Decarbonization scenarios and carbon reduction potential for China’s road transportation by 2060

Decarbonization scenarios and carbon reduction potential for China’s road transportation by 2060

China’s NEVs diffusion and emission estimation: space perspective As shown in Fig. 2, at the

China’s NEVs diffusion and emission estimation: space perspective

As shown in Fig. 2, at the national level, under the support of incentives, the number of NEVs is significantly increasing. In 2016, the stock of NEVs increased from 0.98 million in 2016 to 3.81 million in 2019, with an average annual growth rate of 57.27%. The development of NEVs presents a significant combined effect at the regional level. In 2016, the numbers of NEVs in the eastern, central, and western regions were 643.81 thousand, 19.80 thousand, and 13.88 thousand, respectively, and these values increased to 2.57 billion, 819.1 thousand, and 423.7 thousand in 2019, with annual growth rates of 58.66, 60.52, and 45.07%, respectively. There were only four cities surpassing the threshold of 50,000 NEVs in 2016, and the number increased to 15 in 2019, and these cities are all located in the eastern and central regions, except for Chengdu, Chongqing, and Liuzhou (Supplementary Notes 1 and 2). The higher growth of NEV stocks in the eastern and central regions is mainly due to their higher economic development, excellent supporting facilities, and stronger consumer market demand.

Fig. 2: Distribution of NEV stock and estimation of CO2 emissions for China passenger road transport 2016–2019.
figure 2

ad (The size and color of the triangle represent the volume of NEV stocks; the background color on the map represents the estimated CO2 emissions.) Source data are provided in the Source Data file.

The CO2 emissions of road passenger transport show a significant increasing trend, as shown in Fig. 2, with corresponding emissions increasing from 414.59 Mt in 2016 to 725.77 Mt in 2019 and presenting an average annual growth rate of 20.5%. At the regional level, the eastern coastal regions contribute half of the country’s total road traffic emissions, followed by the central and western regions. Actually, the majority of the top 10 city emitters are located in the eastern and central regions, except the typical new first-tier cities, i.e., Chengdu and Chongqing (Supplementary Note 2). The high levels of economic development and per capita vehicle stock (mainly traditional fuel vehicles) could tell most of the story. By contrast, the rapid development of transportation infrastructure construction, e.g., high-speed roads, partially explains the high growth rate of road traffic emissions in the central and western regions. Along with the expectation of economic prosperity, carbon emissions in these regions could greatly increase in the future without substantial electrification.

China’s NEVs development paths and CO2 estimation: time perspective

We could observe different time features between the sales of traditional fuel vehicles and NEVs, as portrayed in Fig. 3. During the period, the average annual growth rate of gasoline vehicles and diesel vehicles dropped by −6.25 and −4.80%, respectively. In contrast, NEV sales, particularly battery electric vehicles (BEVs), increased year by year, with an average annual growth rate of 45.76%. The annual sales peak of NEVs is in the second half of the year, especially in December at which time most promotion activities happened (Fig. 3a). In addition, every July is the liquidation time of the previous year’s new energy subsidies and the earliest month of the preallocation funds for the sales of NEVs in that year, which also explains the surge in NEV sales in the second half of the year. This indicates the significant role of policy incentives in the prosperity of NEV market26.

Fig. 3: Month-to-month sales, CO2 emissions, and carbon reduction due to vehicle electrification for China’s road passenger transport.
figure 3

a Passenger car sales; b passenger car sales trend; c road passenger transport CO2 emissions; d road passenger transport CO2 emissions trend; e carbon reduction due to vehicle electrification; f trend of carbon reduction due to vehicle electrification (G1 = Gasline Mini Car; G2 = Gasoline Common Class Car; G3 = Gasoline Middle Cass Car; G4 = Gasoline Middle-High Class Car; G5 = Gasoline High Class Car; D2 = Diesel Common Class Car; D3 = Diesel Middle Cass Car; D4 = Diesel Middle-High Class Car; Hybrid Electric Vehicle = HEV; Plug-in Hybrid Electric Vehicle = PHEV; Real = electricity from thermal power; Reduction = real carbon reduction due to vehicle electrification; Hypothetical = electricity from clean energy). Source data are provided in Source Data file.

We also estimated CO2 emissions from vehicles with different fuel types and their differences over time, as shown in Fig. 3c,d. Obviously, gasoline vehicles have the largest carbon emissions, followed by diesel vehicles, BEVs, PHEVs, and HEVs, which are jointly determined by vehicles’ stock, sales, and fuel economy with different fuel types. In terms of time scale, summer and winter are the peak emission periods of the year, especially winter, at which time residents are more likely to choose private cars, taxis or online car-hailing to meet their travel needs than public transportation. Actually, the number of online car-hailing users in China reached 400 million in 201927, and the large-scale development of online car hailing may have an induced effect of emissions. Although the improvement in the road traffic sharing level reduces car purchase intention to a some extent28, the total mileage of trips does not significantly decrease, which leads to an increase in carbon emissions29,30,31. It is therefore also vital to promote the electrification of online car-hailing services32.

The emission reduction potential of road transport electrification mainly comes from the difference in emission intensity between electric vehicles and traditional fuel vehicles33. Basically, we assume that the electricity consumed comes from thermal power by default, according to the current situation. As shown in Fig. 3e, f, the emission reduction caused by vehicle electrification shows a significant increase trend. From 0.94 Mt in 2016 to 4.00 Mt in 2019, the average annual growth rate reached 61.81%. If all the electricity consumed by electric cars comes from clean sources, such as hydropower, then the reductions will expand to 1.81 Mt in 2016 and 10.14 Mt in 2019. Figure 3d also shows the emission reduction potential of different classes, and we find that MCC and MHCC gasoline cars (engine displacement in the range of 1.6–4 L) have the largest potential of carbon reduction, given its dominant role in passenger car consumption. This emphasizes the significance of regulating high-emission vehicle sales to control road traffic emissions (see Supplementary Note 3 for more details).

Relationships among vehicle stock, CO2 emissions, and economic development

The level of economic development of a region largely determines the purchasing power of consumers, which in turn determines the car stock and sales and related carbon emissions. Generally, the higher the per capita GDP is, the higher the vehicle stock and the associated carbon emissions25,34. Compared with 2016, in 2019, the average per capita GDP growth of the 11 provincial capital cities in eastern China was 16.15% (Fig. 4). However, the stock of passenger cars and their CO2 emissions reached 37.6% and 70.2%, respectively. This is particularly true for Guangzhou and Beijing, despite the rapid expansion of motor vehicles in Beijing has been restricted by policies such as lottery and driving restrictions (Supplementary Note 4). Overall, the average per capita GDP growth rate of provincial capitals in the central region is 2.22% higher than that of provincial capitals in the eastern region. This is also true for the growth rates of vehicle ownership and CO2 emissions. To be specific, Wuhan has the most significant increase in CO2 emissions, as high as 2207%, while Zhengzhou is the largest emitter owing to the so-called “stock” effect of motors (Zhengzhou ranks first in terms of vehicle ownership in the central provinces, Fig. 2).

Fig. 4: Relation dynamics of road passenger stock, GDP per capita (2008 = 100), and CO2 emissions from 2016 to 2019.
figure 4

a 11 provincial capitals of Eastern China; b 10 provincial capitals of Central China; c 9 provincial capitals of Western China; d relationship between passenger car stock and economic development in 291 prefecture-level cities. The two bubbles (the small and big bubble) in subfigures show carbon emissions in 2016 and 2019 respectively, and the size gives the number of estimated emissions.

In contrast, the western region had the most significant increase in per capita GDP during the studied period, with an average increase of 19.7% in all provincial capitals. The rise in passenger car stock and its carbon emissions is larger than that in the eastern region but smaller than that in the central region. The relations among vehicle stock, CO2, and economic growth are true in most western cities, except Xi’an and Lanzhou. Actually, economic growth of the two provincial capitals was slow, but their vehicle ownership and carbon emissions have grown enormously, and this situation also occurs in typical eastern cities, like Shenyang, Hangzhou, Tianjin, and Jinan. This provides evidence that economic development is not always positively and linearly related to residents’ willingness and ability to purchase a car.

From a national perspective, we observe a highly positive correlation between passenger car ownership and per capita GDP (Fig. 4d), and the relationship between the two gradually strengthened over the period. In 2016, an extra 1% increase in per capita GDP is associated with a 8.2% increase in passenger vehicles, and this value increased to 9.4% in 2019. Given a lower car ownership per thousand people in China (compared with the developed economies) and a sustained economic growth expectation, residents’ willingness to buy motor vehicles will dramatically increase in the future. In addition, the restriction policy does alleviate road congestion caused by fuel vehicles to a certain extent, but in the long run, whether it effectively reduces CO2 emissions and air pollutants is still controversial35,36,37,38. Therefore, how to coordinate economic growth with structural adjustment in the automobile industry (vehicle electrification) is of great significance to long-term low-carbon transformation of the traffic sector.

Low-carbon transition pathways of road transportation

By developing a CRT-LCTP model, we study the low-carbon transition pathways of China’s road transport across various scenarios (see “Methods” and Scenario setting), and the results are portrayed in Fig. 5a. As the number of traditional fuel vehicles increases, China’s road transport CO2 emissions will increase significantly. Under the BAU scenario, China’s road transport CO2 emissions will increase from 1340.80 Mt in 2020 to 1683.66 Mt in 2030, with an annual growth rate of approximately 2.3%. The emissions will peak in 2058, corresponding to the peak level of 2563.08 Mt. However, the peak time could be advanced to 2045 and 2034, respectively under the CPS and TPS, despite they are still later than the committed 2030. Thus, peak the emissions of transport sector in time depends on more intensified policies. Indeed, when moving to the most stringent scenario, i.e., the EPS, road traffic emissions can peak in 2031, roughly consistent with the promised time of the NDC target, and the corresponding peak level is reduced by 48.3% relative to the BAU scenario.

Fig. 5: CO2 emissions pathways (2020–2060) and distribution of carbon mitigation contribution (2060) for China’s road transportation.
figure 5

a CO2 emissions of road transportation across different scenarios (2020–2060); b mitigation contribution under the CPS; c under the TPS; d and under the EPS (Stock adjustment = Stock, electrification of vehicles = EV, =TD, Other factors (public transport rate, emission standard control, operation efficiency, etc.) =Other, Zero emissions gap = Gap). ‘Other’ includes factors that are not considered in the EV and TD scenarios, such as the increase in the public transportation share, share trips, emission standard control, etc.

From Fig. 5b–d, we find that the maximum contribution of emission reduction under the three policy scenarios all comes from the stock adjustment, and the contribution levels are 75.15, 63.74, and 59.57%, respectively. The contribution of electrification increases significantly with the strengthening of policy intensity, from 17.61% under the CPS to approximately 33.10% under the EPS. The contribution of technological progress, such as fuel efficiency improvement, to emission reduction is relatively limited, with an average level of approximately 2%, and it is much lower than that of ‘Other’ factors, i.e., 5–16% on average.

Overall, due to the persistence of traditional fuel vehicles (Supplementary Fig. 4), it is difficult to achieve net-zero emissions in the road traffic sector by 2060, and it is true when moving to the results under the SSPs (see Supplementary Note 5 for details). However, the optimistic auto electric transformation will finally restructure the future of road transport structure and then determine the long-term trend of carbon emissions, especially in the passenger transport department. In addition, challenges in the low-carbon transition of freight transport39,40 also explains the difficulty in achieving carbon neutrality in road transport (see Supplementary Note 6). Therefore, the realization of carbon neutrality in the transport sector may further rely on the forced phase-out of fuel vehicles, disruptive innovation in the transport system, and the structural adjustment of freight transport from road to rail and water.