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Page 10 of 22 Hao et al. Carbon Footprints 2024;3:15 https://dx.doi.org/10.20517/cf.2024.24

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 2021-2022, the economy
maintains stable growth. The output value of new energy vehicles promotes the growth of the regional
economy’s total output value, and its positive drive leads to an increase in total traffic freight volume. The
demand for electric logistics vehicles will grow with the increase in highway operational freight volume; (4)
Investment in R&D and infrastructure may affect production costs, charging time, and the range capability
of new energy logistics vehicles, contributing to the improvement of technological levels and the perfection
of supporting facilities; (5) The calculated prices used in the model are based on current prices, without
considering future price changes; and (6) The model does not consider the specific profits or losses of
market operators unrelated to the process of logistics vehicle electrification.

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.

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.

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
(Ne), range (SE), replenishment time (Te), replacement cost (CR), intangible cost (CI), purchase cost (CP),
energy cost (CE), and economic benefits of logistics vehicle electrification (EF); constants include the
supporting facility investment ratio (QI), the proportion of normal climate (QW1), and the proportion of
special climate (QW2).