Electrification pathways for light-duty logistics vehicles based on perceived cost of ownership in Northern China
0 Abstract
Urban decarbonization and environmental mitigation necessitate the electrification of light-duty logistics vehicles (LDLVs), including battery electric, plug-in hybrid, and hydrogen fuel cell variants. Although the market uptake of electric LDLVs is ecologically imperative, it is impeded by range anxiety and charging infrastructure limitations, particularly pronounced in Northern China’s cold climates. This paper employs a system dynamics model to assess the Perceived Cost of Ownership of electric LDLVs, integrating both direct expenses - initial investment and energy costs - and indirect factors like energy replenishment, vehicle substitution, and lifecycle carbon emissions. This analysis reveals that, notwithstanding higher upfront costs, electric LDLVs offer substantial economic and environmental advantages, with significant energy and maintenance savings projected by 2030 under various electrification scenarios. This paper predicts that policy incentives, electricity pricing, and technological progress will significantly influence the market dynamics and industry output of new energy vehicles in Northern China. Notably, the findings indicate that by 2030, electric LDLVs could achieve substantial cost savings and environmental benefits, with market penetration and industry output contingent on the interplay of policy support and technological advancements. The baseline scenario forecasts a 48.17% market share and CNY 60.015 billion in industry output, whereas the high-speed electrification scenario projects the most optimistic outcomes, with a 75.29% market share and CNY 306.087 billion in output.
Keywords
INTRODUCTION
Climate change has emerged as one of the most pressing global challenges of the 21st century, necessitating urgent action across all sectors of the economy[1]. As nations worldwide grapple with the imperative to reduce greenhouse gas emissions, China, the world’s largest carbon emitter, has set ambitious targets to peak carbon emissions by 2030 and achieve carbon neutrality by 2060[2]. This commitment underscores the critical role of sustainable development in urban areas, where the concentration of economic activities and population growth intensifies environmental pressures[3].
Within the broader context of urban sustainability, the logistics sector stands out as a significant contributor to carbon emissions and air pollution. In China, the rapid growth of e-commerce and urban delivery services has led to a substantial increase in light-duty commercial vehicles, exacerbating air quality issues and hindering decarbonization efforts[4]. The logistics industry’s vehicle emissions not only contribute to climate change but also pose immediate health risks to urban populations, making the transition to cleaner transportation solutions an urgent priority[5].
Electrification of light-duty logistics vehicles (LDLVs) presents a promising pathway to address these challenges. Battery electric vehicles (BEVs), plug-in hybrid electric vehicles (PHEVs), and hydrogen fuel cell vehicles offer the potential to significantly reduce both carbon footprints and operational costs in the logistics sector[6]. The adoption of these alternative energy vehicles aligns with global trends toward sustainable urban mobility and has garnered support from policymakers and industry stakeholders alike[7].
However, the transition to electric light-duty commercial vehicles (ELCVs) faces substantial barriers that impede widespread market integration. Range anxiety, stemming from limited battery capacity and inadequate charging infrastructure, remains a primary concern for potential adopters[8]. These challenges are particularly pronounced in regions with extreme climatic conditions, such as Northern China, where cold winters significantly impact vehicle performance and energy efficiency[9]. In these areas, low temperatures can reduce battery capacity by up to 40%, increase charging times, and necessitate more frequent charging stops, thereby intensifying the inconvenience and operational challenges associated with ELCVs[10].
Current research lacks a comprehensive assessment framework that incorporates both tangible and intangible costs associated with electric vehicle adoption in diverse climatic conditions. This study fills this gap by introducing a Perceived Cost of Ownership (PCO) model, a novel approach that evaluates the economic viability of electric light-duty commercial vehicles in comparison to traditional counterparts. The PCO model considers costs from the perspective of logistics companies and fleet managers, who are the primary decision-makers in the adoption of ELCVs. The model’s innovation lies in its consideration of spatial heterogeneity and the integration of intangible costs, offering unprecedented insights into the true economic implications of electric vehicle adoption across varying geographic and climatic settings. By examining the economic benefits through this PCO lens, the paper forecasts market penetration trends and assesses the influence of regional economic, social, and environmental factors on the adoption of electric vehicles by 2030. The findings are pivotal for deciphering the barriers to market penetration and for crafting policies that foster sustainable urban logistics solutions. The findings from this research may inform future studies on electric vehicle adoption in regions with similar challenging environments, potentially contributing to a more nuanced understanding of electrification processes in diverse geographical contexts.
The paper is structured as follows: The “Introduction” outlines the motivation for the PCO model. The “Literature Review” critiques existing cost calculation models and underscores the study’s innovative contributions. The “Modeling and Methodology” section elucidates the model’s framework and analytical approach. The “Model Results and Analysis” presents findings and explores their implications for market trends and policy. Finally, the “Conclusion and Policy Implications” synthesizes the study’s insights and suggests avenues for future research.
LITERATURE REVIEW
The traditional framework for assessing the economic viability of new energy commercial vehicles has predominantly relied on the Total Cost of Ownership (TCO) model[7-10], facilitating a quantitative comparison of different vehicle types under various operational scenarios[11-13]. However, this approach has methodological limitations, often focusing on direct monetary costs[11] and lacking a comprehensive assessment of intangible factors[12,13].
In the realm of light commercial vehicles, an influx of literature has emerged, employing diverse analytical techniques such as the Analytic Hierarchy Process (AHP)[14], Data Envelopment Analysis (DEA)[7,15], and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)[16-18]. A comparative analysis of freight electric vehicle schemes across several European countries by Taefi et al. (2014) provides valuable insights into policy frameworks, infrastructure development, and operational challenges relevant to LDLV electrification in various contexts[19]. These methods have been adept at revealing the multifaceted benefits of adopting electric vehicles, including social and environmental impacts. Yet, they frequently fall short in integrating the broader spectrum of economic and environmental costs, particularly those influenced by regional and climatic disparities.
The element limitations are evident in the undervaluation of intangible time costs associated with electric light commercial vehicles (ELCVs)[19-23], such as the time spent on charging[11,23] and the anxiety of range limitations[12,24,25]. While studies have begun to address the economic[26] and environmental costs[27-30], including the lifecycle assessment of carbon emissions, there remains a disconnect in quantifying the true impact of spatial heterogeneity and regional climate on ELCV performance and cost-effectiveness.
Regional limitations have been highlighted by the oversight of cold climate challenges in existing system models[31,32], which are especially pertinent in regions like Northern China[33,34]. The cold temperatures significantly affect battery performance[35], yet there is a dearth of research on the economic implications of these technical hurdles[6] and the potential for policy[36] and technological interventions[37] to overcome them.
This comprehensive review of existing literature reveals several critical research gaps in the economic assessment of ELCVs. Traditional TCO models, while valuable, often fail to capture the full spectrum of costs associated with ELCV adoption, particularly intangible factors such as range anxiety and charging time. Current models largely overlook the impact of regional variations, especially in terms of climate and infrastructure, on ELCV performance and economic viability. The unique challenges posed by cold climates, particularly relevant in regions like Northern China, are underrepresented in existing economic models. Furthermore, there is a notable gap in research that comprehensively evaluates the potential of policy interventions and technological advancements to address ELCV adoption barriers.
To address these gaps, our study introduces the PCO model, a novel extension to the TCO model that incorporates both tangible and intangible costs, providing a more holistic assessment of ELCV economic viability. This model accounts for regional variations in climate, infrastructure, and economic conditions, offering insights into the geographically diverse challenges of ELCV adoption. We provide an in-depth examination of the economic implications of cold climate challenges on ELCV performance and adoption, and evaluate the potential of various policy interventions and technological advancements to mitigate adoption barriers, particularly in challenging climatic conditions. By synthesizing these factors, our research offers valuable projections of ELCV market penetration trends, considering the interplay of economic, technological, and policy factors. This multifaceted approach not only addresses the limitations of existing models but also provides a comprehensive framework for understanding and promoting ELCV adoption across diverse geographic and climatic contexts. Our findings have significant implications for policymakers, industry stakeholders, and researchers working toward sustainable urban logistics solutions. Table 1 summarizes the key contributions and limitations of existing cost calculation models, highlighting the need for the PCO model.
Literature review summary table
| Ref. | Methodology | Key contributions | Limitations | Consideration of intangible costs | Regional/Climatic impacts |
| [7-10] | TCO | Direct cost comparison, operational scenario analysis | Limited intangible cost assessment | - | No |
| [14] | AHP | Multi-criteria decision support | Qualitative focus, less on direct costs | Partial | No |
| [15] | DEA | Efficiency and performance evaluation | Lacks environmental cost integration | No | Yes |
| [16-18] | TOPSIS | Comprehensive ranking of alternatives | Does not account for regional differences | No | Yes |
| [19-22] | - | Time cost analysis for ELCVs | Isolated from broader economic analysis | Yes | No |
| [22] | - | Charging time valuation | - | Yes | No |
| [23,24] | - | Range anxiety impact assessment | - | Yes | No |
| [25] | - | Economic cost studies | - | Yes | No |
| [26-30] | - | Environmental cost studies | - | No | Yes |
| [31,32] | - | Cold climate impact on battery performance | - | No | Yes |
| [33,34] | - | Regional challenges in Northern China | - | No | Yes |
| [35,36] | - | Technical hurdles in cold climates | - | No | Yes |
| [37,38] | - | Economic implications of cold climate on ELCVs | - | Yes | Yes |
METHODOLOGY AND MODELLING
Research scenario definition
Electric LDLVs encompass a diverse array of vehicles tailored for a multitude of applications and can be differentiated under various classification criteria. This study categorizes electric LDLVs into three principal types based on their energy sources: BEVs, PHEVs, and Hydrogen Electric Vehicles (HEVs). The research focus is specifically on BEVs, which are commercial vehicles designed and intended for urban settings. These vehicles are characterized by their compact size, lightweight construction, and efficient powertrain systems. Over a typical service life of five years, BEVs are predominantly utilized for intra-urban freight transportation and logistics distribution, highlighting their agility in maneuvering and their proficiency in short-haul transport. Urban LDLVs are commonly recognized for their excellent fuel efficiency, stringent emission standards, and substantial cargo capacity, aligning well with the demands of urban logistics. They serve as a critical component in various sectors, including urban delivery, e-commerce logistics, food service distribution, courier services, supply chain support for supermarkets and retail outlets, and cold chain logistics. Comparative analysis is conducted with traditional logistics vehicles, exemplified by diesel-powered light-duty trucks, as detailed in Table 2.
Research scenario definition
| Research subject | Research elements | Usage elements |
| Traditional light-duty logistics vehicles | Range (km) | 800.00 |
| rated load capacity (tons) | 1.74 | |
| Service life (years) | 5.00 | |
| Annual mileage (km) | 93,000.00 | |
| Daily business operating mileage (km) | 127.88 | |
| Electric light-duty logistics vehicles | Range (km) | 323.54 |
| Rated load capacity (tons) | 1.31 | |
| Service life (years) | 5.00 | |
| Annual mileage (km) | 93,000.00 | |
| Business day mileage (km) | 122.38 |
LDLV electrification: economic assessment
PCO model proposed in this paper encompasses the tangible vehicle costs that can be directly monetized, as included in the traditional TCO model. These tangible costs include purchase price, energy costs, maintenance costs, taxes, and insurance costs. Additionally, the model takes into account the intangible costs incurred by users during the usage phase, which are defined in this paper as time costs. Both tangible and intangible costs together constitute the economic costs of LDLVs, as detailed in Figure 1.
Figure 1. The structure of economic cost evaluation.
Tangible costs module
In this comprehensive study, the economic evaluation of electrified LDLVs reveals a nuanced picture of cost dynamics that significantly impact the total cost of ownership. The analysis focuses on three primary cost components: vehicle purchase cost, energy expenditure, and maintenance, each playing a crucial role in determining the long-term economic viability of electric LDLVs. The initial purchase price of pure electric LDLVs presents a notable financial hurdle, with these vehicles typically commanding a premium over their conventional diesel counterparts. However, the implementation of strategic fiscal and tax policies has substantially alleviated this barrier. These policy interventions have effectively reduced the upfront cost by approximately 26,800 yuan, a significant decrease that narrows the price gap between electric and diesel options. This reduction not only makes electric LDLVs more accessible to fleet operators but also shortens the payback period for the initial investment. Over a five-year period with an annual mileage of 93,000 km, the total energy cost for a pure electric logistics vehicle is projected to be 627,800 yuan, a substantial saving of 250,700 yuan compared to diesel vehicles, which have a total energy cost of 878,500 yuan. Additionally, electric vehicles exhibit lower maintenance and repair costs, further enhancing their economic viability. These insights suggest that the higher initial investment in electric LDLVs is offset by significant operational and energy cost savings, making them an increasingly attractive option in the transition toward sustainable transportation solutions. The tangible costs module, as detailed in Table 3, provides a comprehensive overview of these financial considerations, highlighting the long-term economic benefits of electrification in the logistics sector.
Tangible costs module[38]
| 2022 | Tangible costs (Yuan) | |||||
| (1) Purchase cost | (2) Energy cost | (3) Maintenance, taxes, and insurance costs | ||||
| Pure electric logistics vehicle | List price | 233,800 | Annual mileage | 93,000.00 (km) | Maintenance | 10,300 |
| Policy subsidies | 26,800 | Electricity consumption | 90 (kWh/ | Insurance | 15,000 | |
| Actual price | 206,00 | Electricity price | 1.5 (Yuan/kWh) | Vehicle and vessel tax | 600 | |
| Annual electricity cost | 125,500 | Calculation period | 5 (years) | |||
| Calculation period | 5 (years) | Total cost | 129,600 | |||
| Total energy cost | 627,800 | |||||
| Diesel logistics vehicle | Purchase price | 126,900 | Diesel price | 7.36 | Maintenance | 12,000 |
| Urea price | 2.46 | Insurance | 15,000 | |||
| Fuel consumption | 25 (kg/100 km) | Vehicle and vessel tax | 500 | |||
| Urea consumption | 2 (kg/100 km) | Calculation period | 5 (years) | |||
| Mileage | 93,000.00 (km) | Total cost | 137,700 | |||
| Annual fuel cost | 171,100 | |||||
| Annual urea cost | 4,600 | |||||
| Total energy cost | 175,700 | |||||
| Calculation period | 5 (years) | |||||
| Total energy | 878,500 | |||||
| Electrification of logistics vehicles | Difference | 7.91 | Difference | -250,700 | Difference | -8,100 |
Intangible costs module
In this study, we extend the traditional TCO model to incorporate intangible costs, introducing the PCO. This innovative approach captures not only the direct expenses but also the inconvenience fees associated with charging and the potential costs of vehicle replacement[38]. The PCO model acknowledges the time value lost due to charging and the impact of regional climate conditions on electric logistics vehicle performance, which can limit operational efficiency and affect the bottom line for logistics operators.
where CI represents intangible costs, CF represents energy replenishment costs, CR represents alternative vehicle cost, and Ce represents energy inconvenience costs.
This study segments intangible costs into three components: energy replenishment costs, energy inconvenience costs, and alternative vehicle costs. Energy replenishment costs are calculated based on the time spent locating charging stations, influenced by the distribution of charging infrastructure, road network length, and urban average speed. This is compounded by the total number of charging cycles over the vehicle’s life cycle[39].
where CF represents energy replenishment costs, S represents road network length, NS represents the distribution of charging stations, Va represents urban average speed, Ne represents the number of charging times in the entire life cycle of the vehicle, and Vt represents the time cost of logistics workers.
Charging time costs reflect the waiting period during each charging session, a significant consideration given the longer charging times for electric vehicles compared to traditional counterparts. This waiting time can curtail the operational window and, consequently, the earning potential of logistics practitioners.
where Te represents the single charging time, Ne represents the number of charging times in the entire life cycle of the vehicle, and Vt represents the time cost of logistics workers.
Lastly, the alternative vehicle cost accounts for the expenses incurred when electric vehicles cannot meet service demands due to climate constraints or limited range. This cost represents the strategy of logistics companies to maximize profitability by utilizing traditional vehicles as a fallback option when electric ones falter.
where M3 represents the entire life cycle mileage that cannot be normally completed by the electric logistics vehicle under abnormal climate conditions and must be completed by the traditional logistics vehicle, and CEcv represents the energy cost of the alternative traditional logistics vehicle.
Carbon emission costs module
This study estimates the lifecycle carbon emission costs of traditional LDLVs and electric LDLVs in urban distribution logistics scenarios, considering direct vehicle emissions (weighted carbon emissions of the vehicle), upstream and downstream carbon emissions caused during vehicle production, and the average price of carbon emission allowances in China’s carbon emission trading market.
As shown in Table 4, the lifecycle carbon emissions of diesel vehicles in 2022 are 107,360.40 kg, while the carbon emissions of pure electric vehicles are only 9,890.14 kg, nearly 1/11th of that of diesel vehicles. By 2030, the carbon emissions of pure electric vehicles will be only 6,155.20 kg, indicating a significant carbon emission advantage for pure electric vehicles.
Lifecycle carbon emission of light-duty urban logistics vehicles
| Light-duty urban logistics | 2022 | 2025 | 2030 | 2040 | 2050 |
| Diesel | 107,360.40 | 94,365.88 | 88,302.36 | 80,674.38 | 77,819.32 |
| Natural gas | 98,053.53 | 89,002.43 | 81,459.85 | 69,348.79 | 64,826.04 |
| Pure electric | 9,890.14 | 7,749.75 | 6,155.20 | 2,988.53 | 958.17 |
Total vehicle carbon emission cost
Table 5 presents a comprehensive analysis of the costs and profits associated with the electrification of LDLVs. The total economic profit, which is the net result of all cost elements, indicates a saving of
Economic profit of light-duty logistics vehicle electrification in 2022
| Cost type | Cost element | Cost (Yuan) |
| Tangible costs | Purchase cost | 79,100 |
| Energy cost | -250,700 | |
| Maintenance cost | -8,100 | |
| intangible costs | Search & energy inconvenience costs | 105,400 |
| alternative vehicle cost | 2,400 | |
| Carbon emission costs | Carbon emission cost | -4,400 |
| Economic cost for electrification | Total | -76,300 |
Electrification simulation: LDLV simulation model
Model parameter settings
This study takes Beijing as an example and selects urban economic, social, and natural climate conditions as the boundary conditions for the system model baseline area. The year 2021 is set as the start of the simulation, with a simulation period of 10 years and a step length of 1 year.
Macroeconomic elements mainly include regional GDP, GDP growth rate, new energy industry output value, the proportion of new energy industry output value in GDP, new energy automobile output value, the proportion of automobile output value in the industry output value, permanent resident population, natural population growth rate, fixed asset investment in the transportation industry, motor vehicle stock, highway operation freight volume, total retail sales of social consumer goods, etc. The macroeconomic data of Beijing from 2018 to 2022 are shown in Table 6.
Macroeconomic situation of Beijing from 2018 to 2022
| Economic element | 2018 | 2019 | 2020 | 2021 | 2022 |
| Regional GDP (100 million yuan) | 33,106 | 35,445 | 35,943 | 41,045 | 41,611 |
| GDP growth rate (%) | 6.70 | 6.10 | 1.10 | 8.80 | 0.70 |
| New energy industry output value (100 million yuan) | 276.80 | 341.50 | |||
| Proportion of new energy industry output in GDP (%) | 0.67 | 0.82 | |||
| New energy automobile output value (100 million yuan) | 77.90 | 184.40 | |||
| Proportion of automobile output in industry output (%) | 0.28 | 0.54 | |||
| Permanent resident population (ten thousand people) | 2,192 | 2,190 | 2,189 | 2,189 | 2,184 |
| Natural population growth rate (%) | -0.03 | -0.07 | -0.05 | -0.02 | -0.20 |
| Fixed asset investment in transportation (100 million yuan) | 1,283 | 1,085 | 983 | 949 | 877 |
| Motor vehicle stock (ten thousand vehicles) | 608 | 637 | 657 | 685 | 713 |
| Highway operation freight volume (ten thousand tons) | 20,278 | 22,325 | 21,789 | 23,075 | 18,549 |
| Total retail sales of consumer goods (100 million yuan) | 14,422 | 15,064 | 13,716 | 14,868 | 13,794 |
As shown in Table 7, through channels such as the China Society of Automotive Engineers and the China Federation of Logistics and Purchasing, and in combination with big data crawlers and GIS vector map verification, the supporting environmental information for electric logistics vehicles in the main urban area of Beijing is organized.
Supporting environment of electric logistics vehicles in Beijing in 2022
| Social element | Value |
| Road length (km) | 8,681.35 |
| Road network area (ten thousand square meters) | 15,374.80 |
| Total number of public charging and battery swap stations | 3,990 |
| Distance between gas stations (km) | 1.60 |
| Refueling time (h) | 0.08 |
| Average urban speed (km/h) | 22.10 |
| Distance between charging and battery swap stations (km) | 2.18 |
| Time value (yuan/h) | 32.00 |
| Number of charging stations | 3,922 |
| Number of battery swap stations | 68.00 |
| Average charging time (h) | 1.50 |
Additionally, field research has shown that climate conditions have a significant impact on the range and charging time of electric logistics vehicles. Under normal climate conditions (temperature above 10 °C), electric logistics vehicles can usually operate at rated parameters; between -10 and 10 °C, the operation of electric logistics vehicles will be affected to some extent, with the range of electric vehicles reduced by 50% and the charging time at charging stations increased by 70%, referred to as special climate conditions in this study; at temperatures below -10 °C, electric logistics vehicles cannot operate normally, and using them forcibly carries great vehicle damage risks and safety hazards. Traditional logistics vehicles are usually used to complete the set work, referred to as abnormal climate conditions in this study.
This study organizes and statistically analyzes the average daily temperature of Beijing from 2019 to 2023 for five years, using three indicators: the proportion of normal climate, the proportion of special climate, and the proportion of abnormal climate, to form a climate factor that measures the level of natural conditions in Beijing. The statistical results are shown in Table 8.
Climate factors in Beijing
| Climate proportion in Beijing | Proportion |
| Normal climate proportion (above 10 °C) | 62.79% |
| Special climate proportion (-10 to 10 °C) | 36.94% |
| Abnormal climate proportion (below -10 °C) | 0.27% |
Model assumptions
This paper primarily investigates the impact of policy environment, energy prices, and automotive technology changes on the benefits, market share, and industry output value of the electrification of LDLVs in the northern region of China. The main assumptions are as follows: (1) The model simulation step is 1 year, with the total model cycle spanning from 2021 to 2030; (2) In the process of constructing the model, some relatively minor factors, such as personnel, management, and administrative costs, will be excluded; (3) Within the simulation time frame, except for the period of the pandemic in
Comprehensive benefit model causal relationship diagram
Building upon the previously defined scope of the systematic research and the basic assumptions for model construction, this study has created a causal relationship diagram for the electrification path of LDLVs. The diagram allows researchers to visually identify the causal logic between key elements in the market system and the corresponding systemic feedback mechanisms. The specific causal relationship diagram is shown in Figure 2.
Figure 2. Causal relationship diagram of the comprehensive benefit model for logistics vehicle electrification.
Model stock and flow diagram
Based on the causal loop diagram, a system dynamics stock and flow diagram are constructed using Vensim software. In this model, the output value of the new energy vehicle (NEV) industry, the number of permanent residents, the stock of electric logistics vehicles, and the number of charging stations are the level variables, while the increments of new energy, population increments, stock increments, scrapping amounts, and supporting facility increments are the rate variables of this model. Direct input variables such as purchase subsidies, unit supporting costs, supporting facility investment ratios, the stock of traditional logistics vehicles, and scrap rates are constants, and the rest are auxiliary variables. As shown in Figure 3, under the interconnection and joint action of the above variables, a stock and flow diagram of the electrification path of LDLVs is formed.
Figure 3. Flow diagram of the benefit evaluation model system for logistics vehicle electrification.
As shown in Figure 4, the Economic Benefit Subsystem flow diagram of the economic benefit subsystem includes one level variable, namely the number of charging stations (NS), and its corresponding rate variable is the increment of supporting facilities (IS). Auxiliary variables include investment in electric logistics vehicle supporting facilities (FIS), search time (TF), the number of lifecycle replenishment times
Figure 4. Flow diagram of subsystem (A) Flow diagram of economic benefit subsystem, (B) Flow diagram of environmental benefit subsystem, (C) Flow diagram of market efficiency subsystem.
In the environmental benefit subsystem, there are two auxiliary variables: carbon price (Pc) and the environmental benefit of logistics vehicle electrification (EE), with the whole vehicle carbon emissions of electrified logistics vehicles (Nc) being a constant. The purpose of establishing the environmental benefit subsystem is to evaluate the environmental management cost savings that can be achieved by replacing traditional logistics vehicles with electric logistics vehicles during the process of electrification. Additionally, environmental benefits play a key role in the comprehensive benefits of logistics vehicle electrification and are an indispensable part of the overall assessment.
In the market efficiency subsystem, there are three level variables: New Energy Vehicle Industry Output Value (NEVPV), Permanent Resident Population (PRP), and Electric Logistics Vehicle Inventory (ELVI); the rate variables for these level variables are New Energy Increment (NEI), Population Increment (NPGR), Electric Logistics Vehicle Increment (ELVG), and Electric Logistics Vehicle Scrapping (ELVS); the Traditional Logistics Vehicle Inventory (DLVI) and the Scrapping Rate (DLVSR) are constants, with the rest being auxiliary variables. The market benefit model reflects the impact of the increase in the comprehensive revenue of the whole vehicle brought by the electrification of logistics vehicles on the new energy logistics vehicle industry and the new energy industry. At the same time, the increase in the output value of the new energy industry also plays an important role in the process of electrification of logistics vehicles.
Model validity test
This research has identified three pivotal indicators for evaluating the model’s validity: Beijing’s new energy industry output value, the permanent resident population, and the number of charging stations. The temporal scope of our validity assessment encompasses the period from 2021 to 2023. The assessment is conducted annually, thus covering a three-year period. A meticulous analysis of the data presented in the table yields a test conclusion. The simulation error for the permanent resident population is a negligible 0.013%, which is attributed to the model’s reliance solely on the natural growth rate to predict population changes, thereby ensuring a high degree of accuracy between the estimated and actual values. The specific test results are shown in Table 9.
Validity test results of comprehensive benefit model of logistics vehicle electrification
| Time (Year) | 2021 | 2022 | 2023 |
| Permanent resident population (ten thousand people): estimated value | 2,188.60 | 2,184.59 | 2,180.30 |
| Permanent resident population (ten thousand people): actual value | 2,188.60 | 2,184.30 | 2,180.00 |
| Permanent resident population (ten thousand people): error value | 0 | 0.0001 | 0.0001 |
| New energy vehicle industry output value (billion yuan): estimated value | 77.90 | 178.09 | 205.39 |
| New energy vehicle industry output value (billion yuan): actual value | 77.90 | 184.40 | 206.53 |
| New energy vehicle industry output value (billion yuan): error value | 0 | -0.0342 | -0.0055 |
| Number of charging stations (units): estimated value | 3,990 | 5,472 | 6,956 |
| Number of charging stations (units): actual value | 3,990 | 5,500.000 | 6,700 |
| Number of charging stations (units): error value | 0 | -0.0050 | 0.0383 |
Scenario settings
Recent studies indicate that electricity prices, vehicle technology advancements, and policy support are critical factors influencing the electrification rate of logistics vehicles, particularly in regions with distinct climatic challenges, such as Northern China[40,41]. The cold climate in this region significantly impacts battery performance and charging efficiency, making these factors even more crucial. Key parameters for these scenarios were derived from projections by the International Energy Agency[42], China’s 14th Five-Year Plan (2021-2025)[43], and Bloomberg New Energy Finance (BNEF, 2023)[44], with special attention to their implications for Northern China. The IEA report projects a doubling of China’s renewable energy capacity by 2030, which could lead to reduced electricity costs, crucial for offsetting the higher energy consumption in colder climates. BNEF’s analysis shows an 89% reduction in battery pack prices from 2010 to 2022, supporting projections for improvements in range and cold-weather performance. The Five-Year Plan outlines targets for new energy vehicle adoption and infrastructure development, which this study interpreted in the context of Northern China’s unique challenges.
To analyze and simulate the dynamic impacts of policy optimization adjustments, technological measures, and energy price fluctuations, Gillingham et al. (2020) divided policy scenarios into a baseline scenario, accelerated electric energy substitution, and low-speed electric energy substitution to simulate the effects of policy changes on the implementation of new energy vehicle electrification[45]. This paper selects three policy variables: purchase subsidies, range mileage, and electricity price fluctuations for comparative analysis.
Baseline Scenario: This scenario is calibrated to mirror current conditions, with the subsidy phase-out rate set at a moderate 10% per annum. It represents a stable evolution of the market, reflecting the status quo of policy support and technological capabilities. The range mileage for LDLVs is benchmarked at
Low-Speed Electrification Scenario: In this scenario, we explore a more conservative trajectory of electrification, characterized by the absence of purchase subsidies, reflecting a scenario where initial policy support has ceased. The range mileage is projected to increase by a modest 5% annually, acknowledging a slower pace of technological advancement. Electricity pricing is expected to hover at 1 yuan/kW·h[46], representing a gradual adjustment in line with market conditions. This scenario examines the resilience and self-sustainability of the LDLV market in the face of reduced policy incentives.
High-Speed Electrification Scenario: Conversely, this scenario envisions an aggressive push toward electrification, with subsidies phasing out at an accelerated rate of 5% per annum, indicative of a market gaining momentum and requiring less fiscal support. Technological progress is anticipated to be robust, with range mileage increasing by 10% each year, underscoring the potential of breakthroughs in battery technology and energy efficiency. The electricity price is optimistically projected to drop to
The model scenario settings are as shown in the Table 10.
Model scenario settings
| Scenario | Purchase subsidy phase-out rate | Range mileage (km) | Electricity price (yuan/kW·h) |
| Baseline | 10% per year | 323.54 | 1.5 |
| High-speed electrification | 5% per year | Annual increase of 10% | 0.7 (expected future price) |
| Low-speed electrification | No subsidy | Annual increase of 5% | 1.0 (expected future price) |
RESULTS
Sensitivity analysis under single scenarios
The core analysis of this study focuses on electricity prices, vehicle range, and policy subsidies as primary factors influencing LDLV electrification in Northern China. An expanded sensitivity analysis was conducted to address a broader range of variables relevant to Northern China’s unique context.
Sensitivity analysis of purchase subsidy variations
As shown in Figure 5, Uncertainty in the NEV sector, particularly surrounding purchase subsidy adjustments, is scrutinized through the lens of this study. The pace of technological innovation, the stringency of policy enforcement, and the unpredictability of market demands are pivotal factors that sway the course of vehicle electrification. This analysis reveals a nuanced relationship between subsidy policies and the projected growth of the NEV industry. The Baseline Scenario, with a gradual subsidy phase-out, forecasts a steady 9.23% growth in comprehensive benefits by 2030. In stark contrast, the High-Speed Electrification Scenario, with an ambitious reduction in subsidies, anticipates a more robust growth rate of 15.55%, suggesting an industry primed for swift technological adoption. Conversely, the Low-Speed Electrification Scenario, devoid of subsidies, foresees a decline, illustrating the market’s reliance on policy support. These insights, depicted in this comparative analysis, underscore the significance of balanced policy mechanisms and the imperative for technological advancements to align with market responsiveness. The study’s findings advocate for strategic policy formulation that considers the interplay of these uncertainties, ensuring the NEV sector’s sustainable progression.
Figure 5. Sensitivity analysis of purchase subsidy variations.
Uncertainty analysis of range mileage variation
As shown in Figure 6, this paper explores the uncertainty surrounding range mileage variations and their impact on the NEV industry’s growth, set against a Baseline Scenario with an initial electric light-duty logistics vehicle (LDLV) range of 323.54 km. The analysis juxtaposes a Low-Speed Electrification scenario, with a 5% annual range increase, against a High-Speed scenario, with a 10% increase. The Low-Speed scenario forecasts a measured growth in comprehensive benefits, reaching 4.89% by 2030, indicative of a more tempered advancement in technology. Conversely, the High-Speed scenario, with its accelerated range improvements, projects a markedly higher growth rate of 19.53%, signifying the potential for swift market adoption of LDLVs. This delineated the NEV industry’s sensitivity to technological progress. The Baseline Scenario serves as a reference, while the variance in range mileage underscores the critical role of innovation in shaping market trajectory.
Figure 6. Sensitivity analysis of range mileage variation.
Uncertainty analysis of different electricity prices
As shown in Figure 7, this paper investigates the uncertainty associated with different electricity prices and their impact on the NEV industry, particularly focusing on comprehensive benefits by 2030 in comparison to the baseline scenario. It is worth noting that our current electricity price model uses the actual charging costs at commercial charging stations, which include both the electricity price and service fees. The analysis examines the effects of varying electricity prices on the industry’s growth, efficiency, and overall economic viability. The study considers a range of electricity prices, with
Figure 7. Sensitivity analysis of electricity price changes.
Expanded sensitivity analysis
Findings indicate that charging infrastructure improvements could significantly impact adoption rates, with a 20% increase in charging station density, potentially leading to a 5%-10% increase in LDLV adoption. Advancements in cold weather battery performance, such as a 15% increase in low-temperature range, could result in a 7%-12% increase in market penetration. Grid reliability and renewable energy integration also play crucial roles, with combined improvements potentially enhancing LDLV adoption by 3%-6%. Regional economic growth above average could drive 8%-13% higher adoption rates, while stricter emissions standards in urban areas could boost LDLV adoption by 10%-15%.
To address the impact of excluding minor factors such as administrative costs, a sensitivity analysis was conducted. Administrative costs, estimated at 1%-3% of total vehicle ownership costs, were incorporated into the model. Results showed that including these costs led to a marginal increase in the overall perceived cost of ownership, ranging from 0.8% to 2.5%. This slight increase did not significantly alter the comparative advantage of electric LDLVs over conventional vehicles in most scenarios. However, in cases where the cost difference between electric and conventional LDLVs was already narrow, the inclusion of administrative costs could delay the break-even point by 3-6 months.
In addition to the aforementioned factors, considering the economic and environmental impacts of battery recycling is crucial for a comprehensive understanding of the total cost of ownership and environmental footprint of electric LDLVs, especially given the shorter lifespan of commercial vehicles in this study
Simulation results under combined scenarios
As shown in Figure 8, the combined scenarios for the development of electrified LDLVs project a comprehensive benefit of 4.499 million yuan for the whole vehicle, a market share of 48.17% for electric LDLVs, and an industry output value of 60.015 billion yuan by 2030 under the baseline electrification scenario. This reflects a steady increase in market penetration and gradual expansion of the industry’s output value due to current policies and technological conditions. The convergence of policy support and market acceptance propels this trend, laying a foundation for sustainable industry growth. The low-speed electrification scenario further estimates a comprehensive benefit increase to
Figure 8. Simulation results of combined scenarios for LDLV electrification.
The market and industry progression of electrified LDLVs is subject to a confluence of factors, including technological innovation, policy support, market demands, and competitive industry strength. Key drivers of electrification include advancements in range mileage, reductions in battery costs, development of charging infrastructure, and supportive government policies. As environmental consciousness grows and the need for sustainable transportation solutions intensifies, so does the market demand for electric vehicles. The expansion of industry output value signifies not only a broadening market scale but also an ascent in the industry’s global competitiveness.
Comparative analysis of predicted market share
This study presents a comparative analysis of the predicted market share for new energy light trucks by 2030, featuring forecasts from a range of sources. Figure 9 offers a visual juxtaposition of these predictions, including those from “Made in China 2025,” the “Automotive Industry Green Low-Carbon Development Roadmap 1.0,” and Marianna Rottoli’s 2021 study, alongside the scenarios modeled in this paper: Baseline, Low-Speed Electrification, and High-Speed Electrification. The estimates from this study project a market share of 48.17% for the Baseline scenario, with the Low-Speed and High-Speed Electrification scenarios forecasting increases to 54.18% and 75.29%, respectively. To assess the consensus and variance among these forecasts, this paper calculates and presents the normalized average of the scenarios as a red dashed line in Figure 9, indicating the general trend of the predictions. Together, these data points and the trend line provide a comprehensive view of the industry’s potential, as forecasted by different models.
Figure 9. Side-by-side comparison of market share predictions for new energy light trucks by 2030[48].
CONCLUSION AND POLICY IMPLICATION
This study provides a comprehensive analysis of the electrification of LDLVs in Northern China, based on the PCO model. Our findings reveal several key insights: (1) Economic viability: Despite higher initial purchase and intangible costs, electrified LDLVs demonstrate significant economic advantages. The overall cost benefit reaches 76,300 CNY, primarily driven by substantial energy cost savings; (2) Impact of electricity pricing: Energy pricing emerges as a critical factor influencing future market dynamics. Compared to the baseline electrification scenario, market share projections for 2030 increase by 30.71% and 46.19% in low and high-speed electrification scenarios, respectively, under varying electricity prices. Notably, electricity pricing is identified as a key economic lever that could accelerate the transition to electric vehicles; and (3) Promising future for LDLV electrification: Even in the face of an inevitable decline in government financial and economic policy support, our projections indicate a positive trajectory for electrified LDLVs. Considering scenarios of energy price fluctuations and technological breakthroughs, market share is expected to reach 54.18% and 75.29% by 2030 in low and high-speed electrification scenarios, respectively.
To capitalize on these benefits, we recommend that governments implement targeted policies. Specifically, subsidy phasing-out should be gradual to sustain market growth. Electricity pricing must be competitive to reduce operational costs for LDLVs. Additionally, governments should incentivize the rapid deployment of charging infrastructure to mitigate range anxiety and bolster consumer confidence in electric vehicles. These measures will not only expedite the transition to sustainable urban logistics but also stimulate the new energy vehicle sector’s development.
The research opens avenues for future studies to delve into the long-term economic impacts of vehicle electrification, encompassing lifecycle costs and battery degradation effects. It is essential to continue monitoring the evolution of electric vehicle technologies and their influence on performance and cost. Additionally, tracking market dynamics, including consumer preferences and competitive industry shifts, will provide valuable insights into consumer adoption patterns. Evaluating the effectiveness of existing and forthcoming policies on electrification adoption rates and usage is crucial for refining strategic approaches. Furthermore, assessing the comprehensive environmental footprint of electric vehicles throughout their lifecycle will contribute to the holistic understanding of their sustainability profile.
Limitations of the study
While this study provides valuable insights into LDLV electrification in Northern China, several limitations should be acknowledged. The geographic focus on Northern China may limit the generalizability of our findings to regions with different climatic and policy environments. Our projections for 2030 may not capture long-term technological breakthroughs or policy shifts. Additionally, the PCO model, while comprehensive, necessarily simplifies some complex real-world interactions and decision-making processes.
Expanding on the limitations, it is important to note that our analysis did not account for the nuances of peak/off-peak electricity pricing, which could significantly impact the operational costs and charging strategies for LDLVs. The economic model could be further refined by incorporating these pricing variables to provide a more accurate representation of cost implications. Moreover, the study’s scope did not extend to the end-of-life management of batteries, including recycling and disposal, which are critical factors in the overall environmental and economic assessment of LDLV electrification. The omission of these aspects may underestimate the long-term costs and environmental burdens associated with battery usage in LDLVs. Despite these limitations, our study provides a robust foundation for understanding LDLV electrification in Northern China and offers valuable insights for policymakers and industry stakeholders.
DECLARATIONS
Authors’ contributions
Made substantial contributions to the conception and design of the study, and performed data analysis and interpretation: Hao X, Zhou D
Performed data acquisition and analysis: Zhong R, Li S
Provided crucial input in refining the study’s assumptions and enhancing the methodology: Meng X
Provided administrative, technical, and material support, and substantively revised the work: Liu B
All authors have read and approved the final manuscript.
Availability of data and materials
The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.
Financial support and sponsorship
This study was supported by the National Key R&D Program of China (2022YFB2503504), the National Natural Science Foundation of China for Young Scholars (72304031), and Fundamental Research Funds for the Central Universities in China (FRF-TP-22-024A1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Conflicts of interest
Meng X is affiliated with China Automotive Technology and Research Center Co., Ltd. The authors declared that this affiliation does not influence the research. The other authors have declared that they have no conflicts of interest.
Ethical approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Copyright
© The Author(s) 2024.
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Cite This Article
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Hao, X.; Zhou, D.; Zhong, R.; Li, S.; Meng, X.; Liu, B. Electrification pathways for light-duty logistics vehicles based on perceived cost of ownership in Northern China. Carbon Footprints. 2024, 3, 15. http://dx.doi.org/10.20517/cf.2024.24
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