Will agglomeration improve the energy efficiency in China’s textile industry: Evidence and policy implications

Applied Energy 237 (2019) 326–337

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Applied Energy journal homepage: www.elsevier.com/locate/apenergy

Will agglomeration improve the energy efficiency in China’s textile industry: Evidence and policy implications Hongli Zhaoa, Boqiang Linb,

T

⁎

a

School of Economics and Management, Beijing Institute of Petrochemical Technology, Beijing 102617, PR China School of Management, China Institute for Studies in Energy Policy, Collaborative Innovation Center for Energy Economics and Energy Policy, Xiamen University, Fujian 361005, PR China

b

H I GH L IG H T S

relationship between industrial agglomeration and energy efficiency. • AA nonlinear threshold effect exists when industrial agglomeration affects energy efficiency. • When degree is less 1.3865, it will promote energy efficiency. • Futureagglomeration policy for energy efficiency in China’s textile industry is suggested. •

A R T I C LE I N FO

A B S T R A C T

Keywords: Industrial agglomeration Energy efficiency Threshold effect

Based on provincial panel data on the textile industry in China, this paper calculates the total-factor energy efficiency of this industry as the dependent variable. Additionally, based on linear panel analysis of the relationship between industrial agglomeration and energy efficiency, in-depth analysis of the industry is performed at different industrial agglomeration levels. The paper identifies different impacts of industrial agglomeration on energy efficiency, uses the threshold regression model to extend the research to a nonlinear framework, and constructs a double threshold regression model in which the threshold of the textile industry agglomeration level serves as the threshold variable. The results show, first, a threshold effect occurs when industrial agglomeration affects total-factor energy efficiency. Second, a significant positive correlation exists among the degree of economic development, energy prices, research and development investment (R&D), enterprise scale, and total factor energy efficiency of the textile industry. Third, a non-linear relationship exists between industrial agglomeration and energy efficiency in the industry. When industrial agglomeration is low, promoting it improves energy efficiency. However, when industrial agglomeration reaches a certain level, agglomeration and energy efficiency show a negative relationship. Finally, based on the empirical results, ways of improving energy efficiency in the industry are suggested.

1. Introduction Agglomeration is the most prominent geographical feature used to characterize social and economic activities. Industrial agglomeration describes the phenomenon wherein an industry’s development is highly concentrated in a specific geographical scope, along with the continuing spatial convergence of industrial capital, leading to formation of external economies of scale for an industry in a specific area; this in some industries results in a sustained competitive advantage [1,2]. Industrial agglomeration becomes inevitable at a certain stage of

economic development [3]. With increasing attention being paid to new forms of organization, industrial agglomeration has attracted considerable research attention. It can improve production efficiency through specialized division of labor and cooperation and, to a certain extent, it is associated with cost reduction. Internal technological spillover accelerates technological innovation, while both competition and cooperation among enterprises promote industry development and enhance regional competitiveness. Industrial agglomeration is now an important strategy for adjusting the economic structure in many regions, and has attracted increasing attention in China as a new form of

⁎ Corresponding author at: School of Management, China Institute for Studies in Energy Policy, Collaborative Innovation Center for Energy Economics and Energy Policy, Xiamen University, Fujian, 361005, PR China. E-mail addresses: [email protected], [email protected] (B. Lin).

https://doi.org/10.1016/j.apenergy.2018.12.068 Received 2 January 2018; Received in revised form 4 December 2018; Accepted 30 December 2018 0306-2619/ © 2019 Elsevier Ltd. All rights reserved.

Applied Energy 237 (2019) 326–337

H. Zhao, B. Lin

studied the relationship between economic agglomeration and labor productivity in US states, finding a significant positive correlation between economic agglomeration and labor productivity. Mitra and Sato [6], Brülhart and Sbergami [7] found industrial agglomeration has a substantial impact on productivity. In China, researchers have also studied the relationship of industrial agglomeration with labor productivity, and with economic growth [8,9]. However, there has been limited research on industrial agglomeration and energy efficiency. Industrial agglomeration has an economies of scale effect, technology spillover effect, and correlation effect, and can promote economic growth and improve total factor productivity (TFP). Chen et al. [10] made an empirical study on the relationship between energy efficiency and industrial agglomeration in the industrial sector. The results showed that industrial agglomeration could significantly promote the improvement of energy efficiency in the industry. Zhao and Zhang [11] found industrial spatial agglomeration can effectively improve TFP by promoting continuous progress of production technology. In an empirical study, Wang and Chan [12] found industrial agglomeration promotes technological efficiency and considerably impacts TFP. Based on existing research, researchers have extensively analyzed energy efficiency, fully considering the role and impact of industrial agglomeration. Li [13] used micro survey data to perform an empirical analysis and found industrial agglomeration promotes energy efficiency, and mainly depends on labor sharing and knowledge spillover. Han et al. [14] focused on provinces and municipalities in analyzing the relationship between spatial agglomeration and energy efficiency during a sample period, and found spatial agglomeration helps improve energy efficiency. Liu et al. [2] used statistical data from 2004 to 2013 to analyze the effect of industrial agglomeration on energy efficiency, and found the former can promote an increase in the latter at a country level. Ning et al. [15] examined how urban industrial agglomeration interacts with the intra- and inter-regional externalities resulting from foreign direct investment in urban innovation in an emerging economy. There are also some scholars who have studied the non-linear relationship between TFP and industrial agglomeration and its impact mechanism, and the conclusions are not exactly the same. Shi and Shen [16] calculated the energy efficiency value through EBM model and analyzed the factors affecting energy efficiency. The research found that with the improvement of urbanization level, energy efficiency showed a u-shaped feature of decreasing first and then increasing. Industrial agglomeration can play a positive role when the urbanization rate reaches the threshold of 55.46%. Ji and Zhao [17] take the manufacturing industry as the research object and use the panel threshold regression to conduct an empirical analysis of their energy efficiency and industrial agglomeration. The empirical results show that for the whole industry sample and non-technically intensive industry samples, In the lower industrial agglomeration interval, industrial agglomeration has a positive effect on energy efficiency; in a relatively high concentration range, industrial agglomeration has a negative impact on energy efficiency. Qiao et al. [18] took the panel data of inter-provincial manufacturing industry from 2000 to 2010 as the research object, established the theoretical model between industrial agglomeration and energy efficiency through the analysis of the intermediary variable – industrial competitiveness, and explored the mechanism of the two and energy efficiency respectively by using the systematic GMM method. The research shows that the former has a greater impact on energy efficiency than the latter, but when there is excessive competition, the former is not conducive to the improvement of energy efficiency. Lin et al. [19] used panel data for Chinese textile industry enterprises during 2000–2005. The sample in that study was divided into three groups according to the Ellison–Glaeser index and the agglomeration of different groups within the industry was analyzed together with enterprise production efficiency using virtual variables. The results showed an inverted U-shaped nonlinear relationship between industrial agglomeration and enterprise production efficiency in the

organization. The textile industry is a traditional pillar industry in China. It has clear international competitive advantages, while being extremely important in the development of people’s livelihoods. The industry substantially contributes to stimulating the market, absorbing labor, increasing farmers’ incomes, accelerating urbanization, and promoting harmonious social development. According to the China Statistical Yearbook [4], the manufacturing industry in China includes 29 industries. In 2013, the industrial added value of the textile industry accounted for 6.23% of the entire manufacturing industry, evidencing the important role it plays within it. With China’s rapid economic development and gradual opening to the outside world, the textile industry faces increasing inter-regional mobility, resulting in some areas gradually forming advantageous regional groupings. These industrial clusters promote the development of the textile industry and of local economies through internal collaboration and resource sharing, and improve regional competitiveness. Since China’s reform and opening, the domestic textile industry has developed rapidly but unevenly, being concentrated mainly in the country’s eastern region, with the central and western regions remaining relatively lagging. Against a backdrop of energy resource constraints and associated pressure for sustainable development, China’s textile industry will inevitably face strong energy constraints in the future, leading to emphasis on effective resource allocation, reduced energy consumption, and improved energy efficiency becoming the top priorities in its development. Given China’s severe problems with energy resource shortages and environmental pollution, improving energy efficiency in the textile industry is seen as an effective way to conserve energy and reduce emissions. Based on panel data on the textile industry in 28 provinces, autonomous regions, and municipalities in China during 1995–2013, this paper calculates total-factor energy efficiency (TFEE), using the location entropy index to measure the textile industry’s degree of spatial agglomeration. Additionally, based on linear panel analysis of the relationship between industrial agglomeration and energy efficiency, the paper analyzes the impact of industrial agglomeration on energy efficiency to further examine the different levels of such agglomeration in the industry. The threshold regression model is used to extend the study to a nonlinear framework, and construct a double threshold regression model with industrial agglomeration as the threshold variable for answering the following questions: (1) Is China’s textile industry agglomeration related to total -factor energy efficiency (TFEE)? (2) Does the agglomeration exert a threshold effect on energy efficiency? (3) How can the energy efficiency of the textile industry be improved from the perspective of industrial agglomeration? The contributions of this study are as follows. First, although some studies have examined the industrial agglomeration and energy intensity of China’s industrial sectors, there is insufficient research on the relationship between industrial agglomeration and energy intensity when taking the textile industry as the study object. Second, for datarelated reasons, previous research on China’s textile industry has been concentrated on the national level. This study is the first attempt to systematically collect the output and input data of the industry in 28 provinces in China for the first time, broadening the possibility for research on China’s textile industry. The rest of this paper is organized as follows. The next section is a review of pertinent literature. The third part outlines model setting and data sources. The fourth part presents the empirical analysis. Finally, the conclusions are summarized and recommendations are offered. 2. Literature review After a thorough review of literature on industrial agglomeration, we found researchers in China and abroad mainly focused on industrial agglomeration with regard to areas such as labor productivity, technical efficiency, and economic growth. For instance, Ciccone and Hall [5] 327

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fixed effects by using non-dynamic balanced panel data, and uses the bootstrap technique to test the significance of the threshold effect. Without considering the cross-section data, the general form of the K segment threshold model can be expressed as the time series framework:

industry. The above review makes it clear that prior studies have focused on the linear relationship between industrial agglomeration and energy efficiency. Taking China’s textile industry as the research object, it is insufficient to further explore the possible nonlinear relationship between the two, especially the threshold effect.

yt = α + βi1 x1t + βi2 x2t + ⋯βin x nt +⋯+βiN xNt + εit ri − 1 ⩽ θt < ri

3. Model setting and data sources

Among them:

This paper builds an econometric model to conduct empirical research. This is for the purpose of exploring the influence of the agglomeration effect on energy efficiency in China’s textile industry, and is based on panel data for the industry for 28 provinces, autonomous regions, and municipalities during 1995–2013.

(1) yt and x nt are the interpreted variables and explanatory variables, respectively, n = 1,2,…,N; (1) K is a positive integer, which represents the threshold number; (2) − ∞ = r0 < r1⋯ < rk < rk + 1 = ∞, (ri − 1, ri ) is the first i threshold interval, i = 1,…,k; (3) θt is the threshold variable1; (4) \{ εit \} is the error term.

3.1. Linear regression model setting To effectively avoid heteroscedasticity, this paper treats all variables logarithmically and determines the linear regression model as follows:

TFEEjt = αj + β1 EDjt + β2 EPjt + β3 RDjt + β4 ESjt + β5 Lqjt + εjt

(2)

After adding the section data, the general form of the K section panel threshold model can be expressed as:

(1)

yjt = αj + βi1 x j1t + βi2 x j2t + ⋯βin x jnt +⋯+βiN x jNt + εjit

TFEEjt is the total factor energy efficiency (TFEE) in region j at t , EDjt is the economic development level in area j at t , EPjt is the energy price level in region j at t , ESjt is the enterprise scale (ES) in region j at t , RDjt is the internal expenditure on research and development (R&D) in area j at t , and Lqjt is the concentration level of the textile industry in area j at t .

ri − 1 ⩽ θjt < ri

(3)

Among them: (1) yjt and x jnt are the interpreted variables and explanatory variables, respectively, j = 1,2,…,M; n = 1,2,…,N; (2) K is a positive integer, which represents the threshold number (3) − ∞ = r0 < r1⋯ < rk < rk + 1 = ∞, (ri − 1, ri ) is the first i threshold interval, i = 1,…,k; (4) θjt is the threshold variable2; (5) \{ εit \} is the error term.

3.2. Threshold regression model setting In macroeconomics, the relationship between variables is not always symmetrical; it is often asymmetrical and nonlinear [20]. For example, energy prices exert an asymmetric impact on economic development, with their impact being greater during periods of price rise than decline. Domestic and overseas researchers mainly use methods such as the threshold model, smooth transition regression model, and Markov mechanism switching model to analyze and study such problems. Hoover et al. [21] believed in the existence of an optimal scale of industrial agglomeration; when this optimal scale is exceeded, a crowding effect will somewhat offset the positive externalities of the agglomeration. In other words, industrial agglomeration promotes energy efficiency within a reasonable range. Beyond this range, its effect depends on the magnitude of positive and negative externalities. Given large positive externalities, industrial agglomeration will promote energy efficiency; otherwise, it will have the reverse effect. The development conditions of the textile industry differ by location, and the influence of industrial agglomeration on energy efficiency faces varying constraints among different provinces, autonomous regions, and municipalities. Industrial agglomeration can only promote energy efficiency when it possesses a suitable value. Accordingly, this paper attempts to study the relationship between industrial agglomeration and energy efficiency in the textile industry, extend previous simple linear research, and use the nonlinear regression method threshold model proposed by Hansen [22] for endogenous grouping of systems. The threshold regression method proposed by Tong [23], as well as Tong and Lim [24], was used for analysis at specific points in time. Situational changes in time series result in quick positional jumps, and when data processing continues, with the jump to classified sample values, the coefficient value for the return continues after comparing differences. Economists have built on Tong’s method in beginning to construct different threshold models, such as the panel threshold regression model and threshold cointegration model. Among these, Hansen [22] takes full account of the timeliness and richness of sample data, constructs a panel threshold regression model with individual

This paper takes the total factor energy efficiency (TFEE) of the Chinese textile industry as the dependent variable, uses location entropy to the approximate industrial agglomeration degree, and introduces a panel threshold model to build an econometric model of the relationship between the total factor energy efficiency and industry agglomeration degree of the industry in 28 provinces, autonomous regions, and municipalities. Studies show that other factors may also affect the textile industry’s energy efficiency; these include the economic development level, energy prices, investment in R&D and technology, and enterprise scale [25,26]. Therefore, we introduced provincial percapita gross domestic product (GDP) to the model to approximate economic development level, energy prices and internal expenditures on R&D to the approximate energy price and technical R&D investment intensity, and the ratio of the number of large-scale textile enterprises to the total number of textile enterprises to approximate the textile enterprise size. To eliminate the influences of dimensions and heteroscedasticity, the variables were processed by a logarithm, the panel threshold model was determined as follows: 1 The function of threshold variables is to divide the samples in the model into several sub-samples, and to regression each sub-sample separately. The threshold value can be searched automatically by finding the points at different fractal points that minimize the sum of squared errors of the model. Taking industrial agglomeration as threshold variable and based on different agglomeration levels of China's textile industry, this paper divides China's textile industry into different sections. Specifically, the positive impact of industrial agglomeration on energy efficiency is within a certain range. If it exceeds a certain range, the energy efficiency will decrease with the deepening degree of agglomeration due to the congestion and hitchhiking effects, and this cut-off point is the threshold value. 2 Idem.

328

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This paper refers to Lin and Liu [35] and Lin and Li [36]. The price of the several kinds of energy China’s textile industry consumes is weighted and processed in accordance with the energy consumption structure of the textile industry in each year of the study period. Prices of each energy resource should be converted into standard coal units before a weighted average is produced. The research data mainly came from past issues of the Chinese Industry Economy Statistics Yearbook, CEIC database [37], and the statistical yearbooks of provinces, autonomous regions, and municipalities. (5) Investment in research and development (RD). The connotation of technological progress is very rich, and its impact on energy efficiency is reflected in energy consumption and energy production. In terms of energy consumption, improving the efficiency of equipment can directly reduce the energy consumption per unit of product. In terms of energy production, the full use of existing new technologies will also lead to significant improvements in energy efficiency. Technological progress is the main way to improve energy efficiency. As the main source of technological progress, R&D investment is an important way to improve energy efficiency. In this paper, internal expenditures on R&D made by textile industry companies is taken as an index of R&D investment. All such internal expenditure has been adjusted to 1995 prices based on the consumer price index. Data from the relevant calendar year from the Chinese Statistical Yearbook and Science and Technology [38], Second National Resources Inventory Data Compilation were used to calculate R&D expenditure.

TFEEjt = αj + β1 EDjt + β2 EPjt + β3 RDjt + β4 ESjt + β5 Lqjt + εjt ri − 1 ⩽ Lqjt < ri

(4)

Among the variables: TFEEjt is the total factor energy efficiency of region j at time point t , EDjt is the economic development level of region j at t , EPjt is the energy price level in region j at t , ESjt is the ES of region j at t , RDjt is the R&D expenditure of region j at t , and Lqjt is the textile industry concentration level in area j at t . 3.3. Data sources and processing Owing to a lack of data in individual provinces and cities, the sample used for empirical analysis comprises 28 provinces, municipalities, and autonomous regions in China from 1995 to 2013 (Chongqing, Hainan, and Tibet were excluded because of a lack of data). The main indicators are the industrial output value of the textile industry (current prices), average number of employees, original value of fixed assets, accumulated depreciation, current year depreciation, net value of fixed assets, total energy consumption, industrial output value of various provinces, autonomous regions, and municipalities (current prices), price index of fixed assets investment, consumer price and producer price indexes of the textile industry in China, and retail price index of fuel commodities. Data used in this study were from the Chinese Energy Statistical Yearbook, China Statistical Yearbook [4], and statistical yearbooks of provinces, autonomous regions, and municipalities. The main variables are explained as follows:

4. Empirical analysis and discussion of results

(1) Gross industrial output value of the textile industry. The total industrial output value of the textile industry in all provinces, autonomous regions, and municipalities (current price) approximates industry output adjusted relative to the factory price index of the national textile industry in 1995. In this paper, industrial output value at the provincial level of the textile industry is adjusted to the whole industry caliber. (2) Enterprise scale (ES). Economies of scale can improve energy efficiency and reduce energy intensity, Compared with small and medium-sized enterprises, large and super-large enterprises have significant economies of scale and relatively high energy efficiency. He [27] showed that there is a positive correlation between enterprise scale and energy efficiency. In this paper, ES is represented by the ratio of the total output value of the textile industry and the number of textile enterprises. Data are mainly derived from the Chinese Textile Industry Report and China Industry Economy Statistical Yearbook. (3) Economic development level (ED). Different modes of social production at different levels of economic development have different levels of energy utilization, which will directly affect energy efficiency. Literature studies have shown that when considering the inter-provincial panel data for energy efficiency influencing factors, regional economic development level indicators should be considered to better explore the influencing factors of differences [28,29]. Shi [30] believes that per capita GDP has an important impact on regional energy productivity. The provincial per-capita GDP is used to reflect the economic development level of individual provinces, autonomous regions, and municipalities, and is adjusted relative to 1995 levels in this study. Data were obtained from the annual China Statistical Yearbook [4]. (4) Energy prices (EP). Rising energy prices will lead to a significant increase in energy costs. In view of increasing profits and saving costs, there is an incentive to constantly tilt towards the adoption of new technologies to improve energy efficiency, thus ultimately improving energy efficiency. Existing studies have found that rising energy prices significantly promote energy efficiency [31–34]. Enterprises are incentivized to save energy and reduce consumption because rising energy prices increase enterprise production costs.

4.1. Spatial distribution As shown in Fig. 1, the textile industry output for the 10 provinces and municipalities in eastern China represents 68.70% of the total national output, while the eight provinces and municipalities in central China account for about 22.56% and the 10 provinces, autonomous regions, and municipalities in western China account for only about 8.73% of total national output. At the level of individual provinces, autonomous regions, and municipalities, in the eastern region, each accounted for an average 6.87% of the total national output, while this was 2.26% for the central region and only 0.873% for the western region. Obviously, whether in terms of overall output scale or that of individual provinces, the textile industry is much more developed in the eastern region than in the other two regions. Fig. 1 shows the absolute output value of the textile industry is rising in all three regions, most obviously in the eastern and central regions. The industry’s total output value in the eastern region rose from 282,756 million yuan in 1995 to 267,604 million yuan in 2013, from 151,199 million yuan to 899,119 million yuan in the central region, and from 70,538 million yuan to 349,125 million yuan in the western region. China’s textile industry recently has also been transferring from the eastern region to the central and western regions. Fig. 1 shows that before 2005, the proportion of national textile output in the eastern region was rising; from 56.05% in 1995 to a peak of 77.55% in 2005. After 2005, that output began to slowly decline as a proportion of national output, and in 2013 it dropped to 68.12%. In comparison, the proportion in the central region was basically declining before 2005; from 29.97% in 1995 to a low of 16.09% in 2004. After 2005, it gradually rose; from 16.35% in 2005 to 22.96% in 2013. Meanwhile, the western region also exhibited a gradual decline before 2006; from 13.98% in 1995 to 6.03% in 2006. In 2007, a slow upward trend set in, but even in 2013, it still accounted for only 8.92% of the total national output. This situation directly reflects changes in the regional economic development policy. With the steady implementation of the Western Development Strategy and the Rise of Central China Plan, China’s textile industry has gradually shifted from the eastern region to the central and western regions. 329

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Fig. 1. Distribution of China’s textile industry. Source: The data come from China Industry Economy Statistical Yearbook (1996–2014) [45].

increasing. This changed to a decline starting in 2005, and has been especially obvious since 2008. The steepening decline is mainly due to the outbreak of the global economic crisis in 2008, which has to a degree influenced China’s foreign trade; China, as a major exporter of textiles, has shown a substantial decline in textile industry concentration since 2008. The location entropy index, also known as the regional specialization index, is an important index of the degree of an industry’s local specialization, and can also be used to judge the possibility of industry agglomeration. It is generally believed that a regional entropy index exceeding 1 indicates an industry in a region has an obvious competitive advantage at the national level, and also indicates relatively strong gathering capacity. The calculation formula is:

4.2. Industry agglomeration in the textile industry Various indicators exist for measuring industrial agglomeration. The most commonly used include location quotient, Ellison–Glaeser index, concentration ratio, and locational Gini coefficient. Location quotient and concentration ratio mainly compare the degree of industry agglomeration between different regions, while the Ellison–Glaeser index and locational Gini coefficient focus on the degree of industry agglomeration between different industries in a certain region. We use the industry concentration and location entropy index to measure industrial agglomeration because this study is a statistical analysis of the textile industry, comparing different provinces, autonomous regions, and municipalities. The calculation formula for the industry concentration index is:

Qij = n

Cn =

N

∑ Sij/∑ Sij i=1

i=1

Sij / ∑i Sij ∑j sij / ∑i ∑j sij

i = 1, 2⋯m ; (5)

j = 1, 2⋯k

(6)

Qij is the location entropy index of industry j in province i , and sij is the industrial output value of industry j in the first i provinces and municipalities. The calculation formula shows the location entropy index is actually the comparison between the concentration degree of industry j in province i , and that of the whole industry in province i . When the location entropy exceeds 1, the concentration degree of industry j in province j exceeds that of the industry as a whole, indicating agglomeration of industry j . When the location entropy is less than 1, the industry concentration degree of industry j in province i is lower than that of the industry as a whole; thus, industrial agglomeration is not obvious. Table 1 shows the average location entropy of China’s textile industry in the eastern, central, and western regions during the sample period. As Table 1 shows, the textile industry’s average annual entropy in the eastern region is 1.1887, indicating the industry’s concentration in that region during the sample period exceeds the national average; a clear indication of industrial agglomeration. The average annual location entropies of the textile industry in the central and western regions were 0.8208 and 0.6007, respectively, being slightly lower in the eastern region, and suggesting no obvious agglomeration advantage. The regional entropy in the eastern region showed an increasing trend from 1995 to 2004, but since 2005, it has gradually been decreasing. In

Among the variables, Cn denotes industry concentration, while Sij is the total industrial output value of industry J in the ith provinces, autonomous regions, and municipalities. Industry concentration expresses the proportion of total industry output value contributed by n provinces, autonomous regions, and municipalities, which can directly reflect the industry’s concentration in specific areas. In practice, C4 or C8 are often used to represent industry concentration. C4 refers to the total output value of the four provinces with the largest output values in an industry, expressed as a proportion of the national output value. C8 denotes the same for the eight provinces with the largest output values. Generally, C4 > 30% indicates obvious industrial agglomeration. Fig. 2 shows the change in concentration degree of the textile industry in China from 1995 to 2013. As per Fig. 2, we found that, first, from the overall situation of the industry, the industry concentration index C4 and C8 of the 28 provinces, autonomous regions, and municipalities from 1995 to 2013 both exceeded 30%. This shows the textile industry exhibits obvious agglomeration characteristics during the sample period. Second, the industry’s vertical change exhibits two stages since 1995: an increase in industrial agglomeration followed by a fall. During 1995–2004, the degree of industrial agglomeration of China’s textile industry was 330

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Fig. 2. Change in the concentration degree of China’s textile industry during 1995–2013. Source: The data come from China Industry Economy Statistical Yearbook (1996–2014) [45].

factor and total-factor. Energy intensity, expressed by energy consumption per unit of GDP, is the most commonly used single-factor energy efficiency index, and was the most widely adopted in early research on energy efficiency. Although this is a simple measure and is easy to calculate, it takes energy as the only factor of production, ignoring other factors of production, and does not consider the elasticity of substitution between energy and other factors of production. Total factor energy efficiency (TFEE) takes into account capital, labor, and other factors of production in the efficiency analysis. The interaction is considered among factors, and it measures the ability to achieve the maximum output or the minimum input when the factors are given, which is more in line with the connotation of Pareto efficiency. Hu and Wang [39] first proposed the concept of total factor energy efficiency, and considered the traditional energy efficiency index inappropriate because it regarded energy as the sole input factor. At the same time, the data envelopment analysis (DEA) method not only does not need to provide prior weight information and set specific function form, but also can easily deal with the efficiency measurement problem under the condition of multi-input and multi-output. Currently, it has become the preferred method to measure distance function. Therefore, under the assumption of constant return on scale and based on the DEA model of input orientation, this paper realizes the minimization of input on the premise of constant output and the complete integration of the idea of energy saving and the decision of production unit. The distance function value can be solved by means of linear programming, as follows:

Table 1 Average location entropy in different regions for the Chinese textile industry during 1995–2013. Source: The data come from China Textile Industry Yearbook (1996–2014) [46]. Years

Eastern

Central

Western

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

1.5646 1.6215 1.6729 1.6588 1.5372 1.2068 1.1893 1.1055 1.0697 1.1052 1.0411 1.0128 1.0223 0.9625 0.9507 1.0044 0.9337 0.9650 0.9605

0.9843 0.9121 0.9735 0.8647 0.8268 0.6251 0.6137 0.6071 0.6475 0.6843 0.7290 0.7726 0.8456 0.8487 0.8442 0.9096 1.0150 0.9275 0.9632

0.7541 0.7177 0.6957 0.6282 0.6280 0.5193 0.5168 0.5011 0.5012 0.4328 0.4219 0.4827 0.5506 0.6205 0.5932 0.7015 0.6844 0.7094 0.7548

The average

1.1887

0.8208

0.6007

2005, the geographical entropy of the textile industry in the eastern region was 1.0411, compared with 0.9605 in 2013. In contrast, in the central region the location entropy of the textile industry has gradually increased since 2005, with regional entropy of 0.7290 in 2005 increasing to 0.9632 in 2013. The regional entropy in western China has also been increasing slowly since 2006, and reached 0.7548 in 2013. This trend partly reflects the textile industry beginning to shift from eastern to central and western China after the national government proposed the Rise of Central China Plan. The shift to the central region is most conspicuous. Despite this shift, concentration remains in the eastern region; eastern, central, and western have the most to least industry agglomeration.

[Dt (x it , yit )]−1 = TE (x it , yit ) = minθ, λ θ s. t .

λ ⩾ 0; ⎧−yit + Yt θx it − Xt λ ⩾ 0; ⎨ 0, 1, 2, ⋯I λ i ⩾ = ⎩

(7)

D (x , y ) is the distance function under the group frontier or common frontier in the tth phase. TE (x , y ) represents technical efficiency, and in this paper, it represents total factor energy efficiency. X , Y and λ represent input, output and weight matrices respectively. I is the number of decision-making units at the group frontier or common frontier. θ is the optimum level that the factor input decreases with the proportion under the condition that the output level remains unchanged. It reflects the technical efficiency level of a given production unit. Therefore, based on the above analysis, the present study uses TFEE to measure the energy efficiency of China’s textile industry; Table 2

4.3. Energy efficiency of the textile industry Research on methods for measuring energy efficiency has made key progress in recent years. Such methods fall into two categories: single331

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method and TSLS method were used, as mentioned above. In the dynamic GMM method, the instrumental variable selects the lag and the two phases of the endogenous variables in the model. In the TSLS method, the lag phase and two periods of the endogenous variables were also used as the tool variables. Furthermore, we conducted a series of tests on whether the tool variable selection is effective. Table 3 shows the results of econometric tests of instrumental variables. First, the accompanying probability P value of the Kleibergen-Paap rk LM (KPLM) statistic obtained by the unrecognized test is far below 1%. The original hypothesis that the instrumental variable is not recognized is rejected, and the instrumental variable is evidently related to the endogenous explanatory variable. Second, the weak tool variable test adopted the Kleibergen-Paap rk Wald F (KP-F) statistic. KP-F statistics were compared with the size of the critical value under different confidence level intervals. As can be seen from Table 3, the value is 43.9802, which is considerably larger than the critical value at the 10% significance level; therefore, the null hypothesis of rejecting weak instrumental variables is rejected. Third, the Hansen J-test value and the accompanying probability obtained by the over-identification test indicate there is no over-recognition problem, and the tool variable is strictly exogenous. The empirical results are shown in Tables 3 and 4. Tables 3 and 4 report the empirical results of the TSLS method and dynamic GMM method, both of which exhibit significance at the 5% level. These results show the textile industry’s agglomeration significantly and positively impacts its energy efficiency. For brevity, this paper focuses on the conclusions from the dynamic GMM method. Table 4 shows the Sargan test results, namely that the J-statistic = 23.965 and p(j) = 0.4057 > 0.05, demonstrating the effectiveness of the estimation of dynamic panel GMM. The industry’s industrial clustering correlates positively with national energy efficiency at a significance level of 5%. Given an increase of 1% in industrial agglomeration, the energy efficiency of the textile industry increases by 0.0343%. The industry scale correlates positively with R&D investment, energy prices, and energy efficiency at a significance level of 1%. Furthermore, given a 1% increase in the scale of Chinese textile enterprises, R&D investment, and energy prices, the energy efficiency will increase by 0.4121%, 0.1125%, and 0.1453%, respectively. Economic development, thus, does not significantly affect the industry’s energy efficiency.

Table 2 Total factor energy efficiency in different regions for the Chinese textile industry during 1995–2013. Source: The data come from China Energy Statistical Yearbook (1996–2014) [47]. Years

Eastern Region

Central region

Western Region

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

0.7087 0.6701 0.6491 0.6796 0.6883 0.6325 0.6914 0.7250 0.7708 0.7396 0.8364 0.8182 0.8480 0.8440 0.8529 0.8692 0.8976 0.9121 0.9381

0.4882 0.5045 0.4906 0.4619 0.5042 0.5259 0.4695 0.5045 0.5236 0.5314 0.5284 0.5125 0.4176 0.4901 0.4103 0.3448 0.3471 0.3613 0.3737

0.2565 0.2521 0.2528 0.2383 0.2308 0.3299 0.2654 0.3637 0.3213 0.2950 0.2346 0.2074 0.1857 0.1719 0.2017 0.2062 0.2094 0.2502 0.2504

The average

0.7775

0.4600

0.2486

shows the results. As shown in Table 2, without considering specific regional conditions, the average value of dynamic and static total factor energy efficiency for the textile industry is higher in the eastern than in the central and western regions. 4.4. The influence of industrial agglomeration on energy efficiency in China’s textile industry Industrial agglomeration affects energy efficiency through knowledge sharing. Social resources and talents are optimally used and reasonably distributed in agglomeration areas [26,40]; this is the fundamental driving force for the technology spillover effect. Scientific and technological progress promotes improved energy efficiency; therefore, enhancing industrial agglomeration is beneficial to enhancing energy efficiency. However, there are crowding and freeriding effects therein. If energy efficiency exceeds a certain range, it will decrease as the concentration becomes deeper, owing to crowding and free-riding effects [41]. There are two main reasons for endogeneity problems in empirical research3: the first is mutual causality among variables, namely, interactions among different explanatory variables. The second is missing variables, that is, not all the variables that can influence the energy efficiency of China’s textile industry can be included in the regression equation. Based on the literature, to effectively overcome these endogeneity problems, this study used the TSLS method and dynamic panel data method (GMM) to empirically study the model (1), and in using the model (4), we treated the interpreted variables with a lag phase.

(2) Threshold effect test and result analysis Panel linear estimation was divided into the panel fixed effect model and random effect model. Before estimating the threshold panel, we first used formula (3) to determine model to use. The Hausman test is often used to judge whether to use the fixed or random effect model [42,43]. Therefore, before the threshold model was introduced, this test should be conducted on the model without two items. Table 5 shows the results. The P-value of the Hausman test was 0, so the random effect model was rejected. Therefore, the present study uses the panel fixed effect model for sample estimation. To avoid pseudo-regression, we test the stationarity of each variable before constructing the panel threshold model. The Levin–Lin–Chu (LLC) test [44] is used as the inspection method; Table 6 shows the test results. Based on the test results of trend and no trend, all variables can be considered stable. The threshold in the panel was tested step-by-step in this study, and the bootstrap was repeated 1000 times to obtain the confidence interval of the threshold variable Y. Tables 7 and 8 show the results. Table 7 shows that the single threshold model under the threshold value of the variable is 0.1727, falling within the 95% confidence interval. The threshold variable values under the double threshold model are 0.1727 and 0.3268, respectively; all within the 95% confidence interval, indicating the threshold of each model is significant at the significant level of 5% level. We examined whether a nonlinear relationship exists between

(1) Linear regression model estimation In an empirical study, endogeneity of variables can lead to estimation bias. To effectively solve the endogeneity problem involving variables, and to avoid the impact of individual effects and realize more scientific and accurate empirical conclusions, the dynamic GMM 3 In specific research, measurement error is also an important cause of endogeneity problems, but the main and most relevant empirical research samples in the sample are not the focus of this paper.

332

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Table 3 TSLS regression results.

Table 7 Threshold value of industrial agglomeration and its 95% confidence interval.

Variables

Coefficient

Standard deviation

T

P

C ES ED RD EP LQ

−0.7973** 0.3832*** 0.0360*** −0.0106 0.3112*** 0.0485**

0.2960 0.0160 0.0059 0.0071 0.0639 0.0163

−2.6936 23.8945 6.1513 −1.4850 4.8683 2.9794

0.0073 0.0000 0.0000 0.1382 0.0000 0.0030

Sample size

448

448

448

448

Hansen test

Hansen J-statistic = 0.4395 p-value = 0.4997＞0.1

Kleibergen-Paap rk LM Statistic

KP-LM statistic = 56.1297 p-value = 0.0000 < 0.01

Kleibergen-Paap rk Wald F Statistic

KP-F statistic = 43.9802

Table 4 Dynamic panel GMM regression results. Standard deviation

T

P

0.4121 0.0143 0.1125*** 0.1453*** 0.0343**

0.0126 0.0111 0.0166 0.0212 0.0122

32.5964 1.2902 6.7584 6.8471 2.8156

0.0000 0.1976 0.0000 0.0000 0.0051

Sample size

448

448

448

448

Sargan test

J-statistic = 23.9650 p(j) = 0.4057＞0.05

ES ED RD EP LQ

Coefficient ***

Note: “**” and “***” are represented at 5% and 1% respectively. Table 5 Fixed effect and random effect Hausman test. Variables

Coefficient of fixed effect and random effect Fixed effect

Random effect

ES ED RD EP Lq

0.4287 0.0205 0.0441 0.1179 −0.0269

0.4274 0.0250 0.0140 0.0819 −0.0320

Hausman test

chi2(5) = 1469.09 Prob > chi2 = 0.0000

Difference

Standard deviation

0.0013 −0.0045 0.0301 0.0360 0.0051

0.0017 0.0005 0.0072 — 0.0034

Table 6 Levin-Lin-Chu unit root test results. No trend of intercept Statistic TFEE ES ED RD EP Lq

−6.4598 −6.7064** −1.8299 −5.8509** −16.5902*** −10.1794*** **

Trend of intercept P

Statistic

P

0.0434 0.0203 0.3803 0.0186 0.0000 0.0000

−10.0112 −10.4345** −10.3652*** −11.1892*** −19.1481*** −9.4782*** *

Single threshold

Th-1 Th-21 Th-22

0.1727

Double threshold

95% confidence interval

0.1727 0.3268

0.1694 0.1694 0.3172

0.1828 0.1828 0.3351

industry agglomeration and total factor energy efficiency. Combining the results from Tables 7 and 8, the threshold number of the panel threshold model (4) was determined as 2, and the results of econometric analysis of the model were obtained using Stata software, as shown in Table 9. The coefficients of each variable in Table 9 and the test results show the following. First, a significant positive correlation exists among the degree of economic development, energy prices and R&D investment, enterprise size, and the total factor energy efficiency of the textile industry. The coefficient of ES is 0.4414, which indicates that with each 1% increase in ES, the total factor energy efficiency increases by 0.4414%. For the industry, the expansion of enterprise scale can be realized through specialization and vertical expansion. Moreover, the coefficient of the degree of economic development is 0.0201, indicating that with each 1% increase in the level of regional economic development the total factor energy efficiency increases by 0.0201%. In accordance with the actual economic development of the province, the government determines its own economic development policy and then promotes economic development through scientific and rational planning of market economy development. At the same time, the government needs to further improve the public service function and promote economic development. The coefficient of the energy price is 0.1442, indicating with each 1% increase in energy prices, the total factor energy efficiency of the textile industry increases by 0.1442%. Finally, the coefficient of R&D input is 0.0348, indicating with each 1% increase in R&D investment, the total factor energy efficiency increases by 0.0348%. The government, thus, needs to further increase R&D investment, improve the R&D efficiency of the textile industry, and reduce R&D costs. Second, in the first and second threshold intervals, a significant positive correlation exists between China’s textile industry agglomeration and total factor energy efficiency. In the third threshold interval, no significant correlation exists between textile industry concentration and total factor energy efficiency. In the first and second threshold intervals, a significant correlation exists between textile industry agglomeration and total factor energy efficiency, and the impact coefficients are 0.7468 and 0.2098, respectively. This shows that below a certain level of agglomeration, each 1% increase will improve total factor energy efficiency by 0.7468%. Taking the first threshold value of 0.1727 as the inverse logarithm, the industry agglomeration index is 1.1885; that is, when the industry agglomeration degree is < 1.1885, each 1% increase in agglomeration degree will lead to a 0.7468% increase in total factor energy efficiency. Similarly, the second threshold value of 0.3268 is taken as the inverse logarithm, and the industry agglomeration index is 1.3865. When the agglomeration degree of the industry is between 1.1885 and 1.3865, it will play a positive role in promoting the industry’s total factor energy efficiency. At this point, each 1% increase in industry agglomeration degree will increase TFEE by 0.2098%. Notably, the industry’s increased energy efficiency does not simply correlate with increased concentration. From the estimation results, the coefficient of the industry agglomeration index is negative in the third threshold interval, showing that once the agglomeration degree reaches this level, further increases in agglomeration no longer promote energy efficiency. However, the coefficient fails to pass the significance test, indicating the effect is not clear. The continued increase in industrial agglomeration begins to be associated with negative externalities. As the number of enterprises in a specific region becomes excessive,

Note: “**” and “***” are represented at 5% and 1% respectively.

Variables

Model

0.0602 0.0122 0.0006 0.0041 0.0000 0.0006

Note: “*”, “**” and “***” are represented at 10%, 5% and 1% respectively.

industrial agglomeration and total factor energy efficiency in China’s textile industry. Table 8 shows the results, namely, acceptance of the hypothesis of the single and double threshold effects at a 5% confidence level, demonstrating a nonlinear relationship between China’s textile 333

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Table 8 Threshold effect self-sampling test. Threshold

RRS

Single Double

MSE

1.7398 1.6357

0.0034 0.0032

F **

40.30 32.67**

P

Significant 10%

Significant 5%

Significant 1%

0.0130 0.0270

23.8875 23.0329

29.5075 27.9961

41.2847 39.3593

Note: “**” is represented at 5% respectively.

4.5. Influence of the western development strategy and the rise of Central China Plan on the textile industry

Table 9 Estimation results of the panel threshold model (bootstrap = 1000 1000). TFEE

ES ED RD EP Lq_0 Lq_1 Lq_2 _cons

Coefficient

***

0.4414 0.0201*** 0.0348*** 0.1442*** 0.7468*** 0.2098*** −0.0071 −0.0323

Standard deviation

t

0.0105 0.0064 0.0094 0.0432 0.1001 0.0456 0.0104 0.2575

41.92 3.15 3.69 3.34 7.46 4.61 −0.68 −0.13

P > |t|

0.000 0.002 0.000 0.001 0.000 0.000 0.497 0.900

95% confidence interval

0.4207 0.0075 0.0163 0.0593 0.5501 0.1203 −0.0275 −0.5383

First, the Chinese government’s regional economic development plan has promoted improvement of the total factor energy efficiency of China’s textile industry. As seen in Table 2, during 1995–2013, the total factor energy efficiency in the three regions increased beyond 1, and exhibited a continued increasing trend. Progress has been especially remarkable in the western region. Energy efficiency of the textile industry in that region increased from 0.9984 in 1996 to 1.0561 in 2013, with a mean value of 1.1350 during the period. That mean exceeds the 1.1037 in the central region during the same period, indicating that the total factor energy efficiency in the western region improved faster based on the original. Second, China’s regional development strategy promotes gradual transfer of its textile industry from the eastern to the central and western regions. From Table 1, it can be seen that during 1995–2004, textile industry location entropy in eastern China had continuous growth, which changed to gradual decline after 2005. Simultaneously, the location entropy in the central and western regions increased significantly after 2005, especially in the former. As can be seen from Fig. 1, the eastern region’s share of national textile output value was increasing during 1995–2004, and began to decline after 2005, but the textile industry in the central region exhibited opposite characteristics; decreasing continuously during 1995–2004, before gradually increasing after 2005. The western region exhibits a similar trend to that of the central region, with a slow decline followed by a gradual rise. This further confirms how adjustment of the national regional development strategy shifted the industry from the eastern region to the

0.4620 0.0327 0.0534 0.2291 0.9436 0.2993 0.0134 0.4736

R2_within = 0.8607 R2_between = 0.9313 R2_overall = 0.9032

Note: “***” is represented at 1% respectively; Lq_0 is the coefficient of industry concentration index in the first threshold interval; Lq_1 is the coefficient of industrial agglomeration index in the second threshold interval; Lq_2 is the coefficient of industrial agglomeration index in the third threshold interval.

enterprises increasingly rely on freeriding for their growth rather than competition and cooperation. They also gradually stop focusing on technological innovation and instead pursue cost savings through blind imitation of others, hindering development and dissemination of new technologies, as well as further improvement in energy efficiency.

Fig. 3. Location entropy of the textile industry in Eastern China during 1995–2013. Source: The data come from China Textile Industry Yearbook (1996–2014) [46]. 334

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Fig. 4. Location entropy of the textile industry in Central China during 1995–2013. Source: The data come from China Textile Industry Yearbook (1996–2014) [46].

promoted increased textile industry concentration in the central and western regions and decreased the developmental unevenness for the industry between the eastern and western regions. Although China’s textile industry agglomeration contributes to improving energy efficiency, the present study found this association does not hold true in all provinces and municipalities. To better understand the needs of the textile industries in specific provinces and municipalities from the perspective of increasing agglomeration and improving energy efficiency, we made a further calculation to obtain two thresholds and, hence, three threshold intervals. We therefore calculate the location entropy for the textile industry of each province, autonomous region, or municipality and assign the results to three categories, as shown in Table 10. The first category, made up of provinces,

central and western regions. Third, China’s regional economic development strategy is conducive to decreasing the unevenness of textile industry agglomeration. Figs. 3–5 show the location entropy change of the industry in 28 provinces, autonomous regions, and municipalities during 1995–2013. The location entropy index is declining in eight of the 10 provinces and municipalities in the eastern region. In the central region, it is gradually increasing in five (Jilin, Jiangxi, Henan, Hunan, and Hubei) of eight provinces, autonomous regions, and municipalities, and this trend is especially marked after 2005. Finally, among the 10 provinces, autonomous regions, and municipalities of the western region, the index increased slowly in five (Guangxi, Sichuan, Qinghai, Ningxia, and Xinjiang). Thus, China’s regional economic development strategy has

Fig. 5. Location entropy of the textile industry in Western China during 1995–2013. Source: The data come from China Textile Industry Yearbook (1996–2014) [46]. 335

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Table 10 Classification of provinces, autonomous regions, and municipalitiesin the threshold region. Range

Tiai < 1.1885

[1.1885, 1.3865]

Tiai > 1.3865

Provinces

Beijing, Hebei, Tianjin, Liaoning, Shanghai, Guangdong, Shanxi, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Inner Mongolia, Guangxi, Sichuan, Shaanxi, Ningxia, Yunnan, Guizhou, Gansu, Qinghai, Xinjiang

Fujian, Hunan

Jiangsu, Zhejiang, Shandong, Hubei

Note: Tiai is the textile industry agglomeration index.

resulting in phenomena such as land price increases, high pollution, and vicious competition. The potential of production enterprises in terms of efficiency improvement, technological innovation, and other aspects is weakened by the accumulation of agglomeration costs, decreasing the impact of industrial agglomeration on total factor energy efficiency, consistent with Hoover’s [21] notions that industrial agglomeration has an optimal degree. Second, a significant positive correlation exists between the degree of economic development, energy prices, R&D investment, enterprise scale, and the total factor energy efficiency of the textile industry. Third, a nonlinear relationship exists between industrial agglomeration and energy efficiency in the industry. When agglomeration is low, increased agglomeration helps improve energy efficiency. However, when agglomeration reaches a certain level, agglomeration and energy efficiency exhibit a negative relationship. Restated, simply increasing the concentration of the textile industry in each province, autonomous region, or municipality is not the correct route for increasing the industry’s energy efficiency. Agglomeration is already high in Jiangsu, Zhejiang, Shandong, and Hubei, exceeding the index of 1.3865. If agglomeration continues to increase in these four provinces, energy efficiency will be hindered rather than improved. Fourth, the adjustment of China’s regional development strategy has the textile industry shifting from the eastern region toward the central and western regions, contributing to overall improvement in energy efficiency. Based on the above conclusions, we offer some recommendations and research implications: The empirical results show that R&D investment and energy price are positively correlated with the energy efficiency of Chinese textile industry. Raising energy prices and increasing investment in technological research and development will promote the improvement of the energy efficiency of Chinese textile industry. Therefore, the Chinese government should continue to deepen market-oriented price reform of the energy system so as to reflect the real cost of energy utilization. In addition, the government can increase investment in science and technology research through financial or taxation, and improve the energy production efficiency of the textile industry from the perspective of technological progress. The government should close or eliminate backward production capacity according to the specific situation in each province to promote the overall energy efficiency of textile industry. Local governments should actively seek to break down the barriers formed between regions, strengthen the supervision of industrial agglomeration areas, and actively promote and encourage regional cooperation to ensure the smooth flow of production factors in different regions. Simultaneously, governments should continually strengthen and improve construction of facilities, such as financial, logistics, and communication services.

autonomous regions, and municipalities where the agglomeration index is below 1.1885, comprises Beijing, Hebei, Tianjin, Liaoning, Shanghai, Guangdong, Heilongjiang, Jilin, Henan, Shanxi, Anhui, Guangxi, Jiangxi, Inner Mongolia, Sichuan, Yunnan, Shaanxi, Ningxia, Guizhou, Gansu, Qinghai, and Xinjiang. Increased agglomeration in these 22 provinces will promote energy efficiency in the textile industry. Specifically, the empirical study found that each 1% increase in textile industry concentration in these 22 provinces would increase energy efficiency by 0.7468%. The second category was made up of two provinces, Fujian and Hunan, with an agglomeration index of 1.1885–1.3865. The study found that increasing the industry concentration in these two provinces also increased industrial energy efficiency. Specifically, if that concentration could be increased by 1%, energy efficiency would increase by 0.2098%. Finally, the third category comprised provinces with an index greater than 1.3865; namely, Jiangsu, Zhejiang, Shandong, and Hubei. In contrast with the other two categories, in this category, an increased agglomeration degree not only will fail to promote energy efficiency, but will actually have a negative effect. Restated, the industry in these four provinces already has a high degree of agglomeration, and further increases are unnecessary, and even counterproductive, toward improving energy efficiency. Among the four provinces with industrial agglomeration indexes above 1.3865, three are in the eastern region—Hubei is the lone exception. Clearly, agglomeration is already relatively high in the eastern region. Whether from the overall performance of textile industry agglomeration, or from the individual performance of agglomeration in specific provinces, autonomous regions, and municipalities, the following conclusions were obtained. First, China’s textile industry exhibits obvious clustering; moreover, the clustering pattern displays a shift from the eastern region to the central and western regions, which correlates with strategic adjustments in the national development strategy. Second, as the regional strategic adjustment in the national development strategy caused the textile industry’s shift from eastern to central and western, this both narrowed the regional development gap and substantially improved the energy efficiency of China’s textile industry. 5. Conclusions and recommendations Based on panel data on the textile industry in 28 provinces, autonomous regions, and municipalities in China from 1995 to 2013, the present study calculates the industry’s total factor energy efficiency. Linear panel analysis of the relationship between industrial agglomeration and energy efficiency, undertaken as part of an in-depth analysis of differences in the industry agglomeration level, indicated the impact of agglomeration on energy efficiency varies with the agglomeration’s level. The threshold regression model was used to extend this research to a nonlinear framework. The study analyzed the relationship between total factor energy efficiency and industrial agglomeration degree for the textile industry in 28 provinces by constructing a double threshold regression model with the textile industry agglomeration level as the threshold variable. The results were as follows. First, there is a threshold effect when industrial agglomeration affects total factor energy efficiency. As the level of industrial agglomeration increases, its influence gradually weakens. The effect of agglomeration on total factor energy efficiency is, thus, subject to the law of diminishing marginal returns. Excessive industrial agglomeration exerts a crowding effect on production factors,

Acknowledgements The paper is supported by Report Series from Ministry of Education of China (No. 10JBG013), China National Social Science Fund (No. 17AZD013), China Postdoctoral Science Fund (No. 2018M631376), and Beijing Municipal Social Science Fund Research Base Project (No. 18JDYJB022). 336

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