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

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 and lacking a comprehensive
[11]
assessment of intangible factors [12,13] .

In the realm of light commercial vehicles, an influx of literature has emerged, employing diverse analytical
[14]
techniques such as the Analytic Hierarchy Process (AHP) , 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 . These methods have been adept at revealing the multifaceted benefits
[19]
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
[26]
limitations [12,24,25] . While studies have begun to address the economic 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 , yet there is a dearth of research on the economic implications of
[35]
[37]
these technical hurdles and the potential for policy and technological interventions to overcome them.
[6]
[36]
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,