Impact of battery electric vehicle usage on air quality in three Chinese first-tier cities

China, the world leader in automobile production and sales, confronts the challenge of transportation emissions, which account for roughly 10% of its total carbon emissions. This study, utilizing real-world vehicle data from three major Chinese cities, assesses the impact of Battery Electric Vehicles (BEVs) on air quality. Our analysis reveals that BEVs, when replacing gasoline vehicles in their operational phase, significantly reduce emissions, with reductions ranging from 8.72 to 85.71 kg of CO2 per vehicle monthly. The average monthly reduction rate is 9.47%, though this effect is less pronounced during winter. Advanced BEVs, characterized by higher efficiency and newer technology, exhibit greater emission reduction benefits. While private BEVs generally contribute positively to environmental outcomes, taxi BEVs, due to their intensive usage patterns, show less environmental advantage and may sometimes worsen air quality. Looking ahead, we project substantial emission reductions from the replacement of gasoline vehicles with electric alternatives over the next decade. Policymakers are urged to adopt proactive measures, focusing on promoting medium to large electric vehicles and fostering the use of private and ride-hailing electric vehicles.

Industrialized countries, particularly China, are grappling with the challenge of mitigating air pollution amidst rapid urbanization.As per the International Energy Agency, China's CO 2 emissions in 2021 accounted for a staggering 33% of the global total, amounting to 11.9 billion tonnes.The transportation sector, a major contributor to China's carbon footprint, is responsible for about 10% of its total emissions 1,2 .This environmental issue is particularly acute in China's super-tier-1 cities, where air pollution significantly hinders socioeconomic growth and public health 3,4 .
In response to these challenges, the Chinese government has emphasized the development and adoption of New Energy Vehicles (NEVs), particularly Battery Electric Vehicles (BEVs), as a clean alternative to conventional automobiles 5,6 .By June 2022, out of 312 million civilian vehicles, only 8.104 million were BEVs, representing roughly 2.6% of the total 7,8 .This shift towards electric mobility is part of China's broader strategy to achieve carbon neutrality, as outlined in the State Council's Action Plan for Achieving Carbon Peaks by 2030, which targets a 40% share for new energy and clean energy-powered transport vehicles by 2030 [9][10][11] .Globally, policy support has been instrumental in increasing the total number of electric vehicles to about 16.5 million, a threefold increase from 2018 12 .However, the realization of these targets hinges on the usage patterns and frequency of BEV utilization 13 .
Despite the rising global adoption of electric vehicles, the academic community remains divided over the environmental impact of New Energy Vehicles (NEVs).Proponents highlight NEVs' potential in reducing carbon intensity and enhancing air quality 14 .In contrast, critics caution that BEVs might shift emissions from transportation to electricity production, particularly in regions dependent on non-renewable energy sources 15,16 .Further, there are concerns that subsidies for NEVs could divert resources from other vital environmental initiatives, potentially impeding broader urban environmental progress 17 .
This debate largely centers around different phases of NEV utilization.Advocates often focus on the usage phase and life cycle assessment (LCA) 18,19 , while detractors draw attention to the energy production phase and its possible diversion of resources from other environmental policies 20 .The environmental footprint of BEVs, including their electricity consumption and resultant greenhouse gas emissions, varies depending on operational conditions and the battery charging/discharging process 21 .Many studies have explored BEV emission reductions

Emission reduction benefits of BEVs replacement for fuel vehicles
Utilizing real-world driving data, our study demonstrates a notable carbon reduction effect when Battery Electric Vehicles (BEVs) replace fuel vehicles over equivalent mileages (refer to Fig. 1).We observed a consistent upward trend in emission reductions over the study period.In January 2019, the emission reduction per BEV was approximately 8.72 kg of CO 2 , which escalated to around 63.83 kg of CO 2 by October 2020, averaging a monthly increase of 9.47% (details provided in Supplementary Data 1).
Initially, the new energy vehicle market in China, including BEVs, was largely dependent on government support.However, diverse support policies have subsequently catalyzed substantial growth in this sector, positioning

Differences in emission reductions between BEV models
This study delves into the emission reduction differences among various BEV models when they replace equivalent fuel vehicles (see Fig. 2).We found a consistent trend: the larger or heavier the vehicle, the more substantial the carbon emission reduction achieved when it is replaced with a BEV of the same category.This observation can be attributed to the higher energy release and larger displacement of larger vehicles, leading to notable emission reductions when substituted with equivalent BEVs 34 .
Among the evaluated models, C-type BEVs demonstrated the most significant emission reduction benefits.On average, these vehicles yielded an annual reduction of 14.75 kg CO 2 per 100 km.B-type vehicles followed closely, with an average annual reduction of 13.86 kg CO 2 per 100 km.In contrast, A0-type BEVs showed the least benefit, with an average annual reduction of only 8.86 kg CO 2 per 100 km.
Extreme weather conditions were found to increase the carbon emissions across all models, likely due to the use of air conditioning and the diminished performance of batteries in such conditions 35 .Cold temperatures, in particular, have a more pronounced impact on BEVs.The decrease in battery activity and storage capacity in colder climates, coupled with increased rolling resistance and aerodynamic drag, results in heightened energy consumption and power loss.Studies have also noted a significant drop in average EV mileage in cold climates 36 .

Air quality impacts of BEVs for different applications
Utilizing the method outlined in Eq. ( 2), we analyzed the air quality impacts of private BEVs, taxi BEVs, and online ride-hailing BEVs, each detailed in Tables 2, 3, and 4, respectively.
Table 2 indicates that private BEV travel substantially benefits air quality.A negative correlation was found between the amount of private BEV travel frequency and the AQI index, accompanied by a decrease in of PM 2.5 , PM 10 , CO, SO 2 and O 3 levels, but an increase in NO 2 levels, aligning with the overall sample results.
Conversely, Table 3 shows that taxi BEVs do not significantly impact environmental improvement and may even contribute to a decline in air quality.This is evidenced by increased levels of PM 2.5 , PM 10 , CO, SO 2 and O 3 , with a decrease in NO 2 .The inefficiency of taxi BEVs in air quality improvement is attributed to their high energy consumption, particularly at lower temperatures and during nighttime charging 23 .As the power grid in the examined super-first-tier cities is predominantly coal-based, the electrification of taxi BEVs leads to increased emissions of greenhouse gases and pollutants 39,40   www.nature.com/scientificreports/ in increased electricity consumption and accelerated battery aging, further diminishing efficiency and exacerbating air quality issues.In contrast, Table 4 highlights that travel in online ride-hailing BEVs significantly reduces emissions, leading to improved air quality.Although there is an increase in PM2.5, PM10, CO, SO 2 , and NO 2 , the reduction in O 3 is noteworthy.Despite similar operating conditions to electric taxis, online ride-hailing cars are comparatively more efficient, consuming 36% less fuel and producing 44% fewer hydrocarbons 41 .Thus, while they contribute positively to air quality, they also generate additional pollutants.

Discussion
This study evaluated the impact of Battery Electric Vehicles (BEVs) on air quality in China's three first-tier cities, utilizing real-world vehicle data and carbon emission modeling.Our analysis reveals that BEVs, when replacing fuel vehicles for equivalent mileages, generally contribute to better emission reductions.However, this effect varies across different cities, months, and vehicle models, with a notable reduction in efficacy during winter.Larger vehicles, such as B, C, and MPV models, exhibit higher carbon efficiency over 100 km compared to smaller vehicles (e.g., A0, A) and show less sensitivity to climatic changes.
The study also found a significant decrease in total vehicle mileage (fuel vehicles + BEVs) in 2020, largely attributable to COVID-19 related restrictions 42 .Interestingly, BEVs experienced a smaller change in total travel mileage but an increase in travel frequency, possibly influenced by rising fuel prices due to the Russia-Ukraine conflict 43 , making electric vehicles a more attractive travel option 44 .
Further analysis using a two-way fixed-effect model on air quality and BEV trip frequency revealed that increased BEV usage significantly improves air quality by reducing levels of PM 2.5 , PM 10 , CO, SO 2 and O 3 .Environmental benefits vary across BEV classes, with A0-, A-, B-, and SUV-type BEVs making a substantial impact Table 1.Baseline estimates.Note: The independent variable, BEVtrips, represents the natural logarithm of the monthly travel frequency of battery electric vehicles (BEVs).The unit-of-analysis is at the BEV-month level.Columns (1) and ( 2) detail the relationship between BEV monthly travel frequency and the Air Quality Index (AQI).Columns (3) through (8) explore the relationship between BEV monthly travel frequency with individual air pollutants: PM 2.5 , PM 10 , CO, SO 2 , NO 2 and O 3 , respectively.Both month and city fixed effects are controlled in all regressions.The control variables are not included for the regression in Column (1) and are included in the regressions in Columns ( 2)- (8).Robust standard errors are shown in parentheses.*p < 0.1; **p < 0.05; ***p < 0.01.www.nature.com/scientificreports/ on reducing AQI and improving air quality.In contrast, A00-, C-, and MPV-type BEVs show less pronounced benefits due to lower emissions, fewer trips, and higher energy consumption for longer ranges.
The study also differentiates the environmental impacts of BEVs based on their usage.Private passenger BEVs substantially improve air quality, while taxi BEVs have lesser environmental benefits due to their operational characteristics.Online ride-hailing BEVs, having lower energy consumption per unit than taxi BEVs, offer slightly better environmental advantages.
Based on our findings, we propose several strategic recommendations to enhance the environmental efficacy of Battery Electric Vehicles (BEVs).We advocate for the prioritization of larger BEVs, as A-class and higher models demonstrate more significant carbon emission reduction benefits over their entire lifecycle compared to smaller BEVs.Additionally, the continuous monitoring of pollutants, especially NO2, is crucial to understand and mitigate the potential environmental impacts of widespread BEV adoption.Advancements in battery technology are also imperative, with the development of new-generation solid fuel cells and low-temperatureresistant ternary lithium batteries essential for maintaining emission efficiency in cold weather.Ternary lithium batteries, functional down to -30℃, exhibit superior low-temperature discharge performance, and the use of iron phosphate lithium batteries, with less than 15% mileage degradation in winter, could significantly enhance EV performance in extreme conditions.Furthermore, promoting online ride-hailing services can offer a more sustainable alternative to traditional taxi BEVs, covering wider areas and providing cost-effective, time-efficient commuting options 41 .Implementing energy-efficient air conditioning and electric power systems is pivotal for reducing winter energy consumption and enhancing EV efficiency, addressing the increased energy demands of EVs during colder months.Finally, policymakers should encourage more eco-friendly travel methods during winter, such as buses, subways, shared bicycles, trolley buses, and carpooling, which are particularly suitable for short-distance journeys and in areas with challenging road conditions.These recommendations collectively aim to maximize the environmental benefits of BEVs, contributing to sustainable urban transportation solutions and addressing the challenges posed by climate variability and technological limitations.These recommendations Table 2. Heterogeneity analysis: private BEVs.Note: The independent variable, BEVtrips, represents the natural logarithm of the monthly travel frequency of battery electric vehicles (BEVs).The unit-of-analysis is at the BEV-month level.Column (1) details the relationship between private BEV monthly travel frequency and the Air Quality Index (AQI).Columns (2) through (7) explore the relationship between private BEV monthly travel frequency with individual air pollutants: PM 2.5 , PM 10 , CO, SO 2 , NO 2 and O 3 , respectively.All regressions account for both month and city fixed effects, and incorporate control variables to ensure robustness and accuracy in capturing the true impact of private BEV travel frequency on air quality.Robust standard errors are shown in parentheses.*p < 0.1; **p < 0.05; ***p < 0.01.www.nature.com/scientificreports/aim to maximize the environmental benefits of BEVs, contributing to sustainable urban transportation solutions and addressing the challenges posed by climate variability and technological limitations.However, this study does not account for specific conditions such as extreme winter temperatures, which may increase power consumption, indicating a need for further research.Future studies should delve deeper into the interplay between BEVs, carbon emission reduction, air quality, and the substitution patterns between BEVs and fuel vehicles.

BEVs driving data and air quality data
In this paper, we collected available data from the open lab of the National Big Data Alliance of New Energy Vehicles.We cleaned the data and kept all normal driving segment data.The rules followed were as follows: (1) remove all PHEV vehicles; (2) remove samples with abnormal energy consumption where the post-driving battery percentage difference SOC ≥ 0, which is the remaining power of the battery; (3) remove samples with an average speed greater than 200 km/h in the driving segments; (4) remove samples with a driving range less than 0.1 km in the driving segments.
Each driving segment in this study database contained the number of each type of electric vehicles, the time of the start and end of the drive, the start and end mileage, the start and end SOC, the duration of the drive, the average speed, the maximum speed, the longitude and latitude of the start of the drive, the longitude and latitude of the end of the drive, the distance difference, the type and use of the vehicle, the battery material and capacity, the maximum range and the net weight of the vehicle.
In 2016, the National Monitoring and Management Centre for New Energy Vehicles was established in China.It serves as the national big data platform for EVs.The centre has the only datasets in the world that contain Table 3. Heterogeneity analysis: Taxi BEVs.Note: The independent variable, BEVtrips, represents the natural logarithm of the monthly travel frequency of battery electric vehicles (BEVs).The unit-of-analysis is at the BEV-month level.Column (1) details the relationship between taxi BEV monthly travel frequency and the Air Quality Index (AQI).Columns (2) through (7) explore the relationship between taxi BEV monthly travel frequency with individual air pollutants: PM 2.5 , PM 10 , CO, SO 2 , NO 2 and O 3 , respectively.All regressions account for both month and city fixed effects, and incorporate control variables to ensure robustness and accuracy in capturing the true impact of taxi BEV travel frequency on air quality.Robust standard errors are shown in parentheses.*p < 0.1; **p < 0.05; ***p < 0.01.real-time operating data of nationwide EVs.The coverage of the platform can reach up to 80% in Beijing and Shanghai 45 .In recent years, Shenzhen, as a super-first-tier city in China, has also vigorously promoted new energy vehicles.By the end of 2020, the number of new energy vehicles in Shenzhen reached 480,000, accounting for about 14% of the city's motor vehicle ownership 46 .Our data sample in this paper consisted of panel data for three super-tier-1 cities in China (Beijing, Shanghai and Shenzhen) from January 2019 to October 2020.In the empirical model analysis, we classified the BEVs data sample by city and month to obtain the total number of trips per vehicle per month as one observation.If a vehicle did not make a trip in a month, no observation was recorded for that month for that vehicle.In total, there were 1,826,095 observations for 196,387 vehicles in the three cities, including 1,288,697 trips for 132,644 vehicles in Beijing, 239,742 trips for 22,240 vehicles in Shanghai and 297,656 trips for 41,503 vehicles in Shenzhen.The detailed data are shown in Supplementary Data 1.In addition to BEVs data, air quality data and meteorological data for each district and county in the three cities were obtained based on publicly available data in terms of year, quarter and month.AQI, CO 2 , SO 2 , O 3 , PM 2.5 , PM 10 and other data indicating air quality for the three cities were obtained from the real-time national urban air quality release platform of the China General Environmental Monitoring Station at https:// air.cnemc.cn: 18007/; GDP per capita, share of secondary industry in GDP and urban population density are taken from the China Urban Statistical Yearbook for previous years; implementation of policies restricting the driving and purchase of conventional gasoline engines from the official websites of municipalities and public security bureaus, etc 47 .Average temperature, humidity, precipitation, sunshine hours, wind speed and other meteorological data for the three cities were taken from the National Climatic Data Centre (NCDC) at https:// ngdc.noaa.gov/.Table 4. Heterogeneity analysis: Ride-hailing BEVs.Note: The independent variable, BEVtrips, represents the natural logarithm of the monthly travel frequency of battery electric vehicles (BEVs).The unit-of-analysis is at the BEV-month level.Column (1) details the relationship between ride-hailing BEV monthly travel frequency and the Air Quality Index (AQI).Columns (2) through (7) explore the relationship between ride-hailing BEV monthly travel frequency with individual air pollutants: PM 2.5 , PM 10 , CO, SO 2 , NO 2 and O 3 , respectively.All regressions account for both month and city fixed effects, and incorporate control variables to ensure robustness and accuracy in capturing the true impact of ride-hailing BEV travel frequency on air quality.Robust standard errors are shown in parentheses.*p < 0.1; **p < 0.05; ***p < 0.01.www.nature.com/scientificreports/

Factors leading to carbon emission in fuel vehicles
As the BEVs considered in this study were mainly sedans, the fuel for the corresponding car model of the gasoline vehicles was mainly petrol.Therefore, the carbon emission factor for gasoline vehicles driving was taken as the carbon emission factor for petrol.The carbon emission factors of fuel vehicles, E co 2 23,27 was calculated as follows: where, E co 2 denotes CO 2 emissions per litre of gasoline in kg CO 2 , CQ g denote default gasoline net calorific value in KJ/kg.According to the General Rules for Calculating Comprehensive Energy Consumption (GBT2589-2020) issued by the State Administration of Market Administration of China and the State Standardization Administration, the net calorific value of gasoline is 43,124 kJ/kg 48 .CF co 2 is the default value of the effective CO 2 emission factor for petrol in KG/TJ.We referred to the IPCC guidelines for fuel carbon emission factors to obtain a default value of 74 100 kg/TJ for the effective CO 2 emission factor of petrol 49 .Therefore, 3.1954884 kg CO 2 is emitted per kilogram of gasoline.According to information from PacificCar.com, there are three gasoline brands in China, 90, 93 and 97, which have an average density of 0.720 g/mL, 0.725 g/mL and 0.737 g/mL, respectively 50 .We took the average of the densities of the three types of gasoline as the density of gasoline, i.e., 0.7273333 g/ mL.Therefore, each litre of gasoline emitted 2.3241852 kg CO 2 , i.e., the carbon emission factor of the gasoline vehicles was 2.3241852 kg CO 2 /L.

Carbon emission model for the driving phase of BEVs
We developed a model for calculating the carbon emissions of BEVs during their driving phase based on real vehicle data from three cities: Beijing, Shanghai and Shenzhen.As BEVs do not produce carbon emissions during driving, all references to BEVs carbon emissions in the text refer to the carbon emissions from electricity production corresponding to the energy consumed while driving.The model was primarily divided into two parts: carbon emissions from fuel vehicles and those from BEVs.The emission savings per kilometre of a BEVs were calculated by taking the difference between the emissions calculated in Eq. ( 2) with the corresponding gasoline vehicle emissions 23 , represented as: where, ΔCE denotes emission savings per kilometre of a BEVs in kg CO 2 ; F co 2 denotes carbon emission factors of fuel vehicles in kg CO 2 /L; C f denotes fuel consumption of fuel-powered vehicles in L/100 km; E co 2 denotes elec- tric carbon emission factor in kg CO 2 /(kW•h) and C e denotes actual grid energy consumption in kW•h/100 km.

Air quality model for the driving phase of BEVs
Based on the analysis (Supplementary information 1, 2 and 4), the air quality model of BEVs travel was constructed as follows: The dependent variable ln AQI c,t represents the natural logarithm of the Air Quality Index (AQI) for city c in month t, which represents the level of air pollution in the city.Contaminants for AQI calculations and evaluation include sulfur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), suction particulate matter (PM 10 ), fine particulate matter (PM 2.5 ), carbon monoxide (CO) and ozone (O 3 ).To more comprehensively reflect the emission reduction effect of BEV trips replacing fuel vehicle trips, we also included CO, SO 2 , O 3 , PM 2.5 and PM 10 as dependent variables, and recorded their logarithmic values as lnCO, lnSO 2 , lnO 3 , lnPM 2.5 , and lnPM 10 .
The independent variable BEVtrips c,t is the log of BEV's monthly trips.The more the fuel vehicles are driven, the more emissions they produce 28 .Assuming a constant total monthly travel demand, more BEVs trips per month meant fewer fuel vehicle trips per month and stronger air quality improvement.Control c,t are control variables affecting air pollution, including GDP (lngpd), share of secondary industry in GDP (Industry), urban population density (lnpopula), a dummy variable Govern for whether policies restricting driving and purchasing of conventional gasoline engines are implemented, average temperature (lntemp), humidity (lnhumi), precipitation (lnprecip), sunshine hours (lnsun), and wind speed (lnwind).The classical environmental Kuznets curve (EKC) theory postulates that the relationship between air pollution and economic growth is a significant inverted U-shaped curve 51 .In general, the higher the GDP per capita, the higher the vehicle stock and associated carbon emissions 52,53 .Therefore, GDP (lngpd) was included in the model.The secondary sector contributed the most to China's energy consumption and emissions 54 ; hence, the higher the proportion of the secondary sector, the more serious the air pollution.Therefore, the share of the secondary sector in the GDP (Industry) was used to control the impact of the secondary sector 55 .Urban population concentrations and activities can contribute to air pollution 56 , so we controlled for the possible effects of urban population concentrations using the urban population density (lnpopula), which was the urban population divided by the area of the urban administrative district.Vehicle emissions are a significant source of urban air pollution, and the Chinese government has implemented policies to restrict the driving and purchasing of conventional gasoline engines 57 hich restricted vehicle trips.We added a dummy variable (Govern) for whether the city implements these two restrictive policies.Air pollution is most likely related to natural climatic conditions, such as local dispersion and the deposition of pollutants.Hence, we used annual average temperature (lntemp), humidity (lnhumi), precipitation (lnprecip), sunshine duration (lnsun) and wind speed (lnwind) to control the effects of different natural climatic factors on air pollution.µ c are city fixed effects.µ t are city fixed effects.Robust standard errors ε c,t are clustered at the city-month level.

Fuel consumption by fuel vehicles
The fuel mileage of gasoline vehicles is based on the weighted average of the sales volume and fuel mileage of gasoline vehicles sold each year, and the annual sales volume and fuel consumption information are obtained from the owner's home website (www.16888.com).The price for getting on the website ranges from 30,000 yuan to 650,000 yuan, and fuel consumption spans from 6 L/100 km to 18 L/100 km, which is a good representation.The formula for calculating the 100 km fuel consumption for fuel vehicles is as follows: where C f is the fuel consumption of a gasoline vehicle in L/100 km per 100 km, Q i is the annual sales of fuel vehicles in units, f denotes the model, i is the annual sales ranking and n takes the value according to different models.For convenience, this paper compares gasoline vehicle models and BEVs models as follows: according to the length, wheelbase and displacement of vehicles, BEVs models can be divided into A00-, A0-, A-, B-, C-, SUV-and MPV-type vehicles.Amongst them, gasoline vehicle models of micro car, small car, medium car and medium and large car correspond to A00-, A0-, A-, B-and C-type models of BEV, respectively; gasoline vehicle SUV models correspond to BEV's SUV models; gasoline vehicle MPV models correspond to BEV MPV models and gasoline vehicle sedan models correspond to BEV's A00-, A0-, A-, B-and C-type models.

BEV energy consumption
The 100 km energy consumption of BEVs was calculated based on the driving segment and charging segment data of the vehicle, and the actual grid energy consumption of each vehicle was obtained based on the changes in the driving range and battery power of BEVs, as shown in Eq. ( 5) 58 : where, C e is the power consumption in kW•h/100 km over a period of time.We calculated the total power con- sumption in travelling 100 km for each vehicle for one month; soc i1 was the remaining power at the end of the ith charging segment; soc i0 was the remaining power at the beginning of the ith charging segment; P i is the battery capacity in kW•h corresponding to the ith charging segment and S j is the mileage in km of the jth driving segment.

Electric carbon emission factors
We calculated the local carbon emission factor for electricity based on the regional energy mix, i.e. the carbon dioxide emissions generated per 1 kW•h of electricity output from the grid in each region.In this paper, we refer to the BEV energy conversion method and the reference value of the National Standard of the People's Republic of China (GB/T 37340-2019) to calculate the carbon emission factor of electricity.The specific calculation method is shown in Eq. ( 6): where E co 2 was the electricity CO 2 emission factor in kg CO 2 /(kW h).T E was the standard coal consumption for thermal power supply in kg/(kW h), which was 0.3064 kg/(kW h) and 0.3055 kg/(kW h) in 2019 and 2020, respectively 59 .T C was the CO 2 emission factor for fuel coal, using the parameter value of 3.09; ϕ was the thermal power ratio, i.e. the share of thermal power generation in total power generation, which is the main source of power generation in China.According to the China Energy Statistics Yearbook, China emits more than 90% of greenhouse gases from coal and thermal power, which is 10 times more than other technologies (hydropower, nuclear and wind) 60 .Because carbon emissions from power generation are mainly produced by thermal power generation, we assumed in this paper that other energy generation does not produce carbon emissions.According to the National Bureau of Statistics, the national electricity generation in 2019 and 2020 was 750,342,428 million kW h and 777,960 million kW h, respectively, while thermal power generation was 5,220,150 million kW/h and 5,330,250 million kW h, making the share of thermal power generation in 2019 and 2020 69.57% and 71.04%, respectively 61 .t M was the discount factor between fuel coal and standard coal; the parameter value used was 1.07.i ch was the charge efficiency, i.e. the ratio of the electrical energy input to the power battery to the electri- cal energy from the grid, based on the initial charge/discharge efficiency of BEVs batteries calculated by Yang et al. 62 , which is 98%.i tr was line loss rate, which is the percentage of power supply, and the amount of power supply according to the data released by the National Energy Administration of China.The integrated line loss rates of the grid in 2019 and 2020 were 5.93% and 5.62%, respectively 61 .According to Eq. ( 4), the carbon emission factors for electricity in 2019 and 2020 were 0.6818506 kg/(kW h) and 0.6776147 kg/(kW h), respectively. (4)

Figure 1 .
Figure 1.Comparative carbon emissions of gasoline vehicles and BEVs at equivalent mileages.Note: This figure illustrates the carbon emissions during the driving phase for both electric vehicles (BEVs) and gasoline vehicles over the same driving mileage.The monthly emission reduction per vehicle are calculated as the difference in carbon emissions between the gasoline vehicles and BEVs for equivalent mileages.The detailed formula used for this calculation is outlined in the "Models" section, and the specific data utilized can be found in Supplementary Data 2-1.

Figure 2 .
Figure 2. Monthly CO 2 emission reductions per 100 km for Six BEV Types.Note: This figure displays the CO 2 emission reductions per 100 km on a monthly basis for six different types of Battery Electric Vehicles (BEVs).The BEV types include A00-type, A0-type, A-type, B-type, C-type, SUV-type, and MPV-type BEVs.The data from January to October represent the total emission reductions for each BEV model across both 2019 and 2020.For November and December, the data shown reflect only the emission reductions for each model in 2019.Detailed source data can be found in Supplementary Data 2-2.

Figure 3 .
Figure 3. Correlation between air quality and vehicle travel frequency in three first-tier Chinese cities. Note: This figure presents an analysis of vehicle travel data and air quality across various districts in Beijing, Shanghai, and Shenzhen, based on vehicle latitude and longitude information.For Beijing, the data encompasses Changping, Chaoyang, Daxing, Dongcheng, Fangshan, Fengtai, Haidian, Huairou, Mentougou, Miyun, Pinggu, Shijingshan, Shunyi, Xicheng, and Yanqing Districts.In Shanghai, the analysis includes Baoshan, Fengxian, Hongkou, Huangpu, Jiading, Jinshan, Jing'an, Minhang, Pudong New, Putuo, Qingpu, Songjiang, Xuhui, Yangpu, and Changning Districts.For Shenzhen, the focus is on Futian and Nanshan Districts.Air quality levels were evaluated using various indicators from meteorological stations in each district, with the Air Quality Index (AQI) serving as a reference.Higher AQI values indicate more severe air pollution.The data for Shenzhen includes only Futian and Nanshan Districts; thus, the figure specifically illustrates changes in BEV travel frequency in these districts.Detailed data can be found in Supplementary Data 2-3.

( 5 )
C e = 100 × n i=1 (soc i1 − soc i0 ) × P i m i=1 S j (6) E co 2 = T E × T C × ϕ t M × i ch × (1 − i tr ) . Additionally, the unique operating conditions of taxi BEVs, such as extensive outdoor travel, frequent braking and throttling, and high charging demands, result