Life Cycle Cost and Environmental Performance of Electric and Gasoline Vehicles in Cold Climate and Coal-Dependent Regions: A Case Study of Heilongjiang Province, China

Abstract

This study conducts a comparative life cycle assessment (LCA) and life cycle cost (LCC) analysis of battery electric vehicles (BEVs) and gasoline vehicles (GVs) in Heilongjiang Province, China, under cold climate conditions and a coal dominated electricity grid. Environmental impacts were assessed using SimaPro with the ReCiPe 2016 Midpoint (H) method, while cost performance was evaluated over 5-, 10-, and 15-year ownership periods. Results show that BEVs offer lower total ownership costs than GVs, even without subsidies, primarily due to reduced energy and maintenance expenses. In terms of global warming potential, BEVs show a 4.52% reduction compared to GVs. However, BEVs demonstrate higher impacts in several non-climate categories—including ionizing radiation, particulate matter formation, eutrophication, toxicity, and water use—largely due to emissions from coal-based electricity. The derived grid emission factor of 1.498 kg CO₂/kWh underscores the critical role of regional energy structure. These findings suggest that while BEVs provide economic and climate benefits, their overall environmental performance is highly dependent on local grid carbon intensity and seasonal energy demand. Policy recommendations include accelerating grid decarbonization, improving cold weather efficiency, and incorporating multidimensional environmental indicators into transport planning.

1. Introduction

Switching to greener transport modes can significantly reduce carbon emissions, and the number of newly registered battery electric vehicles (BEVs) has surged over the past decade. China, accounting for over 60% of global electric vehicle (EV) sales, has set ambitious goals to cease selling combustion engine vehicles by 2035 and achieve carbon neutrality by 2060 [1]. However, achieving these goals requires addressing regional barriers to adoption and solving existing infrastructure and technological challenges.

BEVs and gasoline vehicles (GVs) present distinct advantages and disadvantages. BEVs offer zero direct tailpipe emissions [2], lower operating costs [3], and reduced dependence on fossil fuels. Their environmental effectiveness largely depends on the carbon intensity of the electricity grid. Nevertheless, BEVs face high upfront purchase costs [4,5], technological immaturity, high manufacturing costs [6], limited driving range [7], longer charging times, reliance on charging infrastructure [1,2], and environmental burdens associated with battery production [8]. In contrast, GVs benefit from lower initial purchase prices [9], mature technology [3], well-established refueling infrastructure, and longer driving ranges [10], but they are associated with significant carbon dioxide emissions and local air pollution compare with BEVs [11]. These trade-offs underscore the necessity of comprehensive evaluations that consider both economic and environmental dimensions across different life cycle stages.

Operational costs further influence consumer decisions. Informed consumers assess total ownership costs, including fuel, maintenance, repair, and insurance expenses [12]. Subsidy programs and reductions in parking, tolls, and road taxes have made BEVs more attractive [13]. However, their adoption heavily depends on government subsidies and high battery costs [3]. With China planning to abolish subsidies, including purchase tax exemptions, by 2027 [14], evaluating the long-term cost competitiveness of BEVs becomes critical. A life cycle perspective is essential for this purpose [15]. However, most BEV studies in China focus on national averages, with few addressing consumer costs under region-specific conditions.

China’s vast geographic and climatic diversity further complicates BEV adoption. Temperature effects on usage costs, electric consumption, and battery life [16,17], alongside limited access to affordable private charging facilities [18], pose significant barriers. Heilongjiang Province, the coldest region in China, exemplifies these challenges [19]. In colder climates, battery efficiency decreases, energy consumption rises, and inadequate energy replenishment infrastructure hinders BEV sales [20]. The northeast, northwest, and southwest grids act as sending ends, while north China, east China, and central China are receiving ends [21]. Northeast China relies heavily on coal-fired power, leading to high fuel consumption and carbon emissions [22]. Furthermore, EV sales in the northeast power grid region are the lowest nationwide, with Heilongjiang ranking at the bottom [23]. These factors—combined with public attitudes and infrastructural limitations—have slowed BEV adoption in northeast China.

The environmental impacts of BEVs depend heavily on the local energy mix. This study explicitly considers regional power grid characteristics and seasonal temperature effects to reduce bias. Uneven electricity distribution may result in varying regional environmental outcomes. Although BEVs are often promoted as solutions to air pollution, their actual emission reduction potential remains unclear [24]. While China is transitioning toward full electrification [25], key issues such as energy consumption, emissions, and well-to-wheel efficiency still depend on grid composition [26], making the use phase particularly critical in coal-dependent areas [27]. Depending on local conditions, BEVs can outperform or underperform GVs and hybrids [28,29,30,31], necessitating case-specific analyses [32]. Temperature variations also affect charging and discharging efficiency, impacting both energy consumption and user convenience [33].

This study investigates how regional characteristics and economic variables influence the overall cost and environmental performance of BEVs and GVs. It further examines the impacts of government subsidies and evaluates whether BEVs are more environmentally sustainable than GVs under cold climate, coal-dependent conditions.

The specific aim is to evaluate and compare the life cycle costs (LCCs) and environmental impacts of BEVs and GVs during the use phase in Heilongjiang Province, with a particular focus on regional temperature variations, grid emissions, and user costs. To achieve this aim, the following research hypotheses are proposed:

H1: 

BEVs have lower total life cycle costs than GVs over different ownership periods (5, 10, and 15 years).

H2: 

BEVs maintain a net present value (NPV) advantage over GVs during the early ownership years, but the gap changes over time.

H3: 

The real discount rate significantly influences the total cost of ownership for BEVs and GVs, with GVs being more sensitive to changes in discount rates.

H4: 

The phase-out of government subsidies reduces the cost advantage of BEVs but does not eliminate it in the long term.

H5: 

BEVs have lower global warming potential than GVs under NECG conditions but may exhibit higher impacts in other environmental categories such as particulate matter formation and toxicity.

This study provides several significant contributions to the existing literature. While previous analyses often generalize findings across broader national or global contexts or focus on the complete vehicle life cycle, this research uniquely isolates and examines the use phase—the stage where actual environmental and economic burdens are most influenced by regional characteristics. This study investigates how regional characteristics and economic variables influence the overall cost and environmental performance of BEVs and GVs. It further examines the role of government subsidies and assesses whether BEVs offer superior sustainability under cold climate, coal-dependent conditions, thereby setting the stage for a focused life cycle cost and environmental impact assessment in the following sections.

2. Methodology

Below is the framework of the LCA and LCC analysis conducted in this study. The present study is defined based on the procedure and recommendations of ISO 14040:2006 [34], which includes four main steps of goal and scope definition, inventory analysis, impact assessment, and result interpretation.

The process involves defining the goal and scope; collecting inventory data related to vehicle specifications, energy consumption, emissions, and costs; performing environmental and economic impact assessments; and interpreting the results through a comparative analysis between BEVs and GVs, with a particular focus on the use phase, as shown in Figure 1.

The goal of the study is to evaluate the environmental and economic impacts of replacing GVs with BEVs from a consumer perspective, with a particular focus on the use phase. The scope is defined by the selection of representative vehicle models—BYD Dolphin (BEV) and Volkswagen Lavida (GV)—and the specific regional characteristics of Heilongjiang Province, China. The analysis incorporates the impacts of subsidy removal and discount rate variations on cost competitiveness and assesses environmental performance based on the local energy mix and seasonal temperature fluctuations. This framework provides a realistic, localized assessment of vehicle sustainability in coal-dependent, cold climate conditions.

This study selected the BYD Dolphin, the second-best-selling BEV in Heilongjiang Province in 2023 (RMB 116,800–139,800), and the Volkswagen Lavida, the second-ranked gasoline vehicle (RMB 93,900–151,900), as representative models for studying the sustainability performance of BEVs and traditional gasoline vehicles. According to the Electric Vehicle (BEV/PHV/FCV) Sales Monthly Report for 2022 and January to September 2023, the top-selling BEV model in Heilongjiang Province is the Wuling Hongguang Mini EV, priced between RMB 32,800 and RMB 99,900 [35]. Despite the Wuling Hongguang Mini EV’s sales success, it is not representative due to its lower price [9]. BEVs, in general, are sold at higher prices compared to gasoline vehicles of similar performance.

Heilongjiang Province, referred to as Heilongjiang, is located in northeastern China. Its capital is Harbin. It is the northernmost and highest latitude province in China, stretching from 121°11′ east longitude in the west to 135°05′ east longitude in the east and from 43°26′ north latitude in the south to 53°33′ north latitude in the north. It spans 14 degrees of longitude from east to west and 10 degrees of latitude from north to south. It faces Russia across the river in the north and east, borders the Inner Mongolia Autonomous Region in the west, and borders Jilin Province in the south. Regional and environmental characteristics of Heilongjiang province category (People’s Government of Heilongjiang Province) are shown in

Table 1. Regional and environmental characteristics of Heilongjiang Province (People’s Government of Heilongjiang Province).

Category	Details
Province Name	Heilongjiang Province
Capital City	Harbin
Total Land Area	473,000 km2 (6th largest in China)
Geographic Boundaries	Borders Russia (N/E), Inner Mongolia (W), and Jilin Province (S)
Urban Land Use (est.)	15%
Industrial Land Use (est.)	10%
Natural Zones (forests, rivers, wetlands)	65%
Pedestrian/Leisure Zones (est.)	10%
Major Rivers	Heilongjiang, Songhua, Wusuri, and Suifen
Major Lakes	Xingkai Lake, Jingbo Lake, and Wudalianchi
Climate Type	Cold temperate and mid-temperate; continental monsoon
Frost-Free Period	90–160 days, decreasing from south to north

.

Heilongjiang province, in terms of electricity, is part of the northeast regional power grid, according to [36], and the northeast regional power grid covers Liaoning Province, Jilin Province, and Heilongjiang Province. This study only considered Heilongjiang Province, which consists of a total of 13 cities, namely, DaHinganLing, Heihe, Qiqihar, Daqing, Suihua, Yichun, Harbin, Hegang, Jiamusi, Shuangyashan, Qitaihe, Jixi, and Mudanjiang. The study area is illustrated in Figure 2.

This study considers the four distinct seasons of Heilongjiang Province and specifically examines the impact of temperature on electric vehicle power consumption, as well as the effect of charging frequency on battery life.

According to the climate bulletin from the Heilongjiang Provincial Meteorological Bureau [37], the average temperature in Heilongjiang during the winter months (December 2019–February 2020) was −16.3 °C. In spring (March–May 2020) the average temperature was 6.0 °C, while in summer (June–August 2022) the province experienced an average of 20.5 °C, considered a normal temperature. In autumn (September–November 2020) the average temperature was 5.1 °C.

When temperatures drop below 10 °C the power consumption per 100 km increases as the temperature falls, leading to a reduction in the vehicle’s cruising range. At around 10 °C, energy consumption is at its lowest. However, once the temperature exceeds 10 °C there is a slight increase in power consumption per 100 km, along with a slight decrease in cruising range. Excursions conducted at temperatures below 10 °C or above 30 °C displayed greater variations in energy consumption compared to those conducted within this optimal range [38]. Therefore, this study considers 10–30 °C as the optimal operating temperature range for BEVs.

A report on the actual driving performance, energy consumption, cruising range, and charging modes of China’s pure electric passenger vehicles, released in June 2023, analyzed 10 private electric passenger cars in five cities. The models closest to those in this study were observed under high-temperature conditions (30–35 °C), where energy consumption increased by approximately 4%. In “low temperature” conditions (environments ≤ 0 °C, including temperatures ≤ −7 °C) energy consumption per 100 km increased by 40%. Under “extremely low temperature” conditions (≤−7 °C) energy consumption increased by 70% [39]. The report also indicates that energy consumption rises by 40% when the temperature drops from 10 °C to 0 °C, equivalent to a 4% increase in energy consumption for each 1 °C drop.

Thus, this study accounts for the significant temperature variations across the four seasons in Heilongjiang Province and calculates energy consumption accordingly. Winter is classified as an “extremely low temperature” season where energy consumption per 100 km increases by 70%. In spring energy consumption increases by 24% on average. Summer represents the optimal operating temperature, while in autumn energy consumption rises by 20%. The annual average energy consumption per 100 km is 13.74 kWh/100 km.

Private charging piles in Heilongjiang Province offer lower electricity rates, but they account for a small proportion of charging infrastructure. Efforts are being made to promote the installation of private charging piles. Public charging piles, however, have higher fees, which include commercial electricity charges and service fees. A field survey conducted in Harbin found that the average service fee at public charging piles is 0.4 RMB. Considering variations in electricity prices across different periods and the difference between private and public charging fees, this study calculates an average charging price for electric vehicles of 0.919 RMB/kWh, which includes service fees.

Electric vehicles experience a decline in performance over time. During the warranty period, once the power battery’s capacity degrades by 20% the cruising range fluctuates significantly. If degradation reaches 30% or more it can impact daily use [40]. To enhance the reliability of key components, electric vehicle manufacturers are required to offer a quality guarantee for energy storage components, including power batteries, drive motors, and motor controllers. According to regulations, manufacturers must provide a minimum warranty of 8 years or 120,000 km for passenger cars [41]. Moreover, BYD offers free lifetime battery replacement and free installation of charging piles [42]. Therefore, this study does not consider battery replacement fees.

The average annual mileage of passenger vehicles nationwide in 2013 was approximately 19,000 km [43]. The BYD Dolphin’s owner manual shows that maintenance is at 20,000 km or 1 year [44]. Therefore, this study assumed an average annual mileage of 20,000 km.

Ignoring the effect of temperature, the BEV’s energy consumption is 10.69 kWh per 100 km (battery capacity of 44.9 kWh/100 km of cruising range [cruising range 420 km/100 km] = 10.69 kWh per 100 km). Assuming that consumers drive 20,000 km a year, the energy consumption for the entire year is 10.69 × 200 = 2138 kWh. The total annual carbon emission is 3100.92 kg of CO₂-eq divided by 2138 kWh = 1.45 kg of CO₂-eq. Hence, 1 kW of electricity consumption will emit approximately 1.45 kg of CO₂-eq.

The GV’s consumption of gasoline is 5.98 L/100 km, and the average annual fuel consumption is 200 × 5.98 = 1196 L. The total annual carbon emission is 4149.63 kg of CO₂-eq divided by 1196 L = 3.47 kg CO₂-eq. Therefore, 1 L of gasoline consumption will emit approximately 3.47 kg of CO₂-eq.

The findings shows that BEVs can substantially reduce GHG emissions, consistent with the results of the studies conducted by [45,46]. However, ref [3] found that BEVs’ emissions and climate change impacts were significantly higher than those of the ICEVs. Nonetheless, the study had a limitation in that it used foreign data, which might lead to errors in the conclusions [3].

2.1. Life Cycle Costing Inventory Analysis and Impact Assessment

Life cycle costing (LCC) is an economic approach to determining the overall cost of an asset. It takes into account both the initial cost and the future expenses that will be discounted during the asset’s lifespan. This strategy was implemented by the US Department of Defense throughout the 1960s [47]. It takes into account all costs, including initial costs, future costs, and any costs associated with resale, salvage, or disposal [48]. LCC has been applied in this study, guided by [49], and only internal real costs from the consumer’s perspective in the use stage and end of life of electric vehicles and gasoline vehicles are consider in this study. The LCC technique is crucial in this connection because it emphasizes cost optimization across the whole life cycle [50].

This study examined the whole cost of automobiles, taking into account several factors such as initial purchase cost, operating cost, maintenance cost, replacement cost, and end of life residual valuations (recycling cost). In the meantime, in LCC all the collected economic data in the inventory phase are classified into specific cost categories.

Table 2. Inventory analysis of LCC indicators.

Impact Category	Indicators	Sources
Initial purchase cost	Purchase price	[42,51]
	Purchase tax	[52,53]
	Subsidy	[42,51]
	Charging Pile Fee	[42]
	License Plate Fee	[54]
	Insurance Cost	[55,56,57]
Operating and maintenance cost	Vehicle And Vessel Tax	[58,59]
	Annual Vehicle Inspection Cost	[60,61]
	Energy Cost	[62]
	Cleaning cost	Based on general price at market
	Replacement Cost and Service Cost	[44,63,64]
End of life	Resale price	Calculate based on vehicle depreciation rate
	Resale cost	[65]
Inflation rate	2%	[66]
discount rate	5%	[67]
Energy consumption	5.98 L/100 km 44.9 kWh/420 km	[42,51]
Average electricity fee	0.9192583125 RMB/kWh	[40] State grid Heilongjiang electric power company limited
Average gasoline fee	8.312083333 RMB/L	[62]

shows a classification of LCC indicators.

Operating costs consider insurance costs, vehicle and vessel tax, and annual vehicle inspection costs. Maintenance and replacement costs include energy costs, cleaning costs, and replacement and service costs. In the replacement and service costs consider all items based on an official guide [49]. For data gathering and analysis refer to

Table A1. Life cycle inventory cost results for BEV.

Optiona 1: Electric Vehicles																		Unit:	CNY
Real Disscont Rate:																			2.94%
Year	Initial Purchase Cost					Operating Cost			Maintenance and Replacement Cost			Residual Valuation		Present Value	Cumulative Present Value	Net Present Value	Net Present Value (USD)	Net Present Value (EUR)	Year
	Purchase Price	Purchase Tax	Merchant Discount	Charging Pile Fee	License Plate Fee	Insurance Cost	Vehicle and Vessel Tax	Annual Vehicle Inspection Cost	Energy Cost	Cleaning Cost	Replacement and Service Cost	Resale Price	Resale Cost
0	123,800.00	0.00	7000.00	0.00	130.00									116,930.00	116,930.00	116,930.00	16,709.06	15,582.36	0
1						4979.00	0.00		2526.00	240.00	240.00	95,615.69	2448.39	7756.86	124,686.86	34,181.48	4884.46	4555.10	1
2						4979.00	0.00		2526.00	240.00	1463.00	85,461.62	2194.54	8689.35	133,376.20	54,799.27	7830.70	7302.67	2
3						4979.00	0.00		2526.00	240.00	240.00	76,159.28	1961.98	7319.94	140,696.14	72,678.63	10,385.63	9685.32	3
4						4979.00	0.00		2526.00	240.00	2963.00	67,664.13	1749.60	9535.68	150,231.83	91,533.65	13,079.97	12,197.98	4
5						4979.00	0.00		2526.00	240.00	240.00	59,931.58	1556.29	6907.63	157,139.46	106,640.39	15,238.69	14,211.14	5
6						4979.00	0.00	230.00	2526.00	240.00	1463.00	52,917.07	1380.93	7931.32	165,070.78	121,761.87	17,399.52	16,226.26	6
7						4979.00	0.00		2526.00	240.00	240.00	46,576.04	1222.40	6518.55	171,589.33	134,564.91	19,229.05	17,932.42	7
8						4979.00	0.00	230.00	2526.00	240.00	2963.00	40,863.90	1079.60	8674.11	180,263.44	148,713.48	21,250.85	19,817.89	8
9						4979.00	0.00		2526.00	240.00	240.00	35,736.11	951.40	6151.38	186,414.82	159,617.82	22,809.06	21,271.03	9
10						4979.00	0.00	230.00	2526.00	240.00	1463.00	31,148.08	836.70	7062.99	193,477.81	170,794.08	24,406.13	22,760.41	10
11						4979.00	0.00	230.00	2526.00	240.00	240.00	27,055.25	734.38	5972.10	199,449.91	180,315.29	25,766.69	24,029.22	11
12						4979.00	0.00	230.00	2526.00	240.00	2963.00	23,413.06	643.33	7724.46	207,174.38	191,094.29	27,306.99	25,465.66	12
13						4979.00	0.00	230.00	2526.00	240.00	240.00	20,176.92	562.42	5635.71	212,810.09	199,354.01	28,487.28	26,566.37	13
14						4979.00	0.00	230.00	2526.00	240.00	1463.00	17,302.29	490.56	6289.73	219,099.82	207,896.04	29,707.92	27,704.70	14
15						4979.00	0.00	460.00	2526.00	240.00	240.00	14,744.58	426.61	5467.17	224,566.99	215,297.75	30,765.61	28,691.06	15

Note: Discount Rate is 5%, Escalation Rate is 2%.

and

Table A2. Life cycle inventory cost results for GV.

Optiona 2: Gasoline Vehicles																		Unit:	CNY
Real Disscont Rate:																			2.94%
Year	Initial Purchase Cost					Operating Cost			Maintenance and Replacement Cost			Residual Valuation		Present Value	Cumulative Present Value	Net Present Value	Net Present Value (USD)	Net Present Value (EUR)	Year
	Purchase Price	Purchase Tax	Mechant Discount	Charging Pile Fee	License Plate Fee	Insurance Cost	Vehicle and Ve-sel Tax	Annual Vehicle Inspection Cost	Energy Cost	Cleaning Cost	Replacement and Service Cost	Resale Price	Resale Cost
0	110,000.00	9734.50	30,000.00	0.00	130.00									89,864.50	89,864.50	89,864.50	12,841.45	11,975.55	0
1						4906.00	480.00		9941.15	240.00	504.00	84,957.40	2181.94	15,611.97	105,476.47	25,066.02	3581.88	3340.35	1
2						4906.00	480.00		9941.15	240.00	1771.00	75,935.20	1956.38	16,361.55	121,838.03	52,026.18	7434.44	6933.13	2
3						4906.00	480.00		9941.15	240.00	3290.00	67,669.80	1749.75	17,286.56	139,124.59	78,694.93	11,245.35	10,487.06	3
4						4906.00	480.00		9941.15	240.00	3196.00	60,121.60	1561.04	16,708.95	155,833.54	103,684.22	14,816.26	13,817.19	4
5						4906.00	480.00		9941.15	240.00	2404.00	53,251.00	1389.28	15,546.41	171,379.96	126,515.61	18,078.82	16,859.76	5
6						4906.00	480.00	310.00	9941.15	240.00	2576.00	47,018.40	1233.46	15,507.28	186,887.24	148,411.42	21,207.69	19,777.64	6
7						4906.00	480.00		9941.15	240.00	3829.00	41,384.20	1092.61	15,834.04	202,721.28	169,829.26	24,268.26	22,631.83	7
8						4906.00	480.00	310.00	9941.15	240.00	1771.00	36,308.80	965.72	13,995.43	216,716.71	188,688.75	26,963.24	25,145.09	8
9						4906.00	480.00		9941.15	240.00	3290.00	31,752.60	851.82	14,526.93	231,243.64	207,438.68	29,642.57	27,643.75	9
10						4906.00	480.00	310.00	9941.15	240.00	3196.00	27,676.00	749.90	14,273.52	245,517.16	225,366.83	32,204.46	30,032.89	10
11						4906.00	480.00	310.00	9941.15	240.00	2404.00	24,039.40	658.99	13,289.94	258,807.10	241,810.12	34,554.18	32,224.16	11
12						4906.00	480.00	310.00	9941.15	240.00	2576.00	20,803.20	578.08	13,031.70	271,838.80	257,555.73	36,804.19	34,322.46	12
13						4906.00	480.00	310.00	9941.15	240.00	3829.00	17,927.80	506.20	13,518.96	285,357.76	273,406.06	39,069.17	36,434.71	13
14						4906.00	480.00	310.00	9941.15	240.00	1771.00	15,373.60	442.34	11,761.19	297,118.95	287,168.36	41,035.78	38,268.71	14
15						4906.00	480.00	620.00	9941.15	240.00	3290.00	13,101.00	385.53	12,609.23	309,728.18	301,496.36	43,083.22	40,178.09	15

Note: Discount Rate is 5%, Escalation Rate is 2%.

. The end-of-life stage considered resale price and resale cost.

Net present value (NPV), present value (PV), and cumulative present value are key financial metrics used in evaluating future expenses such as maintenance and repairs, with an appropriate discount rate applied. The choice of discount rate is crucial as a lower rate results in a greater impact, and vice versa. Present value (PV) is the current value of a future sum of money or cash flow series, considering a specific rate of return (refer to (2) for the PV formula). Net present value (NPV) quantifies the difference between the present value of cash inflows and outflows over a given time frame [68], making it an essential metric for assessing project investments [67]. NPV represents the current value of a project’s cash flows, factoring in the required rate of return, and compares this to the initial investment [62]. For the NPV equation refer to (1). The cumulative present value is determined by adding the present value of the current year to the cumulative present value from the previous year (3). The three equations are as follows:

$${\mathrm{L}\mathrm{C}\mathrm{C}}_{\mathrm{N}\mathrm{P}\mathrm{V}}={\mathrm{C}\mathrm{P}\mathrm{V}}_{\mathrm{n}}-\mathrm{R}\mathrm{V}\times {(1+{\mathrm{I}}_{\mathrm{r}\mathrm{d}})}^{-\mathrm{n}}$$

(1)

$${\mathrm{P}\mathrm{V}}_{\mathrm{n}}=\left({\mathrm{O}\mathrm{C}}_{\mathrm{n}}+{\mathrm{M}\mathrm{R}\mathrm{C}}_{\mathrm{n}}\right)\times {(1+{\mathrm{I}}_{\mathrm{r}\mathrm{d}})}^{-\mathrm{n}}$$

(2)

$${\mathrm{C}\mathrm{P}\mathrm{V}}_{\mathrm{n}}={\mathrm{P}\mathrm{V}}_{\mathrm{n}}+{\mathrm{C}\mathrm{P}\mathrm{V}}_{\mathrm{n}-1}$$

(3)

where

• LCC_NPV: life cycle costing net present value;
• n: current year;
• CPV: cumulative present value;
• I_rd: real discount rate;
• PV: present value;
• OC: operating cost;
• MRC: maintenance and replacement cost.

The initial purchase cost includes the sum of the purchase price, purchase tax subsidy, charging pile cost (if applicable), and license plate fees (4). Any LCC analysis must factor in both operational and maintenance costs. Operational costs (5), along with maintenance and replacement strategies (6) for the asset, can be established based on the LCC analysis results. Variations and uncertainties in operational phase expenses are influenced by factors such as projected inflation rates, emerging technologies, regulatory changes, inspection fees, insurance, and local taxes or labor costs [69].

Replacement costs, which occur over longer intervals, may be evaluated separately or as part of capital costs [49]. Maintenance activities follow the standard maintenance and replacement schedule as outlined in the maintenance manual, excluding exceptional cases like accidents. Such special circumstances are covered by insurance, and as a result, this study does not include separate calculations for replacement costs. Equations (4), (5), and (6) are as follows:

$$\mathrm{I}\mathrm{P}\mathrm{C}=\mathrm{P}+\mathrm{P}\mathrm{T}-\mathrm{M}\mathrm{D}+\mathrm{C}+\mathrm{L}$$

(4)

$$\mathrm{O}\mathrm{C}=\mathrm{I}\mathrm{C}+\mathrm{V}\mathrm{V}\mathrm{T}+\mathrm{V}\mathrm{I}\mathrm{C}$$

(5)

$$\mathrm{M}\mathrm{R}\mathrm{C}=\mathrm{E}\mathrm{C}+\mathrm{C}\mathrm{C}+\mathrm{R}\mathrm{C}+\mathrm{S}\mathrm{C}$$

(6)

where

• IPC: initial purchase cost;
• P: purchase price;
• PT: purchase tax;
• MD: merchant discount;
• C: charging pile fee;
• L: license plate fee;
• IC: insurance cost;
• VVT: vehicle and vessel tax;
• VIC: annual vehicle inspection cost;
• EC: energy cost;
• CC: cleaning cost;
• RC: replacement cost;
• SC: service cost.

Residual valuation (7) combines fundamental valuation methods, employing a hybrid approach. It uses a combined revenue and cost method [70]. The residual method is rooted in a straightforward economic principle: the land’s value is determined by subtracting the estimated development costs from the projected value of the completed development [49,71]. Residual valuation is equal to resale price minus resale cost. Subtract the salvage value of the asset from the total of the purchase, ownership, and disposal costs to arrive at the LCC, which is depicted as follows:

$$\mathrm{R}\mathrm{V}=\mathrm{R}\mathrm{P}-\mathrm{R}\mathrm{C}$$

(7)

where

• RV: residual valuation;
• RP: resale price;
• RC: resale cost.

Resale price, vehicle depreciation rate, and resale cost are all considered. The resale cost (10) is the total number extension fee and production cost and is RMB 58. The transfer fee is the handling fee for car transfer and is based on the evaluation fee and is charged at a rate of 2.5%: this study does not consider the additional costs arising from uncertainties [65]. Determining a vehicle’s depreciation rate can be difficult as trade-in or salvage values vary depending on the model, which can depreciate at different rates, and fluctuations in future material prices further complicate the estimation. Therefore, this study refers to [72] equitation, as (9). Resale price is the purchase price plus one minus vehicle depreciation rate, as shown in (8). All three equations are discussed as follows:

$$\mathrm{R}\mathrm{P}=\mathrm{P}\times (1-{\mathrm{I}}_{\mathrm{v}\mathrm{d}})$$

(8)

$${\mathrm{I}}_{\mathrm{v}\mathrm{d}}=6\times {10}^{-5}\times {\mathrm{n}}^3-0.0038{\mathrm{n}}^2+0.093\mathrm{n}+0.1384$$

(9)

$$\mathrm{R}\mathrm{C}=\mathrm{R}\mathrm{P}\times 2.5\mathrm{\%}+58$$

(10)

where

• I_vd: vehicle depreciation rate.

In this study, NPV calculations used real discount rate and discount rate (5%). A suitable discount rate should be applied to future expenses like maintenance and repairs. The value of the discount rate is crucial; the bigger the impact, the lower the discount rate, and vice versa [67]. Escalation rate (2%) is China’s CPI, which was up 2% in 2022 [66]. The real discount rate is the rate used to adjust present and future monetary values to make them comparable. It is calculated using the Fisher equation formula, as seen in (11):

$${\mathrm{I}}_{\mathrm{r}\mathrm{d}}={\displaystyle \frac{1+\mathrm{r}}{1+\mathrm{e}}}-1$$

(11)

where

• r: discount rate;
• e: escalation rate.

2.2. Environmental Performance (Life Cycle Assessment) Inventory Analysis and Impact Assessment

BEVs generate almost zero exhaust emissions during the operating phase. However, researchers have different perspectives on BEVs reducing CO₂ emissions. Even though BEVs do not use fossil fuels and emit no emissions, these vehicles transfer pressure to the power grid when they use electricity for energy consumption. Power generation consumes non-renewable energy and causes emissions. Some studies highlight that electricity generation has a significant effect during the BEV use phase [73]. Hence, given China’s current power structure, is BEV an environmentally friendly product compared with GVs? This question needs to be considered.

LCA, a system of assessment developed under ISO 14040, is one of the most effective ways for exploring the resource and environmental impact of a life cycle [34]. Data collection ensured high-quality, region-specific data were used, particularly for the electricity mix in Heilongjiang Province. The data were derived from the latest database compiled in January 2023, Ecoinvent 3.9.1 data, as unit processes with links to other methods [74]. Data were also obtained from literature publications or government policy.

In the use phase comparison of a GV and a BEV, both are assessed over a driving distance of 20,000 km. The GV consumed 1196 kg of low-sulfur petrol per year (20,000 km), while the BEV consumed 2138 kilowatt-hours (kWh) per year (20,000 km) of grid electricity, based on temperature conditions in Heilongjiang Province, China. These energy inputs were used in SimaPro to model the respective environmental impacts under a well-to-wheel boundary.

To enable direct comparison, all emissions were calculated using the ReCiPe 2016 midpoint (H) method. ReCiPe is a widely used method for conducting a life cycle impact assessment (LCIA) within the life cycle assessment (LCA) framework. LCIA translates emissions and resource consumption into a set of environmental impact categories using characterization factors which quantify the relative contribution of each flow to specific environmental issues. Midpoint indicators focus on single environmental problems, for example, climate change or acidification.

The hierarchical perspective is based on scientific consensus with regard to the time frame and plausibility of impact mechanisms [75]. The electricity consumed by the BEV was sourced from the “Electricity, medium voltage {CN-NECG}| market for electricity, medium voltage | Cut-off, U”, which represents a regional electricity mix dominated by coal-fired generation. This market activity dataset aggregates electricity from local production and imports, accounting for transmission losses and delivery to end-users. It models the transmission of 1 kWh of electricity at high voltage, beginning from entry into the medium-voltage grid and ending with distribution over aerial lines and cables. With a production volume of 846.65 TWh, this dataset reflects the average consumption mix of electricity in northeast China, including all upstream and transmission-related processes.

The petrol used by the GV was modeled using the Ecoinvent database under the “market for petrol, low-sulfur {RoW}” process, which includes upstream emissions from extraction, refining, and distribution. This dataset represents a market activity for low-sulfur petrol at the global level, capturing the average consumption mix of this refined fossil fuel across different regions. It aggregates production from petroleum refineries and includes imports where applicable. The system boundary begins at the gate of fuel production and ends with delivery to the final consumer (e.g., household, vehicle, or power plant), incorporating all necessary transport processes. The product has a calorific value of 42.5 MJ/kg, and an additional 6% energy input is required for conversion from conventional unleaded petrol to its low-sulfur variant. The dataset assumes negligible product losses during distribution. It is primarily used as an automotive fuel but also serves domestic and industrial energy needs. The modeled production volume is 666,560,757,760 kg, reflecting large-scale global availability. The database does not cover direct tailpipe emissions from gasoline combustion. The life cycle inventory of petrol (low-sulfur) is based on the Ecoinvent v3.9.1 [74] dataset under the global market activity, which includes upstream refining processes and fuel distribution to the point of consumption. According to [76], the combustion of one liter of gasoline releases approximately 2339 g of CO₂ from vehicle tailpipes. In addition, the China National VI B emission standard regulates N₂O emissions at 20 mg/km for light-duty vehicle. Accordingly, this study accounts for both CO₂ and N₂O emissions from gasoline vehicle use.

However, CH₄ and HFC emissions were excluded due to their minimal contribution to total GHG emissions—typically 1–5% combined [77]—and because the China VI B standard does not regulate these gases. Their exclusion aligns with both the regulatory scope and inventory limitations [78].

3. Result

3.1. Assessing the Overall Cost for Consumers in the Utilization of BEVs and GVs

Details of the estimated life cycle costs (LCCs) related to BEVs and GVs in Heilongjiang Province, China, are provided in Table A1 and Table A2. The total nominal costs of a BEV over 5 years, 10 years, and 15 years are 162,357.29 RMB, 207,421.70 RMB, and 252,262.61 RMB, respectively. For a GV, the corresponding nominal costs over 5 years, 10 years, and 15 years are 180,254.53 RMB, 273,042.90 RMB, and 366,244.28 RMB, respectively. Based on this study, if the current policies remain unchanged, the nominal cost of a GV is higher than that of a BEV.

Figure 3 illustrates the comparison of total costs between BEVs and GVs by service life year. Figure A1 shows the year-by-year total life cost comparison for GVs vs. BEVs (15-year horizon). As the service life increases, the initial cost accounts for a decreasing portion of the total cost. For GVs, the initial cost represents 49.85% of the total cost at year 5, but this drops to 32.91% at year 10 and further to 24.53% at year 15. For BEVs, the initial cost constitutes 72.02% of the total cost at year 5, decreasing to 56.37% at year 10 and 46.35% at year 15. While the initial cost of BEVs is a significant component of the total cost, for GVs the initial cost represents a comparatively smaller portion.

The proportion of operating costs relative to total costs increases for both vehicle types, but does so more markedly for BEVs. For BEVs this share rises from 15.33% at year 5 to 30.43% at year 15; however, for GVs the increase is more gradual. Despite this, the absolute operating costs of GVs remain higher than those of BEVs across all years. Current policy exemptions from vehicle and vessel taxes [58] and annual emissions inspections [61] contribute to the lower BEV operating costs.

3.2. EVs and GVs Net Present Value (NPV) from Year 1 to Year 15

NPV is the sum of all present values and is determined by applying a discount rate to future cash flows, which includes both inflows and outflows [79]. Figure 4 shows that the NPV trends for BEVs and GVs over 15 years resemble a check mark. From year 0 to almost year 2, the NPV of BEVs is higher than that of GVs. Around year 2, the NPVs of both vehicles converge, but after approximately year 2.5 the NPV of GVs surpasses that of BEVs.

As the service life extends, the NPVs for both vehicle types continue to rise. However, the NPV of BEVs grows at a slower rate than that of GVs, resulting in a widening NPV gap in favor of GVs over time. This suggests that the financial value recovery of GVs outpaces that of BEVs in the long run under current assumptions.

3.3. Analysis of the Real Discount Rate Affecting the Overall Cost for Consumers in Using BEVs and GVs

LCC calculations consider the time value of money. The NPV is often used to calculate LCC [80]. The NPV discount rates usually range from 2% to 6.1%, or are based on local bank rates [81]. During the 15-year vehicle life cycle, variables that determine the discounted value, such as interest rates and inflation rates, may change. Because interest rates have a profound impact on company investments, different discount rate scenarios are compared [82].

Sensitivity analysis is an investigation into how projected performance varies with changes in the key assumptions on which the projections are based. It also enables the examination of how, for example, uncertainty in international prices can alter project outcomes [83]. The discount rate is a key factor in assessing the economic feasibility of a project. In this study, the LCC analysis was conducted based on a 5% discount rate and a real discount rate, but changes in this value can significantly affect the LCC analysis results. Therefore, a sensitivity analysis is needed to determine how changes in the discount rate affect the cost to consumers in the use stage to accommodate investors of different categories and needs [84].

Based on Figure 5 and Figure 6, a comparison of the cost curves of GVs and BEVs shows that GVs have a larger slope, which means that GVs are more sensitive to the discount rate than BEVs during the 15-year use cycle.

Figure 5 depicts the sensitivity analysis of BEVs’ LCC results with adjusted real discount rates. First, high depreciations due to high real discount rates will lead to higher life cycle costs. This is because a high depreciation rate results in a lower vehicle resale price, that is, a lower residual value. Therefore, the higher the real discount rate in the first 7 years, the higher the NPV result, which means higher life cycle costs. Over time, the NPV cost curve becomes smoother and the slope becomes flatter. When the vehicle’s residual value continues to decrease and accounts for a smaller proportion of the total cost than other costs, the NPV result changes. Starting from the 7th year, the higher the real discount rate then the lower the change in NPV, which means that other costs are significantly affected by changes in the real discount rate. A high real discount rate means higher life cycle costs in the first 7 years, but the opposite is true after 7 years when a high real discount rate means lower life cycle costs.

Figure 6 shows a similar pattern to the BEV sensitivity analysis. Initially, a higher real discount rate is accompanied by a higher NPV. However, after 4.5 years, a higher real discount rate lowers the NPV. Furthermore, GVs’ LCC results are more sensitive than BEVs’ LCC results to the actual discount rate. This is because GVs incur higher operating costs than BEVs. The slope of the cost curve in Figure 5 also shows that higher costs will respond significantly to the discount rate. However, BEVs’ LCC results are more sensitive than GVs’ LCC results to real discount rates at the early stage.

3.4. Analyzing Subsidies Affecting the Overall Cost for Consumers in the Utilization of BEVs and GVs

Subsidies for BEVs are gradually being reduced and have been extended several times. Currently, these subsidies have been extended until 2027. From 1 January 2024 to 31 December 2025, vehicle purchase tax exemptions for new energy passenger vehicles will remain in place. From 1 January 2026 to 31 December 2027, the vehicle purchase tax will be halved. After 2027, there will be no more vehicle purchase tax exemptions for new energy passenger vehicles [14]. This gradual reduction in subsidies may affect the third year shown in Figure 7, in which the cost of GVs is nearly the same as that of BEVs with a 50% subsidy. By the fourth year, the cost of GVs is almost between the cost of BEVs without subsidies and those with half the subsidy. By the fifth year, the cost of a GV is nearly the same as that of a BEV without any subsidy. However, from the sixth year onward, the cost of a GV exceeds that of a BEV, even without subsidies. Over time, the cost of GVs rises linearly, while the cost of BEVs increases at a slower rate. This suggests that BEVs will maintain their cost advantage even if subsidies are eliminated, making it timely to reconsider the need for BEV subsidies [85].

3.5. Use Phase Energy Consumption and Environmental Impacts

3.5.1. Overview of Energy Consumption

According to SimaPro-based life cycle modeling, a battery electric vehicle (BEV) operating under the northeast China grid (NECG) and adjusted for seasonal temperature variations consumes 2706.5 kWh of electricity over a 20,000 km driving distance. This results in a total global warming potential (GWP) of 4053.67 kg CO₂ equivalent, or 0.203 kg CO₂ eq/km. In comparison, a gasoline vehicle (GV) consuming 1196 kg of low-sulfur petrol emits 4245.51 kg CO₂ eq, or 0.212 kg CO₂ eq/km. These figures reflect full life cycle emissions, including upstream processes such as crude oil extraction, refining, transportation, and combustion. A summary of energy use, emission factors, and total emissions is provided in

Table 3. Comparative use phase energy use and CO₂ emissions of GVs and BEVs.

Parameter	Unit	GV	BEV (NECG, Four-Season Adjusted)
Energy consumption (20,000 km)	kg fuel/kWh electricity	1196 kg petrol	2706.5 kWh electricity
Emission factor (full life cycle of petrol or electricity)	kg CO2/kg or kg CO2/kWh	3.55	1.498
Total CO2 emissions	kg CO2 eq	4245.51	4053.67
CO2 emissions per kilometer	kg/km	0.212	0.203

.

The BEV emission intensity was not calculated using a fixed grid emission factor. Instead, it was derived directly from modeled life cycle results. Dividing total BEV emissions (4053.67 kg CO₂ eq) by electricity consumption (2706.5 kWh) yields an effective emission factor of 1.498 kg CO₂/kWh. This is significantly higher than both the national average of 0.537 kg CO₂/kWh [86] and NECG’s regional average of 0.556 kg CO₂/kWh [22].

The GV’s life cycle emission factor—3.55 kg CO₂ per kg of petrol—covers both direct (tailpipe) and indirect (upstream) emission [87]. The tank-to-wheel component alone is estimated at 3.17 kg CO₂/kg, based on IPCC (2006) default values and data from the U.S. EPA and DEFRA. Despite being relatively lower than coal-fired electricity in upstream intensity, petrol still contributes substantially to the GV’s total carbon footprint.

The stability of petrol’s emission factor across regions contrasts with the variability of electricity emissions, which fluctuate significantly based on the local grid mix. As a result, the BEV’s advantage is subject to regional and climatic conditions.

3.5.2. Extended Environmental Impacts: Trade-Off Analysis

This section presents a comparative environmental analysis of GVs and BEVs operating under the northeast China grid (NECG), based on a driving distance of 20,000 km. The assessment was conducted using the ReCiPe 2016 midpoint (H) method in SimaPro and characterizes multiple environmental impact categories during the use phase, as shown in

Table 4. Characterized midpoint environmental impacts during the use phase for GVs and BEVs under NECG conditions.

Impact Category	Unit	GV	BEV (NECG)	Δ (%) BEV vs. GV
Global warming	kg CO2 eq	4245.514	4053.67	−4.52%
Stratospheric ozone depletion	kg CFC11 eq	0.005	0.001	−83.84%
Ionizing radiation	kBq Co-60 eq	19.498	73.750	278.24%
Ozone formation: human health	kg NOx eq	3.602	11.239	212.04%
Fine particulate matter formation	kg PM2.5 eq	1.670	5.927	261.51%
Ozone formation: terrestrial	kg NOx eq	4.409	11.272	155.67%
Terrestrial acidification	kg SO2 eq	4.675	12.815	174.09%
Freshwater eutrophication	kg P eq	0.083	0.736	784.22%
Marine eutrophication	kg N eq	0.115	0.049	−57.67%
Terrestrial ecotoxicity	kg 1,4-DCB	3460.524	2725.750	−21.23%
Freshwater ecotoxicity	kg 1,4-DCB	11.233	47.878	326.24%
Marine ecotoxicity	kg 1,4-DCB	20.084	64.616	221.73%
Human carcinogenic toxicity	kg 1,4-DCB	42.788	122.072	185.29%
Human non-carcinogenic toxicity	kg 1,4-DCB	355.613	1801.315	406.54%
Land use	m2a crop eq	29.278	47.857	63.46%
Mineral resource scarcity	kg Cu eq	2.328	1.543	−33.73%
Fossil resource scarcity	kg oil eq	1466.821	778.063	−46.96%
Water consumption	m3	3.224	9.932	208.05%

.

BEVs demonstrate clear environmental advantages in climate-related categories. Global warming potential (GWP) is reduced by 4.52% compared to GVs (4053.67 vs. 4245.514 kg CO₂ eq), while stratospheric ozone depletion decreases by 83.84% (0.001 vs. 0.005 kg CFC11 eq). Fossil resource scarcity also shows a 46.96% reduction (778.063 vs. 1466.821 kg oil eq). These benefits are mainly attributable to the elimination of direct tailpipe emissions and the partial substitution of gasoline with electricity. GWP and energy use are commonly considered dominant environmental factors for electric vehicles [88], and several studies have verified the emission-reduction advantages of BEVs [45,46,47,48].

In terms of resource use, BEVs also reduce mineral resource scarcity by 33.73% (1.543 vs. 2.328 kg Cu eq), likely due to reduced dependency on internal combustion components. However, land use increases by 63.46% (47.857 vs. 29.278 m²a crop eq), suggesting a higher spatial footprint from electricity generation and upstream infrastructure.

BEVs exhibit substantial increases in ionizing radiation (278.24%) and fine particulate matter formation (261.51%), which are associated with upstream power generation and battery-related activities. Specifically, BEVs generate 73.750 kBq Co-60 eq of ionizing radiation compared to 19.498 for GVs, and 5.927 kg PM₂.₅ eq compared to 1.640 for GVs.

Additionally, ozone formation impacts rise significantly. For human health, BEVs increase ozone formation by 212.04% (11.239 vs. 3.602 kg NO_x eq), and for terrestrial environments the increase is 155.67% (11.272 vs. 4.409 kg NO_x eq). These shifts highlight potential urban air quality trade-offs.

Terrestrial acidification increases by 174.09% (12.815 vs. 4.675 kg SO₂ eq), and freshwater eutrophication increases by 784.22% (0.736 vs. 0.083 kg P eq), both indicating substantial upstream pollutant burdens. Interestingly, BEVs reduce marine eutrophication by 57.67% (0.049 vs. 0.115 kg N eq), possibly due to lower direct nitrogen-based emissions.

Toxicity indicators show a consistent and significant increase in the BEV scenario. Human non-carcinogenic toxicity increases by 406.54% (1801.315 vs. 355.613 kg 1,4-DCB eq), and carcinogenic toxicity rises by 185.29% (122.072 vs. 42.788 kg 1,4-DCB eq). Ecotoxicity indicators also rise as freshwater ecotoxicity increases by 326.24%, marine ecotoxicity by 221.73%, and terrestrial ecotoxicity by 21.23%. These increases are linked to the complex material extraction, refining, and electricity generation processes that dominate BEV life cycles.

In summary, while BEVs present significant climate and fossil resource benefits during the use phase, these gains are accompanied by increased burdens in radiation, particulate formation, acidification, and toxicity. As emphasized in earlier life cycle studies [89,90,91], evaluating electric vehicle sustainability requires a balanced, multi-indicator perspective that accounts for both benefits and trade-offs across environmental dimensions. Table 4 clearly supports this conclusion, particularly for cold climate and coal-dominated electricity regions such as NECG.

4. Discussion

4.1. Interpretation of Cost Structure Results

The results indicate that although BEVs have a higher initial cost compared to GVs, they offer better long-term cost performance under current policy conditions. This cost advantage is driven by lower operating and maintenance costs, as well as exemptions from specific regulatory and tax-related expenses 60].

Lower BEV maintenance costs are partially attributable to industry practices such as BYD’s free lifetime battery replacement and complimentary charging pile installation [42]. These cost reductions offset the higher purchase price and slow depreciation of BEVs. Furthermore, BEV operating costs benefit from stable electricity pricing and fewer mandatory inspections, further widening the cost gap between BEVs and GVs.

While some researchers argue that BEVs can be more expensive than GVs over time [92], this largely depends on contextual factors including battery lifespan, annual mileage, learning curve effects in battery production, and regional power grid configurations [93]. In this study, those variables—combined with local policy incentives—favor BEVs.

The resale value of BEVs remains higher than that of GVs over comparable lifespans, though the value retention rate is not proportionately stronger. This indicates that while BEVs depreciate more slowly, their higher upfront costs still influence total asset value. Nonetheless, short-range BEVs can recoup initial premiums more quickly, especially in policy-supported markets [94].

Overall, the LCC findings in Heilongjiang align with international evidence, such as findings from Norway [95] where a supportive fiscal and regulatory environment makes BEVs more cost-competitive than internal combustion engine vehicles.

4.2. Interpretation of NPV Comparison

This trend highlights that electric vehicles have a higher NPV in the early years of ownership, mainly due to their slightly higher initial purchase price. However, over time, traditional gasoline vehicles eventually exceed the NPV of BEVs, potentially because GVs benefit more from the higher initial investment as time goes on. BEVs, in contrast, hold a greater long-term advantage due to their lower operating and maintenance costs. This pattern underscores the importance of considering service life when evaluating the economic benefits of BEVs and GVs. As noted by the authors of [9], the purchase price of BEVs remains a significant barrier despite their long-term savings. Although electric vehicles may present higher upfront costs, owners benefit from lower fuel and maintenance expenses during the vehicle’s lifetime.

However, consumers do not always have sufficient information about potential cost savings on fuel, maintenance, and other expenses, which can result in suboptimal purchasing decisions [95]. However, consumers do not always have sufficient information about potential cost savings on fuel, maintenance, and other expenses, which can result in suboptimal purchasing decisions [96]. This highlights the need for enhanced consumer education and transparent total cost of ownership communication to support rational decision-making in the vehicle market.

4.3. Discussion on Discount Rate Sensitivity

The results demonstrate that the economic feasibility of both BEVs and GVs is highly sensitive to variations in the discount rate—a crucial variable in present value-based evaluations. While BEVs incur higher depreciation-related costs in the early years under high discount rates, their relatively lower operating and maintenance expenses offer advantages over the long term.

GVs, on the other hand, are more vulnerable to discount rate effects due to their higher operating expenditures. A higher discount rate reduces the present value of these future costs, which disproportionately benefits GVs in longer service durations. This explains why the GV cost curve exhibits greater slope changes under varying discount scenarios.

These findings are consistent with those of [15], which also observed that BEV cost advantages tend to persist across a range of discount rate scenarios, particularly when fuel and maintenance costs are substantial and sustained.

Furthermore, these results underscore the importance of tailoring financial assumptions—such as discount rates—to reflect real-world economic conditions. Policymakers and investors should account for this sensitivity when designing incentive schemes or making investment decisions. For instance, a lower real discount rate can enhance the relative attractiveness of BEVs by reducing the impact of initial depreciation and highlighting savings in operating costs over time.

4.4. Discussion on Subsidies Affecting Overall Cost for Consumers in the Utilization of BEVs and GVs

The observed trends align with a broader shift in China’s policy landscape, which now emphasizes cost-effectiveness and market-driven EV development. Recent policy reforms, including the planned phase-out of subsidies by 2027, reflect concerns about the long-term fiscal sustainability and efficiency of BEV incentives. Studies show that the per-ton cost of GHG abatement via BEV subsidies may exceed the estimated social cost of carbon under current grid and manufacturing conditions [97,98]. This raises concerns about whether subsidies are the most economically viable climate policy tool.

Additionally, the recent BEV price wars in China—triggered by intensified competition among domestic automakers following subsidy removal—suggest that market forces are increasingly capable of sustaining BEV adoption without continued public support. These dynamics indicate a maturing market and support the rationale for shifting future policy efforts from broad subsidies to more targeted measures such as innovation incentives and infrastructure investment.

Electric vehicle incentive programs may lead to reduced tax revenues, particularly if these policies are not designed to be revenue-neutral [95]. While subsidies can have a positive economic effect, they may also raise overall emissions and increase the demand for continuous technological advancements. This suggests that promoting technological advancement is a more sustainable policy option compared to providing subsidies [99,100]. Government intervention through subsidies alone may struggle to achieve an ideal steady state or break the evolutionary standstill. Therefore, subsidies should be used alongside phased regulations and supervision. Increasing subsidies alone may weaken the market and prove counterproductive [101].

Transitioning to BEVs is crucial for reducing transport emissions but faces slower adoption than GVs due to high upfront costs, limited range, charging constraints, and battery life concerns [4,5]. Consumers also consider total ownership costs—including fuel, maintenance, and insurance—which can make GVs appear more economically attractive in the short term [12]. While LCC studies show BEVs may become competitive with technological advancements and policy incentives [92,102], key factors such as battery lifespan, annual mileage, and grid composition remain influential [93].

In this context, reward–penalty mechanisms—such as differentiated taxation based on environmental impact—are proving more effective than flat-rate subsidies in improving both environmental outcomes and social welfare [103]. Likewise, fiscal tools that influence fuel and vehicle prices can significantly shift fleet composition, energy consumption, and the climate footprint over the long term [104].

The analysis highlights the risk of relying on subsidies for the industry, which could stifle innovation and market competitiveness. A shift away from direct fiscal incentives towards mechanisms that promote technological advancement and cost reductions could ensure continued industry growth and consumer acceptance without the need for long-term fiscal support [3]. Increased overall emissions and the requirements for continued technological advancement indicate that promoting technological progress is a more sustainable alternative than maintaining subsidies [99,100]. Effective policy implementation and taxation, as outlined by [105], further influences consumers’ decisions and contribute to emission reductions.

Compared with the subsidy mechanism, the reward–penalty mechanism presents more significant effects on the recycling rate and social welfare [103]. A combination of subsidy and supervision, or phased regulation, should be adopted. Increasing subsidies is likely to weaken the function of the market and have a counterproductive effect [101]. Also, stricter credit targets for new EVs could increase production while curbing the sales of ICEVs [106].

According to the results, despite the elimination of subsidies EVs still have economic benefit if the usage is maintained for more than 5 years. More subsidies are not always more beneficial. Thus, subsidies may be phased out and replaced by a penalty or compensation mechanism (such as a carbon tax) to encourage consumers to buy EVs. By understanding the impact of subsidies on the economic positioning of BEVs relative to GVs, policymakers can design more effective strategies that not only promote initial adoption but also support sustainable growth of the EV market

4.5. Discussion on Use Phase Energy Consumption and Environmental Impacts

4.5.1. Overview of Energy Consumption Discussion

According to the latest official statistics released by the Ministry of Ecology and Environment and the National Bureau of Statistics of China [86], the national average CO₂ emission factor for electricity in 2022 was 0.5366 kg CO₂/kWh. However, regional disparities in power generation fuel mixes result in a significant variation in carbon intensity across different grid regions [107]. In this study, rather than using a predefined grid emission factor, the life cycle emission intensity under NECG conditions was derived from modeling results. Specifically, the total BEV emissions (4053.67 kg CO₂ eq) divided by electricity consumption (2706.5 kWh) yields an effective emission factor of 1.498 kg CO₂/kWh. This value is significantly higher than the statistical regional average of 0.5564 kg CO₂/kWh [22], primarily due to the broader system boundary in the life cycle model. The model includes upstream emissions such as coal mining, transportation, and power infrastructure, in addition to direct combustion. Therefore, conventional grid emission factors are not suitable for life cycle assessment (LCA) or product carbon footprint (PCF) calculations. PCF accounts for both direct and indirect greenhouse gas emissions across a product’s full life cycle—from raw material extraction to disposal. National averages typically reflect only combustion-related emissions and neglect other significant sources—particularly for renewable electricity generation. For instance, the northeast China grid (NECG), which supplies electricity to Heilongjiang Province, reported a regional average emission factor of 0.5564 kg CO₂/kWh. This value is notably higher than regions like south China (0.3869 kg CO₂/kWh) or southwest China (0.2268 kg CO₂/kWh) [86], where hydropower and other renewables play a dominant role [22]. Average grid emission factors typically only include emissions from fuel combustion, excluding upstream emissions from fuel extraction, infrastructure, and end-of-life waste—especially relevant for renewable energy [108]. Several studies have addressed this by using LCA to calculate more comprehensive emission factors for various power generation technologies and plants in China, for instance the study by the authors of [108] which estimated it at 0.972 kg CO₂/kWh in 2020. This discrepancy can be attributed to several factors. While ref [108]’s study employed a carbon footprint methodology, it did not include upstream transportation emissions or power grid seasonal variations in electricity consumption. In contrast, our study incorporates these components and adjusts electricity demand based on seasonal load changes, especially under cold climate conditions. Therefore, the emission factor in this study reflects a more comprehensive and realistic assessment for high-energy-use scenarios in northern China.

These findings demonstrate that although BEVs generally have lower life cycle greenhouse gas emissions, their environmental advantage is highly sensitive to both regional electricity generation profiles and seasonal climatic conditions. In cold climates, like northeast China, energy consumption increases due to reduced battery efficiency and the need for additional cabin heating. This leads to higher electricity demand—2706.5 kWh in this study, compared to an estimated average of 2138 kWh in moderate climates—thereby diminishing the relative emissions benefit of BEVs. Because of the reliance on coal-fired power generation and the colder climate, BEVs in most northern provinces tend to have higher emission intensities compared to those in southern regions [109].

In contrast to the electricity-based emissions of BEVs, the GV’s total life cycle emissions are primarily driven by petrol consumption. In this study, the GV consumes 1196 kg of low-sulfur petrol over 20,000 km, resulting in a total of 4245.51 kg CO₂ equivalent, or 0.2123 kg CO₂ eq/km. The emission factor used—3.55 kg CO₂/kg petrol—includes both direct and indirect emissions across the fuel’s life cycle. This encompasses upstream processes such as crude oil extraction, refining, and fuel transportation, as well as tailpipe (tank-to-wheel) emissions.

The tank-to-wheel CO₂ emission factor for petrol combustion is approximately 3.17 kg CO₂ per kg of fuel based on IPCC (2006) default values, which is consistent with estimates from the U.S. EPA and DEFRA [87]. However, upstream activities—including the energy-intensive refining process and long-distance fuel distribution—contribute significantly to the remaining emissions. While these upstream emissions are lower in intensity compared to electricity generation from coal, they still form a considerable portion of the GV’s carbon footprint.

Unlike electricity, whose emission intensity varies by region and energy mix, the emission factor for petrol is relatively stable across locations due to standardized production and combustion characteristics. As such, while BEVs may benefit from cleaner grids in certain regions, GVs offer little variability in emissions regardless of geographic location or climate. However, it is important to note that the overall GWP of GVs still slightly exceeds that of BEVs under NECG conditions, despite the coal-heavy grid. From a policy perspective, two critical implications emerge. First, realizing the full climate benefits of BEVs requires simultaneous progress in electricity grid decarbonization, particularly in coal-dependent regions like northeast China. Without significant reductions in power sector emissions, BEVs will continue to deliver only marginal improvements over internal combustion engine vehicles.

Another critical factor is seasonal temperature variation, which increases energy consumption in cold climates due to battery inefficiency and cabin heating. This study found that BEV electricity use was 2706.5 kWh under NECG conditions—substantially higher than the 2138 kWh typical for moderate climates. This 26.5% increase in energy demand directly contributes to the elevated GHG emissions observed. In contrast, the GV’s emissions remain stable across regions because fuel characteristics and combustion conditions are standardized. While BEVs have the potential to achieve lower emissions in clean-grid areas, their advantage is limited in coal-dominated grids.

Policy implications from these findings are twofold. Grid decarbonization is essential to unlock the full climate benefit of BEVs, particularly in fossil-heavy provinces like Heilongjiang. Without cleaner electricity, BEVs will offer only marginal GHG reductions over conventional vehicles. Vehicle-level innovation, especially in battery thermal efficiency and cabin heating systems, is critical to reduce seasonal energy spikes that compromise BEV performance in cold regions.

These insights reinforce the necessity of adopting a regionally contextualized, life cycle-based approach to electrification policy and infrastructure planning. The one-size-fits-all view of BEVs as inherently clean must be revisited in light of these complex, location-specific dynamics.

4.5.2. Discussion on Extended Environmental Impacts: Trade-Off Analysis

The environmental trade-offs identified in this study underscore the need for a holistic and context-sensitive approach to BEV deployment. Although BEVs offer substantial reductions in GHG emissions and fossil energy use, the increases observed in other impact categories—particularly toxicity, particulate matter, and ionizing radiation—highlight the limitations of carbon-focused policy frameworks.

Emerging energy storage technologies, such as lithium–sulfur and solid-state batteries, hydrogen fuel cells, and solar integration, are expected to transform the economic and environmental calculus of electric mobility [110,111]. Comparative life cycle analyses by the authors of [112,113] highlight the advantages of emerging battery technologies like lithium–sulfur and all-solid-state batteries. BEVs offer environmental and societal benefits such as lower emissions and fuel savings [114]. Moreover, life cycle and sensitivity analyses underscore the need to consider uncertainty in fuel prices, inflation, and technological change [115,116]. These dynamics highlight that both policy design and technological pathways must be aligned to ensure a just, sustainable transition. Ensuring a just and effective transition requires synchronizing policy interventions with these evolving technological pathways, particularly in regions facing unique climatic and infrastructural constraints.

BEVs can reduce GHG emissions by ~60% in most EU member states, with an average 50% saving compared to diesel, although some regions show no savings [117]. BEVs can only fully realize their environmental potential when powered by renewables, minimizing electricity production impacts [118,119]. Canadian studies [120,121] reinforce the significance of regional factors such as climate and energy mix in evaluating the environmental performance of BEVs. These insights align with our findings in Heilongjiang, where cold temperatures and a coal-dominated energy structure impact BEV adoption. The comparative analysis suggests that policies promoting lightweight materials, energy-efficient technologies, and comprehensive life cycle assessments are crucial for the successful deployment of BEVs in cold climate, fossil-fuel-reliant regions. Incorporating these international perspectives strengthens the applicability of our study’s conclusions and provides a framework for policymakers in similar regions to develop effective strategies for BEV adoption.

Local factors like climate and geography affect BEV performance, with the operating phase being most vulnerable to spatial variations [88]. Refs. [122,123] emphasize that the ambient climate impacts EV range and market penetration, while effective policy and taxation influence consumer decisions and emissions reductions [105]. Hou et al. (2022) project significant global GHG reductions from BEVs by 2030 and 2050, aiding the transition to a low-carbon transportation industry [124]. Cai et al. (2017) suggest using hybrid life cycle and multi-criteria decision analysis for city taxi fleets to assess environmental, financial, and political impacts [125]. Reducing driving and adopting green consumption patterns, such as fewer car trips and energy-saving practices, can lower household GWP [126,127].

In cold climate regions like Heilongjiang, BEV adoption faces challenges such as battery degradation in low temperatures, increased energy demand for heating, and inadequate weather-adapted infrastructure. In response, regional authorities have launched targeted policies. The 2025 Harbin Interim Measures mandate 100% charging readiness in new residential parking and promote centralized, climate-resilient charging hubs [128]. The 2024 opinions of the CPC Heilongjiang Provincial Committee and the People’s Government of Heilongjiang Province on comprehensively implementing Xi Jinping’s thoughts on ecological civilization and accelerating the construction of a green Heilongjiang point out, develop, and expand the green energy industry and promote the transformation and upgrading of the energy structure [129]. Despite progress, region-specific gaps remain. Further support should include battery thermal standards, winter-adjusted electricity pricing, and public education to build consumer confidence. These actions are critical for ensuring cold, coal-intensive provinces like Heilongjiang keep pace with China’s national electrification goals.

In conclusion, the transition to BEVs offers promising environmental benefits but is far from universally sustainable. A successful pathway requires coordinated action across technology development, electricity grid reform, regional policy, and behavioral shifts. Policymakers must adopt evidence-based, multi-indicator frameworks that reflect the complex trade-offs and regional specificities highlighted by this and other life cycle studies.

5. Conclusions

This study conducted a comprehensive life cycle comparison of BEVs and GVs in Heilongjiang Province, China, incorporating both cost and environmental dimensions under the specific conditions of a cold climate and a coal-based electricity grid. Over a 20,000 km driving distance, the findings reveal that BEVs outperform GVs in terms of total ownership cost across 5-, 10-, and 15-year periods, primarily due to reduced operational and maintenance expenses. Even in the absence of subsidies, BEVs maintain a cost advantage, and sensitivity analysis confirms that their economic viability is less affected by changes in discount rates compared to GVs.

From an environmental perspective, BEVs demonstrate a 4.52% reduction in global warming potential relative to GVs, attributable to the elimination of tailpipe emissions. However, this benefit is largely offset by the high carbon intensity of electricity in the NECG, with a life cycle emission factor derived at 1.498 kg CO₂/kWh—significantly higher than the national average. Furthermore, BEVs impose greater burdens in other environmental categories, including ionizing radiation, fine particulate matter formation, eutrophication, toxicity, and water consumption, due to upstream emissions associated with fossil-based electricity generation.

These results suggest that while BEVs contribute to decarbonization and deliver long-term cost savings, their environmental performance remains highly context-dependent. A multi-pronged strategy is essential. Firstly, accelerating grid decarbonization through investments in renewable energy and energy storage is critical in coal-dependent regions. Secondly, advancing cleaner battery technologies and responsible sourcing—such as through circular economy principles and traceable, low-impact supply chains—can help reduce environmental burdens associated with resource extraction and processing. Thirdly, vehicle design innovations, particularly in battery thermal management and cold-weather efficiency, are needed to reduce seasonal energy penalties. Finally, policy frameworks must adopt a multi-indicator lens that extends beyond CO₂ emissions to include broader impacts like toxicity, land use, and resource depletion, ensuring that BEV adoption does not shift burdens across. Fiscal reforms could include eco-modulated vehicle taxes that account not only for CO₂ emissions but also for life cycle impacts such as metal toxicity and resource depletion. Governments might also consider implementing differentiated subsidies or tax credits that favor BEV models with lower overall environmental footprints, verified through standardized LCA metrics. On the regulatory side, tighter environmental standards for battery manufacturing, mandatory recycling quotas, and incentives for domestic battery reuse could significantly mitigate upstream impacts.

In conclusion, BEVs represent a more economically and climatically sustainable alternative to GVs in cold, coal-intensive regions. However, their broader environmental advantages can only be fully realized through parallel improvements in energy infrastructure, emission accounting practices, and regional policy design that reflects local energy and climate realities. Overall, by incorporating life cycle cost, environmental impact, real-world climatic variability, and regional power grid characteristics into a unified framework, this study provides a more comprehensive and practical basis for evaluating electric vehicle sustainability in coal-dependent, cold climate regions.

Nevertheless, several methodological limitations should be acknowledged. The LCA analysis focused solely on the use phase and relied on static emission datasets, without accounting for future decarbonization or system degradation. The LCC model was based on fixed assumptions and did not incorporate vehicle residual values or user behavior variability. This study only considers the BEV and GV use stage in Heilongjiang province and consumers as a stakeholder. While this study focuses specifically on Heilongjiang Province—a region characterized by a cold climate and coal-dominated electricity generation—the findings offer insights that may be transferable to similar latitude or fossil-fuel-reliant regions globally However, the results are less directly applicable to regions with mild climates and low-carbon energy systems. These limitations suggest that the findings should be interpreted within the study’s defined scope.

Future research should adopt dynamic modeling approaches, extend the analysis to full life cycle stages, and incorporate uncertainty analyses to enhance the robustness and applicability of sustainability evaluations for emerging vehicle technologies. It should also consider incorporating behavioral economic insights or even survey-based data to complement the LCC model, thereby grounding the consumer perspective in more realistic assumptions to reflect more dynamic and empirically grounded consumer decision-making processes. Also, it may include battery degradation and partial replacement costs to validate the assumption. Also, it may consider including hybrid vehicles (HEVs and PHEVs) to enable a broader technology comparison. Moreover, the integration of dynamic electricity decarbonization pathways, end-of-life recycling and reuse strategies, battery second-life applications, and user behavior modeling would further enhance the comprehensiveness and robustness of sustainability evaluations across the vehicle life cycle.