Measuring the sustainable performance of industrial land utilization in major industrial zones of China

Technological Forecasting & Social Change 112 (2016) 207–219

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Technological Forecasting & Social Change

Measuring the sustainable performance of industrial land utilization in major industrial zones of China Hualin Xie a,b, Wei Wang b,⁎, Zihui Yang c, Yongrok Choi d a

School of Public Administration, China University of Geosciences, Wuhan 430074, China Co-innovation center of institutional construction for Jiangxi eco-civilization, Jiangxi University of Finance and Economics, Nanchang 330013, China Lingnan College, Sun Yat-Sen University, Guangzhou 510275, China d Department of International Trade, Inha University, Inha-ro 100, Nam-gu, Incheon 402-751, Republic of Korea b c

a r t i c l e

i n f o

Article history: Received 29 October 2015 Received in revised form 21 May 2016 Accepted 14 June 2016 Available online 24 June 2016 Keywords: Industrial land Green use Global generalized directional distance function Global Malmquist-Luenberger index Shadow price China

a b s t r a c t In this study, we analyze the dynamic changes of industrial land green use efficiency (ILGUE) for the four main industrial zones in China during 2003–2013, using the global generalized directional distance function (GGDDF) and global Malmquist-Luenberger index (GML) approaches. Further, we investigate the impacts of influencing factors on the ILGUE, including industrial land policy and prices. The results show that (1) The Pearl River Delta zone enjoys the highest ILGUE, while the Central and South Liaoning zone suffers the worst. All four zones have potential for using industrial land more efficiently. The relatively low ILGUE is mainly due to the low level of industrial production technology. (2) The central government's land policies apply only in the Beijing-Tianjin-Tangshan zone. The local government's land financing policy has a significant negative effect on improving ILGUE, except in the Yangtze River Delta zone. Active governmental pursuit of foreign industrial investment leads to ILGUE improvement in all four of the zones. (3) The shadow prices of industrial land indicate upward trends in all four of the zones, with the actual prices in most cities being significantly lower than the shadow prices. Therefore, the actual prices of these cities should be appropriately adjusted over time. © 2016 Elsevier Inc. All rights reserved.

1. Introduction According to the China Statistical Yearbook 2004–2014, the average annual growth rate of industrial GDP (gross domestic product) in China is N20%, which is an outstanding achievement compared with most other countries in the world. Most of this success comes from preferential policies for the development of industrial zones designated by the Chinese government. In 2013, the industrial sector of GDP accounted for approximately 37% of the total GDP in China. Unfortunately, the Chinese industrial economy has been developing at too high a speed, resulting in many side issues, including relatively low resource utilization efficiency, as well as environmental degradation (He et al., 2014; Zhang et al., 2016). In 2013, the total area of industrial zones in China accounted for N 19% of built-up urban land, which is N4500 km2, while the proportion in most developed countries is b10% (Bertaud and Renaud, 1997). However, the massive input of industrial land resources has been accompanied by relatively low economic efficiency, with the industrial GDP created by 1 km2 of industrial land being far lower than that in developed countries (Xiong and Guo, 2013). In addition, as the main carrier of industrial production activities, ⁎ Corresponding author. E-mail addresses: [email protected] (H. Xie), [email protected] (W. Wang), [email protected] (Y. Choi).

http://dx.doi.org/10.1016/j.techfore.2016.06.016 0040-1625/© 2016 Elsevier Inc. All rights reserved.

industrial land suffers most of the industrial pollutants (Wang et al., 2016a). Thus, whether China can improve both industrial land use efficiency and environmental eco-friendliness is a key issue in the realization of sustainable development regarding the Chinese economy (Zhang et al., 2015). China has a vast territory, but the level of industrial development varies from region to region (Zhang et al., 2012). According to the China Statistical Yearbook 2004–2014, because the reform and opening up of the late 1970s, industrial GDP in the eastern provinces of China has always accounted for N65% of the total industrial GDP of the country. There are four large major industrial zones in the eastern provinces, with the GDP of these zones being half of the entire sum of the eastern provinces combined (and N 32.5% of the total industrial GDP of China). As shown in Fig. 1, the four industrial zones are the Beijing-Tianjin-Tangshan zone, the Central and South Liaoning zone, the Yangtze River Delta zone and the Pearl River Delta zone. The areas of industrial land in the four zones have shown a consistently rising trend (Fig. 2), and the four zones combined measured up to 4629.17 km2 in 2013, accounting for approximately 47% of the total area of industrial land in China. Therefore, the four industrial zones are expected to be representative of the general conditions and situation of industrial land use in China. This study will make three main contributions to the study of China's industrial land green use efficiency (ILGUE). Firstly, it is the first study to

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Fig. 1. Geographical distribution of four industrial zones.

analyze industrial land use efficiency in the consideration of undesirable environmental impacts in the main industrial zones. Most previous studies always focus on the economic efficiency while neglecting the

Fig. 2. Trends of the areas of industrial land in the four zones, 2003–2013.

negative impacts on the environment caused by industrial production (Pascoe and Tingley, 2010; Byrnes et al., 2010). However, as noted by Zhang and Choi (2013), ignoring the environmental pollutants when evaluating the sustainability of the industrial sector or the utilization of resources would cause inaccurate result, we have considered several major industrial pollutants in this paper. Secondly, as noted by Zhang et al. (2014a), many previous studies tend to use cross-sectional data other than time-series data when evaluating efficiency, which may lead to bias results because production technology varies in different years. Thus, we apply the global generalized directional distance function (GGDDF) approach to calculate the ILGUE, which envelops all of the contemporaneous technologies in the study period. Thirdly, we propose a non-radial Malmquist industrial land performance index (NMILPI) to measure the dynamic changes of the ILGUE. We further decompose the NMILPI into several indices (e.g., technical change (TC), scale efficiency change (SEC) and pure technical efficiency change (PTEC)) to obtain further insight into the dynamic changes of the ILGUE. Lastly, we compute the shadow price of industrial land, and makes recommendations regarding price adjustments of industrial land for the purpose of improving ILGUE and in support of sustainable industrial land management.

H. Xie et al. / Technological Forecasting & Social Change 112 (2016) 207–219

Therefore, the following questions are addressed in this study. (1) What is the overall situation and dynamic change of the ILGUE? Are there obvious regional differences? Which one of the three decomposition indices of the NMILPI is the main driver of the growth of the ILGUE? (2) What is the impact of central government and local government land policies on ILGUE in the four industrial zones? (3) To what extent do the actual prices of industrial land deviate from the ideal or potential prices in the cities? What adjustments can we make?

The remainder of this paper is organized as follows. Section 2 presents the literature review. Section 3 introduces the methods and data. Section 4 reports the empirical results, and Section 5 presents conclusions and some policy implications. 2. Literature review 2.1. Directional distance function and Malmquist–Luenberger productivity index Sustainable development of industry in China has attracted more and more attention in recent years, and reasonable evaluations of the actual situation of industrial development is necessary before taking effective improvement measures (Wu et al., 2016). Many studies have tried to address this issue by evaluating the sustainable development of industry sector including the iron and steel industry (Xu and Lin, 2016; Wang et al., 2016,b), the manufacturing industry (Li and Lin, 2016), the power industry (Lin and Yang, 2014; Zhang et al., 2014a), the energy sector (Liu and Lu, 2015), the pulp and paper industry (Yu et al., 2016), the coal industry (Cui et al., 2015), the petroleum industry (Song et al., 2015) and the transportation industry (Zhang and Wei, 2015) at the national level (Zhang et al., 2012; Zhang and Xie, 2015; Wang and Lin, 2016; Zhang et al., 2015), the regional level (Sueyoshi and Yuan, 2015; Wang and Feng, 2015; Xie and Wang, 2015) and the city level (Chen et al., 2015; Li et al., 2016). The resource utilization is an important issue for the sustainable development, and realizing the effective use of resources is the key problem to achieve sustainable development (Stigson and Stigson, 2015). Thus, a number of studies have described and evaluated the use of water (Zhang et al., 2016; Deng et al., 2016), energy (Wang et al., 2016a), labor force (Chen et al., 2014), and capital (Salike, 2016) in China. Regarding research method, many previous studies of land efficiency preferred using single factor indexes such as the economic output per square kilometer of land (Huang et al., 2009). However, as Hu et al. (2006) noted, land cannot produce products unless accompanied by other inputs such as labor and capital, and land efficiency computed under the total factor framework is more reasonable. Therefore, the directional distance function (DDF) approach has been widely applied by current studies, because the input and output variables are incorporated into the model. Specifically, the earlier DDF approach developed by Shephard (1970) aimed at expanding all types of outputs proportionally, regardless good or bad outputs. However, we hope to expand the good outputs and contract the bad outputs at the same time in the actual production activities. To solve this problem, Chambers et al. (1996) proposed the directional distance function which was able to distinguish the good and bad by setting positive sign for the good outputs and negative sign for the bad outputs in the model. In addition, this approach is regarded as radial efficiency because that the expanding of the good outputs and the contracting of the bad outputs are at the same rates, which is not in line with the actual production and may lead to bias results (Fukuyama and Weber, 2009; Zhou et al., 2007). Moreover, as Zhang et al. (2015) pointed out, many current studies applied cross-sectional data other than time-series data, and the studies

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based on cross-sectional data are regarded as contemporaneous efficiency evaluation. It is obvious that production technologies of each year are not the same, and results based on contemporaneous production technology may not be reasonable. Therefore, Zhang et al. (2014b) developed a global generalized DDF (GGDDF) approach to overcome this problem, which envelops all of the contemporaneous technologies. To obtain further insight into the status of land use efficiency, dynamic changes of land use efficiency should be explored to identify the source that contributes most to increasing land use efficiency. The traditional DEA method and its extensions can only depict the static relative efficiency. Thus, it cannot provide comprehensive implications for policymakers. The Malmquist productivity index approach proposed by Färe et al. (1994) can be used for measuring dynamic productivity change, and it can be decomposed into two indices (e.g., efficiency change and technical change) to explore the main driver of the productivity growth. Chung et al. (1997) extended the Malmquist index by setting the direction of good and bad outputs' vectors to expand the good output and contract the bad output simultaneously, which is more in line with the actual production. This relative advanced approach is named as Malmquist–Luenberger productivity index, and it has been widely applied in the studies on energy sector (Xue et al., 2015), agricultural sector (Lin and Fei, 2015), pulp and paper industry (Yu et al., 2016), power industry (Munisamy and Arabi, 2015) at the national level (Chen et al., 2015), the regional level (Chen et al., 2006), the provincial level (Zhang et al., 2011) and the city level (Fan et al., 2015) in China. In addition, Zhang et al. (2012) extended this approach by applying the global production technology and renamed it as global Malmquist–Luenberger (GML) index, which can provide more accurate results because the global production technology was employed. In general, there are a large number of studies on the sustainable industrial development and resource utilization. Regarding the studies on industrial land use in China, Guo and Xiong (2014) measured the industrial land use efficiency in China's 30 regions by employing a traditional DEA model. However, they did not take the environmental pollutants into account, which might overestimate the efficiency. Xiong and Guo (2013) modeled dynamic changes of the industrial land use efficiency using a Malmquist index at the provincial level in China, however, the study was actually a static analysis because it was based on contemporaneous production technology, and it could not depict the actual dynamic changes of industrial land use efficiency. Therefore, in this paper, we will compute the ILGUE and its dynamic changes over the 2003–2013 period using GGDDF and GML approaches, and then we investigate the impacts on the ILGUE of central and local government land policies and other related influencing factors. Finally, we compute the shadow prices of industrial land for each city, based on the dual model of the GGDDF approach, and put forward some policy recommendations base on the empirical results. 2.2. Influential factors Land policies are often considered to be important factors in the sustainable use of industrial land (Tu et al., 2014). The Chinese government has always paid great attention to the feasibility of applying selective concentration to industrial land use. In 1986, the Land Management Law took effect, and the Bureau of Land Administration was founded, leading to a large quantity of land being converted from agricultural land into industrial land. Due to these policies, industrial GDP experienced unprecedented improvement. In 1998, the Bureau became the Ministry of Land and Resources, and the Land Management Law was further amended to encourage the construction of industrial parks and to raise land compensation standards, which resulted in very rapid increases in the scale of industrial production, as shown in Fig. 2. To satisfy the large demand for industrial land and to improve land use efficiency, the Ministry of Land and Resources and the Ministry of Supervision issued the “Notice on the implementation of the relevant issues of the industrial land bidding auction listing transfer system.” This

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land policy advocated saving land and curbed the inefficient use of industrial land. The process was adjusted to examine more strictly requests to approve construction on urban land, and the utilization of industrial land was further scrutinized, greatly improving ILGUE across China (Long, 2014). However, as the implementing local partners of the central government's land policy, local governments at times do not fully implement central land policies if the policies conflicts with their own interests. The situation has become more serious since 1994 due to reforms of the tax system that have enabled the central government to retain a large portion of tax revenue, with local governments only receiving a small portion, placing them under much greater financial pressure. In response, they quickly found several effective methods to finance the budget deficit, such as obtaining more land-transfer fees by greatly expanding new industrial land, via the so-called “land financing policy”, and by attracting foreign investment through various preferential policies, such as setting very low prices or even pricing land at zero for industrial production (Du et al., 2016). However, this shortsighted behavior of pursuing short-term economic interests is not good for sustainable development, and it results in low utilization efficiency of industrial land. Zhang (2014) found that the “land financing policy” effectively alleviates financial problems in the short term only. It could have a seriously negative impact on the effort to improve land use efficiency in the long term. Considering the relatively short period of rapid development in China, foreign industrial enterprises could still introduce more advanced technologies in industrial production and management, compared with domestic ones (Jiang et al., 2015); therefore, most regions are eager to invite industrial investment from foreign countries. However, there have been few studies of the impacts of local government policies on the productivity of foreign investment. Hu (2001) used a sample of high tech enterprises in Beijing and found that imported technology had a significantly positive impact on productivity. Jefferson et al. (2006) performed a similar study of manufacturing enterprises in China and came to the same conclusion. In addition, Zhang (2014) found that, although the policy of attracting foreign investment is based on good intentions regarding the improvement off industrial production and management technologies, it has turned regions across China into vicious competitors due to manipulation by local governments. The unreasonably low prices of industrial land and the great number of preferential policies have led to a prevalence of unusual profit-seeking activities and a serious waste of industrial land, which have threatened industrial land use sustainability and social stability in China. Industrial land is an important input factor in industrial production; therefore, land prices can have a direct impact on land use efficiency and sustainable development (Wu et al., 2014). In general, reasonable land price is determined by the supply and demand of land. According to some previous studies, land prices less than a reasonable level resulted in lower production costs and more industrial GDP, but they could also easily lead to a waste of resources, resulting in low land use efficiency. In contrast, price greater than a reasonable level can increase production costs. However, if we can improve production and management technology through innovation, we can raise not only the GDP per unit of land, but we can also more effectively protect the environment, which is in compliance with the sustainable development policy of achieving the green usage of industrial land (Porter and van der Linde, 1995). Before the reform and the opening up of the late 1970s, land transactions were banned because land resources were state-owned assets; consequently, the market for transferring land and a price for land did not actually exist. Under the planned economic system, the allocation of land for industrial production is completely based on the production plan, which follows the arbitrary decisions of the central government rather than the supply and demand of the land market. Therefore, land property ownership uncertainties and the lack of an appropriate land market have led to extremely low land use efficiency levels (Ding, 2003). After that, the central government of China established several special

economic zones in the eastern coastal provinces, where they have attempted to separate the ownership and management rights to land. Individuals are permitted to obtain land and to maximize their profits in these specific zones. This innovation has greatly promoted local industrial economic development. In 1991, the State Council issued a “Provisional regulation on the granting and transferring of the land rights over state-owned land in cities and towns,” which officially allowed transfers of land and related transactions (Valletta, 2001). However, this beneficial policy has not been thoroughly implemented, and industrial land prices currently are mainly determined by local governments and land developers across China. Due to economic benefits and other reasons, the price of industrial land has been deliberately lowered in most cases, and it is obvious that such administrative interventions can easily lead to the inefficient use of land resources. Therefore, analysis with the goal of establishing appropriate prices for industrial land and recommendations for land price adjustments would be significantly important for the sustainable development of industrial land in China.

3. Methods and data 3.1. Global generalized directional distance function (GGDDF) We assume that there are N cities in our study, and each city has M inputs (x) to produce J desirable outputs (y) and K undesirable outputs (b), with the matrices of inputs, desirable outputs and undesirable outputs in city n as follows (Zhang et al., 2014a):

X ¼ ½x11 ; … ; xMn ∈RMn ; Y ¼ ½y11 ; … ; yMn ∈R Jn ; B ¼ ½b11 ; … ; bMn ∈RKn where X N 0, Y N 0, and B N 0. The production possibility set T (x) can be expressed as follows: T ðxÞ ¼ f ðx; y; bÞ jx can produce ðy; bÞ; x ≥Xλ; y≤Yλ; b ¼ Bλ; λ ≥0g ð1Þ

where the production possibility set T (x) is assumed to satisfy the production function theory (Tone et al., 2001), and a benchmark for global technology can be expressed as the accumulation of each period: that is, TG = T1 ∪ T2 ∪ … ∪ TN. In addition, the traditional radial DDF approach always assumes that the linear programming solution allows for both inputs and outputs to expand or contract in proportion to the original inputs and outputs, which is almost impossible in real production. To overcome this shortcoming, a generalized DDF approach was developed and has become widely used in studies of resource efficiency evaluation. Moreover, wT = (x, y, b)T in Eq. (3) is the standard weight matrix of inputs and outputs, and g = (−gx, gy, −gb) are the direction vectors. ϕ = (x,y, b) represents the adjustment ratios of all of the inputs and outputs, which are nonnegative numbers. diag is a diagonal matrix. Thus, the adjustment ratios of all of the inputs and outputs can be different, more likely reflecting the actual production reality. Eq. (4) represents the efficiency evaluation model under the contemporaneous benchmark technology set, and Eq. (5) is under the global benchmark technology set. ! D ðx; y; b; gÞ ¼ supfϕ : ððx; y; bÞ þ g  ϕÞ∈T g

ð2Þ


 ! D ðx; y; b; gÞ ¼ sup wT ϕ : ððx; y; bÞ þ g  diag ðϕÞÞ∈T

ð3Þ

H. Xie et al. / Technological Forecasting & Social Change 112 (2016) 207–219

  ! D ¼ max α 1 þ … þ α i þ β1 þ … þ β j þ γ 1 þ … þ γk 8 N X > > > λn xmn ≤ ð1−α m gm ÞX m0 > > > > n¼1 > > > N   X > > > λn yjn ≥ 1 þ β j g j Y j0 > > < n¼1 s:t: X N > > > λn bkn ¼ ð1−γk g k ÞBk0 > > > > > n¼1 > > λ ≥0; α m ≥0; β j ≥0; γk ≥0 > > > m ¼ 1; 2; …; M; > j ¼ 1; 2; …; J; > : k ¼ 1; 2; …; K

approach to measure the dynamic changes of ILGUE using the GML index (Zhang et al., 2014a), which could be called the generalized Malmquist industrial land performance index (NMILPI) as follows: ð4Þ

ILGUE ¼

 1 1 α land þ ðγwater þ γ so2 þ γ soot Þ 2 3 1 þ βgdp

where the ILUEG(. t) is given by solving the model in Eq. (6), and if the NMILPI index is greater than, equal to, or b 1, it respectively represents the ILGUE enjoying positive progress, not changing, or suffering a deterioration during times t and t + 1. The NMILPI index can also have several decompositions as follows (Zhang et al., 2015):

¼



LDt;tþ1 ; LBt;tþ1 ; Kt;tþ1 ; GDPt;tþ1 ; WAt;tþ1 ; SO2t;tþ1 ; SOOTt;tþ1   ILUEG :tþ1 jCRS

NMILPI

ILUEG ð:t ÞjCRS   ILUEG :tþ1 jCRS

ILUED

 tþ1  : jCRS

ð8Þ

 tþ1  D : jVRS ILUED ð:tþ1 ÞjCRS ILUED ð:tþ1 ÞjVRS ILUE   ¼ ILUED ð:t ÞjCRS ILUED ð:t ÞjVRS ILUEG ð:t ÞjCRS ILUED ð:t ÞjCRS



ILUED ð:t ÞjVRS

¼ TCt;tþ1  SECt;tþ1  PTECt;tþ1

where CRS and VRS imply constant returns to scale and variable returns ð5Þ

N

to scale, respectively. It is VRS when the constraint of ∑ λt ¼ 1 is i¼1

In our study, we assume that the inputs are industrial land, labor and net fixed assets in industrial land. The desirable output is industrial GDP, and the undesirable outputs are discharge of wastewater, waste gas, and dust (the three famous industrial wastes) from industrial land. According to previous studies, because there are three inputs (industrial capital (LK), industrial labor (LB), and industrial land (LD)), one desirable output (industrial gross domestic product (GDP)), and three undesirable output (industrial waste water discharge (WA), industrial sulfur dioxide emission (SO2), and industrial soot emission (SOOT)) in our study, we set the weight vector to (1/9, 1/9, 1/9, 1/3, 1/9, 1/9, 1/9) and the directional vectors to g = (− K, − LB, − LD, GDP, − WA, −SO2, −SOOT). Then, the industrial land green use efficiency (ILGUE) could be expressed as follows: 1−

  NMILPI LDt;tþ1 ; LBt;tþ1 ; Kt;tþ1 ; GDPt;tþ1 ; WAt;tþ1 ; SO2t;tþ1 ; SOOTt;tþ1   ILUEG LDtþ1 ; LBtþ1 ; Ktþ1 ; GDPtþ1 ; WAtþ1 ; SO2tþ1 ; SOOTtþ1   ¼ ILUEG LDt ; LBt ; Kt ; GDPt ; WAt ; SO2t ; SOOTt

ð7Þ

where the superscripts m, j, k, respectively represent the mth input, the jth desirable output, and the kth undesirable output of the city under evaluation. αi, γk and βj are the adjustment ratios of the inputs, desirable outputs and undesirable outputs, respectively, and λ is a non-negative vector. The superscripts t and n refer to the year t in the study period and the number of cities in the sample, respectively. The city is located on the frontier of production if αi, γk and βj have zero values. Additionally, we can use the global generalized directional distance function (GGDDF) model to perform the study under the global benchmark technology set, which is expressed in Eq. (5), and the solutions of different years can be compared with each other.   ! D ¼ 8 max α 1 þ … þ α i þ β1 þ … þ β j þ γ 1 þ … þ γk T X N >X > > > λtn xtmn ≤ ð1−α m g i ÞX tm0 > > > t¼1 n¼1 > > > T X N   > X > > > λtn ytjn ≥ 1−β j g j Y tj0 > > < t¼1 n¼1 s:t: X T X N > t > > λtn bkn ¼ ð1−γk g k ÞBtk0 > > > > t¼1 n¼1 > > > λt ≥0; α i ≥0; β j ≥0; γ ≥0 > k > > > > m ¼ 1; 2; …; M; j ¼ 1; 2; …; J; > : k ¼ 1; 2; …; K

211

ð6Þ

where αland, βgdp, γwater, γso2 and γsoot are the adjustment ratios of the corresponding indicators. The ILGUE is obviously between 0 and 1, where industrial land is efficiently used when the ILGUE is equal to 1 and is inefficiently used when the ILGUE is b1. 3.2. Global Malmquist index for measuring industrial land productivity growth The traditional ML index faces potential limitations of linear programming that cannot be resolved, and it does not have cyclicity or transitivity. In response, Oh (2010) combined the concept of productivity and the directional distance function, constructing a global MalmquistLuenberger (GML) index to replace it. Consequently, we adopted this

imposed in Eqs. (7) and (8). The superscripts G and D relate to the solutions under the global benchmark technology set TG and the contemporaneous benchmark technology set TD, respectively. In addition, technical change (TC) refers to the shift of the production frontier, and if the value of TC is greater than, equal to, or b 1, it indicates that production technology is enjoying progress, is not changing, or is suffering deterioration, respectively. The scale efficiency change (SEC) and pure technical efficiency change (PTEC), which occur on the same production frontier, have values greater than, equal to, or b 1, having the same meaning as those of values of TC. With these tools, we can investigate the impacts of the decomposition indices of the NMILPI index on the ILGUE. 3.3. Influencing factors analysis As mentioned in Section 1, the four industrial zones are distributed from the north to the south of the eastern seaside of China. These zones are bound to have large differences in political conditions and geographic environment. Therefore, in the second stage, we explore the factors influencing policy in the four regions. The econometric model is as follows: ln yit ¼ α it þ β1 POLit þ β2 ln LFP it þ β3 ln FDI it þ β4 lnPGDP it 2 3 þ β5 ð lnPGDP it Þ þ β6 ð lnPGDP it Þ þ β7 lnLUIit þ β8 UBRit þ μ it ð9Þ where i and t (t = 2003, …, 2013) represent city i and year t in each zone, respectively. The term, αit is a constant, and μit is the random error term. yit is the ILGUE for city i. For the land policy-related factors, POL represents the central government land policy. Considering that the regulatory framework of “Notice of the relevant issues concerning the adjustment for approval of urban construction land from the State Council,” which was implemented by the Ministry of Land and Resources in 2007,1 and “Measures for the management of the national 1

http://www.mlr.gov.cn/zwgk/zytz/200701/t20070123_79059.htm.

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eco-industrial demonstration zone” issued in the same year, which aimed to establish a national eco-industrial system,2 the policy variable is assigned the value of 1 from 2007 and the value of 0 before 2007. LFP refers to the land financing policy for financing local governments by leasing land for industrial production, which is very popular in cities with fiscal challenges in China. Here, it is defined by the fiscal gap ratio. FDI represents the foreign direct investment, which refers to proportion of foreign enterprises' output account for total industrial output, on behalf of the local government's land policy (Zhang, 2014). As for the non-political factors, PGDP is the per capita industrial GDP by industrial workers. LUI is land use intensity, which is represented by economic output per square kilometer of urban construction land. URB is the urbanization rate, which is expressed as the proportion of the population in the municipal district versus the total population of the city (Xiong and Guo, 2013). Additionally, a higher level of GDP per capita is assumed to lead to greater land use efficiency because a region with relatively higher GDP per capita always focuses on the quality of its industrial economic development, which generally requires greater land use efficiency. However, according to the environmental Kuznets curve (EKC) hypothesis, the PGDP does not necessarily have a linear relationship with ILGUE in most cases, and there can be quadratic or cubic terms in the relationship between the PGDP and ILGUE (Choi et al., 2012). Thus, we add the quadratic and cubic terms to Eq. (9) to explore the nonlinear relationship between PGDP and ILGUE. In addition, land use intensity generally refers to the investment intensity or economic output per unit area of land, thus, we choose the latter in this study. It is obvious that the higher the industrial GDP per unit area of land is, the greater the intensity of land use is; therefore, we hypothesize that there might be a positive correlation between land use intensity and ILGUE. Moreover, as is well known, due to the scale effect of urban development, the higher the urbanization rate is, the more advanced the production and management technology of a city are, which can be helpful for realizing relatively high resource use efficiency. Therefore, we hypothesize that there might be a positive correlation between the urbanization rate and ILGUE (Xiao et al., 2015).

is converted into 1 Chinese yuan (CNY) at the constant value in 2013. Then, the relative shadow price of inputs (PX) with regard to the desirable output (PY) can be expressed as follows: PX ¼ 1 CNY 

v w

ð11Þ

3.5. Data The ILGUE could be affected by many factors from social, economic, and environmental perspectives; therefore, there is requirement to consider environmental, as well as economic, outputs. We construct an indicator system for the evaluation of ILGUE, as has been done in many previous studies (Zhang et al., 2014a), using the following input and output indicators. (1) Input indicators: The input factors mainly include land, capital and labor, in accordance with production function theory, and they respectively refer to the area of industrial land, the annual average net value of industrial assets — which can well represent real assets used in industrial production (Zhang, 2014) — and the workers for industrial production. (2) Output indicators: We chose industrial GDP as the desirable output in the process of industrial production, and the three infamous by-products of industrial production (namely emissions of industrial sulfur dioxide, industrial wastewater, and industrial soot) as undesirable outputs. In this study, the data of 55 cities in this paper are sourced from the China City Statistical Yearbook (2004–2014), Regional Economic Statistical Yearbook (2004–2014) and China Statistical Yearbook (2004–2014). To eliminate the effects of price factors, the industrial GDP and the annual average net value of industrial assets are converted to 2003 constant prices according their own deflectors. 4. Empirical results

3.4. Shadow price of industrial land 4.1. ILGUE As the third step of the evaluation for the purpose of making reasonable adjustments to the actual price of industrial land, we can use the dual DDF model to compute the appropriate price for each city, and the dual form of Eq. (4) can be expressed as follows (Zhang et al., 2015): min vm xm −w j y j þ r k uk s:t: vm xm −w j y j þ r k uk ≥0  1 1 1 ; … ; ; … ; vm ≥ Xm X X 1 M 1 1 1 ; … ; ; … ; wj ≥ Yj YJ  Y1 1 1 1 rk ≥ ; … ; ; … ; B1 Bk BK

ð10Þ

According to Eq. (5), we can compute the ILGUEs for the four industrial zones. As shown in Fig. 3, rising ILGUE was found in all four zones during the research period. This finding might be attributable to increasing industrial economic efficiency and the more effective treatment of industrial pollution during the industrial development of China in recent years (Yi and Liu, 2015) and this is not consistent with the conclusions of Guo and Xiong (2014), which found that industrial land use efficiency showed a downward trend in the provinces of eastern China during 2005–2010, perhaps because these authors did

where v ∈ Rm, w ∈ Rj and r ∈ Rk, respectively, refer to the dual variables of the inputs, desirable outputs and undesirable outputs, which are solved by the Eq. (10). The purpose of Eq. (10) is to minimize the virtual cost of industrial production such that when a city under evaluation is absolutely efficient in industrial land utilization, the virtual cost is zero. Additionally, v ∈ Rm and r ∈ Rk refer to the shadow prices of the inputs and undesirable outputs, which are non-negative numbers. w ∈ Rj can be expressed as the marginal virtual income of the desirable outputs. We can assume that the absolute shadow price of the desirable outputs (e.g., land) is equal to its market price (PX), and the absolute shadow price of industrial GDP is equal to its market price (PY), which 2

http://www.zhb.gov.cn/gkml/zj/wj/200910/t20091022_172490.htm.

Fig. 3. Trends in the ILGUEs of four industrial zones, 2003–2013.

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not employ a global DEA approach, and the industrial pollutants were not considered. According to the statistical data from the China City Statistical Yearbook 2004–2014, the discharge of industrial wastewater and sulfur dioxide on 1 km2 has, respectively, decreased by 39.2% and 57.8% in 2013 compared to 2003 in the four zones. These data suggest that industrial pollution problems have been effectively mitigated, explaining the improvement in ILGUE. In addition, there are noticeably higher values for the ILGUEs for Pearl River Delta zone in 2009 and BeijingTianjin-Tangshan zone in 2010. This may due to the famous global financial crisis in 2008, which caused obvious negative impact on industrial development across China, especially regions with relatively high reliance on export (Wu et al., 2014). The local government of Pearl River Delta zone was forced to stop the blind expansion of industrial land and try to improve land use efficiency.3 Beijing-Tianjin-Tangshan zone took similar measures after a short while, which was beneficial to raise land use efficiency. However, the Yangtze River Delta zone was actively expanding the scale of the industry, and the Central and south Liaoning zone has been focused on heavy industry sector with considerable economic benefits but serious environmental pollution (Li et al., 2016). This may be the main reason why the ILGUEs of Yangtze River Delta zone and Central and south Liaoning zone showed poorer performance at the same time. Additionally, none of the four zones has achieved effective use of industrial land. The Pearl River Delta zone has the highest value of ILGUE, with the exception of 2010, and it has the highest average value of ILGUE (0.467) of the four zones, followed by the Beijing-TianjinTangshan zone, which has an average ILGUE value of 0.312. The average values of ILGUE in the Yangtze River Delta zone and the Central and south Liaoning zone are 0.213 and 0.194, respectively. These values indicate that the ILGUEs of these four zones – 53.3%, 68.8%, 78.7%, and 80.6%, respectively – fall far short of the production technology frontier. In other words, if they were to use industrial land more effectively, they could respectively save 151.26 km2, 310.9 km2, 1593.08 km2, and 761.1 km2 per year. Therefore, the industrial land in the four zones that could be saved is as much as 2816.32 km2 per year, accounting for approximately 71.8% of the total area of industrial land in China, which suggests that the waste of industrial land appears to be quite serious. We can perform comparative analysis using the average values of ILGUE at the city level during the research period. As shown in Table 1, the three cities with the highest average values of ILGUE are located in the Pearl River Delta zone (i.e., Shenzhen, Foshan and Zhongshan), and the average values of ILGUE are 0.778, 0.858 and 0.773, respectively, indicating that even these three most efficient cities could improve their ILGUEs by 22.2%, 14.2% and 22.7%, respectively. In other words, the three cities can respectively save industrial land of 30.71 km2, 8.35 km2 and 5.02 km2 per year. The ILUGEs of other cities are far less than those of these three cities, which are all b0.5, indicating that ILGUE could be improved by N50% in the research period. The city with the lowest ILGUE is Jinhua in the Yangtze River Delta zone, the average ILGUE of which is only 0.048, indicating that the ILGUE can be improved by as much as 95.2%, if the industrial land were used more effectively. All of these findings suggest that there are large differences in ILGUE in different cities and, thus, in the potential for saving industrial land. In short, the ILGUE of most of the cities in the four zones has increased considerably, but few cities have approached perfect utilization of industrial land, as would be indicated by an ILGUE value of 1. A conceivable explanation is the long-standing habit of China's industrial development, which overly focuses on industrial economic benefits without considering land use efficiency or ecological and environmental protection (Xiao et al., 2015). In fact, China's central government has 3 Ministry of Housing and Urban-Rural Construction of the People's Republic of China. http://epub.cnki.net/kns/detail.aspx?DBCode=CYFD&fileName= N2013120079000013&DBName=CYFDLM_total. (In Chinese).

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long emphasized this problem, issuing a series of regulatory notices in support of efforts to achieve green industrial land use. For example, the Land Management Law explicitly requested that Chinese cities perform scientific development planning for the purpose of curbing blind expansion, to protect the ecological environment and to promote sustainable development4 (Ding, 2003). However, due to the conflicting interests between the central and local governments, local government officials are often eager to make great displays of achievement (e.g., great expansion of city size and GDP) by disobeying the scientific planning of the central government. In addition, a large amount of money is needed to maintain the normal operations of eco-friendly industrial land promotion, and financial difficulties place great pressure on local governments (Ding et al., 2014). Moreover, the implementation of the reform of the tax system in 1994 enabled the central government to collect the largest portion of taxes, which led to a much heavier financial burden on the local governments. Therefore, making up for the budget deficit through massive urban expansion and the illegal transfer of industrial land has become a common phenomenon in most cities in China (Liu and Wang, 2015). A research report from the National Development and Strategic Research Institute showed that the ratio of industrial land to urban built-up areas in the city is as high as 40 or even 50%, while this ratio in some foreign developed countries (e.g., France and Japan) is usually b 15% (Bertaud and Renaud, 1997). Therefore, the severe oversupply of industrial land has led to a waste of industrial land for many years. Even in the Pearl River Delta zone, the ILGUE of which is relatively high, the phenomena of illegal land transfers and oversupply are quite common. In the Dongguan City, for example, the area of idle industrial land is 21.75 km2, constituting more than half of the total urban built-up area. The area of idle industrial land was N150 km2 in 2003.5 Considering that new industrial land in Chinese cities is converted from agricultural land, the unreasonable expansion of industrial land has led to a great loss of potential land resources (Deng et al., 2015). This loss might be a major reason why Chinese central government has issued the command to defend the bottom line of 1.8 billion mu (1.2 million km2) of arable land in recent years. In addition, all of the industrial land in China suffers from much lower relative economic efficiency, compared with other countries. It has been reported that, even in the Kunshan export processing zone in the Yangtze River Delta zone, which is known for its intensive land use, the industry investment and GDP on one unit of land are less than one third of those in Singapore and Taiwan.6 Moreover, enterprises in industrial parks discharge a large number of pollutants in the process of production, and many industrial pollutants directly contaminating the surrounding areas due to a lack of supervision, seriously damaging the local ecological environment (Xiao et al., 2015). The Ministry of Environmental Protection has reported that the economic loss caused by water pollution in China is N 240 billion CNY per year, mainly attributed to industrial pollution. To curb the irrational expansion and inefficient use of industrial land, the central government is promoting urgent adjustments. Stricter land policies and closer supervision of local governments have led to evident improvements in ILGUE. Furthermore, the Ministry of Land and Resources delineated the urban development boundaries of large cities (e.g., Beijing, Shanghai and Guangzhou) under the twelfth five-year plan (2011–2015), aiming to regulate the excessive expansion of the cities strictly. The ministry also reported that the boundaries of urban development of cities across China would be determined in a few years, indicating that the central government is determined to manage comprehensively and reasonably the development scale of cities across the entire country. To alleviate serious ecological environment problems caused by traditional industrial parks, the State Environmental Protection Administration issued the “Measures for the management of 4 5 6

http://www.tdwq.net/a/2010225941480197.shtm http://www.chinapark.net/ http://www.guotuzy.cn/html/1501/n-210292.html

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Table 1 Average values of the ILGUE for each city. City

ILGUE

City

ILGUE

City

ILGUE

City

ILGUE

Shenzhen (P) Foshan (P) Zhongshan (P) Tianjin (J) Huizhou (P) Changzhou (Y) Shenyang (C) Guangzhou (P) Hangzhou (Y) Beijing (J) Zhuhai (P) Yangzhou (Y) Shanghai (Y) Panjin (C)

0.858 0.778 0.773 0.462 0.450 0.444 0.437 0.434 0.398 0.391 0.375 0.336 0.323 0.304

Ningbo (Y) Hefei (Y) Nanjing (Y) Zhoushan (Y) Xuzhou (Y) Wuhu (Y) Dongguan (P) Qinhuangdao (J) Wuxi (Y) Nantong (Y) Taizhou (Y) Dalian (C) Wenzhou (Y) Zhenjiang (Y)

0.295 0.294 0.289 0.288 0.266 0.265 0.251 0.246 0.239 0.228 0.222 0.211 0.209 0.200

Jinzhou (C) Yingkou (C) Suzhou (Y) Yancheng (Y) Benxi (C) Lianyungang (Y) Huaian (Y) Fushun (C) Lishui (Y) Ma'anshan (Y) Huzhou (Y) Jiangmen (P) Huludao (C) Tangshan (J)

0.192 0.192 0.191 0.191 0.191 0.189 0.188 0.183 0.166 0.161 0.153 0.151 0.150 0.149

Dandong (C) Suqian (Y) Zhaoqing (P) Taizhou (Y) Shaoxing (Y) Jiaxing (Y) Tieling (C) Chuzhou (Y) Liaoyang (C) Anshan (C) Huainan (Y) Quzhou (Y) Jinhua (Y)

0.137 0.134 0.133 0.129 0.128 0.118 0.118 0.113 0.107 0.106 0.102 0.084 0.048

Note: B, C, Y and P represent the Beijing-Tianjin-Tangshan zone, the Central and South Liaoning zone, the Yangtze River Delta zone and the Pearl River Delta zone, respectively. ILGUE refers to industrial land green use efficiency.

the national eco-industrial demonstration zone” in 2007, advocating vigorous construction of eco-friendly industrial parks and upgrading the production technology of traditional industrial parks.7 The ecoindustry park contains many manufacturing and service enterprises that attempt to manage environmental and economic issues simultaneously to maximize economic benefits and minimize environmental damage. It has been reported that there were 12 national ecoindustrial demonstration zones in 2010, and another 39 were under construction. Most cities have their own eco-industrial parks.8 To avoid repeating the mistakes of environmental pollution, the State Council and some relevant departments have issued many environmental regulation policies, such as the “Water pollution prevention action plan” issued in 2015.9 This policy warns that some industrial parks could be closed down if they do not seriously address pollution problems. Stronger promotion of scientific planning and strengthening of the supervision of local governments will certainly be beneficial for the improvement of ILGUE, and this is consistent with the Porter hypothesis, which insists that strict supervision is conducive to the improvement of enterprise competitiveness and resource use efficiency (Porter and van der Linde, 1995). 4.2. NMILPI and its decompositions According to Eqs. (7) and (8), we can compute the NMILPI and its decompositions indices. Fig. 4 shows the trends of the NMILPI in the four industrial zones, and we find that the NMILPIs of the Yangtze River Delta zone during the study period are always N 1, indicating that the ILGUE in the Yangtze River Delta zone is always increasing, whereas the NMILPIs for the other three zones are b1 in some single years, indicating that the ILGUEs in these zones decreases for some years. Specifically, the Yangtze River Delta zone enjoys the highest average value of the NMILPI, which is 1.206, followed by the BeijingTianjin-Tangshan zone with an average value of 1.205. The average NMILPIs in the Central and south Liaoning zone and Pearl River Delta zone are relatively low, at only 1.146 and 1.142, respectively. The average annual growth rates of industrial land green productivity in these zones are 20.6%, 20.5%, 14.6% and 14.2%, respectively. Regarding the decompositions of the NMILPI, Fig. 5 shows that the values of TC of the four zones are more than one in most years of the research period before 2011, perhaps because these industrial zones have paid greater attention to improving industrial production technology. However, the values of TC in the four zones demonstrate sudden falloffs since 2011. A plausible explanation is that the central 7 8 9

http://www.zhb.gov.cn/gkml/hbb/bwj/201512/t20151224_320098.htm http://www.chinapark.net/ http://www.zhb.gov.cn/gkml/hbb/qt/201504/t20150416_299173.htm

government has introduced a series of economic stimulus plans to maintain the rapid development of the industrial economy in China due to its being greatly affected by the financial crisis of 2008. Consequently, industrial production in the four zones has returned to the old mode of focusing on industrial GDP, while ignoring technological progress and environmental management. Given the situation, there is a need for future development of the industrial economy to return to paying greater attention to improving the quality of industrial economic development, by investing more funds in R&D and by welcoming foreign enterprises with advanced production and management technologies. As shown in Fig. 6, the PTECs of the four zones were b 1 in most years before 2010, indicating that the PTE levels of industrial land are often moving backward. This finding is consistent with the conclusions of Xiong and Guo (2013). Since then, all of the regions in China have actively responded to the order of the central government to improve industrial production technology more vigorously, achieving remarkable results. Regarding the SEC, as shown in Fig. 7, the four zones have different patterns of SECs, but they all show downward trends during 2008–2011, with the SECs of the four zones running to b1 during 2009–2010, indicating that the scale efficiency levels of industrial land production were demonstrating declining trends. This finding might have arisen due to the vigorous expansion of the industrial development scale in response to the negative impact of the international financial crisis. However, this policy has led to the excessive use of input resources in industrial production (e.g., land and energy) due to its focus on boosting industrial output, which leads to diminishing returns of scale on industrial land. Notably, conditions have improved

Fig. 4. Trends in the NMILPIs of the four industrial zones.

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Fig. 5. Trends in the TCs of the four industrial zones. Fig. 6. Trends in the PTECs of the four industrial zones.

since 2011, when the SEC rates of all four of the zones began to increase, possibly because of a series of policies that were issued by the central government in approximately 2010 for the purpose of curbing the blind expansion of industrial land. In addition, local governments have been trying to use idle land and to improve land use intensity — actions that have been helpful in improving industrial land green productivity. In a word, the improvement of ILGUE can be found in all four of the zones, with the remaining reason for inefficient industrial land use possibly being low technical efficiency. Therefore, developing and importing advanced production and management technologies are key issues in future industrial production. In fact, central and local governments have been cooperating on this issue; consequently, advanced foreign production equipment and technologies have been imported in recent years. In the most recent five-year plan (the twelfth), the state council of China issued the Plan for industrial restructuring and upgrading to introduce a series of methods to transform and upgrade Chinese industry, such as enhancing the ability of independent innovators and opening up to the foreign industrial market.10 Notably, industrial enterprises have a relatively weak capacity for independent research, which has prevented full absorption of innovative knowledge and technology. Therefore, greater awareness of these challenges is required, not only in support of innovation going forward but also to encourage increased R&D investment intensity in industrial enterprises (Wu, 2006).

4.3. Influential factors analysis Based on Eq. (9), we explore the specific impacts of the influencing factors on the ILGUE. Considering regional heterogeneity, we further separately perform the regression analysis for each zone. Because the Hausman test results support the fixed effect, and the results, which only incorporate lnGDP and its quadratic term, have a higher adjusted R-squared value than that of lnGDP and of its quadratic and cubic terms, we therefore adopt the result with lnGDP and its quadratic term only, as shown in Table 2. As shown in Table 2, the adjusted R-squared values of the models are all N0.7, indicating that the results explain these models well. According to the sixth column of Table 2, we can find that, in general, the coefficients of all the influencing factors are statistically significant. Specifically, the coefficients of POL, LFP, FDI and UBR are positive, which indicates that land management policy has realized expected purpose to improve land use efficiency, and increases in fiscal gap ratio of local government, the proportion of foreign enterprises' output account for total industrial output, and the urbanization rate have positive impacts on the ILGUE. However, the land financing policy has an unexpected positive impact on the ILGUE, this may be attributed to the short-term economic benefit 10

http://www.gov.cn/zhengce/content/2014-08/06/content_8955.htm

caused by money from selling land. In addition, there is a positive sign for lnGDP and a negative sign for its quadratic term, which indicates a U type relationship between lnGDP and the ILGUE. At the regional level, regarding land policy-related factors, the coefficient of POL is significantly positive in the Beijing-Tianjin-Tangshan zone, the coefficients are not statistically significant in the other two zones, and the coefficient is actually negative in the Yangtze River Delta zone. These findings indicate that the central government's land policy has had a significant, positive impact only on the ILGUE in the Beijing-Tianjin-Tangshan zone. The implication is that, because Beijing is located in the Beijing-Tianjin-Tangshan zone, the central government's policies are more thoroughly implemented. In contrast, the Yangtze River Delta zone is rapidly expanding its scale and has recently become a large region with 30 cities, rendering local governments unable to address land problems effectively. It is observed that local governments do not always or fully implement the central government's land policy, especially in regions far from the Chinese capital of Beijing. Therefore, more strict supervision of local government is necessary. The coefficients of lnLFP are significantly negative in the BeijingTianjin-Tangshan and Pearl River Delta zones and significantly positive in the Yangtze River Delta zone, indicating that the land financing policy has had a significantly negative impact on ILGUE in the Beijing-TianjinTangshan and Pearl River Delta zones but a positive impact in the Yangtze River Delta zone. The areas of the former two zones are relatively small, making it difficult to earn financial revenue from transferring industrial land. However, its successful expansion in the second half of 2010 has brought the Yangtze River Delta zone a large amount of undeveloped land, making it easier to earn money by transferring land for industrial production (Liu and Shen, 2010).

Fig. 7. Trends in the SECs of the four industrial zones.

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Table 2 Results of regressions.

POL lnLFP FDI lnGDP (lnGDP)2 lnLUI UBR Constant Adjusted R2 F-statistic Prob.

Beijing-Tianjin-Tangshan

Central and South Liaoning

Yangtze River Delta

Pearl River Delta

Together

0.299*** (2.717) −0.604*** (−2.718) 0.004 (0.891) −0.533*** (3.795) 0.231** (2.215) 0.300** (2.261) 0.028*** (4.486) −4.161*** (−6.253) 0.885 37.752 0.0000

0.013 (0.057) −0.033 (0.547) 0.003*** (3.142) 0.001 (0.02) 0.337 (0.012) 0.513*** (9.642) 0.006*** (2.679) −3.901*** (−12.523) 0.720 20.798 0.0000

−0.049 (−0.974) 0.059*** (3.862) 0.009*** (6.009) 0.036 (1.145) 0.008 (1.022) 0.660*** (18.008) 0.001 (0.637) −4.596*** (−33.891) 0.760 144.124 0.0000

0.178 (0.254) −0.005** (2.311) 0.523*** (2.932) −0.866** (−2.641) 0.089** (2.921) 0.523*** (7.943) 0.177* (1.931) −2.023** (−2.023) 0.763 41.982 0.0000

0.005*** (5.525) 0.016*** (2.895) 0.005*** (5.525) −0.472*** (−4.681) 0.058*** (5.358) 0.612*** (25.100) 0.003*** (4.637) −3.241*** (−13.820) 0.762 276.879 0.0000

Note: *, **, and *** indicate significance levels of 10%, 5% and 1%, respectively. POL refers to land management policy, LFP refers to the land financing policy, LUI refers to land use intensity, FDI represents foreign direct investment, GDP represents per capita gross domestic product, and UBR represents the urbanization rate.

The coefficients of FDI are significantly positive in the Central and south Liaoning, Yangtze River Delta, and Pearl River Delta zones, indicating that higher foreign investment is helpful in raising the ILGUE in these zones. Additionally, the coefficient in the Pearl River Delta zone is significantly larger than that in the Central and south Liaoning and Yangtze River Delta zones such that the Pearl River Delta zone has enjoyed the greatest positive impact due to its having the longest history of the special economic zone policies across China, resulting in the strongest capacity for the adoption of new technology. However, this is inconsistent with the conclusions of Zhang (2014). A plausible explanation is that Zhang (2014) only used the data of Chinese cities in 2010, while our study has performed a dynamic analysis of the ILGUE using 11 years of data. Regarding non-policy factors, there is an inverted U curve relationship between the lnGDP and the ILGUE in the Beijing-TianjinTangshan and Pearl River Delta zones. However, this relationship does not exist in the Central and south Liaoning and Yangtze River Delta zones because the coefficients of the lnGDP and its quadratic term are not statistically significant in these two zones. The coefficients of the lnLUI and UBR are significantly positive in all four of the zones, indicating that an increase in land use intensity and urbanization rates in the four zones could improve ILGUEs.

4.4. Shadow price of industrial land According to previous studies of land prices (Wu, 2006), the prices of most resources are underestimated for production in China, which is not helpful for sustainable development. As mentioned in Section 2, even when industrial land prices are an important factor affecting ILGUE, the actual prices of industrial land in China are mainly determined by local governments and land developers, and they easily diverge from the reasonable prices of the land market due to the significant benefits of industrial economic development. Therefore, computing an appropriate price for industrial land would greatly enhance its sustainable use. According to Eqs. (10) and (11), we can compute the trends in the shadow prices of industrial land for each city in the four zones. As shown in Fig. 8, the shadow prices of industrial land in the four zones share the same pattern of rising trends. The price in the Beijing-Tianjin-Tangshan zone has ranked the highest since 2010, at up to 1.34 billion CNY per square kilometer. The price in the Pearl River Delta zone ranks the second highest, with a value of billion 1.24 CNY per square kilometer. The prices in the Yangtze River Delta and Central and south Liaoning zones are relatively low, with values of 0.99 and 0.87 billion CNY per square kilometer, respectively. According to the “Standard of lowest price in Chinese industrial land transfer” issued by the Ministry of Land and Resources in 2007,11 the price of industrial land in Shanghai ranks the highest of Chinese cities, with a value of only 0.84 billion CNY per square kilometer, which is far less than the average shadow prices in all four of the zones. Therefore, the prices of industrial land for cities in the four zones are generally much lower than the shadow prices. As mentioned above, it is evident that a modest increase in price can improve ILGUE and sensitivity to the need to save land, as demonstrated in Fig. 9, which shows the significantly positive relationship between actual prices of industrial land (APIL) and the ILGUE of the cities. To provide a reasonable adjustment to the industrial land prices for cities, we established an industrial land price performance index (ILPPI) to reflect the deviation of industrial land prices at the end of the research period:

 APIL ILPPI ¼ 1− SPIL

Fig. 8. Trends in shadow prices of industrial land for each zone, 2003–2013. Unit: 108 CNY/km2.

11

http://www.mlr.gov.cn/xwdt/zytz/200612/t20061227_78789.htm

ð12Þ

H. Xie et al. / Technological Forecasting & Social Change 112 (2016) 207–219

Fig. 9. The relationship between the APIL and ILGUE of the cities, 2007–2013.

where APIL and SPIL refer to the actual price of industrial land and the shadow price of industrial land, respectively. In consideration of the conditions of the industrial land market in China, we assume that a shadow price N5 times the actual price can have a significantly negative impact on sustainable economic development. Thus, as shown in Fig. 10, in the 12 cities marked with red circles, the shadow prices of industrial land of which are N 5 times the actual prices, and their ILPPI is between 0.8 and 1, so these cities require the utmost effort to encourage more appropriate prices for land. Of these 12

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cities, Wenzhou has the highest ILPPI of 0.95, indicating that the shadow price of industrial land is 20 times the actual price. Therefore, Wenzhou and the other cities with red circles should raise prices to an appropriate and sustainable level. For another 39 cities marked with green circles, their values of ILPPI are between 0 and 0.8, indicating that the shadow prices of industrial land in these cities are 1 to 5 times the actual prices. Most of these cities do not have effective usage of industrial land, but their industrial land waste is not as serious as that of the above 12 cities, thus, their prices for industrial land should be increased moderately. For the 4 remaining cities – namely Shanghai, Anshan, Jinhua and Dandong, which are marked with blue circles – their ILPPIs are b0, indicating that the shadow prices of industrial land in these cities are less than the actual prices. Considering that these cities cannot effectively use industrial land, the actual prices could be maintained or moderately reduced, according to each situation. In a word, the cities with red circles are mainly located in the Yangtze River Delta and Pearl River Delta zones, with 8 of them belonging to the Yangtze River Delta zone (i.e., Wenzhou, Zhoushan, Wuhu, Nantong, Yancheng, Xuzhou, Changzhou, and Yangzhou) and 3 belonging to the Pearl River Delta zone (i.e., Foshan, Zhongshan, and Zhuhai). The explanation for this departure of land prices from the norm might come from the relatively faster industrial economic development of these two zones. Therefore, industrial land prices should be increased accordingly. However, Shanghai and Jinhua are exceptional cases, and their shadow prices for industrial land are less than the actual ones. A plausible explanation could be that the actual price of industrial land in Shanghai is the highest in China, which is close to the shadow price; moreover, significant increases in the demand for land in recent years, in Pudong and other districts in Shanghai, have led to a large-scale expansion of the

Fig. 10. ILPPIs for each city, 2013.

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industrial land base and price increases. Regarding Jinhua, its industrial development has been relatively backward compared to some other cities in the Yangtze River Delta zone (e.g., Shanghai and Wenzhou) such that the price of industrial land could be decreased to reduce industrial production costs and to accelerate the development of the industrial economy. A similar situation can be found in Dandong and Anshan, which belong to the Central and south Liaoning zone. Cities with green circles account for approximately 71% of all of the cities in the research region, and they are distributed across all four of the industrial zones. In short, industrial land prices for all cities deviate from the reasonable price, and we must make reasonable adjustments according to the actual situation in each city. 5. Conclusions Based on GGDDF and GML approaches, we compute the dynamic changes in ILGUE in the 4 industrial zones of China, the NMILPI, and its decompositions. Then, we analyze the impacts of the factors influencing ILGUE. In addition, we compute the shadow price of industrial land employing the dual GGDDF model, proposing some suggestions to adjust industrial land prices by comparing the difference between actual land prices and shadow prices for each city. The major points from the results are summarized as follows. First, the Pearl River Delta zone enjoys the highest ILGUE, followed by the BBT zone, while the ILGUEs in the Yangtze River Delta and Central and south Liaoning zones are relatively low. At the city level, Shenzhen in the Pearl River Delta zone has the highest average ILGUE, while Jinhua in the Yangtze River Delta zone suffers from the worst average ILGUE during the study period. This finding indicate there is a great potential to improve eco-friendly efficiency in the cities, especially in the Yangtze River Delta and Central and south Liaoning zones in the southern part of China. Second, the NMILPIs for each zone are basically greater than one in the research period, indicating that the ILGUEs are rising. The low ILGUE is mainly due to low technical efficiency of the process of industrial production, but it could be enhanced by scale efficiencies, given that, among the three decompositions of the NMILPI, the scale efficiencies of industrial land in the four zones have the best performance. Third, the central government's land policy is only significantly effective in the Beijing-Tianjin-Tangshan zone due to its physical proximity to the central government; thus, supervision of the local governments is necessary to ensure the implementation of the central government's land policy. The local government land policy, known as the “land financing policy,” has negative impacts on ILGUE in all but the Yangtze River Delta zone. In addition, other local government land policies, which attract foreign investment in industrial production, are helpful in increasing the ILGUE of all four of the zones. Finally, the shadow prices of industrial land in the four zones indicate a rising trend during the research period, with the actual prices of industrial land in most cities running less than the shadow prices. Of the 55 cities in the four zones, 12 cities (e.g., Wenzhou, Foshan, and Zhoushan) should raise prices substantially, 39 cities (e.g., Taizhou, Tianjin, and Huai'an) could moderately increase the prices of industrial land, and the 4 remaining cities (namely, Anshan, Shanghai, Jinhua and Dandong) should consider maintaining or moderately reducing their actual prices of industrial land. There are also some limitations of this study. For instance, we have only selected three industrial pollutants as environmentally undesirable outputs, but some important pollutants from industrial wastewater are not considered, such as chemical oxygen demand and ammoniac nitrogen, mainly due to data being unavailable. In addition, factors from a human health perspective, such as human capital, are not considered in this study, for the same reason of data unavailability. However, the inclusion of these factors could enhance future studies.

Acknowledgments This study was supported by the National Natural Science Foundation of China (41561040; 71273286), the Natural Science Foundation of Jiangxi Province (20143ACB21023), the Fok Ying Tung Foundation (141084), and the Technology Foundation of Jiangxi, Education Department of China (KJLD14033), the Key project of National Social Science Foundation (15AZD075), and the Key project of Social Science Foundation of Jiangxi Province (15ZQZD10).

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