Impact of energy technology patents in China: Evidence from a panel cointegration and error correction model

Energy Policy 89 (2016) 214–223

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Impact of energy technology patents in China: Evidence from a panel cointegration and error correction model Ke Li a,b, Boqiang Lin b,n a

College of Mathematics & Computer Science, Hunan Normal University, Changsha 410081, PR China Collaborative Innovation Center for Energy Economics and Energy Policy, China Institute for Studies in Energy Policy, Xiamen University, Fujian 361005, PR China b

H I G H L I G H T S








Energy technology patents in China are analyzed. Relationship between energy patents and funds for R&D activities are analyzed. China's energy price system hinders energy technology innovation. Some important implications for China's energy technology policy are discussed. A panel cointegration model with FMOLS estimator is used.

art ic l e i nf o

a b s t r a c t

Article history: Received 19 April 2015 Received in revised form 6 October 2015 Accepted 30 November 2015

Enhancing energy technology innovation performance, which is widely measured by energy technology patents through energy technology research and development (R&D) activities, is a fundamental way to implement energy conservation and emission abatement. This study analyzes the effects of R&D investment activities, economic growth, and energy price on energy technology patents in 30 provinces of China over the period 1999–2013. Several unit root tests indicate that all the above variables are generated by panel unit root processes, and a panel cointegration model is confirmed among the variables. In order to ensure the consistency of the estimators, the Fully-Modified OLS (FMOLS) method is adopted, and the results indicate that R&D investment activities and economic growth have positive effects on energy technology patents while energy price has a negative effect. However, the panel error correction models indicate that the cointegration relationship helps to promote economic growth, but it reduces R&D investment and energy price in the short term. Therefore, market-oriented measures including financial support and technical transformation policies for the development of low-carbon energy technologies, an effective energy price mechanism, especially the targeted fossil-fuel subsidies and their die away mode are vital in promoting China's energy technology innovation. & 2015 Elsevier Ltd. All rights reserved.

Keywords: Energy technology patents R&D activities Energy price Panel cointegration model China

1. Introduction 1.1. Background and motivation Owing to rapid economic growth and rising level of industrialization and urbanization, China's energy consumption is increasing rapidly. During the period 1999–2013, China's gross n Corresponding author at: Collaborative Innovation Center for Energy Economics and Energy Policy, China Institute for Studies in Energy Policy, Xiamen University, Fujian 361005, PR China. Tel.: þ 86 5922186076; fax: þ86 5922186075. E-mail addresses: [email protected] (K. Li), [email protected], [email protected] (B. Lin).

http://dx.doi.org/10.1016/j.enpol.2015.11.034 0301-4215/& 2015 Elsevier Ltd. All rights reserved.

domestic product (GDP) increased from US$ 4202.9 billion in PPP (purchasing power parity at 2005 price) to US$ 15,643.2 billion in PPP (World Bank, 2014); and its energy consumption increased from 934.7 million tones oil equivalent (Mtoe) to 2852.4 Mtoe (BP, 2014). However, because of the coal-dominated energy consumption structure and low energy efficiency, China has become the biggest CO2 emitter in the world since 2007. According to a preliminary estimate, China emits over 10 GtCO2 in 2013, which accounts for about 32% of the global CO2 emissions, and the level of emission is still increasing rapidly (Friedlingstein et al., 2014). Thus, China faces an increasing pressure to reduce CO2 emissions. In a China–US joint statement on climate change on November 12, 2014, China first publicly pledged to peak CO2 emissions around 2030.

K. Li, B. Lin / Energy Policy 89 (2016) 214–223

On the other hand, severe air pollution such as a wide range of fog and haze, which is directly related to massive use of fossil fuel (especially coal), has caused a strong public appeal to address environmental problems. It is widely acknowledged that the share of coal in total primary energy consumption must be reduced as much as possible to address the problem of air pollution. Thus, it can be seen that air pollution management, energy-conservation and emission-abatement are consistent with one another. From the perspective of energy policies, energy technology innovation and economic structural transformation are two main ways to enhance energy efficiency and air pollution governance. However, the contribution of structural transformation is greatly affected by economic fluctuations (Li and Lin, 2014). In this sense, energy technology innovation is a fundamental determinant of energy-saving and pollution management performance. On the one hand, the substitution of clean or carbon-free energy for coal requires technological advancement. On the other hand, enhancing fossil fuel efficiency also depends on the advancement of fossil fuel technologies such as clean coal technologies. Patent counts are widely used to measure technological innovation performance, and they are the outputs of technology research and development (R&D) activities (Lee and Lee, 2013; Oltra et al., 2010; Wang et al., 2012). But there are some limitations in measuring innovation using patent counts. For example, not all innovations are patentable, and patents do not reflect the actual value of innovation because not all patented inventions are actually implemented in market applications (Albino et al., 2014). Furthermore, variations in the number of patents granted has no clear interpretation (Pakes, 1985). But there are still some attractive reasons to measure innovation by patent counts. One of the main reasons is that it provides standardized and objective information about technology, and the data is available (Lee and Lee, 2013). In this sense, energy technology patents are suitable to measure the output of innovation performance and the latest advancement in energy technologies (Johnstone et al., 2010; OECD, 2008; Wang et al., 2012; WIPO, 2009). Most studies analyze the trend of energy technological progress using energy patent counts. Albino et al. (2014) collected 131,661 patents granted at the United States Patent and Trademark Office (U.S.P.T.O.) during the period 1971–2010 and analyzed the development trends of low-carbon energy technologies. Wong et al. (2014) analyzed the production trends of carbon-free energy technologies in Asian emerging economies using publications and patents. To use energy in an environmentally friendly manner, it is necessary to adopt clean or carbon-free energy technologies through effective investment in R&D activities. Margolis and Kammen (1999) investigated the relationship between R&D activities and energy patent counts in the United States, and found they are highly correlated. Bointner (2014) indicated that it is urgent to introduce appropriate public energy R&D funding because private investments may be crowded out by public investments. Popp et al. (2011) found that technological advancement results in more investment in renewable energy carriers. A few studies investigate energy technology patents in China. Hu and Phillips (2011) analyzed China's biofuel industry by using biofuel patents. After comparing shale gas-related patent applications between America and China, Lee and Sohn (2014) found China's shale gas development was limited by lack of technologies, especially those related to hydraulic fracturing and directional drilling technologies. Wang et al. (2012) explored the effects of energy technology patents on China's CO2 emissions using a panel cointegration model. Despite the increased use of patent counts to measure energy technology innovation, very few studies analyze the development of China's energy technology patents. Wang et al. (2012) argued

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that China's patents for low carbon energy technologies were conducive to CO2 emission abatement, but it did not reveal how to enhance energy technology innovation performance, which is the gap this paper wants to fill. This paper investigates the influencing factors of China's energy technology innovation performance. Specifically, it answers the following questions: (1) are China's R&D activities effectively and significantly conducive for energy technology innovation? In other words, is there a long-term equilibrium relationship between energy technology patents and R&D activities in China? (2) If the answer is yes, then is this relationship affected by energy price policy? Because energy technology innovation is a crucial determinant of energy-saving and air pollution management, the answers to the above questions have important policy implications. On the one hand, it would reveal the effect of R&D activities on energy technology innovation. On the other hand, it would help to introduce a policy mix for enhancing energy technology innovation performance. 1.2. Theoretical background In the current study, energy technology innovation is measured by energy technology patents. It is well known that patents have three types—invention, utility models and designs. In addition, patent counts can be divided into patent applications and patents granted. In this paper, energy technology patents refer to the number of energy technology invention patent applications. Generally speaking, utility models and designs refer to the new solutions for the shape or appearance of products, and they do not involve new technical solutions for products or production process, or their improvement (SIPO, 2014). Thus, the literatures usually take invention patents as an index for technology innovation. We choose patent applications rather than patents granted because patent applications involved the information contained in patents granted. Furthermore, the “Patent Law” indicates that the duration of a patent starts from the date of its application. Based on an extensive literature on energy technology development, a set of keywords are identified to obtain data on energy technology patents (Johnstone et al., 2010; Margolis and Kammen, 1999). Data are generated from title or abstract searches in the “Patent Search of Incopat Innovation Intelligence Platform” (http:// www.incoshare.com/), which is a commercial and intelligentized database for patent. After a comprehensive search, the duplicate patents are removed or merged. The search terms are as follows: (Coal or petroleum or oil or gas or hydro or solar or hydrogen or nuclear or biomass or chemical energy or wind or ocean energy or geothermal) and (electric* or energy or power or generat* or turbine). These search terms result in a small data set, but we believe it is a properly focused data set on China's energy technology patents. Overall, China's energy technology is developing rapidly, and energy technology patents increased from 1198 in 1999 to 31,571 in 2013. It developed relatively slowly during 1999–2006, but has experienced rapid growth during 2006–2013 (Fig.1).1 Furthermore, most energy technology patents are produced in the eastern provinces. The biggest five provinces are Beijing, Jiangsu, Shandong, Shanghai and Guangdong. They have 93,258 energy technology patents during 1999–2013, accounting for about 56.5% of the total during the same period (Fig. 2). As outputs of energy technology innovation, the patents series is highly correlated with energy R&D activities (Margolis and Kammen, 1999). However, it is impossible to obtain the data on 1 Though China's patent system starts from the year of 1985, but the data range of this paper is 1999–2013 because of the limitation data range of R&D investment.

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K. Li, B. Lin / Energy Policy 89 (2016) 214–223

Fig.1. Energy technology patents and R&D investment in 1999–2013.

China's investment on energy R&D activities. The government has special R&D funds for energy-conservation and emission-abatement and this funds increase rapidly. For enterprises, they invest billions of funds in energy-saving which is induced by government's motivation and the choice of future development. This is consistent with the trend of China's investment in R&D activities. So, we take investment in R&D activities as a proxy variable for investment in energy R&D activities, and the data are obtained from China Economic and Industry Database (CEIC). It has been deflated by CPI (2000 ¼1) in the study. Figs. 1 and 2 indicate that energy technology patents have a high positive correlation with investment in R&D activities. Adopting a simple linear regression on funds for R&D activities and energy technology patents, we find that the R2 is 0.979, and the t-statistic is 25.121. Fig. 2 shows that both series are consistent, implying that higher funds for R&D activities correspond to higher energy technology patents. Guangdong is an exception. Its investments in R&D activities totaled 64.17 billion Yuan during 1999–2013 and ranked second, but its energy technology patents ranked fifth. Compared with the first four provinces (Beijing, Jiangsu, Shandong and Shanghai), Guangdong has a few high level universities, thereby its technology innovation mainly depends on corporate R&D activities. Universities are one of the important

players in technology innovation and technology patent applicants. In addition, Guangdong's information technology industry is developing rapidly, and it has a number of excellent information technology companies such as Huawei and ZTE. Therefore, most patents in Guangdong are for information technology. We believe these two factors may be the important reasons behind Guangdong's relatively small energy technology patents counts despite its high level of R&D investment. From the technical requirements perspective, except for R&D activities, energy innovation is also induced by energy price. For example, the international oil price is closely negatively related with the global energy technology R&D investment. It means that increase in oil price directly contributed to the rise in global energy R&D investment, and thereby energy innovation (Albino et al., 2014; Bayer et al., 2013; Wei and Jiao, 2013). Generally speaking, low energy prices are not conducive for the promotion and application of new energy technology, especially clean or carbon-free energy technology. On the one hand, low fossil-fuel prices will reduce the reasonable return on the use of carbon-free energy technologies, and thus depressing the driving forces of energy technology development. On the other hand, if clean energy carriers do not have cost advantage relative to fossil fuel energy carriers (such as coal, oil, etc.), its further development is also

Fig.2. The accumulative total of energy technology patents and R&D investment across provinces in 1999–2013.

K. Li, B. Lin / Energy Policy 89 (2016) 214–223

undermined. Popp (2002) also confirmed that higher energy prices are conducive for increasing energy technology patents, and thereby improving energy efficiency. A reasonable assumption is that the expectation regarding energy patents application does matter for energy R&D activities and their results (energy technology patents).The most important motivation of R&D activities is the competitive advantage by using the latest technologies. A direct result of energy R&D activities is the decline in energy cost in production, which is an important aspect of competitive advantage. Furthermore, the Chinese government pays more attention to energy-conservation and emission-abatement, and this policy can potentially influence both the pace and direction of energy innovation through its impact on industrial, consumer, and public service demands (Herrera and Nieto, 2008) and can encourage the application of the latest energy patents (especially carbon-free patents). The above analysis means there is a close relationship between government policy and the expectation regarding energy patents application, which can potentially influence energy price according to the induced innovation theory (Popp, 2002). In summary, energy prices not only affect energy innovation directly, but also reflect the effects of the expectation of energy patents application through government policy, though the expectation is difficult to measure in the econometric model. The remaining parts of this paper are organized as follows. Section 2 is the model and the related estimation and test methods. The results and discussion are provided in Section 3 and Section 4 respectively. Section 5 focuses on the conclusion and policy implications.

2. Method 2.1. Model specification Based on the above analysis, we want to investigate the effects of investment in R&D activities and energy price on China's energy technology innovation performance. Thus the model we are interested is

ln patentit = αi + β1 ln rgdpit + β2 ln rdit + β3 ln priceit + uit

(1)

where patentit is the counts of energy technology patents; rgdpit is GDP per capita (in constant 2000 price), and represents the level of economic development; rdit is the funds for R&D activities (in constant 2000 price); priceit is energy price and stands for the effects of market power on energy technology innovation, and it is measured by purchasing price indices (PPI) for fuel and power which are obtained from the National Bureau of Statistics; αi is the fixed effects and represents the heterogeneity among cross-sections; the subscripts i and t represent cross-sections (provinces) and periods (year), respectively. In addition, ln implies that all variables are in logarithm form. We add rgdpit to model (1) for the following reason. According to the environmental Kuznets curve (EKC) theory, environmental pollution level is highly related with economic development stage. At an advanced economic development stage, environmental deterioration will be alleviated because of scale effect, structure effect and technology effect. In fact, environmental pollution level has a relationship with energy technology innovation, while innovation is affected by economic development. Thus, we take GDP per capita as a proxy for economic development, and the data are collected from the National Bureau of Statistics. Furthermore, economic development has a direct impact on industrial structure, people's consumption preference on clean energy and so on. Thus adding this variable to the empirical model helps to avoid the problem of omitted variable bias.

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For model (1), if the data of all the variables are generated from panel unit root processes, and the residuals of model (1) are a stationary process ( uit ∼ I (0)), then model (1) is a panel cointegration model. Hence the panel vector error correction models (PVECM) are

Δ ln patentit = φ1ecmi, t − 1 + δ11Δ ln rgdpit + δ21Δ ln rdit + δ31Δ ln priceit + ς1it

(2)

Δ ln rgdpit = φ2 ecmi, t − 1 + δ12 Δ ln patentit + δ22 Δ ln rdit + δ32 Δ ln priceit + ς 2it

(3)

Δ ln rdit = φ3 ecmi, t − 1 + δ13 Δ ln patentit + δ23 Δ ln rgdpit + δ33 Δ ln priceit + ς 3it

(4)

Δ ln priceit = φ4 ecmi, t − 1 + δ14 Δ ln patentit + δ24 Δ ln rgdpit + δ34 Δ ln rdit + ς4it

(5)

where ecmi, t − 1 is the residual of panel cointegration model; ϕi (i = 1 − 4) is the short-term adjustment effect, and it represents the effect of long-term (stability or equilibrium) relationship among energy technology patents, economic growth, R&D activities and energy prices on changes in each variable in short-term; Δ implies first difference of variables. Further, ϕ1 < 0 implies that the long-term relationship inhibits the changes in energy technology patents in the short-term. From the perspective of econometric method, it also confirms that model (1) is a panel cointegration model. In addition, ϕ2 > 0 and ϕ3 > 0 indicate that the cointegration relationship helps to improve economic growth and R&D activities in the short-term; while ϕ3 < 0 implies that the cointegration relationship inhibits the growth of R&D investment. For model (5), ϕ4 < 0 shows that the cointegration relationship inhibits changes in energy prices, which means that technological advancement in the energy sectors would result in the decline in energy price. This relationship is an important prerequisite for the existence of energy “rebound effect” (Lin and Liu, 2012).2 2.2. Panel unit root test methods Before conducting a panel cointegration model, there is need to first test whether the variables in model (1) are generated by panel unit root process. The pioneer study on panel unit root test is Bhargava et al. (1982). They used a modified DW statistic to test whether the residual of a fixed panel data model was a random walk process. The panel unit root test methods have developed rapidly in recent years. Assume the data generation progress (DGP) of panel data (taking lnpatenti, t as an example) is

ln patentit = ρi ln patenti, t − 1 + dit pi

+

∑ φij Δln patenti, t − j + uit , j=1

i = 1, …, N; t = 1, …, T

(6)

where dit = α0i + α1i t is the deterministic component (constant and trend); Δstands for the first difference. For model (6), δ i < 1 2 Rebound effect refers that energy saved by technological advancement may offset because there are “bounce quantity” of energy consumption due to economic growth promoted by technological advancement.

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implies that ln patentit is a stationary process. Hurlin and Mignon (2004) divided the unit root test methods into two generations. The first generation methods assume the independence of the cross-sections, and they include LLC test (Levin et al., 2002), Harris–Tzavalis test (Harris and Tzavalis, 1999), IPS test (Im et al., 2003), combining p-values tests or the Fisher tests (Choi, 2001; Maddala and Wu, 1999) and Hadri test (Hadri, 2000). These tests also can be divided into two categories. LLC test and Harris–Tzavalis test assume the homogeneity of each crosssection, namely ρi = ρ . Other tests allow each cross-section sequences with individual unit root process. For example, IPS test constructs the t¯ (ρ) statistic to test the follow assumption: H0 : ρ1 = ⋯ = ρN = 1 vs. HA : ρi < 1 ( ∀ i ). The statistic of the combining p-values test or the Fisher test is as follows: N

P = − 2 ∑ ln (pi ) ⇒ χ22N i=1

yit = αi + q′it β + uit qit = qi, t − 1 + εit , uit = γi, t + υit , γit = γi, t − 1 + θυit

(7)

Compared with the IPS test, the Fisher tests can adopt various lag lengths for the individual ADF regressions, and it also can be used for a unbalanced panel data. The simulation process shows they have a much higher test power than the IPS test. The independence of the cross-sections assumption is a rigorous assumption, and it is difficult to meet in empirical studies. Thus, the second generation methods relax this assumption, and they include SN test (Chang, 2002), tests based on bootstrap (Chang, 2004) and CADF test (Pesaran, 2007). The SN test is only used in a situation of weak dependence of the cross-sections, and it is biased when there is deterministic trend. Tests based on bootstrap are too complex to calculate. Pesaran (2007) suggested a simple way to eliminate the cross-sectional dependence. It is known as the cross-sectionally augmented Dickey–Fuller (CADF) test, and is based on CIPS statistic. Experimental results show that this test perform well (Baltagi, 2005). Based on the above analysis, we put emphasis on Fisher tests and CADF test in the follow empirical study.

2.3. Fully modified OLS (FMOLS) and cointegration test This paper takes China's provinces as cross-sections, and analyses the influencing factors of energy technology patents’ development based on model (1). Thus the model may have heterogeneity among cross-sections.3 Furthermore, according to economic theory, there may be simultaneity bias among the explanatory variables ln rdit, ln priceit and the explained variable ln patentit. Specifically, increasing R&D activities helps to improve energy innovation while the breakthrough technologies in energy field will also stimulate increased R&D activities. According to “induced innovation” theory, raising energy prices will be conducive for the development of energy-saving technologies, but energy technology innovation and its application are conducive for reducing the relative price of energy, resulting in energy “rebound effect” or increased energy consumption. In addition, simultaneity bias is also observed between economic growth ln rgdpit and energy technology innovation ln patentit (that is, energy technology innovation will contribute to economic growth). Theoretically, Phillip (1986) confirmed that the cointegration or the long-term relationship would lead to biased OLS estimators. The above analysis implies that an important empirical issue—endogeneity— should be addressed to avoid bias and inconsistency of parameter estimation. 3

There are two methods to obtain consistent estimators in a panel cointegration model. The methods are the fully-modified OLS (FMOLS) (Phillips and Hansen, 1990) and the dynamic least squares (DOLS) (Saikkonen, 1991; Stock and Watson, 1993). Because we have a short panel data (1999–2013 with 15 years), the FMOLS is adopted. Further, if u^it ~I (0), then model (1) is a panel cointegration model, and the estimate results indicate the long-term (stability or equilibrium) relationship among energy technology innovation, economic growth, R&D activities and energy prices. We discuss the FMOLS for model (1) as follows. To facilitate the presentation, assuming yit = ln patentit , qit = (ln rgdpit , ln rdit , ln priceit )′ and β = (β1, β2, β3 )′, then model (1) can be expressed as

Here, the heterogeneous in model (1) refers that the long-run covariance of random error terms have related with cross-sections, and the heterogeneous among cross-sections for fix effects αi .

where υit ~i. i. d. N (0, procedure for Eq. (8).

σv2 ).

(8)

Eq. (9) will be yielded by an iteration

t

yit = αi + q′it β + θ ∑ υij + υit ≜ αi + q′it β + εit j=1

(9)

If wit = (υit , ε′it )′, then the long-run covariance and its decomposition of wit are

Ω = lim

T T ⎡ ϖ2 ϖ ⎤ 1 12 ⎥ E ( ∑ wit )( ∑ wit )′ = Σ + Γ + Γ ′ = ⎢ 1 T ⎣ ϖ 21 Ω22 ⎦ t=1 t=1

1 T

T−1

Γ = lim

1 T

∑ E (wit w′it ) = ⎢ σ1

T →∞

T →∞

Σ = lim

T →∞

T

∑ ∑ k=1 t=k+1

⎡ Γ11 Γ12 ⎤ E (wit w′i, t − k ) = ⎢ ⎥ ⎣ Γ21 Γ22 ⎦

⎡

T

2

⎣ Σ21

t=1

Σ12 ⎤ ⎥ Σ22 ⎦

2 −1 we define , ϖ1,2 = ϖ12 − ϖ12 Ω22 ϖ 21, and −1 ^ + ^ 12 Ω22 ε^it , and then the FMOLS estimator is given by yit = yit − ϖ

Further,

⎛ α^ ⎞ + i β^i, FM ≜ ⎜⎜ ⎟⎟ = (X ′i Xi )−1(X ′i yi+ − πTδ^ ) ^ ⎝ βi ⎠FM

(10)

where Xi = (lT , qi ) is a (T × 4) matrix, lT represents an (T × 1) vector with all elements equal to unity, π = (0, 1, 1, 1)′ and −1 + ^ ^ ^ ^ ^ . δ =Π −Π Ω ϖ 21

22

22

21

Eq. (10) corrects for the presence of serial correlation and endogeneity in the OLS estimator for each province i. Further, Phillips and Moon (1999) provided the pooled FMOLS estimator to get a consistent estimator of the regression coefficient for the homogeneous and near homogeneous cointegration cases. It is used for the pooled sample after removing the deterministic components from both the dependent variable and the regressor. Given estimates of the average long-run covariance, Ω^ and Γ^ , we define the modified dependent variable

^ −1^ϵ ^ 12 Ω y˜it+ = y˜it − ϖ 22 it

(11)

where y˜it and q˜ ′it are the corresponding data purged of the individual deterministic trends, y˜it = yit − y¯i. and q˜ ′it = q′it − q¯ ′i. . The pooled FMOLS estimator is then given by

⎛ α^ ⎞ + i β^PFM ≜ ⎜⎜ ⎟⎟ = (X˜ ′X˜ )−1(X˜ ′y˜ + − πTδ^ ) ^ ⎝ βi ⎠PFM

(12)

where X˜ = (l, q˜ ). In essence, the pooled estimator simply sums across cross-sections separately in two different parts of Eq. (10).

K. Li, B. Lin / Energy Policy 89 (2016) 214–223

219

Table 1 Panel unit root tests. Variable

IPS

Fisher-ADF

Levels

ln patent ln rgdp ln rd ln price

3.881 4.238 1.491  8.109***

(0.999) (1.000) (0.932) (0.001)

First difference

ln patent ln rgdp ln rd ln price

 15.305***  2.428***  22.932***  17.065***

(0.000) (0.008) (0.000) (0.000)

Fisher-PP

39.034 (0.984) 65.459 (0.293) 51.689 (0.769) 167.634 ***(0.000) 304.786*** 80.272** 417.149*** 335.521***

(0.000) (0.041) (0.000) (0.000)

CADF

63.339 16.820 99.664*** 154.999***

(0.359) (1.000) (0.002) (0.000)

 1.060 4.269 2.413  0.506

(0.145) (1.000) (0.992) (0.306)

398.457*** 73.277 441.663*** 465.951***

(0.000) (0.117) (0.000) (0.000)

 3.696  2.669***  2.662***  7.709***

(0.000) (0.003) (0.004) (0.000)

Note: 1. All tests are based on the model with a constant. 2. Lag lengths are chosen from 0 to 2 based on Schwarz Information Criterion. *** **

Denotes statistical significance at 1% level. Denotes statistical significance at 5% level.

Based on the residuals of FMOLS, McCoskey and Kao (1998) derived the LM-test for the null of cointegrationin panels. For model (9), θ = 0 implies that the random error does not accumulate in the stochastic trend, thus ε^it ~I (0). It means there is a cointegration relationship in {yit , q′it }; otherwise, there is no cointegration relationship. Therefore, the hypothesis of the LM-test is H0 : θ = 0 vs. HA : θ ≠ 0. The statistic is defined as follows: +

LM =

1 N

N

∑i = 1

1 T2

2 ^ 1,2 ϖ

(13)

t ^ 1,2 is the consistent estiwhere Sit+ = ∑ j = 1 e˜ij+ , e˜ij+ = yit+ − Xi′ βi, FM , ϖ mator of ϖ1,2. Assuming qit itself does not have a cointegration relationship, E (wit ) = 0 and the existence of sup E wit p when

t

2 ≤ p < ∞ ( ∃ p), the asymptotic result for the test is (Kao and Chiang, 2000)

N (LM + − μ u ) ⇒ N (0, σu2 )

(14) 1

where μu = E ( ∫ W (r )2dr ), σu2 = Var ( ∫ W (r )2dr ), W (r ) is a stan0

0

dard Brownian motion. The moments, μu and σu2, can be obtained through a Monte-Carlo simulation process. The LM-test statistic can be obtained on the basics of the re^ 1,2 through FMOLS, while μ^u and siduals e˜ + and correction factor ϖ 2 σ^u through Monte-Carlo simulation. Standardizing it yields N (LM + − μu ) σu

The FMOLS is adopted to estimate model (1), and the process is discussed as follow: ① estimate model (1) by OLS, and the residual is denoted as u^ . Define it

ϵit =

T

∑t = 1 Sit+2

1

3.2. Panel cointegration estimation and panel error correction models

⇒ N (0, 1). Therefore, we can test the null hypothesis of

cointegration. If we cannot reject it, then model (1) is the cointegration model and the cointegration vector is given by Eq. (12).

3. Results 3.1. Unit root tests This paper applies IPS test, Fisher tests (including Fisher-ADF test and Fisher-PP test) and CADF test to conduct the panel unit root tests, and the results are shown in Table 1. It indicates that all the variables except energy price are non-stationary. However, when considering the dependence of China's provinces, energy price is non-stationary based on the CADF test. The Fisher-PP test for the first differences of ln rgdp implies it is non-stationary, but the other two tests (Fisher-ADF test and especially CADF test) conclude it is stationary. Thus, we can conclude that all the variables became stationary after taking first differences. These results not only reveal the non-stationary characteristics of economic growth, R&D activities, energy prices and energy technology patents of China's provinces, but also lay the foundation for the following panel cointegration analysis.

( ( Δln rgdp

it

)(

)(

− Δln rgdpi • , Δln rdit − Δln rdi • , Δln

priceit − Δln pricei •

))

② for u^it and εit , using the Bartlett kernel function (bandwidth ¼3) to adjust their long-run covariance matrix, and + ^ + and get consistency estimators of Γ and Ω , and then get δ^ , Ω 22

−1 ^ 12; ③ adjust the explained variable by using y+ = yit − ϖ ^ 12 Ω^ 22 εit ; ϖ it ④ based on Eqs. (10) and (12), adopt the pooled FMOLS method to estimate model (1) to obtain the consistent estimators, and the result is presented below (t statistics in parentheses):

^it ln patentit = 3.547 + 0.255 ln rgdpit + 0.835 ln rdit − 0.159 ln priceit + u (71.01)

(1237.33)

(−1.30)

(15)

Whether Eq. (15) is a cointegration relationship depends on the statistic prosperity of u^it . According to Eq. (14), we need to cal2 culate μ^u and σ^u firstly through Monte-Carlo simulations so as to conduct LM-test. A Monte Carlo simulation process is designed and conducted as follow: ① generate random numbers for αi ( i = 1, … , 30) based on uniform distribution [0, 5]; generate random numbers for β1 and β2 based on uniform distribution [0, 1]; and generate a random number for β3 based on uniform distribution [  1, 0]; ②generate random numbers for εit based on three-dimensional normal distribution with the mean is zero and the covariance matrix4 is

0.481 0.853 0.001 0.852 2.564 − 0.001 , 0.001 − 0.001 0.006 and then generate three explanatory variables by xit = xi, t − 1 + εit ; ③generate uit by uit = bi εit , where bi is a (3 × 1) vector and generated on the basis of uniform distribution [0, 0.5]; ④ generate ⌢ yit on the basis of model (1), thus we get a four variables system {⌢ yit , x′it } ( xit = (x1it , x2it , x3it )′) with a cointegration relationship; ⑤ adopt FMOLS introduced in Section 2.3 to estimate these simulation data, and calculate the values of LM + statistic. Take the sample mean and sample variance of LM + as estimators of μu and σu2 in 4 This setting is the same as the covariance matrix of the sample data of ln rgdp, ln rd and ln price.

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Table 2 Panel cointegration tests: Kao test and Pedroni test. Method

Statistic

Kao residual cointegration test

ADF stat

 5.806***

Pedroni residual cointegration test

Panel v-Statistic Panel rho-Statistic Panel PP-Statistic Panel ADF-Statistic Group rho-Statistic Group PP-Statistic Group ADF-Statistic

 1.900 0.024  6.565***  3.572*** 2.386  9.149*** 1.964**

Note: 1. Trend assumption: no deterministic trend; Null Hypothesis: no cointegration. 2. Newey–West automatic bandwidth selection and Bartlett kernel. *** **

Denotes statistical significance at 1% level. Denotes statistical significance at 5% lvel.

small samples. After repeating the above process 3000 times, we get the follow results: μ^u ¼0.421 and σ^u ¼2.110. The value of LM + statistic on the basis of Eq. (15) is LM + = 0.284 , thus

LM =

(

)

N LM + − μ^u /σ^u = − 0.355

The p-value of the lower-tail of the standardized normal distribution is 0.361. It implies that rejecting the null of cointegration results in 36.1% probability of type I error (the null hypothesis is rejected with the same frequency when it is in fact true). In addition, Table 2 indicates that the cointegration relationship is further confirmed by Kao test (Kao, 1999) and Pedroni test (Pedroni, 2004),5 which are widely used in the literature. Therefore, Eq. (15) reveals the long-term relationship among energy technology patents, economic growth, R&D activities and energy prices. Based on the panel cointegration relationship revealed in Eq. (15), we further estimate models (2)–(5) in order to investigate the adjustment effects of this long-term relationship on changes in variables in model (1) in the short-term. The results are shown in Table 3 and the estimators of ϕi depict these adjustment effects. For model (2), ϕ^ (¼  0.423) is negative and significant, which 1

further confirmed the above panel cointegration relationship.

4. Discussion 4.1. The long-term relationship between economic growth, R&D activities, energy price and energy technology patents Eq. (15) indicates that economic growth and R&D activities have significant positive effects on energy technology patents while energy price has a negative but insignificant effect. These conclusions imply that economic growth and R&D activities are the driving forces of energy technology innovation, and increasing energy prices does not necessarily improve energy technology innovation. Specifically, the long-term elasticities of economic growth and R&D activities are 0.225 and 0.835 respectively, which shows that energy technology innovation is mainly driven by R&D activities. This finding is supported by many previous literatures, such as Lee and Lee (2013) and Bointner (2014), and it is also consistent with the intuitive meaning of Figs. 1 and 2. Patents are the direct 5 Pedroni test contains 7 tests and it can be classified into two categories. Panel ADF-Statistic and Group ADF-Statistic have better small sample properties, so the result is induced by these two tests.

outputs of R&D activities, so this result is also straightforward. In other words, promoting R&D activities is a crucial way for energy technology innovation. So one question arise: how do we promote R&D activities? Market stimulus is a major source of innovation (Guo, 2005). Enterprises are the main body of R&D activities, and the main target of their R&D activities is to establish core competence through a deliberate layout of intellectual property rights, thereby gaining high returns by using the advanced technologies (Hu and Phillips, 2011). In this sense, they are the direct beneficiaries of patents, and they will strive to improve the efficiency and quality of R&D outputs. However, R&D investment activities will face market failure. For one thing, technology innovation has positive externalities. Because of technology spillovers, technical inventors often do not exclusively complete new technologies or they cannot control the direction and speed of technology spillovers, which results in a lower return rate on R&D investment than expected. For another thing, due to insufficient information, the actual values of R&D activities are uncertain, which increases risk and make investors more cautious towards R&D activities (Dits and Berkhout, 1999). The above aspects imply that investments in R&D activities will be lower than the optimal level. Thus, government's investment in R&D activities is an important and supplementary support mechanism. Compared to traditional energy carriers (such as coal, oil and so on), using new energy technologies often do not have a cost advantage, especially in China because of irrational energy pricing policies. So the funds from the government towards new energy or clean energy technologies would go a long way in promoting energy technology innovation through efficient R&D. The support for R&D activities from the government is effective in the short- and middle-term (Guan and Yam, 2015; Wang et al., 2012). In the long run, it should improve patent protection system so as to make the energy patents owner enjoy exclusive benefits, and establish a sound technological market to ensure the commercial values of the energy patents, which is critical to promote energy R&D activities. Contrary to expectation, the effect of energy price on energy technology patents is a small and negative value, but it is insignificant. It also goes against the induced innovation theory, which shows that energy prices play an important positive role in inducing new energy innovations (Popp, 2002). We believe the possible reason is the energy market distortions, which could send wrong signals to the market and has also been found to have a limited or insignificant effect on China's energy consumption or energy intensity in previous literatures (Song and Zheng, 2012). The government pays huge fossil fuel subsides to maintain fossil fuel price at a low level in order to support economic growth, which distorts the energy market (Li and Lin, 2015). In recent years, the government makes some upward adjustments for fossil fuel prices. But after considering inflation, the adjustments are too small to reflect the scarcity of energy resources, environmental degradation, and imbalances in domestic demand and supply (Jiang et al., 2014). In this case, although the government gives some support on new energy technologies, they still do not have cost advantage compared to traditional fossil fuel. Furthermore, cheap energy prices will deepen energy rebound effect, which offset the energy savings induced by energy technological progress (Li et al., 2013). In summary, because of the distorted energy price sending wrong signals to the market, the driving force of energy technology innovation is frustrated, which results in the insignificant effect of energy price on energy technology patents. The economic implication of the above conclusion is that in the long term, China's energy technology innovation strategy should increase support towards energy technology R&D activities. Meanwhile, the government must increase the pace of energy

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221

Table 3 Results of models (2)–(5). Variable

Model (2) Δ ln patentit

Model (3) Δ ln rgdpit

ecmi, t − 1

 0.423*** (  10.44)

Δ ln patentit

Δ ln rgdpit

0.564 (0.70)

Δ ln rdit Δ ln priceit

0.111 (1.09)  0.073 (  0.38)

Model (4) Δ ln rdit

0.009 (1.08) 0.013** (2.02) 0.017* (1.52) 0.044*** (3.58)

Model (5) Δ ln priceit

 0.058** (  2.54) 0.028 (1.09)

 0.023 (  0.35)  0.008 (  0.18)

0.763** (2.01)

 1.329* (  1.90) 0.061 (0.51)

0.143* (1.47)

Note: t statistics in parentheses. *** ** *

Denotes statistical significance at 1% level. Denotes statistical significance at 5% level. Denotes statistical significance at 10% level.

price reforms. The primary thing is to eliminate the unreasonable fossil fuel subsidies, and to form a new energy pricing mechanism which reflects fossil fuel depletion governance premium and its environmental costs. At the same time, the government should give price supports and preferential policies to new and clean energy, thereby resulting in its cost-competitiveness. In this way, the development and application of the latest energy technologies will be accelerated. In June 2014, the Chinese government proposed to actively promote energy production and consumption revolution, and its main content is “energy sources are general goods, and its prices should be determined by its supply and demand; thus the prices could be signals to guide the effective development and rational use of energy”. Obviously, this is a long-term strategy and it is conducive for energy technology innovation based on our findings. 4.2. The short-term relationship between economic growth, R&D activities, energy price and energy technology patents In Table 3, ϕ^1( ¼ 0.423) for model (2) is negative and significant, which confirms the existence of the panel cointegration relationship again, and implies that deviation from cointegration system of energy technology patents will cause the energy technology patents to change about  42.3% in the next term. It is consistent with the data characteristics of China's energy technology patents. For example, during the period 2000–2013, the growth rates of China's energy technology patents were 52.09%, 11.64%, 35.11% and 14.92%, showing typical alternating features. For model (3), ϕ^ (¼0.009) is positive but insignificant, in2

dicating that the cointegration relationship is conducive for economic growth. In fact, energy technology innovation itself is the driving force of economic growth. For model (4), ϕ^ ( ¼ 0.058) is negative and significant, which implies that the 3

cointegration relationship has inhibitory effect on R&D investment activities in the short-term. In other words, energy technology patents growth will make R&D investment activities to decline by 5.8% in the next term. The deeper meaning is that a breakthrough in energy technologies may undermine R&D activities in the next term because the applications of the latest technologies are not smooth or the possibility of breakthrough of a new technology may become more difficult. Finally, ϕ^ ( ¼  0.023) in model (5) 4

is negative and insignificant, indicating that the cointegration relationship is conducive for energy price reduction. Though energy technology progress is conducive for decline in energy consumption, the energy price reduction will increase energy consumption, thus resulting in energy rebound effect. The conclusions of the above analysis have important economic and policy implications. Induced by economic growth and R&D investment, energy technologies would achieve a breakthrough. However, cheap fossil fuel price and the energy rebound effect

may discourage the use of the latest energy technologies, leading to a decline in R&D investment in the later period. In the longterm, this will further hinder energy technology innovation. The above analysis means it creates a negative feedback loop. Therefore, reforming the energy pricing system, and promoting energy technologies development and its application in order to effectively reduce the energy rebound effect caused by energy technological advancement, should be an important part of China's energy development strategy at this stage. In sum, the conclusions based on the panel error correction models have an important practical significance for China's energy policies in the near future. The conclusions of the panel error correction models further support the conclusions of the panel cointegration model, and its economic implications are mutually complementary and consistent with China's fact. So this analysis of the long-term influencing factors and the dynamic adjustment of China's energy technology patents accurately reveal the relationship between energy technology innovation, economic growth, R&D activities and energy prices.

5. Conclusion and policy implications Patents are the main outputs of technology innovation, and directly induced by R&D activities. However, there are few empirical studies on China's energy technology patents because it is difficult to obtain the related data. In this sense, the current research fills the gap, and also enriches energy technology innovation theory and literatures on China's economics research. Using panel data covering 30 provinces of China over the period 1999–2013, the current study explores the relationship among economic growth, R&D investment, energy price, and energy technology patents. The panel unit root tests confirm that all variables are generated by panel unit root process. The cointegration model, which is estimated by fully modified OLS (FMOLS) method and tested by LM-test introduced by McCoskey and Kao (1998), confirms that there is a long-term relationship among energy technology patents, funds for R&D activities, economic growth and energy price. Finally, panel error correction models are adopted to reveal the short-term relationships. The cointegration relationship indicates that R&D activities and economic growth have positive effects on energy technology patents, while energy price has a negative but insignificant effect. The panel error correction models reveal that in the short-term, this cointegration relationship is conducive for economic growth, but it reduces R&D investment and energy price. Based on the empirical results, some important implications for China's energy technology policy are discussed as follow. First, due to the significant positive effect of R&D activities on energy patents, the Chinese government should introduce more

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energy technology-push policies to support energy R&D activities in order to promote energy technology innovation. These policies include financial support measures and the restrictive targets for energy conservation and emissions abatement. Today, financing and fostering basic and applied R&D activities have become important components of China's government policies (Guan and Yam, 2015). Huge funds are invested in energy R&D activities to address energy and environmental issues (Ke et al., 2012; Zhao et al., 2014), but they are still insufficient for social demand. On the one hand, clean or carbon-free energy R&D investment has a high risk, and its outputs may be imitated by others. Thus it requires government financial support through government sponsorship, tax deduction and exemption and fiscal subsidies. On the other hand, China uses huge amount of fossil fuel, and its energy consumption structure is coal-dominated. Thus, it is nearly impossible to substitute coal for clean or carbon-free energy types in the near future. So the government should also pay attention to R&D investment in fossil fuel technologies, such as clean coal technologies. The government can equally establish some R&D bases, and speed up the absorption of imported energy technologies. Restrictive targets for energy conservation and emissions abatement, such as energy/carbon intensity reduction target, plays an important guiding role in the pace and direction of energy innovation and technology applications, thereby promoting carbon-free R&D activities. Second, the empirical results and discussion show the distorted energy price produces a negative but insignificant effect on energy innovation, and the cointegration relationship is conducive for energy price reduction, which implies that energy price system reform is a crucial measure for promoting energy technology innovation and reducing the rebound effect. From this perspective, the policies mix could be far away from expected. Generally speaking, clean or carbon-free energy carriers do not have cost advantage compared to fossil fuel such as coal and oil, while fossil fuel subsidies make this issue more serious (Li and Lin, 2015). In fact, it amounts to huge waste when government gives financial support to clean energy technologies while also giving subsidies towards fossil fuel. In this sense, adopting the targeted fossil fuel subsidies for the poor to ensure that fossil fuel price reflects its depletion premium and environmental governance cost will be conducive for improving the efficiency of the financial support towards energy technology innovation. Furthermore, because of positive externalities and cost disadvantage, guaranteed price schemes and investment incentives play a major role in carbonfree or low-carbon technology development. However, these policies may be effective in the early phase of the technology life cycle, whereas for relatively more mature technologies, quantitybased instruments seem more suitable (Nesta et al., 2014). Third, as stated in Section 1.2 (theoretical background), the expectation regarding the energy patents application has an effect on energy R&D activities and their results (energy technology patents). So the government can introduce some technical transformation policies to improve the adoption of energy technology patents except for the above incentive policies (financial support, environmental regulation and energy price reform). Although energy technology patents have experienced fast growth, many of them are not been widely used. One possible reason is that unreasonable energy price hinders their application. Another important reason is the gap between energy technologies suppliers and users. In fact, most energy-efficient technologies are produced by academic institutions, and they are far from end-users. The government could make efforts to bridge the gap between them so as to reinforce the R&D outcomes emanating from academic institutions and ensuring technology commercialization (Hu and Phillips, 2011). Forth, as mentioned in the discussion, enterprises are the main

body of R&D activities and market stimulus is a major source of innovation (Guo, 2005), which implies the government should reform the funding system, and encourage private R&D activities. In other words, enhancing energy innovation performance should mainly depend on market activities such as investments from private capital or venture capital while government direct investments in energy R&D activities should be moderate. Specifically, the government should depend more on market mechanism such as tax deduction and exemption, and interest-rate subsidy to support energy R&D activities. Furthermore, it is important for the government to introduce a clear and reasonable incentive mechanism (for example, a reasonable energy price system) to encourage private investments in energy R&D activities. We believe that this measure could help to reduce crowding effect, which means enterprises will reduce their own investments in energy R&D if the funds from the government are high (Bointner, 2014; Popp and Newell, 2012). In theory, enterprises are the direct beneficiaries of R&D activities, so fully mobilizing their enthusiasms to support energy R&D activities is crucial to enhancing sustainable energy technology innovation performance. Government direct investments in energy R&D activities should pay more attention to basic and common technologies. From the perspective of policy implication, this paper has some limitations. Despite the necessity of government financial support to R&D activities, it should pay more attention to the effect of the different forms of support (Blind, 2012). Taking Beijing as a case, Guan and Yam (2015) found three major government financial incentives—special loans, tax credits and direct earmarks—have different effects on firms’ innovation performance. In the current study, we obtain a general result that enhancing R&D activities through government financial support can promote energy technology innovation, while the results of Guan and Yam (2015) imply that we should further analyze how to enhance the effectiveness of government financial support. This is an important issue which is left for future investigation.

Acknowledgment The paper is supported by Xiamen University - Newcastle University Joint Strategic Partnership Fund, the Grant for Collaborative Innovation Center for Energy Economics and Energy Policy (No. 1260-Z0210011), Xiamen University Flourish Plan Special Funding (No. 1260-Y07200), and the China Sustainable Energy Program (No. G-1506-23315).

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