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Hao et al. Carbon Footprints 2024;3:15 https://dx.doi.org/10.20517/cf.2024.24 Page 13 of 22

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.

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 , China’s 14th Five-
[42]
[43]
[44]
Year Plan (2021-2025) , and Bloomberg New Energy Finance (BNEF, 2023) , 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 . This paper selects three policy
[45]
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 323.54 km,
and the electricity price is anchored at the prevailing rate of 1.5 yuan/kW·h. This scenario offers a reference
point to gauge the incremental impacts of the other, more dynamic scenarios.

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
0.7 yuan/kW·h , reflecting anticipated economies of scale in renewable energy production and grid
[46]
modernization. This scenario aims to capture the potential for rapid market expansion and industry
transformation with strong policy and technological tailwinds.

The model scenario settings are as shown in the Table 10.