Finding the best approach for the transportation sector to synergistically reduce CO2 and air pollutants emissions

doi:10.21203/rs.3.rs-1508775/v1

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Finding the best approach for the transportation sector to synergistically reduce CO2 and air pollutants emissions

https://doi.org/10.21203/rs.3.rs-1508775/v1

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The transportation sector has a significant impact on the synergistic reduction of CO₂ and pollutants. Using a structural path decomposition, we reveals the direct and embodied CO₂ and NOx emissions from the transportation sector triggered by key supply chain pathways. Meanwhile, the emission abatement potential and economic costs of 33 vehicle specific abatement technology options are analyzed based on the input-output analysis for life cycle assessment. The results show that most of the vehicle technology options can reduce CO₂ and NOx emissions while saving economic costs. Among these technologies, both the pure electric technology for passenger cars and the parallel hybrid technology for heavy-duty trucks have high abatement potential. Compared to costly pure electrification technologies for passenger cars, parallel hybrid technologies for heavy-duty trucks offer the highest economic benefits as well as a better driving mileage. Therefore, hybrid heavy-duty trucks will be a more comprehensive solution in the near future.

The transportation sector plays an important role in the economic activities of society by linking production, exchange, distribution and consumption. Meanwhile, the transportation sector is a major source of CO₂ and air pollutants emissions^[1][2], with road subsector making the largest contribution^[3]. The China's latest national plan (14th Five-Year Plan) puts forward the requirement of synergy in carbon and air pollutants reduction, and the transportation sector has a significant impact on achieving this goal. China has now become very stringent in managing transportation emissions, especially in vehicles. A range of direct and enforceable end-of-pipe treatment policies, such as improved fuel quality and stricter tailpipe emission standards, have achieved significant emission reductions^[4][5]. However, vehicle emissions remain high as transportation demand increases.

Transportation is the demand generated by economic activities. The analysis of emissions from the transportation sector inevitably involves all sectors of the economy. Therefore, it is important to explore the economic relationship between the transportation sector and other sectors, which requires extremely comprehensive data. The input-output table is the most appropriate tool because it is able to link the production of goods to the exchange of materials between economic sectors. The input-output analysis have shown that heavy industry, electricity, construction and services are the main sectors related to air pollutants and CO₂ emissions from the transport sector^[6][7]. The final demand is the strongest driver of the rapid growth of CO₂ emissions from the transportation sector^[8]. While these studies have contributed significantly to the understanding of transportation emissions and have generated useful production and demand-side policy recommendations. However, facing the prominent position of transportation emission, it is not enough to rely on macro level analysis alone. Research on specific abatement technologies for the transportation sector, especially the road subsector, is essential.

Several studies have assessed the abatement potential and costs of vehicle emission abatement technologies at the micro-level. For example, International Council on Clean Transportation (ICCT) studied the abatement potential and cost of vehicles over the time period 2015 to 2030^[9][10]. It found that the investment cost of vehicle abatement technology rises as abatement rates increase, but consumer savings in fuel costs will be two to three times the cost of vehicle technology investments by 2030. Peng et al. ^[11] studied 55 passenger car abatement technologies in China from 2010 to 2030, with abatement costs ranging from − 1324.59 to 7623.47 yuan/t from 2010 to 2030, with a cumulative CO₂ abatement potential of about 27Mt.

However, these studies mainly analyzed the potential and costs of energy-efficient technologies in the use phase, ignoring the emissions of these technologies in the production phase.. New energy technologies have high CO₂ emissions in the production phase, which can reach up to 50% of the emissions reductions in the use phase^[12]. In the coming decades, production emissions will account for an increasing share of new energy vehicle emissions. Meanwhile, the response of global temperature to the impulse of emitted greenhouse gases is an important factor in taking productive emissions into account. The temperature impulse response of global temperature has a lag and will peak in a few decades. Production emissions now will affect temperatures decades into the future^[13]. Inadequately, the above study only measured the emissions of new energy vehicles, such as hybrid and electric vehicles, at the production stage, without fully analyzing the emissions of the whole transportation industry and comparing more abatement technologies at the production stage.

Based on the above considerations, this paper integrates life cycle assessment based on input-output analysis (IOA-LCA) and structural path analysis (SPA) to trace the CO₂ and air pollutants emission streams hidden in the supply chain of the whole transportation sector and 33 combination scenarios of vehicle types and technologies from a life-cycle perspective, including production phase and use phase. This innovation is that the additional emissions generated by all vehicle abatement technologies at the equipment manufacturing and energy production stages are quantitatively assessed, and the net abatement effect of each technology at the whole economic system level is evaluated. Combined with the net abatement potential and economy cost-benefit analysis, the future motor vehicle abatement technologies for the synergistic reduction of air pollutants and CO₂ in China are proposed in this paper.

Change and driving force analysis of CO₂ and NOx emissions in the transportation sector

According to the CO₂ and NOx emissions of 11 subsectors of transportation over the period 2012–2018, we find CO₂ emissions from the transportation sector increased substantially while NOx emissions remained essentially unchanged, and the road freight contributes the most to the increase of CO₂ emissions, accounting for about 40%, followed by the multimodal transport subsector and the road passenger transport subsector (Fig. 1). Furthermore, we apply the structural decomposition analysis (SDA) method to analyze the effects of emission intensity, industrial structure, and final demand on the drivers of changes in CO₂ and NOx emissions. The overall SDA calculation method can be seen in Supporting Information section 1.2. The factors causing the rapid increase in CO₂ emissions are the simultaneous increase in both emission intensity and final demand. The reduction of NOx emission intensity offsets the increase of final demand, and the impact of industrial structure on CO₂ and NOx is not significant. The reasons for the above phenomenon are related to environmental protection policies. From 2013 to 2018, China introduced a series of mobile source emission reduction measures to reduce NOx emissions, which achieved significant emission reduction effects^[4][5]. However, China has not issued standards or policies for CO₂ emission reduction of motor vehicles, resulting in high and rapid growth of CO₂ emissions in the transportation sector.

Emission flows between consumption and production

Emission flows of transportation sector

To explore more systematically the emissions of transportation sector in its production stage, we use the modified SPA method to provide an exhaustive diagram of the supply chain linkages related to the transport final consumption attribution and final production attribution, highlighting the emissions implicit in the intermediate product streams that connect different economic sectors along the supply chain.

From the perspective of final consumption attribution, the emissions include the direct emissions and embodied emission flows ^[14]. According to Fig. 2, the share of direct emissions from transportation is very high. The direct emissions of CO₂ and NOx account for 50% and 81% of all emissions, respectively. Regarding the embodied CO₂ emission flows, the electricity is significant in each production layer. For other sectors, the story is quite different. Refined oil and auto parts sectors are significant in production layer PL1 (accounting for 28% in the total embodied emissions flows), while the mining, metal products, and equipment manufacturing sectors are significant in PL2 and PL3 (accounting for 35% respectively). For NOx, both the direct emissions and the embodied emissions from transportation are dominant in all production layers, accounting for 65%, 40%, and 34% of the total embodied emissions in PL1, PL2, and PL3, respectively.

From the aspect of final production, the CO₂ and NOx emissions due to transportation during production are very high (leftmost column of Fig. 2). This is similar to the findings revealed in the road freight study by Liu et al. ^[6].

In summary, CO₂ emissions from China's transportation sector do not decrease but increase, while the NOx emissions only decrease slightly. Therefore, it is not enough to focus only on the industry sector scale, and a more detailed analysis of the whole life cycle of the technical path is needed.

Emission flows of typical technologies

In order to analyze the additional emissions generated by motor vehicle emission reduction technologies, we calculate the emission flows caused by the consumption of intermediates in the production process of 33 combination scenarios of vehicle types and technologies, and the comparative analysis of each technology is presented in section 3.3.1 below. We take the example of the purely electric technology for passenger cars scenario (PC12), which had the highest production costs and the most complex production process. By using the SPA method, we quantitatively analyze the emission flows caused by this technology, including additional emissions from electric vehicle manufacturing and power production, as well as additionally reduced emissions from fuel production (Fig. 3).

Different from the results of analyzing the whole transportation sector with SPA method, the auto parts and steel rolling sector has also become a prominent contributor to emission flows in the SPA results of PC12 scenario alone, which is responsible for approximately 50% of all CO₂ embodied emissions and 40% of all NOx embodied emissions in the PL1, PL2 and PL3 (Fig. 3(a)~(b)). The production of electricity and fuel oil is different from the complex motor vehicle manufacturing industry, with simple intermediate production links (Fig. 3(c)~(f)).

The result shows that if all existing passenger cars are converted to electric vehicles, although it will save emissions by 470.44 Mt CO₂ and 648.28 Kt NOx from fuel oil production, it will cause an additional emission about 494.44 Mt CO₂ and 676.13 Kt NOx in the electric vehicles manufacturing phase. These two parts almost completely offset each other. Moreover, the electric vehicles require about 216.33 Mt CO₂ and 80.39 Kt NOx emissions from additional electric power production. Therefore, the low carbon of power production is the key to the emission reduction of electric vehicle technology. At the same time, it should also be taken into account that due to fuel oil conservation, emissions in the production stage are reduced, offsetting the additional emissions generated by the manufacture of electric vehicles.

Analysis of emission abatement potential and costs of typical vehicle energy-saving technologies

In order to analyze the net actual impact of the implementation of typical energy-saving technologies for vehicles on pollutant emissions, we comprehensively consider the emissions in the use stage and production stage, and analyze the engine energy-saving technology, new energy technology and drag reduction technology of passenger cars, light trucks and heavy trucks.

Analysis of emission abatement potential over the whole life cycle

(1) Emission reductions in the production and use stage of each technology

Based on the above, the SPA method is applied to calculate the emissions in the production stage of all 33 combination scenarios of vehicle types and technologies. The results show that the additional emissions resulting from the production phase of all technologies are smaller than emission reductions from their use phase, indicating that all 33 combination scenarios of vehicle types and technologies could achieve net emission reductions (Fig. 4). In terms of vehicle types, passenger cars and heavy trucks have greater potential of CO₂ and NOx emission reductions. Regarding technologies, new energy technology (T-Ⅱ) has the highest emission reduction potential. It is worth noting that the hybrid and pure electrification technologies have higher CO₂ emissions in the production phase than others. Especially, for passenger car plug-in hybrid (PC11) and passenger car pure electric (PC12) technologies, the CO₂ emission of vehicle production could reach 50% of the emission reduction in the use stage. Unlike CO₂, it could be found that the additional NOx emissions caused by each type of technology in their production phase are small, and the emission reduction in the use phase plays a dominant role in the net emission abatement (Fig. 4(b)).

Although there are differences in the main emission stages, these technologies with high CO₂ reduction potential would also have high reduction potential for NOx. Especially, the new energy technologies have the best synergistic effect on reducing both CO₂ and NOx. (Fig. 4(c) and Table S4).

Net benefit analysis

Figure 4(d) shows the net benefits ($NB$) of the 33 vehicle types and technology combination options. It could be found that most technologies are cost-effective. The economic benefits of fuel saving in the use stage are greater than the additional invest costs of energy-saving technologies, except for passenger car plug-in hybrid technology (PC11) and passenger car pure electric technology (PC12). The PC11 and PC12 technology have negative net benefits because their additional investment costs and electricity costs exceed the fuel savings benefits. According to Table 1, the drag reduction technologies category has the highest net abatement benefit of CO₂ on average. For NOx, however, the engine energy-saving technology category has the highest net abatement benefit on average. New energy technology has the lowest net benefit per unit reduction of CO₂ and NOx (see Table 1). For instance, from the whole life cycle perspective, the costs of PC12 technology are the highest and the net abatement benefits per unit of CO₂ and NO_X are − 1424 yuan/t and − 75 yuan/kg, respectively. However, according to the above analysis of abatement potential, compared with other technologies, the PC12 technology scenario has the greatest emission reduction potential. Therefore, the promotion of this technology requires additional long term massive subsidies. However, according to the latest national energy policy, China will no longer give subsidies for the purchase of new electric vehicles on December 31, 2023^[15]. The withdrawal of fuel vehicles may be a mandatory policy in the future. Therefore, the costs would be transferred to consumers. We notice that heavy truck parallel hybrid technology scenario (HT3) also has a large emission reduction potential, with the highest CO₂ reduction potential and the second highest NOx reduction potential after PC12 (Fig. 4(a) - (b)), but the HT3 technology scenario has the highest net benefit among all technologies (Fig. 4(d)). Therefore, this technology is cost-effective and worth popularizing. The net abatement benefits per unit emissions ($AB$) for each technology are shown in Supplementary Information Section 3 and Table S5.

Table 1

Average net abatement benefit per unit for each type of technology
Average net abatement benefit per unit	Engine energy-saving technology				New energy technology				Drag reduction technology
Average net abatement benefit per unit	PC	LT	HT	Average	PC	LT	HT	Average	PC	LT	HT	Average
CO₂ (yuan/t)	1,460	1,865	1,863	1,676	264	1,278	1,750	703	2,165	2,056	1,789	2,019
NOx (yuan/kg)	101	1,386	350	648	26	941	328	293	163	1,533	337	626
Note: PC is passenger car, LT is light truck, HT is heavy truck.

The current emission reduction measures of transportation are weak for CO₂ control. CO₂ emissions from the transportation sector have increased rapidly, from 317.6 Mt in 2012 to 746.2 Mt in 2018. In future, the transportation sector will be critical for the entire economic system to achieve the goal of reducing pollutants and carbon. The transportation policies of China should focus more on the coordinated emission reduction of CO₂ and air pollutants.

To examine the relationship between the transportation sector and other sectors, we use the SPA method to make a detailed profile of production emissions due to the transportation sector, including direct and embodied emissions. The transportation sector causes much higher direct emissions than embodied emissions. Direct emissions of CO₂ and NOx from the production phase account for 50% and 81% of all emissions, respectively. This is because the direct emissions in most production phase are related to road freight^[6]. The transportation industry itself will form a vicious circle, that is, the increase of the scale of the transportation industry will drive the development of electricity, refined petroleum, auto parts, mining, equipment manufacturing, and metal products industries, and these industries need more transportation support for their production, resulting in more emissions. Therefore, it is suggested to formulate low-carbon policies and plans for vehicles and their power production stages. Special attention should be paid to the freight emissions caused by the production stage.

From a more micro technical point of view, 33 vehicle abatement technology scenarios are analyzed in this paper. Most of them have good synergistic CO₂ and NOx reduction effects. In addition, we find that all technology scenarios, except for pure electric (PC12) and plug-in hybrid (PC11) technologies for passenger cars, have net benefits due to fossil fuel savings. Notably, the passenger car pure electric technology (PC12) has the highest potential for emission reduction, but also requires the highest cost. Another noteworthy point is that the parallel hybrid technology for heavy trucks (HT3) has the greatest potential to reduce emissions, which is consistent with the findings of Zhao et al.^[16] and Xu et al.^[17]. Moreover, this technology has the largest net benefit. In terms of technical principle, the parallel hybrid technology of heavy truck is modified based on the traditional fuel vehicle, and its key is dual engine and electronic auxiliary unit^[18]. Therefore, the modification cost is low. The pure electric trucks cannot meet the long-distance transportation requirements, while the pollution emission of traditional diesel power trucks is too high. The parallel hybrid technology for trucks can make up for the shortcomings of diesel power and pure electric technology, and meet the needs of transportation and emission reduction at the same time. Currently, heavy trucks using parallel hybrid technology are more common in Europe^[19], while they are rare in China. The reason is that in China hybrid heavy trucks do not have the same financial subsidies as pure electric passenger cars, resulting in low enthusiasm for hybrid heavy trucks production. Combined with the above research conclusions on the important role of freight transportation in the production stage, it is suggested to formulate policies to promote parallel hybrid technology for heavy trucks. Under the background of China's national strategy of green freight and optimization of traffic structure, the promotion of hybrid heavy trucks may be a more win-win solution for the environment and economy in the near future than the promotion of pure electric passenger vehicle technology.

The life cycle assessment (LCA) is used to assess the overall environmental impact of goods or services, including the entire life cycle of a product: extraction and processing of raw materials, manufacturing, distribution, use, reuse, maintenance, recycling, and final disposal^[20][21]. LCA analysis can be performed based on two main approaches: process-based model and input-output based analysis ^[22]. Compared with the method of process-based model, the input-output analysis (IOA) can be applied to more macro level life cycle assessment. The Input-output analysis (IOA) can comprehensively include the direct and indirect contributions of all economic activities in an impact assessment, and it can be more easily adopted as a basis for assessing the impact of anticipated technological changes in a defined economic system^[20]. With the ability to extend the analysis to the entire economic system, IOA-LCA allows for a more comprehensive assessment of environmental impacts, considering emissions that may be hidden in upstream production processes, which contributes to the reduction of emissions throughout the economic system^[14].

The central formula of the input-output model is that for an economy consisting of $n$ industries, total output $X$ can be expressed as the sum of intermediate use $AX$ and final demand (i.e., products that are no longer processed for production) $Y$ in each industry:

$$X=\text{AX}+Y$$

$$X=(I-A{)}^{-1}Y$$

Where, I is the identity matrix; $(\text{I-A}{)}^{-1}$ is the Leontief inverse matrix, also known as the complete demand factor; $X$ is a N × N matrix.

In order to investigate the difference between final production emissions and final demand emissions and the reasons for their formation, Skelton et al.^[14] proposed a method for mapping embodied emission flows through the Leontief production system. This method is considered as an extension of the traditional SPA and can show a detailed flow analysis map of the supply chain between final production and consumption attribution. The SPA model has been widely used to identify key industrial sectors and supply chain pathways that lead to resource use and associated environmental impacts^[23][24], and is a suitable and effective method for quantifying demand-driven environmental emissions.

An advantage of the Leontief model is the ability to track the intermediate purchasing chain through the layers of the production system triggered by the final demand. This is achieved by solving the Leontief inverse using its power series approximation, as follows ^[14]:

$$L=(I-A{)}^{-1}=I+A+{A}^{2}+{A}^{3}+\text{...}+\text{proveded that li}{\text{m}}_{t\to {\infty }}{A}^{t}$$

The relationship between each two production layers (PL) is as follows:

$$\text{P}{\text{L}}^{t+1}=\text{P}{\text{L}}^{t}A$$

Each sector relates to the environment through a number $m$ of exogenous transactions (resources consumption or waste emissions), collected into the exogenous transactions coefficients vector $\theta$,w, ich is a vector of direct emission coefficient, that is the amount of pollutant emissions generated per unit of economic output in each industry.$\theta =E/X$, where $E$ is the direct pollutant emission vector for each sector and $X$ is the total output vector for each sector. In this paper, we consider CO₂ and NOx emissions where the transportation sector is the main contributor^[25].

The consumption of final products in each sector forms the final demand, which pulls upstream sectors along the production layer to produce intermediate products. Each sector generates direct emissions of pollutants from each production tier as it performs production activities at each production tier. Similarly, the consumption of intermediate goods by each production layer forms the reverse implied pollution flow from final production emissions back through each production layer to final demand emissions. The direct emissions ${D}^{t}$, indirect production embodied emissions ${P}^{t}$ and consumption emissions ${E}^{t}$ for sector $i$ at production layer 0 and production layer $t$ are calculated as follows.

Table 2

Direct, Consumption, and Production Attribution Equations for PL0 to PL3
	Direct	Consumption	Production
Final attribution at PL0	${D}_{i}^{0}={\theta }_{i}{y}_{i}$	${E}_{i}^{0}={m}_{i}{y}_{i}$	${P}_{i}^{0}=\text{M*Y}$
Intermediate attribution at PL1	${D}_{i}^{1}={\theta }_{i}\text{AY}$	${E}_{i}^{0}={m}_{i}\text{AY}$	${P}_{i}^{1}=\text{MAY}$
Intermediate attribution at PL2	${D}_{i}^{2}={\theta }_{i}\text{}{\text{A}}^{2}\text{Y}$	${E}_{i}^{2}={m}_{i}\text{}{\text{A}}^{2}\text{Y}$	${P}_{i}^{2}=\text{M}{\text{A}}^{2}\text{Y}$
Intermediate attribution at PL3	${D}_{i}^{3}={\theta }_{i}\text{}{\text{A}}^{3}\text{Y}$	${E}_{i}^{3}={m}_{i}\text{}{\text{A}}^{3}\text{Y}$	${P}_{i}^{3}=\text{M}{\text{A}}^{3}\text{Y}$

Table 3

Embodied Emissions Flow Equations
	from sector at PL1 to sector at PL0	from sector at PL2 to sector at PL1	from sector at PL3 to sector at PL1
Embodied Emissions Flow	${E}_{\text{ij}}^{1\to 0}={m}_{i}\text{}{\text{a}}_{\text{ij}}\text{}{\text{y}}_{i}$	${E}_{\text{ij}}^{2\to 1}={m}_{i}\text{}{\text{a}}_{\text{ij}}\text{A*Y}$	${E}_{\text{ij}}^{3\to 2}={m}_{i}\text{}{\text{a}}_{\text{ij}}\text{}{\text{A}}^{2}\text{*Y}$

In Tables 2 and 3, $M$ is the N × N matrix of emission multipliers and $m$ is the row vector (1×N) in $M$. $M$ and $m$ can be calculated according to Eqs. (5) and (6), respectively:

$$M=\stackrel{\wedge }{\theta }L$$

$$m={\theta }^{T}L$$

Where $\stackrel{\wedge }{\theta }$ is the diagonal form of the emission intensity and ${\theta }^{T}$ is the row vector form of the emission intensity.

Finally, we should also note that the sum of the final production attributes equals the sum of the final demand attributes (${\sum }_{i=1}^{n}{P}_{i}^{0}={\sum }_{i=1}^{n}{E}_{i}^{0}$).

In this paper, it is assumed that the supply relationship between sectors remains unchanged during the life cycle of the technology implementation (set to 15 years in this paper, based on the current end-of-life requirements for most vehicle models in China), and that the industry emission intensity and product prices remain constant. A similar hypothesis has been set in many studies^[26][27][28].

The implementation of vehicle related emission reduction technologies requires additional investment costs, which is an important reference for countries or enterprises to adopt emission reduction strategies. The application of emission reduction technologies will cause changes in the final consumption. When the increased costs or benefits of applying energy-efficient technologies lead to changes in the final consumption of key sectors, the economic output of other sectors will be indirectly affected due to sectoral linkages, which will lead to changes in pollutant emissions in each sector.

In order to obtain the net emission abatement potential of each technology, the direct emission reductions during the use stage and the indirectly induced emissions during the production stage are considered in this paper, using 2018 as the base year.

Considered from the whole life cycle, the equipment and power used in these abatement technologies generate additional pollutant emissions during the production phase. In addition, the reduction in fuel consumption also reduces the pollutants emitted during production stages. The addition emissions in production stage generated through industry linkages, is denote as $AD{E}_{production}$. The top-down IOA method can be used to estimate the impact on the whole economic sectors from the final demand shock (reflected in monetary value) caused by the application of the technology, resulting in associated additional emissions in production stage. The SPA model introduced above can calculate the emissions in the production stage, and this method does not need too much micro production process data. We use the SPA model to estimate the additional emissions from the production phase of the technology, as described in Eqs. (3)- (6) and Tables 2 and 3, where it is important to note that when calculating the additional emissions of a technology, the variable $Y$ is set to zero in all sectors except for the shock sector, which is a change in value (denoted as y₁, y₂ and y_3, in million of yuan). The ${y}_{1}$ is the fuel cost, which is the final demand reduction in the refined oil sector in the IO table. The ${y}_{2}$ is the technology investment cost, which is the increase in final demand in the IO table for the automotive parts sector, rubber and related industries such as the complete vehicle manufacturing sector (see Table 4) due to the implementation of the motor vehicle retrofit technology. The ${y}_{3}$ is the electricity cost, which in the IO table is the increased final demand in the electricity sector due to the implementation of motor vehicle electrification technology. Here we set the fuel cost (${y}_{1}$) and electricity cost (${y}_{3}$) increase by a discount rate of 5% p.a. in the 15 year^[11], and the technology investment cost (${y}_{2}$) is completed in the first year, without considering discounting. The variation of these three final requirements could be estimated by the following formula.

$${\text{y}}_{1}=\sum _{i=1}^{n}(\text{O}\text{P}\times \text{F}\text{E}\times \text{A}\text{M}\times \text{V}\text{O}\times \text{S}\times {10}^{-6}\times {\left(1+5\text{\%}\right)}^{n-1})$$

$${\text{y}}_{2}=\text{I}\text{C}\times \text{V}\text{O}\times {10}^{-6}$$

$${\text{y}}_{3}=\sum _{i=1}^{n}(\text{E}\text{P}\times \text{P}\text{M}\times \text{A}\text{M}\times \text{V}\text{O}\times {10}^{-6}\times {(1+5\text{\%})}^{n-1})$$

In which, $OP$ (yuan/L) is the oil price, and the average oil price in China in 2018 is 7.27 yuan /L for gasoline and 6.88 yuan/L for diesel^[29]; the variables $FE$, $AM$, $VO$ and $S$ are described in Eq. (7); $IC$ (yuan) is the technology investment cost (see Table S1); $EP$ (yuan/kW·h) is the electricity price, and the average feed-in tariff for power producers in China in 2018 is 0.374 yuan/kW·h^[30]; $PM$ (kW·h/100km) is the power consumption per unit mileage (see Table S2); $n$ is the year of technology use, ranging from 1 to 15.

Table 4

**Major industries where emission reduction technologies are causing** ${\mathbf{y}}_{2}$**changes**
Technology		Description	Direct industries with increased final demand ^[31]
Engine energy-saving technology		Engine and transmission parts improvement	Auto parts and accessories industry
New energy technology		Adopt new power systems such as electrification and hybrid power	Automobile industry
Drag reduction technology	Low friction lubricant	Lubricant improvement	Refined petroleum and processed nuclear fuel products industry
	Low rolling resistance tire	Rubber tire improvement	Rubber products industry
	Lightweight Or Air resistance reduction	Overall body design improvement	Automobile industry

For vehicles, the emission reduction caused by energy-saving or substitution technology could be accounted by the coefficient method of pollutant emission per unit of gasoline. Assuming no change in vehicle ownership is considered, the direct CO₂ abatement could be obtained by multiplying the CO₂ generation factor by the amount of fuel that would be saved by each type of technology in the next 15 years. Since NOx emissions are more likely to be influenced by tailpipe control technologies, the relationship between NOx production factor and final emission factor of fuel consumption is highly uncertain. In the absence of sufficient data, this article set the current emissions multiplied by the corresponding energy-saving efficiency of various technologies as the emissions of this technology.

$${\text{A}\text{B}\text{E}}_{\text{u}\text{s}\text{e}-{\text{C}\text{O}}_{2}}\text{=α ×FE×VO×AM×S×ρ×1}{\text{0}}^{\text{-11}}\text{×15}$$

$$\text{A}\text{B}{\text{E}}_{\text{u}\text{s}\text{e}-\text{N}\text{O}\text{x}}\text{=}\text{β}\text{×S×15}$$

In which, the CO₂ and NOx direct abatement emissions are denoted as ${ABE}_{use-{CO}_{2}}$ and $A{BE}_{use-NOx}$, respectively; $\alpha$ is the CO₂ generation factor, for gasoline is 2.93t/t and for diesel is 3.10t/t; $FE$ is the fuel consumption per unit mileage(L/100km); $VO$ is the current total ownership of vehicles; $AM$ is the average annual mileage (km); $\rho$ is the fuel density, 0.73 kg/L for gasoline and 0.84 kg/L for diesel; 15 is the service life of the vehicle; $\beta$ is the annual statistical NOx emissions (Kt) of current total quantity of vehicles; $S$ is the energy-saving efficiency in %. Detailed data for $FE$, $VO$, and $AM$ are shown in Table S2, and detailed data for $S$are shown in Table S1.

The potential for emission abatement over the whole life cycle is referred to as the net abatement potential/emissions, denoted as ${ABE}_{net}$(CO₂ in Mt, NOx in Kt). The net abatement potential is the direct emission reductions from the use phase of the technology deducting the additional emissions generated in the production phase, which could be expressed as:

$$\text{A}\text{B}{\text{E}}_{\text{n}\text{e}\text{t}}={\text{A}\text{B}\text{E}}_{\text{u}\text{s}\text{e}}-\text{A}\text{D}{\text{E}}_{\text{p}\text{r}\text{o}\text{d}\text{u}\text{c}\text{t}\text{i}\text{o}\text{n}}$$

From the perspective of the ife cycle, the adoption of vehicle energy-saving or substitution technology will produce additional investment costs of technological transformation (above ${y}_{1}$). If electric vehicle technology is adopted, it will increase additional power costs (above ${y}_{2}$), but it will also save fuel and generate energy-saving benefits (above ${y}_{3}$). Economic benefits are the main driving force that may promote the long-term development of technology. We generally refer to emission reduction from the perspective of cost, however, but energy-saving technology may bring economic benefits. From the perspective of cost-benefit analysis, the net benefit ($NB$) is as follows.

$$\text{N}\text{B}={\text{y}}_{3}-{\text{y}}_{1}-{\text{y}}_{2}$$

The net abatement benefit per unit emissions ($AB$, CO₂ unit yuan/t, NOx unit yuan/kg) could be calculated as follows:

$$\text{A}\text{B}=\text{N}\text{B}/{\text{A}\text{B}\text{E}}_{\text{n}\text{e}\text{t}}$$

Data availability

The historical base period of this study is from 2012 to 2018. For the future emission reduction potential, this study takes 2018 as the base year and set the future research period as 15 ye

The historical base period of this study is from 2012 to 2018. For the future emission reduction potential, this study takes 2018 as the base year and set the future research period as 15 years after 2018 (determined based on the current end-of-life requirements for most vehicle models in China[³²^]). We organize the economic sectors in the original input-output tables for 2012 and 2018 into 142 sectors (139 sectors in the original table for 2012 and 153 sectors in the original table for 2018 of China) according to the latest industrial classification and coding requirements of the national economy[³³^].The transportation sector included 11 subsectors: rail passenger transport, rail freight transport, road passenger transport, road freight transport, water passenger transport, water freight transport, air passenger transport, air freight transport, pipeline transport, multimodal transport, and postal service.

rs after 2018 (determined based on the current end-of-life requirements for most vehicle models in China[³²^]). We organize the economic sectors in the original input-output tables for 2012 and 2018 into 142 sectors (139 sectors in the original table for 2012 and 153 sectors in the original table for 2018 of China) according to the latest industrial classification and coding requirements of the national economy[³³^].The transportation sector included 11 subsectors: rail passenger transport, rail freight transport, road passenger transport, road freight transport, water passenger transport, water freight transport, air passenger transport, air freight transport, pipeline transport, multimodal transport, and postal service.

Since the transportation sector is not the main emission sector of sulfur dioxide and particulate matter, and considering the availability of data, NOx is only regarded as the representative of air pollutants in this paper. The NOx emissions data for each sector are from the National Bureau of Statistics of China, and NOx emissions of motor vehicles are provided by the Annual Report on Environmental Management of Mobile Sources in China (2019) [³⁴^] and converted to NOx emission reductions based on energy savings for each type of technology; CO₂ data for each sector of China are from CEADs (https://www.ceads.net.cn/data/), and CO₂ emission reduction data involving specific emission reduction technologies for motor vehicles are based on energy-related emission factors provided by the IPCC[³⁵^], and CO₂ emission reductions are obtained based on the energy savings of each type of technology.

For the future development of automotive emission reduction technology, the "New Energy Vehicle Industry Development Plan (2021-2035)" in China clearly states that pure electric vehicles, plug-in hybrid vehicles, fuel cell vehicle technology and common energy-saving technologies such as light weight and low frictional resistance are the future development direction. However, China lacks systematic and open literature on these technologies. Here, referring to the reports of the National Science Research Council and the National Highway Traffic Safety Administration in the U.S.A[³⁶^][³⁷^][³⁸^] and the research by Peng et al.[¹¹^], this paper classifies emission reduction technologies into 3 categories: engine energy-saving technology, new energy technology, and drag reduction technology. Besides, 3 types of passenger cars, light trucks and heavy trucks are considered. Since upgrading exhaust gas control measures at this stage may lead to a slight increase in fuel consumption and CO₂ emissions, such measures are not involved in this paper. The names, energy efficiency and investment cost of each technical scenario and parameters of each type of automotive are shown in Table S1 and Table S2, respectively.

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	Direct	Consumption	Production
Final attribution at PL0	\({D}_{i}^{0}={\theta }_{i}{y}_{i}\)	\({E}_{i}^{0}={m}_{i}{y}_{i}\)	\({P}_{i}^{0}=\text{M*Y}\)
Intermediate attribution at PL1	\({D}_{i}^{1}={\theta }_{i}\text{AY}\)	\({E}_{i}^{0}={m}_{i}\text{AY}\)	\({P}_{i}^{1}=\text{MAY}\)
Intermediate attribution at PL2	\({D}_{i}^{2}={\theta }_{i}\text{}{\text{A}}^{2}\text{Y}\)	\({E}_{i}^{2}={m}_{i}\text{}{\text{A}}^{2}\text{Y}\)	\({P}_{i}^{2}=\text{M}{\text{A}}^{2}\text{Y}\)
Intermediate attribution at PL3	\({D}_{i}^{3}={\theta }_{i}\text{}{\text{A}}^{3}\text{Y}\)	\({E}_{i}^{3}={m}_{i}\text{}{\text{A}}^{3}\text{Y}\)	\({P}_{i}^{3}=\text{M}{\text{A}}^{3}\text{Y}\)

Finding the best approach for the transportation sector to synergistically reduce CO2 and air pollutants emissions

Status:

Version 1

Abstract

Figures

Introduction

Results

Change and driving force analysis of CO₂ and NOx emissions in the transportation sector

Emission flows between consumption and production

Emission flows of transportation sector

Emission flows of typical technologies

Analysis of emission abatement potential and costs of typical vehicle energy-saving technologies

Analysis of emission abatement potential over the whole life cycle

Net benefit analysis

Discussion

Methods

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1

Finding the best approach for the transportation sector to synergistically reduce CO2 and air pollutants emissions

Status:

Version 1

Abstract

Figures

Introduction

Results

Change and driving force analysis of CO2 and NOx emissions in the transportation sector

Emission flows between consumption and production

Emission flows of transportation sector

Emission flows of typical technologies

Analysis of emission abatement potential and costs of typical vehicle energy-saving technologies

Analysis of emission abatement potential over the whole life cycle

Net benefit analysis

Discussion

Methods

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1

Change and driving force analysis of CO₂ and NOx emissions in the transportation sector