Residential landscapes in suburban China from the perspective of growth coalitions: Evidence from Beijing

Journal of Cleaner Production 223 (2019) 620e630

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Residential landscapes in suburban China from the perspective of growth coalitions: Evidence from Beijing Jiayu Wu a, Yuxiang Wu b, Wei Yu c, Jian Lin d, Qingsong He e, * a

College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, 310058, Zhejiang Province, PR China Master Planning Institute, Xiamen Urban Planning & Design Institute, Xiamen, 361000, Fujian Province, PR China c Zhejiang Urban and Rural Planning Design Institute, Hangzhou, 310007, Zhejiang Province, PR China d Department of Urban and Regional Planning, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, PR China e College of Public Administration, Huazhong University of Science & Technology, 1037Luoyu Road, Wuhan, 430074, Hubei Province, PR China b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 27 January 2019 Received in revised form 7 March 2019 Accepted 12 March 2019 Available online 18 March 2019

Residential landscapes in China manifest characteristics different from those seen in Western countries: high density, facilities improvement, and good quality. This study investigates residential landscape by developing a novel framework to capture a growth coalition that is composed of local government and real estate enterprises. Stemming from institutional insights, this study assumes that the growth coalition in China attempts to acquire residential land far from built-up areas to reduce costs, increase the floor area ratio and the green rate, and promote accessibility and walkability to improve housing prices. Taking Beijing as a case, we first identify the residential expansion type (renewal, infill, edge, outlying). Then, two models are created: the land acquisition cost model and the housing price model. The results provide a good argument in support of our hypothesis. This study contributes institutional knowledge to support an understanding of residential landscapes and urban sustainability in China. © 2019 Elsevier Ltd. All rights reserved.

Keywords: Residential landscape Growth coalition Entrepreneurial government Urban land expansion China

1. Introduction Since the economic reform at the end of the 1970s, China has generated extraordinary economic growth and land-centred urbanization (He and Huang et al., 2014; Huang and Wei et al., 2015). The striking increase in land development in China is not only a policy tool to foster economic development (Tian and Ma, 2009) but also the consequence of China’s unprecedented economic boom (Lin, 2007; He and Huang et al., 2014). According to official statistics, China witnessed an expansion in urban land from 34,166.70 km2 in 2006 to 51,584.10 km2 in 2015. Unexpectedly, China’s residential land also experienced a surge as a critical dimension of urban expansion, from 9,772.15 km2 in 2006 to 16,282.49 km2 in 2015, representing a growth rate of 66.62%, which is significantly faster than the urban area growth rate of 50.98%. China, similar to Western countries, entered a period of rapid residential expansion rather than industrial land expansion. The rapid expansion of construction land, especially residential land, has created major concerns about traffic congestion (Dieleman and

* Corresponding author. E-mail address: [email protected] (Q. He). https://doi.org/10.1016/j.jclepro.2019.03.145 0959-6526/© 2019 Elsevier Ltd. All rights reserved.

Wegener, 2004), environmental pollution (Brueckner, 2000) and a series of urban diseases (Ewing and Schmid et al., 2008; Frumkin, 2016). Although abundant literature has documented residential landscape expansion in Western countries (Brueckner, 2000; Squires, 2002; Dieleman and Wegener, 2004), especially in the US (Song and Knaap, 2004a,b; Song and Knaap, 2004a,b; Ewing and Schmid et al., 2008), the residential landscape of China, which, based on our observations, differs from that of other countries in many aspects, has not received much attention (He, Huang and Wang, 2014; Huang, Wei, He and Li, 2015;). Therefore, this study attempts to add to the literature in three important ways. First, we measured the characteristics of residential landscapes considering two aspects: urban form and location. Urban sprawl throughout many Western countries is often characterized by lowdensity neighbourhoods and commercial strip development made possible by and dependent on extensive automobile use (Jabareen, 2016). According to our investigation, however, the residential landscape in China, especially in Chinese metropolises, has evolved with multi-storied and higher-density mixed land use oriented towards public transport. The only similar feature between China and other countries is outlying development (Tsai, 2005). To quantify these characteristics, we structure a series of measurements to portray residential landscapes in China and compare them

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to the existing literature on traditional urban sprawl. Second, we develop an innovative framework to analyse residential landscapes in China by highlighting institutional foundations. Although many studies document and explain the expansion of urban construction land (Deng and Huang et al., 2008; He and Huang et al., 2014; Wu and Song et al., 2017) or industrial land (Huang and He et al., 2017) in China, the literature seldom focuses specifically on residential land. The residential landscape, however, is particularly important when we realize that different land types display various mechanisms of expansion. In this paper, we consider how the coalition of local governments and real estate enterprises, also called growth coalitions, achieve lower land acquisition costs and higher profits for housing. Based on these frameworks, we explain why residential landscapes have a unique form and location. Third, we take Beijing, the capital of China, as an empirical case using extensive new available data. Prior research on urban sprawl mainly compares the changes in multi-period remotely sensed data (Tian and Ma, 2009; Wu and Yeh, 1997) and census information (Deng and Huang et al., 2008; He and Huang et al., 2014), which may both have limitations in terms of accuracy and scale. In our paper, we collect basic information for more than six thousand neighbourhoods obtained from the SOUFANG website using crawler technology. Furthermore, we crawled for more than 10 thousand POIs (points of interest) from the BAIDU website to capture the level of facilities around the neighbourhoods. This extensive high-precision data will contribute to depicting the spectacle of urban sprawl with greater accuracy on a larger scale. The study proceeds as follows. Section 2 introduces an analysis framework based on the theory of growth coalitions. In section 3, the data are described, and the study’s method is considered. We present the estimated results in section 4. In the Conclusion, the study’s conclusions are summarized, and some policy implications are drawn.

by the state, while rural land is owned by village collectives (Ding and Lichtenberg, 2011). These village collectives administer rural land, but they lack authority to allocate land for non-agricultural uses; in fact, it is expressly prohibited (Lu, 2007). The use of rural land for most residential, commercial, and industrial uses is allowed only after ownership is transferred from the collective to the state (Ping, 2011). Although compensation is given for land acquisition from rural collectives, it is generally low according to the Land Administration Law. Therefore, the government monopolizes the primary market of land. After the government or its authorized enterprises carry out unified land acquisition, demolition, resettlement and compensation for rural collective land within a certain region, it should develop appropriate municipal supporting facilities, including water, electricity, road, communication, and land reclamation (also called wu tong yi ping). Subsequently, the private sector can lease urban land for a specified period, such as 40 years for commercial use, 50 years for industrial use, and 70 years for residential use (Ding, 2003; Lin and Ho, 2005) by paying the local government an up-front land transfer fee. Although compensation is required during the process of requisition, it is much lower than the land transfer fee. In other words, local governments can acquire a large amount of land income by acquiring land from farmers, offering a low compensation fee, leasing or selling it on a large scale to developers and receiving higher land transfer fees (Ding, 2007; Lin and Ho, 2005). Hence, local governments in China have been able to significantly increase their local revenue by relying on land development (He and Zhou et al., 2016). The pursuit of extra-budget revenues to promote urban development is a key source of urban land expansion in China (Tao and Su et al., 2010). Because local governments and enterprises have become growth coalitions (Fig. 1), profits are now the target.

2. Analysis framework

2.2. Growth coalition: a perspective

2.1. Residential zone construction in China

Molotch (1976) first proposed urban growth coalition theory. He believed that financial crisis in the welfare state creates a strong motivation to develop the economy. City officials and the enterprise

China exercises public ownership of land. Urban land is owned

Fig. 1. Residential zone construction in China.

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elite closely combine to create wealth: the political elites and economic elites form a coalition. Labour, trade unions and other actors and organizations are included in Logan and Molotch’s (1987) study on growth coalitions. They were regarded as the power of anti-growth. In the streamlining of China’s urban growth, Wu (1997) noted that growth coalitions had emerged in China, and Zhu (1999) also made exploratory arguments. The growth coalitions that emerged in China were similar to those in Western countries, with two differences. First, the background of governance institutions is different in China. In the West, after the traditional bureaucratic system encountered difficulties, central control of local governments gradually weakened to maintain the legitimacy of the government (Molotch, 1976). Therefore, decentralization is the institutional background of the growth coalition (Logan and Molotch, 1987). After 1994 in China, however, the new tax-sharing system (TSS) introduced a clear distinction between national and local taxes (He and Zhou et al., 2016). Local governments are now required to pay taxes proportional to local income instead of a fixed, lump-sum remittance, directly causing the share of revenue collected by the central government to expand and the share for local governments to shrink (Li and Zhou, 2005). The introduction of TSS and subsequent reforms has given rise to a level of fiscal hardship that varies across localities due to their different revenue generating capacities (Oi, 1992); under this fiscal landscape, subnational governments account for over 70% of total public expenditure while collecting less than 50% of total government revenue (Lin and Liu, 2000). With their decreasing revenue share, local governments have become motivated to seek an independent source of financial support. It can be seen that centralization is the institutional background to the Chinese government seeking to form a growth coalition. Second, the growth coalitions have different interests. The Western growth coalition is based on marketization, and the public affairs originally undertaken by local governments are handed over to the market. The government introduces management concepts and methods from private enterprises and cooperates extensively with enterprises and associations (Hackworth, 2007; Lofland, 2017). This is a market-oriented economic partnership. In contrast, in the growth alliance of local governments in China, the relationship between government and enterprises is much more complicated. In addition to all the features of the Western growth coalition, there are many unique behaviours. Since Chinese local governments have great power to manage local public assets, as the main body of growth alliances (Peng, 2001; Zhou and Li et al., 2003), local governments often go beyond their own functions, from supervisors to participants, and engage in strong interventions such as law and administration. Growth coalitions seem to be developing successfully in China due to the publicly owned land system (Long, 2014). Growth coalitions emerged with the interest bundling of the government and enterprises. Referring to government, growth alliances bring three benefits to the government: First, the land transfer fees paid by enterprises have become the most important source of finance outside the government budget (Li and Wen et al., 2012), effectively solving the financial difficulties of local governments after the taxsharing reform (Lin, 2009). Second, in addition to paying taxes and fees, real estate enterprises have promoted the development of upstream and downstream industries, including steel, cement, building materials, and service industries (Li and Zhou et al., 2018). These enterprises have jointly promoted local economic development (Glaeser and Huang et al., 2017). Finally, the employment and economic development promoted by real estate companies have laid a solid foundation for the legitimacy of local governments (Chen and Liu et al., 2016). Referring to enterprises, growth coalitions also bring three benefits: First, local governments help real

estate companies to remove potential obstacles in the suburbs through strong administrative power (Wu and Phelps, 2008). Second, the government ensures a good environment for capital investment (Wu and Zhang, 2007) by investing in infrastructure and other public facilities. In addition, real estate companies and local governments often reach a tax reduction partnership. Growth coalitions form and begin to develop land value. Acquiring land at the minimal cost and selling housing to maximize benefits is the optimized approach. To minimize land acquisition costs, local governments carefully consider the location of the requisitioned land. Several studies have demonstrated that land acquisition cost decreases as the distance from the urban centre increases (Ding, 2007; Tao and Su et al., 2010; Wu and Song et al., 2017). In addition, landowners near built-up areas are motived to claim higher land requisition compensation, which is generally beyond the government budget or the provisions of law. In this context, local governments and farmers subject to land expropriation usually enter into lengthy negotiations, resulting in high transaction costs. Therefore, local governments prefer to acquire land parcels far from built-up areas. Meanwhile, growth coalitions push housing prices as high as possible. The previous literature has documented serval strategies for increasing urban housing value. First and foremost, the floor area rate (FAR) has a direct influence on the housing price (Davis and Oliner et al., 2016; Wu and Song et al., 2017). The private sector is asked to sign a contract that contains a series of construction conditions, including the FAR (Wu and Song et al., 2018). When local governments deregulate FAR, development companies can build more houses to sell, generating substantial profits. Hence, they are pleased to pay higher land transfer fees in exchange for a higher FAR. The growth coalition can then bear interest. Second, improving the environmental aspects of neighbourhoods, such as achieving a higher green rate, is another strategy for increasing housing value (Song and Knaap, 2003; Wu and He et al., 2018). Third, housing values surge when neighbourhood service facilities improve, such as through better accessibility to commercial lots, improved amenities or increased walkability (Cao and Cory, 1981; Wu and Ta et al., 2018). In summary, entrepreneurial local governments can promote land earnings by changing urban forms. Thus, growth coalitions attempt to increase FAR, increase the green rate, and promote accessibility and walkability to improve the benefit obtained from land. Based on our analysis framework (Fig. 2), we seek a reasonable explanation for the characteristics of residential landscapes in Chinese metropolises. We consider that growth coalitions hope to achieve greater profits by reducing land acquisition costs and maximizing housing prices. Therefore, they consider location and urban form when making decisions. 3. Data and model specifications 3.1. Data resources Based on the analysis framework, the location and urban forms of residential landscapes must be measured. Our data come from five primary sources: (1) Land use data. This study collected a five-year (1996, 2001, 2006, 2010 and 2015) land use dataset for Beijing. The land use classification in 1996 and 2001 is in accordance with the Classification and Implications of Land Use Status issued by the Ministry of Land and Resources in 1984, while the data in 2006 are in line with the Classification Standards for Land Use Status (GB/T 21010- 2007). The land use classification in 2010 and 2015 is in conformity with the National Land

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governments from transferring land with a FAR less than 1 since 2012; as a result, this study only adopts second-level residential land. (5) Residential land transfer data. The collected residential land data were obtained from the China Land Market Network website (http://www.landchina.com/). This website was established by the Ministry of Natural Resources (formerly Ministry of Land and Resources, MLR), and it publicizes all land leasing records from 1998 to 2017. We obtain the physical attributes of the residential land, including land price and location, at this website using a web crawler. Recently, academic studies have used this dataset to research China’s land issues.

3.2. Measurement of the location of the residential zone

Fig. 2. Analysis framework.

Classification (Applicable during the transition period) issued by the Ministry of Land and Resources in 2002. Although the standard of classification differs across the versions, the crucial content for residential land did not change. (2) Residential zone data. The residential zone data were obtained by crawling the SOUFANG website (http://www.fang. com/) in December 2017 and include the 6826 residential zones in Beijing. SOUFANG is a housing service platform that has a market share of approximately 70% of Beijing’s housing market (Wu and Ta et al., 2018). The dataset contains a series of data, including the name, location, area, covered area, FAR, green rate, the number of total and current households, time of completion, and average house price. After data cleaning by merging duplicates and checking abnormal values, 6207 residential zones were ultimately identified and adopted. Recently, many academic studies (Ding and Zhao, 2014; Wu and He et al., 2018) have supplemented traditional neighbourhood sources by exploiting open access social media data. (3) POI data. The POI data are obtained by crawling the BAIDU Map (http://map.baidu.com/), which includes coordinates based on latitude and longitude, names, addresses, and other information. The data collected for this study include shopping centres, transportation facilities, educational facilities, financial institutions, hotels, tourist attractions, community services, recreational facilities, medical facilities, restaurants, and corporate facilities (He and He et al., 2018) and are mainly used to measure the urban form of the residential areas. (4) Standard land price data. We also obtained the standard land price data from the Dynamic Inspection System of City Land Prices (http://www.landvalue.com.cn/); these were evaluated based on the average price in trades of residential land in Beijing. The previous literature (Su and Zhang, 2007; Zheng and Wu et al., 2014) on urban studies in China used these data to estimate land values. According to the standard land prices for Beijing in 2004, the city-wide land price is divided into twelve levels. The standard price for residential land includes first-level residential land (FAR less than 1) and second-level residential land (FAR equal to or greater than 1). However, the central government has prohibited local

As we argue above, entrepreneurial local governments seek to minimize land acquisition costs by choosing a long-distance land development mode. Hence, outlying parcels are preferred over edge and infill parcels. As a result, the first assignment is to identify the location types of residential land. The land patches adopted in the existing research on urban land expansion patterns are polygons, while the residential zone data obtained by network crawlers represent points; thus, the expansion type cannot be directly identified. Therefore, we first identify the location type of newly added construction land patches based on existing studies (Liu and Li et al., 2010; He and Song et al., 2017), and then we identify the location relationship between residential zones and newly added construction land patches. The approach details are presented below. We adopted the land expansion index (LEI) proposed by Liu and Li (2010) to identify the urban land expansion pattern in Beijing. This method has the advantage of capturing the more intuitive dynamic processes of land use expansion patterns rather than reflecting spatial patterns at a single point in time. The formula for LEI is defined as follows:

LEI ¼

A0  100 A0 þ Av

(1)

where LEI represents the expansion index of newly added construction land patches, A0 is the intersection area between the buffer zone surrounding new patches and the buffers surrounding existing urban patches, and Av is the intersection area between the newly added construction land patch buffer zone and non-urban land. According to Liu and Li (2010), the stability of the LEI index is highest when the buffer distance is 1 m. The LEI value ranges from 0 to 100, and the criterion for identifying the expansion pattern is as follows: if LEI is equal to 0, the newly added construction land patch is the outlying type; if LEI is larger than 0 and smaller than 50, the newly added construction land patch is the edge type; and if LEI is larger than 50 or smaller than 100, the newly added construction land patch is the infilling type. In Formula 1, newly added construction land patches refer to urban areas that were developed between 1996 and 2006 and 2006 to 2015, and existing urban areas refer to development prior to 1996 or after 2006 (Fig. 3). The spatial relationship between residential zones and newly added construction land patches includes four types: (1) outlying residential zones: the residential zone is located in newly added outlying construction land patches; (2) edge residential zone: the residential zone is located in newly added edge construction land patches; (3) infill residential zone: the residential zone is located in newly added infilling construction land patches; and (4) renewal

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Perhaps most importantly, however, they are policy relevant. The measures for transportation options, residential proximity to retail and industrial uses, and the accessibility of parks, shops, and transit are of direct concern to citizens and policymakers. Thus, Allen’s indicators serve well to evaluate alternative development proposals and land use plans. For each block-group “neighbourhood”, we computed three sets of urban form measures that have been frequently used in previous literature Song and Knaap, 2004b; Kajtazi, 2010). The definitions of the variables are provided in Table 1. 3.4. Model specifications based on costs and benefits Based on the above theoretical framework, growth coalitions attempt to acquire residential land far from built-up areas to reduce costs. We apply the regression model as follows:

Gcost ¼ a þ b1 Dinfilling þ b2 Dedge þ b3 Doutlying þ b4 D Center þ ε (2)

Fig. 3. An illustration of different urban expansion patterns.

residential zone: the residential zone is located in built-up areas. Fig. 4 shows the method for identifying the location type of residential land.

3.3. Measurement of the urban form of residential zone We define several measures of urban form that capture residential zone characteristics; these measures are developed by and his colleagues at Criterion and popularized by other scholars (Song and Knaap, 2004b). We then use these detailed and policy-relevant measures of urban form to evaluate alternative development scenarios, formulate plans, and monitor plan implementation. These indicators offer a number of advantages over previous measures. Like all good indicators, they are well defined, relatively easy to compute (when GIS data are available), and easily interpreted.

where Gcosts represents the cost of land acquisition. Dinfilling, Dedge and Doutlying are dummy variables that represent the residential zone locations. The renewal residential zones are set as the reference group, and the results for 1996e2006 and 2006e2015 are put into the model separately. The control variable D_Centre is the distance to the urban centre. Special attention must be called to the fact that developing outlying parcels means that local governments have a higher burden through infrastructure costs. However, existing literature has demonstrated that local governments in China are pleased to invest in infrastructure construction for two reasons: first, infrastructure has a significantly positive effect on urban economic growth (Tao and Su et al., 2010; He and Huang et al., 2014); second, infrastructure construction indirectly facilitates official promotion (Wu, 2018a,b). Thus, it is clear that entrepreneurial local governments seldom consider the higher public financing costs brought by outlying expansion. The land acquisition cost is difficult to obtain and cannot be matched one by one with each residential zone. As mentioned above, however, the standard land price can reflect the government’s costs in the process of land development. The Land Administration Law provides that the land acquisition compensation fees include land compensation fees, resettlement subsidies, and compensation for attachments and young crops on the ground. In reality, however, the specific compensation standards are set by the local government. Taking Beijing as an example, the standard land price has a positive correlation with the land acquisition compensation fees. Therefore, we use the standard land price as the proxy variable for the cost of land acquisition. In addition, the standard land price in Beijing cannot directly reflect the land price because it is calculated based on the floor price. It is affected by planning permit conditions (mainly by the FAR). To eliminate the impact of the FAR, the standard land price is amended using the following formula:

LPij ¼ FPij *FARj

Fig. 4. The location identification of residential zones.

(3)

where LPij represents the land price of residential land parcel i in level j. FPij represents the floor price of residential land parcel i in level j. FARj represent the average FAR in level j. As we argued above, the growth coalition may optimize the urban form to increase the total housing price. We use the hedonic model, which is one of the most important tools for assessing the economic consequences of public goods supply, environmental services and urban facilities (Wu and Zhang et al., 2014). The

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Table 1 The definition of variables. Variables

Definition

Neighbourhood attribute FAR Floor area rate Green_rate Green rate Accessibility Access_tra The distance to nearest transportation stop Access_pedtra The number of transportation stops within 500 m Walkability Walk_com The distance to nearest large commercial facility Walk_pedcom The number of large commercial facilities within 500 m Walk_serv The distance to nearest daily commercial facility Walk_pedserv The number of daily commercial facilities within 500 m Walk_pededu The number of education facilities within 500 m Walk_pedhosp The number of hospitals within 500 m Walk_pedop The number of open spaces within 500 m Diversity P Mixed_1  6i¼1 ðpi ÞLnðpi Þ A diversity index H1 ¼ , where H1 is land use diversity in the neighbourhood, including residential use, pi is the proportions of each of the LnðsÞ five land use types (commercial and service sites, business and factory sites, education sites, government sites), and s is the number of land uses. In this case, s ¼ 6. P Mixed_2  5i¼1 ðpi ÞLnðpi Þ A diversity index H2 ¼ , where H2 is also the land use diversity of the neighbourhood, except residential use, and s is the number of types LnðsÞ of land use, in this case s ¼ 5. Control variable D_Centre The distance between residential zones and the urban centre (Tian-An-Men Square)

explanatory variables in our model refer to the previous literature (Song and Knaap, 2004a,b; Wu and Song et al., 2017). The model is as follows:

Ghousing ¼ b1 þ b2 FAR þ b2 Greenrate þ b3 DCenter þ b4 Accesstra þb5 Accesspedcom þ b6 Walkcom þ b7 Walkpedcom þb8 Walkserv þ b9 Walkpedserve þ b10 Walkpededu þ b11 Walkpedhosp þb12 Walk pedop þ b13 Mixed 1 þ b14 Mixed 1 þ b15 C þ ε (4) where Ghousing is the housing price; this is the benefit enjoyed by growth coalitions. b1 - are the coefficients of all the urban form variables, which can be divided into three categories: neighbourhood attributes (FAR, green rate, location), accessibility (access to transportation and commercial sites), and walkability (the number of commercial sites, educational facilities, and other facilities within 500 m). The variable descriptions are listed in Table 1. The control variables C include the location of residential zones and the

distance to the urban centre. It should be noted that the growth coalitions are motivated to minimize costs, which leads them to acquire land far from the builtup area. However, this is not the whole story: the local government will need to think about both minimizing cost and maximizing revenue. To the extent that the land transfer price (that is, the land sales revenue that local governments collect) is very much affected by the distance to the city centre, local governments are motivated to acquire land closer to the city centre to obtain higher sales prices, even though the acquisition cost may be higher. This represents a trade-off between revenue and cost. In our framework, however, the local government and real estate enterprises form a growth coalition; therefore, the land transfer fee, which is important to local revenue, is an internal transaction between local government and real estate enterprises. To verify the trade-off, we draw a scatter diagram (Fig. 5) to represent the relationship between the standard land price/housing price and the distance to the urban centre. Fig. 5 (left) shows

Fig. 5. The trade-off between the land increment and the distance to the urban centre.

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that the standard land price and housing price both decrease as the distance to the urban centre increases. The standardized difference value (Fig. 5 right) has no significant correlation with the distance to the urban centre. Referring to the growth coalition, therefore, the distance to the urban centre is not a primary concern.

4. Results and discussion 4.1. Identifying the location of residential zones Using the above methods, we identify the urban land expansion pattern in Beijing from 1996 to 2006 and from 2006 to 2015. The results are shown in Fig. 6. On the basis of the results for the expansion pattern of urban construction land, the spatial location types of residential zones for 1996e2006 and 2006e2015 are identified and divided into four types: renewal, infill, edge and outlying. The result is shown in Fig. 7. We divide all residential zones into two time periods: 4339 residential zones built in 1996e2006 and 1707 residential zones built in 2006e2015 (Table 2). Residential expansion in Beijing mainly occurred during the 2000s, when China was experiencing rapid urbanization and economic growth. As previous research has argued (Wu, 2018a,b), Beijing, like other metropolises worldwide, entered a period dominated by urban renewal. The residential renewal zones occupied 46.53% and 53.3% of all land, respectively, in 1996e2006 and 2006e2015. The proportion of outlying residential zones increased remarkably, from 19.36% in 1996e2006 to 22.85% in 2006e2015. This result is interesting: on the one hand, Beijing accepted more urban renewal projects to build new residential zones due to a shortage of available urban land resources; on the other hand, the major profits achieved through outlying residential zones are highly attractive. Table 3 shows that the urban forms do not differ much across the residential zones at different locations. Specifically, the accessibility of outlying residential zones is higher than that of other areas. In addition, the distance between residential zones and commercial sites in outlying parcels is lower than it is in other areas. Only educational resources are centralized in existing urban areas, while these are lacking in the suburbs.

4.2. The land acquisition cost model According to the regression results, the standard land price of the residential zones located in infill, edge and outlying areas is lower than that for land located in built-up areas. This result explains the phenomenon observed in the above section, which is the significant increase in the proportion of outlying and edge areas, suggesting that new residential zones are being cumulatively built at a distance from existing urban areas. This suggests that local governments choose to supply residential land with outlying land parcels, which are the least costly (Table 4). In contrast to the research hypothesis, the standard land price of infill residential zones is lower than that of edge zones in 1996e2006. This result may be related to the timing of land transfers in Beijing. Lin (2009) shows that urban construction land expansion in Beijing is closely related to traffic infrastructure. Residential zones are usually developed along major roads and subway lines, especially in outer suburban areas (Wu and Song et al., 2017). Residential land proximate to the traffic axis is the first to be constructed, which is most often in edge expansion areas rather than in infilled areas. Therefore, this may result in relatively poor traffic-location conditions in the infilled areas and therefore a lower base land price. 4.3. The housing price model Table 5 reports the results of the variables measuring urban form from the OLS regressions for a semi-log formulation of the hedonic equation. The estimated effects of the urban form variables on residential property values are of primary interest. The results of the benefit model are within expectations. The average housing price has a significantly negative relationship with the FAR and a nonsignificant negative relationship with the green rate. This means that growth coalition could improve the FAR of residential zones by revising land transfer contracts of their own accord, thereby boosting the housing price and land transfer fee. We also found that price decreases with distance from the urban centre, in line with findings from the previous literature (Song and Knaap, 2004a,b; Liu and Nowak et al., 2016). In terms of accessibility, the distance to the nearest transportation stop has significant effects on

Fig. 6. The identification of urban land expansion (Left: 1996e2006; Right: 2006e2015).

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Fig. 7. The identification of residential land (Left: 1996e2006; Right: 2006e2015).

Table 2 Location types for residential zones in Beijing. Location

Residential zones built in 1996e2006

Residential zones built in 2006e2015

Amount

Percent

Amount

Percent

Renewal Infill Edge Outlying

2019 607 473 840

46.53% 13.99% 10.90% 19.36%

910 207 200 390

53.30% 12.13% 11.72% 22.85%

Sum

4339

100%

1707

100%

Table 3 Averages for the four urban form indicators. Urban form

Renewal

Infill

Edge

Outlying

FAR Green_rate Access_tra Access_pedtra Walk_com Walk_pedcom Walk_serv Walk_pedserv Walk_pededu Walk_pedhosp Walk_pedop

1.610567 0.325498 0.1341 36.9875 0.1298 113.4414 0.122 47.3375 9.73972 7.92222 3.507222

1.971875 0.352701 0.2202 28.91071 0.2116 91.78125 0.1995 38.46429 9.59375 7.008929 3.950893

1.923067 0.373587 0.3882 23.49333 0.3317 78.45333 0.309 34.46667 3.6 5.466667 3.36667

1.581494 0.393683 0.5203 31.73793 0.5425 109.9471 0.4758 40.33103 1.868966 7.464368 3.58621

price, as does the number of transportation stops within 500 m of residential zones. Most walkability indicators are significant. The average housing price is higher if the number of educational institutions, hospitals, and open spaces is greater. Unexpectedly, the number of daily commercial sites within walking distance is not significant. We attribute this result to changing travel behaviour. Residents do not depend on shopping malls and supermarkets near their homes due to advanced public transportation and the increasing ownership of private cars. They may choose to shop online or based on centralized shopping models instead of at commercial sites near home. The estimated coefficients of Mixed_1 and Mixed_2 are interesting. Unlike the previous literature, mixed land use has a (non)significant negative effect on housing prices. Because the not-in-my-backyard attitude is also valid in China, mixed land use may sometimes decrease housing prices.

The control variables strongly prove that the location choice is not a trade-off; as mentioned above, the growth coalition acquires residential land far from built-up areas to reduce costs. Specifically, the rank of residential location selection is outlying > edge > infill > built-up areas. However, the location does not have a direct effect on housing prices by controlling the effect of distance to the urban centre. It should be noted that we do not directly link the residential land transfer fee data to residential zone data. The price of residential housing may be highly correlated with the land transfer fee of the residential zone. We establish spatial correspondence between the residential land transfer fee data and the average housing price of the residential zones. The Kriging interpolation of these two datasets for Beijing is shown in Fig. 8. The correlation between the housing price and the sale price of residential land is calculated. The bivariate spatial association, proposed by Wartenberg (1985), is used to calculate the spatial correlation between these two pieces of data. Sang-II refined Wartenberg’s method and demonstrated that the two-variable spatial correlation coefficients are calculated in accordance with Pearson’s correlation coefficient when establishing point-to-point spatial relations between two variables. For this reason, we establish the point-to-point relationship between the residential land transfer fee and the average housing price of each residential zone through the grid calculator in ArcGIS and calculate its Pearson correlation coefficient. The results are shown in Table 6. The calculation results show that the correlation coefficient between housing and land price reaches as high as 0.771, and a 1% significance level is achieved. Thus, we could conclude that housing prices are remarkably influenced by land transfer fees. In other words, the land increments from agricultural land to residential land are shared by both local government and real estate enterprises. 5. Conclusion Growth coalitions emerged in the context of the neoliberal wave sweeping the globe (Porter, 2000). In a neoliberal institutional atmosphere, cities compete to obtain global capital, operating under tremendous pressure to improve the investment environment and attract investment (Scott, 2001; Miraftab, 2004). Local governments and enterprises are inevitably involved in atypical governing behaviour, and these are referred to here as “growth coalitions” (Tat

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Table 4 The model result for cost minimization.

Dinfilling Dedge Doutlying D_Centre Intercept N F Prob > F R2

Model 1 Period from 1996 to 2006

Model 2 Period from 2006 to 2015

Ref. the renewal residential zone as reference group before 2006 36239.356*** (7156.326) 36534.353** (11557.211) 28273.570*** (5678.188) 3.178*** (.119) 176760.437*** (2145.456)

Ref. the renewal residential zone as reference group before 2015 30030.261** (11173.632) 22726.883* (9370.438) 32748.266*** (7627.598) 3.043*** (.173) 182116.599*** (4042.041)

3630 577.83 0.000 0.234

1230 685.44 0.000 0.279

Note: *, **, *** represent significant at 0.05, 0.01, and 0.005 levels, respectively.

Table 5 The model result for benefit maximization. Variables

Coefficient

Standard deviation

FAR Green_rate Access_tra Access_pedtra Walk_com Walk_pedcom Walk_serv Walk_pedserv Walk_pededu Walk_pedhosp Walk_pedop Mixed_1 Mixed_2 Control variables Dinfilling Dedge Doutlying D_Centre Intercept

850.868*** 2817.306 12.047*** 63.233*** 10.810*** -.230 3.818 7.795 76.173*** 237.239*** 325.005*** 15066.823 46319.629**

209.224 4231.998 2.607 10.097 3.070 1.497 3.500 10.023 9.369 23.033 20.953 18162.903 17843.501

3096.804 2225.931 178.705 -.915*** 76274.750

1519.540 2618.792 1328.879 .031 4145.230

N Prob > F R2

4942 0.000 0.509

Note: *,**, *** represent significance at the 0.1, 0.05, and 0.01 levels, respectively.

Kei Ho, 2002). Consequently, local governments no longer play an irrelevant governing role but rather act as stakeholders (Schneider and Teske, 1992). In this context, cities in different countries are shifting from a traditional governance mode represented by the construction of urban infrastructure and the provision of service facilities to an urban entrepreneurial management mode (Walder, 1995). China is no exception. In addition, the growth coalition in China is remarkable and distinctive due to China’s special institutional background, economic development and urbanization mode. China has witnessed significant residential land expansion in the past two decades. Using residential zone data from 1996 to 2015, this study applied an innovative analysis framework based on growth coalitions to explore the mechanism behind the residential landscape in Chinese metropolises. We assumed that growth coalitions attempt to acquire residential land far from built-up areas to reduce costs, increase the FAR and green rate, and promote accessibility and walkability, thereby improving the housing benefits. Taking Beijing as a case, we first identified the expansion (renewal, infill, edge, outlying) type of each residential zone. Then, two models are created: the land acquisition cost model and the housing price model. The results provide a good argument supporting our hypothesis. This study contributes to the literature on both international urban sprawl and Chinese residential landscapes. Referring to

Fig. 8. The Kriging interpolation of land transfer fees and average housing price (Left: 1996e2006; Right: 2006e2015).

J. Wu et al. / Journal of Cleaner Production 223 (2019) 620e630

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Table 6 The spatial correlation between residential land transfer fees and average housing price.

Residential land transfer fee Average housing price

Residential land transfer fee

Average housing price

1.000 0.771***

1.000

global urban sprawl, urban sprawl in Western countries is characterized by low-density, automobile-oriented and deteriorating facilities (Jabareen, 2016). A large number of neighbourhoods are scattered in the suburbs, which requires local governments to provide the necessary infrastructure, leading to an excessive financial burden on local governments (Carruthers and Ulfarsson, 2003). This phenomenon led to a discussion on smart growth (Downs, 2005; Handy, 2005). This study provides a distinctive picture of urban sprawl. As mentioned above, the residential landscape in Beijing is characterized by high density, facilities improvement, and good quality. The residential landscape mode in China is a specific sample within urban sprawl studies, representing “location sprawl” rather than “form sprawl”. Referring to urban land expansion in China, we consider that residential land does not sprawl based on our interpretation framework of growth coalitions. As the construction land supply in China is monopolized by governments, growth coalitions should carry out land acquisition and infrastructure construction before the land transfer. Local governments must maximize land transfer fees to balance the cost of land acquisition and infrastructure. The residential landscape in China is inevitable in the context of growth coalitions, and we should shed light on governance systems in China instead of solely focusing on land expansion. This paper addresses residential landscapes that could affect global warming (Sun and Lu et al., 2019), quality of urban life
nchez et al., 2012) and employment (Prado-Lorenzo and García-Sa (Tang and McLellan et al., 2016). To the best of the authors’ knowledge, this is the first attempt to study the relationship between residential landscape and urban sustainability through a novel framework of ‘big data,’ and the results of this study can provide guidance on urban planning and design for government leaders and researchers in the future. Funding This work was supported by Social Science Foundation of China (No. 18VSJ041), the Fundamental Research Funds for the Central Universities, and Funds from Key Laboratory of Regional Sustainable Development Modeling, Chinese Academy of Sciences (No. KF2018-04). References Brueckner, J.K., 2000. Urban sprawl: diagnosis and remedies. Int. Reg. Sci. Rev. 23 (2), 160e171. Cao, T.V., Cory, D.C., 1981. Mixed Land Uses and Residential Property Values in the Tucson Metropolitan Region: Implications for Public Policy. Carruthers, J.I., Ulfarsson, G.F., 2003. Urban sprawl and the cost of public services. Environ. Plan. Plan. Des. 30 (4), 503e522. Chen, M., Liu, W., et al., 2016. Challenges and the way forward in China’s new-type urbanization. Land Use Pol. 55, 334e339. Davis, M.A., Oliner, S.D., et al., 2016. Residential land values in the Washington, DC metro area: new insights from big data. In: DC Metro Area: New Insights from Big Data (January 19, 2016). Deng, X., Huang, J., et al., 2008. Growth, population and industrialization, and urban land expansion of China. J. Urban Econ. 63 (1), 96e115. Dieleman, F., Wegener, M., 2004. Compact city and urban sprawl. Built. Environ. 30 (4), 308e323. Ding, C., 2003. Land Policy Reform in China: Assessment and Prospects, vol 20, pp. 109e120, 2. Ding, C., 2007. Policy and praxis of land acquisition in China. Land Use Pol. 24 (1), 1e13.

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