mini, Small, and Medium	Gasoline	98%	86%	68%	34%	0%
		Electricity	1.6%	12.74%	30.72%	64.68%	100%
		Hybrid	0.4%	1.26%	1.28%	1.32%	0%
	Large	Gasoline	75%	50%	30%	15%	0%
		Electricity	25%	50%	70%	85%	100%
Fuel economy	Mini	Gasoline (L/100 km)	5.20	4.03	3.84	3.67	3.53
		Electricity (kWh/100 km)	8.70	6.73	6.40	6.13	5.89
		Hybrid (L/100 km)	2.81	2.18	2.07	1.98	1.91
	Small	Gasoline (L/100 km)	8.30	7.50	6.75	6.00	5.30
		Electricity (kWh/100 km)	13.00	10.00	9.00	8.20	7.80
		Hybrid (L/100 km)	4.22	3.36	2.75	2.60	2.58
	Medium	Gasoline (L/100 km)	17.10	15.20	14.80	14.60	14.50
		Electricity (kWh/100 km)	120.00	116.00	110.00	100.00	92.00
		Hybrid (L/100 km)	9.24	8.22	8.00	7.89	7.84
	Large	Gasoline (L/100 km)	21.80	19.40	18.90	18.50	18.20
		Electricity (kWh/100 km)	144.00	140.00	135.0	128.00	122.00
Average annual VKT (km)	Mini	All	10,000	8917	7917	7000	6500
	Small	All	12,000	10,700	9500	8500	7500
	Medium	All	35,000	35,750	36,500	37,250	38,000
	Large	All	48,300	48,900	49,200	49,500	49,800
Fuel emission factor	All	Gasoline (kg/L)	2.42	2.42	2.42	2.42	2.42
	All	Electricity (kg/kWh)	0.71	0.52	0.46	0.4	0.38

and

Table 2. Parameter settings of freight transport in the BAU scenario.

Influencing Factors of CO2 Emission	Vehicle Type	Fuel Type	2020	2030	2040	2050	2060
Freight vehicle population (ten thousand)	All	All	3042.6	3861.4	4453.5	4793.3	4957.4
Vehicle structure	Mini	Diesel	0.00%	0.00%	0.00%	0.00%	0.00%
	Light		40.20%	37.68%	33.75%	29.12%	24.50%
	Medium		5.70%	3.00%	1.70%	1.10%	0.50%
	Heavy		54.10%	59.33%	64.55%	69.78%	75.00%
	Mini	Gasoline	5.50%	4.00%	2.40%	1.20%	0.00%
	Light		94.50%	96.00%	97.60%	98.80%	100.00%
	Medium		0.00%	0.00%	0.00%	0.00%	0.00%
	Heavy		0.00%	0.00%	0.00%	0.00%	0.00%
	Mini	Electricity	99.50%	83.38%	67.25%	51.13%	35.00%
	Light		0.50%	7.88%	15.25%	22.63%	30.00%
	Medium		0.00%	5.00%	10.00%	15.00%	20.00%
	Heavy		0.00%	3.75%	7.50%	11.25%	15.00%
	Mini	Natural gas	0.10%	0.00%	0.00%	0.00%	0.00%
	Light		3.80%	2.50%	1.60%	0.80%	0.00%
	Medium		0.40%	0.00%	0.00%	0.00%	0.00%
	Heavy		95.70%	97.50%	98.40%	99.20%	100.00%
Fuel structure	All	Diesel	69.60%	60.00%	51.00%	43.00%	35.00%
	All	Gasoline	27.90%	19.30%	14.00%	12.00%	10.00%
	All	Electricity	0.70%	13.40%	24.00%	32.00%	40.00%
	All	Natural gas	1.80%	7.30%	11.00%	13.00%	15.00%
Fuel Economy	Mini	Diesel (L/100 km)	6.80	6.10	5.80	5.60	5.50
		Gasoline (L/100 km)	9.60	8.60	7.40	5.80	4.20
		Natural gas (m3/100 km)	8.40	7.50	6.68	5.84	5.00
		Electricity (kWh/100 km)	18.50	17.40	16.00	15.20	14.70
	Light	Diesel (L/100 km)	8.70	7.80	7.40	7.10	7.00
		Gasoline (L/100 km)	11.00	9.90	8.84	7.72	6.60
		Natural gas (m3/100 km)	11.20	10.10	8.94	7.82	6.70
		Electricity (kWh/100 km)	125.00	119.00	113.00	106.00	102.00
	Medium	Diesel (L/100 km)	15.50	14.70	14.00	13.40	12.90
		Natural gas (m3/100 km)	17.50	15.70	14.02	12.26	10.50
		Electricity (kWh/100 km)	132.00	128.00	123.00	114.00	111.00
	Heavy	Diesel (L/100 km)	32.60	30.80	29.30	28.00	27.00
		Natural gas (m3/100 km)	30.80	27.80	24.66	21.58	18.50
		Electricity (kWh/100 km)	150.00	146.00	140.00	132.00	129.00
Average annual VMT (km)	Mini	All	20,000	20,000	20,000	20,000	20,000
	Light	All	20,000	20,000	20,000	20,000	20,000
	Medium	All	24,000	25,627	27,498	29,288	31,000
	Heavy	All	40,000	40,500	41,143	41,786	42,500
Fuel emission factor	All	Diesel (kg/L)	2.8	2.8	2.8	2.8	2.8
	All	Gasoline (kg/L)	2.42	2.42	2.42	2.42	2.42
	All	Natural gas (kg/m3)	2.62	2.62	2.62	2.62	2.62
	All	Electricity (kg/kWh)	0.71	0.52	0.46	0.4	0.38

.

4.2. Clean Electricity (CE) Scenario

Presently, China’s energy structure is dominated by coal. Therefore, the power of electric vehicles is mainly generated from coal and the CO₂ emissions of electric vehicles are still high. With the diffusion of electric vehicles in the future, the influence of the electricity emission factor on road transport CO₂ emission will be more significant. This study proposes a clean electricity (CE) scenario where renewable energy power generation will be rapidly developed and widely adopted. In the CE scenario, the CO₂ emission factor of electricity should decrease more significantly compared with the BAU scenario. Therefore, the CO₂ emission factors of electricity in the CE scenario are assumed to decrease 2.5 times as fast as in the BAU scenario. The specific parameters that change compared with the BAU scenario are shown in

Table 3. Parameter settings in the CE scenario.

Influencing Factors of CO2 Emission	2020	2030	2040	2050	2060
CO2 emission factor of electricity (kg/kWh)	0.71	0.32	0.23	0.16	0.14

. The rest of the parameters are the same as those in the BAU scenario.

4.3. Fuel Economy Improvement (FEI) Scenario

As one of the main factors affecting CO₂ emissions, the fuel economy of vehicles will be improved with the continuous development of automobile conservation technology in the future. Therefore, we propose a fuel economy improvement (FEI) scenario where the fuel economy of vehicles will be increased faster than that in the BAU scenario. Due to the differences in power types and engine technology, the room for improvement in the fuel economy of freight vehicles is usually larger than that of passenger vehicles. Thus, we set the improvement rate of fuel economy for passenger vehicles and freight vehicles in the FEI scenario to be 1.5 times and 1.8 times, respectively, that in the BAU scenario. The specific parameters that change compared with the BAU scenario are shown in

Table 4. Parameter settings in the FEI scenario.

Influencing Factors of CO2 Emission	Vehicle Type		Fuel Type	2020	2030	2040	2050	2060
Fuel economy	Passenger vehicle	Mini	Gasoline (L/100 km)	5.20	3.54	3.28	3.07	2.89
			Electricity (kWh/100 km)	8.70	5.90	5.48	5.12	4.83
			Hybrid (L/100 km)	2.81	1.91	1.77	1.66	1.56
		Small	Gasoline (L/100 km)	8.30	7.13	6.08	5.09	4.23
			Electricity (kWh/100 km)	13.00	8.75	7.47	6.49	6.02
			Hybrid (L/100 km)	4.22	2.99	2.21	2.03	2.01
		Medium	Gasoline (L/100 km)	17.10	14.32	13.76	13.48	13.34
			Electricity (kWh/100 km)	120.00	114.05	105.30	91.24	80.49
			Hybrid (L/100 km)	9.24	7.74	7.44	7.29	7.21
		Large	Gasoline (L/100 km)	21.80	18.29	17.59	17.03	16.62
			Electricity (kWh/100 km)	144.00	138.04	130.70	120.66	112.26
	Freight vehicle	Mini	Diesel (L/100 km)	6.80	5.59	5.10	4.79	4.64
			Gasoline (L/100 km)	9.60	7.87	6.00	3.85	2.14
			Natural gas (m3/ 100 km)	8.40	6.84	5.55	4.35	3.28
			Electricity (kWh/100 km)	18.50	16.56	14.23	12.98	12.22
		Light	Diesel (L/100 km)	8.70	7.14	6.49	6.03	5.88
			Gasoline (L/100 km)	11.00	9.09	7.41	5.80	4.36
			Natural gas (m3/100 km)	11.20	9.29	7.45	5.84	4.42
			Electricity (kWh/100 km)	125.00	114.39	104.20	92.84	86.62
		Medium	Diesel (L/100 km)	15.50	14.09	12.90	11.92	11.13
			Natural gas (m3/100 km)	17.50	14.37	11.72	9.19	6.94
			Electricity (kWh/100 km)	132.00	124.88	116.33	101.32	96.57
		Heavy	Diesel (L/100 km)	32.60	29.43	26.89	24.78	23.21
			Natural gas (m3/100 km)	30.80	25.59	20.60	16.18	12.24
			Electricity (kWh/100 km)	150.00	142.87	132.46	119.12	114.28

. The rest of the parameters are the same as those in the BAU scenario.

4.4. Shared Autonomous Vehicles (SAV) Scenario

The emerging shared autonomous vehicles may steer a revolution in passenger transport. According to related research [42,43,44,45], people will give up purchasing private cars if shared mobility and autonomous driving services are widely adopted in the future. Since the shared autonomous vehicles usually have high capacity, the vehicle structure of passenger transport will change significantly. In addition, shared autonomous vehicles can also improve transport efficiency and fuel economy of vehicles. Therefore, we propose a shared autonomous vehicles (SAV) scenario where the vehicle population of passenger vehicles, the structure of passenger vehicles, and the average annual VKT of passenger vehicles are changed compared with the BAU scenario. In the SAV scenario, the car ownership per 1000 people is set to 400 in 2060, which is less than that in the BAU scenario. Besides, the proportion of medium and large passenger vehicles is greater than that in the BAU scenario. The proportion of medium and large passenger vehicles is assumed to increase 0.5 times as fast as in the BAU scenario. In addition, we set the improvement rate of fuel economy for vehicles in the SAV scenario to be 1.25 times that in the BAU scenario. The specific parameters that change compared with the BAU scenario are shown in

Table 5. Parameter settings in the SAV scenario.

Influencing Factors of CO2 Emission	Vehicle Type		Fuel Type	2020	2030	2040	2050	2060
Passenger vehicle population (ten thousand)	All		All	24,166.2	34,848.5	38,095.5	38,854.2	38,991.0
Vehicle structure	Passenger vehicle	Mini	All	0.65%	0.30%	0.13%	0.06%	0.03%
		Small	All	98.40%	98.77%	98.81%	98.81%	98.81%
		Medium	All	0.28%	0.43%	0.65%	0.77%	0.84%
		Large	All	0.67%	0.50%	0.41%	0.35%	0.32%
Fuel economy	Passenger vehicle	Mini	Gasoline (L/100 km)	5.20	3.78	3.55	3.36	3.19
			Electricity (kWh/100 km)	8.70	6.31	5.92	5.61	5.33
			Hybrid (L/100 km)	2.81	2.04	1.92	1.82	1.73
		Small	Gasoline (L/100 km)	8.30	7.31	6.41	5.53	4.73
			Electricity (kWh/100 km)	13.00	9.35	8.20	7.30	6.85
			Hybrid (L/100 km)	4.22	3.17	2.46	2.30	2.28
		Medium	Gasoline (L/100 km)	17.10	14.76	14.27	14.03	13.91
			Electricity (kWh/100 km)	120.00	115.02	107.63	95.52	86.06
			Hybrid (L/100 km)	9.24	7.98	7.71	7.58	7.52
		Large	Gasoline (L/100 km)	21.80	18.84	18.23	17.75	17.39
			Electricity (kWh/100 km)	144.00	139.02	132.83	124.28	117.03
	Freight vehicle	Mini	Diesel (L/100 km)	6.80	5.94	5.57	5.33	5.21
			Gasoline (L/100 km)	9.60	8.36	6.93	5.11	3.40
			Natural gas (m3/100 km)	8.40	7.29	6.31	5.33	4.39
			Electricity (kWh/100 km)	18.50	17.13	15.43	14.47	13.88
		Light	Diesel (L/100 km)	8.70	7.59	7.10	6.75	6.63
			Gasoline (L/100 km)	11.00	9.64	8.37	7.06	5.80
			Natural gas (m3/100 km)	11.20	9.84	8.45	7.14	5.89
			Electricity (kWh/100 km)	125.00	117.54	110.18	101.70	96.93
		Medium	Diesel (L/100 km)	15.50	14.51	13.65	12.92	12.32
			Natural gas (m3/100 km)	17.50	15.28	13.26	11.21	9.23
			Electricity (kWh/100 km)	132.00	127.02	120.84	109.88	106.28
		Heavy	Diesel (L/100 km)	32.6	30.36	28.53	26.95	25.75
			Natural gas (m3/100 km)	30.8	27.09	23.32	19.73	16.27
			Electricity (kWh/100 km)	150.00	145.02	137.60	127.84	124.21

. The rest of the parameters are the same as those in the BAU scenario.

4.5. CO₂ Emission Trading (CET) Scenario

CO₂ emissions trading is a cost-effective climate policy to reduce greenhouse gas emissions. Although the road transport sector has not currently been incorporated into the emission trading system, it is very likely to be implemented in the future with the development of emerging technology and the maturity of the carbon market. This study proposes a CO₂ emissions trading (CET) scenario with different levels of carbon prices (low, mid, and high). According to related research [46], the carbon price of CET can stimulate vehicle users to choose low-carbon vehicles (with fuel types of electricity, hybrid, and natural gas) and decrease the average annual VKT. The higher the price, the greater the influences.

In the CO₂ emissions trading scenario with low carbon prices (CET-LCP), the proportion of low-carbon vehicles is set to 1.25 times that in the BAU scenario and the average annual VKT of vehicles in 2060 is set to 0.85 times that in the BAU scenario. In the CO₂ emissions trading scenario with mid carbon prices (CET-MCP), the proportion of low-carbon vehicles is set to 1.5 times that in the BAU scenario and the average annual VKT of vehicles in 2060 is set to 0.75 times that in the BAU scenario. In the CO₂ emissions trading scenario with high carbon prices (CET-HCP), the proportion of low-carbon vehicles is set to 2 times that in the BAU scenario and the average annual VKT of vehicles in 2060 is set to 0.65 times that in the BAU scenario. The specific parameters of the three scenarios that change compared with the BAU scenario are shown in

Table 6. Parameter settings in the CET-LCP scenario.

Influencing Factors of CO2 Emission	Vehicle Type		Fuel Type	2020	2030	2040	2050	2060
Fuel structure	Passenger vehicle	Small and medium	Gasoline	98.00%	82.50%	60.00%	17.50%	0.00%
			Electricity	1.60%	15.93%	38.40%	80.85%	100.00%
			Hybrid	0.40%	1.58%	1.60%	1.65%	0.00%
		Large	Gasoline	75.00%	37.50%	12.50%	0.00%	0.00%
			Electricity	25.00%	62.50%	87.50%	100.00%	100.00%
	Freight vehicle		Diesel	69.60%	56.08%	44.13%	34.20%	24.31%
			Gasoline	27.90%	18.04%	12.12%	9.55%	6.94%
			Natural gas	1.80%	9.13%	13.75%	16.25%	18.75%
			Electricity	0.70%	16.75%	30.00%	40.00%	50.00%
Average annual VMT (km)	Passenger vehicle	Mini	All	10,000	8881	7763	6644	5525
		Small	All	12,000	10,594	9188	7781	6375
		Medium	All	35,000	34,325	33,650	32,975	32,300
		Large	All	48,300	46,808	45,315	43,823	42,330
	Freight vehicle	Mini	All	20,000	19,250	18,500	17,750	17,000
		Light	All	20,000	19,250	18,500	17,750	17,000
		Medium	All	24,000	24,588	25,175	25,763	26,350
		Heavy	All	40,000	39,031	38,063	37,094	36,125

,

Table 7. Parameter settings in the CET-MCP scenario.

Influencing Factors of CO2 Emission	Vehicle Type		Fuel Type	2020	2030	2040	2050	2060
Fuel structure	Passenger vehicle	Small and medium	Gasoline	98.00%	79.00%	52.00%	1.00%	0.00%
			Electricity	1.60%	19.11%	46.08%	97.02%	100.00%
			Hybrid	0.40%	1.89%	1.92%	1.98%	0.00%
		Large	Gasoline	75.00%	25.00%	0.00%	0.00%	0.00%
			Electricity	25.00%	75.00%	100.00%	100.00%	100.00%
	Freight vehicle		Diesel	69.60%	52.17%	37.27%	25.41%	13.61%
			Gasoline	27.90%	16.78%	10.23%	7.09%	3.89%
			Natural gas	1.80%	10.95%	16.50%	19.50%	22.50%
			Electricity	0.70%	20.10%	36.00%	48.00%	60.00%
Average annual VMT (km)	Passenger vehicle	Mini	All	10,000	8719	7438	6156	4875
		Small	All	12,000	10,406	8813	7219	5625
		Medium	All	35,000	33,375	31,750	30,125	28,500
		Large	All	48,300	45,563	42,825	40,088	37,350
	Freight vehicle	Mini	All	20,000	18,750	17,500	16,250	15,000
		Light	All	20,000	18,750	17,500	16,250	15,000
		Medium	All	24,000	23,813	23,625	23,438	23,250
		Heavy	All	40,000	37,969	35,938	33,906	31,875

and

Table 8. Parameter settings in the CET-HCP scenario.

Influencing Factors of CO2 Emission	Vehicle Type		Fuel Type	2020	2030	2040	2050	2060
Fuel structure	Passenger vehicle	Small and medium	Gasoline	98.00%	74.80%	42.40%	0.00%	0.00%
			Electricity	1.60%	22.93%	55.30%	98%	100.00%
			Hybrid	0.40%	2.27%	2.30%	2.00%	0.00%
		Large	Gasoline	75.00%	0.00%	0.00%	0.00%	0.00%
			Electricity	25.00%	100.00%	100.00%	100.00%	100.00%
	Freight vehicle		Diesel	69.60%	44.34%	23.54%	7.82%	0.00%
			Gasoline	27.90%	14.26%	6.46%	2.18%	0.00%
			Natural gas	1.80%	14.60%	22.00%	26.00%	27.27%
			Electricity	0.70%	26.80%	48.00%	64.00%	72.73%
Average annual VMT (km)	Passenger vehicle	Mini	All	10,000	8556	7113	5669	4225
		Small	All	12,000	10,219	8438	6656	4875
		Medium	All	35,000	32,425	29,850	27,275	24,700
		Large	All	48,300	44,318	40,335	36,353	32,370
	Freight vehicle	Mini	All	20,000	18,250	16,500	14,750	13,000
		Light	All	20,000	18,250	16,500	14,750	13,000
		Medium	All	24,000	23,038	22,075	21,113	20,150
		Heavy	All	40,000	36,906	33,813	30,719	27,625

. The rest of the parameters are the same as those in the BAU scenario.

4.6. Comprehensive (CS) Scenario

The CS scenario is the combination of the CE, SAV, FEI, and CET-HCP scenarios proposed above. In this scenario, all six factors affecting road transport CO₂ emissions—vehicle population, vehicle structure, fuel structure, fuel emission factor, fuel economy, and average annual VKT—are changed. The specific parameters that change compared with the BAU scenario are shown in

Table 9. Parameter settings of passenger vehicles in the CS scenario.

Influencing Factors of CO2 Emission	Vehicle Type	Fuel Type	2020	2030	2040	2050	2060
Passenger vehicles population (ten thousand)	All	All	3042.6	5133.3	6864.9	7512.9	8731.0
Vehicle structure	Passenger vehicle	Mini	All	0.65%	0.30%	0.13%	0.06%
		Small	All	98.40%	98.77%	98.81%	98.81%
		Medium	All	0.28%	0.43%	0.65%	0.77%
		Large	All	0.67%	0.50%	0.41%	0.35%
Vehicle structure	Mini	All	0.65%	0.04%	0.00%	0.00%	0.00%
	Small	All	98.40%	98.77%	98.81%	98.81%	98.81%
	Medium	All	0.28%	0.69%	0.78%	0.83%	0.86%
	Large	All	0.67%	0.51%	0.42%	0.36%	0.32%
Fuel structure	Small and medium	Gasoline	98.00%	74.80%	42.40%	0.00%	0.00%
		Electricity	1.60%	22.93%	55.30%	98%	100.00%
		Hybrid	0.40%	2.27%	2.30%	2.00%	0.00%
	Large	Gasoline	75.00%	0.00%	0.00%	0.00%	0.00%
		Electricity	25.00%	100.00%	100.00%	100.00%	100.00%
Fuel economy	Mini	Gasoline (L/100 km)	5.20	3.54	3.28	3.07	2.89
		Electricity (kWh/100 km)	8.70	5.90	5.48	5.12	4.83
		Hybrid (L/100 km)	2.81	1.91	1.77	1.66	1.56
	Small	Gasoline (L/100 km)	8.30	7.13	6.08	5.09	4.23
		Electricity (kWh/100 km)	13.00	8.75	7.47	6.49	6.02
		Hybrid (L/100 km)	4.22	2.99	2.21	2.03	2.01
	Medium	Gasoline (L/100 km)	17.10	14.32	13.76	13.48	13.34
		Electricity (kWh/100 km)	120.00	114.05	105.30	91.24	80.49
		Hybrid (L/100 km)	9.24	7.74	7.44	7.29	7.21
	Large	Gasoline (L/100 km)	21.80	18.29	17.59	17.03	16.62
		Electricity (kWh/100 km)	144.00	138.04	130.70	120.66	112.26
Average annual VMT (km)	Mini	All	10,000	8556	7113	5669	4225
	Small	All	12,000	10,219	8438	6656	4875
	Medium	All	35,000	32,425	29,850	27,275	24,700
	Large	All	48,300	44,318	40,335	36,353	32,370
Fuel emission factor	All	Electricity (kg/kWh)	0.71	0.32	0.23	0.16	0.14

and

Table 10. Parameter settings of freight vehicles in the CS scenario.

Influencing Factors of CO2 Emission	Vehicle Type	Fuel Type	2020	2030	2040	2050	2060
Fuel structure	All	Diesel	69.60%	44.34%	23.54%	7.82%	0.00%
	All	Gasoline	27.90%	14.26%	6.46%	2.18%	0.00%
	All	Natural gas	1.80%	14.60%	22.00%	26.00%	27.27%
	All	Electricity	0.70%	26.80%	48.00%	64.00%	72.73%
Fuel economy	Mini	Diesel (L/100 km)	6.80	5.59	5.10	4.79	4.64
		Gasoline (L/100 km)	9.60	7.87	6.00	3.85	2.14
		Natural gas (m3/100 km)	8.40	6.84	5.55	4.35	3.28
		Electricity (kWh/100 km)	18.50	16.56	14.23	12.98	12.22
	Light	Diesel (L/100 km)	8.70	7.14	6.49	6.03	5.88
		Gasoline (L/100 km)	11.00	9.09	7.41	5.80	4.36
		Natural gas (m3/100 km)	11.20	9.29	7.45	5.84	4.42
		Electricity (kWh/100 km)	125.00	114.39	104.20	92.84	86.62
	Medium	Diesel (L/100 km)	15.50	14.09	12.90	11.92	11.13
		Natural gas (m3/100 km)	17.50	14.37	11.72	9.19	6.94
		Electricity (kWh/100 km)	132.00	124.88	116.33	101.32	96.57
	Heavy	Diesel (L/100 km)	32.60	29.43	26.89	24.78	23.21
		Natural gas (m3/100 km)	30.80	25.59	20.60	16.18	12.24
		Electricity (kWh/100 km)	150.00	142.87	132.46	119.12	114.28
Average annual VMT (km)	Mini	All	20,000	18,250	16,500	14,750	13,000
	Light	All	20,000	18,250	16,500	14,750	13,000
	Medium	All	24,000	23,038	22,075	21,113	20,150
	Heavy	All	40,000	36,906	33,813	30,719	27,625
CO2 emission factor	All	Electricity (kg/kWh)	0.71	0.32	0.23	0.16	0.14

. The rest of the parameters are the same as those in the BAU scenario.

5. Results and Discussions

5.1. CO₂ Emissions of Subsectors of Road Transport in Different Scenarios

Based on the methods and data introduced above, CO₂ emissions from all passenger vehicles and freight vehicles in road transport can be calculated. For better comparison, we further divide passenger transport into subsectors of mini, small, medium, and large passenger vehicles and freight transport into subsectors of mini, light, medium, and heavy freight vehicles. Based on the LEAP, we can analyze CO₂ emissions from different subsectors of road transport in China from 2020 to 2060 under the eight policy scenarios proposed above. The results are shown in Figure 4.

Figure 4a shows that the total CO₂ emissions of road transport in the BAU scenario initially increase and then decrease yearly after reaching a peak of 1419.50 million tonnes in 2033. The CO₂ emissions of passenger transport increase and then decrease, from 54.08% in 2020 to 17.82% in 2060. On the other hand, the CO₂ emissions of freight transport grow steadily from 45.92% in 2020 to 82.18% in 2060. Before 2034, the CO₂ emissions of passenger transport are greater than those of freight transport. After 2034, the CO₂ emissions of passenger transport are gradually less than those of freight transport and continue to decline. The CO₂ emissions of small passenger vehicles and heavy freight vehicles are relatively large. While the CO₂ emissions of small passenger vehicles begin to decline after reaching a peak of 686.27 million tonnes in 2028, the growth trend of freight transport CO₂ emissions gradually slows down and begins to decline after 2056. The CO₂ emissions of other types of vehicles account for a relatively small proportion, with the trend relatively stable.

Figure 4b shows that in the CE scenario, due to the wide application of clean electricity for passenger vehicles, the reduction in passenger transport CO₂ emissions is significant. However, the CO₂ emission reduction of freight transport is small since the implementation of electric vehicles in freight transportation is more difficult. Figure 4c shows that the FEI scenario has promoted a great reduction in CO₂ emissions for all subsectors, with the CO₂ emission peak dropping significantly and the rate of CO₂ emission reduction accelerating after the peak. According to Figure 4d, in the SAV scenario, due to a decrease in the number of passenger vehicles and a change in vehicle structure, with an improvement in the fuel economy of vehicles, CO₂ emissions of passenger vehicles are significantly reduced compared with the BAU scenario. A comparison of Figure 4e–g shows that in the CET scenario, with an increase in carbon prices , the reduction of road transport CO₂ emissions becomes increasingly obvious. This is especially the case in the CET-HCP scenario, where the road transport CO₂ emissions will begin to decrease yearly after 2026 and will only be 488.10 million tonnes in 2060. Figure 4h clearly shows that in the CS scenario, under the combined effect of various emission reduction policies, the downward trend of CO₂ emissions from road transport is the quickest and the emission reduction is the most significant for passenger transport and freight transport.

5.2. CO₂ Emission Reduction Potential of Road Transport in Different Scenarios

5.2.1. Total CO₂ Emissions of Road Transport in Different Scenarios

Based on the LEAP, the total CO₂ emissions of China’s road transport in different scenarios from 2020 to 2060 can also be analyzed and compared, as shown in Figure 5. The CO₂ emission gaps between the BAU scenario and other scenarios are then regarded as the CO₂ emission reduction potential.

Compared with the BAU scenario, the CO₂ emission reduction of road transport in the CE scenario is not significant before 2031 due to the relatively low penetration rate of electric vehicles. However, with the expansion of the vehicle electrification scale, the CO₂ emission of electric vehicles from power generation becomes one of the main emission sources in road transport. Therefore, the CO₂ emission reduction potential in the CE scenario is increasing yearly and even surpasses that in the FEI, SAV, and CET-LCP scenarios by 2060. The CO₂ emission reduction in the FEI scenario is relatively steady every year due to the continuing improvement in the fuel economy of vehicles, whereas the CO₂ emission reduction potential in the SAV scenario is gradually decreasing yearly since the vehicle population tends to be saturated. In the CET scenarios, the CO₂ emission reduction of road transport is gradually increasing. A comparison of CET-LCP, CET-MCP, and CET-HCP scenarios reveals that the CO₂ emission reduction potential increases with the rise of carbon prices. Among all the scenarios, the CS scenario has the greatest potential for road transport CO₂ emission reduction with all the policies implemented.

For better evaluation and comparison of the CO₂ emission reduction potential under different scenarios, the key numerical results are listed in

Table 11. Comparison of road transport CO₂ emissions under different scenarios from 2020 to 2060.

Scenarios	Peak Year of CO2 Emission	CO2 Emission Peak (Million Tonnes)	Cumulative CO2 Emission (Million Tonnes)	Cumulative CO2 Emission Reductions Compared with the BAU Scenario (Million Tonnes)	CO2 Emission Reduction Rate from Carbon Peak Year to 2060
BAU	2033	1419.50	51,073.75	-	34%
CE	2031	1378.69	47,460.70	3613.05	47%
SAV	2029	1315.47	47,274.21	3799.54	34%
FEI	2029	1335.51	46,320.29	4753.46	43%
CET-LCP	2029	1350.32	45,587.65	5486.10	44%
CET-MCP	2027	1307.91	41,560.01	9513.74	53%
CET-HCP	2026	1272.22	37,110.66	13,963.08	62%
CS	2023	1200.37	28,572.73	22,501.22	82%

. It shows that the peak years of CO₂ emission are becoming earlier from the BAU scenario to the CS scenario. The cumulative CO₂ emission in different scenarios are ranked as follows: BAU > CE > SAV > FE I > CET-LCP > CET-MLP > CET-HCP > CS. Among them, the CS scenario has the greatest potential for CO₂ emission reduction. In the CS scenario, CO₂ emissions of road transport will peak at 1200.37 million tonnes in 2023 and decrease to 217.73 million tonnes by 2060. The reduction rate of road transport CO₂ emission from the carbon peak year to 2060 can be up to 82%. From 2020 to 2060, the cumulative CO₂ emissions in the CS scenario are only 28,572.73 million tonnes. Compared with the BAU scenario, the cumulative CO₂ emission reduction is 22,501.22 million tonnes in the CS scenario.

5.2.2. Comparison with Previous Studies

In this study, we separately calculated CO₂ emissions of passenger transport and freight transport and obtained the total CO₂ emissions of road transport. In this section, we compare the results with some previous studies on the CO₂ emissions of passenger transport and freight transport in China. For example, Peng et al. [47] predicted that the direct CO₂ emissions of the road transport sector in mainland China will peak at 1500 million tonnes around 2030 and gradually decline to 1341.3 million tonnes in 2050 in the reference scenario. In the BAU and low carbon scenarios, the direct CO₂ emissions further decrease to 892.6 and 620.6 million tonnes in 2050, respectively. Gambhir et al. [48] demonstrated that road transport CO₂ emissions in China could decrease from 2080 million tonnes in the BAU scenario to 1240 million tonnes in the low carbon scenario by 2050. Yan et al. [49] indicated that CO₂ emissions of China’s road transport sector in 2030 would reach 1303.7 million tonnes in the BAU scenario, which will be reduced to 783.1 million tonnes in the best-case scenario. Through the comparison, it can be found that the CO₂ emission reduction potential of China’s road transport in our study is much greater than that in the previous studies. It indicates that the policy scenarios in our studies may have more significant effects on the CO₂ emission reduction of road transport.

5.3. Factor Contribution to Road Transport CO₂ Emission Reduction

Based on the LMDI method, the factors contributing to the emission reduction of road transport from the peak year to 2060 are composed for each scenario, as shown in Figure 6, Figure 7, Figure 8, Figure 9, Figure 10, Figure 11, Figure 12 and Figure 13.

As shown in Figure 6, among the various influencing factors of CO₂ emissions in the BAU scenario, the increase in vehicle population has a positive role in promoting an increase in CO₂ emissions, with the contribution of CO₂ emissions being as high as 246.53 million tonnes, which is the main reason for the increase in road transport CO₂ emissions. Besides, the vehicle structure also has a positive role in promoting an increase in CO₂ emissions. However, vehicle structure’s contribution is only 48.91 million tonnes, substantially less than the influence of the vehicle population. The factors that contribute to the reduction of CO₂ emissions include the average annual VKT, fuel structure, fuel economy, and fuel emission factor. With an increase in the proportion of electric vehicles and other clean-energy vehicles, an improvement in fuel economy, and a decrease in the electricity CO₂ emission factor, road transport CO₂ emissions will be reduced. The most influential factor of CO₂ emission reduction is fuel structure, with its contribution reaching 387.35 million tonnes in the BAU scenario.

According to Figure 7, Figure 8, Figure 9, Figure 10, Figure 11, Figure 12 and Figure 13, the contribution of factors to the road transport CO₂ emission reduction varies from scenario to scenario. In the CE scenario, the emission reduction contribution of the fuel emission factor increased by 12.01 million tonnes compared with the BAU scenario due to cleaner electricity used in the electric vehicles. Similarly, the contribution of fuel economy factors to emission reduction in the FEI scenario increased from 247.27 million tonnes (BAU) to 422.37 million tonnes since the additional improvement in the fuel economy of vehicles. The role of the vehicle population in the increase of road transport CO₂ emissions is weakened in the SAV scenario because of the reduced demand for private cars. The distributions of the factor contribution to road transport CO₂ emission reduction in the three CET scenarios are similar. Fuel structure is the driving factor of CO₂ emission reduction in CET scenarios with market-based incentives for low-carbon vehicles. In the CS scenario, the contribution of all factors to the road transport CO₂ emission reduction is enhanced.

To comparatively analyze the relative contribution of various factors, the contribution rates of each factor under different policy scenarios are shown in Figure 14. In all scenarios, the contribution rate of the vehicle population to CO₂ emissions is positive and greater than 0.35, which restrains the reduction of road transport CO₂ emissions. However, the average annual VKT, fuel structure, fuel economy, and fuel emission factor contribute to reducing road transport CO₂ emissions, which can promote the reduction of road transport CO₂ emissions. Fuel structure and fuel economy are the most important factors that restrain road transport CO₂ emissions. The contribution rate of fuel structure is up to −0.88 in the CE scenario, with the contribution rate of fuel economy up to −0.74 and −0.72 in the SAV and FE scenarios, respectively.

To sum up, the vehicle population has the greatest impact on road transport CO₂ emission, followed by the vehicle structure, fuel economy, fuel structure, and average annual VKT. The emission reduction contribution of the fuel emission factor is relatively small, but with an increase in the proportion of electric vehicles, the fuel emission factor will be important and non-negligible in the medium and long term for the reduction of road transport CO₂ emission. Therefore, to achieve a deep and comprehensive reduction in road transport CO₂ emissions, all the factors discussed above should be considered when developing a low-carbon policy for road transport.

6. Conclusions and Policy Implications

To help achieve the goal of carbon neutrality in China, this study proposed eight policy scenarios to reduce CO₂ emissions of road transport. These scenarios are defined by several key factors that influence road transport CO₂ emissions, such as vehicle population, vehicle structure, fuel structure, fuel economy, average annual VKT, and fuel emission factors. Based on the scenarios set on the LEAP, road transport CO₂ emissions in China are analyzed from 2020 to 2060 for each scenario. Compared with the BAU scenario, the CO₂ emission reduction potential of the other seven scenarios is evaluated. Furthermore, the factors contributing to the emission reduction of road transport from the peak year to 2060 are composed using the LMDI method. The main findings are summarized as follows:

(1)

Due to the widespread adoption of electric vehicles for passenger transport, they have a greater potential to reduce CO₂ emissions than freight transport in the field of road transport, especially for small passenger vehicles.

(2)

The total CO₂ emissions of road transport will peak at 1419.5 million tonnes in 2033 for the BAU scenario. In contrast, the peaks of road transport CO₂ emissions for the CE, SAV, FEI, CET-LCP, CET-MCP, CET-HCP, and CS scenarios are decreasing and occur progressively earlier, as early as 2023.

(3)

Compared with the BAU scenario, the cumulative CO₂ emission reductions of road transport from 2020–2060 for the other seven scenarios can be up to 22,501.22 million tonnes. The CO₂ emission reduction potential of the seven scenarios can be ranked as follows: CS > CET-HCP > CET-MCP > FEI > CET-LCP > SAV > CE. This finding indicates that CO₂ emission trading may be more effective than other policies, with a combination of policies the best.

(4)

Based on the decomposition of factors that contribute to the CO₂ emission reduction of road transport from the peak year to 2060 for each scenario, it is concluded that fuel structure and fuel economy contribute most to the emission reduction, whereas the increase in vehicle population restrains the CO₂ emission reduction.

The above findings also provide certain policy implications for the government to design a pathway toward low-carbon road transport under the goal of CO₂ emission peak and carbon neutrality in China. The specific policy suggestions are as follows:

(1)

The power industry needs to vigorously increase the proportion of clean energy in power generation, including photovoltaic, hydroelectric, wind, and nuclear powers, to further reduce CO₂ emissions of electric vehicles.

(2)

The government should formulate relevant policies to encourage vehicle manufacturers to improve the fuel economy of both traditional internal combustion engine vehicles and new energy vehicles to reduce the energy consumption and CO₂ emissions of road transportation.

(3)

Since private vehicles account for a large proportion of passenger transport in China, the government could implement downstream emission trading for road transport to encourage more consumers to purchase new energy vehicles.

(4)

To ensure the achievement of the targets of peak CO₂ emissions and carbon neutrality in China, a comprehensive policy package should be designed considering all the contributing factors to the emission reduction of road transport.

This study also has some limitations. Due to the immature technology and the high cost of hydrogen and other synthetic fuel cell vehicles, the future development of these vehicles is uncertain and difficult to predict. In addition, the proportion of these vehicles is currently negligible. Therefore, this study does not include hydrogen and other synthetic fuels in the fuel structure. The COVID-19 pandemic situation may reduce the traffic demand of road transport during the prevention and control period. However, the period was short and the impact was moderate across the year 2020. Therefore, this study does not consider the consequence of the COVID-19 pandemic in the scenario analysis of the road transport CO₂ emissions in China from 2020 to 2060, since we use the average annual VKT in the calculation. These limitations should be further addressed in future studies.