In the contemporary era, technological innovation serves as the primary driver of China’s digital economy, playing a crucial role in instigating industrial transformation and fostering emerging sectors. We investigate the mechanism through which the digital economy enhances technological innovation capabilities in the new energy vehicle industry, with a specific focus on electric vehicles in China. Based on panel data from Chinese electric vehicle listed companies between 2012 and 2022, we conduct empirical analyses to explore the pathways of this influence. Our findings indicate that the digital economy significantly boosts innovation in the China EV sector. Furthermore, the digital economy exerts a stronger innovation-driving effect on state-owned electric vehicle enterprises compared to private ones. Additionally, the impact of the digital economy on technological innovation in electric vehicles is more pronounced in eastern cities than in western regions. Therefore, leveraging big data resources and information advantages is an inevitable trend for leading the development of the electric vehicle industry in China.
The digital economy, characterized by the integration of data resources as core生产要素, has revolutionized production and consumption patterns through information and communication technologies. According to the State Council’s guidelines, accelerating the development of the digital economy and promoting its deep integration with the real economy are essential for building internationally competitive digital industrial clusters. The electric vehicle industry in China has experienced rapid growth, with production and sales volumes showing significant increases. For instance, in 2023, China’s electric vehicle production reached approximately 9.27 million units, reflecting a year-on-year growth of 27.9%. However, challenges such as weak core technological innovation capabilities and inadequate charging infrastructure persist. Enhancing technological innovation in the China EV industry is vital for achieving high-quality development and meeting carbon peak and neutrality goals.
Existing literature highlights the positive effects of the digital economy on innovation. For example, studies suggest that the digital economy facilitates knowledge spillovers and reduces information asymmetry, thereby fostering innovation in strategic emerging industries. In the context of electric vehicles, digital technologies like big data analytics, cloud computing, and artificial intelligence have been applied to improve battery management, driving control, and energy efficiency. These advancements contribute to the development of smart and connected electric vehicles, aligning with global trends in sustainable transportation. Moreover, government subsidies and policies play a significant role in supporting innovation in the China EV sector, particularly in state-owned enterprises where fiscal incentives are more effective.
To systematically analyze the impact of the digital economy on technological innovation in electric vehicles, we propose the following hypotheses based on theoretical frameworks:
- H1: The digital economy positively influences technological innovation in the electric vehicle industry.
- H2: Substantive innovation, as measured by invention patents, has a more significant promoting effect on electric vehicle industry innovation compared to strategic innovation.
- H3: The digital economy drives technological innovation more strongly in state-owned electric vehicle enterprises than in private ones.
Our research employs a fixed-effects model to examine these hypotheses. The dependent variable is the number of patent applications in the electric vehicle sector, which serves as a proxy for technological innovation. We categorize patents into substantive innovation (invention patents) and strategic innovation (utility model patents). The core explanatory variable is the digital economy level (DEL), measured using an index that combines internet development and digital financial inclusion. Internet development is assessed through indicators such as internet broadband penetration, employment in computer services, per capita telecom business volume, and mobile phone penetration. Digital financial inclusion is measured using the China Digital Financial Inclusion Index. Control variables include R&D expenditure (Expend), industrial structure (Structure), industrial scale (Scale), and government investment (Gov).
The baseline regression model is specified as follows:
$$ In_{i,j,t} = \alpha_1 + \alpha_2 DEL_{i,j,t} + \alpha_3 X_{i,j,t} + \lambda_t + \mu_i + \varepsilon_{i,j,t} $$
where \( In_{i,j,t} \) represents the natural logarithm of patent applications for electric vehicles in region \( i \), enterprise \( j \), and year \( t \). \( DEL_{i,j,t} \) denotes the digital economy level, \( X_{i,j,t} \) is a vector of control variables, \( \lambda_t \) and \( \mu_i \) are time and city fixed effects, respectively, and \( \varepsilon_{i,j,t} \) is the error term.
Table 1 summarizes the指标体系 for measuring the digital economy level:
| Primary Indicator | Secondary Indicator | Tertiary Indicator | Attribute |
|---|---|---|---|
| Digital Economy Level Index | Digital Internet Development | Internet Penetration Rate | Positive |
| Employment in Related Sectors | Positive | ||
| Telecom Output per Capita | Positive | ||
| Mobile Phone Penetration Rate | Positive | ||
| Digital Financial Inclusion | China Digital Financial Inclusion Index | Positive |
Data for the dependent variable are sourced from the National Intellectual Property Administration and Wind database, while explanatory and control variables come from the China City Statistical Yearbook and the Peking University Digital Finance Research Center. The sample period spans from 2012 to 2022, starting in 2012 due to the release of the Energy-saving and New Energy Vehicle Industry Development Plan (2012-2020), which emphasized the importance of electric vehicles for industrial transformation.
Benchmark regression results are presented in Table 2. Columns (1) and (2) show results for invention patents (substantive innovation), while columns (3) and (4) display results for utility model patents (strategic innovation). The coefficient for DEL is positive and statistically significant at the 1% level in all specifications, supporting H1. After including control variables, the DEL coefficient remains significant, indicating that the digital economy enhances innovation in electric vehicles. Moreover, the effect is larger for invention patents, confirming H2 that substantive innovation is more strongly promoted.
| Variable | (1) Invention Patents | (2) Invention Patents | (3) Utility Patents | (4) Utility Patents |
|---|---|---|---|---|
| DEL | 20.720*** (9.64) | 7.793*** (3.24) | 18.367*** (7.88) | 8.364*** (3.68) |
| Structure | -0.071*** (-3.40) | -0.053*** (-2.81) | ||
| Expend | -0.000** (-2.47) | -0.000 (-0.45) | ||
| Scale | 0.002*** (5.41) | 0.001*** (4.65) | ||
| Gov | 0.000** (2.14) | 0.000 (0.73) | ||
| Constant | -4.634*** (-5.56) | -0.524 (-0.32) | -7.434*** (-6.73) | -2.210 (-1.12) |
| Observations | 154 | 154 | 154 | 154 |
| R-squared | 0.445 | 0.667 | 0.356 | 0.579 |
| City Effects | Yes | Yes | Yes | Yes |
| Time Effects | Yes | Yes | Yes | Yes |
Note: ***, ** indicate significance at the 1% and 5% levels, respectively; t-values in parentheses.
To ensure robustness, we conduct additional tests. First, we replace the dependent variable with digital economy-related patent counts, as shown in Table 3, columns (1) and (2). The DEL coefficient remains positive and significant, confirming the baseline results. Second, we introduce a micro-level control variable, return on assets (Roa), in column (3). The results are consistent, further validating the robustness of our findings.
| Variable | (1) In | (2) In | (3) In |
|---|---|---|---|
| DEL | 8.768*** (8.07) | 4.988*** (4.71) | 15.463*** (6.79) |
| Structure | -0.006 (-0.56) | ||
| Scale | 0.000** (2.57) | ||
| Gov | 0.000 (1.01) | ||
| Roa | 0.020 (0.79) | ||
| Constant | 8.438*** (8.75) | 8.878*** (9.81) | -2.120 (-1.88) |
| Observations | 154 | 154 | 154 |
| R-squared | 0.556 | 0.703 | 0.634 |
| Time Effects | Yes | Yes | Yes |
| City Effects | Yes | Yes | Yes |
Note: ***, ** indicate significance at the 1% and 5% levels, respectively; t-values in parentheses.
Heterogeneity analysis is performed by grouping samples based on region and ownership. Table 4 presents the results. Columns (1) and (2) compare eastern and western cities, showing that the digital economy’s impact on electric vehicle innovation is stronger in eastern regions, likely due to better infrastructure and industrial base. Columns (3) and (4) distinguish between state-owned and private enterprises. The DEL coefficient is positive and significant for both, but larger for state-owned electric vehicle enterprises, supporting H3. This may be attributed to greater government subsidies and policy support for state-owned entities, which enhance innovation capacity.
| Variable | (1) Eastern | (2) Western | (3) State-owned | (4) Private |
|---|---|---|---|---|
| DEL | 20.613*** (8.79) | 5.830 (0.15) | 12.556*** (7.13) | 10.863*** (5.64) |
| Constant | -4.652*** (-2.97) | -1.826 (-0.19) | -1.438 (-1.13) | -0.997 (-0.68) |
| Observations | 121 | 33 | 55 | 99 |
| R-squared | 0.458 | 0.479 | 0.498 | 0.432 |
| Time Effects | Yes | Yes | Yes | Yes |
| City Effects | Yes | Yes | Yes | Yes |
| Control Variables | Yes | Yes | Yes | Yes |
Note: *** indicates significance at the 1% level; t-values in parentheses.
In conclusion, our study demonstrates that the digital economy significantly enhances technological innovation in China’s electric vehicle industry. The promotion effect is more substantial for substantive innovation and varies by region and enterprise ownership. For electric vehicle enterprises, it is essential to strengthen core technology R&D, particularly in areas like batteries, motors, and electronic controls. Integrating digital technologies such as AI and big data can lead to smarter and more connected electric vehicles. Additionally, ensuring safety and reliability is paramount for gaining consumer trust. At the industry level, collaboration among upstream and downstream players is crucial for optimizing the supply chain and reducing costs. Government policies should continue to support the China EV sector through subsidies and infrastructure development, fostering a conducive environment for innovation. By leveraging the digital economy’s potential, the electric vehicle industry in China can achieve sustainable growth and contribute to global carbon reduction goals.
The digital economy’s role in driving innovation can be further illustrated through a conceptual framework. Let \( I \) represent innovation output, \( D \) the digital economy level, and \( Z \) a set of moderating factors such as ownership and region. The relationship can be modeled as:
$$ I = \beta_0 + \beta_1 D + \beta_2 Z + \beta_3 (D \times Z) + \epsilon $$
where \( \beta_1 \) captures the direct effect of the digital economy, and \( \beta_3 \) reflects interaction effects. Our empirical results align with this framework, highlighting the importance of contextual factors in maximizing the benefits of the digital economy for electric vehicle innovation.
In summary, the rapid development of the digital economy offers unprecedented opportunities for the electric vehicle industry in China. By embracing digital transformation, enterprises can enhance their innovation capabilities, ultimately contributing to the high-quality development of the China EV sector and the realization of national strategic objectives.