Interaction between urban land expansion and land use policy: An analysis using the DPSIR framework
Introduction
Urban land has been expanding at an unprecedented rate during the past decades around the world (Schneider et al., 2015; Liu et al., 2018), and this phenomenon will likely continue over the coming years (Seto et al., 2011; Xu et al., 2019). Rapid urban growth guarantees spatial demands for socioeconomic development. However, it also can lead to some severely negative socio-ecological impacts, such as farmland loss, rural decline, decreases in biodiversity, and ecological degradation (Jago-on et al., 2009; Li et al., 2010; Nassauer and Raskin, 2014; Liu and Li, 2017). Therefore, controlling urban land expansion (ULE) is drawing increasing attention from policy-makers and researchers (Halleux et al., 2012; Tong et al., 2017).
ULE is the result of mutual interactions between human society and natural systems (Lambin and Meyfroidt, 2010; Long and Qu, 2018). Land use policy (LUP) has thus been chosen as an important tool for controlling ULE and reducing its negative effects. In Europe, the European Landscape Convention (Council of Europe, 2000; Déjeant-Pons, 2006) and the European Spatial Development Perspective (Commission of the European Communities, 1999) have been proposed to reduce land development. In the United States, the Farmland Preservation Program has been widely adopted to protect farmland (Daniels, 2019). Additionally, the Chinese government has also proposed a series of land use policies and regulations to protect farmland and improve land use efficiency (Liu et al., 2014; Wu et al., 2017), such as the Basic Farmland Protection Regulation, the Requisition-Compensation Balance of Farmland Policy, and Economical and Intensive Land Use Policy. These LUPs primarily attempted to control ULE in two interrelated but distinguishable ways: first, restricting the conversion of farmland or other open space to urban areas; second, improving urban land use patterns (Nuissl et al., 2009). However, they have often proved to be ineffective in practice (Shao et al., 2018; Shen et al., 2019).
Enhancing our knowledge as to the causes, processes, and effects of ULE is essential to the formulation of LUPs (Herold et al., 2003). Therefore, previous studies have focused on analyzing the patterns and drivers of ULE in order to improve the efficiency of LUPs. In particular, temporally non-stationary expansion rates and urban land use intensity have been detected based on geographic information system (GIS) and remote sensing (RS) techniques (Rahman, 2016; Qiao et al., 2017; Gong et al., 2018). The spatial variations of ULE have been analyzed using spatial analysis methods such as the G-statistic (Huang et al., 2015) and kernel density estimation (KDE) (Cai et al., 2013). Considering the direction and physical distribution of added urban land, various spatial patterns of ULE (e.g., concentric, infilling, and leapfrog) have been identified (Xu and Min, 2013). Since the expansion process has been recognized to be both spatial and temporal, it is preferable to analyze this phenomenon comprehensively by taking both space and time into consideration. In recent years, several frameworks, such as the multi-order urban development (MUD) model, have been proposed for this purpose (Bhatta et al., 2010; Tong et al., 2017). As for the drivers of ULE, researchers have discovered that ULE is driven not only by physical features (e.g., elevation and slope) but also by socioeconomic factors (e.g., GDP, demography, and investment) (Li et al., 2013; Seto et al., 2011). Generally, previous studies have explored the socioeconomic determinants from either neoclassical or institutional perspectives (Huang et al., 2015). For example, Burchfield et al. (2006) and Deng et al. (2008) identified population, income, transportation cost, and agricultural land prices as key drivers of ULE based on the monocentric city model. Wei (2015) analyzed the drivers of ULE in terms of the triple process of globalization, marketization, and decentralization in contemporary China.
Additionally, researchers have also attempted to improve the efficiency of LUPs by analyzing the impacts of the limitations of these policies on ULE. “Growth‐led” development, entrepreneurial governance, and land financing were recognized as the essential reasons for ineffectiveness of LUPs (Shao et al., 2018). Moreover, the restriction of the household registration system on population mobility, the contradiction between demand for urban land and protection of farmland, and insufficient reciprocities and multi-layer connections among the policies were considered to be other challenges that may degrade the land use policy efficiency (Liu et al., 2014). Hence, certain useful suggestions were provided in previous studies. For example, Liu et al. (2015) suggested that the government should make more efforts to develop a trans-regional linkage between urban and rural construction land management. Wang et al. (2018) indicated that there is still a pressing need to reform land policies for the more efficient and effective utilization of limited land resources.
Previous studies have provided crucial information for policy-makers on both the process of ULE and the effectiveness of LUP. However, the complexities of human-natural interactions and feedback loops between ULE and LUP are far from fully understood. Specifically, previous research has primarily suffered from two types of limitations: first, the long-term trends and abrupt changes of urban land expansion, which were hypothesized to cause the transitions of LUP (Nuissl et al., 2009), have attracted little attention; second, attention was typically paid to the direct consequences of these policies by examining the physical elements of farmland and the condition of urban intensive use, whereas few studies have investigated the impacts of LUP on the drivers of ULE.
Given the aforementioned research uncertainties, this study applied the Driver-Pressure-State-Impact-Response (DPSIR) framework to synthesize the processes drivers, and policy feedback of ULE in order to understand the interaction between ULE and LUP. The DPSIR is a causal framework developed by the European Environmental Agency European Environmental Agency, 1999 to describe the cause-effect relationship between human society and the environment (European Environmental Agency, 1999; Haase and Nuissl, 2007; Jago-on et al., 2009; Carr et al., 2009). The basic idea of the DPSIR framework is that the drivers (D) in social and economic activities can lead to an environmental state (S) change due to their imposed pressure (P). This process then generates socio-ecological impacts (I), which trigger policy responses (R). These responses can then influence trends in drivers, pressures, states, and impacts in turn. Although the DPSIR framework is not a thorough quantitative model, it is a heuristic tool which is able to create linkages between the drivers, processes, and LUP of ULE and able to reveal a feedback loop between policy and environmental problems (Svarstad et al., 2008). For example, Haase and Nuissl (2007) described the different components of the DPSIR separately, and further qualitatively discussed the impacts of urban sprawl on water balance and policy by using the information gained from the DPSIR framework. Jago-on et al. (2009) synthesized the driver, state, impact and policy response of subsurface enironments through the DPSIR framework, and also examined the qualitative connections between policy and enironemtal problems within the framework.
The main contributions of this study include: (1) analyzing the trend changes of ULE in China using the abrupt change point detection technique and (2) describing feedback loops among drivers, processes, and LUPs through the DPSIR framework. Notably, although the abrupt change point detection technique has often been used to analyze the trend change of climate data, it is rarely used in the field of ULE. The remainder of this article is arranged as follows: Section 2 presents the data sources and methodology, Section 3 describes the spatiotemporal process, drivers, impacts and policy responses of ULE within the DPSIR framework. The issues concerning the relationship between LUP and ULE are discussed in Section 4. The conclusions and limitations of this study are described in the final section.
Section snippets
Data sources
In this study, the investigation of interactions between ULE and LUP covered the 224 main cities in China. The urban land data (1981–2015) were obtained from the China Urban Construction Statistical Yearbook. The socioeconomic driver data (e.g., population and GDP) of 224 sample cities were collected from the China City Statistical Yearbook (1990–2015). The 1 km spatial resolution land use data was downloaded from the resource and environment data cloud platform (http://www.resdc.cn), and
Temporal change of urban land
In previous studies, researchers commonly selected the subsample size based on the time period of their investigations (Fu et al., 1999; Zhao et al., 2008). However, different subsample sizes may cause variations in the abrupt change point (Wei, 1999). To avoid this bias, five equivalent subsample sizes (n = 4, 5, 6, 7, 8) for the MTT (Fig. 3a–e) were adopted, and the DMC (Fig. 3f) was employed to confirm the MTT result. Based on Fig. 3 and Table 4, we discovered that the only overlapping
The abrupt change of ULE
ULE is a continuous and nonlinear process. Identifying the trend changes of ULE can provide new insights that are useful when analyzing its spatiotemporal processes. It is recognized that ULE is temporally non-stationary. For example, the expansion speed of urban land varied with time in this study (Fig. 7), although only the years 1990 and 2000 were identified as abrupt change points. This indicates that short-term observations or those consisting of only a few separate individual time periods
Conclusions
This study used the DPSIR framework to synthesize existing knowledge on ULE in order to understand the interaction between LUP and ULE. The DPSIR has proved to be effective in revealing the cause-effect relationships between socioeconomic activities and ULE. Based on official urban land data from 1981 to 2015, this study discovered that the two abrupt change points in the process of ULE in China corresponded to the transitions of socioeconomic status. Moreover, by applying the MTT and KDE
CRediT authorship contribution statement
Shijin Qu: Methodology, Data curation, Writing - original draft, Formal analysis. Shougeng Hu: Conceptualization, Methodology, Data curation. Weidong Li: Supervision, Writing - review & editing. Hui Wang: Writing - review & editing. Chuanrong Zhang: Supervision, Writing - review & editing, Resources. Quanfeng Li: Software, Validation.
Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant No. 41671518), Major Project of National Social Science Foundation of China (Grant No. 18ZDA053), Humanities and Social Sciences Foundation of Ministry of Education (Grant No.16YJAZH018), and the Fundamental Research Funds for the Central University, China University of Geosciences (Wuhan) to Shougeng Hu.
References (71)
- et al.
Quantifying the degree-of-freedom, degree-of-sprawl, and degree-of-goodness of urban growth from remote sensing data
Appl. Geogr.
(2010) - et al.
Estimating the relationship between urban forms and energy consumption: a case study in the Pearl River Delta, 2005–2008
Landsc. Urban Plan.
(2011) - et al.
Comparing urban land expansion and its driving factors in Shenzhen and Dongguan
China, Habitat Int.
(2014) - et al.
Urban land expansion and the transitional mechanisms in Nanjing
China, Habitat Int.
(2016) - et al.
Growth, population and industrialization, and urban land expansion of China
J. Urban Econ.
(2008) - et al.
Changing urban forms and carbon dioxide emissions in China: a case study of 30 provincial capital cities
Appl. Energy
(2015) - et al.
Economic transition and urban land expansion in Provincial China
Habitat Int.
(2014) - et al.
A review of the application and evolution of the DPSIR framework with an emphasis on coastal social-ecological systems
Ocean Coast. Manag.
(2015) - et al.
Urban expansion dynamics and modes in metropolitan Guangzhou
China, Land Use Policy
(2018) - et al.
Does urban sprawl drive changes in the water balance and policy?
Landsc. Urban Plan.
(2007)
The adaptive efficiency of land use planning measured by the control of urban sprawl. The cases of the Netherlands, Belgium and Poland
Land Use Policy
The spatiotemporal form of urban growth: measurement, analysis and modeling
Remote Sens. Environ.
Urban land expansion under economic transition in China: a multi-level modeling analysis
Habitat Int.
Analysis on coupling relationship of urban scale and intensive use of land in China
Cities
Urbanization and subsurface environmental issues: an attempt at DPSIR model application in Asian cities
Sci. Total Environ.
How does sprawl differ across urban built-up land types in China? A spatial-temporal analysis of the Beijing metropolitan area using granted land parcel data
Cities
Land use transitions: socio-ecological feedback versus socio-economic change
Land Use Policy
Unit root tests in panel data: asymptotic and finite-sample properties
J. Econom.
Socioeconomic transformations in Shanghai (1990–2000): policy impacts in global–national–local contexts
Cities
Forty years of urban expansion in Beijing: What is the relative importance of physical, socioeconomic, and neighborhood factors?
Appl. Geogr.
The varying driving forces of urban expansion in china: insights from a spatial-temporal analysis
Landsc. Urban Plan.
Key issues of land use in China and implications for policy making
Land Use Policy
Construction land expansion and cultivated land protection in urbanizing China: insights from national land surveys, 1996–2006
Habitat Int.
Conversion from rural settlements and arable land under rapid urbanization in Beijing during 1985–2010
J. Rural Stud.
Strategic adjustment of land use policy under the economic transformation
Land Use Policy
Land use transitions and land management: a mutual feedback perspective
Land Use Policy
Changing man-land interrelations in China’s farming area under urbanization and its implications for food security
J. Environ. Manage.
Urban vacancy and land use legacies: a frontier for urban ecological research, design, and planning
Landsc. Urban Plan.
Environmental impact assessment of urban land use transitions—a context-sensitive approach
Land Use Policy
The changing spatial form of cities in Western China
Landsc. Urban Plan.
Evaluating the effectiveness of land use plans in containing urban expansion: an integrated view
Land Use Policy
Discursive biases of the environmental research framework DPSIR
Land Use Policy
Urban land expansion and arable land loss in China—a case study of Beijing–tianjin–hebei region
Land Use Policy
Multi-order urban development model and sprawl patterns: an analysis in China, 2000–2010
Landsc. Urban Plan.
Does research applying the DPSIR framework support decision making?
Land Use Policy
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