In the context of increasingly severe global climate change, we have prioritized the “carbon peak and carbon neutrality” goals as part of our national strategic development plan. The electric car industry, being a key sector for carbon emissions, has seen its electrification transformation become a critical pathway to achieving these dual carbon objectives. This shift represents an inevitable choice for transitioning toward green and sustainable development. The “New Energy Vehicle Industry Development Plan (2021–2035)” explicitly states that developing electric cars is essential for evolving from a large automotive nation to a powerful one, serving as a strategic measure to address climate change and promote green development. Additionally, the “Medium and Long-Term Plan for the Development of the Hydrogen Energy Industry (2021–2035)” emphasizes the role of central government investments in supporting related industries, particularly in advancing the research and manufacturing of core components and key equipment. In recent years, the electric car industry has experienced rapid growth driven by both policy support and market forces, with production and sales of electric cars leading globally for ten consecutive years since 2015. By 2024, annual production of electric cars surpassed 10 million units, demonstrating the robust upgrading of our automotive sector and the strong resilience of sustainable economic development. However, challenges such as insufficient core technological innovation and reliance on imported key components continue to constrain the long-term competitiveness of the electric car industry. Government subsidies, as a crucial tool for state intervention in markets and incentivizing corporate innovation, have consistently been a focus of academic and policy discussions regarding their impact on innovation performance in the electric car sector.
Currently, some scholars argue that government subsidies significantly enhance corporate innovation performance. For instance, studies have shown that subsidies positively influence technological innovation inputs and outputs in small and medium-sized enterprises, though financing constraints may negatively moderate this relationship. Other research incorporating research and development (R&D) expenditure as a moderating variable found that R&D spending in manufacturing firms amplifies the positive effect of government subsidies on innovation performance. Conversely, some scholars contend that government subsidies can inhibit innovation performance. Empirical evidence from strategic emerging industries indicates that subsidies may have a negative impact on innovation, particularly in non-state-owned enterprises. Further analyses suggest that while subsidies boost the quantity of innovation in early stages, improving innovation quality requires additional policy interventions. Many researchers recognize the complex nature of this relationship, noting both benefits and drawbacks. For example, government subsidies have been found to promote substantive and strategic green innovation while enhancing corporate performance, but they may also lead to inefficient investments and resource wastage. Similarly, R&D subsidies can drive technological progress in small and medium-sized enterprises, though rent-seeking behaviors might distort their positive effects.
In this study, we utilize data from A-share listed companies in China’s electric car industry from 2016 to 2023 to construct a dual fixed-effects model, empirically examining the impact of government subsidies on innovation performance and the underlying mechanisms. Our marginal contributions include: first, focusing specifically on the electric car industry, whereas existing research often centers on the broader manufacturing sector, thereby enriching this field and providing insights for policy formulation; second, employing the latest data from 2016–2023 to address timeliness limitations in previous studies, offering a new perspective on the long-term mechanisms and dynamic effects of industrial policies. The electric car industry, with its rapid technological advancements and market expansions, serves as an ideal context for analyzing how government interventions foster innovation. We emphasize that understanding these dynamics is crucial for shaping future policies that support the sustainable growth of electric car production and adoption.
From a theoretical standpoint, information asymmetry exists between electric car companies and stakeholders such as consumers and investors. Consumers may have concerns about the technical reliability, range, and safety of electric cars, while investors might struggle to accurately assess the profitability and future potential of these firms. According to signaling theory, government subsidies act as a mechanism that conveys positive signals about a company’s development prospects, reducing information asymmetry and bolstering consumer confidence in electric cars. This, in turn, attracts more investors to the electric car industry, providing additional financial support that fosters healthy corporate growth and enhances innovation performance. Moreover, innovation activities in the electric car sector exhibit significant positive externalities. For instance, corporate innovations in areas like battery technology and intelligent driving systems not only improve product competitiveness but also generate technological spillovers across the industry. Additionally, the use of electric cars contributes to environmental benefits, such as reduced greenhouse gas emissions, creating positive environmental externalities. However, these innovations often result in private benefits that are lower than social benefits, leading to underinvestment in innovation. Government subsidies internalize these externalities by compensating for innovation costs, aligning private returns with social gains, and thereby encouraging greater innovation efforts and performance improvements. Based on this, we propose the following hypothesis:
H1: Government subsidies have a positive promoting effect on innovation performance in the electric car industry.
Furthermore, the electric car industry is technology-intensive, with key breakthroughs in areas like battery energy density, charging speed, and integration of smart networking technologies requiring sustained and substantial capital investment. Due to the high uncertainty and risk associated with such R&D activities, firms often face significant funding gaps. Drawing on the resource-based view, government subsidies provide direct financial support, augmenting firms’ R&D resources. On one hand, subsidies can be allocated to basic research,分担前期研发成本, and accelerating the development of the electric car industry. On the other hand, they enable firms to establish a “high-end talent–technology transformation” dual-drive mechanism,注入持续创新动能, and accelerating the output of technological innovations, thereby enhancing innovation performance. Thus, we propose a second hypothesis:
H2: Government subsidies promote innovation performance in the electric car industry by strengthening R&D investment.
To test these hypotheses, we designed our research methodology as follows. The sample selection begins in 2016, as this year marks a restructuring period for subsidy policies following incidents of fraudulent claims, with more standardized subsidy distribution processes. We selected A-share listed companies in China’s electric car industry from 2016 to 2023 as our research sample. To ensure accuracy and reliability, we processed the data by: (1) excluding companies labeled ST or *ST during the study period; (2) removing firms with severe consecutive missing values; and (3) using linear interpolation for firms with missing values to maintain sample continuity. This resulted in a final sample of 462 upstream, midstream, and downstream electric car listed companies, totaling 3,285 observations. To mitigate the impact of outliers, we winsorized all continuous variables at the 1st and 99th percentiles. Data for the electric car industry were sourced from Tonghuashun, patent-related data from the National Intellectual Property Administration, and other data from the CSMAR database.
For variable definitions, the dependent variable is enterprise innovation performance (Innovation). Following common practices, we use the number of patent applications to measure innovation performance, as it better reflects the innovation capability and adaptability of electric car firms given the rapid iteration of innovations. To ensure data stability, we apply a natural logarithm after adding 1. The core explanatory variable is government subsidies (Sub), measured as the natural logarithm of government subsidy amounts recorded in corporate financial statements and notes. The mediating variable is R&D investment (RD), measured as the ratio of R&D expenditure to operating revenue, which directly indicates the emphasis on innovation in core business activities. Control variables include capital intensity (Capital, total assets divided by operating revenue), return on assets (ROA, net profit divided by total assets), ownership concentration (Top1, shareholding ratio of the largest shareholder), asset-liability ratio (Lev, total liabilities divided by total assets), and firm size (Size, natural logarithm of total assets). These controls help isolate other potential influences and accurately identify the impact mechanism of government subsidies on innovation performance in the electric car industry.
| Variable | Symbol | Name | Description |
|---|---|---|---|
| Dependent Variable | Innovation | Innovation Performance | Ln(Total Patents + 1) |
| Core Explanatory Variable | Sub | Government Subsidies | Ln(Government Subsidy Amount) |
| Mediating Variable | RD | R&D Investment | R&D Expenditure / Operating Revenue |
| Control Variables | Capital | Capital Intensity | Total Assets / Operating Revenue |
| ROA | Return on Assets | Net Profit / Total Assets | |
| Top1 | Ownership Concentration | Largest Shareholder’s Holding Ratio | |
| Lev | Asset-Liability Ratio | Total Liabilities / Total Assets | |
| Size | Firm Size | Ln(Total Assets) |
We construct a dual fixed-effects model to verify the impact of government subsidies on innovation performance in the electric car industry:
$$ \text{Innovation}_{i,t} = \alpha_0 + \alpha_1 \text{Sub}_{i,t} + \alpha_2 \text{Controls}_{i,t} + \text{Indiv}_i + \text{Year}_t + \varepsilon_{i,t} $$
where Innovationi,t represents the innovation performance of firm i in year t, Subi,t denotes government subsidies, Controlsi,t includes all control variables, α0 is the constant term, α1 and α2 are regression coefficients, Indivi and Yeart represent individual and time fixed effects, and εi,t is the random error term.
To test the mediating effect of R&D investment, we employ a stepwise regression approach with the following models:
$$ \text{RD}_{i,t} = \beta_0 + \beta_1 \text{Sub}_{i,t} + \beta_2 \text{Controls}_{i,t} + \text{Indiv}_i + \text{Year}_t + \varepsilon_{i,t} $$
$$ \text{Innovation}_{i,t} = \gamma_0 + \gamma_1 \text{Sub}_{i,t} + \gamma_2 \text{RD}_{i,t} + \gamma_3 \text{Controls}_{i,t} + \text{Indiv}_i + \text{Year}_t + \varepsilon_{i,t} $$
If β1 in equation (2) and γ1 and γ2 in equation (3) are significant, and γ1 is less than α1 from equation (1), it indicates a partial mediating effect, supporting H2.
The descriptive statistics for the variables are presented in the table below. The dependent variable, innovation performance, has a maximum value of 7.844 and a minimum of 0, reflecting significant disparities in innovation outcomes across the electric car industry. The core explanatory variable, government subsidies, ranges from 13.482 to 21.154, indicating substantial variation in subsidy allocations among firms. The mediating variable, R&D investment, shows a wide spread from -0.370 to 61.790, underscoring differences in the emphasis firms place on R&D activities. These variations highlight the diverse landscape of the electric car sector, where innovation drivers and resource allocations differ markedly.
| Variable Name | Obs | Mean | Median | SD | Min | Max |
|---|---|---|---|---|---|---|
| Innovation | 3285 | 3.912 | 3.932 | 1.481 | 0.000 | 7.844 |
| Sub | 3285 | 16.803 | 16.677 | 1.431 | 13.482 | 21.154 |
| RD | 3285 | 5.136 | 4.490 | 3.615 | -0.370 | 61.790 |
| Capital | 3285 | 1.861 | 1.689 | 0.865 | 0.553 | 5.421 |
| ROA | 3285 | 0.037 | 0.040 | 0.054 | -0.188 | 0.158 |
| Top1 | 3285 | 32.015 | 30.140 | 14.261 | 8.020 | 71.770 |
| Lev | 3285 | 0.431 | 0.432 | 0.169 | 0.080 | 0.792 |
| Size | 3285 | 22.301 | 22.119 | 1.224 | 20.177 | 26.049 |
The baseline regression results, with control variables added progressively, are shown in the following table. The coefficient for government subsidies (Sub) remains significantly positive at the 1% level across all specifications (e.g., column 6 coefficient = 0.0870), indicating that a one-unit increase in government subsidies boosts innovation performance in the electric car industry by 0.0870 units. This supports hypothesis H1. Among the control variables, firm size and ownership concentration show significant positive effects on innovation performance, whereas the asset-liability ratio, capital intensity, and return on assets exhibit significant inhibitory effects. This suggests that high debt levels and capital intensity may crowd out R&D funds, while high asset returns might encourage short-term profit orientation over long-term innovation investments in the electric car sector.
| (1) | (2) | (3) | (4) | (5) | (6) | |
|---|---|---|---|---|---|---|
| Variable | Innovation | Innovation | Innovation | Innovation | Innovation | Innovation |
| Sub | 0.241*** | 0.0945*** | 0.0942*** | 0.0901*** | 0.0870*** | 0.0870*** |
| (10.71) | (3.98) | (3.97) | (3.80) | (3.66) | (3.67) | |
| Size | 0.716*** | 0.722*** | 0.774*** | 0.796*** | 0.822*** | |
| (14.92) | (15.05) | (15.34) | (15.51) | (15.68) | ||
| Top1 | 0.0102*** | 0.0112*** | 0.0108*** | 0.0118*** | ||
| (3.39) | (3.69) | (3.58) | (3.88) | |||
| Lev | -0.574*** | -0.600*** | -0.739*** | |||
| (-3.27) | (-3.42) | (-4.00) | ||||
| Capital | -0.0696** | -0.0940*** | ||||
| (-2.30) | (-2.94) | |||||
| ROA | -0.860** | |||||
| (-2.38) | ||||||
| Constant | -0.462 | -13.71*** | -14.18*** | -15.07*** | -15.36*** | -15.81*** |
| (-1.26) | (-14.34) | (-14.71) | (-15.07) | (-15.25) | (-15.44) | |
| Controls | Yes | Yes | Yes | Yes | Yes | Yes |
| Individual FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Year FE | Yes | Yes | Yes | Yes | Yes | Yes |
| N | 3285 | 3285 | 3285 | 3285 | 3285 | 3285 |
| R²-adjusted | 0.081 | 0.148 | 0.151 | 0.154 | 0.155 | 0.157 |
Note: t-statistics in parentheses; *p<0.1, **p<0.05, ***p<0.01.
To address potential endogeneity concerns, such as the influence of prior innovation performance on current outcomes and reverse causality between government subsidies and innovation performance, we employ a two-step system GMM approach. We treat government subsidies and lagged innovation performance as endogenous variables, using their lagged terms as instruments, while controlling for firm and time fixed effects. The results, presented in the table below, show that the AR(2) test (p=0.247) and Hansen test (p=0.155) meet the requirements, confirming the validity and exogeneity of the instruments. After controlling for endogeneity, the government subsidy coefficient remains significantly positive at the 1% level, further validating H1 and underscoring the robustness of our findings in the electric car context.
| (1) | |
|---|---|
| Variable | Innovation |
| L.Innovation | 0.520*** |
| (0.063) | |
| L2.Innovation | 0.101*** |
| (0.036) | |
| Sub | 0.126*** |
| (0.041) | |
| Constant | -4.299*** |
| (1.170) | |
| Controls | Yes |
| Number of Individuals | 462 |
| AR(1) p-value | 0.000 |
| AR(2) p-value | 0.247 |
| Hansen p-value | 0.155 |
| N | 2361 |
We conduct robustness checks from three perspectives to ensure the reliability of our results. First, we replace the explanatory variable with the ratio of government subsidies to operating revenue. As shown in column (1) of the table below, the coefficient for government subsidies remains significantly positive at the 1% level. Second, we use robust standard errors to address potential heteroskedasticity, and the results in column (2) confirm the significant positive effect. Third, we lag the dependent variable by one period to account for the time lag in the impact of subsidies on innovation performance; column (3) shows that the coefficient is still significantly positive at the 1% level. These checks consistently support H1, indicating that government subsidies robustly promote innovation performance in the electric car industry.
| (1) | (2) | (3) | |
|---|---|---|---|
| Variable | Innovation | Innovation | L.Innovation |
| Sub1 | 5.409*** | ||
| (2.69) | |||
| Sub | 0.0870*** | 0.0963*** | |
| (2.71) | (3.84) | ||
| Constant | -16.25*** | -15.81*** | -14.61*** |
| (-15.85) | (-9.89) | (-12.86) | |
| Controls | Yes | Yes | Yes |
| Individual FE | Yes | Yes | Yes |
| Year FE | Yes | Yes | Yes |
| N | 3285 | 3285 | 2823 |
| R²-adjusted | 0.155 | 0.275 | 0.079 |
To test the mediating mechanism of R&D investment, we apply the stepwise regression method. The results in the table below show that in column (1), the total effect of government subsidies on innovation performance is significantly positive. Column (2) indicates that government subsidies significantly promote R&D investment in the electric car industry. In column (3), after including the mediating variable, the direct effect of subsidies remains positive but decreases by 15.9% compared to the total effect (calculated as 1 – 0.0732/0.087), demonstrating that R&D investment plays a partial mediating role. This confirms that government subsidies indirectly enhance innovation performance by stimulating R&D investment, thereby supporting H2. The electric car industry’s reliance on continuous innovation underscores the importance of this pathway, as subsidies enable firms to allocate more resources to R&D, leading to advancements in electric car technologies such as battery efficiency and autonomous driving systems.
| (1) | (2) | (3) | |
|---|---|---|---|
| Variable | Innovation | RD | Innovation |
| Sub | 0.0870*** | 0.268*** | 0.0732*** |
| (3.67) | (4.82) | (3.09) | |
| RD | 0.0515*** | ||
| (6.45) | |||
| Constant | -15.81*** | 16.40*** | -16.65*** |
| (-15.44) | (6.83) | (-16.25) | |
| Controls | Yes | Yes | Yes |
| Individual FE | Yes | Yes | Yes |
| Year FE | Yes | Yes | Yes |
| N | 3285 | 3285 | 3285 |
| R²-adjusted | 0.157 | 0.027 | 0.169 |
We further conduct heterogeneity analyses based on firms’ positions in the electric car industry chain and their size. The electric car industry chain includes upstream (raw material supply), midstream (component manufacturing), and downstream (vehicle manufacturing and charging infrastructure) segments, each with distinct business scopes, technological levels, market positions, and efficiency in utilizing government subsidies. The results in the table below reveal that government subsidies have significant positive effects on innovation performance for upstream and midstream firms (columns 1 and 2, coefficients significant at 1%), but no significant effect for downstream firms (column 3). A Chow test for coefficient differences across segments yields a p-value of 0.0008, indicating significant heterogeneity. Specifically, the coefficient for midstream firms (0.281) is higher than for upstream firms (0.0754), suggesting that midstream firms, which often focus on core components like batteries and motors, are more effective in leveraging subsidies to drive innovation in the electric car sector.
Regarding firm size, we divide the sample into large and small-to-medium enterprises based on the mean firm size. Columns (4) and (5) show that government subsidies significantly promote innovation performance for both groups (coefficients significant at 1%), but the effect is stronger for large firms (coefficient 0.235) compared to small and medium-sized firms (coefficient 0.179). A Chow test confirms significant differences (p=0.0000). This implies that large firms, with their greater resources and economies of scale, are better positioned to utilize subsidies for innovation in electric car development, whereas smaller firms may face constraints in absorbing and applying such support.
| (1) Upstream | (2) Midstream | (3) Downstream | (4) Large Firms | (5) SMEs | |
|---|---|---|---|---|---|
| Variable | Innovation | Innovation | Innovation | Innovation | Innovation |
| Sub | 0.0754*** | 0.281*** | -0.0402 | 0.235*** | 0.179*** |
| (3.05) | (2.85) | (-0.33) | (7.29) | (5.01) | |
| Constant | -16.02*** | -16.13*** | -17.22*** | -0.237 | -0.198 |
| (-15.22) | (-2.84) | (-2.79) | (-0.40) | (-0.32) | |
| Controls | Yes | Yes | Yes | Yes | Yes |
| Individual FE | Yes | Yes | Yes | Yes | Yes |
| Year FE | Yes | Yes | Yes | Yes | Yes |
| Chow Test p-value | 0.0008 | 0.0000 | |||
| N | 2820 | 300 | 165 | 1643 | 1642 |
| R²-adjusted | 0.180 | 0.119 | 0.062 | 0.045 | 0.015 |
In conclusion, our study demonstrates that government subsidies positively influence innovation performance in the electric car industry, primarily through enhanced R&D investment. However, these effects vary across industry segments and firm sizes, with midstream and large firms benefiting more significantly. To achieve high-quality development in the electric car industry and maximize the positive impact of subsidies, we propose the following policy recommendations. First, optimize government subsidy policies by tailoring them to different segments of the electric car industry chain. For midstream firms, increase support for key technology R&D and industrial upgrading; for large firms, focus on guiding前瞻性 and strategic R&D to enhance overall competitiveness. Encourage large electric car enterprises to establish technical collaborations with global benchmarks, facilitating the introduction of cutting-edge technologies and management models to boost international competitiveness. Second, strengthen the supervision of subsidy funds by establishing scientific evaluation indicators that cover technological innovation capability, market competitiveness, and economic benefits. Implement transparent监管 mechanisms, regularly disclosing innovation performance data to ensure subsidies are used for technological progress rather than short-term capacity expansion in the electric car sector. Third, reinforce R&D investment management by encouraging firms to establish sound mechanisms for allocating resources. Foster closer industry-university-research partnerships among electric car companies, research institutions, and universities, supported by special funds and policies. Improve intellectual property protection systems to safeguard R&D outcomes and innovations, providing institutional guarantees for corporate innovation activities in the electric car industry. By implementing these measures, we can better leverage government subsidies to drive sustainable growth and innovation in the electric car market, contributing to global efforts in reducing carbon emissions and promoting green transportation.