Industrial land use efficiency under government intervention: Evidence from Hangzhou, China

Habitat International 43 (2014) 1e10

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Industrial land use efficiency under government intervention: Evidence from Hangzhou, China Fan Tu a, *, Xiaofen Yu a, Jianqing Ruan b a b

School of Economics & Management, Zhejiang University of Technology, Hangzhou 310023, China School of Management, Zhejiang University, Hangzhou 310058, China

a b s t r a c t Keywords: Land use efficiency Industrial land Government intervention Urban land China

With a background of rapid urbanization and an already vast population in China, promoting land use efficiency to curb urban sprawl has significant influence upon sustainable urban development. As the “world’s factory”, improving industrial land efficiency is pivotal in optimizing urban land use. Using a binary logistic analysis based on the data from 2000 to 2011 in Hangzhou, this paper analyzes whether the policy from the central government to promote industrial land leasing publicly at the end of 2006 reduced underdevelopment and idled land use behavior. It has been found that the industrial land use is more influenced by industry sub-type, the year of the land lease and land size than policy intervention. In light of strong government intervention, a lack of equity with a bundle of conditions for land development without strictly implementing measures that do not conform with land leasing contracts leads to one-on-one negotiation, relatively low land leasing prices compared to the secondary market land price, and low land use efficiency. Thus, this paper suggests that government intervention should focus more on promoting a more market-oriented environment with strict supervision during land development, rather than on specific conditions on each industry sub-type and factory. Ó 2014 Elsevier Ltd. All rights reserved.

Introduction While not a particularly common problem prior to the 1960s, urban sprawl eventually became an issue in developed countries and larger cities in developing countries after that point (Couch, Leontidou, & Petschel-Held, 2007; Keiner, Koll-Schretzenmayr, & Schmid, 2005; Sorensen, 1999; Squires, 2002). Urban sprawl causes a variety of negative effects, including, but not limited to, loss of farmland, environmentally fragile land, open regional space, as well as risks to developing countries’ food security (Johnson, 2001; Zhang, Chen, Tan, & Sun, 2007). Under improper management, current levels of urbanization and sprawling development could become the root of more serious environmental troubles (UNHabitat, 2008). Many researchers have analyzed theoretically the urban growth controls. In open city models (two-city, three-city)(Engle, Navarro, & Carson, 1992; Helsley & Strange, 1995), optimal growth controls are analyzed with consideration towards the effects on land rent,

* Corresponding author. School of Economics & Management, Zhejiang University of Technology, Hangzhou 310023, China. Tel.: þ86 18357133022. E-mail address: [email protected] (F. Tu). http://dx.doi.org/10.1016/j.habitatint.2014.01.017 0197-3975/Ó 2014 Elsevier Ltd. All rights reserved.

landlord’s welfare, and on the provision costs of public goods with the incorporation of production activities, externalities, public goods, and restricting land supply (Brueckner & Lai, 1996; Cooley & Lacivita; Hannah, Kim, & Mills, 1993). In practice, planning experiments have sought to limit urban expansion by attempting to raise central densities. The discussion in previous research focuses on two kinds of government interventions, direct and indirect. Direct intervention includes zoning, containment, and a controlled supply of land (Bengston, Fletcher, & Nelson, 2004; Dieleman, Dijst, & Spit, 1999; Erickson, 1995; Hill & Kim, 2000; Nelson & Hellerstein, 1997; Tian & Ma, 2009; York & Munroe, 2010). Indirect intervention methods implemented to combat sprawl are land or split-rate taxes. Land or split-rate taxes are in force through capital/land ratios from Australia, Denmark, and parts of Indonesia to the United States (McCluskey & Franzsen, 2005; Youngman & Malme, 1994). Until now, there has been little research on the practice of policy instruments improving industrial land use efficiency. Most literature about industrial land use is focused on location, relocation, and land prices (Ambrose, 1990; Fehribach, Rutherford, & Eakin, 1993; Hodgkinson, Nyland, & Pomfret, 2001; Lockwood & Rutherford, 1996). Costs of transportation, financial capital, land and labor are considered as main locational factors (Harrington, 1995; Miller, 1977). In China, industrial land occupies over 20% of the urban

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F. Tu et al. / Habitat International 43 (2014) 1e10

area. In some developed areas, for example, Shanghai and Suzhou, that percentage is as high as 26% and 32% (Jia, Huang, Yu, Wang, & Zheng, 2010). With industrialization, more focus than ever has been placed on the question of “How can we make more efficient use of industrial land?”. In 2004, China’s central government began to implement the policy of competitive allocation of state-owned land to industrial land use through auction, tender, or requests for listings (participants requested by public advertisement to submit a confidential, sealed order for an industry property are referred to as “listing tenders”, but they are, in fact, much closer to the auction format). The end of 2006 put this policy into practice all across the nation. This paper investigates the role of government as it relates to industrial land use efficiency after the overall implementation of publicly leasing industrial land in China from the end of 2006. It will survey the data of industrial land use parcels from 2000 to 2011, using the end of 2006 as a benchmark. The objective of government policy is to promote a competitive and open market, improve land-leasing prices and prohibit low-density use. Thus, the intent of this paper is to explore industry market and land use efficiency reactions to government policy intervention, using the case study of Hangzhou, China. After the central government’s sevenyear practice of supervising the public leasing of industrial land, the surprising finding is that this form of competitively leasing state-owned land to industries does not achieve the original objective, which was to improve land use efficiency through higher land prices. Industrial land use efficiency and industrial land market Evaluation of industrial land use efficiency China’s land use planning was enacted in 1985, and its effects on arable land protection have been positive (Li & Yeh, 2004; Peng, Cheng, Xu, Yin, & Xu, 2007). The key issues in China’s land use planning are to project the size of cities over the next 20 years and determine the space suitable for construction. In order to achieve a reasonable plan, analyzing the existing land use efficiency, especially the industrial land, is important. The evaluation of industrial land use efficiency involves comparisons from different sides, including comparisons from different industry sectors, both inside and outside the development zones, comparisons between industrial land obtained through transactions and by other means, and comparisons between land use efficiency changes over time (Meng et al., 2008). In terms of indicators for evaluation, in 2010, the Ministry of Land and Resources developed a nationwide evaluation for national level and provincial level industry parks. It used 16 indicators categorized under four objectives: land use situation, land output situation, output profit, and land management (Table 1). Industrial land market under government intervention The rapid development of industry in Asia is due to appropriate government intervention in the markets (Wade, 2003). Government-developed industrial parks are characterized by a government intervention mode, which may involve comprehensive legislation, the establishment of organizations and institutions, offers of competitive production environments and management services, and the formation of independent industrial parks. The government plays a complex role in making decisions regarding location, land use zoning regulations, and sale prices at first tradeoff, while also offering industrial park management services (Lin & Ben, 2009). In China, the first industrial park was set up in 1984. Depending on the levels of benefits, there are many different kinds of industrial districts, including industrial parks, economic development zones, tariff-free zones, special economic zones and hi-tech development zones.

Table 1 Indicators for evaluating land use efficiency across the national and provincial level industry parks. Objective

Sub-objective

Indicator

Land use status

Intensity of land development

Land development ratio Land supply ratio Land built-up ratio Industrial land ratio High technology industrial land ratio Comprehensive FARa Building density Comprehensive ratio of industry land Building density of industry land Fixed assets investment intensity of industry land Capital output intensity of industry land Output capital intensity of high technology industry land Expired project disposal ratio Idle land disposal ratio Land use ratio Land public leasing ratio

Land structure Land output situation

Intensity of land use

Output profit

Industrial land input and output profit

Land management

Land use supervision Land supply marketization

a FAR: the total area of a building divided by the total area of the lot the building is located on. Higher FARs tends to indicate more dense construction. Source: Ministry of Land and Resources.

In China, compared with the commercial and residential markets, the industrial land market is not a completely competitive market. Prior to 2006, although the time period for industrial land can have a maximum of 70 years for use, the price was negotiated between the government and developers. The low industrial land price in China can be explained through several factors: the low cost of production for industrial land, economic contributions, the risk analysis of industrial investment, and the regional competitiveness from local governments (Weidong & Zhonghong, 2008; Wu, 2007). In 2006, China’s economy grew 10.7%, the fastest pace since 1995, amid growing signs of inflation. Soaring exports, stronger retail sales, manufacturing booms, and huge investments in new buildings, roads, and cities largely propelled the stronger-thanexpected growth. The central government tried to cool the economy by raising interest rates and pressuring banks to temper lending, but the economy continued to charge ahead, climbing from 9.1% in 2002 to 10.7% in 2006, the fourth consecutive year of double-digit economic growth. Under these circumstances, the land policies were formally and systematically put forward by the central government as a macroeconomic control tool. The primary objectives of land policies are to control land supply quantities to a reasonable level through comprehensive land planning and yearly land supply plans, to conduct land supply directions, to support the key and advanced industry sectors that are encouraged by the country, and to limit the oversupply industries. Based on the 2002 edition of the “Rules on the Assignment of State-Owned Land Use Rights by Means of Bid Tendering, Auction and Quotation”, the State Council modified the article so the amendment specifies that “Industry, commerce, tourism, entertainment, and profit-oriented residential land, or land that has two intended uses, must be transferred through bidding, auction, or listing approaches”. After the implementation of these regulations, the open industrial land transfer system was finally set up and put into practice. After almost five years of these practices, although land prices may differ across the nation, there are three common characteristics. First, the land transfer price must be set above the ground transfer price, according to the government standard, which would be modified every few years. Secondly, the listing method is the

F. Tu et al. / Habitat International 43 (2014) 1e10

most frequently used approach in industrial land transfers. The content of the public announcement listing became more detailed than ever, covering specific industry types, the investment density, the development specifications and other conditions. Finally, most industry lands were cleared before the land transfer and could be developed directly after the land transferees received the land use rights, which effectively guaranteed the benefits of the developers. Impacts of industrial land policy change in China: a case of Hangzhou Hangzhou, the capital of Zhejiang province, is located in the Yangzi River Delta in East China, 180 km southwest of Shanghai, and is one of the fastest growing cities in China. The city has a total area of 596 km2, with 8 districts, 3 county-level cities and 2 counties. Altogether, there are 193 villages and towns (districts). The urban core area is made up of 8 districts, consisting of about 683 km2. By the end of 2011, there were 873.8 million long-term residents. In 2011, Hangzhou’s GDP reached 701.18 billion CHY,1 ranked eighth among all of China’s cities. The GDP per capita was US$12,447 at the average exchange rate (Hangzhou Statistical Bureau, 2012). This research has selected Hangzhou as a case study based on the two factors: first, the land market in Hangzhou is ahead of other cities, characterized by government support and conduct. After Shanghai, Hangzhou was the second city to have set up a land reserve model in 1997, which was recognized as the “Hangzhou model.” Second, Hangzhou’s economic development and geographical location could be considered a typical case of China’s eastern area, which is more developed compared to the other cities in central and west China. Analyzing of impacts from government policy Using the industrial land use samples from 2000 to 2011, this paper evaluates the land use efficiency changes from the policy changes, and further analyzes the impacts of different factors to the industrial land use changes. The key issues will focus on whether government policy can have influence and whether it can act or not. Crucial to this study was the collection of accurate data in industrial land use. The complete industrial land parcel data came from the Hangzhou Land and Resources Bureau (HLRB), including industrial land leasing cases from the government to enterprises (not including cases on the secondary market). Because the supervision of the land development system was not completely built up, it was difficult to find complete records for each parcel regarding the process of land development, and information on accurate FAR and building density. Consequently, we collected all the parcels, which had already been surveyed regarding the development process by the HLRB until the end of 2011. Altogether, there are 612 samples, about 49.7% of all of the land parcels over twelve years. The periods before and after 2007 are separated into two time phases. Prior to 2007, the industrial land transfer system was not yet completely built up. After policies from the State Council, marked by the modified edition of “Rules on the Assignment of the State-owned Land Use Right by Means of Bid Tendering, Auction and Quotation,” there were many changes that occurred in the industrial land transfer approach and procedure. The following analysis includes three parts: 1. Change of land leasing attributes. The change in industrial land supply, land prices and industry types will be analyzed in this section. These three characteristics can directly reflect the market reaction to the government policies.

1 CHY: the currency of China, exchange rate between the US$ and RMB at the end of 2011 was 6.3.

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2. Change of land use efficiency. Improving land efficiency needs to have common notions from the central government, local governments and industry firms. From the illustration about publicly implementing the industrial land leases, the determination of central government becomes evident. But the influence of the industrial land use efficiency cannot omit the reactions from local governments and the positive reactions of enterprises. From the land use efficiency analysis, we can observe the results of the policy. 3. Impacts from different factors on land use efficiency. This paper uses a binary logistic regression model to analyze the different factors’ influences. The factors are categorized into three types: land parcel attributes, government policy, and industry agglomeration. We’ve used land industry type, land area and the year of the land lease to present the land parcel attributes. Dummy variables representing the period before and after the end of 2006 indicate the different government policies. The land parcels located at the national and provincial level industry parks represent the factors of industry agglomeration. Change of land use attributes: land supply, land price and industry type land supply In China, the Ministry of Land and Resources implemented the land supply plan from top to bottom. Land supply is comprised during the long periods of comprehensive land planning and yearly land supply plans. The former usually lasts about ten to fifteen years, depending on the quantity and space distribution of land that can be transferred from agricultural lands to urban construction land for the local government. The yearly land supply plan decides how much land value will be leased each year. The key issues and interests for the local government include approximately how much value they can get from land leases according to the land plan. Because of China’s tax structure, the land transfer fees are sources of local revenue that support the local government’s necessary public and infrastructure expenditures. Land supplies include commercial, residential, and industry categories. Industry occupies the greatest percentage of land supply quantities every year. Fig. 1 shows the land supply changes from 2000 to 2011. Before 2006, the average industrial land supply was 2,980,823 m2. Obviously, after 2006 the central government reduced the land supply quantities, about one third compared to the previous annual stage. Industry type There are many categories of industry types in this research, according to the land use characteristics and the similarities of different subtype industries. Altogether, eleven industry types are divided, including: (1) equipment manufacturing; (2) food manufacturing; (3) the pharmaceutical industry; (4) textiles; (5) furniture manufacturing and the wood product processing industry; (6) the printing industry; (7) the chemical industry; (8) the plastics industry; (9) the metal smelting and processing industry; (10) electronic communications equipment and other manufacturing industries; (11) civil engineering construction. In terms of internal industry structure (Fig. 2), the land supplies belonging to the equipment manufacturing, textiles, and chemical industries in relation to the whole quantity reduced most, at individual percentages of 7.3%, 4.1% and 6%. During the same period, the percentages of food manufacturing, the pharmaceutical industry, electronic communications equipment and other manufacturing industries were increasing. Among them, electronic communications equipment and other manufacturing industries increased most, from 18% to 37.3%, adding 15.3%.

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Fig. 1. Industrial land supply area from 2000 to 2011. Source: Hangzhou Land and Resources Bureau.

Fig. 2. Percentage of industry type case change during 2000e2011. Source: Hangzhou Land and Resources Bureau.

Land price Compared with the high land prices from commercial lands, industrial land prices before 2006 will always remain at a very low level. Even in China’s more developed eastern area, there is a huge gap between the unit price of industrial land and real estate land prices. As an example, in Lutuo Town, Ningbo, from 1999 to 2002, the residential and commercial land prices rose from 300 CHY/m2, 600 CHY/m2 in 1999 to 600 CHY/m2 to 1200 CHY/m2 in 2002. But in the same period, industrial land still remained from 130 CHY/m2 to 150 CHY/m2 (Lixia & Yongjia, 2003). Fig. 3 is the comparison of land prices before and after 2007. It can be concluded that there are only two average land prices. Before 2007, the industrial land price was about 150 CHY/m2 (100,000 CHY/mu), below land cost. After 2007, the price was pushed upward by the regulation of lowest allowable industrial land prices to more than 450 CHY per m2 (300,000 CHY/mu). But the price line remains stable after 2007, which may indicate that land price is more determined by the government price index than the actual market price. The reason for this abnormal market

reflection on industrial land prices is that in order to attract investors for industry projects, local government uses the low industrial land price as one of the incentives and benefits for investors.2 Change of industrial land use efficiency When land use density increases due to the increasing demands of benefits in limited areas, the land value also tends to increase. Usually land use density is considered as another criterion of land value (Hill & Kim, 2000). The concept of industrial land use intensity originally came from the research of agricultural land (Ricardo, 1927). Based on the assumption of uniformity in the physical conditions of the cities, some of the research discusses the relationship between land use density and other critical factors. Kim and Sohn (2002) uses the space syntax analysis to show that

2 The exchange rate between U.S. dollars and CHY at the end of 1999, 2002, 2007,2011 was 8.28, 8.28, 7.37, 6.4.

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there is no increase in building density as the government expects. It seems that with the exception of the furniture manufacturing/ wood product processing industries and the electronic communications equipment/other manufacturing industries, all of the other industry types appear with a tendency to decline in the building industry. Another interesting finding is that the average level of building density doesn’t exceed 0.4, which shows that the land use efficiency is very low. On one hand, the local government tries to get more land planning quotes from the central government. On the other hand, the existing land is not optimally utilized. From Fig.5, the photo taken in Xiasha provincial industrial area, Hangzhou identifies that some percent of land in the factory has fallen idle, and this phenomenon is not an exception. Factors influencing industrial land use intensity Fig. 3. Median of industrial land price from 2000 to 2011. Source: Hangzhou Land and Resources Bureau.

the office density represented by office building levels is influenced by street configuration variables, such as global integration, connectivity, control, and local location. However, in the application of the phenomenon, the research is still inadequate to explore how the land use density is influenced. In the land transfer contract between local governments and developers who get the industrial land use rights, a mandatory percentage of building density and FAR is assigned as land use monitoring in the event the land is not constructed on time. But in China, there is also a minimum baseline of FAR according to different industry subtypes to avoid the low efficiency of land use behaviors. Similarly, building density is also referred to as the supervision of land use developments. The most important and frequently used indicator for analyzing land use efficiency is FAR, which reflects the land use density from the vertical level. In 2008, the MLR made the lowest national FAR standard for various subtypes for industry. Compared with the national standard, from 2000 to 2006, FAR exceeded national levels. This is a justification that Hangzhou has a higher standard. Furthermore, it can be seen that the FAR of most industries after 2006 has increased, except “food manufacturing.”(Table 2) Another important indicator of land use efficiency, based on China’s specific situation, is building density. The building density is the actual used land in the total land transfer area. In order to prevent the land from going idle or being only partly developed, the government sets an article in the land sale contract that provides a specific minimum standard of building standards for each piece of industry land. Fig. 4 and Table 3 show building density in different industry types before and after the implementation of the modified regulation of “the Assignment of the State-Owned Land Use Rights by Means of Bid Tendering, Auction and Quotation.” It shows that

The numerous factors affecting industrial land use intensity include the general attributes of land, the role of government policy, and industrial agglomeration. The general attributes include land parcel size, land price and industry type, location, and policy influence. But in this research, the influence from land price has to be omitted for two reasons. First, the industrial land price information from 2007 is incomplete. According to historical record, the land price information is not complete because the industrial land price was negotiated between the government and industry firms prior to 2007. The other reason is that even after 2007, the industry prices among different land parcels are uniformly consistent. Therefore, in this research we have omitted that influence from the land price. The role of government can be analyzed from aspects of important land regulations, such as “Rules on the Assignment of the State-Owned Land Use Rights by Means of Bid Tendering, Auction and Quotation,” because the priority of this regulation is to push through a more intensified use model at the end of 2007. The expected result is that after 2007, the industrial land use intensity will be improved through the influence of policy. Firms located together may also have influence on the land use model, including land price from direct side, and land intensity from the indirect side. The land parcel data are from eight districts in Hangzhou’s urban area; two of them are national and provincial level economic zones. Binjiang’s hi-tech zone is a national level industry park and the Hangzhou economic zone in Xiasha is a provincial level industry park. The other land parcels are located in city level industrial parks or otherwise dispersed among the five districts. It is hypothesized that the lands in the national and provincial level parks tend to be more efficiently developed. The analyzed data were industrial land lease parcels selected from all of the data spanning 2000e2011. The method used is a binary logistic regression analysis. The building density and FAR

Table 2 Change of FAR from 2000 to 2011, compared with national standard. National standard

Chemistry industry Metal smelting and processing industry Engineering manufacturing Textile Furniture manufacturing and wood products processing industry Printing industry Food manufacturing Electronic communication equipment and other manufacturing industries Average Source: Hangzhou Land and Resources Bureau.

0.6 0.6 0.7 0.8 0.8 0.8 1.0 1.0

2000e2006

2007e2011

Total

Mean

Median

Mean

Median

Mean

Median

0.8130 0.9263 0.9889 1.0746 1.2190 1.0020 1.0422 1.1828 1.0078

0.6774 0.6467 0.8731 1.0628 1.1870 1.0098 0.9906 1.0653 0.8996

0.9003 e 1.1263 1.1317 1.6281 1.0344 0.8327 1.3238 1.1277

1.0714 e 1.1159 1.0646 1.6281 1.0030 0.8529 1.3314 1.1491

0.8254 0.9263 1.0084 1.0813 1.2505 1.0083 0.9584 1.2200 1.0279

0.6795 0.6467 0.8780 1.0628 1.2058 1.0063 0.9579 1.1303 0.9129

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Fig. 4. Change of building density for different industry type from 2000 to 2011. Source: Hangzhou Land and Resources Bureau.

represent dependent variables. The boundary for dividing FAR and building density is 1.0 and 0.4. The basis comes from two sides; one is according to land parcels’ statistics, the mean and median level of FAR is around 1.0, to building density around 0.4. From the other side, through investigation of government officials regarding which level can be indicated as the division of undeveloped and low efficiency used land, it was agreed that below 0.4 for building density and 1.0 for FAR is indication of a low efficiency used land group. Given the nature of the land use intensity and the influencing factors, we needed to access the different impacts from the general land attributes, government policy, and industry agglomeration. Where the individual, dependent variables are FAR and building density separately, the explanatory variables are the land area in square meters (size), the land parcel location, if located in a national and provincial level industrial park (location), the government policy of public land leasing, represented by the dummy variable before and after the end of 2006 (policy), the industry type in one of the eleven industry types (industry), and the specific year of land leasing (year). Table 4 gives the overall distribution situation of all of the cases. The main sample industries are the equipment manufacturing

industry, the textile industry, the chemical industry and the electronic communications equipment industry, which account for approximately 87% of all cases. The main industries, which can be concluded from the land samples, are in accordance with the city’s industry structure. In terms of the land sample location, they are more evenly distributed in terms of space. With the exception of two areas in which there were fewer case land parcels located, all other areas account for 97% of all land samples. The land leased after the new policy from MLR that requests industrial land should be leased publicly accounts for 16.8% of all the cases. In terms of land location, although there are two industrial parks with high hierarchy levels (a national industrial park and a provincial level industry park), we still can see that 42% of land cases are distributed in other areas (Fig. 6). The mean of the land area is 25,600 m2 and standard deviation is 36,608. The high S.D. reflects the area gap among land cases. Since we are interested in examining the extent to which factors influence the land use density, we performed step-wise models to compare the influence of different factors. We separately added the variables of price and year (representing the specific year of industrial land lease), in order to avoid interaction between the two

Table 3 Change of building density from 2000 to 2011. 2000e2006

Chemical industry Metal smelting and processing industry Engineering manufacturing Textile Furniture manufacturing and wood product processing industry Printing industry Food manufacturing Electronic communication equipment and other manufacturing industries Average Source: Hangzhou Land and Resources Bureau.

2007e2011

Total

Mean

Median

Mean

Median

Mean

Median

0.3826 0.5834 0.4140 0.3985 0.4433 0.4357 0.4426 0.3361 0.4010

0.3702 0.5488 0.4218 0.3963 0.4345 0.4029 0.4538 0.3477 0.4035

0.2354 e 0.3849 0.3413 0.6099 0.3514 0.3347 0.3494 0.3490

0.2449 e 0.4529 0.3425 0.6099 0.4317 0.3556 0.3899 0.3932

0.3618 0.5834 0.4099 0.3918 0.4561 0.4193 0.3994 0.3396 0.3922

0.3561 0.5488 0.4219 0.3871 0.4602 0.4080 0.3993 0.3514 0.4021

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Fig. 5. Undeveloped land in existing planting areas (taken in Hangzhou Binjiang Hi-Tech zone).

variables. Because of FAR’s vertical indicator and the horizontal indicator of building density, we performed two analyses. The first analysis focused on variables influencing FAR. The second regression analyzed the influence on Building density. Our empirical specification is the following:

FAR ¼ a þ b1 size þ b2 location þ b3 policy þ b4 industry þ b5 year þ ei Density ¼ a þ b1 size þ b2 location þ b3 policy þ b4 industry þ b5 year þ ei The result of binary logistics is shown in Table 5. Table 6 shows the results of a set of multivariate models that explore how various factors suggested in the literature relate to Table 4 Description statistics (N ¼ 612).

Dependent variable Independent variable

FAR Density Policy Size (m2) Year Location

Mean

S.D.

1.03 0.391589 0.1683 25,600 3.36 1.58

0.640 0.179093 0.37444 36,608 2.022 0.4940 Count

Industry

Policy

Location

Engineering manufacturing Food manufacturing Textile Furniture manufacturing and wood product processing industry Printing industry Chemistry industry Metal smelting and processing industry Electronic communications equipment and other manufacturing industries Total Before 2006 After 2006 Total Outside national and provincial level industrial park Inside national and provincial level industrial park Total

Column N%

268 20 68 13

43.8% 3.3% 11.1% 2.1%

36 92 9 106

5.9% 15.0% 1.5% 17.3%

612 509 103 612 257

100.0% 83.2% 16.8% 100.0% 42.0%

355

58.0%

612

100.0%

FAR. The variables significant for FAR are at least at a level of 0.05, including year, size and industry type. The effect of the year is in accordance with expectations. The earlier the land was developed, the higher FAR would be. It’s also a justification that industry development requires a great deal of time. Compared with the chemistry industry, only the printing industry has a higher FAR. The other industry types all have a lower FAR compared to reference type. The land size also has a significant influence on FAR. The bigger the land size, the lower the FAR will appear. This result is consistent with the assumption of a relationship between the land size and land use efficiency. The regression analysis of explanatory variables to building density shows different results from FAR (Table 7). The stepwise regression shows all variables entering the estimation, except location and size. The first variable entered is year. It has a negative effect of 0.202 to building density. This indicates that the owners had no intention of increasing the horizontal land development while the building areas were increased in the vertical level from the regression results to FAR from the former analysis. At the same time, the policy factor has a strong negative effect on building density, which is adverse to the government policies’ expectations. As expected, the industry type also has strong influence on building density. Compared with the reference group, most of the other industry groups have a higher building density, except the metal smelting and processing industry. Discussion From the regression, some of the results coincide with expectations, including the reflection of land size and industry type. As land size increases, the FAR will decrease. It reminds us that in order to promote denser land use, the solution is to control large parcels of land. In terms of industry type, we can draw two conclusions. One is that there are variances in land use efficiency, which can be seen from the significant results in the two regression analyses. The other is that, when compared with the chemistry industry, most of the other industry types have a lower FAR and a higher building density. In most cases, the chemical industry will be developed to different stages. But the land is leased in its entirety at one time. There is also an industry need for high investment so the land will be developed over a long period of time. That is the result that is compared with the chemical industry; the other industries have a strong impact on building density.

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Fig. 6. Industrial land distribution in Hangzhou. Source: Hangzhou Land and Resources Bureau.

Note: significance of x2: comparison with previous model.

binary logistic regression estimates, the results regarding building density and FAR in response to specific variables, namely policy and years, give the signal that the behavior is more heavily influenced by time than by governmental policy. Examining more detailed reasons can help us understand the market reflections from industry firms. Two important, deeply rooted causes incurred the unexpected results of low industrial land efficiency. First, the so-called public leasing market for industrial land is still not a fully competitive market, because of the different interests and perspectives between the central and local governments. Although the industrial land market has been open to the public since after 2006, most of the land still follows the “project first, land second” model, which means before the land leasing process, most of the land has a single owner, intentionally. This does not allow for the maturity of real market competition. In all of the land leasing records from 2006, only two parcels of land had two companies bidding for the same pieces of land, which led to

Table 6 Binary logistic regression estimates predicting FAR.

Table 7 Binary logistic regression estimates predicting BUILDING DENSITY.

But there were also some unexpected results. Policy influence is negative, which can be seen in the affects it had on building density after new regulations were implemented at the end of 2006. Additionally, policy had no influence regarding the improvement of FAR, which is more easily affected by outside variables. Through the

Table 5 Comparisons of goodness of fit between different models (Log-likelihood ratio test). Independent variable Step FAR

Building density

þyear for land lease þland size þindustry type þyear of land lease þindustry type þpolicy

B Year Size Chemical industry Metal smelting and processing industry Engineering manufacturing Textile Furniture manufacturing and wood products processing industry Printing industry Food manufacturing Electronic communication equipment and other manufacturing industries Constant Reference group: chemistry industry.

2 Log Df Chi2 likelihood

Prob > x2

812.826 799.271 779.751 839.372 819.017 813.804

0.00 0.00 0.00 0.003 0.000 0.000

S.E.

1 2 9 1 7 1

30.455 44.009 65.530 8.936 29.290 34.504

Wald

Sig.

0.337 0.063 28.558 0.000 0.009 0.003 8.267 0.004 18.580 0.010 1.049 0.315 11.056 0.001 0.727 0.756 0.926 0.336 0.654 0.248 6.954 0.008 0.255 0.331 0.593 0.441

Exp(B) 1.401 0.991 0.350 0.483 0.520 0.775

0.875 0.712 0.239 0.412 0.592 0.524

1.511 0.219 2.398 0.335 0.562 0.788 1.274 0.259 0.553

1.151 0.481

5.720 0.017 0.316

B

S.E.

Wald

Sig.

Exp(B)

Year 0.202 0.060 11.459 0.001 0.817 Chemical industry 21.806 0.003 Metal smelting and processing industry 0.066 0.301 0.048 0.827 1.068 Engineering manufacturing 2.605 1.091 5.706 0.017 13.537 Textile 0.826 0.245 11.397 0.001 2.284 Furniture manufacturing and wood 0.441 0.325 1.839 0.175 1.554 products processing industry Printing industry 1.092 0.625 3.056 0.080 2.980 Food manufacturing 0.884 0.407 4.722 0.030 2.421 Electronic communication equipment 0.585 0.507 1.330 0.249 1.765 and other manufacturing industries Policy 0.670 0.314 4.564 0.033 0.511 Constant 0.902 0.463 3.790 0.052 2.464 Reference group: chemistry industry.

F. Tu et al. / Habitat International 43 (2014) 1e10

land-leasing prices higher than the listing reserve price of 147% and 67%. Under the other circumstances, all the land is leased around the reserve price with only one firm taking participation in the price competition. The reason is not because other companies aren’t interested in gaining land use rights, but more due to conditions combined with “listing, auction and bidding.” The hidden reason is different interest incentives and attitudes between the central and local governments toward industrial land leasing. From the central government’s perspective, the hope is to encourage a formal and transparent public market in order to reduce the low land price leasing and increase land use efficiency. However, from the local government’s point of view, the objective of high GDP growth is more important, because it may decide a local official’s political promotion and evaluation in the political system. On the surface, it seems that high land-leasing prices would not have a direct relationship with political achievement, but local government officials are afraid that the industry firms may feel that increased investment costs propelled by land price will eventually lead to industry owners giving up their investments and changing the factory’s location to one that is less expensive. Losing the industry presence will eventually negatively influence employment and tax income, leading to a drop in GDP growth. Thus, the local government has adopted a strategy of “project negotiation first, land leasing second” in a way that will not offend the regulations for industrial land leasing from the central government. In this way, local government will add more conditions for land listings, auctions, or bidding on the land in hopes that it can be acquired by a firm with which they have already negotiated. The conditions automatically become a threshold on which to preclude other firms from taking part in the leasing action. Eventually, the public leasing policies do not allow for transparency or equal opportunities to all firms. Second, the incentives for any firm receiving the industrial land use rights are another reason behind the inadequate use of land. Compared with the costs and the price levels of residential and commercial land, the price of industry landmass is so low due to the one on one negotiation under the condition of not offending the lowest land-leasing national standard. Some of the firms try to compete for potential benefits from achieving the land use rights. Although, after the implementation of the revised of “Rules on the Assignment of State-Owned Land Use Rights by Means of Bid Tendering, Auction and Quotation,” the industrial land price has greatly improved. However, compared with the cost of land and industrial land prices in the secondary market, the price to lease land is comparatively cheap. The average land-leasing price is 480 CHY/m2 (320,000 CHY/mu3), while the average industrial land cost is 1199 CHY/m2 (800,000 CHY/mu), including land requisite costs, crop compensation, and housing reallocation compensation. Based on the sampling survey of industrial land mortgages in Hangzhou from 2008 to 2011, the average industrial land lease price was 480 CHY/m2 (320,000 CHY/mu). But on the secondary market, the industrial land price increased, from the lowest price of 495 CHY/m2 (330,000 CHY/mu) to the highest of 3733 CHY/m2 (2,490,000 CHY/mu). The low industry land-leasing price has two results. One is that the industry enterprise may lack the incentive to promote more densely utilized land; conversely, through the transfer of land in the secondary market, the firm may gain a profit, which may induce the firm to use the industrial land for profit instead of for industry-related business operations. Apparently, there has been the formation of a two-stage tactic in industrial land management, which has incurred complicated effects on both the market and normal management. The first stage occurs

3

Mu is an area unit used in China, which equals to 667 m2.

9

before land leasing, placing the government in the spotlight; however, in the second stage, after land leasing, the industry firms take a more dominant position. Prior to land leasing, the government had the power to influence legal provisions in the leasing documents. However, after getting the land use rights, the industry firms received the legal rights of land use, land disposal, and land yields; the firms thus have the right to make their own in land and plant development, construction, and factory production. At the same time, the government is passive in monitoring whether the firms’ developments are compliant with leasing document rules. Even if there are detailed articles about the investment quantity, construction planning specifications, and the schedule for starting and finishing the construction of a plant, including liabilities for breaching the contract, many industry firms will not construct the plant in accordance with the contract. The government seems powerless in dealing with this phenomenon. The enterprises will only develop the land from their own interests, schedules and objectives due to a lack of contract consciousness, low costs against contract default, and no powerful government monitoring measures. Conclusion The past literature has focused on the land use market from two sides: the first, the direct government policy, which includes zoning and the supply of land; the second is indirect policy, such as taxes (Alan W. Ewans, UK, ES. Milles and P. Cheshhire, 1999). There is so little attention paid to the literature of government’s role in industrial land use efficiency because in some countries and districts, the conflicts between the people and land resources are not prominent. Another reason is due to mature market mechanisms; the land property belongs to the individuals and governments have little influence over land use behavior. In China, due to the scarcity of land resources, the protection and efficient use of land are priorities in land policy. The land market system began in the 1980s; although public land leasing began late, it developed quickly. In the process of industrialization and marketization, the output from industries has a large percentage of GDP. Analyzing the government’s public land leasing mechanisms and systems has been beneficial to the research on land use behavior, which will allow for more research in this field. From the horizontal and vertical levels of land use intensity indicators, this research focused on the influence of land attributes, government policy on public land leasing, and land locations using a binary logistic model through stepwise regression, analyzing different variables on land use density, and setting up a significant regression model. We found that factors, including the year of the lease, FAR, and the industry type, had a significant influence on building density. The land industry type, building density, and the year of the land lease, government policy, and land size had a close relationship with FAR. The government’s policies on industrial land lease do not achieve the original objectives of improving land use efficiency through regression analysis. Further research found that on the surface this policy looked like public leasing, but with the government taking a leading role. In this contradictory operating system the following two characteristics appear: first are the stable land prices between annual changes after 2006; the other characteristic is that in almost in all of the industry leasing cases, a single participant took part in the bidding and acquisition of land use rights because the local government established special listing conditions available only for those specific bidders. Analyzing these two characteristics reflect distorted land use behaviors. Obviously, the result is to violate the nature of the public lease. Inevitably, it’s difficult to improve land use efficiency.

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Obviously, China is still in the process of industrialization; overcoming the conflicts between attracting industry investments through relatively low land prices and low industrial land use efficiency is a challenge for China’s government. After the global financial crisis at the end of 2007, China’s economic growth faces a great challenge. Until now, the government has tried many different ways of promoting industrial transition while facing an economic growth dropdown, a shrinking export market and environmental pollution. But industrial transition needs time to make great changes, resulting in a shift away from regulations and institutions as support. These changes will not happen overnight, and manufacturing will continue to be the most important support for GDP growth and employment. The key issue is that government should not be the one to decide the threshold for industry development. The revolution of industry will happen under an open market. If the government wants to support an industry company with relatively low land prices and other credits, the conditions for land sales should be firmly combined as the conditions that will have constraint mechanisms for the industry companies. For instance, the government will take over the land use rights after the incentives of low land price have expired, or after a firm closure. Otherwise, the government should let the industrial land leasing be more publicly available to all firms. There are also some limitations regarding the data in this research. Because of the complexity of influencing factors to land use behavior, more land use samples will be more reliable to get statistical test results. In terms of the assessment for industrial land efficiency, it would be more reasonable to include the economic indicators for unit land area. But during the research, it was still very difficult to get economic data for each firm. Acknowledgements The authors gratefully acknowledge the referees for extremely helpful comments. They also thank the National Natural Science Foundation of China (project 71273240), the Humanities and Social Sciences Foundation of Ministry of Education in China (project 13YJA630083), and the Natural Science Foundation of Zhejiang Province (project LY13G030029) for financial support. All faults are solely those of the authors. References Ambrose, B. W. (1990). An analysis of the factors affecting light industrial property valuation. Journal of Real Estate Research, 5, 355e370. Bengston, D. N., Fletcher, J. O., & Nelson, K. C. (2004). Public policies for managing urban growth and protecting open space: policy instruments and lessons learned in the United States. Landscape and Urban Planning, 69, 271e286. Brueckner, J. K., & Lai, F. C. (1996). Urban growth controls with resident landowners. Regional Science and Urban Economics, 26, 125e143. Cooley, T. F., Lacivita, C. A theory of growth controls’. Couch, C., Leontidou, L., & Petschel-Held, G. (2007). Urban sprawl in Europe: Landscapes, land-use change & policy. Real estate issues. Dieleman, F. M., Dijst, M. J., & Spit, T. (1999). Planning the compact city: the Randstad Holland experience. European Planning Studies, 7, 605e621. Engle, R., Navarro, P., & Carson, R. (1992). On thetheory of growth controls. Journal of Urban Economics, 32, 283.

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