Regionally differentiated promotion of electric vehicles in China considering environmental and human health impacts

The present study adopted an integrated modeling approach that combines CO₂ emissions, air quality levels, and health assessments under different fleet electrification scenarios for the 31 provincial regions (i.e. provinces, municipalities directly under the central government, and autonomous regions, as depicted in figure S1 in the supporting information) of China (figure 1). Specifically, we explored the emission changes of CO₂ and four major air pollutants—PM_2.5, SO₂, NOx, and VOCs—in 31 provinces from 2011 to 2018 in China. Air quality was generated by a Global Air Pollution Information and Simulation (GAINS) model, whereby GAINS was used to estimate the ambient concentration of PM_2.5 among five scenarios. The health impact evaluation model assessed the health effects of human exposure PM_2.5 under different scenarios. The adverse health outcomes included mortality, morbidity, and loss of work time, while the health benefits entailed monetary values of illness and the value of a statistical life. Finally, according to the above results, we determined an order to prioritize the development of EVs and ICEVs in 31 provincial regions incorporating their regional heterogeneity.

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The latest data on PV sales volume, vehicle ownership, and energy efficiency were obtained from China Automotive Technology and Research Center Co., Ltd. This data covers all the PVs in China from 2011 to 2018. The annual mileage for vehicle travel for the 31 provincial regions was collected from the National Big Data Alliance of New Energy Vehicles (Wang 2020).

This study set up five scenarios for CO₂ and air pollutant emissions and the health benefits analysis (figure 1). The first scenario is a reference case coupled with four counterfactual scenarios for further research. This reference scenario (REF) was designed based on historical data, corresponding to PVs' technical structure and energy efficiency following the trend of actual changes. Types of PVs include ICEVs, HEVs, PHEVs, and BEVs, and the technology mix of power generation follows the historical development trend of 2011–2018 under this scenario. ICEVI denotes an 'Internal combustion engine vehicles with improving energy efficiency' scenario; it is a counterfactual scenario representing energy efficiency undergoing an improved trend for ICEVs from 2011 to 2018, in which all PVs become ICEVs and the energy efficiency follows the actual data. ICEVF denotes an 'Internal combustion engine vehicles with frozen energy efficiency' scenario; it also supposed all PVs become ICEVs, but their energy efficiency is frozen from 2011 till 2018 (fixed at 2011 levels). BEVI denotes the 'Battery electric vehicles with improved electricity' scenario; it assumes all vehicles in this scenario are BEVs. The power sector follows the factual trends of a decreasing share of thermal electricity, the same as for REF. Finally, BEVF denotes the 'Battery electric vehicles with frozen electricity' scenario; this has the same design as the BEVI scenario regarding vehicle structure; all PVs are presumed to be BEVs. But the power generation technology did not change from 2011 to 2018.

Thus, the difference between ICEVI and REF results would show the effects of ICEVs altered by small-scale electrification (<2.5%). Correspondingly, the difference between ICEVI and ICEVF would demonstrate the impact of improving the energy efficiency of ICEVs. Moreover, by comparing the results of BEVI and REF, the effects of small-scale electrification transfer to all BEVs could be gleaned. The discrepancies between BEVI versus BEVF scenarios can indicate the impact of a slightly cleaner power source (an average of 70% thermal power in China in 2018).

Energy consumption in this study is calculated by multiplying three quantified terms: energy efficiency per vehicle per kilometer, vehicle ownership, and vehicle traveled miles (equation (1)).

$$\begin{equation}{\text{EC = EE}} \times {\text{VO}} \times {\text{VMT}}\end{equation} \tag{ 1 }$$

where, EC is the total energy consumption from different types of PVs under various scenarios from 2011 to 2018 in the 31 provincial regions (toe); EE is energy efficiency (toe km⁻¹); VO is the vehicle ownership, and VMT is the vehicle traveled miles (in km).

The emission factor of CO₂ for gasoline came from the GAINS model. The Multi-Resolution Emission Inventory for China calculated the electricity emission factor. The respective emission factors of four air pollutants were derived based on vehicle classification (ICEVs, HEVs, PHEVs, and BEVs), fuel type (gasoline, electricity), and emission standard level (from Pre-State I to State V). We also obtained data from the On-road Vehicle Air Pollutants Emissions Inventory Guidebook (MEE 2014) and relevant published research studies (Feng and Xu 2016, Sun et al 2019).

The GAINS model simulated PM_2.5 concentrations under different scenarios. The Integrated Model of Economy, Energy and Environment for Sustainable Development|Health (IMED|HEL) was used here to evaluate the health impacts of PM_2.5 exposure. The PM_2.5 concentration-exposed population and the updated exposure-response functions from epidemiological studies were fed into the IMED|HEL model. The health impacts include five kinds of health endpoints: (mortality), morbidity, number of work-loss days, health expenditures, and the value of a statistical life (Ma et al 2021, Zhang et al 2021).

Temporally, CO₂ emissions under REF and the other four counterfactual scenarios from 2011 to 2018 all exhibited an increasing trend (figure 2(a)). The demand for transportation services generally increases with the population and economic growth (Wang et al 2019). Accordingly, CO₂ emissions have risen from 64.0 Mton in 2011 to 210.5 Mton in 2018, with an average annual growth rate of 18.5% in the REF scenario. The decreasing order of CO₂ emissions in China was ICEVF > BEVF > REF > ICEVI > BEVI in 2018.

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The trend for CO₂ emissions under the ICEVI scenario is similar to those under REF from 2011 to 2018, demonstrating that small-scale changes to vehicle structure only exert a minor effect on this type of emission. One reason is the small magnitude of the shift from EVs to ICEVs, in that the latter only declined from 82.5% in 2011 to 70.4% in 2018 (CCC 2012-2014). Therefore, vehicles fueled with high thermal electricity (BEVs, PHEVs) have similar CO₂ emissions to ICEVs with high energy efficiency.

CO₂ emissions under the BEVI scenario are higher than those under REF from 2011 to 2013, mainly due to the increased emissions of the power sector (Fang et al 2018). However, after 2013, with the power sector now slightly cleaner than in 2011, the total CO₂ emissions for BEVI are lower than for REF, showing the high reduction potential of BEVs. CO₂ emissions increased over time in all scenarios, mainly because of augmented vehicle stocks. Under both ICEVF and BEVF scenarios, the CO₂ emissions respectively increased by 178.4 Mton and 162.9 Mton from 2011 to 2018 (figure 2(b)). The highest CO₂ emissions in 2018 occurred for ICEVF, which indicates that even the power sector in BEVF is thermal-dominant, while it is CO₂ emissions higher than in REF, it showed that EVs might not have the anticipated emission reduction effect in an unclear power system (Holland et al 2016); nevertheless, BEVs still offer a high emission reduction potential when compared with the frozen energy efficiency of ICEVs. Because of the energy efficiency improvement of ICEVs, CO₂ emissions are reduced by 32.6 Mton for ICEVI compared to the ICEVF scenario in 2018. In our research, we have found that high-energy-efficient vehicles have the potential to reduce emissions significantly. Previous studies have also supported this viewpoint. The efficiency technology contributed to reducing vehicular emissions and fuel consumption (Wills and La Rovere 2010). Improving vehicle efficiency and advanced engine and transmission technology were the alternative measures for EVs, and alternative power plants were also an essential method (Atabani et al 2011, Mittal et al 2016). However, the EV promotion order in our research is mainly based on the environmental and human health impacts, without further considering the traffic congestion situation and the financial burden of EV adoption. In addition, the transport infrastructure and charging devices should also be discussed in future research. Thermal electricity decreased by 12% in BEVI relative to BEVF, leading to a 24% reduction in CO₂ emissions in 2018 for BEVI. These results emphasize that clean electricity generation technologies are vital for PVs' decarbonization (Williams 2012).

Spatially, we found that among all provinces, Guangdong had the highest emissions in the five scenarios, followed by Jiangsu and Zhejiang (figure S2). It is due to the high mileage per vehicle and high vehicle ownership in these provinces, both of which are almost two times the national average (MEE 2014, Feng and Xu 2016, Sun et al 2019). However, the increase in vehicle ownership may be associated with a decline in marginal mileage, depending on the circumstances of each individual household (Nunes et al 2022). For example, despite its comparable vehicle ownership to Guangdong, CO₂ emissions are much lower in Shandong (figure S3), primarily because of the latter's lower vehicle-traveled mileage, being almost a quarter that of Guangdong's. Accordingly, Guangdong has the highest CO₂ emission reductions under the ICEVI scenario relative to REF, which accounted for 40% of the total decline. By contrast, its decrease in Shandong amounted to only 6% of the total reductions in China (figure 2(c)). Emission reductions under the ICEVI scenario characterize all provincial regions because of its higher energy efficiency relative to relative to ICEVF (figure 2(d)).

BEVI scenario has the highest potential for CO₂ emission reductions, despite having a high share of thermal power production. Spatially, the highest CO₂ emission reduction occurred in Sichuan under BEVI (figure 2(e)), primarily due to the cleaner electricity production and its significantly more number of vehicles relative to other regions. However, the BEV adoption in Liaoning, Shaanxi, Jiangsu, Xinjiang, and Shandong led to high CO₂ emissions under the BEVI scenario, which exceeded those under REF. The cleanness of the power sector, vehicle ownership, and traveled mileage are the significant determinants of CO₂ emissions of BEVs (Milovanoff et al 2020). If BEVs develop under the context of replacing fossil-fuel-based technologies with renewables for power generation, this will substantially reduce CO₂ emissions (Knobloch et al 2020).

Further, EVs, especially BEVs, are strongly compatible with high economic competitiveness and are apt to earn consumer acceptance after introducing preferential policies for buying them (Luderer et al 2021, Pauliuk et al 2021). Compared with the power production-frozen scenario (BEVF), BEVI resulted in the highest emission reductions in Guangdong (figure 2(f)), primarily because of a slightly cleaner development of electricity there (10% of thermal electricity difference between ICEVI and ICEVF in 2018). Hence, from the perspective of CO₂ emission reductions, we find that BEVs constitute a good choice for road transport's decarbonization as the power sector shifts towards cleaner energy production.

SO₂ and VOCs emission in REF showed a trend of first increasing and then decreasing from 2011 to 2018 (figure 3(a)). The SO₂ emission increased from 59.5 Kton in 2011 to 85.1 Kton in 2014 but declined to 49.6 Kton in 2018. However, PM_2.5 increased continuously from 2011 to 2018 for REF, mainly because of the substantial increase in vehicle ownership over that period. Even introducing high-standard vehicles (such as the State V type) could not offset emissions from greater energy demand caused by rising vehicle ownership. The NO_x emission in REF declined over time, likely influenced by the more significant usage of high-emission standard vehicles from 2011 to 2018. Both ICEVI and REF scenarios have similar emission trends for the two air pollutants from 2011 to 2018, suggesting that energy efficiency improvements and a small-scale change of technical structure (going from ICEVs to EVs) have nearly the same effects on air pollutants. The highest PM_2.5, NO_x , and VOCs emissions under five scenarios occurred for ICEVF. The potential for air pollutants emission is high from ICEVs with low energy efficiency. However, the highest SO₂ emissions arose under the BEVF scenario, primarily due to the increased emissions from unclean power sources, which collectively supply many BEVs. The BEVI scenario had the least PM_2.5, NOx, and VOCs emissions from 2011 to 2018, indicating high mitigation effects from adopting BEVs (Ke et al 2017). Lower air pollutant emissions are also affected by the technology improving the flue gas treatment in power generation plants (Yu et al 2020). The BEVI scenario has higher SO₂ emissions relative to REF because of its higher share of thermal power (higher than 73% in 2018 (NBSC 2019b)).

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Nevertheless, SO₂ emissions diminished with time for BEVI, falling by around 45% by 2018 compared with 2011. It implies that as power plants attain cleaner operations (even at a small scale), SO₂ emission reduction effects become significant. In addition, an SO₂ tax could contribute to regional air pollutants reduction, which favors the renewable utilization of power sectors (Xie et al 2018), especially the penetration of wind power (Dai et al 2016, Herran et al 2016).

Under the REF scenario, the total electricity consumed by vehicles is small, ranging from 0.37 thousand tons of oil equivalent (Ktoe) in 2011 to 0.27 million tons of oil equivalent (Mtoe) in 2018 in China. Low electricity consumption is critical to lower SO₂ emissions for REF. Due to the increased cost-competitiveness and a greater renewable electricity supply (Ludereret et al 2021), EV technologies will increase competitiveness in reducing air pollutants.

Spatially, in the five scenarios, the highest air pollutant emission levels of PM_2.5, SO₂, NOx, and VOCs occurred in Guangdong among 31 provincial regions in 2018 (figure 3(b)). Both PM_2.5 and SO₂ have relatively low emission levels in all provincial regions when compared those of either NOx or VOCs. ICEVI scenario incurred almost similar air pollutant emissions as REF across the 31 provincial areas, with the difference between these two scenarios in most regions being <0.01 Kton. By comparison, the difference in VOCs emissions between ICEVI and REF is >2 Kton in eastern China. Under the BEVI scenario, most regions have lower air pollutant emissions than under REF, and emissions are higher for ICEVF and BEVF than ICEVI and BEVI, respectively, in all of China's mainland regions (figures S4–S7). These air pollutants emission levels show that the BEVI scenario harbors much potential for emission reductions vis-à-vis the other scenarios. It will increase with the development of cleaner power plants. Evidently, the competitiveness of BEVs is pronounced in achieving air pollutant emission reductions in China (Wang et al 2021).

Overall, besides the climate and geographical differences, several factors also contribute to the heterogeneity at the regional level caused by EV penetration. Firstly, the differences in the levels of socio-economic development across regions (e.g. varying population, economy, and consumption patterns) can result in variations in the technological structure and EVs promotion, thus leading to regional differences in emissions. Secondly, the carbon intensity of the provincial electricity grid affects expected carbon emission reduction, and provinces with higher carbon intensive grid could achieve higher emission reduction when gasoline vehicles are substituted with EVs. Thirdly, due to the implementation of different policies and regulations promoting EV in different regions, various incentives and barriers exist for EV development. Consequently, the penetration of EVs varies among provinces.

The electrification of PVs in the BEVI scenario consistently reduced the PM_2.5 concentrations in all provincial regions of China relative to REF, except Heilongjiang, where the PM_2.5 concentration was 0.02 μg m⁻³ higher in the BEVI than REF scenario (figure 4(a)).

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The extraordinary results in Heilongjiang may be due to the high primary PM_2.5 emissions and its relatively high reliance on thermal power generation. The PM_2.5 concentration fell in the BEVI scenario compared with REF, and the reduction ranged from 0.001 μg m⁻³ in Tibet and 1.4 μg m⁻³ in Beijing.

Most central and south China provincial regions have a higher avoided morbidity and mortality risk under the BEVI scenario. According to the air quality analysis, fleet electrification can readily induce more considerable abatement of the ambient PM_2.5 concentration. Because of the slightly cleaner power development in the BEVI scenario, the air quality has improved significantly relative to the BEVF scenario (figure 4(b)). This result highlights the essential role the power sector plays in air pollutants' mitigation by BEVs.

Some regions in western and central China undergo higher reductions (>1 μg m⁻³) in their PM_2.5 concentration in the BEVI scenario compared with BEVF. This result reveals that power plants with a slightly low share of thermal electricity will significantly influence air quality when using BEVs on the road. We also found no significant difference in PM_2.5 concentration for the small-scale transfer of EVs to ICEVs that are highly energy efficient (i.e. the difference between REF and ICEVI scenarios) (figure S8). More than 15 provincial regions in China have substantially improved air quality under the BEVI scenario relative to REF (figure 4(c)). Taken together, and these results reveal that fleet electrification with slightly cleaner-sourced electricity will enhance air quality and decrease CO₂ emission synergistically (figure 4(d)).

Under the BEVI scenario, China avoided 638 599 cases of morbidity and 3167 premature deaths in 2018 compared to the REF scenario (figure 5(a)). The most significant impact was seen in eastern China, particularly in Guangdong, where 72 216 cases of morbidity and 657 premature deaths were avoided, and Sichuan, where 98 517 cases of morbidity and 632 premature deaths were avoided. The BEVI scenario had 554 144 avoided morbidity and 3916 avoided mortalities due to a slightly cleaner power sector than under BEVF, in which the power production structure kept constant at the 2011 level until 2018. Sichuan province featured the highest avoided morbidity and mortality in the BEVI scenario relative to BEVF, having 65 381 and 421 cases, respectively (figure 5(b)).

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Most central and southern China provincial regions experienced higher avoided morbidity and mortality under the BEVI than the REF scenario. We further analyzed the monetary benefits of better human health for BEVI compared with REF as well as BEVF. Large parts of China accrued health benefits because of the complete fleet electrification and slightly cleaner power (figure 5(c)). Compared with REF, the total health benefits under the BEVI scenario are US$4269 million. Under BEVI, we find considerable health benefits in central-south China of US$1744 million, of which Guangdong accounts for 51%. Yet, relative to REF, the BEVI scenario in northeast China incurred a loss of health benefits corresponding to US$75 million more in health costs. The worse-off health benefits in northeastern China are mainly due to the relatively low share of EVs and its high penetration of thermal power under the current status (i.e. REF). Thus, the greater penetration of EVs will lead to more CO₂ and air pollutants emitted in the power sector, which induces a negative health impact in the region.

Nevertheless, slightly cleaner power will lead to overall higher health benefits in China (figure 5(d)). Southwest China has the highest health benefits of US$1732 million in BEVI relative to BEVF. The result demonstrates that clean production in the power sector is the key to realizing low emissions and health benefits during fleet electrification. ICEVs with a high energy efficiency under the ICEVI scenario will also avoid morbidity and mortality relative to ICEVF, though the health burden will rise for ICEVI versus REF (figure S9). It suggests that for health impacts, the high efficiency of ICEVs is inferior to electrification.

Our study demonstrates that the power sector significantly affects CO₂ emission reductions and air quality improvement for BEVs utilization in China. Using BEVs and lower thermal electricity in the BEVI scenario will prevent 3167 premature deaths from PM_2.5 exposure relative to REF in 2018. Thus, BEVs could be the best choice for the decarbonization development and air quality improvement for PVs under a cleaner power supply. However, hydrogen FCVs can also reduce CO₂ emissions and improve air quality (Jacobson et al 2005, Jacobson 2008). Because our study is based on historical data for scenario design, FCVs were not considered. In addition, a previous study reported that FCVs are unnecessary for passenger vehicles and useful in freight road transportation, aviation, and navigation (Plötz 2022).

Our study also shows that enhancing the energy efficiency of ICEVs can also decrease CO₂ and air pollutants emissions in most regions of China, indicating the potential for energy efficiency improvements for climate change mitigation (Kobayashi et al 2009). Meanwhile, our study uncovered a 90% higher energy efficiency of BEVs than ICEVs. Therefore, BEVs are also viable high-energy efficiency technology options for saving energy and advancing decarbonization (Chlopek et al 2018), which should be massively promoted in China's passenger road transport sector.

This study analyzed the temporal and spatial distribution of CO₂, air pollutants, PM_2.5 concentrations, avoided premature deaths, and value of statistical life arising from BEVs' penetration using retrospective analysis. Based on the holistic consideration of these parameters, we categorize three gradient scores (1, 2, 3) for promoting BEVs in the 31 provincial regions, nine of which are ranked first (i.e. =1) for promoting BEVs (figure 6). Given the thermal-dominant power sector in the current situation, we also identified eight provincial regions for developing ICEVs with a high energy efficiency under presently high thermal electricity production (table S3). Inner Mongolia, the northeast, Tibet and some areas in the northwest are appropriate regions for the high energy-efficient ICEVs promotion in the current situation. Our results showed that vehicle electrification under thermal power declined from 82.5% to 70.4% and can prevent 0.55 million cases of morbidity and ca. 3900 cases of mortality, resulting in avoided health expenditures of US$5278 billion. For the ICEV, its energy use efficiency improving by 1 Ktoe 100 km⁻¹ will avoid ca. 1.39 million morbidity cases and ca. 8100 mortality cases, with related health expenditures reduced by US$10 950 billion, this accounting for almost 5.8% of China's GDP (gross domestic product) in 2018 (NBSC 2019a). Thus, BEVs and developing highly energy-efficient ICEVs could be proposed in different provincial regions for achieving deep decarbonization and health benefits.

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EVs are recognized as the most promising technology for decarbonization and health benefits for on-road transportation (Liang et al 2019). Yet the technology mix of power production directly impacts CO₂ and air pollutant emissions, air quality, and the health benefits associated with EVs' development. Our research reveals that since the current power sector still has a high share of thermal electricity, further improving the energy efficiency of ICEVs is essential for realizing climate and health benefits. This also applies to the transitional stage towards a higher percentage of EVs on the road. The burden on the climate and environment from EVs is only at the battery charging stage, which depends on the power production technology (Marczak and Drozdziel 2021). EVs' promotion should be accelerated together with progress in the renewable energy development of the power sector (Vliet et al 2011). The quantitative findings of EV penetration for health impacts and its benefits presented in this study can fill the research gap and provide evidence for policy-making to develop EVs in China.

Road transport technologies electrification has the benefits of carbon and air pollutants emission reduction and human health improvement. However, EV promotion policies also exhibit several trade-offs. From the material side, the surge in demand for power batteries may increase the prices of critical battery materials, such as cobalt (Baars et al 2021), nickel (Elshkaki and Shen 2019), manganese (Feng et al 2022), and lithium (Greim et al 2020), for widespread EVs shortly (Wang et al 2023). The Ministry of Finance has allocated nearly 19 billion yuan in subsidies for constructing new energy vehicles and charging infrastructure in 2023, accounting for around 0.02% of GDP (MFPRC 2022). Financial incentives are also offered to local governments that achieve goals for installing new household chargers. Meanwhile, EV manufacturing entails a much shorter supply chain than ICEV, implying that the job-creating benefits are significantly lower than the conventional vehicles (Papoutsoglou et al 2022).

Several uncertainties in this research remain that should be addressed in future studies. Firstly, the air quality estimated by the GAINS model only considered the energy consumption changes in the road transport sector. Further, the simulation year used by GAINS is 2015 for estimating the results for 2011–2018 in this study. These could cause more uncertainty in the air quality evaluation. Secondly, this study focused only on PVs to reveal the full impact of vehicle electrification on road transportation transition in China, and other vehicle types should be taken into account in future studies. Moreover, the gradient sorting (rank ordering) for EV development is based on the current fleet structure and power mix, with no detailed uncertainty analysis to supplement it. Additionally, the prioritization of promoting EVs was proposed to achieve comprehensive electrification. Still, there was a lack of specific inter-provincial differentiated strategies for gradually promoting EVs. Furthermore, the order conducted from environmental and human health perspectives in our study, for future research should comprehensively consider factors such as EV production, consumption, scrappage, and infrastructure construction.