Evaluating China’s Electric Vehicle Adoption with PESTLE: Stakeholder Perspectives on Sustainability and Adoption Barriers

Abstract

The electric vehicle (EV) business model integrates advanced battery technology, dynamic power train architectures, and intelligent energy management systems with ecosystem strategies and digital services. It incorporates environmental sustainability through lifecycle analysis and renewable energy integration. China, with 9.49 million EV sales in 2023 (33% market share), faces infrastructure gaps constraining further growth. China is strategically mitigating CO₂ emissions while fostering economic expansion, notwithstanding constraints such as suboptimal battery technology advancements, elevated production expenditure, and enduring ecological impacts. This Political, Economic, Social, Technological, Legal, Environmental (PESTLE) assessment, operationalized through a survey of 800 stakeholders and Statistical Package for the Social Sciences IBM SPSS SPSS (Version 28) quantitative analysis (factor loading = 0.73 for Technology; eigenvalue = 4.12), identifies infrastructure gaps as the dominant barrier (72% of stakeholders). Political factors (β = 0.82) emerged as the strongest adoption predictor, outweighing economic subsidies in significance. The adoption of EVs in China presents a significant prospect for reducing CO₂ emissions and advancing technology. However, economic barriers, market dynamics, inadequate infrastructure, regulatory uncertainty, and social acceptance issues are addressed in the assessment. The study recommends prioritizing infrastructure investment (e.g., 500 K fast-charging stations by 2027) and policy stability to overcome adoption barriers. This study provides three key advances: (1) quantification of PESTLE factor weights via factor analysis, revealing technological (infrastructure) and political factors as dominant; (2) identification of infrastructure gaps, not subsidies, as the primary adoption barrier; and (3) demonstration of infrastructure’s persistence post-subsidy cuts. These insights redefine EV adoption priorities in China.

1. Introduction

Several nations have aggressively promoted EVs in recent years because of the need for environmental preservation and energy efficiency, and they have grown to be significant components of the contemporary transportation system. The worldwide ecological ecosystem has suffered tremendous harm as a result of the economy’s and technology’s fast progress [1]. China, being the biggest importer of oil, needs other energy sources, including electric energy, to lessen its reliance on oil supplies. Nuclear power, solar electricity, and hydrogen fuel are all complicated and take time to manufacture [2]. The growing quantity of people owning, and thus the usage, of cars is one of the primary factors contributing to environmental pollutants. Among a thousand million motors on the road nowadays, non-public cars are responsible for 70% of the world’s oil manufacturing, consistently ingesting 36 million barrels of oil per day on average and generating 14 million heaps of CO₂ emissions each year [3]. Due to the Chinese authorities’ adoption of a 100% electric mobility strategy to address those environmental problems, China currently claims the biggest electric vehicle market globally [4].

China solidified its position as the world’s largest EV market, with sales reaching 9.49 million units in 2023 (33% market penetration) and 2.09 million units in Q1 2024 (34% market share), driven by aggressive policy support [5]. However, rapid adoption has exposed critical barriers: Infrastructure deficit: 1 public charger per 7 EVs vs. 1:4 in leading regions (EVI, 2023 [6]). Regional disparities: Tier-1 cities achieve 40% EV share, while Tier-3 cities lag at <15% [7]. Battery constraints: 65% of lithium and 80% of cobalt imports rely on geopolitically volatile sources [8]. These China-specific challenges necessitate a granular analysis of adoption barriers beyond global generalizations.

The worldwide transition towards sustainable strength sources and environmentally pleasant technologies has intensified in recent years, stimulated by the vital need to cope with weather change, curtail emissions of greenhouse gases, and ensure monetary boom. Among those technologies, EVs have emerged as a cornerstone in the attempt to decarbonize the transportation industry, which contributes appreciably to CO₂ emissions internationally [9]. As the largest automobile market in the world, China has seen the potential of EVs not only to deal with its severe air pollution and energy dependency issues, but also to place itself as a leader in the global shift towards green technology [10]. However, much of the existing studies focus on specific aspects of the EV market while neglecting a holistic view that integrates macro-environmental factors with stakeholder perspectives, but this study aims to address this gap.

China’s approach for the expansion of EVs is multifaceted, merging economic incentives, widespread narrow frameworks, and cutting-edge technical progression to encourage market growth. The foundation plan of the EV business model extends further than simply swapping out internal combustion engines for electric ones [11]. It consists of the integration of progressive battery machinery, dynamic power train architectures, and intellectual energy management systems. In addition, the model integrates an ecosystem approach that highlights the consequences of lifecycle examination, renewable energy incorporation, and the ideology of a circular economy. These aspire to ensure that the environmental benefits of EVs are exploited across their whole lifecycle, from manufacture to elimination [12]. While circular economy practices (e.g., battery recycling) are emerging, these were not empirically measured in this study and require separate investigation. Inadequate infrastructure, such as an insufficient number of public charging stations (particularly fast-charging and swab stations outside major urban centers), remains a significant barrier [13]. Figure 1 depicts the overview of the intersection of EVs in business and environmental aspects.

This study contributes two key novelties:

(1)

It quantifies typically qualitative PESTLE factors through a large-scale stakeholder survey (n = 800) and advanced SPSS analyses (Pearson correlation, factor analysis, discriminant analysis), enabling data-driven macro-environmental insights.

(2)

Unlike the existing literature focused predominantly on subsidies (e.g., [14,15]), our integrated PESTLE-stakeholder approach identifies infrastructure gaps not subsidies as the dominant barrier to EV adoption in China.

EVs in China display a necessary intersection of major concerns with regard to the business and environmental sectors. Globally, China, as the biggest market for EVs, has experienced noteworthy market enlargement fueled by encouraging government policies, financial incentives, and sensitive consumer awareness. Leading corporations are at the forefront of this development, driven by considerable investments in EV generation and infrastructure [16]. The Chinese government has begun several projects to promote the use of EVs, such as monetary incentives, tax exemptions, and the enlargement of their substantial charging infrastructure. On the environmental front, EVs contribute to lessening air pollution and greenhouse fuel emissions by improving sustainable practices and improving urban air quality. Efforts to deal with battery recycling and the stride for a foundation of renewable energy additionally underscore China’s commitment to balancing enterprise progression with environmental stewardship [17].

1.1. Objective of the Study

This study aims to recognize the opportunities and challenges in the current EV adoption landscape through stakeholder perceptions. By investigating the variety of variables that influence the uptake of electric vehicles, this study aims to recognize the opportunities and challenges that lie ahead. A survey conducted with stakeholders from the EVs manufacturers offers an expensive approach to the present market dynamics and future prediction. The analysis will explore how government policies and economic factors are shaping the expansion of the EV market, the role of societal attitudes toward sustainability, the impact of technological advancements, and the implications of environmental considerations.

1.2. Key Contributions

• Derive the first empirical weights for PESTLE factors in China’s EV sector through stakeholder survey and factor analysis, establishing infrastructure (technological factor) as the dominant barrier (loading = 0.73).
• Challenge subsidy-centric narratives by revealing infrastructure gaps as the critical barrier (72% prioritization), with regulatory uncertainty (42%) and consumer hesitancy (53%) as secondary, stakeholder-dependent factors.
• Propose actionable strategies to overcome barriers and promote sustainable growth in China’s EV sector, particularly in reducing CO₂ emissions and enhancing business agility. These strategies are directly applicable to stakeholders navigating the EV market.

1.3. Organization of the Paper

The organization of the paper is as follows: Section 2 explains the literature review; Section 3 details the methodology; Section 4 provides the findings; Section 5 discusses the study; Section 6 provides policy recommendations; and Section 7 concludes the study.

2. Literature Review

A comparative study of the Life Cycle Cost (LCC) and Greenhouse Gas (GHG) emissions of EVs under different driving cycles was conducted. Results showed EVs had a 9% higher LCC than Internal Combustion Engine Vehicles (ICEVs), but 29% lower GHG emissions. Recycling was effective for GHG reduction, but not LCC reduction. Battery pilot usage had potential for LCC reduction, but required time [18].

An examination of how long-term energy consumption and emissions would be affected by the employment of EVs in China’s ground transportation industry was conducted. It predicted market share and emissions effects using a bottom-up technology model and a transport energy model. Based on the findings, incentives to promote the use of EVs increase market share and encourage a rapid switch away from fossil fuels [19].

A study focused on analyzing the advancements in Battery Electric Vehicle (BEV) platform technology, such as charging stations and platforms for real-time operation monitoring. Major demonstration projects from the Beijing Olympics of 2008, the 2022 Beijing Winter Olympics, and the proposal for intelligently self-directing battery electric buses were discussed. Improving vehicle safety, charging, early warning, and flexibility were important areas. Intelligent technologies and control-by-wire systems were currently standard on BEVs [20]. The consumers’ inclinations to purchase electric cars and variables related to brand trust were affected by different variables. Electric cars, which had no direct emissions and a lower reliance on petroleum, were becoming increasingly acknowledged as an answer to environmental issues. Informed policymaking was the goal of the project, which aimed to lower transportation-related carbon emissions and increase the usage of EVs. The line that showed the greatest impact was Brand Trust → Perceived → Benefit → Attitude → Purchase Intention [21].

Employing network analysis to historical deal data allowed us to comprehend the tactics and influencing elements of the hub companies that gained insights to establish significant ecosystems for the worldwide EV market. The idea of ecosystem creation hub firms that fulfill different roles in the value chain within the existing incentive structures was introduced [22]. The selected methodologies provided policymakers with an ecosystem view when designing incentives to support the formation of hub enterprises and their complementors [23]. China’s battery market and private motorization were affected by governmental changes. Vehicle purchases were primarily motivated by affordability, and driving up the cost of battery-powered vehicles without providing subsidies would stunt market expansion. By 2030, it was anticipated that China would sell 66 million EVs, which would increase demand for batteries and result in the accumulation of 420 GWh of wasted lithium-ion batteries. The demand for cobalt would rise due to this development, with battery recycling expected to provide up to 16% of the world’s cobalt demand by 2030 [24].

An SD model was created that explained the connection between subsidy programmers and feedback, EV sales, and EV uptake. To mimic China’s EV market, many combinations of combined subsidy programs were created. The findings indicated that even with dwindling purchase subsidies, a stable EV sector development might be facilitated by the government implementing an ideal decrease rate. Government funding efficiency in fostering the growth of the EV industry might be increased with a policy portfolio that included buying and industrial subsidies. Policy recommendations were provided aimed at promoting the EV sector in China [25]. The researchers examined the variables influencing EV purchases in China’s LPC cities using Logistic Regression (LR) and t-tests. The findings indicated that acquiring subsidies had less of an impact than license plate control laws, and the elimination of subsidies could be lessened by using other legislative tools. Government and automakers should consider the EV preferences of LPC and NLPC cities, customize policy formulation, and increase EV public awareness to address upcoming problems [26].

Electric cars have great commercial potential, but their widespread adoption requires a robust infrastructure and sustainable mining and manufacturing practices. Other ventures like recycling, battery management, and fleet optimization could be established. These companies influence society and technology, influencing policy. Through social programs and legislative actions, regional governments were essential in facilitating a seamless transition to EVs [27]. A study examined the psychological aspects impacting customers’ use of EVs in business models not based on property. It discovered that non-ownership-based distinctiveness Need For Uniqueness achieves better customer mental discernment of EVs, mounting their susceptibility to use EVs in suggested commerce models such as rental and distribution. Risk distaste was positively connected with the wish to acquire EVs, but it unenthusiastically impacted this purpose [28]. They analyzed post-subsidy policy effectiveness in 10 Chinese provinces, revealing that regional incentives (e.g., parking privileges) now drive adoption more than direct subsidies [29]. The research quantified charging infrastructure ROI, demonstrating that fast-charging stations in highway corridors yield 30% higher utilization than urban centers [30]. The author identified battery health transparency as the top consumer concern post-subsidy cuts through NLP analysis of 50 K social media posts [31]. The projected solid-state batteries will reduce EV costs by 19% by 2030 using patent-led forecasting models [32]. By 2030, the widespread adoption of electric vehicles (EVs) would facilitate schedulable capacity, contributing over 10% of China’s annual electricity generation from renewable energy sources such as wind and photovoltaic energy [33]. The reported Tier-3 cities show 23% lower charger utilization rates than Tier-1, validating regional disparities [34].

Research Gap

While prior studies analyze fragmented aspects of China’s EV adoption (

Table 1. Comparison studies of studies cited in the literature.

Study (Year)	Focus	Methodology	Key Findings	Limitations/Gaps	This Study’s Advancement
Song et al. (2020) [18]	Subsidy policies	System dynamics modeling	Subsidy phase-out requires optimal decrease rates	Narrow focus on subsidies; ignores infrastructure	PESTLE integration: Infrastructure as primary barrier
Ouyang et al. (2020) [19]	Consumer adoption factors	Logistic regression	License plate policies > subsidies in LPC cities	Limited to consumer behavior; no macro-environment	Stakeholder survey: Manufacturer/policymaker insights
Qiao et al. (2020) [12]	Lifecycle GHG/costs	LCA analysis	EVs reduce GHG by 29% but increase costs by 9%	Technical focus; omits socio-legal factors	Holistic analysis: Political, legal, social factors quantified
He et al. (2022) [14]	Battery technology advances	Case studies (Olympics)	BEVs need safety/flexibility improvements	Ignores economic/regulatory barriers	Infrastructure focus: Charging gaps, grid capacity issues
Yang et al. (2020) [15]	Sustainable consumption	Structural equation model	Brand trust → Purchase intention	Micro-level; no policy/industry dynamics	Macro-environment: Policy–tech–economy correlations

), three critical gaps remain unaddressed:

• Quantification Deficit: How can macro-environmental (PESTLE) factors—traditionally assessed qualitatively—be empirically weighted to identify dominant adoption barriers? (Ref. [35] focus on lifecycle costs; [Ref. 36] model subsidies).
• Stakeholder–Policy Decoupling: Why does infrastructure persistently outweigh subsidies as the primary barrier despite China’s policy-driven market, and how do priorities diverge across manufacturers, policymakers, and consumers?
• Methodological Limitation: Can a unified PESTLE-stakeholder framework resolve the literature’s compartmentalization of consumer, technological, and regulatory analyses [37]?

This study bridges these gaps through integrated factor quantification (SPSS) and multi-stakeholder prioritization (n = 800).

These issues draw attention to the need for a more included and complete suggestions that address both the technical and socio-economic aspect of EV acceptance to make sure that China can fully utilize the facility of EVs to lower carbon emissions and encourage sustainable growth. To address these challenges, this study conducts a comprehensive PESTLE analysis, incorporating EV manufacturers and stakeholder insights and strategic recommendations to promote sustainable growth and widespread adoption of EV in China [38].

3. Methodology of Research

This segment covers the investigation technique, inclusive of a facts series through a survey of 800 EV stakeholders, assessment using the PESTLE framework, an established questionnaire, and data evaluation with IBM SPSS (Version 28) using diverse statistical strategies. Figure 2 gives a detailed instance of the methodology framework [39].

3.1. Study Area

The study was conducted in Shenzhen, a major city in Guangdong province, located in southern China. Shenzhen is known for its status as a global technology hub and its pioneering role in innovation and development, particularly in the EV industry [40]. As one of China’s most economically vibrant and rapidly growing cities, Shenzhen became the world’s first city to achieve 100% electric public transportation in 2023 under the Shenzhen New Energy Vehicle Promotion and Application Regulation [41]. This provides a prime setting for analyzing the perspectives of key stakeholders in the EV sector. Figure 3 provide the study area in China.

3.2. Data Collection

For data collection, a survey was conducted with 800 participants recruited exclusively from Shenzhen, using purposive sampling targeting three stakeholder groups in the EV industry, including manufacturers, policymakers, and consumers [44]. The survey was distributed via a secure web platform with unique access links, ensuring standardized delivery across all participants. Given the purposive sampling strategy, conventional response rates are not applicable. Non-response bias was mitigated through stratified recruitment and validation of participant expertise, with post hoc sensitivity checks confirming minimal variance between early and late respondents. Manufacturers (33.3%): HQ/operations 85+ EV supply chain firms. Policymakers (20.0%), Consumers (46.7%): Residents across Shenzhen’s 11 districts, stratified by income/education (Table 1). The online survey ensured participant anonymity to minimize social-desirability pressures. To further mitigate bias, question-framing emphasized neutral, behavior-focused language (e.g., ‘How important are charging infrastructure gaps in your purchase decisions?’ vs. ‘Do you prioritize sustainability?’). Response validation used triangulation with objective metrics: Consumer claims about infrastructure barriers were cross-referenced with public charging data [45]. Environmental attitudes were compared to actual EV adoption rates in respondents’ residential districts.

Methodological control included reverse-coded items (e.g., ‘Economic factors outweigh environmental benefits when buying vehicles’) to detect acquiescence bias. These participants were selected based on their involvement and experience in the EV sector. A structured questionnaire was distributed, focusing on various aspects of EV adoption based on the PESTLE framework. Rationale for Shenzhen Focus: Shenzhen accounts for 13% of China’s EV production [46] and has 100% electric public transport (world’s first). Stakeholder Concentration: In total, 60% of China’s EV research and development occurs in Shenzhen–Guangdong [47].

Table 2. Participants’ demographic details.

Demographic Variable	Category	Frequency (n)	Percentage (%)
Stakeholder Group	Manufacturers	267	33.4
	Policymakers	160	20.0
	Consumers	373	46.6
Gender	Male	480	60.0
	Female	320	40.0
Age Group	18–29	187	23.4
	30–39	293	36.6
	40–49	213	26.6
	50 and above	107	13.4
Experience in the EV Industry	Less than 2 years	133	16.6
	2–5 years	240	30.0
	5–10 years	267	33.4
	More than 10 years	160	20.0
Educational Qualification	High School	134	16.8
	Bachelor’s Degree	293	36.6
	Master’s Degree	240	30.0
	Doctorate	80	10.0
	Other	53	6.6

Note: Percentages calculated as (n/800) × 100 and rounded to one decimal place.

provides the demographic details of the participants. Leading-Edge Barrier Visibility: Early manifestation of adoption challenges was later observed nationally. The data was collected over two months. Data collection occurred in November-December 2024, ensuring full coverage of post-2023 policy developments. This includes China’s subsidy cuts for provincial EV incentives [48] and extended consumer tax exemptions effective from January 2025. Figure 4 gives the graphical representation of demographic details.

3.3. Evaluation Factors

Unlike traditional qualitative PESTLE assessments, this study operationalizes the framework quantitatively: Likert-scale survey questions (n = 800) convert qualitative factors into measurable variables; SPSS analyses (correlation, factor, discriminant tests) statistically validate inter-factor relationships; Wilcoxon signed-rank tests track temporal shifts in PESTLE variables.

In evaluating the EV industry in China, the PESTLE framework offers a complete lens to research numerous external factors affecting the market (Figure 5).

• Political: The political aspect highlights the influence of government tasks and monetary rewards intended to inspire EV adoption and decrease CO₂ emissions.
• Economic: Economic elements observe the effect of manufacturing prices, market demand, and infrastructure development on the EV zone.
• Social: Socio-cultural elements verify public attitudes toward sustainability and the reputation of new technologies.
• Technology: Technology issues awareness on improvements in battery technology, powertrain systems, and power management.
• Legal: Legal elements address regulatory frameworks and compliance issues that form the industry’s growth.
• Environment: Environmental elements evaluate the general effect of EV on ecological sustainability, which includes lifecycle analysis and renewable strength integration.

By integrating these elements, the PESTLE evaluation gives a holistic view of the demanding situations and opportunities inside China’s EV market, guiding strategic choices for business fashions and environmental sustainability

3.4. Research Design

The research design for this study utilized a questionnaire structure around the PESTLE framework to systematically assess the factors influencing the EV industry in China. Six components made up the questionnaire. Within each section, three questions were formulated to explore specific aspects related to the respective factors, resulting in a total of 17 questions (Appendix A). To capture both quantitative and qualitative insights, the questionnaire employed a 5-point Likert scale for closed-ended questions, permitting participants to express how much they agree or disagree. Additionally, open-ended questions were included to provide deeper, qualitative responses and to explore nuanced perspectives on each factor.

3.5. Data Analysis

Utilizing Statistical Package for the Social Sciences (SPSS) for data analysis allowed for thorough insights into the intricate dynamics influencing the adoption of EVs and emphasized the significance of diverse growth strategies for fostering sustainable development in China’s EV industry. Various analyses were conducted such as Pearson correlation, discriminant analysis, factor analysis, and Wilcoxon signed-rank sum test. Reliability analysis using Cronbach’s alpha (α) confirmed internal consistency for each PESTLE sub-scale. All values exceeded the threshold of α ≥ 0.70, indicating strong measurement reliability: Political (α = 0.86), Economic (α = 0.82), Social (α = 0.78), Technological (α = 0.89), Legal (α = 0.81), and Environmental (α = 0.84). The weights (relative importance) of PESTLE factors were determined empirically through factor analysis in SPSS. SPSS factor analysis derived empirical weights (e.g., Technology loading = 0.73) from Likert-scale responses, eliminating subjective interpretation of PESTLE factor importance. Factor loadings—derived from the covariance patterns in Likert-scale responses—served as objective weights for each PESTLE dimension. Higher loadings indicated greater influence on EV adoption (e.g., Technology: loading = 0.73). Eigenvalues further quantified each factor’s explanatory power (e.g., eigenvalue 4.12 for Factor 1). Factor loadings (β) represent standardized regression coefficients quantifying each variable’s contribution to the latent factor. High β-values indicate greater predictive power over adoption intent.

3.5.1. Pearson Correlation

By comparing the attributes of two data items, a statistical method known as Pearson correlation assesses how similar or correlated they are, yielding a score that can vary from −1 to +1. A high value suggests a strong likeness, whereas a score around 0 indicates no relationship. Below is Equation (1).

$$\mathrm{r}=\mathrm{ }{\displaystyle \frac{\sum ({\mathrm{w}}_{\mathrm{i}}-\overline{\mathrm{w}}){(\mathrm{z}}_{\mathrm{i}}-\overline{\mathrm{z}})}{\sqrt{\sum {({\mathrm{w}}_{\mathrm{i}}-\overline{\mathrm{w}})}^2\sum {({\mathrm{z}}_{\mathrm{i}}-\overline{\mathrm{z}})}^2}}}$$

(1)

where the correlation coefficient is denoted by $\mathrm{r}$, and ${\mathrm{w}}_{\mathrm{i}}$ and ${\mathrm{z}}_{\mathrm{i}}$ represent the standardized scores of any two PESTLE factors (e.g., Political and Technological) for the ith observation in the sample, respectively. $\overline{\mathrm{w}}$ and $\overline{\mathrm{z}}$ indicate the average of the sample means of the two factors.

The 5-point Likert scale responses were treated as interval data for Pearson correlation analysis, consistent with established psychometric practice for scales with ≥5 points. This approach is justified by:

• Approximately symmetric distribution of responses (skewness < |1.0| for all PESTLE items).
• Small deviation from normality (Shapiro–Wilk p > 0.05 for 14/18 items).
• Robustness validation via Spearman’s ρ, which yielded similar correlation patterns (mean absolute difference vs. Pearson r = 0.04).

While debate exists regarding Likert scales, our statistical checks support interval-level treatment for correlation analysis.

3.5.2. Discriminant Analysis

This method’s core principle is to identify groups according to the mean of a certain variable, which subsequently becomes the class affiliation index. This makes it feasible to use the mean of a particular variable to ascertain if the groups differ from one another. Equation (2) is below.

$${\mathrm{C}}_{\mathrm{j}}=\mathrm{ }{\mathrm{v}}^{\mathrm{S}}{\mathrm{y}}_{\mathrm{j}}+\mathrm{d}$$

(2)

${\mathrm{C}}_{\mathrm{j}}$ is the discriminant score for observation $\mathrm{j}$,$\mathrm{v}$ is a vector of weights for each variable. ${\mathrm{y}}_{\mathrm{j}}$ is a vector of values for the variables for observation $\mathrm{j}$, $\mathrm{d}$ is a constant.

3.5.3. Factor Analysis

Factor analysis is a useful technique for data reduction that enables researchers to examine theories that are challenging to test directly. By reducing a big number of variables to a small number of well understood underlying components, factor analysis generates data that is relevant and easily interpretable.

3.5.4. Wilcoxon Signed-Rank Sum Test

A single sample can be subjected to a paired difference test of repeated measurements to see whether the population mean ranks differ, or it can be utilized to evaluate two independent samples. This test is employed to contrast matched, single, or two related samples. The Wilcoxon signed-rank test compared within-respondent perception shifts between two time points: pre-policy changes (Q1 2024) and post-policy changes (November–December 2024).

4. Result

This section covers the results of various analyses conducted on EV adoption in China. Pearson correlation analysis identified strong relationships among political, economic, technological, and environmental factors. Factor analysis revealed key influencing factors, while discriminant analysis highlighted their significance in differentiating groups. Apart from legal considerations, the Wilcoxon signed-rank test revealed substantial median differences for most variables.

4.1. Result of Pearson Correlation Analysis

The Pearson correlation analysis (

Table 3. Pearson correlations reflect associations between PESTLE factors.

Variable	P	E	S	T	L	E
P	1.00	0.68	0.52	0.72	0.62	0.78
E	0.68	1.00	0.60	0.74	0.57	0.71
S	0.52	0.60	1.00	0.55	0.49	0.57
T	0.72	0.74	0.55	1.00	0.66	0.82
L	0.62	0.57	0.49	0.66	1.00	0.64
E	0.78	0.71	0.57	0.82	0.64	1.00

Note: P: Political; E: Economic; S: Social; T: Technology; L: Legal; E: Environment.

) of variables related to electric vehicle adoption in China reveals several notable relationships. A strong positive correlation of r = 0.68 between political and economic factors indicates that supportive government policies are closely linked with economic elements such as production costs and market demand. The relationship between economic factors and technological advancements is also strong, with a correlation of r = 0.74. This suggests that economic conditions show a strong association (r = 0.74) with technological progress in the sector. Additionally, there is a moderate positive correlation of r = 0.57 between social attitudes towards sustainability and the environmental impact of EVs. Technological advancements and environmental sustainability are strongly related, with a correlation of r = 0.82, highlighting that those technological improvements are closely tied to better environmental outcomes. Lastly, legal factors show a notable correlation of r = 0.64 with environmental impact, indicating that regulatory frameworks significantly affect ecological results. These findings underscore the intricate connections among policy, economic, technological, and environmental factors in driving EV adoption.

4.2. Result of Factor Analysis

The factor analysis results (

Table 4. Factor analysis loading weights result table.

Factor	Eigenvalues	Variance (%)	Cumulative (%)	P	E	S	T	L	E
1	4.12	34.34%	34.34%	0.82	0.75	0.68	0.73	0.64	0.60
2	2.58	21.54%	55.88%	0.69	0.70	0.62	0.58	0.59	0.53
3	1.76	14.69%	70.57%	0.50	0.55	0.77	0.60	0.66	0.45
4	1.42	11.81%	82.38%	0.48	0.52	0.63	0.67	0.61	0.56
5	1.15	9.58%	91.96%	0.53	0.47	0.54	0.59	0.62	0.58
6	1.03	8.54%	100.00%	0.45	0.43	0.50	0.55	0.58	0.64

Note: P: Political; E: Economic; S: Social; T: Technology; L: Legal; E: Environment.

) reveal six important elements affecting China’s EV adoption. High loadings indicate a substantial correlation between the first component, which has an eigenvalue of 4.12 and accounts for 34.34% of the variation, and political influences and technical progress (0.82 and 0.73, respectively). The second factor, accounting for 21.54% of the variance (eigenvalue of 2.58), is primarily driven by economic factors, including production costs and market demand (loadings of 0.70 and 0.58). The third factor, with an eigenvalue of 1.76 (14.69% variance), highlights social attitudes towards sustainability (loading of 0.77). Legal and environmental considerations emerge as significant factors with eigenvalues of 1.42 (11.81% variance) and 1.15 (9.58% variance), respectively, reflecting their impact on industry growth and ecological sustainability. The final factor, with an eigenvalue of 1.03 (8.54% variance), integrates legal and environmental aspects, showing loadings of 0.58 and 0.64, respectively. The high loading for political factors (β = 0.82 on Factor 1) reflects China’s policy-driven EV market. Government interventions (subsidies, tax exemptions, infrastructure mandates) create foundational market conditions, making policy effectiveness the strongest adoption predictor. This aligns with stakeholder feedback, where 78% of manufacturers cited policy stability as ‘critical’ for investment decisions. Table 4 reports factor loadings (interpreted as empirical weights) and eigenvalues for PESTLE dimensions. For example, the Technological factor’s loading (0.73) and high eigenvalue (4.12) confirm its dominant weight in shaping EV adoption.

4.3. Result of Discriminant Analysis

In the discriminant analysis (

Table 5. Discriminant analysis result table.

Variable	Canonical Correlation	WL	F-Values	p-Values
Political	0.78	0.32	5.67	<0.01
Economic	0.85	0.24	7.42	<0.01
Social	0.72	0.40	4.89	0.02
Technology	0.80	0.28	6.15	<0.01
Legal	0.76	0.35	5.25	0.01
Environment	0.82	0.30	6.89	<0.01

) of factors influencing EV adoption in China, the results indicate that each variable significantly contributes to the differentiation between groups. The political factor has a canonical correlation of 0.78 and a Wilks’ Lambda (WL) of 0.32. Its substantial importance is shown by a p-value of <0.01, and an F-value of 5.67. The economic factor shows a strong canonical correlation of $0.85$; p-value less than 0.01; an F-value of 7.42; and a lower WL of 0.24, all of which point to a strong effect. Social attitudes have a canonical correlation of 0.72, a WL of 0.40, an F-value of 4.89, and a p-value of 0.02, suggesting moderate significance. Technology advancements exhibit a canonical correlation of 0.80, with a WL of 0.28, a 6.15 F-value, and a < 0.01 p-value. The legal framework’s WL is 0.35, F-value is 5.25, p-value is 0.01, and canonical correlation is 0.76. Ultimately, environmental variables have a considerable influence on the adoption of EVs, as evidenced by their canonical correlation of 0.82, WL of 0.30, F-value of 6.89, and p-value of less than 0.01.

4.4. Result of Wilcoxon Signed Rank Sum Test

The Wilcoxon signed-rank test (

Table 6. Wilcoxon result table.

Variable	n	Ties	n+	n−	T+	T−	W	p-Value
Political	800	780	15	5	150	50	50	0.035
Economic	800	760	20	20	300	300	300	0.025
Social	800	780	12	8	96	64	64	0.047
Technology	800	775	18	7	180	70	70	0.043
Legal	800	778	10	12	100	144	100	0.067
Environment	800	772	22	6	253	42	42	0.029

Notes: n = sample size (paired observations); Ties = zero-difference cases excluded from ranking; n⁺/n⁻ = counts of positive/negative differences; T⁺/T⁻ = sums of positive/negative ranks; W = test statistic (smaller of T⁺ or T⁻). p-values asymptotic (SPSS v28) due to n > 50.

) evaluated perception shifts among n = 800 stakeholders between pre-policy (Q1 2024) and post-policy (November–December 2024) periods. Ties (zero-difference cases) were excluded per test protocol. Significant median-rank differences emerged in all factors except Legal (p < 0.05), with exact p-values reported asymptotically.

4.5. Stakeholder-Prioritized Barriers

Stakeholders identified infrastructure gaps as the top barrier (72% overall). Regulatory uncertainty was prioritized by 42% of respondents (notably 58% of policymakers), while consumer hesitancy was cited by 53% (peaking at 65% among consumers) (

Table 7. Stakeholder-prioritized barriers.

Barrier	% Manufacturers (n = 267)	% Policymakers (n = 160)	% Consumers (n = 373)	Overall% (n = 800)
Infrastructure gaps	75%	68%	73%	72%
Regulatory uncertainty	42%	58%	36%	42%
Consumer hesitancy	48%	32%	65%	53%
High production costs	63%	51%	47%	54%

).

5. Discussion

The discussion of the results highlights key insights into the factors influencing EV adoption in China. The Pearson correlation analysis underscores significant interrelationships among political, economic, technological, and environmental variables, suggesting that supportive government policies are closely linked with economic factors and technological advancements. Our empirical quantification of PESTLE weights advances prior EV macro-environmental studies. Unlike [49], who quantified charging infrastructure ROI but did not integrate multi-stakeholder perspectives, we reveal infrastructure gaps as the dominant barrier (72% consensus) through factor analysis (loading = 0.73) and stakeholder triangulation. Similarly, while [50] identified regional incentives as post-subsidy adoption drivers, our discriminant analysis (political β = 0.82) confirms that policy interventions remains the strongest predictor even amid subsidy cuts—a finding further validated by [51]’s NLP analysis of consumer concerns. This resolves a key tension in the literature: infrastructure deficits persist as the primary barrier (Table 3), but policy stability (not subsidies) is the most effective lever to accelerate adoption. Discriminant analysis confirms that each factor plays a distinct role in differentiating groups, emphasizing the importance of economic and environmental considerations. The Wilcoxon signed-rank test reveals significant changes in political, economic, social, technological, and environmental factors, reflecting dynamic shifts in these areas. However, legal factors did not exhibit significant changes, signifying that rigid frameworks may necessitate additional consideration.

Quantified PESTLE weights (Table 3) reveal infrastructure gaps (β = 0.73) as the dominant barrier, outweighing subsidies in stakeholder prioritization (72% vs. 42%). This redefines adoption priorities, contradicting the subsidy-centric literature [52]. This empirical reorientation toward structural barriers (e.g., charging distribution, grid capacity) provides a replicable model for macro-environmental analysis in transitioning sectors.

Factor analysis (Table 3) reveals that technological factors (eigenvalue: 4.12) explain 34.34% of EV adoption variance—driven primarily by infrastructure gaps (loading: 0.73). Stakeholder feedback further underscores this: 72% cited ‘uneven charging distribution’ as critical; 68% highlighted ‘lack of fast-charging stations’; and 61% noted ‘grid capacity concerns’. This contrasts sharply with the literature emphasizing subsidies [53], confirming infrastructure as the pivotal barrier.

Despite the absence of substantial alterations in the Wilcoxon test due to legal factors, their statistical insignificance in certain analyses warrants further examination. This suggests that the existing legal framework for electric vehicles (EVs) in China, while established, may be perceived by stakeholders in Shenzhen as relatively static or less immediately impactful compared to dynamic economic incentives or rapid technological advancements. It also implies that national-level laws are uniformly applied, rendering them insufficiently distinctive in the perception of the surveyed group. Alternatively, the current legal structures may not yet be sufficiently developed or enforced to effect substantial shifts in adoption behavior. For policymakers, this underscores the necessity of not only creating regulations but also ensuring their perception as enabling, responsive to market demands, and effectively implemented to foster confidence and guide the industry. Future reviews of the legal framework should prioritize more adaptable and clearly communicated regulations.

While infrastructure remained the dominant barrier (72%), regulatory uncertainty (42%) and consumer hesitancy (53%) emerged as critical secondary challenges. Regulatory uncertainty was most acute among policymakers (58%), reflecting concerns about evolving compliance standards. Consumer hesitancy was highest in the consumer cohort (65%), driven by range anxiety and brand trust issues.

The study’s focus on Shenzhen, a technologically advanced and economically vibrant city, necessitates consideration that findings may not be directly generalizable to other regions in China with varying socio-economic conditions, policy support, or infrastructure levels. While Shenzhen has been a pioneer in EV adoption, consumer attitudes and governmental support may exhibit significant disparities in less developed or rural areas.

Regarding inadequate infrastructure, specific gaps identified by stakeholders include an uneven distribution of public charging stations, a scarcity of fast-charging facilities in residential areas and along highways, interoperability issues between different charging networks, and concerns regarding the electricity grid’s capacity to accommodate a substantial EV charging demand.

Environmental sustainability was primarily linked to renewable energy integration (r = 0.82 with technology) and lifecycle emissions reduction—areas directly measured in our survey. Furthermore, emerging technologies are poised to profoundly reshape the EV adoption landscape in China. Solid-state batteries, for instance, offer the potential for higher energy density, improved safety, and extended lifespans, potentially addressing range anxiety and battery degradation concerns [54]. Artificial Intelligence (AI) integrated into energy management systems can optimize charging schedules, enhance battery performance, and facilitate the seamless integration of EVs with smart grids [55]. These advancements have the potential to reduce costs, enhance user experiences, and further accelerate the adoption of EVs.

The global context also provides valuable insights. While China has led in electric vehicle (EV) sales, countries such as Norway have achieved higher market penetration rates through a combination of robust financial incentives, comprehensive charging infrastructure, and disincentives for internal combustion engine (ICE) vehicles [56]. These international experiences, particularly in developing robust and user-friendly charging networks and fostering a supportive regulatory environment, could be instrumental in China’s efforts to refine its EV promotion strategies. Similarly, global collaboration on sustainable battery supply chains and recycling technologies is essential, considering the shared challenge of resource constraints and the environmental impact associated with battery production.

Our late-2024 data captures recent policy turbulence, including 2024 subsidy cuts. Notably, 62% of policymakers (vs. 42% overall) cited these cuts as exacerbating regulatory uncertainty. However, infrastructure gaps remained the dominant barrier (75% prioritization post-2024), underscoring that policy changes do not alleviate structural limitations in charging networks.

These results together emphasize the comprehensive nature of EV adoption and underline the need for an incorporated strategy addressing both business and environmental features to promote a sustainable increase in the sector. Government regulations need to be continuously evaluated and adapted to ensure they effectively drive the market towards sustainability, encompassing the entire lifecycle of EVs, from production (including battery manufacturing emissions) to end-of-life management.

Policy effectiveness (β = 0.82) emerged as the strongest predictor because of the following:

(1)

Market Creation: Overall, 68% of consumers reported subsidies/tax exemptions as their primary purchase motivator;

(2)

Risk Mitigation: In total, 74% of manufacturers cited policy clarity as reducing investment uncertainty;

(3)

Catalytic Effect: Policies accelerate complementary investments (e.g., charging infrastructure).

This dominance mirrors China’s state-led industrial model, where policy signals directly shape market trajectories [57].

6. Policy Recommendation

Based on empirical findings, we propose targeted policy interventions to accelerate EV adoption in China:

• Infrastructure-Led Investment Strategy: Allocate public funding to deploy 500,000 fast-charging stations by 2027, prioritizing highways/rural areas (aligned with 72% stakeholder prioritization). Mandate interoperability standards for charging networks to reduce fragmentation (cited by 68% manufacturers).
• Regulatory Stability Framework: Establish 5-year policy lock-ins for subsidies/tax exemptions to mitigate uncertainty (demanded by 82% policymakers). Create a cross-ministerial EV taskforce (MIIT + MOF + MEE) to harmonize regulations.
• Consumer Confidence Building: Launch national awareness campaigns highlighting lifetime cost savings (e.g., fuel/maintenance reductions). Expand battery warranty mandates to 8 years/200,000 km to address longevity concerns (key for 65% consumers).
• Environmental Impact Transparency: Mandate public reporting of lifecycle CO₂ emissions for EVs (cradle-to-grave) to align with consumer sustainability priorities (57% rated this critical).

7. Conclusions

This study underscores the importance of EVs in China for advancing both commerce opportunity and ecological sustainability. By integrating higher battery technology, powertrain architectures, and intellectual energy organization systems, EVs align with China’s planned goal of decreased CO₂ production and financial development. This study redefines EV adoption priorities by empirically identifying infrastructure gaps as the persistent core barrier, necessitating targeted investment over subsidy adjustments. Our post-2024 data further shows infrastructure’s persistence amidst policy volatility, urging sustained investment in charging networks. This study’s distinctive contribution is its comprehensive PESTLE analysis integrated with direct stakeholder inputs (manufacturers, policymakers, and consumers) within the specific context of China’s rapidly developing EV market. Despite challenges such as suboptimal battery advancement, high manufacture expenses, and persistent ecological impacts, the PESTLE-weighted analysis (Technology loading = 0.73; Political β = 0.82) identifies infrastructure gaps as the primary barrier (72% stakeholder consensus), enabling targeted policy interventions. These analyses offer comprehensive insights into the associations amongst variables, recognize fundamental factors influencing EV adoption, discriminate between marketplace sectors, and assess change over time. The practical application of this research is substantial, as it provides actionable intelligence for policymakers to enhance regulations and incentives for manufacturers. This enables policymakers to develop strategic plans for market entry and product development. Additionally, it assists investors in identifying pivotal growth drivers and potential risks. Addressing market dynamics, insufficient communications (such as charging infrastructure gaps), dictatorial doubts, and social issues remains crucial. The findings suggest that overcoming these barriers through targeted policies, technological innovation, and enhanced public engagement could unlock important business opportunities and contribute to long-term ecological sustainability, emphasizing the need for practical strategies and sustained modernism. Ultimately, policy interventions should prioritize infrastructure investment to overcome the dominant barrier identified in stakeholder responses.

Limitations and Future Research Directions

A limitation of this study is the potential local variation in EV acceptance and policy within China. The survey was primarily conducted in Shenzhen, a city renowned for its advanced EV market. This focus on a single leading city may restrict the generalizability of the findings to other Chinese regions with varying economic development levels, consumer preferences, and policy implementations. Consequently, the specific weights and perceptions of PESTLE factors may differ significantly in Tier-2 or Tier-3 cities or rural areas. This could impact the study results by potentially overstating the influence of technological factors or understating the challenges associated with basic infrastructure in less developed regions. While anonymity and question framing mitigated social-desirability bias, self-reported consumer perceptions remain susceptible to subtle social influences. Future studies could incorporate implicit association tests (IAT) to complement survey data.

Stakeholder perceptions (e.g., infrastructure prioritization) may reflect social desirability or optimism biases. For example, policymakers might underreport regulatory uncertainty, while consumers overestimate willingness to adopt EVs. We mitigated this by anonymizing responses to reduce social pressure, including objective metrics (e.g., charger-to-EV ratios), to triangulate subjective claims. This study captures perceptions at a single post-policy-cut timeframe (November–December 2024), limiting causal inference. For instance, the strong political factor loading (β = 0.82) may reflect temporary subsidy-cut impacts rather than stable trends, infrastructure gaps (72%) could evolve with rapid technological changes, and future longitudinal studies should track these dynamics.

Future studies ought to aim for gathering information from a broader range of areas within China to capture regional versions in EV adoption and policy effects, thereby providing a more nationally representative picture. This could involve comparative case studies between different types of cities. Additionally, exploring the outcomes of emerging technologies (such as next-generation batteries or autonomous driving features in EVs) and evolving authority’s regulations in the EV market in more depth ought to offer more complete insights and guide extra effective techniques for selling sustainable increases. Longitudinal studies that monitor stakeholder perceptions and PESTLE factors over time would help us to understand the dynamic development of the EV market. Further research could explore the specific CO₂ emissions generated by battery manufacturing across diverse provinces with varying energy compositions and the practical effectiveness of circular economy initiatives.

While this study incorporates policies through December 2024, China’s EV regulatory landscape remains fluid. Subsequent 2025 changes (e.g., proposed carbon credit adjustments) warrant monitoring.