Do government subsidies promote efficiency in technological innovation of China’s photovoltaic enterprises?

Journal Pre-proof Do government subsidies promote efficiency in technological innovation of China's photovoltaic enterprises? Boqiang Lin, Ranran Luan PII:

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https://doi.org/10.1016/j.jclepro.2020.120108

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JCLP 120108

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Journal of Cleaner Production

Received Date: 22 May 2019 Revised Date:

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Accepted Date: 9 January 2020

Please cite this article as: Lin B, Luan R, Do government subsidies promote efficiency in technological innovation of China's photovoltaic enterprises?, Journal of Cleaner Production (2020), doi: https:// doi.org/10.1016/j.jclepro.2020.120108. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.

#The wordcount is 7926. Do government subsidies promote efficiency in technological innovation of China’s photovoltaic enterprises?

Boqiang Lin a, *, Ranran Luan a a

School of Management, China Institute for Studies in Energy Policy,

Collaborative Innovation Center for Energy Economics and Energy Policy，Xiamen University, Fujian 361005, PR China *Corresponding author: School of Management, China Institute for Studies in Energy Policy, Collaborative Innovation Center for Energy Economics and Energy Policy，Xiamen University, Fujian 361005, PR China. Tel: +86 5922186076; fax: +86 5922186075 E-mail addresses: [email protected], [email protected] (B. Lin).

Do government subsidies promote efficiency in technological innovation of China’s photovoltaic enterprises?

Boqiang Lin a, *, Ranran Luan a a

School of Management, China Institute for Studies in Energy Policy, Collaborative Innovation

Center for Energy Economics and Energy Policy，Xiamen University, Fujian 361005, PR China *Corresponding author: School of Management, China Institute for Studies in Energy Policy, Collaborative Innovation Center for Energy Economics and Energy Policy，Xiamen University, Fujian 361005, PR China. Tel: +86 5922186076; fax: +86 5922186075 E-mail addresses: [email protected], [email protected] (B. Lin).

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Highlights This paper estimates the innovation performance of China’s photovoltaic industry based on the micro perspective. This paper conducts a comprehensive analysis by a two-stage DEA-Tobit model. The overall innovation efficiency of Chinese photovoltaic enterprises is high relatively but still has progress potential. Government subsidies make a positive influence on technological innovation efficiency of China’s photovoltaic industry. The findings will serve as a reference for policy-making of government and enterprises.

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Abstract Supported by preferential policies and government funding, the technological innovation of China’s photovoltaic industry has been improved greatly. As a capital-intensive practice, will innovation activities in China’s photovoltaic industry be affected by the continuous decline of government subsidies in recent years? Therefore, it is essential to study the relationship between government subsidies and innovation performance of Chinese photovoltaic industry. However, there is less empirical research on this topic. In this context, this study aims to supplement existing studies by proposing a micro-level perspective to measure the innovation performance based on the data of Chinese listed photovoltaic companies from 2012 to 2016. Moreover, we assess the effects of government subsidies and other influencing factors on innovation performance. The findings reveal that, firstly, the average innovation efficiency of Chinese listed photovoltaic companies is over 0.9, which is relatively high. Secondly, government subsidies make a positive influence on innovation performance. Thirdly, financial leverage and ownership concentration have significant positive impacts, while firm size has a significant negative effect on the innovation efficiency of China’s photovoltaic industry. The findings will serve as a reference for policy-making to promote further technological progress and sustainable development of China’s photovoltaic industry. Keywords: Chinese photovoltaic industry; government subsidy; innovation performance; DEA -Tobit model

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1. Introduction Energy consumption is exploding along with the rapid development of the world economy. Energy prices have increased largely since the oil crisis broke out in 1973. Global energy shortage and environmental pollution have become severe problems (Xu, 2016). Paramati et al. (Paramati et al., 2017) believed that developing new energy sources can significantly help reduce carbon dioxide emissions. Therefore, under the pressure of energy consumption growth and climate change, alternative energy resources have developed into a crucial concern (Mekhilef et al., 2011). In the past decade, the clean energy economy has experienced explosive growth worldwide (Yi, 2014). As solar power is free, clean and abundant (Singh, 2013), it has attracted wide attention all around the world and the solar photovoltaic industry is developing rapidly (Jia et al., 2016; Pillai, 2015). Solar power will make a huge contribution to the sustainable development of economic society and environment with its great potential to replace fossil fuel (Xu, 2016). China is rich in solar energy that over 2/3 of the country has more than 2200 hours of sunshine annually (Zhang and He, 2013). Coal has long dominated China’s energy structure (Song et al., 2015b; Wei et al., 2018) that has threatened heavily the safety of energy and environment in China. In 2007, the carbon dioxide emissions of China from energy consumption sector reached 6.027 billion tons, surpassing the United States as the largest CO2 emitter (Shao et al., 2016; Song et al., 2015a). Further, Zhao et al. (Zhao et al., 2013) estimated that the primary energy demand in China will achieve 4.8 billion tons of standard coal by 2020, while the fossil fuels can only meet 70% of it. Although some of the advanced technologies for CO2 mitigation have been emerging (Sepehri and Sarrafzadeh, 2018; Sepehri et al., 2019), the fundamental way to solve the contradiction between 4 / 34

environmental pollution and energy consumption growth in China is to develop new energy actively (Xu and Lin, 2018), especially PV solar energy. In recent years, as energy-saving and emission reduction is always on the agenda (Yang et al., 2017), the Chinese government has paid high attention and given vigorous support to the photovoltaic industry. The Chinese PV industry has been designated as a strategic emerging industry for its rapid development (Sun et al., 2014). From 2013 to 2016, China ranked first continuously in the world in terms of new installed capacity of photovoltaic power generation. Since more than 55% of the world’s photovoltaic products were manufactured by Chinese companies in 2009, China has been the world’s biggest manufacturer of photovoltaic products for many years (Zhang et al., 2010). Although Chinese PV industry has experienced explosive development, the path of this growth is very erratic (Zhang et al., 2014), along with several challenges that Chinese PV industry is facing, such as overcapacity problem and so on. Suffered from the anti-dumping and anti-subsidy policies in Europe and the US, Chinese exports of photovoltaic products had experienced a sharp decline. Moreover, it is an indisputable fact that there exists a shortage of core technology including polysilicon and the key PV equipment relies on import. Furthermore, more than 95% of Chinese PV products are sold abroad. China’s photovoltaic industry has not established a comprehensive system of R&D and innovation; however, technological progress has been significant in recent years. Most of the key equipment for China’s photovoltaic manufacturing have been localized and intelligent manufacturing has gradually been implemented. Meanwhile, the international market of China’s photovoltaic products shows a diversified development trend, that has extended from developed countries to developing countries. The situation of “put both ends of Chinese photovoltaic industry chain outside” 5 / 34

(Wang et al., 2016) has been greatly improved. In recent years, photovoltaic power generation technology in China is increasingly maturing, with the falling of solar electricity costs. As a strategic emerging industry, the rapid expansion and technological innovation of Chinese PV industry cannot be separated from the support of national policies and funds. What is the current status of innovation in Chinese photovoltaic industry? Is it effective? As a capital-intensive practice, will the innovation activities of photovoltaic enterprises be affected by the continuous decline of photovoltaic subsidies? These are matters worthy of concern. However, there are only a few studies on the innovation of China’s photovoltaic industry and most of them are all about a specific innovation issue, we still cannot know the innovation status of the overall industry. Moreover, the performance measurement of China’s photovoltaic industry basically focuses on operating performance. As far as we know, researches on innovation performance evaluation of China’s photovoltaic industry is rare at present. Meanwhile, previous studies on the relationship between government subsidies and innovation performance mainly focused on large industries such as agriculture and manufacturing industry, and most of them were based on the macro perspective. However, photovoltaic enterprises are the subject of receiving government subsidies and innovation activities, it is more reasonable to study how government subsidies affect the innovation performance of China’s photovoltaic industry from the micro perspective. Therefore, it is of great significance for the government and enterprises to investigate technological innovation performance of Chinese photovoltaic industry and explore how government subsidies affect innovation efficiency. Based on the above analysis, the contributions of this study are summarized into three points: First, while the prior studies mainly focused on large-scale industry from the macro perspective, 6 / 34

this paper reveals the impacts of government subsidies on innovation efficiency from the micro perspective to help both government and enterprises to better make policies and measures for sustainable development of Chinese photovoltaic industry. Second, this article aims to supplement existing studies on the innovation problem of Chinese photovoltaic industry. It also explores the internal differences of innovation performance among the enterprises and explores how government subsidies affect innovation performance from the empirical level. Third, the DEA-Tobit model makes it possible to evaluate the innovation performance of Chinese photovoltaic enterprises and study the influence of government subsidies on innovation efficiency. Meanwhile, by controlling the heterogeneity among enterprises, the influence of other internal factors on innovation efficiency of enterprises can be detected. The remaining of this paper is organized as follows. Section 2 presents the literature review. Section 3 introduces the methodology. Data and variables are described in Section 4. Section 5 analyzes and discusses the results in detail. Finally, conclusions and policy implications are shown in Section 6. 2. Literature review 2.1. Research status of China’s photovoltaic industry With the development of China’s photovoltaic industry, many studies have focused on this industry in recent years. However, due to short time span data characteristics, qualitative researches are more than quantitative researches. Most qualitative articles focus on the following aspects. First, development status and prospects (Liu et al., 2010; Zhao et al., 2011) of China’s PV industry. Liu and Wang (Liu and Wang, 2009) 7 / 34

presented the status and outlook of wind-solar hybrid energy system that can avoid problems of unpredictable electric power output. Zhao et al. (Zhao et al., 2015) summarized the current situation and future development trend of China’s photovoltaic industry, studied the turning point problem and concluded that the turning point would appear not later than 2020. The second is a review of policies (Zhang and He, 2013; Zhang et al., 2013) about the PV industry. Some studies focused on tariff policy (Zeng et al., 2013) and some studies provided detailed descriptions of government incentive policies for photovoltaic industry (Huo and Zhang, 2012; Zhang et al., 2014). Zhi et al. (Zhi et al., 2014) examined the evolution of Chinese PV policies by employing policy instruments analysis and suggested that Chinese government should continue to strengthen the market demand-side management and release the production supply-side gradually. The last part is about specific PV projects and hot topics including targeted poverty alleviation using PV power (Zhang et al., 2018), photovoltaic agriculture (Xue, 2017) and distributed photovoltaic generation (Liu and Tan, 2016). There are less quantitative researches on Chinese photovoltaic industry, most emerged in the last five years. Wang et al. (Wang et al., 2016) and Ye et al. (Ye et al., 2017) discussed the effect of feed-in tariff policies on the sustainable development of Chinese PV industry. Zhao and Zhang (Zhao and Zhang, 2018) calculated the operating performance of China’s photovoltaic industry by SFA method. Zhang et al. (Zhang et al., 2016) conducted 58 Chinese listed PV enterprises to analyze the operating performance, industry agglomeration and spatial characteristics of China’s PV industry. Lin et al. (Lin et al., 2017) furthered the researches on spatial distribution of urban photovoltaic utilization in China and made a point that government policy was the most important influencing factor of spatial distribution. The quantitative studies also involved particular PV projects and hot topics, such as the competitiveness of China’s photovoltaic products under the Belt and Road (Shuai 8 / 34

et al., 2018), the employment effects of China’s PV industry (Zhang et al., 2017). Wu et al. (Wu et al., 2018) used a three-phase DEA model to assess performance efficiency of photovoltaic poverty alleviation projects (PPAP) and proposed that most PPAPs suffered the inappropriate production scale and the excessive labor input. Xiong and Yang (Xiong and Yang, 2016) studied subsidy effects on PV companies at different developing stages and made suggestions on the entry and exit occasions of government subsidies. Studies on innovation issue of China’s solar PV industry are few . Zhang and Gallagher (Zhang and Gallagher, 2016) reviewed how China fitted into the global solar PV innovation system, while Huang et al. (Huang et al., 2016) analyzed how China became a leader in solar PV by the framework of Technological Innovation System (TIS). In the different development stages of the photovoltaic industry, Chen et al. (Chen et al., 2014) found the suitable innovation intermediaries. It is easy to find that these are all based on a macro-level perspective. However, to our knowledge, research on the technological innovation efficiency estimation of Chinese photovoltaic industry, particularly from a micro-level perspective is also rare. The technological innovation of Chinese PV companies would not have developed so rapidly without government funding support. However, there is less empirical research on how government subsidies affect the innovation performance of Chinese photovoltaic enterprises. In this context, this article aims to supplement existing studies on Chinese photovoltaic industry by proposing a micro-level perspective to measure the innovation performance using data of Chinese listed PV companies from 2012 to 2016. Moreover, we assess that how government subsidies and other factors influence the innovation efficiency, which have important significance to technological progress. Simultaneously, the conclusion also provides policy references for the sustainable development of China’s photovoltaic industry. 9 / 34

2.2. Effect analysis of government subsidies on technological innovation Most scholars support the idea that government subsidies make a positive influence on promoting technological innovation. It is not difficult to understand that government subsidies can directly or indirectly make up for the lack of enterprises’ innovation input, thus contributing to the improvement of technological innovation capacity. Moreover, the R&D and innovation activities of enterprises are characterized by high risks. Government subsidies can effectively reduce the cost and risk of innovation, thus having a positive impact on enterprises’ innovation decisions (Hewitt-Dundas and Roper, 2010). However, due to the “crowding-out effect” caused by government subsidies on enterprises’ own R&D investment (Clausen, 2009), companies are not as innovative as they used to be, and government subsidies have a negative impact on enterprises’ innovation performance. 3. Methodology Since innovation is a very complex process involving many factors, it should not be evaluated by a single input or output. Hence, this paper abandons a single indicator to represent the innovation efficiency, instead, prefers to choose DEA method to measure the innovation performance of Chinese photovoltaic industry. The efficiency scores calculated from DEA method are limited between 0 and 1. To further understand how government subsidies and other factors influence the innovation efficiency, the second step adopts Tobit regression analysis because it can easily handle the case that the dependent variable is truncated or censored. 3.1. DEA Data envelopment analysis (DEA) is a linear programming technique to assess the relative efficiency of a homogeneous set of multi-input and multi-output decision-making units (DMUs). As 10 / 34

there involve too many factors in the innovation process, DEA model is ideal for analyzing the innovation performance. In the overall efficiency measurement, it should be better to treat technological innovation process as a ‘black box’ and ignore the interaction effects of sub-processes (Chen and Guan, 2012). DEA can determine the efficiency of DMUs without a specific functional form, which can avoid errors caused by model-misspecification. Moreover, different measurement units are allowed in DEA, and variables do not have to be dimensionless. Given its obvious advantages, DEA method has been widely used to measure the efficiency of technological innovation (Piao et al., 2017; Sharma and Thomas, 2008). In addition, there are two kinds of DEA models: CCR (Charnes, Cooper, and Rhodes) model under constant returns to scale (CRS) assumption and BCC (Banker, Charnes, and Cooper) model under variable returns to scale (VRS) assumption. We use BCC model in this study since the technical efficiency (TE) obtained by CCR model can be divided into pure technical efficiency (PTE) and scale efficiency (SE). PTE is the efficiency of eliminating scale factors based on TE, measuring the ability of enterprises to provide corresponding output under the optimal production scale, reflecting the efficiency of internal management and technology of enterprises. SE is the ratio of the technical efficiency under the assumption of constant returns to scale to that under variable returns to scale assumption (Coelli et al., 2005). It measures whether the company has made technological innovation at the optimal scale. Considering the expectation of output quantities to be expanded without altering the input quantities in the technological innovation process, output-oriented approach is more practical than input-oriented approach. Hence, the output-oriented BCC model is adopted in this paper. 3.2. Tobit 11 / 34

Most of the innovation efficiency values calculated from DEA method fall between 0 and 1. There are very few DMUs reaching the effective frontier, where values equal to 1. As the dependent variable is restricted, ordinary least square (OLS) would give inconsistent and biased parameter estimates. Tobit regression, which is one of the limited dependent variable models, can handle the truncated or censored dependent variable data effectively. Therefore, it is suitable to adopt the Tobit model for further investigations to study how government subsidies and other influencing factors affect the innovation efficiency. The application of Tobit method can be found in the literature (Saglam, 2018; Song et al., 2018). The dependent variables in Tobit model should be non-negative and must meet the condition of P(y=0) > 0, that is, the dependent variable values must include zero (Zhang and Lin, 2018). As TE is greater than 0 and less than or equal to 1 (0 0

Yi t = 0 , otherwise

(1)

Where Xi t is a vector of independent variables and β is a vector of parameters to be estimated, α is the constant term. Assuming εi t is normally distributed error term, we can use the maximum likelihood estimation method in this step. 4. Variable and data resource Refer to Wang et al. (Wang et al., 2016), we used the following two criteria to determine whether an enterprise is a listed solar photovoltaic enterprise or not: first, we searched using 12 / 34

keywords, solar and photovoltaic, including enterprises in the concept stocks of solar power generation; second, we searched using keywords, monocrystalline silicon, polycrystalline silicon, silicon wafers, solar photovoltaic cells, cell components and Hull cells as main products. Further, we excluded ST or *ST companies and samples with missing value. Finally, 44 listed solar PV companies on the Shanghai and Shenzhen stock exchanges were selected for the analysis. In 2007, the CSRC (China Securities Regulatory Commission) formally required listed and proposed listed companies to disclose their R&D expenses. Nevertheless, solar PV companies in China came into the market massively from 2012, furthermore, there were deficiencies in patent data of listed companies in 2017. As a result, we assess innovation performance by the firm-level data from 2012 to 2016 and investigate the effects of selected influence factors. The R&D, patent and other indicators discussed in this paper are from the Companies’ Annual Report 2012–2016, the CSMAR and Wind database. 4.1. Input and output indicators of innovation performance measurement Input in the innovation process includes human and material investment. There are two indicators of human investment in most listed companies: numbers of employees by education level classification, and numbers of technical personnel by functional classification. The education level category was different among our samples and they did not have a united statistical caliber, therefore, the number of technical personnel was an indicator of human investment and its logarithm was taken. In 1964, OECD suggested that the R&D expenditure was material investment of innovation process, which has been widely recognized by academia. Therefore, R&D expenditure was chosen as the material investment indicator and its logarithm was taken (Sharma and Thomas, 2008). The use of patents to represent technological innovation is a common practice in previous 13 / 34

literature and Hagedoorn and Cloodt (Hagedoorn and Cloodt, 2003) have indicated that the use of patents to measure innovation output has certain reliability. Rather than patent grants, we preferred to use the number of patent applications per year as the enterprise’s annual innovation production, because the licensing process of the patent is full of uncertainties and instabilities (Piao et al., 2017) and patents have had an impact on operating performance before they are granted. However, the number of patent applications cannot accurately reflect the transformation ability and market value of innovations. Based on the previous research, we had two parts of innovation output: patent output and other output. The knowledge commercialization process transforms the outcomes of the knowledge production process to commercial or economic results, including mainly exports, sales, and revenues (Carayannis et al., 2016). So, we selected the widely used total revenue to ensure comprehensive analysis (Zhang et al., 2016; Zhao and Zhang, 2018), and its logarithm was taken. Since some of the patent data were zero, we added 1 to the patent application variable and then took a natural logarithm. Compared with the innovation inputs, patent applications and achievements are translated into total revenue with a certain degree of hysteresis. Meanwhile, considering that the patent application will be faster than the patent licensing, and industrial enterprises will have a strong impetus to transform innovative achievements in the face of increasingly fierce market competition (Feng et al., 2011). So, this paper considered a 0-year and 1-year lag situation (Czarnitzki and Licht, 2006; Piao et al., 2017) at the same time. The current output indicators match the current and one-stage lag of innovation input indicators. Table 1 shows the descriptive statistics of input and output indicators. Table 1 here 4.2. Influential factors of innovation efficiency 14 / 34

Regarding the influencing factors of innovation efficiency, we selected the following four factors: government subsidy (GOV), firm size (SIZ), financial leverage (FL) and ownership concentration (OC). The reasons for such selections and their proxies are addressed as follows. (1) Government subsidy (GOV). Due to the externality of research and development activities, enterprises are often unable to monopolize the full benefits of their innovation results, which reduces the enthusiasm of innovation investment. Based on this consideration, the government’s support can make up for the insufficiency and stimulate the enthusiasm of enterprises’ research and development, thus improving the ability of technological innovation (Czarnitzki and Licht, 2006). With regard to the photovoltaic industry, Lu and Shao (Lu and Shao, 2016) believed that government subsidies could improve the enthusiasm of photovoltaic enterprises for innovation and promote healthy development. This paper chose government subsidies to study the impact of government subsidy on the innovation efficiency of Chinese listed solar photovoltaic enterprises. (2) Firm size (SIZ). According to Scherer and Ross (Scherer and Ross, 1990), when the enterprise-scale keeps expanding, the R&D efficiency will be damaged due to the reduction of management control ability or excessive bureaucratic control. Chen et al. (Chen et al., 2004) found that the technological innovation activities of enterprises were positively correlated with firm size, and economies of scale could enhance enterprises’ technological innovation ability. Refer to Hamberg (Hamberg, 1964), we used the number of employees as the proxy variable of firm size in this paper. (3) Financial leverage (FL). Asset-liability ratio (the ratio of total indebtedness and total assets) is generally used as the indicator of financial leverage. There is no unified opinion about the effect of financial leverage on technological innovation performance. Some scholars believe that high 15 / 34

financial leverage means that enterprises do not have enough capital for technological innovation. In addition, it is also believed that the existence of agency problem leads to the operators’ main concern about personal wealth, job security and the maximization of personal utility, which will seriously weaken their pursuit of innovation and the long-term goal of the company (Wright et al., 1996), and thereby inhibit the technological innovation efficiency of enterprises. However, some scholars believe that high financial leverage can also reduce the inefficient investment of managers, which improves the utilization of funds and enterprises’ performance, and it is conducive to improving the efficiency of technological innovation. (4) Ownership concentration (OC). When the controlling shareholders are responsible enough to appoint and supervise the management to participate in the operation and management of the listed companies to a certain extent, it will promote the technological innovation activities and improve the production speed of new products. However, when the interests of controlling shareholders are inconsistent or even in conflict with those of minority shareholders, the controlling shareholders may ignore the enterprises’ daily operation, especially the technological innovation activities because of “moral hazard”. In general, there are two common indicators to represent the ownership concentration: shareholding ratio of top ten shareholders and shareholding ratio of the largest shareholder. The shareholding ratio of the largest shareholder is usually used to measure control rights of the major shareholder. By contrast, the shareholding ratio of top ten shareholders is more comprehensive for analysis. Similar to Xu and Wang (Xu and Wang, 1999) and Piao et al. (Piao et al., 2017), we selected the top 10 shareholders’ shareholding ratio as the indicator of ownership concentration. Table 2 and Table 3 show the descriptive statistics and correlation coefficient of the above influencing factors respectively. The value indicators such as government subsidy and firm size, are 16 / 34

conducted logarithmic treatments. The units of financial leverage and ownership concentration are percentages (%). Obviously, the correlation coefficients between variables are all less than 0.7, indicating that the correlation is not apparent. The multicollinearity problem can be excluded from the following analysis. Table 2 here Table 3 here 5. Results and discussion 5.1. DEA-evaluation of innovation performance In this study, we put all the 5 years of 44 samples together for a 0-year lag situation, as well the 4 years for a 1-year lag situation so as to study the changes in innovation efficiency. Table 4 shows that in the 0-year lag, the mean value of TE ranges from 0.917 to 0.936, annual average TE is 0.921. Overall TE is high but still below the efficiency frontier, which indicates that there remains 7.9% of innovation potential to be further developed. From the perspective of decomposition, the mean value of PTE ranges from 0.949 to 0.972, and that of SE ranges from 0.943 to 0.966. Annual average PTE and SE are 0.962 and 0.957 respectively. The high scores of PTE mean the high innovation efficiency on the condition of constant scale. We can improve the SE through appropriate adjustment of industrial scale. In a 1-year lag situation, annual average TE is 0.911, which indicates a potential room for 8.9% improvement in innovation efficiency. Annual average PTE and SE are 0.957 and 0.951, which are lower than those of 0-year lag. That means current inputs have a greater impact on innovation outputs than lagging inputs. Table 4 here In terms of the trend analysis, Fig 1 and Fig 2 show that whether the lag period is set at 0 or 1 17 / 34

year, the TE follows the same trend as the PTE roughly. In 2012-2013, SE had a sharp increase which drove growth for TE even though there was a decline in PTE. During 2014-2015, both TE and PTE were improved while SE slightly declined, which was the reason why TE growth was lower than PTE growth. Fig 1 here Fig 2 here According to Seiford and Zhu (Seiford and Zhu, 1999), the returns to scale (RTS) can be estimated. The RTS includes increasing returns to scale (IRS), constant returns to scale (CRS), and decreasing returns to scale (DRS). The categories of RTS are shown in Table 5. According to Table 5, whether it is in the 0-year lag or 1-year lag period, there are about 80% of DMUs operating in DRS, which are not producing sufficient outputs for the given inputs; about 15% of DMUs operating in IRS, which means outputs will increase more-than-proportionately relative to increasing inputs. Only 5% of DMUs have the best production scale. Most of Chinese PV listed companies must improve their innovation efficiency through technological progress. Table 5 here 5.2. Determinants of the TE Based on the previous discussion in 3.2, the dependent variable of model 0 is inefficiency value (1-TE0), that of model 1 is inefficiency score (1-TE1). TE0 and TE1 are obtained from 5.1, which TE1 considered the lag effect (1-year lag) of research and development on innovation output. Model 1 can be viewed as a robustness test for model 0 and vice versa. The estimation results are presented in Table 6. Table 6 here 18 / 34

As shown in Table 6, most of the variables are significant, and the results of the joint parameter test show that both model 0 and model 1 are effective at the 1% level. It indicates that the results in this paper are robust, and the selected variables have an important impact on the innovative performance of China’s listed PV enterprises. Regarding the influential factors of Tobit regression equation, a negative coefficient of certain variable means that this variable makes a positive influence on innovation efficiency, and vice versa. On the basis of model 0, the regression coefficient of GOV (government subsidy) is significantly negative, which means the impact of government subsidies on the innovation efficiency of China’s photovoltaic industry is significantly positive. Model 1 shows the same results as model 0, but the impact of GOV is not significant in model 1, indicating GOV’s contribution to the next period’s innovation output is not strong. We observed a positive correlation between SIZ (firm size) and innovation inefficiency. Therefore, SIZ has a negative effect on the innovation performance. Such a result is consistent with that of Bayona-Saez and Garcia-Marco (Bayona-Saez and Garcia-Marco, 2010). As expected, FL (financial leverage) is significantly negative across all models, which suggests that the higher asset-liability ratio enhances innovation performance. Although the coefficient of OC (the shareholding ratio of top ten shareholders) is significant at different levels of significance in model 0 and model 1, the coefficient of OC is negative, indicating that the impact of OC on the innovation performance of China’s photovoltaic industry is significantly positive. 5.3. Discussion In this part, the results presented above are discussed in detail to unravel the mechanism and reasons behind these results. First, considering the trend analysis of the innovation efficiency, it is worth noting that 19 / 34

fluctuations in overall TE are related to the domestic and international development environment of Chinese PV industry. From 2005 to 2011, Germany, Spain, Italy and other European countries were the core regions of global photovoltaic installation. During this period, Chinese photovoltaic enterprises rapidly occupied the market, formed a relatively complete solar photovoltaic industry chain, and laid a good foundation for the rapid development of domestic photovoltaic industry. Europe accounted for the world’s share of newly added PV power generation capacity quickly decreased as a result of the 2011 European debt crisis. Subsidies for the photovoltaic industry in EU countries were rapidly cut and the market demand shrank sharply. China’s photovoltaic industry fell into a downturn in the second half of 2011. In 2013, given the benefit from national policies and financial support, China, Japan, and the US became the major areas of growth in photovoltaic installations worldwide, and their market shares continued to rise. Chinese photovoltaic industry has seen a wave of photovoltaic installations, SE in this year came to ahead. In 2014, Chinese photovoltaic industry deeply got stuck in the overcapacity problem and international trade dispute. The average capacity utilization of modules fell below 50%, more than 50% of the component manufacturers were still at a loss. TE and PTE reached the lowest point in this year. Following market reshuffling and industrial upgrading, most of the key equipment of China’s photovoltaic industry has been localized and gradually implemented intelligent manufacturing, which was at the world-leading level. Both TE and PTE were improved continuously during 2015-2016. Second, government subsidies make a positive influence on the innovation performance of China’s photovoltaic listed companies. On the one hand, government subsidies facilitate the operating performance of enterprises, thus improve the innovation environment and increase the innovation efficiency of enterprises. On the other hand, government subsidies are directly used into 20 / 34

the company’s R&D and innovation activities, including employing top technical talents, strengthening the construction of the R&D department, introducing advanced technologies and purchasing high-end instruments, which will directly and significantly boost the innovation performance of enterprises. However, the impact of government subsidies on innovation performance in the next period is not as strong as that in the current period. This may be caused by the loose constraint mechanism and imperfect management mechanism of government subsidies, which only pay attention to the function and direction of government subsidies in that year and ignore the follow-up tracing on the use of government subsidies. Third, firm size has a negative effect on the company’s innovation performance. There is no consensus about the impact of firm size on innovation efficiency. Teece (Teece, 1986) have argued that large firms have higher capacities to profit from R&D activities as they have the needed assets to appropriate the research and development results. Nevertheless, with the expansion of enterprise-scale, the internal division of functions became more detailed, and the interface between departments widened, thus it increased the difficulty of the coordination of enterprise technological innovation. Meanwhile, the R&D efficiency will be weakened due to the excessive bureaucratic control of large companies. In contrast, small enterprises are more competent to overcome organizational inertia flexibly, integrate the target of the functional departments into the innovation activities, and incorporate technological innovation into the daily work. Moreover, small companies are more readily to achieve major technological breakthroughs. Cohen and Klepper (Cohen and Klepper, 1996) also supported the opinion that small firms were more innovative than large firms, or that small firms were more efficient innovators than large firms. Forth, a higher asset-liability ratio enhances innovation performance. Many studies have found 21 / 34

that companies could raise financial leverage and reduce the capital cost of enterprises by debt financing. As pointed out by Easterbrook (Easterbrook, 1984) and Jensen (Jensen, 1986), shareholders can restrain managers’ arbitrary allocation of resources by reducing the cash flow controlled by managers, and one of the ways to reduce the cash flow controlled by managers is to increase the debt level of companies. Jensen (Jensen, 1986) further indicated that the advantage of enterprise liabilities was that it could improve the operating efficiency of managers and their organizations, thus playing a role in controlling managers’ behaviors. From the above, companies use debt financing to reduce conflicts of interest between operators and owners (Piao et al., 2017) and make them work together for the long-term development goals of enterprises. Brander and Lewis (Brander and Lewis, 1986) believed that increasing financial leverage would cause shareholders to pursue a riskier strategy. As a high-risk activity, technological innovation would get more attention. In a competitive environment, debt promotes value-added research and development as a result of the threat of liquidation (Aghion et al., 1999). Hosono et al. (Hosono et al., 2004) studies Japanese manufacturing industry and concludes that asset-liability ratio has a positive impact on R&D intensity. Chinese listed PV enterprises have high asset-liability ratio generally. These debts, to some extent, can increase the activity of enterprises and optimize the capital structure to improve enterprise performance and strengthen the efficiency management of technological innovation. In addition, the increase in indebtedness requires enterprises to have long-term profitability. Technological progress can improve the core competitiveness of enterprises and profit from innovation activities. Fifth, the impact of OC on the innovation performance of China’s photovoltaic industry is significantly positive. This result is consistent with previous studies. Because the enterprise’s 22 / 34

technology innovation investment has high uncertainty and irreversibility, the board would fire the managers if innovation investments failed or the short-term interests of companies declined. So, managers lack the motivation to invest in R&D innovation. The major shareholders of an enterprise own large shares. In order to improve the long-term profitability of the enterprise, major shareholders have the obvious initiative to be involved in the decision-making behavior of managers (Alchian and Demsetz, 1972). Thus, they can supervise the behavior of operators more effectively, which will help reduce the agency problem caused by the separation of powers. Scholars found that the higher ownership concentration could urge managers to pay more attention to the long-term interests of enterprises, which was conducive to the improvement of innovation performance (Shleifer and Vishny, 1997). 6. Conclusions and policy implications 6.1. Main conclusions Based on the data of China’s 44 listed solar PV companies from 2012 to 2016, this study evaluated the innovation performance of Chinese photovoltaic industry by DEA method based on the micro perspective and used the Tobit model to carry out an empirical analysis on whether government subsidies promote the innovation efficiency. The main conclusions are as following. Firstly, from the findings on innovation performance of Chinese photovoltaic industry, the average innovation efficiency of China’s listed photovoltaic companies is more than 0.9, which is relatively high but still has 10% innovation potential to be further developed. From the perspective of decomposition, the pure technical efficiency (PTE) is higher than the scale efficiency (SE), which indicates that the PTE is the main reason for the high innovation efficiency of Chinese PV industry, rather than the SE. 23 / 34

Secondly, from the external factors affecting the innovation performance of China’s listed photovoltaic companies, government subsidies play a significant role in improving innovation efficiency, but the influence is not significant for the 1-lag case. Thirdly, from the internal factors influencing innovation efficiency of China’s listed photovoltaic companies, financial leverage and ownership concentration have significantly positive effects, while enterprise scale has significantly negative effects on innovation efficiency. 6.2. Policy implications Government funding is crucial for an emerging industry, especially for capital-intensive R&D innovation activities in this industry. Although the technology in China’s photovoltaic industry has advanced greatly in recent years, there are no studies that tell us how effective government subsidies are in promoting the technological innovation of China’s photovoltaic industry. Therefore, this study aims to supplement existing studies by evaluating the current status of technological innovation performance and exploring the impacts of government subsidies and other important influencing factors. On the basis of the above conclusions, this paper puts forward the corresponding policy implications as follows. First, there is innovation potential that can be further developed in Chinese photovoltaic industry and enterprises should focus on maintaining PTE at a high level and improving the SE to promote their innovation performance, such as strengthening capability of independent innovation and adjusting industrial scale. Under the important strategic opportunity of “the Belt and Road Initiatives”, relying on policy support, photovoltaic enterprises should accelerate the photovoltaic industry agglomeration and improve the scale efficiency. Also, it is believed that government subsidies are necessary and effective. While encouraging 24 / 34

the government to further increase support strength for PV enterprises, the authorities should also improve the follow-up supervision and strengthen the constraint mechanism of government subsidies, thereby increasing the utilization efficiency of government funding. Efforts should be made by the government to guide enterprises to choose the path of innovative development, optimize the innovation environment of Chinese photovoltaic industry, improve enterprises’ innovation enthusiasm and innovation efficiency. Addition, improving the financial leverage and equity concentration of enterprises can effectively alleviate the conflict of interests between managers and owners caused by the agency mechanism, improve the operating performance of enterprises, create positive environmental externalities for R&D and innovation activities, and play a very important role in improving enterprises’ innovation efficiency. Technological innovation cannot do without high capital input of enterprises. Given the long-term and high risks of innovation capital input, the government should improve the financial supporting system for PV industry, expand the financing channels of enterprises, guide and encourage more private investment, and increase the investment intensity of technological innovation funds. Under the macro background of “mass entrepreneurship and innovation”, the government should encourage and support the newly established small and medium-sized PV enterprises, perfect the industrial chain supporting system, establish the scientific and technological resource sharing platform, and provide a good innovation and learning environment for small and medium-sized enterprises. Furthermore, governmental policies should be instrumental in granting small and medium-sized enterprises a more relaxed policy environment to help them build confidence for long-term development. In summary, though government subsidies drive the enterprises’ innovation performance, the 25 / 34

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based on policy instruments. Applied Energy 129, 308-319. Figures

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Fig 1. Kernel density on innovation efficiencies of Chinese solar PV industry for 0-year lag.

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Fig 2. Kernel density on innovation efficiencies of Chinese solar PV industry for 1-year lag.

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Tables Table 1 Descriptive statistics of input and output indicators. Category 1

Category 2

Variables

N

Mean

Standard

Minimum

Maximum

Input indicators

Human

Technical personnel

220

5.7808

1.1358

2.6391

9.0472

Material

R&D expenditure

220

17.9170

1.3931

13.1963

21.7543

Output indicators

Patent

Patent application

220

3.3197

1.4755

0.0000

6.4552

Others

Total revenue

220

21.4108

1.2750

18.6842

25.0954

Table 2 Descriptive statistics of influential factors. Variables’ name Government subsidy (GOV) Firm size (SIZ) Financial leverage (FL) Ownership concentration (OC)

N

Mean

Std. dev.

Min

Max

220 220 220 220

16.4393 7.7416 45.0161 56.1446

1.4165 1.0355 20.1874 17.7447

11.0021 5.6454 3.5121 12.7200

20.4226 10.3386 104.4356 94.4300

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Table 3 Correlation coefficient between variables. GOV SIZ FL OC

GOV

SIZ

FL

OC

1 0.6575 0.2892 0.0768

1 0.2804 0.1039

1 -0.0866

1

Table 4 Innovation efficiencies of Chinese solar PV industry. Technical efficiency (TE)

Mean 2012 2013 2014 2015 2016 Average

Pure technical efficiency (PTE)

Scale efficiency (SE)

T0 period

T-1 period

T0 period

T-1 period

T0 period

T-1 period

0.917 0.936 0.913 0.920 0.920 0.921

0.926 0.940 0.886 0.890

0.972 0.968 0.949 0.955 0.964 0.962

0.977 0.963 0.931 0.955

0.943 0.966 0.962 0.962 0.954 0.957

0.947 0.975 0.951 0.932

0.911

0.957

0.951

Note: TE is overall technical efficiency, and equal to the product of PTE and SE. Table 5 Returns to Scale classifications of 220 DMUs (T0) and 176 DMUs (T1). RTS 2012 2013 2014 2015 2016 Sum Proportion

T0 period (220 DMUs)

T-1 period (176 DMUs)

IRS

CRS

DRS

IRS

CRS

DRS

0 1 2 3 2 8 3.64%

7 11 6 5 6 35 15.91%

37 32 36 36 36 177 80.45%

0 4 3 4 NA 11 6.25%

8 10 5 4 NA 27 15.34%

36 30 36 36 NA 138 78.41%

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Table 6 Estimation results of Tobit models. Model 0 Variable GOV SIZ FL OC Constant LRchi2 Prob>chi2 Log-likelihood No. of observations Uncensored observations Censored observations

Coefficient *

-0.0070 0.0145*** -0.0007*** -0.0005** 0.1395*** 21.37 0.0003 232.6959 220 187 33

Model 1 t-statistic

Coefficient

t-statistic

-1.87 2.79 -3.45 -2.35 2.98

-0.0065 0.0166*** -0.0007*** -0.0007*** 0.1385** 18.51 0.0010 173.9012 176 150 26

-1.43 2.67 -2.88 -2.75 2.35

Notes: ***Indicates the significance at the 1% level. **Indicates the significance at the 5% level. *Indicates the significance at the 10% level.

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Boqiang Lin: Conceptualization, Methodology, Software, Data curation, Writing- Original draft preparation Ranran Luan: Methodology, Software, Data curation, WritingOriginal draft preparation

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: