Government subsidies and innovation in new energy vehicle companies: An empirical study of new energy vehicle listed companies based on Shanghai and Shenzhen A-shares

Abstract

The panel data of 50 new energy vehicle enterprises in Shanghai and Shenzhen A-shares from 2012 to 2021 are selected to empirically analyze the impact of government subsidies on the innovation of new energy vehicle enterprises and to further discuss the differences between such an impact in different forms and regions. The study finds that, first, government subsidies have a certain promotion effect on the innovation of new energy vehicle enterprises, and an inverted U-shaped relationship exists thereof. Second, at the enterprise level, government subsidies have a significant effect on the innovation of non-state enterprises, downstream vehicle enterprises, and enterprises with lower establishment years, and the inverted-U trend is evident. Third, at the regional level, government subsidies have a more significant effect on the innovation of enterprises in non-eastern regions and low-environmental regulation regions, and the inverted-U-shaped relationship trend is more apparent. The study establishes the nonlinear relationship between government subsidies and the innovation of new energy vehicle enterprises through empirical research, which expands the theory of enterprise innovation and has a certain guiding significance for improving the innovation capability of new energy vehicle enterprises in the future.

1. Introduction

Owing to the unique external advantages of new energy vehicles in connection with energy conservation, emission reduction, and low-carbon environment protection [1, 2], they are gradually becoming an emerging industry, strongly supported by the world nowadays. Moreover, promoting the development of the new energy vehicle industry is a robust approach toward seizing the high ground of science and technology innovation and competing for the national environmental protection discourse [3, 4]. China’s rapid GDP growth has been accompanied by increasingly severe environmental pollution problems [5], and the development and popularization of new energy vehicles are of great significance for China to cope with environmental problems and achieve the goal of carbon neutrality and carbon peaking. Unlike traditional industries, the new energy vehicle industry, as a high-tech industry, faces multiple challenges, such as high production costs, large investments in research and development (R&D), and large market uncertainty [6]. Therefore, government departments need to provide certain policy support through macro-control, especially in the early stages of development. China launched the "863" program in 2001 to help develop new energy vehicles, an innovation that ushered in the development of China’s new energy vehicles, and by 2022, China’s new energy vehicle production and sales ranked first worldwide. Although the overall volume of China’s new energy vehicles is large, the development process has caused some controversy. The subsidy-based form of support inevitably induces some market failures, such as the "rent-seeking" behavior of enterprises due to subsidies, and special subsidies cannot be earmarked for specific purposes. Furthermore, the frontier technology innovation capability remains slightly inadequate, as indicated by the "lack of core" of new energy vehicles [7]. These problems seriously restrict the pace of upgrading China’s new energy vehicle industry.

Determining whether government subsidies play a role in promoting new energy vehicle enterprises is an extensively discussed topic in the current research on new energy vehicles. The existing literature roughly divides the mechanism of the role of government subsidies on enterprise innovation into three broad categories. First, government subsidies have incentive effect on enterprise innovation [8]. Second, government subsidies inhibit enterprise innovation [9]. Third, there is no significant effect [10]. However, from a nonlinear perspective, this study explores the nonlinear relationship between government subsidies and new energy vehicle enterprises based on the linear relationship, which enriches the innovation theory of new energy vehicle enterprises, further expands the study of nonlinear mechanisms, and provides evidence for the inverted U-shaped relationship between government subsidies and enterprise innovation in China’s new energy vehicle industry.

Different types of enterprises are affected differently by government subsidies. As the enterprises may receive different degrees of support from government departments due to the nature of property rights [11], industrial chain division [12], and enterprise age [13], the policy effects may differ. Herein, we divide the enterprises into three industrial chains—upstream, middle, and downstream—which are more detailed and thorough relative to the existing literature. In addition, the location of the enterprise is also an important factor that influences the effect of the policy [14]. Some studies show regional imbalances in government subsidies [15]. This can lead to various marginal effects due to differences in regional innovation factors and government resource tilting. In this study, when considering regional factors, the intensity of environmental regulations is considered for two reasons. On the one hand, environmental regulations may considerably reflect the degree of regional aspirations for developing new energy vehicles. On the other hand, the intensity of environmental regulations can reflect the implementation of government department policies from the side. Most existing literature only considers the geographical division of the region while ignoring the differences in policy implementation, which are considered in this study.

How do government subsidies affect innovation in new energy vehicle companies? How are different types and regions of firms affected? This study analyzes the mechanism of government subsidies on the innovation of new energy vehicle enterprises and the differences in the influence on different types of enterprises and regions from a quantitative perspective, based on panel data of enterprises from 2012 to 2021, which enriches the research on new energy vehicle micro-enterprises. This is especially of great reference value for the policy formulation and stimulation of enterprise innovation of new energy vehicles in China. The remainder of this paper is organized as follows: Section 2 presents the literature review and theoretical analysis, Section 3 focuses on the research design, Section 4 analyzes the empirical results, Section 5 presents the heterogeneity analysis, and, finally, Section 6 provides the conclusion and policy implications.

2. Literature review and theoretical analysis

2.1 Government grants and business innovation

Owing to the significant externalities associated with new energy vehicles, government innovation incentives in the form of government subsidies to enterprises help them achieve industrial upgrading, optimize resource allocation, and regulate market failures [16]. The development of China’s new energy vehicle industry currently relies mainly on government subsidies and technological innovation [17], and research on new energy vehicle micro-enterprises is basically centered on these two aspects. In the early stages of development, new energy vehicle enterprises face the dual pressures of low-cost competition from traditional fossil energy and the high cost of new energy [18]. Government subsidies can relieve the pressure of capital investment and financing restrictions [19], in addition to signaling R&D institutions, promoting cooperation between enterprises and R&D institutions, and improving the level of enterprise innovation [20]. However, in the long run, it may lead to the "subsidy dependence" of enterprises, which gives rise to difficulties in breaking through core technologies and overcapacity [21].

Research on the incentive effect of government subsidies on enterprise innovation are abundant. They show that government subsidies can directly or indirectly compensate for the shortage of enterprise innovation investment, effectively reduce the cost and risk of enterprise innovation, and facilitate enterprise innovation [22]. However, there are also views that government subsidies have a "crowding out" effect on enterprise innovation, thereby inhibiting enterprise innovation behavior, and government subsidies have a restrictive effect on enterprise technological innovation [17]. Additionally, some scholars point out that due to information asymmetry, the government tends to select enterprises with low risk and high short-term returns when screening target enterprises, which makes those enterprises with low short-term returns but good long-term development benefits often fail to receive subsidies [23]. The purpose of government subsidies is essentially to reduce the cost and potential risks of enterprise innovation through financial support and compensate for the shortage of enterprise innovation factors in response to market failure. However, inappropriate and excessive subsidies may result in the government paying for the inputs that should be provided by market mechanisms for enterprise technological innovation, and selective subsidy policies may cause the "crowding out" effect. Therefore, government subsidies need to be kept within a certain range, which can effectively promote enterprise innovation when the subsidies are below the threshold [24] and be counterproductive when they exceed the threshold [25]. Moreover, excessive government subsidies may also increase the risk of enterprise "rent-seeking" and "subsidy fraud," as well as increase the inertia of enterprises and generate negative externalities. Therefore, there may be a nonlinear mechanism between government subsidies and new energy vehicle enterprises, and the following hypothesis is proposed:

1. H1: There is an inverted U-shaped relationship between government subsidies and the innovation output of new energy vehicle enterprises, which is first incentivized and then inhibited.

2.2 Enterprise level heterogeneity

Under China’s special economic system, state owned enterprises (SOEs) and non-SOEs are subject to different institutional restrictions, project approval processes, and access to government subsidies [26]. Owing to the government’s "underwriting," SOEs have certain advantages over non-SOEs in terms of market competition. Even if the marginal output of SOEs’ innovation is low, they will not be eliminated from the market. Non-SOEs, as self-sufficient market players, are subject to market laws and regulations in a perfectly competitive market, but they have to continuously innovate themselves to adapt to market competition. This self-financing model highlights the incentive effect of government subsidies on enterprise innovation and reduces the risk of failure and the cost of institutional regulation [27].

The new energy vehicle industry chain is a complex ecosystem wherein upstream, midstream and downstream enterprises interact with each other, and a clear industrial division of labor system helps to study the innovation mechanism of new energy vehicle enterprises more thoroughly [28]. According to the division of labor in the new energy vehicle industry chain, the industry chain can be roughly divided into upstream, raw material development and utilization; midstream, core components and R&D and manufacturing of batteries, motors, and electric controls; and downstream, vehicle manufacturing and sales. With downstream vehicle enterprises as an important hub in the new energy vehicle industry chain, the innovation results will directly affect consumers, and better innovation results will receive positive feedback from consumers, which will lead to greater marginal gains and continuous technology iteration, forming a virtuous cycle of innovation [29].

As an important indicator of the maturity of a firm’s development, the number of years of firm growth has been a factor that many scholars should consider in influencing firm performance. The development of new energy vehicles in China is just over twenty years old. In general, emerging firms are becoming more active in the market; their market share is increasing; and their incentive to innovate is increasing, but they are less likely to receive government subsidies, probably due to their weaker political connections. Older enterprises have achieved economies of scale and may receive more government subsidies because of their strong political connections and monopoly position in the market, resulting in "subsidy fraud" and political rent-seeking behavior, which reduces the incentive for innovation [30] The following hypothesis is proposed:

1. H2: Government subsidies have a greater stimulating effect on the innovation of non-state owned, downstream industry chain, and less established new energy vehicle companies, with a more obvious trend of inverted "U" shaped relationship.

2.3 Regional level heterogeneity

On account of the higher level of economic development in the eastern region of China, enterprises generally have a higher level of innovation [31], and more diverse financing channels. The concentration of manufacturing industries in the eastern region also generates spatial agglomeration effects and positive externality effects, which lead to a higher level of innovation among enterprises in the eastern region and a higher threshold of policy stimulation, while the approval of subsidies in the eastern region is more stringent compared to the non-eastern region. As for the non-eastern regions, due to the inherent shortage of resource factors, government subsidies can quickly help their development and make up for the shortage of resources [32].

The intensity of environmental regulation reflects, to a certain extent, the level of governance and development in the region. Most of the regions with high environmental regulation belong to the more dense manufacturing regions, which have urgent requirements for the development of new energy vehicles and pay more attention to the promotion of innovation in new energy vehicles. This to a certain extent reduces the occurrence of market failure, while most of the regions with low environmental regulation belong to the less developed regions, where the development of new energy vehicles is at a preliminary stage and the government subsidies are more rugged and the incentive effect of the policy is more obvious [33]. The following hypothesis is proposed:

1. H3: Government subsidies have a greater effect on stimulating innovation in non-eastern, low-environmental-regulation regions, with a more pronounced inverted "U"-shaped relationship.

3. Research design

3.1 Sample selection

Considering that the subsidies for new energy vehicle enterprises began to gradually decline after 2012, which may affect the innovation output of new energy vehicle enterprises, this study selects new energy vehicle enterprises in Shanghai and Shenzhen A-shares from 2012 to 2021 as the research sample. To accurately distinguish new energy vehicle enterprises from traditional fuel vehicle enterprises, we refer to the division of new energy vehicle enterprises by the Ministry of Industry and Information Technology in conjunction with the YEARBOOK OF ENERGY-SAVING NEW ENERGY VEHICLE. Fifty listed new energy vehicle enterprises are selected as the research sample, among which 10 belong to upstream enterprises, 16 belong to midstream enterprises, and 24 belong to downstream enterprises (Table 1).

[Figure omitted. See PDF.]

3.2 Model setting

To further investigate the relationship between government subsidies and firm innovation, based on the relevant theoretical analysis, the following static panel estimates are constructed, where, M1 is estimated linearly and M2 is estimated nonlinearly by adding a quadratic term of government subsidies on the base of M1.

Considering that the innovation output of the previous period may have an impact on the innovation output of the current period, the following dynamic estimated panel model is constructed with reference to Li et al. [34], where, M3 is a linear dynamic panel model and M4 is a nonlinear dynamic panel model.

Patent is the dependent variable, which indicates the innovation output of the new energy vehicle enterprises in the current year. Subsidy is the core explanatory variable, which indicates the government subsidies received by the enterprises in the current year. X is a series of control variables, including R&D expenditure (Expenditure); R&D staff input (Staff); the year of establishment (Age); the asset and liability ratio (Debt); Tobin Q (Tobin Q); the shareholding ratio of the first largest shareholder (Share); cashflow ratio (Cashflow); return on assets (ROA); γ is a time-fixed effect; μ is an individual fixed effect; and σ is an error term.

3.3 Variable description

Explained variables: Two common indicators for measuring firm innovation are generally used: one is the number of patents, including applications, grants and citations of patents [35], while the other is the use of sales revenue from new products [36]. As the sales revenue of new products has not been published in the annual reports of enterprises, based on the availability of data, this study uses the number of patent applications to indicate the innovation capability of enterprises, which includes invention patents, utility model patents and design patents, and searches the patent applications of sample enterprises on new energy vehicles on the pages of the State Intellectual Property Office of China (SIPO) and the 2010 World Intellectual Property Organization provided. The number of new energy vehicle-related patents filed by enterprises each year is obtained by matching the IPC classification numbers of new energy vehicle-related patents with the types of enterprise-level patents obtained from the SIPO search.

Core explanatory variables: Government subsidies are usually divided into direct and indirect subsidies. The former usually refers to government subsidies for enterprises’ innovation activities, while the latter usually connotes tax rebates, tax incentives, and other relief measures for enterprises [37]. As the annual reports of Chinese A-share companies do not disclose data related to direct government subsidies, the "government subsidies included in current profit and loss" in the annual reports are used as a proxy variable, which may be greater than the actual amount of direct government subsidies, but the difference is not too large [38].

Control variables: The number of enterprise R&D personnel and enterprise R&D expenditure can respond to the scale of enterprise R&D investment, and the intensity of enterprise R&D innovation activities will increase with the scale of enterprise R&D investment [39]. Enterprises with larger R&D scales may have achieved economies of scale with better R&D innovation capability. They are more likely to be favored by the government and receive more subsidies compared to enterprises with smaller R&D investments [40]. Older firms are more likely to receive subsidies compared to those with lower years of establishment [41], while younger firms do not have a significant advantage in receiving subsidies. Innovation activities are characterized as long-term and high-risk for enterprises. Moreover, when the financial situation of enterprises is poor, the innovation behavior of enterprises will be significantly affected, while corporate governance can influence the innovation activities of enterprises by affecting the enthusiasm of employees, and the concentration of equity can better reflect the governance of enterprises [42], represented by the shareholding ratio of the first largest shareholder in this study. The fundamental motive of corporate innovation is to obtain excess profit and higher return on capital, and the profitability condition and financial status of the company itself greatly determine the innovative behavior of the company. Thus, this study controls for the liability ratio, Tobin’s Q, return on assets, and cashflow ratio.

Table 2 presents the measures and sources of each variable. To reduce noise from extreme data, all data are winsorized at the 1% and 99% quartiles.

[Figure omitted. See PDF.]

3.4 Variable data analysis

Table 3 shows the descriptive statistics of each variable (some variables have been logarithmically processed). The patent output of new energy vehicle enterprises varies from 0.00 to 8.503, with a mean value of 4.501 and a standard deviation of 2.054, indicating large differences between the innovation outputs of different new energy vehicle enterprises and that the sample is informative. Additionally, the mean value of government subsidies is 18.047 with a standard deviation of 1.771, and the overall representativeness of the sample is good. To avoid pseudo-regression, the correlation coefficient test is conducted for each variable, and the correlation between most of the variables is significant at the 1% level, with a high degree of correlation, which reduces the possibility of pseudo-regression to a greater extent. In addition, all the variables pass the variance inflation factor test, and the coefficients of each are much less than 10, indicating absence of multicollinearity among the variables, which provides the basis for the subsequent empirical study.

[Figure omitted. See PDF.]

4. Empirical results

4.1 Basic regression analysis

Panel regression is used to empirically test 50 new energy vehicle companies, and both static and dynamic panels are used for estimation to ensure the robustness of the results. All regressions are subjected to the Hausman test before running a regression, and the results show that fixed effects should be selected for estimation. Furthermore, all the AR(2) values in dynamic panel estimation are greater than 0.1 and pass the Sargon test to ensure the accuracy of dynamic panel estimation. Two methods, namely, differential and systematic gaussian mixture model (GMM), are adopted for dynamic panel estimation. The results of the underlying regression are shown in Table 4. Columns (1), (3), and (4) verify the linear mechanism of government subsidies on the innovation of new energy vehicle enterprises. This indicates a significant promotion effect of government subsidies on the innovation of new energy vehicle enterprises, while the dynamic panel estimation shows that the innovation output of enterprises in the previous period would significantly increase the innovation output in the current period, thereby verifying the inertia of enterprise innovation [43, 44]. Columns (2), (5), and (6) further investigate the nonlinear mechanism of government subsidies based on linear estimation, and the results show that the coefficients of Subsidy² are significantly negative at a significance level greater than 5%, indicating that there is a certain crowding-out effect of government subsidies on enterprise innovation. There is an inverted "U" shaped relationship, which is consistent with the findings of Dai, Liu et al. [45, 46] thus, H1 is verified. Further analysis shows that the inflection point of the inverted U curve of government subsidies is 18.593, while the average value of government subsidies for the sample enterprises is 18.047, which indicates that the current government subsidies are basically at the inflection point of the inverted U curve vis-à-vis the promotion of innovation in new energy vehicle enterprises.

[Figure omitted. See PDF.]

4.2 Robustness tests

There may be a strong endogeneity problem between government subsidies and enterprise innovation, and the more innovative enterprises may receive more government subsidies. To ensure the reliability of the regression results and to avoid the endogeneity problem, this study also conducts the following robustness tests.

Considering the possible long time lag of government subsidies, the explanatory variable patent is regressed again with one period lag to test the robustness of the findings, and the results are shown in Table 5(1), where the coefficient of the core explanatory variable Subsidy² is -0.039, which is significantly negative at the 5% significance level, again confirming the inverted U-type relationship between government subsidies and corporate innovation.

[Figure omitted. See PDF.]

To overcome the endogeneity in sample selection, the two-stage least squares method is used for instrumental variable (IV) testing, aiming to effectively circumvent the possible endogeneity problem. Government subsidies with a one-period lag are selected as the IVs, which usually have an impact on current corporate innovation, while current corporate innovation did not have an impact on government subsidies in the previous period, and the use of this IV can avoid the endogeneity problem to some extent. The IVs all pass the weak IV and non-identifiable tests, and the regression results are shown in Table 5(2), where the coefficient of Subsidy² is -0.306, which is significantly negative at the 5% significance level.

Corporate innovation is divided into two processes, namely, input and output, and can be measured from both the input and output perspectives. In this section, the number of patent applications from the output perspective is replaced with the RD input intensity, which can reflect the size of corporate innovation capability [47]. The measure is corporate R&D investment divided by total corporate assets, and the regression results are shown in Table 5(3), where the coefficient of Subsidy² is significantly negative at the 5% significance level and the conclusion remains robust.

Replacing the control variables, R&D personnel and R&D expense input are the key variables that directly affect the innovation output of enterprises. They may be highly correlated with the innovation output of enterprises; the higher the profits of enterprises, the more they may invest in R&D expenses; and the total number of employees can also reflect the number of R&D personnel on the side. Replacing the R&D expenses and R&D personnel input with operating profit and the total number of employees in service regression again. The results are shown in Table 5(4), and the coefficient of Subsidy² is -0.059, which is significantly negative at the 1% significance level. The results remain consistent with the previous conclusion.

5. Heterogeneity analysis

5.1 Enterprise heterogeneity analysis

The samples of new energy vehicle enterprises are divided into state- and non-state-owned new energy vehicle enterprises. The division is based on the ownership of the first largest shareholder, defined as an SOE if the first largest shareholder is a state-owned legal person and a non-SOE if it is a non-state-owned legal person. The results of the heterogeneity of property rights are shown in Table 5(1)(2). The Subsidy² coefficient of non-SOEs is -0.043 (p-value <0.1), while that of SOEs is -0.035 (p-value >0.1), which indicates that the incentive effect of government subsidies is more prominent for non-SOEs than SOEs. The inverted U curve is steeper, and the incentive effect of government subsidies on SOEs is not statistically significant. This is possibly because SOEs will have the underwriting guarantee from the government, and they may form a dependence effect on innovation incentives due to this underwriting guarantee and will not be eliminated from the market due to the decline of innovation ability. However, non-SOEs must continuously innovate their products to adapt to the severe market competition due to their self-financing characteristics, and the characteristics of the market economy determine the inevitability of innovation incentives for non-SOEs.

According to the main businesses of the sample enterprises, they are divided into upstream, midstream, and downstream industrial chains (see Table 1), and the results of industrial chain heterogeneity are shown in Table 5(3)(4)(5). The coefficients of downstream vehicle enterprises, midstream component enterprises, and upstream raw material Subsidy² are -0.053 (p-value <0.05), 0.026 (p-value >0.1), and 0.001 (p-value >0.1), respectively. This indicates that government subsidies significantly stimulate the innovation activities of vehicle enterprises, while the innovation incentives of component enterprises and raw material enterprises are not significant. In addition, the trend of the inverted U curve for downstream vehicle enterprises is significant, while that for component enterprises and raw material enterprises is not significant. This is because the value-added products of downstream vehicle enterprises are higher, their innovation power will directly affect the market, and the feedback effect of the market on vehicle innovation is greater, which is more likely to promote the public’s interest in and consumption of new energy vehicles. Furthermore, this is more conducive to the prosperity of the new energy vehicle market, which helps expand the consumer market and is more likely to be favored by the government, thus obtaining more subsidies and forming a virtuous circle. Conversely, component companies and raw material companies may not be able to play a direct role in the market because they are at the middle and upper ends of the industry chain, so they have fewer chances to obtain government subsidies and fewer incentives to innovate.

To examine the variability of the years of establishment, this study groups the sample enterprises according to the actual situation of the development of new energy vehicle enterprises and previous research results [48], using 20 years of establishment as the cut-off point, and the difference in the years of establishment of the enterprises is shown in Table 6(6)(7). The coefficients of Subsidy² for enterprises with more than 20 years of establishment and those with less than 20 years of establishment are -0.028 (p-value>0.1) and -0.230 (p-value<0.01), respectively, indicating that government subsidies are more significant for enterprises with fewer years of establishment and that the inverted U curve tends to be significant. The government subsidies provide these young enterprises with the opportunity to innovate and make trial-and-error decisions, while the older enterprises may have formed a monopoly in the market, so the incentive to innovate is insufficient and that the government subsidies can hardly stimulate their innovation activities. After the above analysis, H2 is verified.

[Figure omitted. See PDF.]

5.2 Regional heterogeneity analysis

Referring to Han [49], the sample firms are classified into eastern and non-eastern firms based on the address of each firm’s registered office and the provincial administrative divisions of China (eastern regions include: Hebei Province, Beijing, Tianjin, Shandong Province, Jiangsu Province, Shanghai, Zhejiang Province, Fujian Province, Guangdong Province, and Hainan Province). The regional differences are presented in Table 6(1)(2). The coefficients of Subsidy² for eastern and non-eastern enterprises are 0.006 (p-value>0.1) and -0.078 (p-value<0.1), respectively, indicating that government subsidies are more significant for non-eastern enterprises. Additionally, the inverted U curve tends to be significant, probably because of the higher level of economic development in the eastern region, and the technology level of enterprises in the eastern region is also relatively higher. Thus, the policy incentive effect of enterprises in the eastern region is lesser than that of enterprises in the non-eastern region, while the marginal benefit of the incentive effect of subsidies is greater in the non-eastern region due to the backwardness of resources and technology. Furthermore, in the eastern region, the economic development level is generally higher, as is the policy implementation level, and the transparency of government departments is also higher than that of the non-eastern region.

Referring to Chen [50], to avoid endogeneity, the ratio of keywords about environmental issues in the work report of the government of the district where the new energy vehicle enterprises are located to the total number of words in the report is taken to measure the intensity of environmental regulation. The keyword extraction tool is Python. The sample enterprises are divided into enterprises in high environmental regulation areas and enterprises in low environmental regulation areas according to the median of the intensity of environmental regulation. The difference in environmental regulation is shown in Table 7(3)(4). The coefficients of Subsidy² are -0.047 (p-value < 0.05) and -0.081 (p-value < 0.05), respectively, for firms in high and low environmental regulation areas, compared to government subsidies for firms in low environmental regulation areas, and the inverted U curve tends to be significant. The possible reason for this is that the marginal benefit of the subsidy incentive is greater in areas with low environmental regulations, where the development of new energy vehicles is slow due to technology and resource constraints. Thus, H3 is verified.

[Figure omitted. See PDF.]

6. Conclusion

This study empirically analyzes the relationship between government subsidies and innovation of new energy vehicle enterprises, taking 50 listed new energy vehicle enterprises in Shanghai and Shenzhen A-shares from 2012 to 2021 as a sample. It is found that, first, government subsidies promote the innovation of new energy vehicle enterprises to a certain extent, but beyond a certain limit, they will play a suppressive role, that is, there is an inverted U-type relationship. The current level of subsidies has reached the inflection point of the inverted U curve, and the stimulating effect of government subsidies has reached the threshold. Second, the incentive effect of government subsidies on the innovation of new energy vehicle enterprises is heterogeneous at the enterprise and regional levels. The incentive effect of government subsidies on the innovation of non-SOEs, enterprises in the downstream of the industry chain, and enterprises with lower establishment years is significant. Moreover, the trend of an inverted U-type relationship is evident, while the incentive effect on SOEs, enterprises in the middle and upper streams of the industry chain and enterprises with higher growth years is not significant, and the inverted U-type relationship is not evident. Third, the promotion effect of government subsidies on enterprises in non-eastern regions is significant, and the trend of an inverted U-type relationship is evident, while that in eastern regions is not significant. The inverted U-shaped relationship trend is more pronounced in low environmental regulation areas than in high ones, where government subsidies contribute more significantly.

Our study also provides two policy implications. First, improve the subsidy policy for new energy vehicle enterprises. In the general environment of the gradual retreat of new energy vehicle subsidies, the government’s role in regulating and leading the process of changing new energy vehicles from government-led to market-led is indispensable, and the subsidies should be gradually and gently retracted during the retreat stage [51]. Simultaneously, improve the threshold for new energy vehicle enterprises to apply for government subsidies, reduce subsidies while achieving optimization and upgrading of the new energy vehicle industry, improve the technical capabilities of new energy vehicle enterprises, increase the supervision of new energy vehicle enterprises applying for government subsidies, and avoid wrong subsidies and abusive subsidies to prevent "bottom-up competition" in the new energy vehicle market.

Second, develop a differentiated government subsidy system. At present, China’s new energy vehicle industry is at a critical market-led development stage. For the government, blindly providing subsidies will not only mean high policy costs but also reduce the efficiency of enterprise innovation. Therefore, in the process of providing subsidies, they should be targeted and clearly defined, reflecting a differentiated strategy for different regions and the nature of enterprises. To accelerate the high-quality development of new energy vehicles, the Chinese government should focus its subsidy level and policy regulation on SOEs, midstream and upstream enterprises, as well as enterprises in non-eastern regions with lower environmental regulation intensity. It should also allocate limited resources to such enterprises to improve the efficiency of subsidies and accelerate the transformation and upgrading of the new energy vehicle industry.

Supporting information

S1 Data.

https://doi.org/10.1371/journal.pone.0284693.s001

(DTA)

Citation: Sun J, Tian M, Zhang W, Ning J (2023) Government subsidies and innovation in new energy vehicle companies: An empirical study of new energy vehicle listed companies based on Shanghai and Shenzhen A-shares. PLoS ONE 18(4): e0284693. https://doi.org/10.1371/journal.pone.0284693

About the Authors:

Jianguo Sun

Roles: Conceptualization

Affiliation: School of Economics, Henan University, Kaifeng, Henan, China

Mingfu Tian

Roles: Investigation, Methodology

E-mail: [email protected]

Affiliation: School of Economics, Henan University, Kaifeng, Henan, China

ORICD: https://orcid.org/0000-0002-7777-1079

Weitong Zhang

Roles: Formal analysis

Affiliation: Institute of Zhongyuan Development, Henan University, Kaifeng, Henan, China

Jingyi Ning

Roles: Visualization

Affiliation: School of Business, Zhengzhou University, Zhengzhou, Henan, China

References

1. Gu J, Renwick N, Xue L. The BRICS and Africa’s search for green growth, clean energy and sustainable development. Energy Policy. 2018;120:675–83.

2. Kaplan YA. Overview of wind energy in the world and assessment of current wind energy policies in Turkey. Renewable and Sustainable Energy Reviews. 2015;43:562–8.

3. Akintande OJ, Olubusoye OE, Adenikinju AF, Olanrewaju BT. Modeling the determinants of renewable energy consumption: Evidence from the five most populous nations in Africa. Energy. 2020;206:117992.

4. Vidadili N, Suleymanov E, Bulut C, Mahmudlu C. Transition to renewable energy and sustainable energy development in Azerbaijan. Renewable and Sustainable Energy Reviews. 2017;80:1153–61.

5. Liu W, Hu G, Tang L, Wang Y. China’s global growth in social science research: Uncovering evidence from bibliometric analyses of SSCI publications (1978–2013). Journal of informetrics. 2015;9(3):555–69.

6. Jaffe AB, Stavines RN. The energy efficiency gap: What does it means? Energy Policy 1994;(22):804–10.

7. Fang-sheng L, Shi-jia Z, You-bo H. Key Issues and Measure Suggestions for Iternational Development of China·s New Energy Automobile Industry. Scientific Management Research. 2021;39(05):72–8.

8. Bronzini R, Piselli P. The impact of R&D subsidies on firm innovation. Research Policy. 2016;45(2):442–57.

9. Shu C, Wang Q, Gao S, Liu C. Firm patenting, innovations, and government institutional support as a double-edged sword. Journal of Product Innovation Management. 2015;32(2):290–305.

10. Klette TJ, Møen J. R&D investment responses to R&D subsidies: A theoretical analysis and a microeconometric study. World Review of Science, Technology and Sustainable Development. 2012;9(2–4):169–203.

11. Dong L, Yan S, wei Z, Dan Y. Political Achievement Demands of Local Government, Goverment Control Right and Corporate Value. Economic Research Journal. 2014;45(01):56–9.

12. Jiang C, Zhang Y, Bu M, Liu W. The effectiveness of government subsidies on manufacturing innovation: Evidence from the new energy vehicle industry in China. Sustainability. 2018;10(6):1692.

13. Liping W, Ziting Y, Chuang L. Quasi-natural experimental study on new environmental protection law on the performance of listed industrial enterprise. Industrial Economics Research. 2021;(04):115–28.

14. Herrera L, Nieto M. The national innovation policy effect according to firm location. Technovation. 2008;28(8):540–50.

15. Květoň V, Horák P. The effect of public R&D subsidies on firms’ competitiveness: Regional and sectoral specifics in emerging innovation systems. Applied Geography. 2018;94:119–29.

16. Xu L, Su J. From government to market and from producer to consumer: Transition of policy mix towards clean mobility in China. Energy Policy. 2016;96:328–40.

17. Clausen TH. Do subsidies have positive impacts on R&D and innovation activities at the firm level? Structural change and economic dynamics. 2009;20(4):239–53.

18. Siddiqui AS, Marnay C, Wiser RH. Real options valuation of US federal renewable energy research, development, demonstration, and deployment. Energy policy. 2007;35(1):265–79.

19. Almus M, Czarnitzki D. The effects of public R&D subsidies on firms’ innovation activities: the case of Eastern Germany. Journal of Business & Economic Statistics. 2003;21(2):226–36.

20. Bianchi M, Murtinu S, Scalera VG. R&D subsidies as dual signals in technological collaborations. Research Policy. 2019;48(9):103821.

21. Liu Y, Kokko A. Who does what in China’s new energy vehicle industry? Energy policy. 2013;57:21–9.

22. Luo G, Liu Y, Zhang L, Xu X, Guo Y. Do governmental subsidies improve the financial performance of China’s new energy power generation enterprises? Energy. 2021;227:120432.

23. Wallsten SJ. The effects of government-industry R&D programs on private R&D: the case of the Small Business Innovation Research program. The RAND Journal of Economics. 2000:82–100.

24. Howell ST. Financing innovation: Evidence from R&D grants. American economic review. 2017;107(4):1136–64.

25. Hussinger K. R&D and subsidies at the firm level: An application of parametric and semiparametric two-step selection models. Journal of applied econometrics. 2008;23(6):729–47.

26. X YF. Analysis on the Strategies of the Improvement of the Independent Innovation Ability of State-Owned Enterprises. Economic Review Journa. 2021;(6):97–3.

27. Zhang P, Deng R, K ZL. A Study on the Relationship between Entrepreneur’s Social Capital and Entrepreneurial Performance. Science Research Management. 2015;(8):120–8.

28. B J , J GJ. The influence of economic growth and policy adjustment on enterprise competitiveness:Empirical analysis on China`s industrial panel data. China Industrial Economics. 2014;32(3):5–17.

29. Zheng-xia HE, Chang-shuai C, Jian-ming W. Research on the Impact of Policy Incentives on New Energy Vehicle Technology Innovation. Scientific Decision Making. 2022;298(05):71–85.

30. Du J, Mickiewicz T. Subsidies, rent seeking and performance: Being young, small or private in China. Journal of business venturing. 2016;31(1):22–38.

31. La Porta R, Lopez-de-Silanes F, Shleifer A, Vishny R. Investor protection and corporate valuation. The journal of finance. 2002;57(3):1147–70.

32. Cai W, Ye P. How does environmental regulation influence enterprises’ total factor productivity? A quasi-natural experiment based on China’s new environmental protection law. Journal of Cleaner Production. 2020;276:124105.

33. Xiao-qi W, Shuang-guang H, Jun-min Z. New Environmental Protection Law and corporate green innovation: forcing or forcing out? China Population,Resources and Environment. 2020;30(07):107–17.

34. Li Q, Wang M, Xiangli L. Do government subsidies promote new-energy firms’ innovation? Evidence from dynamic and threshold models. Journal of Cleaner Production. 2021;286:124992.

35. Cornaggia J, Mao Y, Tian X, Wolfe B. Does banking competition affect innovation? Journal of financial economics. 2015;115(1):189–209.

36. Lin C, Lin P, Song FM, Li C. Managerial incentives, CEO characteristics and corporate innovation in China’s private sector. Journal of comparative economics. 2011;39(2):176–90.

37. Shao W, Yang K, Bai X. Impact of financial subsidies on the R&D intensity of new energy vehicles: A case study of 88 listed enterprises in China. Energy Strategy Reviews. 2021;33:100580.

38. Jun W. The experiential study on the impact of government R&D subsidies on the business R&D input and innovation output. Studies in Science of Science. 2010;28(09):1368–74.

39. Levin RC, Cohen WM, Mowery DC. R&D Appropriability, Opportunity, and Market Structure: New Evidence on Some Schumpeterian Hypotheses. American Economic Review 1985;75(2):20–4.

40. Busom I. An empirical evaluation of the effects of R&D subsidies. Economics of innovation and new technology. 2000;9(2):111–48.

41. Czarnitzki D. Research and development in small and medium-sized enterprises: The role of financial constraints and public funding. Scottish journal of political economy. 2006;53(3):335–57.

42. Lu G, Wang Z, Zhang C. Research on the performance of government innovation subsidies for strategic emerging industries in China. Economic Research Journal 2014;49(07):44–55.

43. Sung B. Do government subsidies promote firm-level innovation? Evidence from the Korean renewable energy technology industry. Energy Policy. 2019;132:1333–44.

44. Yang F, Cheng Y, Yao X. Influencing factors of energy technical innovation in China: Evidence from fossil energy and renewable energy. Journal of Cleaner Production. 2019;232:57–66.

45. Dai X, Cheng L. The effect of public subsidies on corporate R&D investment: An application of the generalized propensity score. Technological Forecasting and Social Change. 2015;90:410–9.

46. Liu D, Chen T, Liu X, Yu Y. Do more subsidies promote greater innovation? Evidence from the Chinese electronic manufacturing industry. Economic Modelling. 2019;80:441–52.

47. Hagedoorn J, Cloodt M. Measuring innovative performance: is there an advantage in using multiple indicators? Research policy. 2003;32(8):1365–79.

48. Zhan-ren Z, -li LY. The international R&D investment transfer of global innovation value chain model. Studies in Science of Science. 2015;33(10):1496–503+5.

49. Han C, Hu H. How Does Clean Production Standards Regulation Dynamically Affect TFP-A Quasi-Natural Experiment Analysis with Policy Interference Eliminated. Chin Ind Econ. 2015;326(5):70–82.

50. Shiyi C, Dengke C. Air Pollution, Government R egulations and High-quality Economic Development. Economic Research Journal. 2018;53(02):20–34.

51. Yue G. The Effect of the Subsidy Reduction Policy and its Implication———the Case of China’s Electric Vehicles. Soft Science. 2020;34(12):28–32+46.