Policy scenario analysis of achieving the "dual carbon" goal for the road transportation sector in China

Abstract

The goal of "dual carbon" has brought the pressure of reducing carbon emissions to the road transportation sector in China. This study aims to identify and analyze possible policy measures that would help to achieve this goal from the perspective of the whole life cycle. We build a bottom-up computing framework based on the LEAP model. Six policy scenarios are designed to quantitatively assess their effects. The results show that promoting pure electric vehicles (PEV) and plug-in hybrid electric vehicles (PHEV) is appropriate for the passenger vehicles (PV), light-duty trucks (LDT), and mini trucks (MT) sub-sectors, while hydrogen fuel cell vehicles (FCV) are more suitable for the promotion of the heavy-duty trucks (HDT), heavy-duty buses (HDB), and medium-duty buses (MDB) sub-sectors. Additionally, the recycling of vehicle materials can effectively reduce the carbon emissions during the life cycle of motor vehicles. Furthermore, reducing the carbon emissions from the vehicle cycle of PV and the fuel cycle of HDT should be taken seriously. The main contribution of this study lies in establishing a comprehensive list of vehicle cycle carbon emission factors, covering various sub-sectors of China's road transportation sector. Besides, this study considers China's strategic planning and "dual carbon" goal, and proposes policy recommendations specifically tailored to the practical issues faced by China.

1 Introduction

The transportation sector plays an important role in promoting social development and improving people's living standards. However, this is also a major area of carbon emissions (He et al. 2005). Recently, the carbon emissions of this sector have significantly increased, making it one of the fastest growing sectors. In particular, the road transportation sector contributes the most to carbon emissions in the sector (Li et al. 2021a, b). According to IEA (2022), in 2021, the carbon emissions of the global transportation sector accounted for 37% of total emissions, among which road transportation alone accounted for 75% of global transportation sector emissions. This trend is likely to continue, especially in developing countries and emerging economies, where transport demand is expected to continue. Failure to implement various measures aimed at reducing carbon emissions in the road transportation sector could lead to a continuous increase in emissions.

China is the largest developing country in the world, and it is also the country with the highest carbon emissions. According to General Secretary Xi Jinping, China will strive to reach the peak of carbon emissions by 2030 and achieve carbon neutrality by 2060, thus establishing China's "dual carbon" goal (Xinhua News Agency 2020). In 2021, the transportation sector contributed 10% to China's total carbon emissions. Additionally, China's road transportation sector is responsible for 86.8% of the transportation sector's carbon emissions (CAICT 2021). It is estimated that there will be 417 million motor vehicles in China in 2022 (Ministry of Public Security of the PRC 2023). As economic growth continues and transportation demand rises, motor vehicles will become more prevalent. Considering China's continued reliance on fossil fuels for fuel in traditional internal combustion engine vehicles (ICEV), there will likely be significant pressure in the future to reduce carbon emissions from the road transportation sector.

In order to achieve the "dual carbon" goal for China's road transportation sector throughout its life cycle, it is very important to take effective measures. In addition, these measures play a vital role in helping society achieve the "dual carbon" goal at large. These measures can also provide reference for other developing countries and emerging economies.

From the industry, provincial, national and global perspective, the current studies have extensively analyzed future development trends for the road transportation sector, as well as directions and measures to reduce carbon emissions (Hu et al. 2010; Ou et al. 2010; Yin et al. 2015; Hao et al. 2015; Peng et al. 2018a; Li et al. 2021a, b). It can be found that, no matter from which point of view, this sector needs to analyze policy scenarios to identify appropriate policies to reduce carbon emissions in the future. However, the existing studies have not sufficiently addressed updated key data and assumptions and research combining updated national strategic planning and the "dual carbon" goal to forecast carbon emissions trends in the road transportation sub-sectors by 2060.

In the road transportation sector, carbon emissions are primarily composed of fuel cycle and vehicle cycle emissions when considering the whole life cycle. Many studies focus on the technical aspects of vehicle electrification and its impact on carbon emissions, particularly the application of PEV and PHEV (He and Chen 2013; Wang et al. 2017; Peng et al. 2018b; Buberger et al. 2022). The current research focuses mainly on fuel cycle carbon emissions within the road transportation sector, while neglecting vehicle cycle emissions. However, it should be noted that PEV and PHEV incur a much larger proportion of carbon emissions in their vehicle cycle compared to ICEV (Qiao et al. 2019). Therefore, the carbon emissions during the vehicle cycle of road transportation should not be ignored.

In addition, countries worldwide regard hydrogen energy as a crucial link between renewable and traditional fossil energy. With the promotion of hydrogen fuel cells to the national strategic level through "Made in China 2025", vehicle hydrogenation emerged as a significant method for carbon emission reduction in the road transportation sector. In recent years, more and more studies have analyzed the carbon emissions during the life cycle of FCV (Ren et al. 2020, 2022; Ajanovic et al. 2021; Li and Taghizadeh-Hesary 2022). It can be found that the existing literature primarily focuses on life cycle carbon reduction achieved by using single or whole fleets of commercial FCV to replace ICEV under various hydrogen production modes, with most of the literature concentrating on the fuel cycle. However, the lack of research is not only limited to the vehicle cycle, but also encompasses the future trends of the road transportation sector's vehicles under varying hydrogen production ratio structures until 2060.

Hao et al. (2017) compared carbon emissions between recycling and non-recycling EV during production. The results showed that recycling EV materials would decrease carbon emissions by 34%. Qiao et al. (2019) found that recycling raw materials from EV can reduce carbon emissions in the vehicle cycle by an even greater margin of 50%. However, Golroudbary et al. (2019) found that recycling lithium-ion batteries using current technology increases carbon emissions by 16% and 20%. To sum up, there is still no consensus on the effect of recycling vehicle materials on carbon emissions in the literature. Therefore, exploring the carbon emissions impact of recycling vehicle materials and future trends in the road transportation sector is imperative.

The following are three aspects that reflect the contribution of this paper. First, it takes the lead in establishing a comprehensive list of vehicle cycle carbon emission factors associated with various sub-sectors of China's road transportation sector; Thus, it makes up for the deficiency of previous studies which had been restricted to direct or fuel cycle carbon emissions. Second, it quantitatively evaluates China's road transportation sector carbon emissions, considering the recycling of vehicle materials; Thus, it resolves the dispute regarding whether recycling vehicle materials reduces carbon emissions. Third, it incorporates the latest data and key assumptions in light of China's strategic planning and "dual carbon" goal; Thus, it identifies and provides policy recommendations for practical issues faced by China through rational scenario analysis.

The paper is structured as follows: Section 2 introduces the LEAP model, the Life Cycle Assessment (LCA), the method for calculating the carbon emissions during the life cycle, the method for calculating the fuel cycle carbon emission factors, and the method for calculating the vehicle cycle carbon emission factors. Section 3 provides the data and corresponding data sources. Section 4 contains six scenario analyses. Section 5 discusses the research results from both historical and future perspectives. Finally, the paper summarizes its conclusions and provides targeted policy recommendations.

2 Methodology

2.1 The LEAP model

The Stockholm Environment Institute's LEAP model will be used in this study. It is an adaptable and user-friendly instrument that provides scenario analysis on energy and the environment, aiding policymakers in anticipating upcoming events and assessing problems (SEI 2009). This study will analyze various policy scenarios to identify the impact of different policy measures on motor vehicles. We can use the LEAP model to predict future stocks, sales, and carbon emissions in China's road transportation sector, identify the principal factors contributing to carbon emissions reduction, and evaluate policy effectiveness in various circumstances. Ultimately, the policy insights uncovered from these different scenarios promote the identification of the most effective strategy and offer recommendations to policymakers, thereby contributing towards the objective of "dual carbon" in China's road transportation sector.

2.2 The Life Cycle Assessment (LCA)

Life Cycle Assessment (LCA) is an environmental management method that assesses the energy consumption and pollution emissions of a product or technology throughout its life cycle. Figure 1 shows the study's research boundary, which covers both the vehicle and fuel cycle. In particular, we focus on three greenhouse gases: ${{\text{CO}}}_{2}$, ${{\text{CH}}}_{4}$ and ${{\text{N}}}_{2}{\text{O}}$. In this study, carbon emissions are commonly used to refer to greenhouse gas emissions, as they account for the greatest proportion of them. The carbon emissions of the vehicle cycle include those generated during vehicle production, assembly, distribution, and recycling. The carbon emissions during the fuel cycle refer to those caused by feed stock production, feed stock transportation, fuel production, fuel transportation, refueling, and vehicle operation.

Fig. 1

Research boundary

2.3 The method for calculating the life cycle carbon emissions

Life cycle carbon emissions comprise both vehicle and fuel cycle carbon emissions, which are represented in Eq. 1:

$${{\text{LC}}}_{{\text{i}},{\text{j}},{\text{k}},{\text{t}}}={{\text{VC}}}_{{\text{i}},{\text{j}},{\text{k}},{\text{t}}} + {{\text{FC}}}_{{\text{i}},{\text{j}},{\text{k}},{\text{t}}}$$

(1)

Here, LC denotes the carbon emissions during the life cycle (g ${{\text{CO}}}_{2}$ eq); VC is the carbon emissions during the vehicle cycle (g ${{\text{CO}}}_{2}$ eq); FC is the carbon emissions during the fuel cycle (g ${{\text{CO}}}_{2}$ eq). The subscript i indicates the fuel type (diesel, gasoline, natural gas, electricity and hydrogen); j denotes the vehicles type (trucks, buses, passenger vehicles and motorcycles); k denotes the vehicles model (heavy-duty, medium-duty, light-duty and mini); t is the year.

Equation 2 illustrates the calculation for the carbon emissions during the vehicle cycle.

$${{\text{VC}}}_{{\text{i}},{\text{j}},{\text{k}},{\text{t}}}=\sum {{\text{Stock}}}_{{\text{i}},{\text{j}},{\text{k}},{\text{t}}}\times {{\text{DT}}}_{{\text{i}},{\text{j}},{\text{k}},{\text{t}}}\times {{\text{VCRat}}}_{{\text{i}},{\text{j}},{\text{k}},{\text{t}}}$$

(2)

Here, Stock denotes the remaining stock; DT is the average annual travel distance (km); VCRat is the vehicle cycle carbon emission factor (g ${{\text{CO}}}_{2}$ eq/km).

Equation 3 demonstrates the computation of fuel cycle carbon emissions.

$${{\text{FC}}}_{{\text{i}},{\text{j}},{\text{k}},{\text{t}}}=\sum {{\text{Stock}}}_{{\text{i}},{\text{j}},{\text{k}},{\text{t}}}\times {{\text{DT}}}_{{\text{i}},{\text{j}},{\text{k}},{\text{t}}}\times {{\text{FE}}}_{{\text{i}},{\text{j}},{\text{k}},{\text{t}}}\times {{\text{FCRat}}}_{{\text{i}},{\text{t}}}$$

(3)

Here, FE denotes the average on-road fuel economy (MJ/km); FCRat is the fuel cycle carbon emission factor (g ${{\text{CO}}}_{2}$ eq/MJ).

Equation 4 provides the calculation for the remaining stock of motor vehicles.

$${{\text{Stock}}}_{{\text{i}},{\text{j}},{\text{k}},{\text{t}}}=\sum {{\text{Stock}}}_{{\text{i}},{\text{j}},{\text{k}},{\text{t}}-1}\times {{\text{SurRat}}}_{{\text{i}},{\text{j}},{\text{k}}}+{{\text{Sale}}}_{{\text{i}},{\text{j}},{\text{k}},{\text{t}}}$$

(4)

Here, SurRat denotes the percentage of vehicle survived (%); Sale is the sales volume.

2.4 The method for calculating the fuel cycle carbon emission factors

This study shows that the fuel cycle carbon emission factors for various FCV models using different hydrogen production methods are calculated using Eq. 5.

$${{\text{FC}}}_{{\text{p}},{\text{j}},{\text{k}},{\text{t}}}={{\text{FCRat}}}_{{\text{p}},{\text{t}}}\times {{\text{LHV}}}_{{\text{H}}2}\times {{\text{FE}}}_{{\text{j}},{\text{k}},{\text{t}}}\times {\text{UtiRat}}\times \frac{1}{100}$$

(5)

Here, ${{\text{LHV}}}_{{\text{H}}2}$ is the low calorific value of hydrogen (MJ/kg), according to the data of reference (Ren et al. 2020), ${{\text{LHV}}}_{{\text{H}}2}$=119.64 MJ/kg. FE is fuel economy (kg/100 km); UtiRat is energy efficiency (%), summed up the data in the literature (Ren et al. 2020) to get UtiRat = 88%. The subscript p indicates hydrogen production methods (coal-based, natural gas-based, renewable energy water electrolysis-based, and industrial by-product-based).

2.5 The method for calculating the vehicle cycle carbon emission factors

Each vehicle cycle carbon emission factor represents the carbon emissions during the vehicle cycle associated with one kilometer of driving for various vehicles in the study. It includes the carbon emissions produced during various stages of the vehicle cycle, including production, assembly, distribution, and recycling.

Regarding carbon emissions emitted during vehicle production process, firstly, representative vehicles powered by diesel, gasoline, and compressed natural gas and their corresponding equipment quality were determined based on the state of China's automobile market (see Table 1). Then, we referenced the raw material composition and corresponding mass distribution data for vehicle bodies in the GREET model of the Argonne National Laboratory (ANL 2022) and combined this with carbon emissions data from the literature (Gao et al. 2008; Jing et al. 2014; George and Bressler 2017; Yadav and Banerjee 2018; Peng et al. 2019; Yang et al. 2020; He et al. 2020; Rani et al. 2021; Li et al. 2021a, b; Golroudbary et al. 2022; Chu et al. 2022; Duan et al. 2022; Wulf and Zapp 2022; Lu et al. 2022; An et al. 2022) concerning the raw material production phase (see Table 2). Equation 6 can be used to estimate carbon emissions emitted during vehicle production process.

Table 1 Representative vehicles for various types and their corresponding gross weight (kg)

Table 2 Carbon emissions in the raw material production stage (kg ${{\text{CO}}}_{2}$ eq/kg) and corresponding mass distribution (%)

$${{\text{PC}}}_{{\text{i}},{\text{j}},{\text{k}},{\text{t}}}=\sum_{{\text{m}}}{{\text{M}}}_{{\text{i}},{\text{j}}.{\text{k}},{\text{t}}}\times {{\text{MD}}}_{{\text{m}}}\times {{\text{C}}}_{{\text{m}},{\text{t}}}$$

(6)

Here, PC denotes carbon emissions in the vehicle production process (kg ${{\text{CO}}}_{2}$ eq); M is the gross weight of vehicles (kg); MD is the mass distribution of raw materials (%); C is carbon emissions in the raw materials production process (kg ${{\text{CO}}}_{2}$ eq/kg). The subscripts m indicates the category of raw materials (steel, stainless steel, cast iron, wrought aluminum, cast aluminum, copper, zinc, magnesium, glass, average plastics, rubber, carbon fiber and glass fiber).

The data from the GREET model is utilized for the following reasons. On the one hand, data on the composition of vehicle raw materials and their detailed mass distribution in China are lacking. On the other hand, by estimating data through American disassembly data, disassembly reports, enterprise surveys, and literature reviews, these data are also applicable to Chinese vehicles with modified rated total weight due to similar mass distribution (Qiao et al. 2019).

Regarding carbon emissions during the vehicle assembly process, Eq. 7 shows the calculation process at this stage.

$${{\text{AC}}}_{{\text{i}},{\text{j}},{\text{k}},{\text{t}}}= {\text{UEN}}\times {\text{UC}}\times {{\text{M}}}_{{\text{i}},{\text{j}},{\text{k}},{\text{t}}}\times {10}^{^-6}$$

(7)

Here, AC denotes carbon emissions in the vehicle assembly process (kg ${{\text{CO}}}_{2}$ eq); UEN is the unit energy consumption of vehicle assembly process (KJ/kg); UC is the unit carbon emissions (kg ${{\text{CO}}}_{2}$ eq/GJ). According to Qiao et al. (2019), UEN = 19750 kJ/kg, UC = 38.65 kg ${{\text{CO}}}_{2}$ eq/GJ.

Regarding carbon emissions during the vehicle distribution process, vehicle distribution emissions are generally proportional to energy consumption, which is in turn proportional to distribution distance and vehicle weight. This study assumes diesel fuel for vehicle distribution. Equation 8 illustrates the calculation process at this stage.

$${{\text{DC}}}_{{\text{i}},{\text{j}},{\text{k}},{\text{t}}}= {\text{PEN}}\times {\text{D}}\times {\text{DCRat}}\times {{\text{M}}}_{{\text{i}},{\text{j}},{\text{k}},{\text{t}}}\times {10}^{^-6}$$

(8)

Here, DC denotes carbon emissions in the vehicle distribution process (kg ${{\text{CO}}}_{2}$ eq); PEN is the unit energy consumption of vehicle distribution process (KJ/kg km); D is the distance of vehicle distribution in China (km); DCRat is the life cycle carbon emission factor of diesel (g ${{\text{CO}}}_{2}$ eq/MJ). According to Qiao et al. (2019), PEN = 0.6 kJ/kg km, D = 1207.44 km, DCRat = 92.4 g CO₂ eq/GJ.

Regarding carbon emissions during the vehicle recycling process, the calculation method for carbon emissions is similar to that used during vehicle production. After confirming raw material composition and corresponding weights, carbon emissions data from existing literature on raw material recycling can be used to calculate emissions during the raw material recycling process for different vehicle models (Yang et al. 2020; Rani et al. 2021) (see Table 3), using Eq. 9:

Table 3 Carbon emissions in the raw material recycling stage (kg ${{\text{CO}}}_{2}$ eq/kg)

$${{\text{RC}}}_{{\text{i}},{\text{j}},{\text{k}},{\text{t}}}= {\sum }_{{\text{m}}}{{\text{M}}}_{{\text{i}},{\text{j}}.{\text{k}},{\text{t}}}\times {{\text{MD}}}_{{\text{m}}}\times {{\text{RRC}}}_{{\text{m}},{\text{t}}}$$

(9)

Here, RC denotes carbon emissions in the vehicle recycling process (kg C0₂ eq);

RRC is carbon emissions of raw materials in the process of recycling (kg C0₂ eq/kg).

The current vehicle cycle analysis only considers the body of the vehicle. It disregards the fact that a tire change is necessary during the vehicle cycle. Additionally, it fails to consider the batteries present in ICEV and MC. According to the GREET model (ANL 2022), this study assumes that every vehicle undergoes tire replacement three times during its vehicle cycle, taking into account the lead-acid battery (LAB) utilized in ICEV and MC. To obtain accurate data, it is imperative to establish the ratio of the tire and battery weight to the total vehicle weight, as well as the composition and mass distribution of the tire raw materials contained in the GREET model (ANL 2022). Equation 10 can be used to compute the carbon emissions throughout the life cycle linked to the raw materials of a tire.

$${{\text{Tire}}}_{{\text{i}},{\text{j}},{\text{k}},{\text{t}}}=\sum_{{\text{n}}}{{\text{M}}}_{{\text{i}},{\text{j}}.{\text{k}},{\text{t}}}\times {{\text{MD}}}_{{\text{tire}}}\times {{\text{MD}}}_{{\text{n}}}\times {{\text{C}}}_{{\text{n}},{\text{t}}}$$

(10)

Here, Tire denotes life cycle carbon emissions of tire (kg C0₂ eq); ${{\text{MD}}}_{{\text{tire}}}$ is the ratio of tires to total gross weight, MD_tyre = 0.74%; ${{\text{MD}}}_{{\text{n}}}$ indicates the mass distribution of the nth kind of raw materials, when n = rubber, MD_n = 66.7%, when n = steel, MD_n = 33.3%. The life cycle carbon emission factor for LAB was obtained by literature (Mc Manus 2012). And the life cycle carbon emissions for LAB can be calculated according to Eq. 11.

$${{\text{LABC}}}_{{\text{i}},{\text{j}},{\text{k}},{\text{t}}}={{\text{M}}}_{{\text{i}},{\text{j}}.{\text{k}},{\text{t}}}\times {{\text{MD}}}_{{\text{lab}}}\times {\text{LABCRat}}$$

(11)

Here, LABC denotes life cycle carbon emissions of LAB (kg C0₂ eq); ${{\text{MD}}}_{{\text{lab}}}$ is the ratio of LAB to total gross weight, MD_lab = 0.87%. LABCRat is the life cycle carbon emission factor for LAB (kg C0₂ eq/kg), LABCRat = 0.9 kg C0₂ eq/kg (Mc Manus 2012).

By adding up all the stages, the vehicle cycle carbon emissions for various vehicles can be determined, as shown in Eq. 12.

$${{\text{VC}}}_{{\text{i}},{\text{j}},{\text{k}},{\text{t}}}={{\text{PC}}}_{{\text{i}},{\text{j}},{\text{k}},{\text{t}}} +{{\text{AC}}}_{{\text{i}},{\text{j}},{\text{k}},{\text{t}}}+{{\text{DC}}}_{{\text{i}},{\text{j}},{\text{k}},{\text{t}}}+{{\text{RC}}}_{{\text{i}},{\text{j}},{\text{k}},{\text{t}}}+{{\text{Tire}}}_{{\text{i}},{\text{j}},{\text{k}},{\text{t}}}+3\times {{\text{LABC}}}_{{\text{i}},{\text{j}},{\text{k}},{\text{t}}}$$

(12)

3 Data

3.1 Classification of vehicle types

According to the Classification of Motor Vehicles and Trailers (2001) (GB/T15089-2001) and the Types of Motor Vehicles for Road Traffic Management (2019) (GA802-2019), motor vehicles can be categorized into two main types: automobiles and motorcycles. Automobiles can be further divided into two categories, which are commercial vehicles and passenger vehicles. Commercial vehicles consist of trucks and buses. Table 4 presents a detailed classification description.

Table 4 Classification of vehicle types

3.2 Stocks and sales

3.2.1 ICEV and MC

Here, the terms stocks and sales denote the number of ICEV (diesel, gasoline and natural gas vehicles) and MC that are owned and sold in China, respectively. This study adopts 2013 as the base year, and does not include sales data in the stock value for this year. Data regarding the stocks of commercial and passenger vehicles were sourced from the China Automobile Market Yearbook (NBS 2014), and the sales data were originated from the China Automobile Industry Yearbook (NBS 2014). The motorcycle stocks data were gathered from China Annual Report on Motor Vehicle Pollution Control (Ministry of Ecology and Environment of the PRC 2013), and the sales data were retrieved from China Automobile Industry Yearbook (NBS 2016). Due to the lack of official data on the annual sales growth rate, the sales growth rate of vehicles in 2013 and the average sales growth rate from 2014 to 2020 are calculated by the actual sales data obtained by the China Association of Automobile Manufacturers (CAAM 2014-2021a; CAAM 2014-2021b). Future estimates of annual sales growth rates were inferred from previous literature (Ou et al. 2010; He and Chen 2013), as seen in Table 5.

Table 5 Stocks (Thousand) and the future estimates of annual sales growth rates (%)

The LEAP model utilizes the motor vehicle stocks structure to depict the ratio between the various lifespan categories of motor vehicles and the existing motor vehicles. Owing to the absence of data on the vehicle stocks structure for 2014 and the stable nature of the vehicle stock structure over time, the 2017 vehicle stocks structure data was selected instead. The data were derived from the Energy Saving and New Energy Vehicle Development Report (CATARC 2017), as shown in Table 6, which provided the motor vehicle stocks structure information.

Table 6 Vehicle stocks structure (%)

3.2.2 PEV and PHEV

The stocks and sales here refer to the quantities of PEV and PHEV that have been owned and sold in China, respectively. The data on stocks and sales of PEV and PHEV was compiled based on data from the China Association of Automobile Manufacturers (CAAM 2014-2021a; CAAM 2014-2021b). It should be noted that the stocks data from 2014 does not include the sales data from the same year (refer to Table 7 for details).

Table 7 The stocks and sales of PEV and PHEV (Thousand)

3.2.3 FCV

Stocks and sales here refer to the quantities of FCV owned and sold in China, respectively. According to official reports from the China Association of Automobile Manufacturers (CAAM 2014-2021a; CAAM 2014-2021b), the stocks of FCV held in 2017 were zero, which did not include the quantities of new sales in 2017. The information on FCV sales after 2017 is presented in Table 8, which was compiled based on data from the China Association of Automobile Manufacturers (CAAM 2014-2021a; CAAM 2014-2021b).

Table 8 The stocks and sales of FCV

3.3 Vehicle annual travel and fuel economy

3.3.1 ICEV and MC

Fuel economy pertains to a vehicle's capacity to undertake a unit of transport workload with minimal fuel consumption, which is measured by the amount of fuel used for every 100 km. As a result of insufficient official data regarding the average annual travel and fuel economy of motor vehicles and advancements in technology, fuel economy is anticipated to improve slightly. Therefore, we referred to previous literature (Ou et al. 2010; He et al. 2013) for guidance. Fuel economy data can be transformed into gasoline units within the LEAP model (see Table 9).

Table 9 Vehicle annual travel (km) and fuel economy (km/MJ; L/100km) for various ICEV and MC models

3.3.2 PEV and PHEV

The fuel economy of PEV here is evaluated by determining the 100 km power consumption. Meanwhile, the fuel economy of the PHEV is evaluated by determining the 100 km power consumption and fuel consumption of the vehicle. Due to the variations in the vehicle models of PEV and PHEV, their annual travel and fuel economy also differ. Therefore, this study utilized different data sources to collect relevant data for specific vehicle models. To be specific, the vehicle annual travel and fuel economy data for trucks were gathered from Zhu and Zhang (2018). The data for buses were acquired from two sources, which were Teng and Zhang (2019) and Guo and Zhang (2019). Lastly, the data for passenger vehicles were collected from Fan et al. (2019) (see Table 10).

Table 10 The vehicle annual travel (km) and fuel economy (Kwh/100 km; L/100 km) for various PEV and PHEV models

3.3.3 FCV

The fuel economy of FCV here is evaluated by calculating the hydrogen consumption over 100 km. Since different models of FCV have different average annual travel and fuel economy, this paper collected data from different sources, specific to each model. The average annual travel and fuel economy data of HDT, MDT and HDB were obtained from the Whole Life Cycle Economic Analysis of Hydrogen Fuel Cell Vehicles (EV100Plus et al. 2020). The data for LDT was sourced from Fan et al. (2018). Additionally, data for MDB and PV were collected from the World Hydrogen and Fuel Cell Vehicle Industry Development Report (China SAE 2018), as detailed in Table 11.

Table 11 The vehicle annual travel (km) and fuel economy (kg ${{\text{H}}}_{2}$/100 km) for various FCV models

3.4 Vehicle life and survival rate

The Compulsory Motor Vehicle Scrapping Standard Provisions (Ministry of Commerce of the PRC 2020) specify Chinese regulations for scrapping vehicles based on their maximum driving mileage or scrapping life. A vehicle must undergo dismantling once it reaches its maximum mileage or scrapping life. Vehicle survival rate refers to the survival proportion of vehicles that increase with age (Hao et al. 2011). The LEAP model uses a life curve to represent the survival rate of a vehicle during different life spans. Based on the data of stocks, sales, stocks structure, and survival rate, the alternating evolution model of new and old vehicles can be constructed to predict the future scenario (Lin et al. 2019). The existing studies (Hao et al. 2011; Huo and Wang 2012) show that there are some differences in vehicle life and survival rate among different economic levels, different countries, different models and different policies. Due to the shortage of consistent and accurate data, this study refers to the survival data provided by Ou et al. (2010) in Table 12.

Table 12 Vehicle life (Year) and survival rate estimate (%)

3.5 Life cycle carbon emission factors for various fuels

Fuel types analyzed in this study primarily consist of diesel, gasoline, and compressed natural gas. The life cycle carbon emission factors of these fuels consider the carbon emissions caused by the whole life cycle process of raw material production, transportation, fuel production, and transportation to use. The carbon emission factors are influenced by various factors, such as oil quality, engine technology and efficiency level, and driving conditions. As there are no official statistics available (Xie 2019), this study refers to the data for 2013 (Ou et al. 2010) and 2020 (Ren et al. 2022), which is presented in Table 13.

Table 13 Life cycle carbon emission factors for various fuels (g ${{\text{CO}}}_{2}$ eq/MJ)

3.6 Life cycle carbon emission factors for various power generation methods

At present, the electricity used to power PEV and PHEV is primarily generated by the State Grid, using diverse methods of power generation, such as thermal, hydro, wind, photovoltaic, and nuclear power. Various power generation methods result in different life cycle carbon emissions. As no official statistics are available currently, this paper references the data from Ren et al. (2022) and presents it in Table 14. The China Statistical Yearbook (NBS 2022) provides varying proportions of power generation methods for different years, resulting in the State Grid having distinct life cycle carbon emission factors for each year (see Table 15).

Table 14 Life cycle carbon emission factors for various power generation methods (g ${{\text{CO}}}_{2}$ eq/MJ)

Table 15 Power generation method proportion structure (%) and life cycle carbon emission factors of State Grid in 2014–2021 (g ${{\text{CO}}}_{2}$ eq/MJ)

3.7 Life cycle carbon emission factors of hydrogen under different hydrogen production methods

FCV require hydrogen for operation, and there exist several methods for hydrogen production. The life cycle carbon emission factors of hydrogen vary significantly depending on the production method used. This study primarily examines four hydrogen production methods: coal-based, natural gas-based, renewable energy water electrolysis-based, and industrial by-product-based. Table 16 provides data on the life cycle carbon emission factors of hydrogen, corresponding to each production method. The data is sourced from Ren et al. (2020).

Table 16 Life cycle carbon emission factors of hydrogen under different hydrogen production methods (g ${{\text{CO}}}_{2}$eq/MJ)

3.8 Fuel cycle carbon emission factors for various vehicles

3.8.1 ICEV and MC

Each fuel cycle carbon emission factor represents the fuel cycle carbon emissions associated with one kilometer of driving for various vehicles in the study. By combining the life cycle carbon emissions for various fuels with fuel economy data, fuel cycle carbon emission factors for various ICEV and MC models can be determined. Table 17 presents the fuel cycle carbon emission factors for various ICEV and MC models.

Table 17 Fuel cycle carbon emission factors for various ICEV and MC models (g ${{\text{CO}}}_{2}$eq/km)

3.8.2 PEV and PHEV

In this study, we obtained life cycle carbon emission factors of State Grid and fuel in different years. These were then combined with fuel economy data, and the LEAP model was utilized for unit conversion. By conducting these calculations, fuel cycle carbon emission factors for various PEV and PHEV models can be determined (see Table 18).

Table 18 The fuel cycle carbon emission factors for various PEV and PHEV models (g ${{\text{CO}}}_{2}$eq/km)

3.8.3 FCV

According to Eq. 5, the data of fuel cycle carbon emission factors for various FCV models under different hydrogen production methods can be acquired (see Table 19).

Table 19 The fuel cycle carbon emission factors for various FCV models under different hydrogen production methods (g ${{\text{CO}}}_{2}$eq/km)

3.9 Vehicle cycle carbon emission factors for various vehicles

3.9.1 ICEV and MC

The vehicle cycle carbon emission factors for various ICEV and MC models can be obtained by dividing it with the driving mileage of the vehicle in the whole life cycle (see Table 20).

Table 20 The vehicle cycle carbon emission factors for various ICEV and MC models (g ${{\text{CO}}}_{2}$eq/km)

3.9.2 PEV and PHEV

The methodology used to calculate the vehicle cycle carbon emission factors for different models of PEV, PHEV, and ICEV is similar. However, due to the difference in mass distribution and batteries used, the raw materials and batteries of PEV and PHEV differ from ICEV. Data on representative vehicles for various models and the corresponding gross weight of PEV and PHEV were obtained from the search engine (see Table 21). Furthermore, mass distribution data for PEV and PHEV were obtained from the GREET model (ANL 2022) (see Table 22). The life carbon emission factor for Lithium-ion battery (Li-ion) was obtained from (Jiang et al. 2022) (see Table 23). Using the above mentioned data and calculations, the vehicle cycle carbon emission factors of various vehicle models are presented in Table 24.

Table 21 Representative vehicles for various PEV and PHEV models and their corresponding gross weight (kg)

Table 22 The mass distribution of PEV and PHEV (%)

Table 23 The life cycle carbon emission factor of Li-ion battery (kg ${{\text{CO}}}_{2}$ eq/kg)

Table 24 The vehicle cycle carbon emission factors of various PEV and PHEV models (g ${{\text{CO}}}_{2}$ eq/km)

3.9.3 FCV

The process for calculating the vehicle cycle carbon emissions factors for various FCV models is similar to that used for ICEV. However, as the raw materials and batteries of FCV differ from those of ICEV, the mass distribution is different. Data regarding representative FCV models and their corresponding gross weights were obtained through the search engine (see Table 25). Mass distribution data for FCV were obtained from the GREET model (ANL 2022) (see Table 26). Data on the life cycle carbon emission factor of Ni-MH battery was obtained from Jiang et al. (2022) (see Table 27). Based on the above mentioned data and calculations, the vehicle cycle carbon emission factors for different FCV models are presented in Table 28.

Table 25 Representative vehicles for various FCV models and their corresponding gross weight (kg)

Table 26 The mass distribution of FCV (%)

Table 27 The life cycle carbon emission factor of Ni-MH battery (kg ${{\text{CO}}}_{2}$ eq/kg)

Table 28 The vehicle cycle carbon emission factors for various FCV models (g ${{\text{CO}}}_{2}$ eq/km)

3.10 Vehicle material recycling data

Vehicle materials recycling in this study refers to the recovery of body and battery materials from scrapped motor vehicles, followed by a series of processes such as recovery and disassembly to obtain raw materials for the production of new motor vehicles. In this study, the fuel cycle carbon emission factor for recycled materials is assumed to be equivalent to that of the original materials, and the calculation process for the vehicle cycle carbon emission factor of recycled materials is the same as the process used for the original materials. Table 29 shows the life cycle carbon emission factors for reused batteries. The vehicle cycle carbon emission factors of recycled materials for ICEV, MC, PEV, PHEV, and FCV are calculated using the above-mentioned method, as displayed in Tables 30, 31, and 32.

Table 29 The life cycle carbon emission factors of three kind of reused battery (kg ${{\text{CO}}}_{2}$ eq/kg)

Table 30 The vehicle cycle carbon emission factors of recycled materials of ICEV and MC (g ${{\text{CO}}}_{2}$ eq/km)

Table 31 The vehicle cycle carbon emission factors of recycled materials of PEV and PHEV (g ${{\text{CO}}}_{2}$ eq/km)

Table 32 The vehicle cycle carbon emission factors of recycled materials of FCV (g ${{\text{CO}}}_{2}$ eq/km)

4 Scenario design

The early studies (Cai et al. 2007; He and Chen 2013) focused on reducing carbon emissions in the road transportation sector, setting their future scenario in 2030. While some other related studies (Ou et al. 2010; Ren et al. 2020) set their future scenario in 2050. This study adopts a scenario designed for 2060 in line with China's aim to attain carbon neutrality by that year.

Firstly, a BAU scenario is established. Secondly, the ELE scenario, HYD scenario and VMR scenario are established. Next, the advantages of these three scenarios are combined to develop a combined scenario. Subsequently, on the basis of the combined scenario, the market permeability of PEV, PHEV and FCV will be improved and updated by 2060. Simultaneously, the proportion of hydrogen production methods is adjusted to establish an improved combined scenario.

4.1 BAU scenario (BAUS)

In the BAU scenario, 2013 is taken as the base year and 2060 is the target year. Any policies will not affect the future development trend of the road transportation sector. This scenario further assumes that the share and growth rate of motor vehicles will remain unchanged, and the fuel structure and vehicle technology are expected to remain at current levels. Therefore, it is important to note that the BAU scenario is intended to serve as a reference rather than a predictive scenario.

4.2 Electrified scenario (ELES)

When considering the whole life cycle, the carbon emissions of PEV and PHEV are lower compared to the carbon emissions produced by ICEV. Furthermore, as the State Grid continues to undergo improvements in cleanliness in the future, the difference in carbon emissions between these vehicle types will continue to increase.

This study considers ELE scenario, which views the State Grid structure as a crucial factor in affecting carbon emissions from the road transportation sector. The State Grid structure for China's road transportation sector in 2025, 2035, 2050, and 2060 is set based on existing State Grid infrastructure and relevant literature (Zhou et al. 2021; Lin 2022), as displayed in Table 33.

Table 33 Setting of State Grid structure (%)

In addition to State Grid structure, the market sales share, 100 km power consumption, and 100 km fuel consumption of PEV and PHEV are crucial in determining carbon emissions in the road transportation sector. This study analyzes these factors in detail for the years 2025, 2030, 2035, 2040, 2045, 2050, and 2060, using relevant forecasts from the Energy Saving and New Energy Vehicle Technology Road Map 2.0 (China SAE 2020). Linear interpolation is used for years that are not included. If the parameter value is a range, the middle value of the range is adopted, as shown in Tables 34 and 35. This study subdivides the market share of PEV and PHEV into several models based on the proportions for various models in previous years, since Energy Saving and New Energy Vehicle Technology Road Map 2.0 (China SAE 2020) does not specify market shares by vehicle model, as shown in Table 36.

Table 34 Market sales share setting for PEV and PHEV (%)

Table 35 100 km power consumption (Kwh/100 km) and 100 km fuel consumption (L/100km) settings of PEV and PHEV (%)

Table 36 Market sales share setting for various vehicle models (%)

4.3 Hydrogenated scenario (HYDS)

From a whole life cycle perspective, FCV emit less carbon emissions when powered by renewable energy hydrogen production through water electrolysis and industrial by-product hydrogen production compared to ICEV. The HYD scenario considers the market permeability of FCV as an essential factor affecting carbon emissions in the road transportation sector. This study determines the permeability of FCV in 2025, 2035, 2050, and 2060 based on the China Hydrogen Energy Industry Development Report (ChinaEV100 and HC 2020), and the missing years are treated according to the linear difference, as shown in Table 37.

Table 37 Market permeability setting of FCV (%)

Furthermore, the hydrogen consumption of FCV for 100 km is also an essential factor. In this study, 2025, 2030, 2035, 2040, 2045, 2050 and 2060 are taken as the key time points, and the future hydrogen consumption 100 km is determined based on the Energy Saving and New Energy Vehicle Technology Road Map 2.0 (China SAE 2020). For years not included, linear interpolation is used to estimate the value, as shown in Table 38.

Table 38 Hydrogen consumption settings for 100 km of FCV (kg ${{\text{H}}}_{2}$/100km)

Additionally, the production ratio of hydrogen is another significant factor. The hydrogen proportion structure settings for 2020, 2030, 2040, 2050, and 2060 are established based on China's Hydrogen Energy and Fuel Cell Industry White Paper (CHAG 2019), which are given in Table 39.

Table 39 The proportion structure settings of hydrogen production methods (%)

4.4 Vehicle materials recycling scenario (VMRS)

Improper disposal of scrapped vehicles can cause significant harm to the environment, including serious pollution to soil, water, and the atmosphere (Gan and Ma 2017). Studies (Tian et al. 2016; Chen et al. 2018) show that more than 90% of the spare parts, such as steel and nonferrous metals on scrapped cars can be recycled, and the recycling rate of glass and plastics can exceed 50%. Therefore, recycling vehicle materials can promote energy conservation, emission reduction and sustainable development in the vehicle industry.

In the VMR scenario, the standard recovery rate of scrapped vehicles is a critical factor. It refers to the proportion of vehicles that meet scrapping standards and are formally scrapped, relative to the total number of scrapped vehicles. Based on previous studies (Li 2020; Wang et al. 2022), this study establishes the standard recovery rate for 2014, 2020, 2030, 2040, 2050 and 2060. For missing years, the rates are extrapolated using linear interpolation, as shown in Table 40.

Table 40 Standard recovery rate setting of scrapped vehicles (%)

4.5 Combined scenario (CS)

The combined scenario integrates all the advantages of the ELE scenario, the HYD scenario and the VMR scenario. In the combined scenario, we introduce PEV, PHEV and FCV at the same time. We also recycle the materials of scrapped vehicles and investigate whether the combined scenario is more effective in reducing carbon emissions than the other three scenarios.

4.6 Improved combined scenario (ICS)

The improved combined scenario builds upon the improved situation of the combined scenario and utilizes the existing scenario data to determine the most effective technical advancements to adopt for different vehicle models. This study establishes the improved market penetration rates for PEV, PHEV, and FCV in 2025, 2030, 2035, 2040, 2045, 2050 and 2060, as displayed in Table 41.

Table 41 The improved market penetration of PEV, PHEV and FCV (%)

5 Results and discussion

5.1 The historical trend

5.1.1 The estimation of motor vehicle stocks

In this study, data are imported to the LEAP model to estimate the stocks for various motor vehicle models used in China's road transportation sector from 2013 to 2022. The resulting estimate, as shown in Table 42, reveals an increase in the quantities of motor vehicle stocks from 222.1 million to 417.0 million in the given period, with vehicle stocks increasing from 127 to 283 million. The actual stock data for the same period was acquired from the Ministry of Public Security of the PRC. Figure 2 shows that the estimated historical motor vehicle stock trend is largely consistent with the actual data, which indicates that the data and assumptions used in this study and the scenario design are scientifically grounded.

Table 42 Estimated stocks of motor vehicles from 2013 to 2022 (Million)

Fig. 2

Comparison between actual stocks and estimated stocks from 2013 to 2022

5.1.2 The estimation of carbon emissions for motor vehicles in the whole life cycle

Figure 3 illustrates the carbon emissions for various motor vehicle models during their life cycle in China's road transportation sector from 2013 to 2022. The carbon emissions increased from 784.10 million tons in 2013 to 1.75 billion tons in 2022, with an average annual growth rate of 9.36%. The emissions reached 1.58 billion tons in 2020, which differ significantly from the reported carbon emissions of 784 million tons for China's road transportation sector in the same year (Lu 2022). The difference stems from the different statistical methods used. The carbon emissions estimated in this study were not only limited to direct emissions during the driving process but also included fuel and vehicle cycle carbon emissions. According to Fig. 3, the PV sub-sector is the primary contributor to carbon emissions, followed by the HDT sub-sector. By 2022, the carbon emissions from the PV sub-sector are projected to represent 67.23% of all carbon emissions, while the HDT sub-sector is expected to contribute 19.78%.

Fig. 3

Life cycle carbon emissions for various motor vehicle models from 2013 to 2022

5.2 The future trend

5.2.1 The estimation of motor vehicle stocks

Table 43 presents the estimated stocks for various models of motor vehicles from 2025 to 2060. By 2030, it is projected that there will be a total of 455.9 million motor vehicles in China, with 314.6 million being vehicles. In addition, it is estimated that by 2060, the number of motor vehicles in China will increase to 650 million, with vehicles accounting for 618.9 million of that total.

Table 43 Estimated stocks for various motor vehicle models from 2025 to 2060 (Million)

5.2.2 The estimation of carbon emissions in BAU scenario

According to the BAU scenario, the carbon emissions during the life cycle of motor vehicles are projected to rise from 784 million tons in 2013 to 3.86 billion tons in 2060, with an average annual growth rate of 3.45%, as illustrated in Fig. 4. The figure also reveals that the PV sub-sector and HDT sub-sector are the primary sources of carbon emissions. Furthermore, the PV sub-sector has a slower life cycle carbon emission growth rate than the HDT sub-sector.

Fig. 4

Life cycle carbon emissions for various motor vehicle models from 2013 to 2060 under the BAU scenario

5.2.3 The estimation of carbon emissions in ELE scenario vs. BAU scenario

In the ELE scenario, PEV and PHEV are encouraged for promotion. Figure 5 displays carbon emissions during the life cycle for various motor vehicle models from 2013 to 2060 under the ELE scenario. During this period, life cycle carbon emissions are projected to increase from 784 million tons in 2013 to 2.37 billion tons in 2060, at an annual growth rate of 2.38%. This rate is 1.07% lower than the rate projected by the BAU scenario.

Fig. 5

Life cycle carbon emissions for various motor vehicle models from 2013 to 2060 under the ELE scenario

In this paper, the scenarios of ELE and BAU are compared, as shown in Fig. 6, to determine the direction of promoting PEV and PHEV. The ELE scenario is projected to reduce carbon emissions by 1.49 billion tons compared to the BAU scenario, by 2060. More specifically, PV-G, PV-D, HDT, LDT, and MT are expected to reduce carbon emissions by 1.49 billion tons, 676, 122, 86, and 21 million tons, respectively.

Fig. 6

Comparison between ELE scenario and BAU scenario

We can find that promoting the application of PEV in the PV sub-sector is considered the most effective approach. This is mainly because single PV-PEV has lower life cycle carbon emissions than single PV-ICEV. As the State Grid structure continues to advance, the carbon emissions during the life cycle of PEV are expected to decrease further. Additionally, PEV offer several advantages over fuel-based vehicles, including low noise, power grid stability, high energy efficiency, and comparatively inexpensive charging. Consequently, promoting the application of PEV in the ELE scenario can notably reduce carbon emissions. Meanwhile, although the incorporation of HDT-PEV in the HDT sub-sector can reduce carbon emissions, the self-weight, slow charging, low efficiency, limited mileage, inconvenient charging, and high purchasing cost of such vehicles restrict their suitability for widespread promotion and application in the future.

5.2.4 The estimation of carbon emissions in HYD scenario vs. BAU scenario

In the HYD scenario, FCV have been widely promoted. According to Fig. 7, the life cycle carbon emissions for various motor vehicle models from 2013 to 2060 under the HYD scenario are displayed. The emissions are expected to rise from 784 million tons in 2013 to 2.8 billion tons in 2060, indicating a 2.75% annual growth rate. This is 0.7% lower compared to the BAU scenario.

Fig. 7

Life cycle carbon emissions for various motor vehicle models from 2013 to 2060 under the HYD scenario

To determine the direction of FCV promotion, this study compares the HYD scenario to the BAU scenario, as illustrated in Fig. 8. By 2060, the HYD scenario will curtail carbon emissions by 1.1 billion tons in comparison to the BAU scenario. Specifically, the HDT, PV-G, PV-D, and LDT will reduce carbon emissions by 535, 354, 164, and 43 million tons, respectively.

Fig. 8

Comparison between HYD scenario and BAU scenario

We can find that promoting the deployment of FCV in the HDT sub-sector is deemed the most efficient approach. The primary reason behind this is that the carbon emissions during the life cycle of a single HDT-FCV are lower compared to those of a single HDT-ICEV from 2040. Furthermore, as the proportion of hydrogen production by environmentally friendly electrolysis water rises and clean State Grid structure become more prevalent, FCV life cycle carbon emissions will decrease further. Additionally, FCV hold unique advantages in terms of driving range, low-temperature adaptability, and energy supply speed, making them ideal for long-distance HDT within the technical route of new energy vehicles. Meanwhile, although FCV promotion and deployment are effective in reducing carbon emissions in the PV sub-sector, large-scale PV-FCV adoption is impractical. This is due to the high cost of PV-FCV and their need for maturation of both technology and infrastructure.

5.2.5 The estimation of carbon emissions in VMR scenario vs. BAU scenario

In the VMR scenario, the body materials and battery materials of motor vehicles are dismantled and recycled. Figure 9 illustrates the life cycle carbon emissions for various motor vehicle models from 2013 to 2060 under the VMR scenario. These emissions will rise from 784 million tons in 2013 to 3.51 billion tons in 2060, indicating a 3.24% annual growth rate. This is 0.21% lower compared to the BAU scenario.

Fig. 9

Life cycle carbon emissions for various motor vehicle models from 2013 to 2060 under the VMR scenario

To determine the exact carbon emission reductions of the VMR scenario, this study compares the VMR and BAU scenarios, as shown in Fig. 10. By 2060, the VMR scenario will reduce carbon emissions by 355 million tons, when compared to the BAU scenario. Specifically, carbon emissions reductions for PV-G, HDT, PV-D, LDT, and MT will amount to 137, 115, 64, 24, and 11 million tons, respectively. Notably, carbon emissions across all sub-sectors are decreasing, primarily due to the recycling of vehicle materials that effectively mitigates carbon emission factors during the vehicle cycle. Furthermore, increasing the standard recovery rate for scrapped vehicles in the future would lead to a reduction in the carbon emissions during the life cycle of motor vehicles.

Fig. 10

Comparison between VMR scenario and BAU scenario

5.2.6 The estimation of carbon emissions in ELE scenario vs. HYD scenario

According to existing literature (Ou et al. 2010; Peng et al. 2018b; Ren et al. 2020), vehicle electrification and vehicle hydrogenation are two key technical routes for reducing carbon emissions in the road transportation sector. Further, the New Energy Automobile Industry Development Plan (2021–2035) (General Office of the State Council of the PRC 2020) emphasizes the significance of pure electric drive technology to achieve the complete electrification of public vehicles and the commercialization of FCV. Therefore, the objective of this study is to analyze the distinct impacts of vehicle electrification and hydrogenation on the carbon emissions generated during the life cycle of various models of motor vehicles in the road transportation sector. To achieve this objective, we undertake a comparative analysis of the ELE and HYD scenarios, as depicted in Fig. 11.

Fig. 11

Comparison between ELE scenario and HYD scenario

The ELE scenario will reduce carbon emissions by 438 million tons by 2060 when compared to the HYD scenario. Among them, PV-G, PV-D, LDT, and MT will reduce carbon emissions by 1.14 billion tons, 512, 43, and 10 million tons, respectively. In contrast, HDT, HDB, and MDB will increase carbon emissions by 413, 72, and 37 million tons, respectively. Thus, it is clear that different motor vehicle sub-sectors are suitable for particular scenarios that reduce life cycle carbon emissions. Specifically, the PV, LDT, and MT sub-sectors are more suited to electrification, whereas the HDT, HDB, and MDB sub-sectors are more suited to hydrogenation.

5.2.7 The estimation of carbon emissions in combined scenario

This study investigated the carbon emissions during the life cycle of motor vehicles in the road transportation sector under three single-type scenarios, namely the ELE, HYD, and VMR scenarios. The results showed continuously increasing carbon emissions, which can not meet the requirements of China's "dual carbon" goal. In pursuit of a more effective scenario, this study proposes a new combined scenario that integrates the three existing scenarios. Figure 12 depicts the life cycle carbon emissions for various motor vehicle models under the combined scenario from 2013 to 2060. Emissions will rise from 784 million tons in 2013 to 1.92 billion tons in 2030 and reduce to 1.48 billion tons in 2060. The potential for carbon emission reduction of the combined scenario exceeds that of any single scenario, enabling it to attain China's carbon peaking goal in the road transportation sector by 2030. However, the magnitude of carbon emission reduction may decrease gradually, reaching zero after 2050.

Fig. 12

Life cycle carbon emissions for various motor vehicle models from 2013 to 2060 under the combined scenario

5.2.8 The estimation of carbon emissions in improved combined scenario

The improved combined scenario builds on the existing combined scenario to provide a better solution. Previous results suggest that PV, LDT, and MT are appropriate sub-sectors for promoting PEV and PHEV. However, HDT, HDB, and MDB are better suited for the mass promotion of FCV. Consequently, we redefined the market permeability and integrated the existing settings of the ELE and HYD scenarios. Figure 13 illustrates the improved combined scenario that we proposed. The improved combined scenario predicts an increase in life cycle carbon emissions from 784 million tons in 2013 to 1.90 billion tons in 2030. Then, these emissions are expected to decrease to 1.27 billion tons by 2060.

Fig. 13

Life cycle carbon emissions for various motor vehicle models from 2013 to 2060 under the improved combined scenario

5.2.9 Comparisons among five scenarios

Table 44 compares the five scenarios' ability to decrease life cycle carbon emissions in the road transportation sector against the BAU scenario in the given years 2015, 2020, 2025, 2030, 2035, 2040, 2045, 2050, 2055, and 2060. The ranking of scenarios from the most effective to the least, in terms of reducing carbon emissions, is ICS, CS, ELES, HYDS, and VMRS. ELES and HYDS are based on fuel cycle carbon emissions, whereas the VMRS is based on the carbon emissions during the vehicle cycle.

Table 44 Reduction in life cycle carbon emissions of motor vehicles under five scenarios compared to the BAU scenario (Million tons)

6 Conclusion and policy recommendations

To address the challenges of global climate change, reduce reliance on fossil fuels, and achieve China's "dual carbon" goal for the whole life cycle of the road transportation sector, it is necessary to simulate BAUS, ELES, HYDS, VMRS, CS, and ICS in the LEAP model. This will enable us to determine the effectiveness of various policy scenarios and make specific policy recommendations.

The scenario analysis yields the following five conclusions:

Firstly, in order to achieve China's "dual carbon" goal in the road transportation sector, it is crucial to promote the application of PEV, PHEV, and FCV. Specifically, the PV, LDT, and MT sub-sectors are suitable for the large-scale promotion of PEV and PHEV, while the HDT, HDB, and MDB sub-sectors are more suitable for FCV applications.

Secondly, the recycling of vehicle materials can effectively reduce the carbon emissions during the life cycle of motor vehicles. By disassembling and recycling materials, carbon emissions can be reduced for all sub-sectors. Furthermore, higher standardized recovery rates for scrapped vehicles will further reduce the carbon emissions during the life cycle of motor vehicles.

Thirdly, the PV sub-sector has the highest life cycle carbon emissions within the road transportation sector. As cleaner State Grid structure becomes the norm in the future, the carbon emissions during the fuel cycle of PV-PEV are expected to decrease, but the carbon emissions during the vehicle cycle will account for 85% of the life cycle carbon emissions by 2060. Therefore, reducing the carbon emissions during the vehicle cycle of PV-PEV should be taken seriously.

Fourthly, the HDT sub-sector generates the second greatest amount of life cycle carbon emissions in the road transportation sector, after the PV sub-sector. Although the use of cleaner State Grid and hydrogen produced through renewable energy water electrolysis are expected to reduce the carbon emissions during the fuel cycle of HDT-FCV, such emissions will still constitute 74% of the carbon emissions during the life cycle of the vehicle by 2060. Therefore, we need to continuously focus on reducing carbon emissions from the fuel cycle of the HDT-FCV.

Finally, the ICS has the greatest potential for reducing carbon emissions compared to all other scenarios. Although the life cycle carbon emissions in the road transportation sector can peak by 2030 under the ICS, it cannot achieve the carbon neutrality goal by 2060. This is mainly due to the significant carbon emissions from the vehicle cycle of PV-PEV and the fuel cycle of HDT-FCV by 2060.

Based on the study's findings, the following four policy recommendations are put forward:

Firstly, the government can provide policy support and financial subsidies for specific motor vehicle models. More substantial one-time subsidies can be reserved for PV-PEV, LDT-PEV and MT-PEV, whereas relatively lower rates can be allotted for PEV and PHEV in other sub-sectors. The subsidies for HDT-FCV, HDB-FCV, and MDB-FCV can also be set to be relatively higher. A gradual reduction in the subsidy standard can be enforced for PEV, PHEV, and FCV until it reaches a certain scale or until subsidies are terminated.

Secondly, the government can enhance its effort to implement the life cycle carbon emissions reduction of the road transportation sector by focusing on the standard recovery rate of scrapped vehicles. To achieve this, the government can work on formulating and improving policies for the recovery of scrapped vehicles. Special financial support can be given to recycling and disassembling enterprises, and car owners can be offered preferential discounts when they exchange their scrapped vehicles for new ones. The government can also strengthen its supervision of the recycling management of scrapped vehicles by strictly dealing with illegal recovery activities and increasing the level of penalties. Additionally, the government can utilize the penalty money received to subsidize the recycling of vehicles, forming a virtuous circle for recycling and disassembly of vehicles.

Thirdly, to achieve China's "dual carbon" goal in its road transportation sector, the carbon emissions reduction effect of the vehicle cycle of PEV must be improved. The government can achieve this by issuing carbon emission quotas to the upper, middle, and lower enterprises of the industrial chain of PV-PEV. This would force enterprises to enhance their energy-saving and emission reduction technologies to transform the entire PV-PEV industrial chain from high energy consumption and high emissions to low energy consumption and low emissions.

Finally, the carbon reduction effect of the fuel cycle of FCV is another essential factor in achieving China's "dual carbon" goal for the road transportation sector. The government could create a government-managed investment fund for the development of hydrogen production technology using water electrolysis from renewable energy sources. By providing guidance to private investments in this technology layout, along with policies to introduce and train professionals, the government can transform hydrogen production using water electrolysis from being expensive and high in emissions to low costs and low emissions.

Besides, there are some limitations in this research, and some research fields need to be considered in the future. Although this study achieves the carbon peaking goal in the road transportation sector by 2030 under the ICS, it neglects to address the carbon neutrality goal by 2060. The vehicle electrification, hydrogenation, and material recycling are analyzed, but other factors, such as transportation structure adjustments and technical improvements of ICEV fuel economy are not taken into consideration. The key factors affecting carbon emission reduction in the whole life cycle of the road transportation sector must be fully considered in future research. Furthermore, a more advanced policy scenario should be designed to analyze the effects of carbon emissions, so as to achieve carbon neutrality goal by 2060. Additionally, although this study puts forward specific policy recommendations for decision-makers in this field, it lacks policy feasibility analysis. Therefore, future research may try to evaluate the feasibility of various policy scenarios from the perspective of green finance, so as to provide more practical reference for decision-makers in this field.