The effect of industrial relocation on industrial land use efficiency in China: A spatial econometrics approach

Journal of Cleaner Production 205 (2018) 525e535

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The effect of industrial relocation on industrial land use efficiency in China: A spatial econometrics approach Wei Chen a, Yue Shen b, Yanan Wang a, *, Qun Wu c a

College of Economics and Management, Northwest A&F University, Yangling, 712100, China School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, 710049, China c College of Public Administration, Nanjing Agricultural University, Nanjing, 210095, China b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 25 February 2018 Received in revised form 2 September 2018 Accepted 12 September 2018 Available online 14 September 2018

The variation of industrial land use efficiency caused by industrial relocation is of great significance to the land use, but little attention has been paid to the spatial effect of industrial relocation on industrial land use efficiency. This study investigates the temporal features of industrial land use efficiency at the regional level and the spatial features of industrial relocation at the provincial level. The industrial land use efficiency exhibits an obvious rising trend in the eastern region, central region, western region and northeast region from 2002 to 2013. There is significant spatial correlation among 30 provinces in industrial land use efficiency and shows obvious trapezoidal growth trends in 2002, 2006, 2010, and 2013. The study estimates the spatial effect of industrial relocation on the industrial land use efficiency in six industries by the spatial Durbin model (SDM). The results show that the industrial relocation of the chemical and rubber industry, mineral manufacturing industry, and machinery manufacturing industry plays a markedly positive role in improving the industrial land use efficiency. It is noteworthy that the impacts of industrial relocation on the industrial land use efficiency in the food and beverage industry, light and textile industry, and high-tech manufacturing industry are not significant. Except for the enterprise ownership structure, three other control variables, namely regional economic development, human capital stock, and industrial fixed capital, can contribute to promoting industrial land use efficiency. © 2018 Elsevier Ltd. All rights reserved.

Keywords: Industrial relocation Industrial land use efficiency Spatial analysis Spatial Durbin model China

1. Introduction Industrial development has entered a stage of accelerated transformation and upgrading, and this process has played an important role in the growth of China's economy during the past 40 years (Chen et al., 2017, 2018a). At present the growing demand for construction land and the scarcity of land resources are the main social contradictions in China. For a long time, the rapid industrialization in China was supported by low land prices and extensive industrial land use. A significant characteristic in the management of industrial land use has been high cost and low efficiency. In order to respond to the changes in resource supply and product demand, industry gradually shifted to the inland. Therefore, reasonable guidance of industrial transfers and diffusion is an important means for improving the industrial land use efficiency (ILUE) (Chen

* Corresponding author. E-mail address: [email protected] (Y. Wang). https://doi.org/10.1016/j.jclepro.2018.09.106 0959-6526/© 2018 Elsevier Ltd. All rights reserved.

et al., 2018b). Different regions have obvious differences in ILUE due to the various industrial development levels that exist at a given time. The land use of traditional industries in the eastern region is categorized as inefficient. Therefore, in this region, higher ILUE could be achieved by global industrial relocation. As the adjustment of the economic structure progresses and the industrialization process advances, the land cost of the eastern coastal areas continues to improve. The traditional manufacturing industries in this region are facing severe challenges with the strengthening of environmental regulations. Meanwhile, the central and western regions and coastal less-developed regions have larger potential, and this can provide a good condition for the development of traditional manufacturing industries. The central and western regions give investors more options with regard to taxation, land, capital, and other production factors in order to incentivize them to undertake industrial relocations. Industrial relocation is an economic process whereby one or more industries transfer from one region to another region (Shafik

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and Bandyopadhyay, 1992; Hoover, 1937). Industrial relocation is accompanied by market transfer, technology transfer, and crossregional investment (Huang et al., 2011). Early industrial relocations occurred mainly in developed countries. The governments encouraged the relocation of industrial enterprises to underdeveloped regions in order to alleviate the imbalance of regional development (Okubo and Tomiura, 2012). When the market economy develops to a certain level, industrial relocation is the inevitable outcome (Huang et al., 2011). This process reinforces the complementary advantages between different regions and promotes coordinated regional economic development. It is important for China to undertake international industrial relocation because it is facing environmental changes and requirements in its industrial development and transformation with the adjustment and evolution of its regional development strategy. From the perspective of the roll-out region, industrial relocation represents the shrinking of an industry, whereas it represents the expansion of the industry scale for the roll-in region. Industrial relocation plays an important role in improving the ILUE. Configuring the land resources that are released by the elimination of traditional manufacturing industries is the key to realizing regional economic development and effective utilization of industrial land in the roll-out region. For the roll-in region, the infrastructure for industrial development consumes a large amount of land resources. It is crucial to allocate new construction land reasonably in order to enhance the ILUE. Therefore, the changes in regional land efficiency brought about by industrial relocation can determine whether full use can be made of scarce land resources. The changes in ILUE caused by industrial relocation are of great significance to land use, and these changes provide a theoretical basis for the adjustment of the industrial structure and the transformation of the industrial land use mode. The impact of industrial relocation on ILUE is shown in Fig. 1. At present, studies on the ILUE and industrial relocation have achieved good results, but few researchers have focused on the impact of industrial relocation on ILUE. Industrial relocation plays an important role in improving the ILUE. It facilitates the optimization and upgrading of the industrial structure, increases the productivity growth, and eventually improves the ILUE. There are significant differences in economic development and industrial development among the provinces, which leads to distinct characteristics of ILUE in different provinces. Therefore, the innovation and contribution of this research lie in the following three aspects. First, this paper expounds the mechanism and the relationship between industrial relocation and ILUE in respect of the roll-out region and roll-in region. Second, in order to reflect the regional

Roll-out Region Capital and technology Roll-in Region

Land release

Cost advantage

Land input

differences, the study investigates the temporal features of the ILUE at the regional level and the spatial features of industrial relocation at the provincial level. Third, the study estimates the spatial effect of industrial relocation on the ILUE in six industries by using a spatial econometric model to reflect the industrial differences. The trade gravity model is applied to determine the spatial weight and considers the geographical features and economic links to do so. The remainder of this paper is as follows. Section 2 is the literature review. Section 3 presents the calculation models and Section 4 describes the research methodology and data. The empirical results are given in Section 5 and Section 6 presents the discussions. The conclusions and policy implications are summarized in Section 7. 2. Literature review The theoretical researchers of industrial relocation have formed many theories, such as industrial gradient relocation and laborintensive industrial relocation (Lewis, 1984), product life cycle (Vernon, 1966), marginal industrial expansion (Kojima, 1978), and the flying geese paradigm (Akamatsu, 1962). These theories explained the motivation and mode of international industry transfers from a macroeconomic viewpoint. The traditional industrial relocation theories hold that industrial relocation occurs between areas with different levels of economic development (Huang et al., 2011); with economic development and change in the industrial structure, industrial relocation is an inevitable trend (Zhao and Yin, 2011). Based on the new economic geography and institutional economics, many factors, such as agglomeration economies, transport infrastructure, human capital characteristics, taxes, and environmental regulations can influence industrial relocation (Arauzo et al., 2010). Land rents, which are another source of congestion costs, operate as a centrifugal force and work against agglomeration (Hanson, 1998). In order to overcome enterprise sunk costs inertia, Rauch (1993) pointed out that developers of industrial parks can let industry move from an old, high-cost site to a new, low-cost site by the discriminatory pricing of land. Okubo and Tomiura (2012) investigated the effect of transfer policies and argued that the average productivity of plants was relatively low as a result of the relocation policy in Japan. Due to market failure and government failure, Zhao and Yin (2011) found that environmental externalities and social costs could occur in the process of industrial relocation accompanied by a series of economic and social issues, such as energy consumption and environment pollution. Other factors, such as the firm's size, the age of the firm, and interregional motorways can also affect industrial relocation (Brouwer

High added value industries

Industrial Agglomeration

Optimization and up u grading upgrading indu d strial of industrial stru r ctu t re structure

Increase average land outp t ut output

Industrial structure adjustment

Regional coordinated development Fig. 1. Mechanism of industrial relocation on industrial land use efficiency.

Improve Imp m rove industrial indu d strial land lan a d eff fficiency use efficiency

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et al., 2004; Conroy et al., 2016), but the location of new establishments and the relocation of existing establishments are driven by different determinants (Manjon and Arauzo, 2011). International industrial relocation from developed countries to developing countries has occurred since the 1930s. Kumar (2002) pointed out that cooperation in different countries would further enhance the productivity level and is conducive to promoting the implementation of industrial relocation. Markusen and Venables (2000) found that incentives for factor mobility may lead to the agglomeration of activity in a single country and may lead to the development of multinational firms. Kim (2007) examined the impacts of regional economic integration on the industrial relocation of participating countries. Technology gaps had relatively great impacts on industrial relocation between the integrating countries. Pennings and Sleuwaegen (2000) found that in the highly industrialized and open economies, labor-intensive industries rather than capital-intensive industries are more likely to migrate to other countries. It is undeniable that industrial relocation could benefit certain countries financially, but it would impose considerable threats to their energy supply security and environmental capacity (Wang et al., 2018a; Wang et al., 2018b). The existing studies on ILUE are mainly focused on evaluation methods, regional differences, and influencing factors. Liu et al. (2018) measured the allocative efficiency of construction land in China by using an extended Cobb-Douglas production function. Ye et al. (2018) evaluated the economic efficiency of construction land in the Pearl River Delta region in China by developing a timely and efficient approach. Wang et al. (2013) proved that the output elasticity of industrial land by bidding, auction, and listing is higher than that using other methods, indicating that market transfer is helpful to improve the efficiency of industrial land use allocation. With the influence of knowledge spillover, labor mobility, trust interests, and the influence of common values, enterprises in industrial parks were more efficient than those outside industrial parks (Huang et al., 2017). Peng et al. (2017) concluded that environmental quality has an obvious “crowding out effect” on urban land use efficiency based on the panel data of 283 cities in China. Wu et al. (2017) found that accessibility, marketization, decentralization, and globalization had a significant effect on urban land efficiency. Tu et al. (2014) found that industrial land use was more affected by the industry sub-type. The industrial land lease policies did not achieve the goal of improving the land use efficiency. Using foreign advanced management in industries showed that market means, combined with government planning and control, were effective ways to improve the ILUE (Zhao, 2008). With the appearance of a contradiction between resources protection and economic development, Zhang and Zhou (2007) showed that the government encouraged the comprehensive intervention of capital and used market activity to improve ILUE. According to the existing research, ILUE is influenced by industrial redistribution in the process of industrial relocations. Therefore, this study examines the spatial effects of industrial relocation on the ILUE in six industries by using a spatial econometric model. Policy suggestions are made to provide a reference for the improvement of ILUE. 3. Calculation methods 3.1. Calculation of industrial land use efficiency Measuring land use efficiency from the perspective of single factor productivity can directly reflect the relationship between land input and economic output, and it is easy to make regional comparisons. In order to explore the total level of ILUE, it is measured by the average land output. The direct impact of

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industrial relocation is the increase in the output value of an industry. Therefore, the average land output can reflect the variation of industrial land output from the spatial and temporal scales. The ILUE is calculated by Eq. (1).

ILUEit ¼

TOVit TLAit

(1)

where ILUEit is industrial land use efficiency in year t of province i., TOVit is the total output value of industry, and TLAit is the total industrial land area in year t of province i. 3.2. Calculation of industrial relocation index The location quotient represents the scale and specialization of an industry in the region (Cummings and Epley, 2014). It can reflect the regional scale advantage and regional specialization rate. We use the location quotient to calculate the industrial relocation index. The industrial relocation amount is measured by the difference between neighboring provinces. The location quotient is defined as Eq. (2).

t LQik ¼P n

ptik

t i¼1 pik

.P m

t k¼1 pik .P P n m i¼1

t k¼1 pik

(2)

t , the location quotient, represents the specialization where LQik

degree in year t of province i and industry k. ptik denotes the total industrial output value of industry k in year t of province i. The difference of the location quotients in year t and t-1 is calculated by Eq. (3).

DLQikt ¼ LQikt  LQikt1

(3)

t denotes the industrial relocation amount in year t of where DLQik t > 0, it means the roll-in of inprovince i and industry k. If DLQik

t < 0, it means the roll-out of dustry k in year t of province i. If DLQik industry k in year t of province i. The total industrial relocation amount in this study is formulated by Eq. (4).

IRAq ¼

X

DLQikt

(4)

where IRAq denotes the industrial relocation amount of industry q, which is classified in this study. 4. Research methodology and data 4.1. Spatial econometric models Considering the spatial correlations among the variables, three models can be used in the study. The two basic spatial norm coefficients regression models are the spatial lag model (SLM) and spatial error model (SEM). The third model is the spatial Durbin model (SDM) (Lesage and Pace, 2009; Elhorst, 2010). As shown in Eq. (5), the SLM contains endogenous interaction effects among the dependent variable.

y ¼ rWy þ X b þ ε

(5)

where y denotes the dependent variable, X denotes the explanatory variables, and it is an n  k matrix. b is a k  1 vector of the explanatory variable coefficient, r represents the spatial regression correlation coefficient, and W is the n  n spatial-weights matrix.

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Wy is the spatial lag dependent variable, which is used to measure the spatial spillover effects of the neighbor in the geographical space, and ε is an independently and identically distributed error term with a zero mean and constant variance s2. n denotes the number of observations and k denotes the number of explanatory variables. The SEM contains interaction effects among the error terms (ε) and is defined as Eq. (6).

y ¼ Xb þ ε ε ¼ lWε þ m

(6)

where l represents the spatial error coefficient, Wε denotes the spatially autocorrelation error term, and m is an independent white noise error component. The SDM considers both the spatially lagged dependent variable and spatially lagged explanatory variables. Therefore, it is widely used in applied research (Lesage and Pace, 2009). It is modelled in Eq. (7).

y ¼ rWy þ X b þ WX q þ ε

(7)

where q is the spatial autocorrelation coefficient of the independent variable. The Lagrange multiplier (LM) test is applied to identify potential spatial effects (Elhorst, 2003). 4.2. Spatial weight model Inter-regional trade is the premise of industrial relocation. The economic link intensity between regions is an important indicator to measure the inter-regional trade. Therefore, this study establishes the spatial weight matrix based on the economic link intensity. The economic link intensity is calculated using the trade gravity model Eq. (8).

Eijt ¼ A

qffiffiffiffiffiffiffiffiffiffiqffiffiffiffiffiffiffiffiffiffi 2 Pit Gti 2 Pjt Gtj

where Eijt

D2ij

(8)

represents the economic link intensity between provinces

t¼1

t i¼1 Eij

4.4. Data Because the data for Hong Kong, Macao, Taiwan, and Tibet are difficult to obtain, 30 provinces of China are used as the research subjects to calculate the ILUE and industrial relocation index. The 30 provinces of China are divided as follows into four regions in accordance with China's economic development level and geographic position: the eastern region (Beijing, Tianjin, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Hebei, Hainan), the central region (Shanxi, Henan, Hubei, Hunan, Anhui, Jiangxi), the western region (Inner Mongolia, Chongqing, Sichuan, Shaanxi, Qinghai, Ningxia, Yunnan, Guizhou, Guangxi, Xinjiang, Gansu), and the northeast region (Heilongjiang, Jilin, Liaoning). The data in respect of the provincial industry output is drawn from the statistical yearbooks of each province from 2003 to 2014, and the data for the national industry output, population, GDP, and college numbers are collected from the China Statistical Yearbook (2003e2014) (NBSC, 2003e2014). The enterprise ownership structure and industrial fixed capital are collected and calculated using the China Industry Statistical Yearbook (2003e2014) (NBSC, 2003e2014). All the price index numbers in the study are converted to the constant price of 2000. According to the Industrial Classification for National Economic Activities (GB/T4754-2002), the manufacturing industries in this study are divided into six types: the food and beverage industry, light and textile industry, chemical and rubber industry, mineral manufacturing industry, machinery manufacturing industry, and high-tech manufacturing industry. The crafts and other manufacturing, waste of resources, and waste materials recycling industries are excluded. The specific classifications are shown in Table 2. 5. Results

i and j in year t. P is the population and G is the GDP of province i and j in year t. D denotes the distance of the spatial line, which is calculated using the geographical coordinates of the capital of each province based on their latitude and longitude. A is the economic gravity coefficient and it is equal to 1 in the study for calculation purposes. The spatial weight is further obtained based on the economic link intensity, which considers the geographical features and economic links. It is defined as Eq. (9).

Eijt Wij ¼ Pp Pn

colleges. The industrial fixed capital (IFC) is expressed by the amount of industrial fixed capital formation based on the calculation method used by Xiong and Guo (2013). It reflects the development speed of the regional industry.

(9)

where p denotes the number of years. 4.3. Definition of variables In this study the industrial land use efficiency is a dependent variable and the industrial relocation amount is an independent variable. Four control variables are considered, and the detailed information on each variable is showed in Table 1. The GDP per capita (PGDP) reflects the regional economic development level. The enterprise ownership structure (EOS) is expressed by the proportion of state-owned enterprises. The human capital stock (HCS) is expressed by the per ten thousand number of students in

5.1. Temporal features of industrial land use efficiency As shown in Fig. 2, the ILUE in the four regions has the same trend with an obvious rise from 2002 to 2013, and the ILUE in the central and western regions is basically the same. The eastern region has higher efficiency than the other three regions. The ILUE of the eastern region in 2002 is 2.077 billion yuan/hm2 and in 2013 it is 5.005 billion/hm2, with an average growth rate of 8.41% each year. The differences in the ILUE in the eastern, central, and western regions reach the greatest extent at this time and then decrease gradually. In 2008 and 2009 there are no obvious promotions of ILUE in the four regions. Fig. 3 shows the spatial distribution of the ILUE of the 30 provinces in 2002, 2006, 2010, and 2013, which presents an obvious trapezoidal growth trend. The ILUE of most provinces in 2002 is relatively low, but in 2006 there is a significant improvement in the provinces of the central region. In 2013 the ILUE in the eastern, central, and western regions increases synchronously, which reflects the good implementation effect of a regional coordinated development strategy by the central government. From the provincial perspective, there is a declining trend from east to west and from the southeast to northwest. Machinery manufacturing, high-tech manufacturing, and other high value-added industries account for a larger proportion in the southeast coastal region, which enables it to rapidly achieve optimization and upgrading of the industrial structure and promote significant improvement in

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Table 1 List of variables. Variables

Description

Unit of measurement

Industrial land use efficiency (ILUE) Industrial relocation amount (IRA) GDP per capita (PGDP) Enterprise ownership structure (EOS) Human capital stock (HCS) Industrial fixed capital (IFC)

Industrial output value divided by land area Difference of location quotient GDP divided by population The proportion of state-owned enterprises The number of students in colleges per ten thousand population The amount of industrial fixed capital formationa

108 CNY/hm2 none CNY % Persons 108 CNY

a

The amount of industrial fixed capital formation ¼ Total fixed capital formation  (industrial fixed assets investment/ investment in fixed assets of whole society).

Table 2 Clasification based on the Industrial Clasification for National Economic Activities. Classification

Industrial classification for national economic activities

Food and beverage industry (FBI)

Agro food processing industry Food manufacturing industry Beverage manufacturing industry Textile industry Textile and garment, shoes, hat manufacturing Leather, fur, feathers (down) and its products Wood processing and wood, bamboo, rattan, palm and grass products industry Furniture manufacturing industry Paper and paper products industry Printing and record medium reproduction Stationery and sporting goods manufacturing industry Petroleum processing, coking and nuclear fuel processing Chemical raw materials and chemical products manufacturing Chemical fiber manufacturing industry Rubber manufacturing industry Plastic products industry Non metallic mineral products industry Ferrous metal smelting and rolling processing industry Non-ferrous metal smelting and rolling processing industry Metal manufacturing industry General equipment manufacturing industry Special equipment manufacturing industry Transportation equipment manufacturing industry Electrical machinery and equipment manufacturing Communications equipment, computers and other electronic equipment manufacturing industry Instrumentation and culture, office machinery manufacturing Pharmaceutical manufacturing industry

Light and textile industry (LTI)

Chemical and rubber industry (CRI)

Mineral manufacturing industry (MII)

Machinery manufacturing industry (MAI)

High-tech manufacturing industry (HTI)

Industrial land use efficiency (billion/hm2)

6 5 4

5.2. Spatial features of industrial relocation Eastern Region

Central Region

Western Region

Northeast Region

y = 0.2785x + 1.7107

3 y = 0.1479x + 1.0606 2 1

y = 0.1378x + 0.623

y = 0.1103x + 0.7648

0 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Fig. 2. Industrial land use efficiency of four regions from 2002 to 2013.

the ILUE. Moreover, there was rapid growth in the ILUE of Qinghai, Sichuan, and Shaanxi in 2013, which reflects the benefits arising from the West Development Strategy. On the whole, the ILUE shows an obvious spatial correlation. Neighboring provinces have the same ILUE and similar growth trends.

Fig. 4 shows the total industrial relocation amount of six industries from four regions from 2002 to 2013. The food and beverage industry and light and textile industry transfer out from the eastern region and transfer into the central, western, and northeast regions. Due to the high cost of land in the eastern region, these low-value-added industries gradually transferred from the east to the west. The chemical and rubber industry transferred from the central region to the eastern, western, and northeast regions. The mineral manufacturing industry transferred out from all regions because the development of resource-dependent industries gradually decreased as a consequence of environmental regulations. The machinery manufacturing industry transferred out from the central, western, and northeast regions and into the eastern region. With the expansion of the market, the high-tech manufacturing industry transferred into the eastern and central regions. These areas have superior geographical locations and access to people with high-technology talents and can provide a good development environment for the high-tech manufacturing industries. Fig. 5 presents the amount of industrial relocation in 30 provinces. There is an obvious roll-out trend for the food and beverage industry in Beijing, Hebei, Inner Mongolia, Shanghai, and other provinces. The chemical and rubber industry shows a roll-out trend

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Fig. 3. Spatial distribution of industrial land use efficiency of 30 provinces in China.

Total industrial relocation amount

10.00 5.00 0.00 Food and beverage

Light and textile

Chemical and Mineral Machinery High-tech rubber manufacturingmanufacturingmanufacturing

-5.00 Eastern region Central region Western region Northeast region

-10.00 -15.00 -20.00

Industry

Fig. 4. The total industrial relocation amount of six industries in four regions for the period 2002e2013.

in some eastern and northeastern provinces, such as Beijing, Tianjin, Hebei, Liaoning, Jilin, and Heilongjiang. There is a significant roll-in trend for the machinery manufacturing industry in Beijing, Tianjin, Liaoning, Hunan, and other provinces, while the high-tech manufacturing industry shows a roll-in trend in Beijing, Shanghai, Jiangsu, Zhejiang, and other provinces.

5.3. Spatial correlation analysis The results of our spatial correlation test using Matlab 2010a are shown in Table 3. In order to examine the potential spatial effects, the LM test is applied to test the spatial correlations, and the SLM, SEM, and SDM models are compared. The coefficients of the spatial fixed effect and time fixed effect of the LM spatial lag are 2.7918 and

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531

Fig. 5. The total industrial relocation amount of six industries in 30 provinces from 2002 to 2013.

Table 3 The LM test for choosing the spatial lag model or the spatial error model. Variables

Panel data OLS

Spatial fixed

Time fixed

Double fixed

IRAFBI IRALTI IRACRI IRAMII IRAMAI IRAHTI LnPGDP EOS LnHCS LnPC Durbin-Watson LM-lag Robust LM-lag LM-error Robust LM-error Spatial fixed effect LR-test Time fixed effect LR-test

0.1471 0.8278 0.8232 1.5290 1.1500 2.4211** 0.4476 2.9597*** 0.8178 3.7463*** 2.0459 1.3232 0.4331 1.0724 0.1824 649.0096*** 59.2779***

0.8248 0.0126 2.0267** 2.9668*** 1.5750 0.4105 9.7574*** 3.5018*** 1.3585 2.6944*** 1.8075 2.7918*** 0.4042 6.5447*** 4.1571***

0.0110 1.0058 1.3616 1.5720 1.8605* 1.8665* 3.3472*** 2.1092*** 3.8155*** 5.8461*** 1.8759 19.8257*** 28.7291*** 3.1976*** 12.1010***

1.1677 0.2660 2.1174** 3.1010*** 1.5011 0.2620 9.5884*** 3.2988*** 0.7909 1.5731 1.8716* 0.0509 1.7710 0.6670 2.3872**

Notes: ***, ** and * represents significance level at 1%, 5% and 10% levels respectively.

19.8257, respectively, at the significance level of 1%. Based on the time fixed effect, the robust spatial lag LM test result is significant at the 1% level. In addition, the coefficients of the spatial fixed effect and time fixed effect of the LM spatial error are 6.5447 and 3.1976, respectively, at the significance level of 1%. Based on the time fixed effect and spatial fixed effect, the robust LM spatial error test results are statistically significant at the 1% level. Based on the test results, the spatial models are more appropriate for analyzing the spatiality than the traditional panel models (Kang et al., 2016). According to the likelihood ratio (LR) test (Table 3) and Wald test (Table 4), the SDM models with time fixed, spatial fixed, and double fixed are significant at the 1% level, which indicates that the SDM is

Table 4 Results of Wald test for SDM. Statistic Wald test spatial lag Wald test spatial error

Spatial fixed ***

27.6455 35.0592***

Time fixed ***

96.7275 95.8152***

Double fixed 37.2753*** 37.4111***

Notes: ***, ** and * represent significance level at 1%, 5% and 10% levels respectively.

more appropriate to explore the spatial relationship between ILUE and industrial relocation (Hao and Liu, 2016). We combine the results of the LM test to choose an SDM model with time fixed effect to analyze the spatial dependence.

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Table 5 Estimation results of spatial econometric model. Determinants

SEM

SLM

SDM

IRAFBI IRALTI IRACRI IRAMII IRAMAI IRAHTI LnPGDP EOS LnHCS LnIFC W*IRAFBI W*IRALTI W*IRACRI W*IRAMII W*IRAMAI W*IRAHTI W*LnPGDP W*EOS W*LnHCS W*LnIFC

0.0721 0.0020 0.1013*** 0.1757*** 0.1120 0.0155 0.5675*** 1.0372*** 0.0004 0.0584***

0.0672 0.0086 0.1037 0.1737*** 0.1185 0.0186 0.5796*** 1.0559*** 0.0004 0.0467

0.0340

0.0875 0.0144 0.0873*** 0.2017*** 0.1243** 0.0885 0.4777*** 1.3927*** 0.0014*** 0.0844*** 0.5885*** 0.1481*** 0.0002 0.4741 0.0371 0.1540 1.1886 0.0877 0.0086 0.6211 0.0770***

0.6176 0.5355

0.7012 0.6368

r l

R2 Adj. R2

0.1300 0.6171 0.5343

Notes: ***, ** and * indicate significance at the 1%, 5% and 10% levels respectively.

5.4. Estimation results of spatial model As shown in Table 5, r represents the spatial correlation parameter of the SDM. The estimation is significantly positive at the 1% level, which indicates that the ILUE in adjacent areas have a positive effect on the local ILUE. It can be seen that the R2 of SDM is the highest, reaching 0.7012, and is followed by the SLM and SEM (Table 5). Therefore, SDM is used to estimate the ILUE, while the others are used for reference purposes. As shown in Table 5, the endogenous interaction relationship may account for the ILUE in Chinese provinces. Based on the estimation results of SDM, the industrial relocation of the chemical and rubber industry (CRI), mineral manufacturing industry (MII), and machinery manufacturing industry (MAI) has a significant positive effect on the ILUE. The coefficient of IRACRI implies that a 1% increase in the industrial relocation should lead to a 0.0873% increase in the ILUE. Moreover, it is noteworthy that the impacts of industrial relocation on the ILUE in the food and beverage industry (FBI), light and textile industry (LTI), and high-tech manufacturing industry (HTI) are not significant. Based on the SDM, the regional economic development (PGDP), enterprise ownership structure (EOS), human capital stock (HCS), and industrial fixed capital (IFC) are selected as the control variables to estimate the influence of the ILUE. PGDP has a positive impact on the ILUE with an elasticity of 0.4777, which suggests that an increase in PGDP would increase the ILUE. The impact of EOS on the ILUE is significantly negative, which indicates that the higher proportion of state-owned enterprises can hinder the regional ILUE. The estimated coefficient of HCS is 0.0014, and it is significant at the 1% level. IFC has a positive impact on the ILUE from 2002 to 2013.

6. Discussion 6.1. Effect of different types of industry Based on the SDM estimation results, it has been found that the industrial relocation of CRI, MII, and MAI has a significant positive

effect on the ILUE. CRI, MII, and MAI are pillar industries of China's economic development. In 2013, the output value accounted for 63% of China's manufacturing industry and increased by 9.38% compared to 2002. Although these three types of industries have varying degrees of roll-in or roll-out in different regions of China, there has been a growing dependence on these capital intensive industries in the development process in recent years under the influence of the market (Zhang and Ning, 2011). Under the guidance of the industrial transformation and upgrading by the central government and local governments, these industries have increased their output efficiency and ILUE by increasing their capital investment and product development efforts. High-speed rail trains and large engineering machinery developments in China are great examples of this. The industry transfers from the eastern region to the central and western regions have continuously eliminated the backward production capacity and improved the ILUE. Petroleum processing, chemical raw materials, and chemical products account for a large proportion of CRI. The petroleum processing industry is the basic energy industry and the oil production from the traditional large terrestrial oil fields are gradually becoming exhausted. These industies in Heilongjiang, Shandong, Henan, and other provinces have gradually transferred out, whereas the industies in Xinjiang, Qinghai, and other inland provinces have begun to transfer in. Moreover, with the increase of offshore oil exploitation and oil imports, a large number of industries in Zhejiang, Fujian, Hainan, and other coastal provinces have begun to transfer in. Some products of chemical raw materials and chemical products are sources of raw materials for other industries, which plays an important role in China's industrial economy and agricultural development. It is undeniable that these industries belong to the high energy consumption and high pollution industries. As China's environmental regulations and ecological requirements continue to strengthen, a large number of enterprises are transferring to the western region with the technological innovation of the eastern region (Yuan et al., 2017). MII includes non-metal and metal product industries. The non-metallic industry segment mainly produces building materials such as cement, lime, and gypsum, which are resource-intensive industries and have a serious impact on the environment. The metal products industry mainly comprises iron and steel products. In recent years the overcapacity problem of the steel industry in China has been serious, and this, combined with the government's increased attention on the environment, has resulted in four regions emerging with regard to industrial relocation. MAI has the largest proportion of China's manufacturing industry, including the manufacturing of general and special transportation as well as electrical and mechanical equipment. Although there is still a gap in respect of machinery manufacturing between China and the United States and Europe, the high-end equipment manufacturing industries of Shanghai, Zhejiang, Guangdong, and other provinces in the eastern coastal areas are developing quickly (Wang and Wang, 2017). However, a large number of state-owned machinery manufacturing enterprises are distributed in the central and western regions. The production technology of these enterprises is relatively backward, and many enterprises with insufficient funds and slow technology renewal are gradually dying out. Therefore, the industrial relocation of MII and MAI reflects the emergence of advanced technology enterprises and the demise of backward enterprises. In the process of industrial development and transfers in to CRI, MII, and MAI, the new industrial land has been fully used and the ILUE has been improved, thereby achieving a virtuous circle of transfer in and transfer out. It is noteworthy that the impacts of industrial relocation on the ILUE in FBI, LTI, and HTI are not significant. FBI and LTI are mainly

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light industries in China as they are involved in the production of daily necessities. These are labor-intensive industries with low product added value, and their output value is relatively low in terms of China's manufacturing industries. With the rise of labor costs in the eastern coastal areas and the development of industries with high additional value, a large number of industries in FBI and LTI, such as the furniture manufacturing industry, began to transfer to the midwest and northeast regions (Yang et al., 2012). On the one hand, the output capacity of the industrial land in the eastern developed areas has improved through the adjustment of the industrial structure. On the other hand, the industrial land in underdeveloped areas can be fully utilized by industrial relocation, but there is broad market demand in various regions for these products, so its distribution is relatively dispersed, which has meant that there has been no significant influence of industry relocation on the ILUE. As a capital- and technology-intensive industry, HTI is composed of a large number of downstream industry chain enterprises in China that are involved in assembly processing. These enterprises have gradually transferred offshore to South Asia and Southeast Asia with the rising labor costs in China (Tan and Chin, 2017). Meanwhile, the upstream enterprises in China have been increasing as a consequence of the growing R&D capability of China. Therefore, the impact of HTI on industrial relocation is not significant.

6.2. Effect of control variables A high economic development level (PGDP) in a region signals its high economic strength, which reflects the strength of the land investment of the enterprises. Investing more funds for the construction of industrial infrastructure, industrial equipment, hightech factories, and the employment of high-quality labor in the same area of land should correspondingly improve the ILUE. Besides, industrial land resources become scarcer in economically developed areas. The basic aim of the improvement of the ILUE is to promote economic development. The optimization and upgrading of the regional industrial structure brought by industrial relocation is the main way to improve the economic development level. A high proportion of state-owned enterprises (EOS) works against the improvement of the ILUE. The land of state-owned enterprises is mainly assigned in large areas by the government, which leads to extensive land use and lack land saving consciousness. Therefore, it is essential to scientifically plan and rationally manage the land allocated to state-owned enterprises to improve regional ILUE. Human capital stock (HCS) has a significant positive effect on the ILUE. Universities and research institutes are concentrated in the regions with high human capital, which facilitates a high transformation speed of the scientific research achievements (Feng and Ke, 2016). The accumulation of human capital can provide the foundation for the optimization and upgrading of the regional industrial structure so as to improve the ILUE. The formation of industrial fixed capital (IFC) can reflect the development speed of a regional industry to a certain extent. Therefore, a high speed of industry development can accelerate regional industrial upgrading and plays an important role in improving the ILUE.

7. Conclusions By calculating the industrial land use efficiency and industrial relocation index, this study estimates the spatial effect of industrial relocation on the industrial land use efficiency in six industries by using spatial econometric models. The main conclusions drawn from this study can be summarized as follows.

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(1) The industrial land use efficiency presents an obvious rising trend in the four regions from 2002 to 2013. The industrial land use efficiency in the central and western regions is basically the same, and the eastern region has higher efficiency than the other three regions. (2) The industrial land use efficiency of 30 provinces presents significant spatial correlation and showed obvious trapezoidal growth trends in 2002, 2006, 2010, and 2013. From the provincial perspective, there is a declining trend from east to west and from the southeast to northwest. (3) The eastern region is the main region in which the high value-added industries (machinery manufacturing, hightech manufacturing) transfer in and the low value-added industries (food and beverage, textile and light) transfer out. The regions that receive the low value-added industries are mainly the central, western, and northeast regions. (4) Industrial relocation has obvious effects on the industrial land use efficiency and plays a different role in different industries. The industrial relocation of the chemical and rubber industry (CRI), mineral manufacturing industry (MII), and machinery manufacturing industry (MAI) has a significant positive effect on the industrial land use efficiency. Moreover, the impacts of industrial relocation on the industrial land use efficiency in the food and beverage industry (FBI), light and textile industry (LTI), and high-tech manufacturing industry (HTI) are not significant. (5) Except for the enterprise ownership structure (EOS), three other control variables, namely regional economic development (PGDP), human capital stock (HCS), and industrial fixed capital (IFC), have significant roles in promoting industrial land use efficiency. The above results have important policy implications. First, by establishing reasonable regional economic planning, industrial planning, and land use planning, each province should be able to take advantage of its location and resources and optimize the industrial layout, so as to improve the industrial land use efficiency rather than rely on the existing industrial base. In addition, the local government should actively introduce the industries that have positive effects on the development of the regional economy and industrial land use efficiency by the use of corresponding industrial policies. Second, the improvement of the industrial land use efficiency should be prioritized. In order to not exceed the carrying capacity of the land resources, it is suggested that the industries that are adapted to the regional development should be introduced and smooth regional industrial relocation should be promoted. Third, according to the incentive mechanism and supervision mechanism, the government should provide land for redevelopment and consolidation to the high value-added industries that are introduced or transferred from other regions. Thus, it can effectively improve the usage level of the idle and inefficient land and promote regional coordinated development. Although this study investigates the spatial effect of industrial relocation on the industrial land use efficiency based on the spatial econometric method, the research is still preliminary. Industrial relocation plays a positive role in promoting the economic development of the roll-in region; however, it inevitably has some negative impacts as well, such as regional environmental pollution. Due to the fragile ecological environment in the western region of China, industrial relocation not only promotes the economic development of that region, it can also cause tremendous harm to the environment, which has been the main obstacle of the industrial relocation in this region. Therefore, future research should pay more attention to evaluating the environmental effect on the industrial land use efficiency in the process of industrial relocation.

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Acknowledgments This study was funded by the National Natural Science Foundation of China (71503200, 71233004, 41602336); the Project of Humanities and Social Sciences of the Ministry of Education of China (18XJC790014); the Fundamental Research Funds for the Central Universities (2452015231, 2017RYWB01, 2017RWYB06). The authors would like to thank the anonymous referees for their helpful suggestions and corrections on the earlier draft of our paper. References Akamatsu, K., 1962. A historical pattern of economic growth in developing countries. Develop. Econ. 1 (1), 3e25. Arauzo, J., Liviano, D., Manjon, M., 2010. Empirical studies in industrial location: an assessment of their methods and results. J. Reg. Sci. 50 (3), 685e711. Brouwer, A.E., Mariotti, I., van Ommeren, J.N., 2004. The firm relocation decision: an empirical investigation. Ann. Reg. Sci. 38 (2), 335e347. Chen, W., He, R., Wu, Q., 2017. A novel efficiency measure model for industrial land use based on subvector data envelope analysis and spatial analysis method. Complexity, 9516267, 1e11. Chen, W., Shen, Y., Wang, Y.N., 2018a. Evaluation of economic transformation and upgrading of resource-based cities in Shaanxi province based on an improved TOPSIS method. Sustain. Cities Soc. 37, 232e240. Chen, W., Shen, Y., Wang, Y.N., et al., 2018b. How do industrial land price variations affect industrial diffusion? Evidence from a spatial analysis of China. Land Use Pol. 71, 384e394. Conroy, T., Deller, S., Tsvetkova, A., 2016. Regional business climate and interstate manufacturing relocation decisions. Reg. Sci. Urban Econ. 60, 155e168. Cummings, J.R., Epley, D.R., 2014. A total requirements view of a tourism and hospitality market is more accurate than traditional location quotients. Tourism Econ. 20 (3), 473e492. Elhorst, J.P., 2003. Specification and estimation of spatial panel data models. Int. Reg. Sci. Rev. 26, 244e268. Elhorst, J.P., 2010. Applied spatial econometrics: raising the bar. Spatial Econ. Anal. 5 (1), 9e28. Feng, P., Ke, S., 2016. Self-selection and performance of R&D input of heterogeneous firms: evidence from China's manufacturing industries. China Econ. Rev. 41, 181e195. Hanson, G.H., 1998. Regional adjustment to trade liberalization. Reg. Sci. Urban Econ. 28 (4), 419e444. Hao, Y., Liu, Y.M., 2016. The influential factors of urban PM 2.5 concentrations in China: a spatial econometric analysis. J. Clean. Prod. 112, 1443e1453. Hoover, E.M., 1937. Location Theory and the Shoes and Leather Industry. Harvard University Press, Cambridge. Huang, Z., He, C., Zhu, S., 2017. Do China's economic development zones improve land use efficiency? The effects of selection, factor accumulation and agglomeration. Landsc. Urban Plann. 162, 145e156. Huang, Z., Lu, J., Sun, H., et al., 2011. Sticky factors in the industrial relocation of a cluster: a case study of Zhili children's garments cluster in China. Soc. Sci. J. 48 (3), 560e565. Kang, Y.Q., Zhao, T., Yang, Y.Y., 2016. Environmental Kuznets curve for CO2 emissions in China: a spatial panel data approach. Ecol. Indicat. 63, 231e239. Kim, Y.H., 2007. Impacts of regional economic integration on industrial relocation through FDI in East Asia. J. Pol. Model. 29 (1), 165e180. Kojima, K., 1978. Direct Foreign Investment: a Japanese Model of Multinational Business Operation. Praeger Publishers, New York. Kumar, P., 2002. Price and quality discrimination in durable goods monopoly with resale trading. Int. J. Ind. Organ. 20 (9), 1313e1339. Lesage, J., Pace, R.K., 2009. Introduction to Spatial Econometrics. CRC Press. Lewis, A., 1984. The Evolution of the International Economic Order. The Commercial Press, Beijing, China. Liu, Y., Zhang, Z., Zhou, Y., 2018. Efficiency of construction land allocation in China: an econometric analysis of panel data. Land Use Pol. 74, 261e272. Manjon, M., Arauzo, J., 2011. Locations and relocations: modelling, determinants, and interrelations. Annu. Reg. Sci. 47, 131e146. Markusen, J.R., Venables, A.J., 2000. The theory of endowment, intra-industry and multi-national trade. J. Int. Econ. 52 (2), 209e234. Okubo, T., Tomiura, E., 2012. Industrial relocation policy, productivity and heterogeneous plants: evidence from Japan. Reg. Sci. Urban Econ. 42 (1e2), 230e239. Peng, C., Xiao, H., Liu, Y., et al., 2017. Economic structure and environmental quality and their impact on changing land use efficiency in China. Front. Earth Sci. 11 (2), 372e384. Pennings, E., Sleuwaegen, L., 2000. International relocation: firm and industry determinants. Econ. Lett. 67 (2), 179e186. Rauch, J.E., 1993. Does history matter only when it matters little? The case of cityindustry location. Q. J. Econ. 108 (3), 843e867. Shafik, N., Bandyopadhyay, S., 1992. Economic Growth and Environ Mental Quality: Time Series and Cross-country Evidence Back Ground. Paper for World Development Report. World Bank.

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Nomenclature

Abbreviations CRI: Chemical and rubber industry FBI: Food and beverage industry HTI: High-tech manufacturing industry ILUE: Industrial land use efficiency LM: Lagrange multiplier LR: Likelihood ratio LTI: Light and textile industry MAI: Machinery manufacturing industry MII: Mineral manufacturing industry SDM: Spatial Durbin mode SEM: Spatial error model SLM: Spatial lag model Variables A: Economic gravity coefficient D: Linear distance between the provincial capitals (km) E: Economic link intensity EOS: Enterprise ownership structure (%) G: GDP of a province (108 CNY) HCS: Human capital stock (Persons) IFC: Industrial fixed capital (108 CNY) IRA: Industrial relocation amount LQ: Location Quotient P: Population of a province p: Total industrial output value of an industry (108 CNY) PGDP: GDP per capita (CNY) TLA: Total industrial land area (hm2) TOV: Total industrial output value of a province (108 CNY) W: Spatial-weights matrix X: Explanatory variables matrix y: Dependent variable b: Explanatory variable coefficient ε: Independently and identically distributed error term q: Spatial autocorrelation coefficient

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l: Spatial error coefficient m: Independent white noise error component r: Spatial regression correlation coefficient Indices i: One of the provinces j: One of the provinces

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k: One of the industries according to Industrial Classification for National Economic Activities m: Number of the industries according to Industrial Classification for National Economic Activities n: Number of the provinces p: Number of years q: One of the industries classified in this paper t: One of the years