Zewei Zhong,Yang Yu,Xiaoli Zhao
Revisiting electric vehicle life cycle greenhouse gas emissions in China: A marginal emission perspective
Energy resources,Energy policy,Energy management,Energy modeling
Summary The accurate estimation of electric vehicle (EV) life cycle greenhouse gas (GHG) emissions is critical for policymakers to predict and manage the reduction of GHG emissions due to transportation electrification. Most previous studies in the Chinese context evaluated the EV life cycle GHG based on the annual average emission factor (AAEF). However, the hourly marginal emission factor (HMEF), which is conceptually more appropriate than AAEF for evaluating the GHG implications of EV growth, has not been applied in China. This study fills this gap by estimating the EV life cycle GHG in China based on the HMEF and comparing it with AAEF-based estimates. It is found that the estimates based on the AAEF substantially underestimate the EV life cycle GHG in China. Moreover, the influences of the electricity marketization reform and changes in the EV charging mode on the EV life cycle GHG in China are analyzed.
Revisiting electric vehicle life cycle greenhouse gas emissions in China: A marginal emission perspective

The accurate estimation of electric vehicle (EV) life cycle greenhouse gas (GHG) emissions is critical for policymakers to predict and manage the reduction of GHG emissions due to transportation electrification. Most previous studies in the Chinese context evaluated the EV life cycle GHG based on the annual average emission factor (AAEF). However, the hourly marginal emission factor (HMEF), which is conceptually more appropriate than AAEF for evaluating the GHG implications of EV growth, has not been applied in China. This study fills this gap by estimating the EV life cycle GHG in China based on the HMEF and comparing it with AAEF-based estimates. It is found that the estimates based on the AAEF substantially underestimate the EV life cycle GHG in China. Moreover, the influences of the electricity marketization reform and changes in the EV charging mode on the EV life cycle GHG in China are analyzed.

The estimation of the carbon emissions of electric vehicles (EVs) is an important basis for the projection of the future greenhouse gas (GHG) trajectory. Globally, the transport sector accounts for 16.2% of GHG emissions, among which 73.5% are attributable to road vehicles. Currently, the electrification of the transport sector is fundamentally reshuffling transportation-based GHG emissions by moving them from roads to the power grid. From 2015 to 2020, the global number of EVs grew 7.7-fold and reached 11.3 million. Among nations worldwide, China has the largest number of EVs and the fastest growth with 5.4 million vehicles, accounting for 48% of the global total. Furthermore, the Chinese government considers the promotion of EVs as one of the important ways to realize its “Carbon peaking and carbon neutrality goals” and plans for EVs to account for 40% of vehicle sales by 2030., The estimation of EV life cycle GHG emissions is critical for policymakers to predict and manage the reduction of GHG emissions due to transportation electrification in China. The GHG emissions from the electricity consumed by driving are the most important component of the EV life cycle GHG (Figure 1A), and usually account for more than 50%. These emissions are also known as well-to-wheel (WTW) GHG emissions. Regarding the choice of the electricity emission factor used to estimate WTW GHG emissions, the annual average emission factor (AAEF) has been adopted in a large number of studies.,,,,, The AAEF represents the ratio of the total annual GHG emissions to the total annual electricity generation of an electricity system. The use of the AAEF to estimate EV life cycle GHG assumes equal proportionate increases in electricity generation from all types of sources with new electricity load additions. However, non-dispatchable generators (e.g., nuclear power and wind power) cannot change their output with the increase of the electricity load. Therefore, some studies have argued that the hourly marginal emission factor (HMEF) is conceptually more appropriate than the AAEF for the evaluation of the GHG implications of EV growth. The HMEF represents the change in GHG emissions from adding 1 MWh of electricity load at a specific time, which is determined by a marginal generator. It should be noted that the difference between the EV life cycle GHG estimates obtained by using the AAEF and HMEF is characterized by different magnitudes and directions. This difference varies between electricity systems and requires specific analysis.,, Due to the shortage of data, few studies have estimated the EV life cycle GHG in China based on the HMEF; most studies have been based on the AAEF (Table S1). The simulation-based calculation of the HMEF requires not only data on the fuel type and efficiency of fossil power generators but also data such as the electricity load and renewable energy profiles on an hourly scale. The neglect of the HMEF will result in the potentially inaccurate estimation of the EV life cycle GHG in China. Moreover, in HMEF-based estimates, there are two factors that may influence the EV life cycle GHG but that have not been fully studied, as follows. (1) The electricity dispatching rule. China is promoting electricity marketization reform, which will change the electricity dispatching rules from the fair dispatch (FD) mode to the economic dispatch (ED) mode., For the FD mode, all fossil power generators are dispatched to meet the net load based on the principle of equal annual operating hours., For the ED mode, fossil power generators are dispatched according to the merit-order principle, via which they are ordered by their operating costs from lowest to highest., Electricity dispatching rules affect the HMEF, which in turn influences the EV life cycle GHG (Figure 1B). To the best of the authors’ knowledge, however, no studies have explored the impact of the electricity dispatching rule on the EV life cycle GHG. (2) EV charging mode. EV charging modes affect charging schedules, which in turn alters GHG emissions (Figure 1B). Although EV smart charging is a future development trend,, to date, there has been no research on the impact of smart charging on the EV life cycle GHG in China. This study intends to fill the aforementioned gaps in the extant research. The EV life cycle GHG in China is estimated based on the HMEF, and the results are compared with AAEF-based estimates. This will help policymakers to calibrate the impact of transportation electrification on GHG emissions in China. Moreover, by conducting an analysis in China, where fossil energy power generation is dominated by coal, this study is favorable for enriching the cognition of the influence of the selection of the electricity emission factor on the estimation of the EV life cycle GHG. It is found that AAEF-based estimates substantially underestimate the EV life cycle GHG in China in 2020 and 2030 by 24% and 31%, respectively. In addition, the influences of the electricity dispatching rules and EV charging modes on the EV life cycle GHG in China are estimated. Regarding the electricity dispatching rules, switching from FD to ED was found to slightly reduce the EV life cycle GHG by 2%, but also increased the discrepancies between HMEF-based and AAEF-based estimates from 31% to 33% in 2030. Regarding the EV charging modes, smart charging was found to reduce GHG emissions in all six power grid regions in China, and this effect is enhanced with the increase of renewable energy.

An electricity dispatching model, EV charging model, and life cycle model were combined to estimate the EV life cycle GHG in 2020 and 2030. The estimation included the following three steps (see the STAR Methods section for more details). (1) The regional HMEF and AAEF were calculated according to the electricity dispatching models. The HMEF and AAEF were respectively calculated for both the FD and ED modes in the six power grid regions in China (North, Northeast, East, Central, Northwest, and South). (2) The EV charging of public and private charging stations was simulated. For each station, two charging modes, i.e., uncontrolled and smart charging, were simulated separately. (3) The regional EV life cycle GHG was calculated and compared according to the HMEF and AAEF. The national average EV life cycle GHG was calculated as a weighted average based on EV sales in each region (Figure S1). All the scenarios considered in this study are summarized in Table 1. Among them, the results for public and private charging stations (Figures 2, 3, S2, and S3) were found to be very similar under the uncontrolled charging mode. For simplicity, the results in the context of the uncontrolled charging mode are discussed using only public charging as an example.

Results based on the AAEF and HMEF were found to lead to substantially different estimates of the EV life cycle GHG in China. In the 2020 FD scenario, the estimate of the national average EV life cycle GHG based on the AAEF was 24% lower than that based on the HMEF (Figure 2). Across the six regions, the AAEF-based estimates were 11%–38% lower than the HMEF-based estimates (Figure 3). These discrepancies in the estimates of EV life cycle GHG originate from the large differences between the HMEF and AAEF of the power grid. The marginal generators in China are mainly coal-powered, which determines the HMEF. In contrast, the AAEF accounts for a large amount of zero-carbon or low-carbon technologies, such as renewable energy, hydropower, and nuclear power. Consequently, the HMEF is higher than the AAEF in China (Figure 4), which caused the EV life cycle GHG estimated by the HMEF to be much higher than that estimated by the AAEF. Substantial discrepancies between the EV life cycle GHG estimates based on the AAEF and HMEF were found to exist in all regions in China (Figure 3). However, the sizes of the discrepancy varied across regions due to different power mixes. Table S2 summarizes the share of non-fossil energy in the power grid regions and the discrepancies between the HMEF-based and AAEF-based estimates. In the North power grid region where non-fossil energy is scarce (17%), the discrepancy between the HMEF-based and AAEF-based estimates was relatively small (11%); in the South power grid region where non-fossil energy is rich (57%), the discrepancy between the HMEF-based and AAEF-based estimates was large (38%). It should be noted that, in contrast to China, the EV life cycle GHG in some U.S. regions estimated by the AAEF is higher than that estimated by the HMEF. This is because the electricity systems in China and the U.S. have diverse power mixes. In some U.S. regions, where the share of coal power is similar to that of natural gas, the HMEF is often determined by natural gas power, which has a lower GHG intensity. In China, however, there are very few natural gas power generators, and the HMEF is often determined by coal power generators.

To analyze future EV life cycle GHG, 2030 scenarios that consider factors such as electricity demand growth, renewable energy growth, and EV efficiency improvement were constructed. From 2020 to 2030, the total electricity demand of the six power grid regions increases from 6.7 to 9.9 PWh, and renewable energy generation increases from 0.7 to 2.4 PWh. Moreover, the electricity consumption of EVs decreases from 16.2 to 13.9 kWh/100 km., In the FD scenario, according to the AAEF-based estimates, the EV life cycle GHG in China is estimated to drop by 7.2 to 24.7 tCO2eq in 2030 (Figure 2). According to the HMEF-based estimates, however, the EV life cycle GHG in China is estimated to drop by 6.1 to 35.7 tCO2eq in 2030 (Figure 2). These results imply that the AAEF-based estimates of the EV life cycle GHG will decrease faster than the HMEF-based estimates, meaning that the estimation discrepancies will be enlarged in the future. Based on the comparison of 2020 and 2030, the estimation discrepancies between the AAEF-based and HMEF-based EV life cycle GHG in the FD scenario will widen from 11%–38% to 20%–42% (Figure 3) across regions, and the national average will widen from 24% to 31% (Figure 2). The discrepancies between the EV life cycle GHG estimates by the AAEF-based and HMEF-based methods affect the cognition of GHG reduction by replacing internal combustion engine vehicles with EVs in China, i.e., the difference between their life cycle GHG. As mentioned previously, estimates of the EV life cycle GHG in China are mostly based on the AAEF. The AAEF-based estimates of this study indicate that, on average, replacing ICEVs with EVs would result in a 21.6 tCO2eq reduction in GHG per vehicle in China in 2030. According to the HMEF-based estimates, however, only 10.6 tCO2eq GHG could be reduced (Figure 2). That said, the AAEF-based estimates were found to overestimate the reduction in GHG emissions from promoting EVs by more than double when compared to the HMEF-based estimates. Therefore, with specific GHG reduction targets, AAEF-based analyses could underestimate the number of EVs needed. Changes in climate policy (e.g., renewable energy subsidies) and cost parameters may accelerate or slow down renewable energy growth. The growth pace of the effect of renewable energy on EV life cycle GHG estimation was analyzed. It was found that the future changes in the discrepancy between the EV life cycle GHG estimates based on the AAEF and HMEF are influenced by the growth pace of renewable energy. Renewable energy substantially reduces the AAEF by replacing fossil fuel energy. However, for the HMEF, in most cases, renewable energy only slightly increases the efficiency of marginal generators by reducing the net load. As a result, in the scenario of faster renewable energy growth, the discrepancies between the AAEF-based and HMEF-based EV life cycle GHG estimates were found to be widened (Figures 2 and 3). For example, in the 2030 FD scenario, a 50% increase in renewable energy growth was found to enlarge the national average discrepancy from 31% to 34%.

The electricity marketization reform in China is switching the electricity dispatching rule from FD to ED, which will also affect the EV life cycle GHG. As shown in Figure 3, in HMEF-based estimates, the impact of electricity marketization reform on EV life cycle GHG varies across regions. In general, switching from FD to ED reduces the EV life cycle GHG by eliminating low-efficiency coal power generators. In 2030, if the dispatching rule is switched from FD to ED, the EV life cycle GHG in China will be reduced from 35.7 to 35.0 tCO2eq. It was found that, compared to FD, ED is more helpful for lowering the HMEF and reducing the EV life cycle GHG via renewable energy growth. In 2030, according to the HMEF-based estimate, a 50% increase in renewable energy growth will reduce the EV life cycle GHG by 0.73 tCO2eq in the ED scenario, but the reduction in the FD scenario will only be 0.58 tCO2eq. These results imply that with the future growth of renewable energy, it will become increasingly important to consider the electricity marketization reform when estimating EV life cycle GHG. Moreover, Figure 2 reveals that the electricity marketization reform from FD to ED increases the discrepancy between the HMEF-based and AAEF-based estimates. In the 2030 FD scenario, the EV life cycle GHG estimate based on the AAEF was found to be 31% lower than that based on the HMEF. In the 2030 ED scenario, however, the EV life cycle GHG estimate based on the AAEF was found to be 33% lower than that based on the HMEF. This is due to the different impacts of the electricity marketization reform on the AAEF and HMEF. The FD mode arranges the annual operating hours of all fossil energy generators equally, while the ED mode prioritizes the dispatch of fossil power generators with higher efficiency to meet the electricity load. Therefore, the electricity marketization reform greatly reduces the AAEF. However, the impact of the electricity marketization reform on the HMEF is relatively complicated. In the FD scenario, the HMEF is generally determined by the average efficiency of all fossil power generators. In contrast, in the ED scenario, the HMEF is determined by the marginal generators. In most regions (North, Northeast, Central, Northwest, and South) of China, where there are many redundant low-efficiency fossil power generators, the efficiency of marginal fossil generators is lower than the average efficiency of all fossil power generators. Therefore, the electricity marketization reform will reduce the HMEF in these regions (Figure 3). On the other hand, in the East power grid region, where there are fewer redundant low-efficiency fossil power generators, the efficiency of marginal fossil generators is lower than the average efficiency of all fossil power generators. As a result, the electricity marketization reform in the East power grid region will increase the HMEF (Figure 3). In general, regarding the national average, the electricity marketization reform greatly reduces the EV life cycle GHG estimates based on the AAEF and slightly reduces the estimates based on the HMEF. Thus, the discrepancies between the two types of estimates are widened.

The HMEF considers hourly changes in the electricity system. This enables an examination of how smart charging affects the EV life cycle GHG by altering the charging time period. In the uncontrolled charging mode, EVs are charged to the target power as soon as possible after they are connected to the grid. Smart charging, however, allows EVs to charge at the proper time when they are connected to the grid. While smart charging has multiple control objectives, here, focus is placed on smart charging with the purpose of minimizing the electricity cost, as this is usually the most important issue for vehicle owners and charging providers. Figure 5 presents the changes in the EV life cycle GHG when switching from the uncontrolled charging mode to the smart charging mode for both public and private charging. The following main findings were elicited. First, smart charging was found to reduce the EV life cycle GHG in all regions and scenarios in China. Moreover, the GHG reduction effect of smart charging will increase in the future, especially with the rapid growth of renewable energy. Renewable energy growth exacerbates the volatility of the net load, thereby exacerbating the difference in the HMEF over time and strengthening the role of smart charging. Second, smart charging is more useful for private charging, as EVs are connected to the power grid for a longer amount of time for private charging than for public charging (Figure S4). Hence, the promotion of smart charging should give priority to private charging stations. It was found that in the 2030 ED scenario, the change from uncontrolled charging to smart charging could reduce the EV life cycle GHG by 0.5% for public charging and 2.5% for private charging (Figure 5). Third, the GHG reduction effect of smart charging in the ED scenario was found to be stronger than that in the FD scenario. In the 2030 FD private charging scenario, smart charging was found to reduce the EV life cycle GHG by only 1.0% by reducing renewable energy and hydropower curtailment. In the 2030 ED scenario, however, smart charging was also found to facilitate the replacement of low-efficiency coal power generators with high-efficiency coal power generators, and thus will reduce the EV life cycle GHG to a greater extent (2.5%). In the ED scenario, smart charging selects hours with low marginal electricity costs. In China, where fossil power is dominated by coal, a lower marginal electricity cost for an hour usually means high efficiency and low GHG emissions for the marginal generator. Therefore, compared to uncontrolled charging, smart charging is more concentrated in hours with a low HMEF (Figures S5–S8). Unlike studies carried out in the U.S. context, this study demonstrates that smart charging in China has a positive effect on reducing GHG emissions in all regions. Studies on smart charging in the U.S. context have found that smart charging aimed at minimizing electricity costs could lead to an increase in GHG emissions. This is because smart charging shifts more electricity demand from higher cost, lower emission natural gas power to lower cost, higher emission coal power.,, However, in China, where natural gas generators are scarce, smart charging mainly enables more efficient coal power generators to be dispatched, thus reducing GHG emissions. Overall, the HMEF-based analysis shows that smart charging in China can reduce the EV life cycle GHG, and could do so to a greater extent in the future.

With the accelerated popularization of EVs, the estimation of the EV life cycle GHG has become an important basis for climate change mitigation strategies in the transport sector. However, previous estimates of the EV life cycle GHG in China have been mostly based on the AAEF. There remains a lack of estimates based on the HMEF, which is conceptually more appropriate for the evaluation of the GHG implications of EV growth. In addition, there is also a lack of research on the impacts of the electricity dispatching rules and EV charging modes on the EV life cycle GHG in China. These two factors are important because they may impact the EV life cycle GHG estimation based on the HMEF. This study fills these existing gaps. By constructing electricity system dispatch models of six power grid regions and combining them with EV charging and life cycle models, HMEF-based and AAEF-based estimates of the EV life cycle GHG in China were compared. On this basis, the impacts of the electricity dispatching rules and EV charging modes were analyzed. It was found that the EV life cycle GHG estimates based on the commonly adopted AAEF in China are 11%–38% lower than those based on the HMEF in different regions, and the national average is 24%. Furthermore, the estimation discrepancies may widen in the future. In 2030, the AAEF-based analysis was found to overestimate the reduction in GHG emissions due to promoting EVs by more than double as compared with the HMEF-based analysis. Therefore, it is critical for policymakers to carefully distinguish between the AAEF and the HMEF when setting climate change action targets for transportation electrification. The electricity marketization reform was found to be relevant to the estimation of EV life cycle GHG in China. The electricity marketization reform in China (the substitution of ED for FD) increases the discrepancy between the HMEF-based and AAEF-based estimates. This conclusion implies that with the deepening of electricity marketization in China, it will become more necessary to distinguish between the AAEF and the HMEF when assessing the impact of transportation electrification on GHG emissions. By introducing the HMEF, the impact of EV smart charging on life cycle GHG can be estimated. Smart charging was found to reduce GHG emissions in all six power grid regions in China. Hence, policymakers should re-examine EV subsidy policies by considering the EV life cycle GHG based on the HMEF. Policymakers should subsidize not only EV purchases but also EV smart charging. Furthermore, the GHG reduction effect of smart charging is greater in private charging stations than in public charging stations, so it should be more strongly considered by policymakers. In addition, the GHG reduction effect of smart charging can be further enhanced with the electricity marketization reform and renewable energy growth, making subsidies for EV smart charging more important in the future.

This study was characterized by some problems that await future research, including but not limited to the following. (1) This study did not provide a detailed breakdown of EV types and charging behaviors. Different types of EVs have different life cycle parameters, and different charging behaviors will lead to different charging curves. These differences may impact the EV life cycle GHG. (2) The simulation of the electricity system did not consider energy storage technologies such as pumped-storage hydroelectricity and lithium-ion batteries, which could participate in peak shaving and alter electricity emission factors. (3) This study did not consider the impact of the massive growth of EVs on the power grid. The mass adoption of EVs will change the electricity load and thus the HMEF and AAEF, and this change awaits further analysis.



Further information and requests for resources should be directed to and will be fulfilled by the lead contact, Xiaoli Zhao (e-mail99zxl@vip.sina.com).

The study did not generate new materials.

The estimation of the EV life cycle GHG can be divided into three parts. The first part of the model simulated electricity system dispatching and calculated electricity emission factors, and the second part simulated EV charging curves. The third part calculated the EV life cycle GHG by combining the electricity emission factors, EV charging curves, and EV life cycle models.

The electricity emission factors of six power grid regions in China were estimated based on hourly electricity dispatching. Figure S10 marks these regions on the map. The AAEF and HMEF were generated by the same electricity dispatch model and data. Therefore, potential biases caused by the use of different methods to separately estimate these emission factors were avoided. The model followed the following steps. (a) Calculating net load Nuclear power, hydropower, renewable energy, and electricity exchange were subtracted from the hourly electricity load to calculate the hourly net load, as given by Equations 1 and 2. In the equations, indicates hours, is the sum of non-fossil fuel generation, is the output of wind power, is the output of solar power, is the output of nuclear power, is the output of hydropower, is the net load, is the hourly electricity load, and is the electricity exchange between the system and outside. If the sum of non-fossil fuel generation and electricity exchange is greater than the hourly electricity load, the net load is set to zero. For ED, the operating costs of non-fossil fuel power (nuclear power, hydropower, and renewable energy) are generally lower than those of fossil power generators. For FD, non-fossil fuel power has higher priority than fossil fuels. Therefore, in both the ED and FD scenarios, the net load refers to the portion that must be met by fossil generation. Electricity load data were sourced from the National Development and Reform Commission. The hourly output of wind and solar power was calculated by combining their profiles and annual power generation. The methodology used in previous research, was followed to generate the wind and solar power profiles. Data on the annual power generation of wind and solar power were collected from the National Bureau of Statistics of China. It was assumed that the power of nuclear power, hydropower, and power exchange was constant during different hours of one month, and the monthly totals were collected from the National Bureau of Statistics of China. (b) Calculating operating costs and GHG emission rates The operating costs for the generation of 1 MWh of electricity produced by fossil fuel power generator were calculated by Equation 3. In the equation, is the fuel consumption rate of the generator, is the fuel cost, and is the variable operations and maintenance costs. The installed capacity of fossil power was determined from the China Electricity Council. The methodology used in a previous study was followed to generate the distributions of fuel consumption rates. The price of coal was set to 0.546 ￥/kg, and the price of natural gas was set to 2.7 ￥/m3. The variable operation and maintenance costs were sourced from a previous study. Three types of GHG emissions were considered, namely carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). To estimate the EV life cycle GHG, the GHG emission rates in this study accounted for not only GHG emissions produced by fossil fuel combustion, but also upstream GHG emissions. Upstream GHG emissions refer to those generated during fuel production, while combustion GHG emissions refer to those generated by the fuel combustion in generators. As given by Equation 4, the fuel consumed per 1 MWh of electricity generation was calculated based on the fuel consumption rate of generators, after which it was converted into various types of GHG emissions based on the upstream GHG emission parameter and the combustion emission parameter . Finally, it was converted it into CO2-equivalent emissions based on the 100-year global warming potential . The upstream and combustion GHG emission rates were respectively adopted from the research by Ou et al. and Eggleston et al. (Table S3). (c) Calculating hourly marginal emission factors and marginal electricity costs For the ED mode, all fossil power generators are dispatched according to the merit-order rule every hour, i.e., they are dispatched according to the order of operating costs from low to high until the net load is met., The dispatched generator with the highest operating costs is the marginal generator, and its operating costs and GHG emission rate are respectively the marginal electricity costs and marginal GHG emission rate of the power system. For hours with zero net load, the marginal emission factor and marginal electricity costs are zero. For the FD mode, all fossil power generators meet the net load according to the principle of equal annual operating hours in a year., Therefore, for the hours when the net load is not zero, the marginal emission factor and marginal electricity costs of the system can respectively be regarded as the capacity-weighted average of the GHG emission rate and the operating costs of all fossil power generators. These are given by Equations 5 and 6, where refers to the capacity. For hours with zero net load, the marginal emission factor and marginal electricity costs are zero. (d) Calculating annual average emission factors The AAEF is equal to the ratio of the total annual GHG emissions to the total annual electricity generation of an electricity system. For the ED mode, the total GHG emission of the system is the product of the GHG emission rates of each fossil energy generator and its dispatched power generation according to the merge-order rule. The AAEF with the ED mode is represented by Equation 7. For the FD mode, because all fossil power generators meet the net load according to the principle of equal annual operating hours in a year, the AAEF can be represented by Equation 8. The estimates for 2030 considered changes such as renewable energy growth, electricity demand growth, and coal power generator efficiency improvements. The data on renewable energy growth, electricity demand growth, and fossil power capacity growth in 2030 were collected from the State Grid Energy Research Institute Co., Ltd. This is the only scientific research institution engaged in soft science research within the State Grid Corporation of China system, and is listed as a "China Core Think Tank". The data on coal power generator efficiency improvements were collected from the work by Tong et al. In addition to the bottom-up approach used in this study, there exists a top-down approach that estimates the HMEF by performing a regression analysis on historical electricity generation and emission data.,, This top-down approach requires high-resolution historical data of electricity system operations, which are unavailable in China. The approach used in the present work does not require these data.

For uncontrolled charging, an EV begins charging to the required volume as soon as it is connected to the power grid. For smart charging, the EV selects the hour with the lowest marginal electricity costs for charging in the available time ranges, which can be expressed as the optimization problem of Equation 9. In the equation, indicates the hour, represents the marginal electricity costs, represents the charging power, represents the maximum charging power, and represents the required charging volume. To indicate whether charging is possible during the hour, and respectively indicate the hours that the EV can and cannot be charged.

The public charging behavior data were collected from the State Grid EV Service Co., Ltd., and contained 23,931 charging records from 118 typical charging piles. Each charging record includes the time the EV was connected to the power grid, the time it left, and the charge volume. The charger used for public charging is a 60-kW direct current charger. A private charging behavior model was constructed based on the National Household Travel Survey (NHTS). The dataset contains the travel time periods and mileage of 150,000 vehicles in a day. The assumptions used in previous research, were followed, namely that the vehicle begins charging after the last journey of the day and ends charging before the first journey of the next day, and the required charging volume corresponds to the mileage driven in the day. The charger used for public charging is a 7-kW alternating-current charger. Figure S9 presents the EV charging curves of public and private charging under the uncontrolled charging mode.

The life cycle of a vehicle includes three stages: manufacturing, use, and scrapping. For the use stage that was of primary focus in this study, referring to related research,, not only was the GHG emissions produced by the combustion of fossil fuels taken into account, but so too were the upstream GHG emissions caused by fossil fuel production. The temperature and EV sales in different provinces are different and located in different regional power grids; thus, the EV life cycle GHG for each province was separately calculated. For regional power grids and national averages, weighted averaging was performed based on provincial EV sales (Figure S1). The life cycle analysis framework was applied to estimate the total GHG emissions of one vehicle , as given by Equation 10. In the equation, is the GHG emissions during the vehicle manufacturing stage, is the GHG emissions during the vehicle scrapping stage, is the life cycle mileage, and is the WTW GHG, i.e., the GHG emissions generated for every 1 km driven by the vehicle. As given by Equations 11 and 12, two different electricity emission factors were used to estimate the GHG emissions during the EV use stage, the annual average emission factors, the hourly average emission factors, and the hourly marginal emission factors; the results are respectively denoted as and . In the equations, is the electricity consumption of EVs for 1 km, is the charging efficiency of EVs, is the EV charging curve, and is the total number of hours. The EV charging curve refers to the distribution of EV charging 1 MWh in the 24 h in a day, is the number of days in month , is the average temperature, and represents the effect of the temperature on the EV efficiency. Referring to the research of Yuksel and Michalek, 17 °C was assumed to be the ideal working temperature for the highest efficiency; when temperatures drop or rise, EVs are less efficient. The GHG emissions of ICEVs during the use stage are given by Equation 13, where is the efficiency of ICEVs, and is the GHG emission rate of gasoline consumption, which is equal to the sum of GHG emissions from gasoline production and combustion. The parameters for EV life cycle GHG estimation are reported in Table S4. Data on the monthly average temperatures by province were sourced from the National Meteorological Information Center.

In this section, the AAEF-based estimation in this study is compared with previous studies. Previous research has used different parameters in the estimation of the EV life cycle GHG, such as the life cycle mileage, manufacturing GHG emissions, and scraping GHG emissions. Variations in these parameters affect the EV life cycle GHG estimates but are beyond the scope of this study. Therefore, this comparison focuses on WTW GHG, which is directly affected by the electricity emission factor. In the calculation of the EV life cycle GHG, the values of WTW GHG (gCO2eq/km) were replaced with the results reported in previous studies,,,, (Figure S11). It can be seen that the AAEF-based estimates in this study are similar to the estimates of previous studies in both the 2020 and 2030 scenarios. Therefore, the conclusion of the discrepancies between HMEF-based and AAEF-based estimates was found to be robust across different datasets.