Differentiated effects of diversified technological sources on energy-saving technological progress: Empirical evidence from China's industrial sectors

Renewable and Sustainable Energy Reviews xx (xxxx) xxxx–xxxx

Contents lists available at ScienceDirect

Renewable and Sustainable Energy Reviews journal homepage: www.elsevier.com/locate/rser

Differentiated effects of diversified technological sources on energy-saving technological progress: Empirical evidence from China's industrial sectors ⁎

Zhenbing Yanga, Shuai Shaob, , Lili Yangc, Jianghua Liub a

School of Economics, Nanjing University of Finance and Economics, Nanjing 210023, China School of Urban and Regional Science, Institute of Finance and Economics Research, Shanghai University of Finance and Economics, Shanghai 200433, China c School of International Economics and Trade, Shanghai Lixin University of Accounting and Finance, Shanghai 201209, China b

A R T I C L E I N F O

A BS T RAC T

Keywords: Energy-saving technological progress Differentiated effect Technological source Industrial sub-sectors Stochastic frontier analysis China

Although it has been a consensus that the promotion of energy-saving technology plays a vital role in impelling the green transformation of economic development, the existing studies pay little attention to whether diversified technological sources present differentiated effects on energy-saving technological progress. Using the stochastic frontier analysis (SFA) based on the translog production function, this paper estimates and compares the energy-saving technological progress rates of various industrial sub-sectors in China over 2001–2011. Furthermore, using the system generalized method of moments (SGMM), which is able to effectively control the endogeneity problem, we investigate the differentiated effects of six basic technological sources on energy-saving technological progress. The results show that although there are evident differences of energy-saving technological progress rates among different industrial sub-sectors, China's industrial energy-saving technological progress presents an overall improved trend. Among six primary technological sources, only the forward technological spillover effect of foreign direct investment (FDI) and the forced effect of competition have a significant positive impact on energy-saving technological progress, while the influences of backward and horizontal technology spillovers, original innovation, and leaning by exporting are all not significant. Moreover, industrial energy-saving technological progress shows an obvious path dependence property, i.e., the previous high-level energy-saving technological progress has an evident positive impact on the current one. Accordingly, we propose that the Chinese government should encourage domestic industrial enterprises to learn and absorb advanced energy-saving technologies from foreign investment enterprises and by exporting products with more advanced technology content and added value.

1. Introduction Since the implementation of reform and opening-up policy in 1978, although China has achieved rapid economic development, the extensive growth mode has caused serious energy and environmental problems. According to the BP Statistical Review of World Energy [8], China surpassed the US to become the world's largest consumer of primary energy consumption in 2010. Due to an unbalanced energy endowment structure and the shortage of oil and natural gas, the green transformation of economic development in China faces a huge difficulty. A coal-dominated energy consumption structure leads to the co-existence of rapid economic development and serious environmental pollution. Recently, a large range of frequent haze further highlights the urgency and necessity to implement energy-

⁎

saving and emission-reduction policies and develop low-carbon economy. The improvement of energy efficiency, which is regarded as the “fifth fuel” [9], has become an effective approach to cope with the conflict between rapid economic development and energy supply insufficiency and the severe challenges of climate change [31]. China has also treated the improvement of energy efficiency as the basic principle of energy-saving and emission-reduction policy. In 2006, China officially launched Comprehensive Work Plan of Energy Saving and Emission Reduction to announce a quantitative energy-saving target, i.e., China's energy consumption per unit gross domestic product (GDP) would reduce from 1.22 t of coal equivalent (tce) per ten thousand RMB in 2005 to less than 1 tce per ten thousand RMB by 2010. In the 11th “Five-Year Plan” (FYP), the 12th

Corresponding author. E-mail address: [email protected] (S. Shao).

http://dx.doi.org/10.1016/j.rser.2016.11.072 Received 9 June 2016; Received in revised form 22 August 2016; Accepted 3 November 2016 Available online xxxx 1364-0321/ © 2016 Elsevier Ltd. All rights reserved.

Please cite this article as: Yang, Z., Renewable and Sustainable Energy Reviews (2016), http://dx.doi.org/10.1016/j.rser.2016.11.072

Renewable and Sustainable Energy Reviews xx (xxxx) xxxx–xxxx

Z. Yang et al.

FYP, and the 13th FYP,1 the reduction targets of quantitative energy intensity were proposed as the constraint goals. Thereby, the estimate, decomposition, and determinants of China's energy efficiency have been paid much attention to as research hot spots. Compared with the traditional single factor energy efficiency (i.e., the output per unit energy use), because the energy efficiency indicator based on total factor productivity (TFP) framework considers the combination of various input factors and thus extends the “singleinput” indicator to the “multi-inputs” structure, it is regarded as a better reflection to energy efficiency2 [20] and is widely adopted [19,20,29,39,45,46]. The methods of calculating and decomposing total factor energy efficiency change can be divided into two categories of frontier analysis methods: nonparametric data envelopment analysis (DEA) and parametric stochastic frontier analysis (SFA). The DEA method has been widely applied. Zhou et al. [47] provided a comprehensive overview of the application of the DEA method in energy and environmental field. Meanwhile, a huddle of studies used the SFA method (e.g. [28,18,11,37]). The above two methods have their respective advantages. Without setting specific function form, the DEA method avoids errors from setting models and can also reflect the production process of multiple outputs and inputs. However, because it does not take into account the effect of random errors, the DEA method requires a higher data accuracy and is susceptible to the statistical errors of sample data [36], causing some bias of estimated efficiency. In addition, because the DEA is a non-statistical or deterministic approach that does not allow a genuine hypothesis test, it is unable to judge the robustness of estimated results. In contrast, the SFA method has the following advantages. First, the SFA model not only introduces random disturbance term, but also assumes that the factors deviated from the frontier production surface are from two aspects: the non-negative random error representing technology invalid and the system random error representing white noise. In this way, the SFA considers the potential deviations from the frontier production surface caused by other uncontrollable stochastic factors. Second, the SFA is a statistical approach with some statistical tests for parameters and the SFA model per se. Third, because the frontier production surface of the SFA model is random, its estimated results may be closer to reality when using panel data. On the contrary, because the frontier surface of the DEA model is fixed, it may ignore the disparity among samples to some extent. Finally, since the estimated efficiency values of the DEA are relative values rather than absolute values, the DEA method fails to provide further comparative analysis among samples whose estimated efficiency values are all equal to (1). On the contrary, the estimated technique efficiency values through the SFA are absolute values to facilitate carrying out a complete comparative analysis [15]. Nevertheless, the SFA can take into account only one output variable rather than multiplicate output

variables. This empirical analysis uses the sample data of China's industrial sub-sectors, which are derived from the estimation or calculation based on raw statistical data. Moreover, there are obvious differences among sub-sectors. Hence, an evident noise is inevitable, and thus the SFA is more suitable for the present empirical investigation. In existing studies, technical efficiency change and energy-saving technological progress are two most prevalent and concernful decomposition indicators of total factor energy efficiency change [24]. The technical efficiency change refers to the capability of the optimal use of existing resources, i.e., the capability of achieving maximum output given the inputs of various factors or minimum inputs given an output level. The energy-saving technological progress refers to the change rate of the production technology frontier over time given the inputs of various factors including energy use [22,43]. Undoubtedly, energysaving technological progress is a main propellent of the improvement of energy efficiency. Previous literature showed that in most years, energy-saving technological progress played a positive role in boosting China's energy efficiency, while technical efficiency change made little contributions to energy efficiency since the 1990s even exerted a negative effect on it in approximately half the investigated period, indicating that energy-saving technological progress became a dominant factor of improving China's energy efficiency [33]. Therefore, this paper focuses on the estimate and determinants of China's energysaving technological progress. The existing studies argue that the progress of energy-saving technology is of great significance to China's green growth. For example, Li and Zhu [23] estimated the cost curve of energy saving and carbon emissions reduction in China's iron and steel sector considering that energy-saving technologies are widely used, and concluded that energy-saving technologies could enhance the potential of energy saving. Focusing on China's cement industry, Wen et al. [40] found that energy conservation and carbon emission reduction can be achieved through the improvement of energy-saving technology in this sector. Although previous studies have indicated that energy-saving technology plays a vital role in the green transformation of China's economic development, they have not distinguished the source of energy-saving technology and have not paid attention to the differentiated effects of diversified technological sources on energy-saving technological progress. Hence, to the best of our knowledge, such an issue is still not explored in existing literature. Considering that various technological sources may make differentiated contributions to improving energy-saving technological progress, the investigation and distinguishing of such disparities can provide more detailed and targeted decision-making reference for the effective implementation of energy-saving policy. Although the existing studies conducted extensive empirical investigations regarding the determinants of total factor energy efficiency and energy-saving technological progress [19,36,37,39,45,46], a specific exploration on the differentiated effects of diversified technological sources on energy-saving technological progress is still absent. In order to fill such a gap, based on the accurate estimate and comparatively analysis of energy-saving technological progress of China's industrial sub-sectors through the SFA, this paper uses the system generalized method of moments (SGMM), which is able to effectively control the potential endogeneity problem, to carry out a specific empirical investigation on the different effects of various technological sources on China's industrial energy-saving technological progress, including the technology spillover of foreign direct investment (FDI), learning by export, original innovation, and market power. Through such a rigorous empirical examination, this paper aims to identify the policy key(s) to promoting China's energy efficiency and energy-saving technological progress, so as to provide some significant decision-making supports for the implementation of China's energy-saving policy. The rest of the paper is organized as follows. In Section 2, we

1 The full name of the "Five-Year Plan" (FYP) is the five year plan for the national economic and social development of China. Since 1953, the Chinese government has formulated a development plan every five years as the core strategy and target of economic development, with an interruption in the year 1963–1965. The starting and ending of the 11th five-year plan were 2006 and 2010, respectively, 2011–2015 for the 12th five-year plan, and 2016–2020 for the 13th five-year plan. In the three FYPs, the quantitative target of energy intensity reduction was proposed, indicating that the Chinese government attaches great importance to the reduction of energy intensity. At the same time, some studies focus on the decomposition and influencing factors of energy efficiency, which have become a hot research issue. 2 Energy efficiency reflects the effect of energy factor on the real output. Some existing studies adopt the ratio of output and energy use to measure energy efficiency, but they ignore the substitution or complementary effects among different factors. Therefore, it is not accurate to measure the change in the actual output caused by the change in energy input, as well as to reflect the change in productivity. In contrast, the total factor energy efficiency considers complementary or substitution effects among capital, labor and energy inputs, reflecting the changes in other factor inputs caused by the change in some factor input, as well as the impact on the actual output. Therefore, the total factor energy efficiency can more fully measure the impact of energy factor on the real output. In this way, the calculation of the energy-saving technology progress is more accurate.

2

Renewable and Sustainable Energy Reviews xx (xxxx) xxxx–xxxx

Z. Yang et al.

industrial added value as the indicator of output,3 similarly to some existing studies (e.g. [30,43],). (2) Capital stock (K). We adopt perpetual inventory method to estimate the capital stock of each sub-sector as Kt = (1 − δt ) Kt −1 + It , where K denotes the fixed capital stock, and the initial stock is estimated by referring to Chen [12]; I denotes annual physical capital investment, and the gross investment in fixed asserts is chosen as its proxy. In addition, capital depreciation rate δ is calculated as the ratio of current depreciation to the original value of fixed assets. Obviously, compared with most previous studies using a constant depreciation rate, the timevarying depreciation rates we use are more accordant with reality to facilitate more precise estimated results of capital stock. (3) Labor input (L). We choose the average annual employment as its proxy. (4) Energy consumption (E). We use total industrial energy consumption with the unit of ten thousand tce to measure it.

describe the estimation methodology of energy-saving technological progress, the econometric model of diversified effects of diversified technological sources, and data treatment. Based on the estimated and regression results, the further discussion is given in Section 3. Section 4 presents conclusions and policy implications. 2. Methodology and data 2.1. Estimation method of energy-saving technological progress The stochastic frontier production function was independently proposed by Aigner et al. [2] and Meeusen and van den Broeck [27]. Its initial form is based on cross-sectional data, and its panel data form is adopted as follows: (1)

Yit = f (xit , β)exp(vit − uit )

where Y denotes output; f (xit , β) represents the production frontier; X is the vector of input factors, and β is the corresponding parameters vector to be estimated; v is the random error term, and we suppose that v~N (0, σv2 ) and is mutually independent with u , indicating the effect of statistical error and random environmental factors on the output of the frontier; u ≥ 0 and is an unilateral error term, which is subjected to non-negative truncated normal distribution, and u refers to the technology inefficiency changing over time to measure the production efficiency; v is a white noise. Following Battes and Coelli [4], we can get uit = ui exp[−η (t − T )] and ui ~N +(μ, σu2 ), where η represents the change rate of u . Because the above stochastic frontier model violates the classical hypothesis of the ordinary least squares (OLS), OLS method is not suitable for its estimation. However, according to the Battes and Coelli [4], we let γ = σu 2 /(σu 2 + σv 2 )(0 ≤ γ ≤ 1) and can obtain all the estimators by using maximum likelihood estimation (MLE). Thus, γ can also be used to judge the proportion of production inefficiency in the total variance. When γ is close to 1, the error is mainly from u , indicating that the gap between actual output and the frontier output is mainly caused by technical inefficiency. Therefore, γ can be regarded as a basis of testing the rationality of model setting. Compared with the Cobb-Douglas (C-D) production function and the constant elasticity of substitution (CES) production function, the translog production function can reflect the substitution relationship among various input factors, loosen the hypothesis of neutral technology, and thus reveal more characteristics of economic system. Hence, we use the following translog production function to estimate the energy-saving technological progress:

Generally, the existing studies introduce energy, capital, and labor together into the production function to calculate the total-factor energy efficiency [19], which is frequently decomposed into three parts: technical efficiency change, technological progress, and scale efficiency change [22,43]. The technological progress refers to the change of the production technology frontier over time given the inputs of various factors including energy. Considering that such change can reflect the contribution of the application of energy-saving technology to the improvement of total-factor energy efficiency to a great extent, following the existing studies [22,43], we define the technological progress as “energy-saving technological progress”, and it can be calculated as follows:

TCit =

Since the technical level of developed countries is generally in the leading position, they mainly rely on their technological innovation to realize the continuous technological progress. In contrast, developing and underdeveloped countries need to import advanced and appropriate technologies from developed countries based on their comparative advantage determined by their factors endowment, in order to promote their technical levels with a faster speed [25]. The technology spillover effect from foreign direct investment (FDI) is frequently regarded as one of the crucial approaches to improve the technological progress in developing countries. Many studies have confirmed the existence of technology spillover effect of FDI and its significant influences on the technical change of the host country (e.g. [5,21],). The technology spillover of FDI can be divided into horizontal and vertical technology spillovers [21]. The horizontal technology spillover mostly comes from the competition, demonstration and imitation effect among various industries, while the vertical technology spillover mainly includes forward and backward technology spillovers. The forward technology spillover refers to the process that foreign investment enterprises provide intermediate product to downstream industries to promote the technological progress of domestic downstream enterprises, while the backward technology spillover refers to the process that foreign enterprises receive intermediate products supplied by domestic upstream enterprises to promote the technological progress of domestic upstream enterprises [5]. The measured indicators of

1

+ α7 t × ln L it 1

+ α8 t × ln Eit + 2 α9 ln Kit × ln L it + 2 α10 ln Kit × ln Eit 1

+ 2 α11 ln L it × ln Eit 1

1

(3)

2.2. Econometric model and estimation method of differentiated effects

ln Yit = α0 + α1 t + 2 α2 t 2 + α3 ln Kit + α4 ln L it + α5 ln Eit + α6 t × ln Kit 1

∂ ln f (xit , β) = α1 + α2 t + α6 ln Kit + α7 ln L it + α8 ln Eit ∂t

1

+ 2 α12 (ln Kit )2 + 2 α13 (ln L it )2 + 2 α14 (ln Eit )2 + vit − uit (2) where the subscript i and t denote the industrial sub-sector and the time in years, respectively; Y, K, L, and E are output, capital stoke, labor input, and energy consumption, respectively; α0 -α14 are coefficients to be estimated. The measured indicators of the input and output variables in Eq. (2) are described as follows. (1) Output (Y). Because the energy-saving technological progress we concern about reflects the change in the production technology frontier induced by factor inputs including energy use, which is an intermediate input, we choose the gross industrial output value of each sub-sector that contains intermediate inputs rather than

3 In addition, since 2008, the data of industrial added value have been absent in China Industry Economy Statistical Yearbook.

3

Renewable and Sustainable Energy Reviews xx (xxxx) xxxx–xxxx

Z. Yang et al.

dent technological innovation activities in developing countries play an irreplaceable role in enhancing their technological progress. Undoubtedly, the independent technological innovation of domestic industry mainly comes from the R & D activities of domestic industrial enterprise. Hence, referring to the existing literature [1,14,7], we also take into account three crucial determinants of independent innovation of domestic enterprise: original innovation activities, the forced effect of competition, and learning-by-exporting effect. (4) Original innovation activities (rd). Since research and development (R & D) is a primary approach to obtaining technological progress, the original technical innovation activities of industrial enterprises play an essential role in promoting energy-saving technological progress and improving energy efficiency [7]. Referring to Bointner [7], we employ the ratio of R & D input to output value in each industry to measure such an effect. (5) Forced effect of competition (li). The fierce competition within a certain industry can impel enterprises to pursue excess profits through technological innovation in order to enhance the competitiveness of enterprises [1]. Therefore, market competition is conducive to the promotion of energy-saving technology and can be considered as an important source of energy-saving technological progress. Generally, the degree of market competition is reflected by the degree of market monopoly in a certain industry, which can be measured by the Lerner index, i.e., a price-cost margin (PCM) index:

the above three kinds of technology spillover effects are described as follows. (1) Forward technology spillover (fs). This effect refers to the weighted average of intermediate inputs used by industry j and produced by the foreign investment enterprises in the upstream industries k of industry j, i.e., the technology transfer mode through the media of upstream suppliers providing raw materials and spare parts. Since competitive enterprises within a certain industry are faced with an optimal choice of the upstream suppliers, intense competition makes the supplier with a larger market share give priority to the enterprises with more advanced production technology when choosing partners. Such a reversed effect activates the enterprises to learn from FDI enterprises to enhance technological innovation and competitive advantage [38]. Referring to Wang et al. [38], the calculation formula of forward technology spillover is as follows:

∑

fsk =

ζkj ×

k,k≠j

TSkf − ESkf TSk − ESk

(4)

where ζkj is the direct consumption coefficients in the input-output table, reflecting the amount of intermediate inputs from industry k per unit output of industry j. It can be obtained from the column vector in the input-output table (excluding the diagonal data). TSkf and TSk represent the sale value of the foreign investment enterprises in industry j and the entire sale value of industry j, respectively. ESkf and ESk denote the export value of foreign investment enterprises in industry j and the entire export value of industry j, respectively. (2) Backward technology spillover (bs). This effect refers to backward linkages effect, i.e., the weighted average of intermediate inputs produced by industry i and used by all foreign investment enterprises in the downstream industries s of industry i. Due to the fierce competition among industries, the dealers of final products in downstream industries have access to a lot of product information, where some production technologies are hidden. Since the technological information of competitive enterprises gather in such downstream dealers, asymmetry production technological information is improved to facilitate domestic enterprises to obtain advanced production technologies from FDI [21]. Referring to Javorcik [21], the calculation formula of backward technology spillover is as follows:

∑

bss =

s, s ≠ i

ζsi ×

Ysf Ys

li = (P − MC )/ P

where P and MC are the price and marginal cost of product in a certain industry, respectively. A higher PCM index means a stronger market monopoly and a weaker market competition, and vice versa. Considering that the data of MC is usually unavailable, we use an alternative index proposed by Cheung and Pascual [13] to measure market competition as follows:

li = (VAI − LC )/ Y

(5)

In summary, the classification of various technological sources of energy-saving technological progress as shows in Fig. 1. In addition, as mentioned above, the Chinese government put forward an energysaving constraint target in 2006 for the first time in the 11th FYP, that is, energy consumption per ten thousand RMB GDP would drop from 1.22 tce in 2005 to 1 tce in 2010. Industrial energy-saving technological progress was expected to be significantly impacted on by such a target. Therefore, following Shao et al. [30], we also introduce a policy dummy variable (gov) as a control variable to reflect such a policy intervention factor, which is equal to (1) during the year 2006–2011 and 0 if otherwise. In summary, we build the following panel regression model to investigate the differentiated effects of diversified technological sources

TSjf TSj

(8)

where VAI is the industrial added value, which is calculated by using the income approach, i.e., industrial added value=compensation of employees + depreciation of fixed assets+net taxes on production +operating surplus; LC stands for the cost of labor measured by the compensation of employees in each industry; Y is industrial output measured by the total output value in each industry. (6) Learning-by-exporting effect (exp). Clerides et al. [14] found that export enterprises can learn from their export behaviours to improve their technological progress and factor productivity, that is, learning-by-exporting effect. Urpelainen [35] also argued that the export-oriented countries have strong incentives to invest in technological innovation in order to improve energy-saving technological progress. Hence, the degree of export can be regarded as an important influential factor of energy-saving technological progress. We employ an index of industrial export capability, i.e., the share of export value in industrial total output value, to reflect such an effect.

where ζis is the direct consumption coefficients in the input-output table, reflecting the amount of intermediate inputs from industry i per unit output of industry s. It can be obtained from the row vector in the input-output table (excluding the diagonal data). Ysf and Ys denote the output value of the foreign investment enterprise in industry s and the entire output value of industry s, respectively. (3) Horizontal technology spillover (hs). This effect refers to the competition, imitation and demonstration effects from FDI within a certain industry, inducing the technology spillover from foreign investment enterprises to domestic enterprises. Following the premise that technology transfer accompanies with commodity trading, such an effect can be measured by an indicator with realistic significance, that is, the share of the sale value of foreign investment enterprises in that of the entire industry [10] as follows:

hsj =

(7)

(6)

TSjf

and TSj represents the sale value of the foreign investment where enterprises in industry j and the entire sale value of industry j, respectively. Besides the above technology spillover effects of FDI, the indepen4

Renewable and Sustainable Energy Reviews xx (xxxx) xxxx–xxxx

Z. Yang et al.

Table 1 Estimated results of Eq. (2). Variable

coefficient

t value

Variable

coefficient

t value

α0

2.2283* (1.1317)

1.9690

α7

−7.3850

α1

−0.1074* (0.0613) 0.0032 (0.0039)

−1.7525

α8

−0.0506*** (0.0068) 0.0149* (0.0077)

0.8074

α9

9.4956

3.6270

α10

0.9560*** (0.1007) −0.0968 (0.1860)

5.5637

α11

−9.9317

−7.7522

α12

3.7238

α13

−0.7018*** (0.0707) −0.4288*** (0.1320) −0.0856 (0.0680)

8.1605

α14

4.8523

274632

μ

0.3881*** (0.0800) 2.1454*** (0.3361)

α2 α3

1.2697*** (0.3501) 1.2895*** α4 (0.2318) −1.3972*** α5 (0.1802) 0.0421*** α6 (0.0113) 2 1.8141*** σ (0.2223) γ 0.9999*** (0.0000) Log likelihood function LR test

Fig. 1. Classification of technological sources of energy-saving technological progress.

on energy-saving technological progress:

TCit = δ 0 + δ1 fsit + δ 2 bsit + δ3 hsit + δ4 rdit + δ5 liit + δ6 expit + δ 7 gov + εit (9) where δ 0 -δ 7 are coefficients to be estimated; ε is random disturbance term. Eq. (9) implicitly assumes that the changes in energy-saving technological progress are instantaneous with the changes in its influential factors, i.e., there is on lagged effect. However, the reality is not in this case. The previous technical level will exert an inevitable influence on the current, i.e., the so-called “inertial technology”. Therefore, referring to Shao et al. [30], we extend Eq. (9) to the following dynamic panel model to capture such a lagged effect:

1.9365

−0.5202

−3.2485 −1.2594

6.3829

−111.6720 156.7740***

Note: Standard errors for coefficients are in parentheses; ***, **, and * denote statistical significance at the levels of 1%, 5%, and 10%, respectively.

multicollinearity problem. 3. Results and discussion

TCit = δ 0 + φTCi, t −1 + δ1 fsit + δ 2 bsit + δ3 hsit + δ4 rdit + δ5 liit + δ6 expit + δ 7 govit + εit

3.1. Estimated results and discussion of energy-saving technological progress

(10)

Because the first-order lagged dependent variables in the right of Eq. (10) will cause an endogeneity problem resulted from the correlation between independent variable and error term, we use the two-step system GMM (SGMM) method to estimate Eq. (10), which can effectively control the endogeneity problem in such a dynamic panel model [6,41].

We employ Frontier 4.1 to estimate Eq. (2) and present the results in Table 1. It can be clearly seen that most coefficients of Eq. (2) are statistically significant, indicating its strong explanatory power, which also can be testified by the results of the log likelihood function and single LR test. Moreover, the mean of inefficiency term μ is 2.1454 and statistically significant at the 1% level, indicating that the hypothesis of non-negative truncated normal distribution is accepted. The total variance σ 2 and the share of production inefficiency in the total variance inefficiency term γ are 1.8141 and 0.9999, respectively, and statistically significant at the 1% level. Such results show that the variation of error component can be mostly attributed by technical inefficiency. When controlling input factors and other random factors, the gap between actual output and frontier output levels is mainly caused by technical inefficiency. Hence, the stochastic frontier model we establish is suitable for describing the production technology of China's industrial sector. Based on Eq. (3), we further calculate the energy-saving technological progress rate of each industrial sub-sector and show the results in Table 2. As a whole, China's industrial sector experienced an energysaving technological progress with an annual average growth rate of 7.69%. With respect to the results of three basic industrial categories, the energy-saving technological progress of “Production and Supply of Electricity, Gas and Water” sector is the fastest with an annual average growth rate of 14.26%; the second is “Mining” sector with 9.53%; and the energy-saving technological progress of “Manufacturing” sector is the slowest with 5.88%. Such results are further depicted in Fig. 2. Although both the overall industrial sector and three basic industrial sectors presented the continuous energy-saving technological progress during 2001–2011, only “Manufacturing” sector showed a lower level than the overall industrial sector. This indicates that the development of China's manufacturing still holds an extensive mode of energy use

2.3. Data description Based on the availability of data, we select the panel data of 22 industrial sub-sectors in China over 2001–2011 as research sample. Although China Industry Economy Statistical Yearbook reports the related data of 36 industrial sub-sectors, the above three technology spillover effects of FDI need to be calculated based on China Inputoutput Table, which presents the data of 22 industrial sub-sectors. To keep a uniform data caliber throughout the empirical analysis in this paper, we merge and recalculate the corresponding data of 36 subsectors reported in China Industry Economy Statistical Yearbook according to the data diameters of 22 sub-sectors presented in China Input-output Table. The changes in the data calibers of industrial subsectors before and after being merged are listed in Table A1. The input and output data in Eq. (2) are derived from China Industry Economy Statistical Yearbook, China Labor Statistical Yearbook, China Energy Statistical Yearbook, and China Compendium of Statistics 1949–2008. The raw data of variables in Eq. (10) are from the China Input-output Table, China Industry Economy Statistical Yearbook, and China Statistical Yearbook on Science and Technology. To ensure the comparability of data, we deflate all the raw data at current price to constant 2000 prices. Descriptive statistics of main variables in this paper are listed in Table A2. As shown Table A3, because correlation coefficients among explanatory variables in Eq. (10) are less than 0.6, we can ignore the

5

Renewable and Sustainable Energy Reviews xx (xxxx) xxxx–xxxx

Z. Yang et al.

Table 2 Estimated results of energy-saving technological progress rates of industrial sub-sectors (unit: %). Sector

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

Average

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 S21 S22 Mining Manufac- turing Electricity, gas and water Overall

4.81 15.50 5.12 2.81 5.35 1.56 −7.48 0.00 3.26 12.57 9.05 4.77 8.64 −0.58 2.41 3.34 −0.75 2.73 −1.82 14.69 10.34 8.40 7.06 2.87 11.14 4.76

5.20 16.48 5.79 3.53 5.93 2.23 −6.85 0.57 3.79 13.51 9.82 5.26 10.88 0.29 3.11 4.04 −0.07 3.24 −1.07 15.49 10.88 8.87 7.75 3.64 11.75 5.50

5.91 15.86 6.60 4.61 6.55 2.86 −6.01 0.76 4.34 13.87 10.53 6.18 11.77 1.52 3.43 4.50 0.37 3.54 −0.99 16.17 11.84 9.54 8.24 4.21 12.51 6.08

6.29 15.66 6.76 5.29 6.90 3.25 −5.32 1.05 4.75 14.11 10.88 6.94 12.15 2.02 3.72 5.00 0.42 3.06 −1.04 16.56 12.36 10.33 8.50 4.53 13.08 6.41

6.31 15.79 7.39 6.17 7.43 3.60 −4.28 1.77 5.19 14.03 11.41 7.48 12.80 2.95 4.37 5.35 0.72 2.94 −0.57 16.90 12.78 11.02 8.92 5.01 13.57 6.89

6.48 15.91 8.14 6.89 8.20 4.34 −3.06 2.79 5.88 14.40 12.02 8.34 13.47 4.16 5.23 5.93 1.49 3.44 −0.06 17.49 13.76 11.77 9.36 5.77 14.34 7.59

7.20 16.62 8.81 7.53 8.92 5.11 −1.96 3.95 6.47 14.75 12.52 9.05 13.95 5.31 6.08 6.52 2.38 3.68 0.60 18.22 14.05 13.07 10.04 6.49 15.11 8.31

7.50 16.19 9.48 7.58 9.31 5.55 −1.08 4.75 6.94 15.02 12.75 9.74 14.15 5.95 6.54 6.76 3.11 3.93 1.37 18.65 14.07 13.60 10.19 6.98 15.44 8.72

8.47 17.14 10.24 8.10 9.60 6.22 −0.50 5.38 7.64 16.59 13.30 10.40 15.19 6.71 7.27 7.49 4.08 4.69 2.25 19.13 14.54 14.09 10.99 7.76 15.92 9.46

9.11 17.50 11.00 8.32 9.69 6.62 0.17 5.52 8.12 17.27 13.71 10.92 15.98 7.32 7.82 8.09 4.87 5.78 3.13 19.99 14.87 14.72 11.48 8.33 16.53 10.02

10.19 18.15 12.00 9.07 10.27 7.56 1.20 6.28 9.25 17.75 14.64 12.04 16.79 8.28 8.72 8.95 6.04 5.60 3.54 21.19 15.16 16.15 12.35 9.13 17.50 10.85

7.04 16.44 8.30 6.36 8.01 4.45 −3.20 2.98 5.97 14.90 11.88 8.28 13.25 3.99 5.34 6.00 2.06 3.87 0.48 17.68 13.15 11.96 9.53 5.88 14.26 7.69

of seven sub-sectors are more than 10%. Among these sub-sectors, the value of S20 (production and supply of electric power and heat power) is the highest, reaching 17.68%; the second is S2 (extraction of petroleum and natural gas), reaching 16.44%; and others arranged from high to low are S10 (processing of petroleum, coking, and processing of nucleus fuel) (14.90%), S13 (manufacture and processing of metals) (13.25%), S21 (production and supply of gas) (13.15%), S22 (production and supply of water (11.96%), and manufacture of chemical (11.88%). Meanwhile, the sub-sectors with the first three lowest values are S19 (manufacture of measuring instrument and machinery) (0.48%), S17 (manufacture of electrical machinery and apparatus) (2.06%), S8 (processing of timbers, manufacture of wood, bamboo, rattan, palm, and straw products, manufacture of furniture) (2.98%). Although the three sub-sectors experienced the slowest energy-saving technology progress, they all presented a continuous upward trend over 2001–2011. According to our results, the annual average energy-saving technological progress rate is up to 7.69%. Considering that the Chinese government and enterprises are paying more attention to reducing energy intensity during industrial production process, we speculate that energy-saving technology progress will speed up in China's industrial sector, and the allocative efficiency of production factors including energy will continue to optimize in industrial production process, promoting the green transformation of China's economic development.

Fig. 2. Trends of energy-saving technological progress rate of industrial sector (unit: %).

Fig. 3. Annual average energy-saving technological progress rates of industrial subsectors (unit: %).

and thus its energy-saving technological progress needs to be effectively improved. Fig. 3 illustrates the annual averages of energy-saving technological progress rates of 22 industrial sub-sectors. It is easily seen that among industrial sub-sectors, only sub-sector S7 (manufacture of textile wearing apparel, footwear, caps, leather, fur, feather, and related products) presents a drop of energy-saving technological progress rate with a negative value (−3.20%). The energy-saving technological progress rates of other sub-sectors are all more than 0, indicating an overall improved trend of industrial energy-saving technology progress. However, there are distinct differences among various subsectors. The annual average energy-saving technological progress rates

3.2. Empirical results and discussion of differentiated effects The estimated results of Eq. (10) are shown in Table 3. To distinctly observe the influences of various technological sources on energysaving technological progress and enhance the robustness of empirical results, we successively introduce independent variables into the regression. In Model 1, We first examine the impacts of three technology spillover effects of FDI (fs, bs, hs), and original innovation activities (rd), the forced effect of competition (li), learning-by-exporting effect (exp), and policy dummy variable (gov) are introduced into Models 2–5 in sequence. “Arellano-Bond” tests show that the error

6

Renewable and Sustainable Energy Reviews xx (xxxx) xxxx–xxxx

Z. Yang et al. Table 3 Effects of various technological sources on energy-saving technological progress. Variable

Model 1

Model 2

Model 3

Model 4

Model 5

TCt−1

0.9849*** (0.0050) −0.0013 (0.0024) −0.0033 (0.0041) −0.0038*** (0.0014)

0.9818*** (0.0048) 0.0050* (0.0028) 0.0028 (0.0042) −0.0014 (0.0015) 0.1096** (0.0553)

0.9681*** (0.0049) 0.0073*** (0.0028) 0.0059 (0.0042) −0.0033* (0.0016) 0.1002* (0.0555) −0.0078*** (0.0019)

0.9793*** (0.0061) 0.0094*** (0.0030) 0.0076* (0.0043) 0.0003 (0.0025) 0.0918* (0.0556) −0.0069*** (0.0020) −0.0054 (0.0049)

0.0086*** (0.0008) 77.39 0.0000 −0.2163 0.0267

0.0084*** (0.0008) 59.74 0.0000 −2.0771 0.0378

0.0096** (0.0008) 53.68 0.0000 −2.0509 0.0403

0.0095*** (0.0008) 92.65 0.0000 −2.0106 0.0444

0.9707*** (0.0061) 0.0100*** (0.0030) 0.0040 (0.0042) −0.0038 (0.0025) 0.0621 (0.0542) −0.0074*** (0.0019) −0.0027 (0.0028) 0.0099*** (0.0008) 0.0099*** (0.0008) 91.17 0.0000 −2.2287 0.0258

−0.4668 0.6407

−0.5041 0.6142

−0.7613 0.4465

−0.6659 0.5055

0.6559 0.5119

21.1862 0.6822

21.2245 0.6800

20.9393 0.6960

20.6858 0.7100

18.8388 0.8048

fs bs hs rd li exp gov Constant F test Pvalue AR (1) test Pvalue AR (2) test Pvalue Sargan test Pvalue

Table 4 Comparative analysis of the effects of diversified sources on energy-saving technological progress. Technological sources Technology spillover of FDI

Domestic independent technological innovation

Result

Reason

Forward technological spillover

Positive

Backward technological spillover

Not significant

Horizontal technological spillover

Not significant

Enterprises can learn from FDI enterprises in the upstream subsectors through the purchase of intermediate inputs to enhance technology Domestic upstream enterprises lack the effective impetus of energy-saving technology, backward technology spillover is not significant Enterprises within a certain industry tend to prevent technological diffusion through technical protection and barrier for the sake of competition

Original innovation activities

Not significant

Forced effect of competition

Positive

Learning by exporting

Not significant

Note: Standard errors for coefficients are in parentheses; ***, **, and * denote statistical significance at the levels of 1%, 5%, and 10%, respectively.

terms of all regressions are significantly first-order serial correlated (AR(1)) at the 5% level, but not significantly second-order serial correlated (AR(2)). Therefore, the basic assumption of the SGMM is satisfied. The Sargan tests show that there is no over-identification problem of instrumental variables in all regressions, indicating that instrumental variables are valid and thus the estimated results in Table 3 are credible. We find that the coefficients of TCt−1 remain significantly positive, implying a significant “path dependence” property of energy-saving technological progress, i.e., the previous energy-saving technical level has an significant positive influence on the current one. This is consistent with general economic rules. The higher technology and knowledge level in previous period provides more abundant knowledge reserve for the current technological innovation and thus is conducive to achieve the durative technological progress. Such a result also accords with the continuous upward trends of energy-saving technological progress depicted in Table 2 and Fig. 2. Next, we will turn to discuss the effects of various technological sources on energy-saving technological progress.

Most enterprises do not concern about saving energy in the production and innovation process With the increasing environmental awareness of the government and public, the products with less energy use would be more competitive in the market Many export-oriented corporations transfer some energy-intensive industries into China, export-oriented domestic enterprises also export products with high energy consumption

FDI played a dominant role in promoting the industrial productivity of Guangdong province of China, but the influence of horizontal technology spillover was not significant. The promotion effect of forward technology spillover can be attributed to that when foreign investment enterprises select domestic enterprises supplying production inputs like raw materials, their relatively high product quality standards impel the technological innovation of domestic enterprises to promote energy-saving technology progress. Since upstream foreign investment enterprises generally present a relatively high level of energy-saving technology, embodying in their products which are reprocessed by downstream domestic enterprises, the energy-saving technology of upstream foreign investment enterprises can effectively transfer to the downstream domestic enterprises through the supply of raw materials and other products. Such a course impalpably promotes the overall energy-saving technological progress. On the contrary, because of a relatively low energy-saving technology of domestic enterprises and a relatively small demand of downstream foreign investment enterprises for the intermediate inputs of upstream domestic enterprises in China [17,38], such domestic upstream enterprises lack the effective impetus of energy-saving technological innovation. Therefore, the effect of backward technology spillover on

(1) The technology spillover effects of FDI. As shown in Table 3, the coefficients of fs are significantly positive in Models 2–5, indicating that the forward technology spillover effect of FDI plays a positive role in promoting the energy-saving technological progress of China's industrial sector. In contrast, the coefficients of bs and hs are not significant in most Models, especially in Model 5, indicating the backward and horizontal technology spillover effects fail to exert an evident promotion influence on energy-saving technological progress. Some of previous studies also draw similar conclusions when not taking into account energy inputs. For example, Yang [44] found that the backward technology spillover effect of

7

Renewable and Sustainable Energy Reviews xx (xxxx) xxxx–xxxx

Z. Yang et al.

(5) Policy intervention. The coefficient of policy dummy variable (gov) is significantly positive, indicating that the implementation of energy-saving and emission-reduction policies in China plays an active role in energy-saving technological progress. The target of China 11th FYP that energy consumption per unit GDP dropped by 20% has achieved. Such an energy intensity constraint policy exerted an evident influence on industrial sub-sectors with high energy consumption. Such sub-sectors had to adjust the structure of factor inputs and actively carry out energy technological innovation, so as to reduce energy intensity and promote energysaving technological progress. In addition, after 2006, the Chinese government promulgated some auxiliary policies, such as income tax reduction policy aimed at promoting energy-saving and environmentally friendly projects and equipment, and value added tax deduction for the investment of energy-saving and environmental projects. Such measures created obvious incentive effects on improving industrial energy-saving technological progress.

energy-saving technological progress is not significant. In addition, due to the fierce competition, enterprises within a certain industry tend to prevent technological diffusion through technical protection and barrier [26] to inhibit the positive impact of horizontal technology spillover on energy-saving technological progress. (2) Original innovation activities. Although the coefficients of rd are significantly positive in Models 2–4, it turns insignificant in Model 5, implying that the promotion effect of original innovation activities of industrial enterprises on energy-saving technological progress is not robust. The production mode extensive energy use in China reveals that the current innovation activities of industrial enterprises are still oriented by products’ upgrading rather than by energy saving. Such a result is consistent with some existing studies. Xuan and Zhou [42] used the DEA-Malmquist index method to estimate the energy efficiency growth rate of China's industrial sector and found that no evidence indicates a significant positive correlation between energy efficiency growth and original innovation activities. Yang et al. [43] argued that because of a marginal output contribute of energy input, most enterprises do not concern about saving energy in the production process. In addition, the realization of enterprise's original innovation outcomes always requires a continuous accumulation process and cannot be accomplished at one stroke, and even there may be some risks of failure. Therefore, enterprises’ original innovation has obvious uncertainty and periodic property, and in most cases it is difficult to obtain returns in the short term. Without appropriate governmental subsidies and supports, the enterprises’ short-term production efficiency may be hindered by the increased cost of innovation. This may be a primary reason why China's industrial enterprises are lack of the enthusiasm to carry out original innovation activities. (3) Forced effect of competition. Since market power (li) reflects the monopoly degree within a sub-sector, the smaller li, and the greater the competition degree. The coefficients of li in Modes 3– 5 are all significantly negative, indicating that industrial market power has a negative impact on energy-saving technological progress, that is, a higher competition degree (a lower monopoly degree) is conducive to improve energy-saving technological progress. This can be attributed to two reasons: on one hand, the fierce market competition motivates enterprises to control energy inputs by improving energy-saving technological progress; on the other hand, with the increasing environmental awareness of the government and public, the products with less energy use would be more competitive in the market, indirectly promoting energy-saving technological progress. Tang and Yang [34] also found that the energy efficiency in a higher competitive industry is higher and that the energy efficiency in a higher monopoly industry is lower. (4) Learning-by-exporting effect. The coefficients of exp in Models 4 and 5 are not significant, implying that the learning effect of exporting on energy-saving technological progress is not obvious. A possible reason is that the technical structure of export trade in China's industrial sector is still not advanced, even inferior to the average level of developing countries [16]. When ignoring processing trade, there is no evidence that export exerts a significant impact on technical progress in China [3]. In addition, some studies found that since China's primary energy consumption structure is dominated by coal with a low price, many exportoriented transnational corporations transfer some energy-intensive industries into China, and export-oriented domestic enterprises also export a large number of products with high energy consumption. This causes that China's industrial export structure goes against improving energy efficiency [32]. Hence, an exportoriented industry does not necessarily lead to energy-saving technological progress.

Table 4 summarizes the above empirical results. It can be obviously seen that among various technological sources, only forward technological spillover and the forced effect of competition have significantly positive effects on energy-saving technological progress, but the effects of the other four technological sources are all not significant. This indicates that for China's industrial sector, the purpose of technological progress is largely to reduce the cost of non-energy factors for production scale expansion, while the concern for energy conservation is still not enough. In contrast, if most technological sources can be used to improve the energy-saving technology, China's current energy and environmental problems will be substantially resolved. 4. Conclusions and policy implications Diversified technological sources may exert differentiated effects on promoting energy-saving technological progress. Such disparities can implicate different energy-saving policy recommendation. In this paper, we use the SFA method based on the translog production function to estimate the energy-saving technological progress rate of China's 22 industrial sub-sectors during 2001–2011. Also, we adopt the SGMM method to empirically investigate the differentiated effects of six primary technological sources on the industrial energy-saving technological progress. The results indicate that as a whole, industrial energy-saving technological progress in China presents a continuous improved trend. However, there are evident differences among various industrial sub-sectors. Especially, the production and supply of electric power and heat power and the extraction of petroleum and natural gas show a relatively high level of energy-saving technological progress rate. In contrast, the energy-saving technologies progress of some subsectors, such as the manufacture of measuring instrument and machinery and the manufacture of electrical machinery and apparatus, presents the slowest rise, despite a continuous upward trend. Moreover, industrial energy-saving technological progress shows an obvious path dependence property, i.e., the previous high-level energysaving technological progress has an evident positive impact on the current one. Among three technology spillover effects of FDI, only forward technology spillover has a significant positive impact on energy-saving technological progress, while the influences of backward technology spillover and horizontal technology spillover are not significant. Among three factors influencing enterprises’ technological innovation, original innovation activities and learning by exporting fail to exert a significantly positive effect on energy-saving technological progress, while the forced effect of competition has a significantly positive influence on energy-saving technological progress, indicating that a strong market competition is conducive to promote energysaving technology. In addition, the implementation of energy intensity

8

Renewable and Sustainable Energy Reviews xx (xxxx) xxxx–xxxx

Z. Yang et al.

constraint policy in 2006 exerts an evident positive effect on energysaving technology progress. Under the background of worsening ecological environment and increasing conflict between energy supply and demand, reducing energy intensity and improving energy efficiency have become a requirement of realizing the sustainable growth of China's industrial sector. Considering that to enhance energy-saving technological progress is the key to improving energy efficiency, the auxiliary policies should be targeted at encouraging industrial enterprises to actively develop and introduce advanced technologies to promote energy efficiency, especially to impel the original innovation of domestic enterprises, which can be regarded as the current fatal weakness in improving energy-saving technological progress in China. In addition, policy-makers should encourage the technology exchange between domestic and foreign enterprises to ensure domestic enterprises to effectively absorb advanced energy-saving technologies from foreign enterprises. Also, the government should actively guide enterprises to export products with more advanced technology content and added value to avoid being locked in the bottom of international industrial chain by learning advanced energy-saving technologies from overseas markets.

Table A1 (continued)

Acknowledgments

Sub-sectors before being merged

Sub-sectors after being merged

Code

Manufacture of paper and paper products printing and reproduction of recording media Manufacture of articles for culture, education, and sport activity Processing of petroleum, coking, and processing of nucleus fuel

Manufacture of articles for culture, education, sport activity, paper and paper products, printing, reproduction of recording media

S9

Processing of petroleum, coking, and processing of nucleus fuel

S10

Manufacture of raw chemical material and chemical products Manufacture of medicines Manufacture of chemical fiber Manufacture of rubber Manufacture of plastic Manufacture of non-metallic mineral products Manufacture and processing of ferrous metals Manufacture & processing of non-ferrous metals Manufacture of metal products

Manufacture of chemical

S11

Manufacture of non-metallic mineral products Manufacture and processing of metals

S12

Manufacture of metal products

S14

Manufacture of general purpose machinery and special purpose machinery

S15

Manufacture of transport equipment Manufacture of electrical machinery and apparatus Manufacture of computers, communication and other electronic equipments Manufacture of measuring instrument and machinery Production and supply of electric power and heat power Production and supply of gas Production and supply of water

S16

Manufacture of general purpose machinery Manufacture of special purpose machinery Manufacture of transport equipment Manufacture of electrical machinery and apparatus Manufacture of computers, communication and other electronic equipments Manufacture of measuring instrument and machinery Production and supply of electric power and heat power Production and supply of gas Production and supply of water

We acknowledge the financial support from the National Natural Science Foundation of China (Nos. 71373153, 71503168, 71503156, and 71402085), the Program for New Century Excellent Talents in University (No. NCET-13-0890), Shanghai Philosophy and Social Science Fund Project (Nos. 2014BJB001 and 2015BJB005), the “Shuguang Program” supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission (No. 14SG32), the Key Project of Zhejiang Statistics Research Program (No. 201630), and the Cultivation Program of Shanghai University for Young Teachers. Appendix See Tables A1–A3.

S13

S17 S18

S19 S20 S21 S22

Table A1 Classification of merged industrial sub-sectors. Sub-sectors before being merged

Sub-sectors after being merged

Code

Mining and washing of coal Extraction of petroleum and natural gas

Mining and washing of coal Extraction of petroleum and natural gas

S1 S2

Mining and processing of ferrous metal ores Mining and processing of nonferrous metal ores Mining and processing of nonmetal ores

Mining of metal ores

S3

Mining and processing of nonmetal ores

S4

Processing of food from agricultural products Manufacture of foods Manufacture of beverage Manufacture of tobacco Manufacture of textile

Manufacture of foods, beverage, and tobacco

S5

Manufacture of textile

S6

Manufacture of textile, wearing apparel, footware, and caps Manufacture of leather, fur, feather, and related products Processing of timbers, manufacture of wood, bamboo, rattan, palm, and straw products manufacture of furniture

Manufacture of textile, wearing apparel, footware, caps, leather, fur, feather, and related products

S7

Table A2 Descriptive statistics of main variables used in current paper. Variable

Unit

Sample size

Mean

Standard error

Minimum

Maximum

Y

100 million RMB 100 million RMB 10 thousand persons 10 thousand tce N.A. N.A. N.A. % N.A. % N.A.

242

9251.7

11604

48.70

60297.6

242

3300.8

3717.6

159.00

22747.0

242

330.14

237.92

14.54

930.84

242

7537.5

12485

163.70

76320.7

242 242 242 242 242 242 242

0.0834 0.0838 0.2531 0.8084 0.1700 0.1536 0.5455

0.0843 0.0531 0.1698 0.6852 0.1185 0.1644 0.4990

0.0030 0.0218 0.0023 0.0043 0.0320 0.0012 0

0.3151 0.3124 0.7261 3.0770 0.7128 0.6814 1

K

L

E

fs bs hs rd li exp gov

S8 Processing of timbers, manufacture of Wood, bamboo, rattan, palm, and straw products, manufacture of furniture (continued on next page)

9

Renewable and Sustainable Energy Reviews xx (xxxx) xxxx–xxxx

Z. Yang et al.

Table A3 Correlation coefficients among independent variables in Eq. (10). Variable

fs

bs

hs

rd

li

exp

gov

fs bs hs rd li exp gov

1 −0.3032 −0.1846 0.2846 0.2757 −0.1048 0.0237

−0.3032 1 0.3196 0.3415 −0.1845 0.4644 −0.0045

−0.1846 0.3196 1 0.4805 −0.5405 0.4378 0.1011

0.2846 0.3415 0.4805 1 −0.2583 0.5033 0.0304

0.2757 −0.1845 −0.5405 −0.2583 1 −0.4383 −0.1000

−0.1048 0.4644 0.4378 0.5033 −0.4383 1 0.0322

0.0237 −0.0045 0.1011 0.0304 −0.1000 0.0322 1

[25] Lin JY, Zhang PF. Advantage of latter comers, technology imports and economic growth of developing countries. China Econ Q 2005;5(1):53–74. [26] Luo L. Harmonization of law and technology in social norms: an examination of anti-technique infringement rules in China. Soc Sci China 2006;1:72–86. [27] Meeusen W, van den Broeck J. Efficiency estimation from Cobb-Douglas production functions with composed error. Int Econ Rev 1977;18(2):435–44. [28] Murty MN, Kumar S, Dhavala K. Measuring environmental efficiency of industry: a case study of thermal power generation in India. Environ Resour Econ 2006;38(1):31–50. [29] Özkara Y, Atakb M. Regional total-factor energy efficiency and electricity saving potential of manufacturing industry in Turkey. Energy 2015;93:495–510. [30] Shao S, Yang LL, Yu MB, Yu ML. Estimation, characteristics, and determinants of energy-related industrial CO2 emissions in Shanghai (China), 1994–2009. Energy Policy 2011;39:6476–94. [31] Shao S, Huang T, Yang LL. Using latent variable approach to estimate China's economy–wide energy rebound effect over 1954–2010. Energy Policy 2014;72:235–48. [32] Shahbaz M, Nasreen S, Chong HL, Sbia R. Causality between trade openness and energy consumption: what causes what in high, middle and low income countries. Energy Policy 2014;70:126–43. [33] Sun GS, Huang Y, Tian HF, Wang FP. Total factor productivity, inputs substitution and regional energy efficiency disparity. Econ Res J 2012;9:99–112. [34] Tang L, Yang ZL. Energy efficiency and industrial economic transition. J Quant Tech Econ 2009;10:34–48. [35] Urpelainen J. Export orientation and domestic electricity generation: effects on energy efficiency innovation in select sectors. Energy Policy 2011;39:5638–46. [36] Wang K, Yu SW, Zhang W. China’s regional energy and environmental efficiency: a DEA window analysis based dynamic evaluation. Math Comput Model 2013;58(5):1117–27. [37] Wang QW, Zhou P, Shen N, Wang SS. Measuring carbon dioxide emission performance in Chinese provinces: a parametric approach. Renew Sustain Energy Rev 2013;21:324–30. [38] Wang R, Yan B, Deng WG. Impact of FDI on the independent innovation capability of China's indigenous industries. China Ind Econ 2010;11:16–25. [39] Wang ZH, Zeng HL, Wei YM, Zhang YX. Regional total factor energy efficiency: an empirical analysis of industrial sector in China. Appl Energy 2012;97(9):115–23. [40] Wen Z, Chen M, Meng F. Evaluation of energy saving potential in China's cement industry using the Asian-Pacific Integrated Model and the technology promotion policy analysis. Energy Policy 2015;77:227–37. [41] Windmeijer F. A finite sample correction for the variance of linear efficient two-step GMM estimators. J Econ 2005;126(1):25–51. [42] Xuan Y, Zhou SD. Technological innovation, return effect and energy efficiency of China's industrial sectors. Financ Trade Econ 2011;1:116–21. [43] Yang LL, S, Cao JH. Decomposition and determinants of industrial total factor energy efficiency of urban agglomeration in the Yangtze River delta of China. J Shanghai Univ Financ Econ 2014;3:94–105. [44] Yang YP. Intra-industry spillovers or inter-industry spillovers of FDI. China Ind Econ 2007;11:73–9. [45] Zhang XP, Cheng XM, Yuan JH, Gao XJ. Total-factor energy efficiency in developing countries. Energy Policy 2011;39:644–50. [46] Zhao XL, Yang R, Ma Q. China's total factor energy efficiency of provincial industrial sectors. Energy 2014;65(2):52–61. [47] Zhou P, Ang BW, Poh KL. A survey of data envelopment analysis in energy and environmental studies. Eur J Oper Res 2008;189:1–18.

References [1] Aghion P, Harris C, Howitt P, Vickers J. Competition, imitation and growth with step-by-step innovation. Rev Econ Stud 2001;68(3):467–92. [2] Aigner D, Lovell CAK, Schmidt P. Formulation and estimation of stochastic frontier production function models. J Econ 1977;6(1):53–66. [3] Amiti M, Freund C. The anatomy of China's export growth. In: Feenstra RC, Wei S, editors. China's Growing Role in World Trade. Chicago: University of Chicago Press; 2010. p. 35–56. [4] Battes GE, Coelli TJ. A model for technical in efficiency effects in a stochastic production frontier for panel data. Empir Econ 1995;20(2):325–32. [5] Blalock G. Technology from foreign direct investment: strategic transfer through supply chains (PhD Dissertation of Haas School of Business). Berkley: University of California; 2001. [6] Blundell R, Bond S. Initial conditions and moment restrictions in dynamic panel data models. J Econ 1998;87(1):115–43. [7] Bointner R. Innovation in the energy sector: lessons learnt from R & D expenditures and patents in selected IEA countries. Energy Policy 2014;73:733–47. [8] British Petroleum. BP statistical review of world energy. London, 2011; 2011. [9] Brookes L. The fifth fuel and the greenhouse effect. Energy World 1990;18(2):199–201. [10] Cappelli R, Czarnitzki D, Kraft K. Sources of spillovers for imitation and innovation. Res Policy 2014;43(1):115–20. [11] Chen S, Paulino AU. Energy consumption restricted productivity re-estimates and industrial sustainability analysis in post-reform China. Energy Policy 2013;57:52–60. [12] Chen SY. Reconstruction of sub-industrial statistical data in China: 1980–2008. China Econ Q 2011;10(3):735–76. [13] Cheung YW, Pascual AG. Market structure, technology spillovers, and persistence in productivity differentials. Int J Appl Econ 2004;1(1):1–23. [14] Clerides SK, Lach S, Tybout JR. Is learning by exporting important? Micro-dynamic evidence from Colombia, Mexico, and Morocco. Q J Econ 1998;113(3):903–47. [15] Coelli TJ, Rao DSP, O’Donnell CJ, Battese GE. An introduction to efficiency and productivity analysis, 2th edition. Berlin: Springer; 2005. [16] Du XL, Wang WG. Technology structures of China's exports and their changes: 1980–2003. Econ Res J 2007;7:137–51. [17] Eskeland G, Harrison E. Moving to greener pastures? Multinationals and the pollution haven hypothesis. J Dev Econ 2003;70:1–23. [18] Herrala R, Goel RK. Global CO2 efficiency: country-wise estimates using a stochastic cost frontier. Energy Policy 2012;45:762–70. [19] Honma S, Hu JL. Industry-level total-factor energy efficiency in developed countries: a Japan-centered analysis. Appl Energy 2014;119(4):67–78. [20] Hu HL, Wang SC. Total-factor energy efficiency of regions in China. Energy Policy 2006;34:3206–17. [21] Javorcik B. Does foreign direct investment increase the productivity of domestic firms? In search of spillovers through backward linkages. Am Econ Rev 2004;94(3):605–27. [22] Kumbhakar SC. Estimation and decomposition of productivity change when production is not efficient: a panel data approach. Econom Rev 2000;19(4):425–60. [23] Li Y, Zhu L. Cost of energy saving and CO2 emissions reduction in China's iron and steel sector. Appl Energy 2014;130:603–16. [24] Lin BQ, Wang XL. Exploring energy efficiency in China's iron and steel industry: a stochastic frontier approach. Energy Policy 2014;72:87–96.

10