Elsevier

Land Use Policy

Volume 99, December 2020, 104856
Land Use Policy

Interaction between urban land expansion and land use policy: An analysis using the DPSIR framework

https://doi.org/10.1016/j.landusepol.2020.104856Get rights and content

Highlights

  • The DPSIR framework is used to analyze the cause-effect relationship between urban land expansion and land use policy.

  • Two abrupt change points in the process of urban land expansion correspond to the socioeconomic transitions in China.

  • Negative effects have led to the implementation of some farmland protection and land-intensive use policy.

  • Existing land-use policies show some weaknesses in controlling urban land expansion.

Abstract

Many studies of urban land use have focused on understanding the process and drivers of urban land expansion (ULE). However, these studies rarely examined the long-term trends and abrupt change points of ULE. Moreover, the feedback loop between ULE and land use policy (LUP) has less been investigated. Thus, stakeholders sometimes experience difficulties when utilizing the existing ULE theories in urban land management practices and policy-making. In this study, we used the moving t-test method to detect the trend changes of urban land in China from 1981 to 2015 and a fixed-effects model to recognize the main drivers. The Driver-Pressure-State-Impact-Response (DPSIR) framework was applied to synthesize the existing knowledge of ULE to understand the interaction between LUP and ULE. Empirical analysis of 224 main cities indicated that China witnessed varying patterns of ULE through three different stages from 1981 to 2015. It revealed that the accelerated conversion of farmland to urban uses, along with inefficiency land use, led to a variety of farmland protection policies and intensive land use policies. In turn, however, these policies possibly affected the drivers of ULE. The impacts of drivers associated with land demand exhibited an increasing trend. Based on our findings, we suggest that land use policies should take the complexity and systematicity of ULE into consideration in urban land management. Overall, the framework, method, and findings of this study can help to increase the effectiveness of land use policy, not only in China but also in other developing countries.

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.

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