Examining the efficiency of biomass energy: Evidence from the Chinese recycling industry

Energy Policy 119 (2018) 77–86

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Examining the efficiency of biomass energy: Evidence from the Chinese recycling industry Xin-long Xua, Hsing Hung Chenb, a b

T

⁎

College of Tourism, Hunan Normal University, Changsha 410081, China School of Business, Macau University of Science and Technology, Taipa, Macau

A R T I C LE I N FO

A B S T R A C T

Keywords: Recycling enterprises Biomass energy Stochastic frontier analysis (SFA) Technical efficiency

Developing biomass energy could help to solve problems which limit the sustainable development of human society. These problems include environmental degradation and resource depletion. Using the stochastic frontier analysis model, we conducted an empirical analysis of technical efficiency for the recycling industry and the factors that impact it in 20 provinces in China. We found that technical efficiency is at a moderate level. There are large gaps in efficiency among the eastern, central and western regions. Technical efficiency is highest in the eastern provinces. Ownership structure and technical efficiency exhibit a significant inverted "U" shaped correlation. While urbanization does not affect technical efficiency, economic, industrial and education levels do have a positive impact on it. Based on the results of our study we have proposed a number of policies to promote the development of the recycling industry.

1. Introduction Waste production, environmental degradation, resource depletion and energy shortfalls have become constraints to sustainable development (Xu et al., 2017). Policy makers depend on recycling companies to convert feedstocks into electricity and to transport liquid fuels (Rentizelas et al., 2009). China is a big agricultural industrial country and has abundant feedstocks including crop and forest residues, energy crops, poultry and livestock manure, organic liquid wastes and manure effluent from industrial and municipal waste water (Binod et al., 2017; Zhao and Liu, 2013). The recycling industry helps to harmonize social and environmental development issues through biomass power generation, biodiesel and bio-ethanol utilization, and the development of bio-hydrogen technology (Carneiro and Ferreira, 2012; Gosens, 2015). Rapid growth in recycling resources has occurred in developed countries that have invested in the recycling business, developed supportive government policy, and provided technical support to their companies (Bajare et al., 2012; Ceballos and Dong, 2016). China has made many provisions for efficient resource consumption and waste recycling, and developed support measures for recycling enterprises (Chen, 2005; Liu, 2008). These policies contain feed-in tariffs, installation rebates for renewable energy systems, investment grants, tradable green certificates, and tax incentives to attract investment capital (Wu et al., 2010; Zhang et al., 2009). When compared with developed countries, the recycling rate of

⁎

feedstocks is still low in China (Li et al., 2016). Due to variation among regions with respect to economic and technological development, and policy support, it is also very uneven (Zhang and Zhu, 1999). Most foreign studies have focused on technical improvement (Agrawal et al., 2016; Islam et al., 2008; Kumar et al., 2015), while Chinese researchers have studied the development status, national policy and market organization of the recycling industry (Feng and Zhang, 2009; Huang and Wang, 2010). There is little research on technical efficiency. Analyzing technical efficiency can lead to reduced costs and higher recycling rates, and can add value to the industry. More quantitative research needs to be done on how policy support affects technical efficiency. Local and central governments need to time and optimize policy support to maximize their impact on technical efficiency. Studying the quantitative and qualitative relationships between economic and technological development, and the technical efficiency of biomass energy is both important and urgent. The most common process for input-output efficiency analysis is the production frontier method. It includes non-parametric and parametric frontier methods. The non-parametric method is the most popular for efficiency estimation, but it does not consider random error. It is represented by the data envelopment analysis (DEA) proposed by Charnes et al. (1978). The parametric frontier method is represented by the stochastic frontier approach (SFA). The SFA method can simultaneously test the parameters and the model itself. It deals with the random error term and can quantitatively analyze the impact of various factors on

Corresponding author at: School of Business, Macau University of Science and Technology, Taipa, Macau. E-mail address: [email protected] (H.H. Chen).

https://doi.org/10.1016/j.enpol.2018.04.020 Received 29 August 2017; Received in revised form 30 March 2018; Accepted 11 April 2018 0301-4215/ © 2018 Elsevier Ltd. All rights reserved.

Energy Policy 119 (2018) 77–86

X.-l. Xu, H.H. Chen

efficiency. The stochastic frontier model includes the Cobb-Douglas production function, constant elasticity of substitution (CES) production function, and translog production function. The translog production function is a second-order Taylor approximation and is the most flexible. It considers the technical progress and the substitution effect between input factors. We have adopted the translog production function model to measure technical efficiency. This paper has contributed to the research in the following ways. It is the first to quantitatively and qualitatively examine the efficiency of biomass energy from the perspectives of economic and technological development, and policy support. We chose the SFA method to estimate efficiency and took into account the random error factors. This method was applied to the new field of recycling and has extended the research field of industrial technical efficiency. We studied the factors that impact technical efficiency, and have provided a new analytical method. This paper includes a review of the status of the biomass energy and recycling industry and technical efficiency; a discussion of proposed methodology; empirical analysis of technical efficiency; and conclusions and policy implications in the final section.

Fig. 1. The industrial output and growth rate of recycling industry.

the development of the recycling industry toward information and intelligence, and promoted the transparency and convenience of recycling resource transactions. However, 2015 was the closing year of the government's “12th Five-Year Plan” and also a critical period for China's economic structure adjustment. Affected by the economic situation, the domestic market of recycling industry has been in fluctuations and sluggishness during recent years. Main types of recycling resources have suffered from continuously decreasing prices, which has resulted in the decline of profits of recycling companies. At present, there is no market access threshold for the recycling industry. Employees in this industry are generally less qualified. Most of the companies adopt the extensive operations and basic management models. The development of recycling industry is unbalanced in different areas. In addition, the recycling companies have insufficient capacity of innovations. The number of R&D institutions either established by companies themselves or based on long-term stable partnership is very small. In the combination of “production, development, and research”, recycling companies are basically in a subordinate position. They emphasize on production rather than research and development, introduction rather than digestion and absorption, imitation rather than innovation. Many companies are still in a state of “manufacturing” without “innovation”. At the same time, the application of the technologies and equipment in the recycling industry are continuously expanding. The quality of domestic technologies and equipment is far from satisfying the market demand. Therefore, for the smooth implementation of the government's “13th Five-Year Plan” and the adjustment and upgrading of the industrial structure of recycling industry, it is urgent to study the factors impacting on technical efficiency.

2. Literature review 2.1. The current status of biomass energy and recycling industry in China Potential reserves for biomass energy including crop residues, forestry residues and energy crops, poultry and livestock manure, and municipal waste are detailed in Table 1 (Li et al., 2010; Qiao et al., 2016). To promote the development of renewable energy in China, the government issued “The Law of Renewable Energy” in 2005, followed by the “Medium and Long-term Development Program for Renewable Energy” in 2007. These programs encouraged the industrialization of bio-gas, biomass liquid fuels and biomass electricity. The Ministry of Finance issued the “Interim Measure for the Administration of Subsidies for Renewable Power Price Appendix” in March 2012. This provided funding for project investment, operation, and maintenance of renewable power generation. It provided subsidies of 4000 yuan per kW per year for public independent renewable power systems. The “Interim Measure for the Administration of Subsidies for the Utilization of Straw as an Energy Resource” released in 2015 provided funding for plants to use straw as raw material (He et al., 2016; Zhao et al., 2016). These programs will drive investment capital towards developing technology and growing recycling industry (Zhang et al., 2009). The industrial output and growth rate of recycling industry from 2006 to 2015 are shown in Fig. 1. In 2015, the value of recycled industrial products reached 377 billion RMB and recycling recourse was 180 million tonnes. The scale of recycling recourse is expected to reach 300 million tonnes by 2020 (Li et al., 2016). To make best use of the driving effect of government investment, increase the financing support for private investment, create an investment environment for fair competition, and further loosen the access to capital market for private funds, the recycling industry has adopted the Public-Private Partnership (PPP) model to attract social capital participation and improve the operational efficiency of the company. The “Guidelines of the State Council on Actively Promoting the ‘Internet +’ Action” issued by the government has fully exerted the Internet's role of innovations, guided

2.2. The research method of efficiency Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) are the two main frontier analysis techniques. Efficiency is calculated as the relative distance between the frontier and the actual output or input. The DEA method is a non-parametric mathematical programming approach, where the sum of different outputs is not required and model misspecification can be avoided. Chien and Hu (2007) studied 45 economies in 2001–2002, and found that technical efficiency in OECD economies was higher than in non-OECD economies since they get a larger share of their energy from renewable sources. Shi et al. (2010) analyzed the technical efficiency of the industry in China using provincial panel data, and concluded that the main reason for energy wastage is excessive dependence on non-renewable energy sources. Hernandez-Sancho et al. (2011) used DEA to analyze waste water treatment in Spain and found very low (10%) technical efficiency. Enterprise scale and the quantity of organic matter removal were the main factors explaining technical efficiency. Olanrewaju et al. (2012) estimated the technical efficiency of energy consumption in 15 Canadian industries using DEA and other methods and predicted their future value. Toshiyuki and Mika (2012) scored and ranked the Japan's power plants according to their technical efficiency. This was measured using DEA, and they found that their operating performance did not

Table 1 The potential reserves for biomass energy in China. Description

Total resource (Mtce)

Available feedstock (Mtce)

Crop residues Forestry residues and energy crops Poultry and livestock manure Municipal waste

308–360 1242

150–216 166–300

1706–2100 28–35

102–105 7.8–9.3

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regional differences. In general, when compared with cities in the central and western regions, the cities in the eastern region have a more developed economy, a higher degree of urbanization, a higher level of industrial manufacturing technology, and a more educated workforce. This has contributed to the development of their recycling industry. This paper analyses the impact of external factors (ownership structure, economic development, industrial development, urbanization, education level of employees, and regional differences) on the development of the recycling industry, and their impact on technical efficiency.

change between 2005 and 2009. Chang et al. (2013) used DEA to study environmental inefficiency in the transportation sector in China and found that it was below 50% for most provinces, and that CO2 reduction potential ranged from 1.6 million tonnes (Qinghai) to 33 million tonnes. Woo et al. (2015) conducted a static and dynamic study of the environmental efficiency of renewable energy in 31 OECD countries. They demonstrated geographical differences in environmental efficiency, and concluded that dynamic efficiency was affected by the global financial crisis in 2008. Because the SFA method considers stochastic noise, it has higher discriminating power for estimating technical efficiency (Lin and Du, 2013; Xu et al., 2018). Buck and Young (2007) studied the energy efficiency of Canadian commercial buildings, and found that technical efficiency was very high and that the important factors were ownership and the economic activities carried out in the buildings. Boyd (2008) concluded that the advantage of the SFA method was that it avoided the problem of energy intensity definition through technical efficiency analysis of wet corn milling plants. Shi et al. (2008) used SFA to estimate the average contributions of total factor productivity, capital-energy ratio and labor-energy ratio in China from 1980 to 2005. The values were 37%, 46% and 18%. Alfonso et al. (2012) used SFA to find that the energy conservation potential of Spanish food and drink, textile, chemical and non-metallic mineral products was about 20%. Massimo and Lester (2012) used data from 48 U.S. states from 1995 to 2007 to study the technical efficiency and energy consumption of their residential sectors. They demonstrated that energy intensity did not adequately represent technical efficiency. Lin and Yang (2013) estimated the energy conservation potential and technical efficiency of the thermal power industry in China. Cumulative energy conservation potential was 551 Mtce and average technical efficiency was 0.85 from 2005 to 2010. Chen et al. (2015) studied the technical efficiency of fossil-fueled electricity generation industry from 1999 to 2011 in China. They investigated the size, location, ownership and sources of fuel. They found that efficiency varies among companies. Katuwal et al. (2016) estimated the effectiveness of fire suppression resource inputs to production of controlled fire lines using SFA. It increases when bulldozers and fire engines are used, and when the fire lines occur along rivers and roads, and within areas previously burned by wildfires. Many studies use a radial DEA method when calculating two-stage technical efficiency. This method cannot account for the inefficiencies associated with the non-radial slacks of each input and output. Our study used the SFA method to calculate technical efficiency with a focus on recycling industry.

2.3.1. Ownership structure Ownership structure has a far-reaching impact on the development of enterprises as well as the industry (Lin and Wang, 2014). Figs. 2 and 3 illustrate that ownership structures affects the development of the industry. The theory of industrial organization holds that the competitive market structure has a higher efficiency level. If the proportion of state-owned assets is too high, monopolies may restrict development of competing industries. The recycling industry is no exception. We used the ratio of total assets of state-owned enterprises to total assets of all enterprises in the recycling industry to represent ownership structure. 2.3.2. Economic level The level of economic development in a region will directly affect the overall development of the regional recycling industry (Ceballos and Dong, 2016). The construction of infrastructure facilities and the investment in fixed assets will require large investments (Lin and Long, 2015). Generally speaking, economically developed areas have invested more in the recycling industry because of their more advanced management philosophy, higher quality of employees, and more openended thinking. Regional economic development supports industrial development, and the development of resource-related industrial recycling activities. We used GDP per capita to indicate economic level. 2.3.3. Industrial level The level of development in the manufacturing determines the level of development in the recycling industry (Liu, 2009). The manufacturing and recycling industries are mutually reinforcing. The recycling industry can provide raw materials for the manufacturing industry, while the manufacturing industry can impact recycling industry through the development of technology, industrial agglomeration, and industrial scale (Gutowski et al., 2013). Machinery manufacturing enterprises have a direct impact on the technical efficiency of recycling industry. We used the ratio of regional industrial output value and regional total output value to indicate the level of local industrial development.

2.3. The Inefficient factors of efficiency There are many studies on the efficiency of industries or enterprises, the factors studied include ownership structure (Lin and Wang, 2014), economic development (Ceballos and Dong, 2016), industrial development (Gutowski et al., 2013; Liu, 2009), urbanization (Sadorsky, 2013; Wang, 2014) and education level of employees (Katircioğlu, 2014). These studies did not look at these variables together and did not focus on the recycling industry. We selected the variables in this paper on the basis of the operating conditions of the recycling industry. Figs. 2 and 3 suggest that there is a relationship between efficiency and ownership structure in the recycling industry, and that both have obvious

2.3.4. Urbanization rate Urbanization has an impact on the collection, transportation and processing of materials connected with the recycling industry. As urbanization intensifies waste materials generate more demand for recycling. The degree of urbanization has generally been measured as the ratio of non-agricultural population to total population or the ratio of urban population to total population (Sadorsky, 2013; Wang, 2014). We chose the ratio of urban population to total population as our indicator. Fig. 2. The proportion of three types recycling industrial enterprises.

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Fig. 3. The operational status of Chinese recycling industry (100 million/RMB).

While the total capital stock in China has been estimated by many scholars (Ren and Liu, 1997; Su et al., 2012), there is no research which focuses on the capital stock of the recycling industry. We calculated capital stock using the perpetual inventory method (PIM) introduced by Goldsmith (1951). It uses a geometric pattern of declining relative efficiency, and a constant replacement rate. It can be expressed as follows:

2.3.5. Education level The educational level of employees is based on the quality of training and the skills of people in the industry (Katircioğlu, 2014). It is associated with the technical requirements for activities related to the utilization of recycling resources. Investment in employee education, training, and R&D, creates a high technical reserve level for the industry. To represent educational level, we have chosen the ratio of people with post-secondary education to people over the age of six.

Kt = K 0 + 2.3.6. Regional differences Business environment and government policies vary from region to region and the technical efficiency of the recycling industry will also vary because of these regional differences (Ceballos and Dong, 2016). The average industrial output value of the recycling industry in eastern cities is the largest, while the value for cities in the central and western regions is much lower. Regional differences have a great impact on the development of recycling industry.

t

∑t0+1

∆K t P (t )

(1)

where K 0 and Kt represent capital stock in the base year and in year t, respectively; ∆Kt represents the growth of capital stock in year t; and P (t ) represents the price index of fixed-asset investment in year t. Therefore, three variables for calculating capital stock are as follows: (1) Capital stock for the base year: Zhang (2008) used the net fixed assets of Chinese provincial material in 1952 as capital stock for the base year and (Lin and Wang, 2014) used the net fixed assets of the iron and steel industry in 2005 as capital stock for the base year, they obtained the net fixed assets directly from Statistical Yearbook. Therefore, we used the net fixed assets of the recycling industry in 2006 as capital stock for the base year, it can be obtained from the Statistical Yearbook of China. We applied PIM to estimate the capital stock of each provincial recycling industry between 2006 and 2015. (2) Investment: The investment data (∆Kt ) was calculated as the fixed assets in year t minus the fixed assets in year (t-1) using the method of Wang (2004) and Shan (2008). (3) Price index: This data was available from the China Statistical Yearbook, 2007–2016.

3. Methodology and data sources 3.1. Data sources According to Li et al. (2012), the recycling industry is the group of enterprises engaged in the collection, processing, and re-manufacturing of renewable resources; and the businesses that use those resources as raw materials. In 2002, the national economic industry classification (GB/ T4754) identified this segment as the "used resources and used materials recycling industry". We have selected data from 20 provinces in China for 2006–2015 from the Statistical Yearbook of China 2007–2016, the provincial statistical yearbooks, and the China macro industrial database.

3.2.3. Labor input (L) Labor input plays an important role in the recycling industry. The number of employees was adopted as an indicator of labor input. The average annual value and the year-end value data were obtained from the China Industry Economy Statistical Yearbook, 2007–2016. The average annual value was calculated as the average value of the adjacent two years, and missing data was reconstructed by computing overall labor productivity multiplied by the industrial value added.

3.2. Variables 3.2.1. Output (Y) We measured the output of the recycling enterprises from the value of biomass power generation, biodiesel and bio-ethanol utilization, and the development of bio-hydrogen technology in each region, converted to 2006 price level by deflating the industrial product price index. 3.2.2. Capital input (K) Original fixed asset value is measured by purchase price. Net fixed asset value is the purchase price minus accumulated depreciation. Both use purchase price to represent the value of capital goods and are not a good reflection of the real value of capital stock (Huang et al., 2002).

3.2.4. Regional variable (D) To accurately estimate technical efficiency, we divided China into Eastern China (D1), and Central and Western China (D2) following the classification of the China Statistical Yearbook. 80

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ϖit = exp (b0 + Zit δ ), σit2 = exp (b1 + Zit γ )

Table 2 Variables description. Variables

Short

Production variables Industrial gross Y value Capital input K Labor input L Non-efficiency variables Ownership Z1 structure Economic level Z2

Mean

St. Dev.

Max.

Min.

Units

127.5

138.92

1627.2

0.05

2.49

3.14

12.16

0.06

2.9

3.01

10.1

0.14

100 million / RMB 100 million / RMB 104

41.13

16.73

78.51

5.31

%

4.01

7.59

11.6

0.52

Industrial level Urbanization rate Education level Regional variables Eastern China

Z3 Z4

44.37 59.13

6.88 3.92

60.13 90.1

21.49 35.2

ten thousand / RMB % %

Z5

15.93

8.79

99.1

8.77

%

D1

Central and Western China

D2

Beijing, Tianjin, Liaoning, Hebei, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong Jilin, Heilongjiang, Anhui, Jiangxi, Hubei, Hunan, Inner Mongolia, Chongqing, Sichuan, Shanxi

(4)

Where b0 and b1 represent the intercept term, Zit represents the exogenous variable that affects the technical efficiency, δ and γ represent the estimated parameters. The exogenous variable impacts on the mean (ϖit ) and the variance (σit2 ) of uit at the same time. The heterogeneity SFA model can simultaneously analyze the impacts of exogenous variables on the technical efficiency, the uncertainty of the production function, and the loss of technical efficiency. The heterogeneity SFA model analyzes the impacts on efficiency in two ways. (1) The likelihood ratio test. The statistic obeys the chi-square distribution and the degrees of freedom is the number of constraints. We use it to examine the heterogeneity settings of the model. (2) The production efficiency index (TEit ) indicates the deviation of the actual output from the optimal output. It can be expressed as follows:

TEit =

E [f (∙) exp (vit − uit )] = exp (− uit ) E [f (∙) exp (vit )| uit = 0]

(5)

In Eq. (5), E (∙) is a conditional expectation, TEit is the recycling industry technical efficiency. In this paper, Z1, Z2 , Z3 , Z4 , Z5 and D are the inefficient explanatory variables that capture the effect on the productivity and effectiveness of the recycling resource. To further examine the influence of ownership structure and industrial level on technical efficiency, we introduce their quadratic terms. Following Zhang and Xia (2011), the heterogeneity of the SFA model can be expressed as follows:

3.2.5. Inefficient explanatory variables To be consistent with the approach taken in the literature, we selected the Ownership structure (Z1), Economic level (Z2), Industrial level (Z3), Urbanization rate (Z4) and Education level (Z5) as inefficient explanatory variables in this paper. The descriptive statistics of the above variables are collected and sorted in Table 2.

Lnϖit = b0 + δ1 z1t + δ11 z12t + δ2 z2t + δ3 z 3t + δ33 z32t + δ4 z 4t + δ5 z5t + δ6 Dt (6)

Lnσit2

= b1 + γ1 z1t +

γ11 z12t

+ γ2 z2t + γ3 z 3t +

γ33 z32t

+ γ4 z 4t + γ5 z5t + γ6 Dt

3.3. Modeling

(7) In Eqs. (6) and (7), z1t , z12t , z2t , z 3t , z32t , z 4t , z 5t , Dt represent ownership structure, quadratic terms of ownership structure, economic level, industrial level, quadratic terms of industrial level, urbanization rate, education level, and the degree of difference between regions in China, respectively.

Aigner and Chu (1968) proposed a deterministic frontier production function, but did not take into account the impact of stochastic factors on productivity and efficiency. To allow for the impact of technical advances, Battese and Coelli (1995) introduced time into the stochastic frontier model. The stochastic frontier model of technical efficiency by Battese and Coelli (1995) can be expressed as:

yit = f (x it , t )exp (vit − uit )

4. Results and discussion

(2) 4.1. Model test

where yit represents the output value of region i of enterprise at year t, x it represents the input amount in region i, and f (x it , t ) is the estimated function, t is time to measure technical improvement. vit is a random error and uit is the production inefficiency used to measure the stochastic frontier technical efficiency level. The SFA model with translog production function can be expressed as follows:

LnYit = α 0 + α1 LnKit + α2 LnLit + α3 t +

Model test results in Table 3 indicate that the model selected and the production function established in this paper are suitable for the analysis of technical efficiency. The translog function considers much more than the Cobb-Douglas production function, and can treat technical inefficiency and random error terms separately. By imposing constraints on the mean (ϖit ) and the variance (σit2 ) of inefficiency terms, the SFA can be divided into five models. This paper focuses on Model 1. It does not impose any constraints on the parameters of the heterogeneous stochastic frontier functions. Therefore, its technical efficiency is dynamic. Models 2–5 are obtained after applying constraints from Model 1. Model 2 (γ = 0 ) assumes that the exogenous environmental variables have no effect on the uncertainty of the

1 1 α (LnKit )2 + α5 (LnLit )2 2 4 2

1 α6 (t )2 + α 7 (LnLit )(LnKit ) + α8 t (LnKit ) + α 9 t (LnKit ) + vit 2 − uit (3) +

Where, Kit , Lit ,t represent capital input, labor input and the time of the recycling industry respectively. In practice, different types of enterprises have heterogeneous production frontiers due to disparate dissimilar technological levels. Differences in ownership structure, economic and industrial level lead to the use of varying production technologies. Because of this, the traditional SFA method cannot estimate the true technical efficiency of each region. The heterogeneous SFA method makes the assumption that the distribution of uit obeys the truncated normal distribution uit ~ N+ (ϖit , σit2 ), where ϖit represents the deviation of the production function from the production frontier because of exogenous factors, and σit2 represents the vitality of the deviation. The heterogeneity of SFA model can be expressed as:

Table 3 The results of model tests. Test

Hypothesis

LR

χ2

Result

1

H0 :C − D production function accepted α 4 = α5 = α6 = α7 = α8 = α9 = 0 H0:OLS Model accepted γ=0 H0:There is no technical progress α3 = α6 = α8 = α9 = 0

141.37

17.81 * **

Reject

60.12

8.15 * **

Reject

127.25

13.15 * **

Reject

2 3

Note: * ** Means significant at 1%, C – D is short for Cobb-Douglas. 81

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Table 4 The results of heterogeneity SFA model. Variable

Model 1 unconstrained Coe.

Model 2 γ=0

Model 3 δ=0

Model 4 ωit = 0

Model 5 μit = 0

T value

Coe.

T value

Coe.

T value

Coe.

T value

Coe.

T value

Technical efficiency Constant 5.137* * Ln K 0.471 * ** Ln L 1.893 * ** t 0.417 * * 2 0.5 * (Ln K) 0.071 * 0.5 * (Ln L)2 0.811 * 0.5 * t2 − 0.359 * Ln L*Ln K 0.179 * t * Ln K − 0.162 * * t * Ln L − 0.169 *

9.514 3.058 5.011 2.781 0.973 2.057 − 2.191 0.673 − 3.163 − 1.451

3.781 * 0.115 * 3.131 * * 1.115 * 3.019 1.324 − 0.125 * 1.042 − 0.701 * − 2.114

6.371 2.131 4.012 2.015 5.441 2.105 − 2.311 2.301 − 2.114 3.457

1.381 − 0.117 * * 0.131 * 0.704 2.183 * * 0.313 1.071 * 0.713 1.031 * * − 0.135

5.347 − 1.532 1.217 1.705 3.152 2.015 1.901 0.906 1.505 − 1.207

2.579 * 2.115 * ** 0.541 * * − 1.114 * 0.153 3.415 * * − 0.502 * 1.035 2.033 * ** − 1.035

4.128 4.157 2.034 − 2.011 1.241 2.057 − 1.811 2.417 3.015 − 1.927

6.787 * − 0.721 * − 0.135 * * 0.902 * * − 0.457 * 0.135 0.175 * * 3.135 − 1.441 * − 3.457

3.971 − 2.154 − 2.071 3.015 − 1.351 0.957 3.041 2.302 − 0.905 − 5.337

Technical inefficiency of ϖ Constant − 8.743 * Z1 3.163 * ** − 5.371 * ** Z12

− 2.897 2.866 − 4.117

1.541 1.141 * − 2.043 * *

3.051 1.772 − 3.515

4.042 * * – –

7.157 – –

– – –

– – –

– – –

– – –

− 0.724 * * − 2.781 * * − 1.987

− 3.701 − 3.169 − 1.116

2.905 − 0.515 * 2.701

5.107 − 2.078 3.002

– – –

– – –

– – –

– – –

– – –

– – –

2.068 − 3.781 * * − 2.159 * **

1.683 − 4.895 − 4.691

0.575 − 1.569 * 3.017 * **

1.914 − 3.173 5.063

– – –

– – –

– – –

– – –

– – –

– – –

Technical inefficiency of σ 2 Constant − 1.541 Z1 1.351 * ** − 3.357 * ** Z12

− 0.432 0.761 − 2.101

2.137 * * – –

3.015 – –

0.373 0.803 * 1.051 * **

2.151 1.153 3.008

2.305 * − 2.115 * * − 2.016

3.107 − 3.045 − 4.515

– – –

– – –

− 1.226 * * − 3.174 * * − 1.155

− 1.514 − 4.012 − 2.041

– – –

– – –

− 0.612 * * 1.053 * − 2.051

− 3.101 3.104 − 4.144

2.025 * − 4.051 * − 2.352

4.016 − 8.115 − 3.155

– – –

– – –

3.151 − 4.163 * * − 1.347 * **

5.814 − 2.181 − 3.094 − 55.838 686.265

– – –

– – – − 81.461 613.997

1.134 − 1.061 * − 2.535 * *

2.501 − 0.932 − 4.171 − 124.65 587.851 0.000 205.361 0.000

2.072 − 2.051 * * 3.043

4.006 − 3.054 5.157 − 155.469 513.784 0.000 245.893 0.000

– – –

– – – – – – 686.265 0.000

Z2 Z3

Z32 Z4 Z5 D

Z2 Z3

Z32 Z4 Z5 D Log-likelihood LR1 P value LR2 P value

.000 – –

0.000 58.129 0.000

Note: * , * *, * ** Means significant at 10%, 5%, 1% respectively. LR1 and LR2 are the Chi-square value obtained by the likelihood ratio tests of corresponding models to model 5 and model 1 respectively.

of the inefficient terms and corresponds to the DEA model. The estimation and test results of the heterogeneity SFA model are shown in Table 4. The final LR test results in Table 4 show that regardless of the null hypothesis of “no inefficient term (LR1)” or “there is a heterogeneity

inefficiency term (Battese and Coelli, 1995). Model 3 (δ = 0 ) assumes that the inefficiency term is not affected by exogenous variables (Reifschneider and Stevenson, 1991). Model 4 (ϖit = 0 ) assumes that the inefficiency term obeys the semi-normal distribution truncated at zero (Aigner et al., 1977). Model 5 (uit = 0 ) does not consider the effects

Fig. 4. The average of technical efficiency in different zones (%). 82

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quadratic coefficients are − 5.371 and − 3.357 with 1% significance in the technical inefficiency function of ϖ , σ 2 respectively. Ownership structure has a significant "U" shaped relationship with technical inefficiency. The high proportion of state-owned assets in the industry promotes the development of technical efficiency in the early stages, but as the recycling industry develops, it becomes restricting. Demsetz (1997) found that the competitive market structure performed better than the monopolistic market model, and this was validated in the recycling industry. State-owned enterprises rely on government to develop policies that create advantages in cost and access to markets. The greater the proportion of state-owned enterprises, the lower the technical efficiency of the industry. To some extent the advantages enjoyed by state-owned enterprises will suppress other enterprises and lower the technical efficiency of the industry.

Table 5 The average technical efficiency of province (2006–2015). Region

Province

Technical efficiency (%)

Ranked

Eastern

Beijing Tianjin Liaoning Hebei Shanghai Jiangsu Zhejiang Fujian Shandong Guangdong Jilin Heilongjiang Anhui Jiangxi Hubei Hunan Inner Mongolia Chongqing Sichuan Shanxi

69.25 85.15 82.14 83.41 76.91 87.52 90.87 81.22 88.45 82.09 65.53 40.12 62.13 65.01 55.15 79.67 28.81 78.53 45.37 78.03 71.27

13 4 6 5 12 3 1 8 2 7 14 19 16 15 17 9 20 10 18 11

Central and Western

Average efficiency

4.3.2. Economic level The coefficients of economic level are − 0.724 and − 1.226 with a 5% significance in the technical inefficiency function of ϖ , σ 2 respectively. There is a negative correlation between the economic level and the technical inefficiency of the recycling industry. Higher economic level leads to higher technical efficiency.

/

4.3.3. Industrial level The coefficients of industrial level are − 2.781 and − 3.174 with a 5% significance, and the quadratic coefficients are − 1.987, − 1.155 with less than a 10% significance in the technical inefficiency function of ϖ , σ 2 respectively. Industrial level has a negative linear correlation with technical inefficiency. The higher the industrial level, the lower the technical inefficiency. Industrial development will promote technical efficiency in the recycling industry.

exogenous variable constraint” (LR2), the heterogeneity SFA model 1 is significantly superior to the other 4 models. Model 1 is significantly better than Model 5, indicating that inter-provincial heterogeneity does affect technical efficiency. Therefore, our analysis is based on Model 1. 4.2. The analysis of technical efficiency Fig. 4 and Table 5 show that the technical efficiency of the recycling industry is moderate in China. The national average is 60% − 75%, and the level differs significantly between regions and between provinces. The regional technical efficiency of the recycling industry in eastern China is higher than that of central and western China. The average technical efficiency of eastern China is 80.3% in a 10-year sample period and the average value for central and western China is 64%. The technical efficiency of different regions varies with time. In eastern China, it increased from 2006 to 2008, deceased from 2009 to 2011 and increased again from 2012 to 2015. The trend in central and western China is an inverted "U" shape and agrees with the national trend. From 2006–2011, the value moved up from 54% to 68%, followed by a downward trend. Technical efficiency varies significantly among provinces. The technical efficiency of Zhejiang Province (90.87%) is the highest and for Inner Mongolia (28.81%) it is at the lowest level. The provinces with the highest technical efficiency are Zhejiang, Shandong, Jiangsu, Tianjin, Hebei, Liaoning, Guangdong and Fujian (The 10-year average technical efficiency is greater than 80%). These eight provinces are all located in eastern China. The provinces with the lowest technical efficiency (less than 50%) are Sichuan, Heilongjiang and Inner Mongolia.

4.3.4. Urbanization rate The coefficients of urbanization rate are 2.068 and 3.151. in the technical inefficiency function of ϖ , σ 2 respectively, but the significance test failed, indicating that urbanization is not related to technical inefficiency. For example, the ratios of non-agricultural population to total population of Beijing and Shanghai are the highest, but their technical efficiencies are not. 4.3.5. Education level The coefficients of education level are − 3.781 and − 4.163 with a 5% significance in the technical inefficiency function of ϖ , σ 2 respectively, indicating that education level has a negative correlation with technical inefficiency. Higher education leads to higher technical efficiency. Therefore, the development of education will promote the technical efficiency of the recycling industry. 4.3.6. Regional differences The coefficients of the regional factors are − 2.159, − 1.347 with a 1% significance in the technical inefficiency function of ϖ , σ 2 respectively, and show that geographical location has a significant effect on technical efficiency. The technical efficiency of recycling in the eastern cities is higher than in the central and western cities of China.

4.3. The analysis of impact factor

4.4. Endogeneity in SFA models

The technical efficiency results of Model 1 in Table 4 show that the coefficients of capital input and labor input are 0.471 and 1.893, respectively, with 1% significance level. Increasing employee numbers and the input of fixed capital will promote technical efficiency. The technical inefficiency results of Model 1 in Table 4 show that the ownership structure, economic, industrial, and education level, and regional differences have a significant impact on production efficiency, but the impact of urbanization rate is not significant.

Endogeneity problem can arise in stochastic frontier models due to some reasons: First, there might be certain correlation between the determinants of the frontier and the two-sided error term. Second, there might be certain correlation between the inefficiency term and the twosided error term. Third, there might be certain correlation between the inefficiency term and two-sided error term. Particularly, the correlation can be derived from the determinants of the inefficiency. For instance, as far as agriculture is concerned, farmers may feel frustrated because of continuous terrible weather, which may result in bad performance of farmers accordingly. The endogeneity in a stochastic frontier model

4.3.1. Ownership structure The coefficients of ownership structure are 3.163 and 1.351 and the 83

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would cause inconsistent parameter estimates. Therefore, this problem should be settled reasonably. Guan et al. (2010) use a two-step estimation methodology to process the endogenous frontier regressors. In their study, the first step is to use GMM to obtain the consistent estimates of the frontier parameters. The second step is to take the residuals from the first step as the dependent variable to obtain the maximum likelihood stochastic frontier estimates. The standard stochastic frontier estimators are applied to the second step. Therefore, the efficiency estimates are inconsistent if there is certain correlation between the two-sided and the one-sided error terms. Kutlu (2010) tries to solve the endogeneity problem in the maximum likelihood estimation by designing a model to address the endogeneity problem because of the correlation between the regressors and the two-sided error term. Tran and Tsionas (2013) present a GMM variation of the study of Kutlu (2010). The hypotheses of these models are insufficient to solve the endogeneity problem because of one-sided and two-sided error terms. Mutter et al. (2013) believe that ignoring the variable causing the endogeneity problem is not a feasible solution and they list the reasons. Shee and Stefanou (2014) update the research method in Levinsohn and Petrin (2010) and try to solve the problem of endogenous input choice because of production shocks that can be predicted by the productive unit but not by the econometrician. Amsler et al. (2016) present a copula method, in which the more general correlation structures are allowable when modeling endogeneity. But this approach is computationally intensive and needs to choose a copula properly. Besides, the environmental variables affecting efficiency are not included in the model proposed by Amsler et al. (2016), which makes it less applicable when explaining the factors that affect the inefficiency. Although lots of studies were focusing on solving the endogeneity problem in stochastic frontier analysis, the existing studies have not summarized a proper solution. Therefore, to settle the endogeneity problem and solve the limitations of existing methods, Karakaplan and Kutlu (2017) present a test of endogeneity in SFA. one of the main strengths of this test is that it is easier to apply compared to its copula. Besides, this model generalizes type estimators of Battese and Coelli (1995) which is one of the most frequently applied stochastic frontier models. In this test, the joint significance of the components of the η term is checked. If the joint significance of the components is rejected, then correction for endogeneity is not necessary, and the model can be fit by traditional frontier models. However, if the components of the η term are jointly significant, then there is endogeneity in the model, and a correction would be necessary. The endogeneity estimation results are presented in Table 6. Model EX represents the model that ignores endogeneity, and Model EN represents the model that uses the methodology of Karakaplan and Kutlu (2017) to handle endogeneity. After evaluating the mean values of variables, we cannot reject null hypothesis since the production function has constant returns to scale at any conventional level. In the results of η endogeneity test, P > χ2 = 0.237. The result accepts the null hypothesis, which means that a correction for endogeneity is not necessary in SFA model.

Table 6 The endogeneity estimation of SFA model. Dependent variable: ln (Y)

Model EX

Constant Ln K Ln L t 0.5 * (Ln K)2 0.5 * (Ln L)2 0.5 * t2 Ln L*Ln K t * Ln K t * Ln L

4.049 * * 0.453 * ** 1.193 * ** 0.417 * * 0.071 * 0.818 * − 0.359 * 0.179 * − 0.165 * * − 0.159 *

(0.107) (0.418) (0.011) (0.081) (0.073) (0.057) (0.091) (0.072) (0.063) (0.051)

4.081 * * 0.442 * ** 1.231 * * 0.415 * * 0.069 * 0.824 * − 0.355 * 0.172 * − 0.169 * − 0.156

(0.115) (0.411) (0.012) (0.085) (0.071) (0.059) (0.090) (0.075) (0.064) (0.057)

− 5.743 * 2.103 * ** − 4.375 * **

(0.297) (0.266) (0.117)

− 5.541 * 2.141 * ** − 4.243 * *

(0.288) (0.272) (0.115)

− 0.774 * * − 2.585 * * − 1.686

(0.091) (0.165) (0.116)

− 0.765 * − 2.515 * − 1.701

(0.097) (0.178) (0.102)

2.261 − 3.185 * * − 2.051 * **

(0.283) (0.391) (0.295)

2.578 − 3.069 * − 2.017 * **

(0.304) (0.273) (0.263)

− 4.543 * **

(0.032)

Dependent variable: ln (σu2 ) Constant Z1

Z12 Z2 Z3

Z32 Z4 Z5 D Dependent variable: ln (σv2 ) Constant Dependent variable: ln (σw2 ) Constant η

η endogeneity test ( χ 2 = 28.9) Log-likelihood 515.09

Model EN

− 3.145 * * (0.033) 0.443 (0.081) 2 P > χ = 0.237 674.81

Note: * , * *, * ** Means significant at 10%, 5%, 1% respectively. Standard errors are in parentheses.

western. Technical efficiency in the central and the eastern regions was relatively close, but there was a big difference among the provinces of central and western China. Regarding to inefficient factors of technical efficiency for recycling industry, we propose the following suggestions. (1) The relationship between ownership structure and efficiency exhibits a significant inverted "U" shape. Therefore, the state should diversify ownership by giving non-state enterprises incentives and lower their entry threshold to attract more foreign and private capital into the recycling industry. On the one hand, the participation of foreign and private capital in market competition will force state-owned enterprises to reduce costs, and improve technology and production efficiency. On the other hand, the competition between independent stakeholders in the market will lead to the improvement of the resource allocation efficiency and the technical efficiency of recycling industry. (2) The industrial level has a positive impact on technical efficiency. Although the recycling industry has attracted lots of attention in China, its technical level is still low and needs to improve. The dismantling methods used in the small enterprises is still very inefficient and can lead to serious secondary pollution. The recycling industry needs to develop and implement advanced equipment to improve technical efficiency with respect to processing. It is still an emerging industry, and the relevant processing equipment is still in the research and development phase. Because R&D is an expensive process, and the recycling industry creates public benefits, the Chinese government should increase its investment in technology, and provide policy support. At the same time, the Chinese government must encourage large-scale enterprises to increase their own investment in technology research and development, and to constantly update machinery and equipment. (3) The economic level has a positive impact on technical efficiency. The traditional manufacturing industry and the recycling industry develop in a complementary way. On the one hand, recycling can

5. Conclusions and policy implication This paper aims to analyze the technical efficiency of biomass energy from the perspective of recycling industry. We study the quantitative and qualitative relationships between economic and technological development, and the technical efficiency of biomass energy. The SFA method considering the heterogeneity problem proposed by Zhang and Xia (2011) and the endogeneity problem proposed by Karakaplan and Kutlu (2017) is applied. After the empirical analysis, we find the technical efficiency level of the recycling industry in China from 2006–2015 was moderate, and the trend versus time line was represented as an inverted "U" shape. The average efficiency of the recycling industry in east China was higher than in the central and the 84

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provide inexpensive high quality raw materials for traditional manufacturing enterprises, and make up for the lack of resources such as mineral. On the other hand, technical innovation in traditional manufacturing will improve the technical level of recycling industry and the recycling rate of renewable resources. For example, the replacement of manual work with innovative incinerators, copper wire crushers, wire strippers, and plastic granulators can improve resource usage and reduce pollution at the same time. (4) The education level has a positive impact on technical efficiency. Therefore, train compound workers with both professional and management skills. Strengthen cross-regional exchange and cooperation. The development level of recycling industry in eastern region is obviously higher than that in central and western region in China. Therefore, it is necessary to strengthen the cooperation between the eastern region, the central and western regions, and the cities. Through cross-region and cross-city cooperation, the advanced technology can be introduced to the under developed areas of the recycling industry.

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