The global transition toward sustainable energy and the implementation of carbon neutrality goals have catalyzed a historic shift in the automotive industry. Traditional internal combustion engine vehicles are gradually being phased out, with several pilot cities in China aiming to completely halt sales of conventional燃油 vehicles by 2030. This policy-driven transformation is accelerating the adoption of electric vehicles (EVs), which not only restructure the automotive market but also promote green mobility systems. The development of the electric vehicle industry exhibits significant spillover effects, fostering synergies across upstream and downstream sectors and cultivating new economic growth drivers. The Chinese government’s “New Energy Vehicle Industry Development Plan (2021–2035)” emphasizes that electric vehicles are essential for China’s evolution from an automotive giant to an automotive powerhouse, providing strategic direction and policy support. This study employs a multi-condition configuration analysis to explore the factors influencing the development of China’s electric vehicle industry, revealing the synergistic pathways of various elements.
Electric vehicles, particularly in the context of China EV development, have gained substantial market share globally due to policy incentives, technological advancements, and growing consumer demand. China, with its robust industrial chain and expansive market, has led in EV production and sales for several years. Innovations in core components like batteries, motors, and electronic controls have significantly enhanced the performance and competitiveness of electric vehicles. Technological upgrades not only drive product iterations but also increase consumer acceptance of electric vehicles. As global focus on sustainability intensifies, the electric vehicle industry, supported by policies and technological innovation, has become a central pillar of the green economy. The progression of electric vehicle technology has concurrently spurred growth in innovative outputs.
Existing literature often highlights the role of policy support in fostering the electric vehicle industry. However, this study extends the analysis by examining the interactive effects of multiple factors on innovation output growth. Using fuzzy-set Qualitative Comparative Analysis (fsQCA), we systematically identify key drivers and their configurations that influence the development of China’s electric vehicle industry. This approach addresses the nonlinear and asymmetric characteristics inherent in such complex systems.
Literature Review
The rapid development of the electric vehicle industry is deeply intertwined with technological innovation and organizational变革. Compared to traditional vehicles, electric vehicles demand advanced technologies in powertrain systems, intelligent controls, and lightweight designs. Research indicates that technological progress is a primary driver of market expansion and innovation output in the electric vehicle sector. For instance, advancements in lithium-ion and solid-state batteries are expected to be the fastest-growing areas of innovation in the coming decade. Additionally, the rapid development of autonomous driving technology has accelerated the application of artificial intelligence and machine learning in electric vehicles.
Policy support is another critical driver. Since 2009, China has implemented various policies, including subsidies, tax incentives, and infrastructure development, to promote electric vehicle adoption. The “dual-credit” policy, for example, has effectively enhanced market penetration, incentivized technological innovation, and improved environmental performance. By encouraging enterprises to increase R&D investment and participate in standard-setting, this policy has bolstered the innovation capacity and competitiveness of China EV manufacturers. However, policy effectiveness varies across regions due to differences in development levels. Regions like Shanghai and Guangzhou, with higher市场化 levels and better infrastructure, have attracted numerous electric vehicle enterprises, thereby boosting innovation output. In contrast, central and western regions, despite policy support, lag in innovation due to weaker infrastructure and lower economic development.
Organizational resources and structural characteristics also play a pivotal role. Economically developed regions typically exhibit stronger market demand and capital investment capabilities, providing a solid foundation for industry expansion. Increased regional R&D funding offers essential resources for technological progress and industrial upgrading. By augmenting R&D investment and attracting high-skilled talent, enterprises can strengthen their technological advantages and enhance market competitiveness, thereby driving sustained growth in innovation output.
Environmental factors, such as regional市场化 levels and industrial transformation, significantly impact electric vehicle development. Highly市场化 regions can attract capital and technological resources to enhance innovation capabilities, facilitating technological R&D and commercialization. Industrial transformation optimizes产业结构 and resource allocation, promoting the development of the electric vehicle产业链. The combined effect of市场化 and industrial transformation lays a robust foundation for continuous innovation in the electric vehicle industry.
Research Framework
The development of the electric vehicle industry is influenced by multiple interacting factors. This study constructs an analytical framework based on three dimensions: technology, organization, and environment. The technological dimension includes factors like education level and infrastructure, which affect the industry’s innovation capacity and market acceptance. The organizational dimension encompasses economic development and innovation funding, which determine resource availability and structural characteristics. The environmental dimension involves市场化 levels and industrial transformation, which shape the institutional and market context for electric vehicle development.
The interrelationships among these factors can be expressed through mathematical formulations. For instance, the consistency and coverage measures in fsQCA are defined as follows:
For a condition $X$ and outcome $Y$, the consistency of $X$ as a subset of $Y$ is given by:
$$ \text{Consistency}(X \leq Y) = \frac{\sum \min(X_i, Y_i)}{\sum X_i} $$
Similarly, the coverage is defined as:
$$ \text{Coverage}(X \leq Y) = \frac{\sum \min(X_i, Y_i)}{\sum Y_i} $$
These formulas are essential for evaluating the necessity and sufficiency of conditions in configuration analysis.
Research Methodology
This study employs fsQCA to analyze the complex causal relationships affecting the development of China’s electric vehicle industry. Unlike traditional linear methods, fsQCA identifies combinations of conditions that lead to an outcome, accommodating nonlinear and asymmetric relationships. The analysis involves calibrating variables into fuzzy sets, assessing necessary conditions, and identifying sufficient configurations.
The data for this study are derived from 31 provinces in China (excluding Hong Kong, Macao, and Taiwan) for the year 2022. Sources include the China Statistical Yearbook 2023, the IncoPat patent database for electric vehicle-related patents, the Fan Gang Marketization Index Report for regional市场化 levels, and the China Financial Yearbook for fiscal support data.
Data Construction and Calibration
Variables are calibrated into fuzzy sets using direct calibration method. The outcome variable is the total number of authorized invention patents in the electric vehicle field, measuring regional innovation output. Condition variables include education level (average years of schooling), infrastructure investment (public budget expenditure), economic development level (GDP per capita), innovation funding (R&D expenditure), regional市场化 level (Fan Gang index), and industrial transformation (share of tertiary industry in GDP). Calibration anchors are set at the 0.95, 0.5, and 0.05 percentiles.
| Variable Type | Variable Name | Fully In | Crossover | Fully Out |
|---|---|---|---|---|
| Outcome | Authorized Invention Patents | 2292.500 | 155.000 | 5.000 |
| Condition | Education Level | 11.463 | 9.702 | 8.614 |
| Infrastructure Investment | 13515.000 | 6699.790 | 2036.235 | |
| Economic Development Level | 162148.500 | 70923.000 | 51630.000 | |
| Innovation Funding | 23808669.000 | 3544104.000 | 149292.000 | |
| Regional Marketization Level | 12.420 | 9.638 | 5.983 | |
| Industrial Transformation | 0.670 | 0.504 | 0.439 |
The calibration process transforms raw data into membership scores ranging from 0 to 1, indicating the degree to which each case belongs to the set of high values for a variable. This allows for set-theoretic analysis using fsQCA.
Data Analysis and Results
Necessity Analysis
Necessity analysis examines whether a single condition is essential for the outcome. A condition is considered necessary if its consistency exceeds 0.9. As shown in Table 2, no single condition meets this threshold, indicating that the development of the electric vehicle industry is driven by combinations of conditions rather than any one factor.
| Condition Variable | High EV Development | ~High EV Development | ||
|---|---|---|---|---|
| Consistency | Coverage | Consistency | Coverage | |
| Infrastructure Investment | 0.835 | 0.714 | 0.491 | 0.578 |
| ~Infrastructure Investment | 0.507 | 0.420 | 0.757 | 0.863 |
| Economic Development Level | 0.768 | 0.693 | 0.477 | 0.592 |
| ~Economic Development Level | 0.547 | 0.432 | 0.752 | 0.817 |
| Innovation Funding | 0.866 | 0.855 | 0.407 | 0.553 |
| ~Innovation Funding | 0.548 | 0.401 | 0.893 | 0.902 |
| Education Level | 0.725 | 0.660 | 0.555 | 0.696 |
| ~Education Level | 0.666 | 0.521 | 0.728 | 0.785 |
| Regional Marketization Level | 0.897 | 0.800 | 0.447 | 0.549 |
| ~Regional Marketization Level | 0.494 | 0.393 | 0.837 | 0.918 |
| Industrial Transformation | 0.758 | 0.702 | 0.527 | 0.673 |
| ~Industrial Transformation | 0.647 | 0.499 | 0.767 | 0.813 |
Note: ~ denotes the absence of a condition.
Sufficiency Analysis: Configuration Results
Sufficiency analysis identifies combinations of conditions that lead to the outcome. Using a consistency threshold of 0.9 and a frequency threshold of 1, we obtained four sufficient configurations for high levels of electric vehicle development. The results are presented in Table 3.
| Condition Variable | Configuration 1 | Configuration 2 | Configuration 3 | Configuration 4 |
|---|---|---|---|---|
| Infrastructure Investment | ● | ● | ⊗ | • |
| Economic Development Level | • | • | ||
| Innovation Funding | • | • | • | |
| Education Level | ⊗ | ⊗ | • | • |
| Regional Marketization Level | ● | ● | ● | ● |
| Industrial Transformation | ● | ● | ● | |
| Consistency | 0.925 | 0.960 | 0.934 | 0.954 |
| Raw Coverage | 0.465 | 0.499 | 0.338 | 0.523 |
| Unique Coverage | 0.035 | 0.043 | 0.025 | 0.096 |
| Solution Consistency | 0.914 | |||
| Solution Coverage | 0.703 | |||
Note: ● indicates core condition presence; ⊗ indicates core condition absence; • indicates peripheral condition presence.
Configuration 1 highlights the synergy between infrastructure investment and regional市场化 level as core conditions, with innovation funding as a peripheral condition. This suggests that well-developed infrastructure and a高度市场化 environment are crucial for electric vehicle development, supported by innovation funds.
Configuration 2 emphasizes innovation funding and regional市场化 level as core conditions, with economic development level as a peripheral condition. This indicates that sufficient R&D investment and a市场化 economy drive electric vehicle innovation, complemented by economic prosperity.
Configuration 3 features regional市场化 level and industrial transformation as core conditions, with education level and innovation funding as peripheral conditions. This underscores the importance of市场化 and industrial upgrading, aided by human capital and R&D.
Configuration 4 shows infrastructure investment as a core condition, with economic development level and education level as peripheral conditions. This pathway demonstrates that infrastructure, combined with economic and educational resources, fosters electric vehicle growth.
The overall solution consistency is 0.914, and coverage is 0.703, indicating that the configurations are both consistent and covering a substantial proportion of cases.
Discussion
The findings reveal that the development of China’s electric vehicle industry is nonlinear and driven by the interplay of technological, organizational, and environmental factors. No single factor is sufficient; instead, specific combinations of conditions create synergistic effects. For instance, regional市场化 level appears as a core condition in all configurations, highlighting its fundamental role in facilitating resource allocation and innovation. Industrial transformation and infrastructure investment also emerge as critical elements, often interacting with市场化 to enhance outcomes.
Technological innovation, represented by innovation funding and education level, acts as a key driver. However, its impact is magnified when combined with supportive policies and infrastructure. The electric vehicle industry benefits from continuous R&D investment and skilled talent, which are essential for breakthroughs in battery technology and autonomous driving. Policy support, through mechanisms like the “dual-credit” system, incentivizes enterprises to innovate and comply with environmental standards. Nevertheless, policy effectiveness depends on regional contexts, necessitating tailored approaches.
The configurations also suggest that regions with lower市场化 levels can still achieve high electric vehicle development by leveraging infrastructure and industrial transformation. This implies that policy interventions should focus on strengthening these areas in less developed regions. Moreover, the synergy between market mechanisms and industrial upgrading can create a virtuous cycle, where technological advancements drive structural changes, further propelling innovation in the electric vehicle sector.
The mathematical expressions of consistency and coverage help quantify these relationships. For example, the consistency formula ensures that only robust configurations are considered, while coverage indicates their empirical relevance. These measures enhance the rigor of configuration analysis in studying complex phenomena like the electric vehicle industry.
Conclusion and Implications
This study demonstrates that the development of China’s electric vehicle industry is influenced by multiple interconnected factors, including technological innovation, policy support, organizational resources, and environmental conditions. Using fsQCA, we identified four distinct pathways that lead to high levels of innovation output, emphasizing the importance of configuration effects. The electric vehicle industry, particularly in the context of China EV strategies, thrives under specific combinations of conditions rather than isolated factors.
Based on the findings, we propose several policy recommendations. First, establish “university-enterprise joint laboratories” to strengthen industry-academia collaboration. Implement differentiated subsidy policies based on technological maturity to precisely support various R&D stages. Second, enhance the technological innovation support system through tax incentives, innovation funds, and other policy tools to encourage enterprise R&D investment. Foster deep integration between industry, academia, and research institutions. Third, optimize resource allocation mechanisms by strengthening government-market coordination. Guide social capital toward critical areas like charging infrastructure, and improve resource utilization efficiency through industrial chain integration.
Furthermore, policymakers should consider regional disparities when designing interventions. In highly市场化 regions, focus on incentivizing R&D and standard-setting. In less developed areas, prioritize infrastructure investment and industrial transformation. Dynamic policy adjustments are necessary to keep pace with industry evolution. Enterprises should engage in cross-sector collaboration to break down barriers and innovate in technology and business models. Government agencies ought to streamline administrative processes and enhance inter-departmental coordination to create an efficient institutional environment.
In conclusion, the electric vehicle industry represents a cornerstone of sustainable transportation and economic growth. By understanding and leveraging the configurational pathways identified in this study, stakeholders can effectively promote the high-quality development of China’s electric vehicle sector, contributing to global energy transition and carbon neutrality goals.