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Article

Urban Land Expansion Simulation Considering the Increasing versus Decreasing Balance Policy: A Case Study in Fenghua, China

by
Yaya Jin
1,
Jiahe Ding
1,
Yue Chen
1,
Chaozheng Zhang
1,
Xianhui Hou
1,
Qianqian Zhang
2,* and
Qiankun Liu
3,*
1
College of Economics & Management, Northwest A&F University, Xianyang 712100, China
2
Department of Earth System Science, Institute for Global Change Studies, Ministry of Education Ecological Field Station for East Asian Migratory Birds, Tsinghua University, Beijing 100084, China
3
Zhejiang Provincial Development and Reform Institute, Hangzhou 310025, China
*
Authors to whom correspondence should be addressed.
Land 2023, 12(12), 2099; https://doi.org/10.3390/land12122099
Submission received: 23 October 2023 / Revised: 20 November 2023 / Accepted: 20 November 2023 / Published: 23 November 2023
(This article belongs to the Special Issue State of the Art on Agriculture in Rural Areas: For Sustainable Land Management)
Browse Figures
Figure 1
<p>The study area.</p> "> Figure 2
<p>Two situations and three steps in the simulation process.</p> "> Figure 3
<p>Methodological framework.</p> "> Figure 4
<p>The driving factors of urban expansion in Fenghua District.</p> "> Figure 5
<p>The restricted area in the process of urban expansion simulation.</p> "> Figure 6
<p>The simulation results of urban land expansion in Fenghua District (model: FLUS). Notes: (<b>a</b>) indicates spatial pattern of land use in 2017, (<b>b</b>) indicates the first step of village land convert into urban land directly in 2035, (<b>c</b>) indicates the second step of village land convert into arable land and other agricultural land in 2035, (<b>d</b>) indicates the third step of arable land and other agricultural land convert into urban land in 2035.</p> "> Figure 7
<p>Spatial heterogeneity in the first step of urban land expansion. Notes: CH—Chunhu Street, DY—Dayan Town, FQ—Fangqiao Street, JK—Jiangkou Street, JP—Jinping Street, QC—Qiucun Town, ST—Shangtian Street, SD—Songdai Town, XW—Xiwu Street, XK—Xikou Town, XWM—Xiaowangmiao Street, YL—Yuelin Street.</p> "> Figure 8
<p>Expansion near the urban land and traffic roads of Xiwu Street from 2017 to 2035. Notes: (<b>a<sub>1</sub></b>,<b>a<sub>2</sub></b>) represent spatial pattern of land use in 2017, (<b>b<sub>1</sub></b>,<b>b<sub>2</sub></b>) represent spatial pattern of land use of the first step of simulation in 2035.</p> "> Figure 9
<p>Arable land concentration after reclamation in Dayan Town from 2017 to 2035. Notes: (<b>a<sub>1</sub></b>,<b>a<sub>2</sub></b>) represent spatial pattern of land use in 2017, (<b>b<sub>1</sub></b>,<b>b<sub>2</sub></b>) represent spatial pattern of land use of the second step of simulation in 2035.</p> "> Figure 10
<p>Spatial heterogeneity of land types for village land reclamation.</p> "> Figure 11
<p>Spatial heterogeneity in the third step of urban expansion simulation.</p> "> Figure 12
<p>Spatial heterogeneity of urban land stock expansion.</p> "> Figure 13
<p>The simulation results of the urban land expansion (model: PLUS).</p> ">
Versions Notes

Abstract

:
Under the political dominance of urbanization, the policy of increasing versus decreasing balance (IVDB) between urban and rural construction land has had a profound influence on urban land expansion in China. The purpose of this study is to reveal the impact of the IVDB policy on the process of urban land expansion. Considering the transition process among different land use types under the IVDB policy, this study proposes two situations of urban land expansion. A future land use simulation (FLUS) model is applied to simulate the expansion process over three steps. A case study of Fenghua District in Ningbo City, China, shows the following: (1) In the first situation of village land directly transformed into urban land, the transformation is concentrated in the northern and western parts of Fenghua District. The expansion trends are particularly pronounced along existing urban land and main traffic lines. (2) In the second situation of village land reclamation for agricultural land and urban land occupation for agricultural land, the spatial differences in village land conversion to arable land or other agricultural land are relatively small, and the degree of concentration of arable land is significantly increased after reclamation. Urban land expansion mainly occurs close to Ningbo City. With the help of transfer quotas “produced” by other areas, expansion land can be balanced within Fenghua District. This research helps to shed light on the urban land use growth process and provides beneficial insights for stock spatial planning in China.

1. Introduction

The Earth has undergone intense urbanization since the beginning of the Anthropocene [1]. About 1.57 billion people have migrated from the countryside to the city since 2000, accounting for 20.2% of the world’s population in 2020 [2]. The process of urbanization may lead to two pivotal challenges. On the one hand, rapid and disordered urban land expansion has largely swallowed agricultural land and ecological land [3], which could pose serious threats to the natural landscape, ecological environment, climate change, and food security [4,5,6]. On the other hand, as many rural migrant workers have flooded into urban areas to earn a living, massive rural settlements remain unoccupied seasonally or even permanently [7,8,9,10]. As urban expansion cannot automatically balance the optimization of construction land structure, careful attention should be given to the spatiotemporal interaction policy of urban–rural construction land, as well as exploring the policy operation mechanism during the process of urban land expansion, for rational urban growth and sustainable urbanization.
Within global urban expansion movements and governance, China’s urbanization is particularly noteworthy because its development is of worldwide significance [11]. China has experienced a severely excessive expansion of urban land [12]. Over the past two decades, the urban population has increased by 76%, while urban built-up land has risen by nearly 300% [2], with a mean annual expansion of 1710.96 km2 per year [13]. Simultaneously, since reforms and opening up in the late 1970s, China has undergone an unprecedented urbanization process [14]. As land finance was considered to be the main driving force [15], state and local governments formulated corresponding policies to regulate urban land use and cultivate land protection [16,17], along with land-centered urbanization greatly accelerating China’s economic and social development [18]. In other words, China’s urbanization is generally considered to be characterized by political dominance [19]. Among the series of land use and urban management policies, the increasing versus decreasing balance (IVDB) policy of construction land between urban and rural areas is recognized as the most effective tool to mobilize and optimize the urban–rural land use structure [20]. With the shortage of land for urban construction, the IVDB policy was introduced in 2004 to maintain the balance between increases in urban construction land and decreases in rural construction land [21]. The IVDB policy involves construction land reorganization, which includes new houses built in urban areas, old houses demolished in the countryside (jianxin chaijiu), and the demolished parcel reclaimed as arable and other agricultural land [22]. The evolution of IVDB policy follows the mode of “pilot project–typical pilot project–wide-scale promotion–national-scale promotion” [23]. The core of the IVDB policy is to transform construction land quotas into urban areas to support development [22], and quotas are saved by demolishing and reclaiming rural land [24]. Then, under the “top-down” controlling of the total construction land quota allocation [25], the increase in urban construction land on the premise of reducing rural construction land seems a multi-win solution [9], achieving the goals of meeting the local demand for urban construction land, maintaining cultivated land quantity, and optimizing the spatial layout of urban–rural areas, along with coordinating urban–rural development [26]. In general, the IVDB policy has a similar implementation mechanism to the transferable development rights (TDRs) in the United States [27]. Since 2005, 31 provinces in China have implemented the IVDB policy, with the approval of 6816.70 km2 transfer quotas to implement replacement projects [23], which means enormous scales of transfer quotas are involved in the urbanization process. Therefore, integrating the IVDB policy into the process of urban land expansion has important research significance and value for China.
Spatiotemporal modeling of land use and land cover change (LULC) is an effective and reproducible tool for analyzing the characteristics of urban development and revealing existing problems in the urbanization process [28]. The cellular automata (CA)-based model is a common method to simulate urban land spatial evaluation, which estimates the state of pixels according to the initial state of pixels, the surrounding neighborhood effects, and a set of transition rules [29]. In the last two decades, CA-based models, such as the conversion of land use and its effects to a small regional extent (CLUE-s) [30], the land transformation model (LTM) [31], Slope, Land Use, Exclusion, Urban, Transportation, Hillshade (SLEUTH) [32], FOREcasting SCEnarios of future land cover (Fore-SCE) [33], a future land use simulation (FLUS) model [29], and a patch-generating land use simulation (PLUS) [34], have been extensively applied to simulate urban land expansion at different scales [35]. In addition to improvements to technical modeling processes, model calibration, and rules, more attention has been given to the impact of policy on the simulation process [36], including the policy of ecological–agricultural–urban suitability assessments [37], spatial functional zones [38], traffic and development zone planning [39], shared socioeconomic pathways [35], ecological security patterns [40], and administrative division adjustments [14]. There is no doubt that the coupling of land use and urban management policies with simulation models can maximize the recurrence of urban growth processes and provide targeted recommendations for policymakers [41,42]. However, most research has failed to focus on the effect of the IVDB policy on the simulation process of urban land expansion, which may lead to bias in growth simulation results and fail to support effective decision making for sustainable urban–rural development in China. This is mainly because the relationship between the transfer quotas of the IVDB policy and the responses of urban expansion is more complicated [43,44], which makes it hard to fully recognize the impact of transfer quotas [26] and to completely present the process of urban expansion. In addition, though previous research has explored the ecological constraints of urban expansion [25,45], few studies have investigated the limits of permanent prime farmland in the expansion.
To fill in this knowledge gap, this research attempts to integrate the IVDB policy into an urban expansion simulation, as well as simulate the whole process of replacing rural construction land with urban land. This paper is organized as follows: Section 2 displays the multiple sources of spatial data and the simulation framework. Section 3 presents the urban land expansion simulation results based on the FLUS model. Section 4 then outlines discussions and suggestions prior to the concluding remarks, which are provided in Section 5.

2. Materials and Method

2.1. Study Area and Data

2.1.1. Study Area

Considering the vast spatial heterogeneity of urbanization in China, the consolidation of rural construction land and the use of transfer quotas have become more urgent in urban agglomeration with higher urbanization levels along the eastern coast [46,47]. Moreover, administrative division adjustment such as turning a county into a district implies the acceleration of urbanization [11,48]. Thus, scientific replacement and optimal layout of urban–rural construction land needs to be strengthened enormously and interact positively with urban expansion in such areas. To this end, Fenghua District of Ningbo City, Zhejiang Province, which abolished its county identity in 2016, was selected as the research area. As shown in Figure 1, Fenghua District is located in the south of Ningbo City and the eastern coast of Zhejiang Province, China. The geomorphic composition of Fenghua is graphically summarized as “60% mountains, 10% water, and 30% fields”, with a land area of 1277.72 km2, a sea area of 91 km2, a coastline of 63 km, and 24 islands (Figure 2). According to statistics, Fenghua District contains 8 streets and 4 towns, with a resident population of 586,000 and a registered population of 476,500 as of 2022. The urbanization rate is 61.2%, which is slightly lower than the national average (65.2%). The Ningbo 2049 Urban Development Strategy proposes that the city will form a new landscape of one main spatial pattern, two vice and multi-centers, three rivers, and three bays. The southern area of Ningbo belongs to the one main spatial pattern, which underscores the significance of Fenghua District. The Fenghua District Territorial Spatial Master Plan (2021–2035) states that the goal is to create a spatial pattern of “one main, two vice, three rivers, and four districts”, with the street and town layout of “one whole, two wings, and four corridors”. With the core aims of improving the quality of urban development and enhancing land use levels economically and intensively, Fenghua will continue to optimize overall construction land and promote the IVDB policy in an integrated manner.

2.1.2. Data

The data applied in this research include four parts: administrative division, land use and spatial planning, natural environment, and socio-economic information to complete the stock simulation of urban expansion (Table 1). As most of the data are derived from official resources, such as the Natural Resources and Planning Bureau (NRPB) of Fenghua District, Geospatial Data Cloud (GDC), Resource and Environment Science and Data Center (RESDC), Online Supervision System for Increasing Versus Decreasing Balance of Urban-Rural Built Land of Ministry of Natural Resources (MNR), and Gaode Map API, data credibility is guaranteed. To account for differences in data types, all data were geo-referenced, and all results were unified in a 25 m resolution raster format. The starting year of the urban expansion simulation is 2017, and the end is 2035, which is consistent with the target year of the new round of national territorial spatial planning in China.
The incorporation of the IVDB policy into the simulation process involves multiple land use types. Based on China’s Land Administration Law (first promulgated in 1986 and subsequently amended in 1988, 1998, 2004, and 2019), land resources are divided into agricultural land, construction land, and unused land. The results of a land use change survey showed that there are more than 10 land use classifications of land in Fenghua District. In order to simplify the analysis while applying it to a general case, all kinds of land classifications in Fenghua are grouped into 5 types, that is, arable land, other agricultural land, village land, urban land, and water bodies. Except for village land and urban land, all other land is non-construction land. The amount of the five land types and their relationships are shown in Table 2.

2.2. Method and Simulation Framework

2.2.1. The FLUS Model

On the basis of CA, the Sun Yat-sen University team developed a future land use simulation (FLUS) model (available on http://www.geosimulation.cn/FLUS.html, accessed on 12 April 2023, as a free download) [29]. The model FLUS innovatively incorporates an adaptive inertial competition mechanism, which could uncover the interactive relationship between different land types in the dynamic simulation process with high precision [39]. Thus, despite the uncertainty and complexity of land use in the real world, the FLUS model has been used to simulate LUCC widely due to its great potential to improve accuracy [40,49]. Specifically, the FLUS model comprises four parts: neighborhood effects, probability of occurrence of every land use type, inertia coefficient, and conversion cost [29,37]. The comprehensive probability of a grid cell being occupied by a specific land use type is estimated as follows:
P L s i ,   L s j t = Ω L s i ,   L s j t × s p L s i ,   L s j × i n t e r t i a L s j t × s c L s i →   L s j
where P L s i ,   L s j t represents the comprehensive probability of the grid cell L s i to transform the primary land use type into the objective type L s j at iteration time t; Ω L s i ,   L s j t represents the neighborhood effect of the land use type L s j on the grid cell L s i at iteration time t (Equation (2)); s p L s i ,   L s j represents the probability of occurrence of the land use type L s j on the grid cell L s i , which is calculated via an artificial neural network (ANN) model and has been widely used in the analysis and modeling of diverse non-linear geographical issues [50,51,52]; i n t e r t i a L s j t is the inertia coefficient of the land use type L s j at iteration time t (Equation (3)); and s c L s i →   L s j represents the transformation cost from the primary L s i to the target L s j .
Ω L s i ,   L s j t = ∑ N × N c o n s L s i t − 1 = L s j / ( N × N − 1 ) × w L s j
where N × N represents the scope of the neighborhood, and a 3 × 3 molar neighborhood or a 5 × 5 extended molar neighborhood, which is usually used in spatial simulations; c o n s L s i t − 1 = L s j is a binary judgment function, indicating whether L s j exists in the range of N × N for the iteration number t − 1. If L s j exists, the value is 1, otherwise 0; ∑ N × N c o n s L s i t − 1 = L s j denotes the total number of grid cells occupied by L s j at the final iteration time t − 1 within the N × N neighborhood window; w L s j represents the weight of variation among the different land use types, as different land use types have different neighborhood effects. The stronger the expansion ability of L s j , the closer w L s j is to 1; otherwise, w L s j is close to 0.
i n t e r t i a L s j t =       i n t e r t i a L s j t − 1   i f   d L s j t − 1   ≤ d L s j t − 2   i n t e r t i a L s j t − 1 × d L s j t − 2 / d L s j t − 1   i f     0 > d L s j t − 2 > d L s j t − 1   i n t e r t i a L s j t − 1 × d L s j t − 1 / d L s j t − 2   i f     d L s j t − 1 > d L s j t − 2 > 0
where i n t e r t i a L s j t represents the adaptive inertia coefficient of L s j at iteration time t − 1; d L s j t − 1 and d L s j t − 2 represent the difference between the target conversion number and the converted number of L s j at iterations t − 1 and t − 2, respectively. d L s j t − 1 = d L s j t − 2 = 1, and this coefficient comes into play from the third iteration. Generally, the definition of the adaptive inertia coefficient is based on the following three different cases. Firstly, if the specific land use type L s j conforms to the macro demand of the development trend, that is, d L s j t − 1   ≤ d L s j t − 2 , then the coefficient will stay unchanged at iteration time t. Secondly, if the macro demand for a particular land use type L s j is less than the current allocation and the development trend of L s j contradicts the macro demand, that is, 0 > d L s j t − 2 > d L s j t − 1 , then, through multiplying d L j t − 2 / d L j t − 1 , the inertia coefficient at iteration time t will be slightly lower. Finally, if the macro demand of L s j is greater than the current allocation and the development trend of the land use type L s j is in contradiction with the macro demand, that is, d L s j t − 1 > d L s j t − 2 > 0 , then the coefficient will increase marginally at time t by multiplying the previous coefficient of d L s j t − 1 / d L s j t − 2 .

2.2.2. Simulation Framework

(1)
Two situations of the transition process in urban land expansion
Generally, urban expansion involves only two categories of urban construction land and non-construction land [53,54], which can only present a simple transformation of non-construction land into construction land. However, land-type transition relationships may be more complex in the case of considering the IVDB policy. As the IVDB policy involves urban–rural construction land reorganization, and the increase in urban land and the decrease in village land do not have a complete spatial equivalence relationship, the conversion of land types can be attributed to two situations: The first situation is that village land within or around towns and cities are directly transformed into urban land. This is mainly due to the influence of radiation and driving factors, such as industry, capital, transportation, and policy; the closer one is to towns or cities, the easier it is to realize industrialization and urbanization. The second situation refers to the indirect conversion of village land into urban land that is far away from a town or a city or has a lower level of security. In this case, village land is preferentially reclaimed as arable land or other agricultural land; then, rural construction land quotas are saved after deducting the rural resettlement land that is transferred to urban use.
Based on the five land types (Table 2), Figure 2 shows the specific transition process. Figure 2a represents the initial distribution of various categories. Then, in the first situation of conversion, the village land that is close to the urban land, such as L24, can be directly converted into urban land due to geographical and other similar advantages (Figure 2b). In Figure 2c, village land that is far away from the town and city will be given priority to be reclaimed as arable land (such as L44) and other agricultural land (such as L41), which is determined according to natural, geographical, and other conditions. Furthermore, based on the net loss of village land in Figure 2c, arable land (such as L13) and other agricultural land close to the town and city will be occupied by urban expansion (Figure 2d). The latter two graphs of Figure 2 represent the second situation of stock expansion. Generally, the process of land-type conversion is divided into three steps.
Land 12 02099 g002
Figure 2. Two situations and three steps in the simulation process.
Figure 2. Two situations and three steps in the simulation process.
Land 12 02099 g002
(2)
Three steps of the simulation strategy
The simulation strategy of the urban expansion includes three steps using the FLUS model. Step 1 of the interaction between land types is reflected in the transformation of village land into urban land. Step 2 is the simulation of village land reclamation. The interaction between land types is reflected in the transformation of village land into arable land and other agricultural land. Before the simulation of the two steps, it is necessary to comprehensively estimate the scale of village land that can participate in the urbanization process, that is, the ratio of village land turned into urban land directly and indirectly, respectively. In Step 3, on the basis of the simulation results of the previous two steps, the simulation of urbanization in different places is carried out. The interactive relationship between land types is reflected in the conversion of arable land into urban land. The conversion quantity shall be determined according to the scale of the reclamation of village land into arable land and other agricultural land, as well as the scale of village construction and resettlement in the second step.
Furthermore, three points require an additional explanation for the simulation strategy. (1) Considering that water bodies, as an important element of natural resources, have a relatively stable spatial location and change mildly [55], with the reason that the probability of village land conversion into water bodies and water body conversion into urban land is low, the mutual transformation of water bodies and other land types is not considered in the simulation process. (2) The FLUS model provides an optional restricted area input window, and permanent prime farmland and ecological conservation red line are set as restricted areas in this study. (3) In order to prevent the unreasonable phenomenon of the conversion of arable land and other agricultural land formed by reclamation into urban land again, a situation like this can be uniformly included in the restricted area during the second simulation step.
Based on multi-source data and combined with the above analysis, a methodological framework for this study is built, as shown in Figure 3.

3. Results

3.1. The Driving Factors and Restricted Area of Urban Expansion

Spatial analysis of natural, social, and economic driving factors affecting urban land expansion is carried out, as shown in Figure 4. Specifically, the spatial variation amplitude of elevation, slope, and slope direction factors is processed. Euclidian distance analysis is carried out for rivers, roads, railways, and the Municipal People’s Government of Ningbo City, and the spatial density of population and economy is graded based on the township as a unit. Nuclear density analysis is conducted on commercial outlets, such as catering facilities, accommodation facilities, sports and leisure facilities, and finance and insurance companies, as well as distribution points including administrative institutions, science, education, and medical care, industry, and bus stations.
According to the bottom-line constraint requirements of spatial planning in China, combined with the setting of FLUS V2.3, permanent prime farmland and ecological conservation red line are included in the restricted area of the urban expansion simulation, and the results of the corresponding superposition and binary processing are presented in Figure 5.

3.2. Accuracy Assessment

On the basis of two periods of land use data in 2010 and 2020, this study examined the simulation accuracy of a CA-based FLUS model. Taking the quantity of five land use types in both 2010 and 2020 and land use distribution in 2010 as input elements, the FLUS model was used to project the land use spatial pattern of 2020.
An overall accuracy (OA) coefficient of 0.75 is generally considered to be a high simulation accuracy [45], and the OA coefficient reaches up to 0.836 in this study. The FOM coefficient is 0.412, which is also higher than 0.3 in many previous studies [56,57]. The Kappa coefficient is 0.824. The results show that the accuracy of the FLUS model in land use simulation is higher and the simulation performance is better in this experiment.

3.3. Estimation of the Expansion Scale of Urban Land

As mentioned above, the implementation of the IVDB policy involves urban–rural construction land reorganization, which involves new houses being built in urban areas and old houses being demolished in the countryside, with this land reclaimed for arable and other agricultural purposes [20]. Thus, the scale of village land undergoing urbanization with direct and indirect patterns, village land reclaimed for arable land and other agricultural land, and arable land and other agricultural land occupied by urban land need to be estimated in the simulation period of 2017 to 2035. Furthermore, in order to present the integration process more clearly in the simulation, this research assumes that urban construction land quotas allocated by the superior government are 0, which means that the increase in urban land is entirely predicated on the reduction in village land. Under the condition of the tight constraints of territorial spatial planning (2020–2035), the land quotas supplied by the superior government will be scarcer [6,25]; thus, we can safely deduce the assumption above is feasible. The following is a detailed description of the estimation description.

3.3.1. Total Amount of Village Land Involved in Urban Expansion

Combined with the new requirements of Ningbo City for Fenghua District, such as the functional positioning and development goals, as well as the rural revitalization of Fenghua District, village layout planning, and its own development demands, it is predicted that, during the simulation period, the proportion of village land involved in the expansion process of urban stock expansion will account for 35% of the village land scale of 2017. In 2017, the total area of village land in Fenghua District was 79.20 km2, which means that 27.72 km2 of village land will be directly or indirectly converted into urban land.

3.3.2. The Proportion of Village Land in Two Expansion Situations

The proportion of village land in the two expansion situations is mainly determined by the geomorphological characteristics of Fenghua District and the layout of villages. According to the geomorphological composition of Fenghua District, most of the existing villages are small in scale and scattered in layout. Moreover, based on the spatial structure of Fenghua District, as planned in the General Territorial Spatial Plan of Fenghua District (2021–2035), it is inferred that the scale of direct conversion of village land to urban land in Fenghua District will be smaller than the indirect conversion during the simulation period. Thus, this study sets the conversion ratio of these two situations as 40% and 60%. That is, the scale of village land directly converted into urban land is 11.09 km2, while the scale of reclaimed villages is 16.63 km2.

3.3.3. The Proportion of Village Land Reclaimed as Arable Land Versus Other Agricultural Land

The ratio of village reclamation to arable land and other agricultural land is determined based on the implementation of the IVDB policy in Fenghua District. According to statistics, as of the end of March 2019, among the IDVB projects completed in Fenghua District, the proportion of village land reclamation for arable land and other agricultural land is 95% and 5%, respectively. Accordingly, during the simulation period from 2017 to 2035, the ratio of village land reclamation to arable land and other agricultural land in Fenghua District is set at 95:5. In other words, the scale of village land reclamation to arable land is 15.80 km2 and the reclamation to other agricultural land is 0.83 km2.

3.3.4. The Proportion of Arable Land and Other Agricultural Land Occupied by Urban Expansion

During the implementation of the IVDB policy, the ratio of rural resettlement versus urban land was 1:99 in Fenghua District, while the ratio in Ningbo City was 9:91 during the same period. Herein, the average value of the two is taken as the ratio of rural resettlement to urban construction in Fenghua District during the simulation period, that is, 95% of the reclaimed village land scale can “float” into the urban space. The scale, 15.80 km2, is equal to the scale of village land reclamation to arable land. In this way, the two characteristics of reclamation quotas—the balance of arable land and the increase in urban land—can be perfectly reflected.
In addition, although 95% of the reclamation quota can be fully occupied by arable land when meeting urban expansion theoretically, considering the uniqueness of the location, the staggered distribution of land types, and future uncertainties, it is assumed that 1% of other agricultural land may be occupied by urban expansion in Fenghua District during the simulation period. In other words, 15.64 km2 of arable land and 0.16 km2 of other agricultural land will be occupied in the third step of the urban stock expansion simulation.
Based on the above analysis, the scale of different land use types and the conversion amount in the simulation process from 2017 to 2035 are summarized in Table 3.

3.4. The First Step of the Urban Land Expansion Simulation

In the first step of the urban expansion, village land is directly transformed into urban land, and the simulation result is shown in Figure 6b. Comparing the result with the original land types (Figure 6a), the spatial scope of local urbanization in Fenghua District was concentrated in the north and west. Only scattered village land was directly converted into urban land in Dayan Town in the south and Songdai Town, Qiucun Town, and Chunhu Town in the east.
The relative amount of village land reduction and urban land increase for each street and town in the first step of the urban expansion is shown in Figure 7. Fangqiao Street, located in the northernmost part of Fenghua District, has the highest absolute and relative reduction in village land. The reduction in village land or the increase in urban land in Fangqiao Street during the simulation period amounts to 3.34 km2, accounting for 57.23% of the total scale of village land in 2017, which means that nearly half of the village land in Fangqiao Street will be directly converted into urban land by 2035. The urban land increase accounted for 1.91 times the total urban land scale in 2017. Jiangkou Street, located south of Fangqiao Street, had the second-highest total reduction in village land during the simulation period after Fangqiao Street. The total reduction in village land is 1.84 km2, accounting for 21.20% and 70.69% of village land and urban land in 2017, respectively. Although the relative amount of village land reduction in Yuelin Street and Jinping Street is less than that of Jiangkou Street, the relative amount of village land reduction is slightly higher than that of Jiangkou Street.
In addition, the spatial growth trend of the first step in every street and town is generally along the existing urban land to the outward expansion or the traffic routes. In Xiwu Street of Fenghua District, the urban expansion in the north is mainly reflected in that, driven by the existing urban construction, the village land in the north is converted into urban land. The south expansion is reflected in the direct conversion of village land into urban land under the drive of the southeast–northwest Jinhai East Road (Figure 8).

3.5. The Second Step of the Urban Land Expansion Simulation

The spatial pattern of village land reclamation is completely different from that of the first step. By comparing Figure 6c with Figure 6a,b, it can be found that there is no significant spatial heterogeneity in reclaimed villages. Scattered villages are more inclined to undergo reclamation, and the concentration of arable land in Fenghua District is significantly improved after village land reclamation. Figure 9 takes Dayan Town, the southernmost town in Fenghua District, as an example, to compare the concentration of arable land before and after village land reclamation. Statistically, the areas with the largest scale of reclaimed village land during the simulation period are Xiyu Street and Xikou Town, with the scales of reclaiming at 2.46 km2 and 2.04 km2, respectively, accounting for 25.56% and 20.11% of the village land in 2017, respectively. Dayan Town, in the south of Fenghua District, has the largest relative volume of reclaimed village land, and the proportion is 36.53%. The total amount of increased arable land due to reclamation is consistent with the general condition of the distribution of reclaimed village land, but the total amount of increased other agricultural land shows a high regional difference. There is no village land reclaimed as other agricultural land in Fangqiao Street yet, and the increase in other agricultural land in the remaining seven streets is also lower than that of the towns in general. The increment in other agricultural land in Xikou Town is the largest, reaching 0.40 km2. For the simulation process of village land reclamation, the relative changes in land types in each street and town of Fenghua District are shown in Figure 10.

3.6. The Final Step of the Urban Land Expansion Simulation

Under the constraint of the total amount of reclamation, in the third step of the simulation, the spatial expansion of urban land in Fenghua District shows a convergence with the first step. Urban expansion is concentrated in the northern part of Fenghua District, including Fangqiao, Yuelin, Jiangkou, and Jinping Streets (Figure 6d). Specifically, the absolute increase in urban land in Fangqiao Street is 7.10 km2, which is 4.06 times the total urban land in 2017. The total urban land expansion scale of the other three streets is between 1.4 km2 and 2.2 km2. In contrast, the total expansions of towns in the south and east of Fenghua District are only 0.02 km2 to 0.33 km2. In contrast, the total urban expansion of 0.02 km2 to 0.33 km2 occurs in the towns in the south and east of Fenghua District. In general, all areas follow the growth law of “infilling and edge-expansion”. In the third step of simulation, the expansion of urban land implies an equal reduction in arable land and other agricultural land, so the spatial area of the latter two types of land reduction is the same concept as the scope of urban land expansion. The relative amounts of urban land increase and arable land decrease in each region are shown in Figure 11.
In summary, the spatial simulation results of urban land expansion in Fenghua District show that urban land expansion is concentrated in the northern and western streets and towns, especially in the northernmost Fangqiao Street, while village land reclamation mostly occurs in scattered village sites around the district. In addition, compared with the original stage of the simulation, the amount of arable land change is greater than 0 in all streets and towns except for Fangqiao, Yuelin, Jinping, and Jiangkou Streets (Figure 12), which means that the transfer quotas formed after the reclamation of the aforementioned areas are not “digested” by themselves but “floating” to the northern area of Fenghua such as Fangqiao Street, which is closer to the downtown area of Ningbo, with an advantageous location and better development opportunities.

4. Discussion

4.1. Methodological Strengths for Integration of Urban–Rural Development

On the basis of different simulation methods, urban land expansion has been explored in different cities and regions (Table 4), but most previous studies paid little attention to the impact of urban–rural interactions on urban growth. According to the operation mechanism of the IVDB policy in China [10,21,23], the “bottom-up” local spatial simulation based on CA and the “top-down” regulation based on urban–rural construction land structure optimization are put forward in this study.
Focused on urban–rural construction, the IVDB policy is a typical “top-down” decision-making model that is promoted by the central government and implemented by local governments [61] and participates in the process of urban land expansion through the operation of transfer quotas [62,63]. Nevertheless, the impact of transfer quotas on urban land expansion is rarely considered in traditional studies (Table 4). Under the dynamic balance assumption in total construction land, this study measures the increase in the scale of urban land on the premise of a reduction in rural construction land. Then, the relationships between village land reclamation and urban land growth are incorporated into the CA-based FLUS model. In distinguishing from the results of the urban land simulation, the expansion process is presented simultaneously in this study (Figure 6), which can enrich the exploration of urban land expansion simulations and be of benefit to policymakers in coordinating urban–rural land use and sustainable development.
Existing studies have fully considered the impact and limiting effect of ecological factors on urban land expansion (NO.4–5, 7–9, as shown in Table 4). In addition to incorporating the ecological conservation red line into comprehensive urban development, this study adds permanent prime farmland to the restricted area, intending to help local governments meet the requirements of “three control lines” with no overlapping in territorial spatial planning (2020–2035).

4.2. Comparisons with the Simulation of the PLUS Model

Considering the inconsistency between the data source of accuracy assessment (2010–2020) and the data adopted by future urban expansion simulation (2017–2035), as well as the difference between individual land use type conversion (non-urban land conversion into urban land) in the verification process and multiple land use type transformation in target simulation, the patch-generating land use simulation (PLUS) model is applied to test the robustness of the simulation results of the FLUS model. The CA-based PLUS model employs a random forest classification (RFC) algorithm to reveal the relationship between land use growth and multiple driving elements. This model can be downloaded for free from https://github.com/HPSCIL/Patch-generating_Land_Use_Simulation_Model, accessed on 12 April 2023 [34]. Figure 13 shows the two-simulation result of urban expansion in Fenghua District.
A comparison of the simulation results is presented in Table 5. Based on the first situation of directly converting village land into urban land, by comparing the simulation results of the FLUS model and PLUS model, it is found that the OA index and Kappa coefficient are as high as 0.989 and 0.978, respectively, and the FOM index is 0.381. In the second situation of urban land expansion, the OA index is 0.987, the Kappa coefficient is 0.974, and the FOM index is up to 0.623. The high similarity between the simulation results proves the reliability of the research results.

4.3. Policy Implication

With the explosion of the population and economy, the increasing demand for urban construction leads to the abandonment of a large amount of rural land, which has intensified the contradiction between economic sustainable development, food security, and urban ecological security [59]. To coordinate urban–rural-integrated development, the research results could provide policy implications for urban sprawl governance and have important significance for other rapidly urbanizing areas:
(1)
More attention is needed on the IVDB policy to realize urban stock growth patterns. According to China’s basic national conditions of human– land tension, incremental growth in terms of speed, fighting scale, consumption of resources, and seeking expansion is no longer feasible [64] after years of development. The central government has repeatedly emphasized the strict control of new urban construction land use quotas, improved the mechanism of “linking the increase to the stock” of construction land, and promoted the change in urbanization development from sprawling expansion to internal enhancement. Thus, urban land stock growth can be achieved via a reduction in rural construction land through the use of the IVDB policy, whereby the total scale of construction land maintains a dynamic balance.
(2)
The allocation of rural construction land for demolition and reclamation should be approached more scientifically. With the implementation of the IVDB policy and the comprehensive land consolidation policy, more and more idle and abandoned land in rural areas will be demolished and reclaimed. A previous site selection experience of a demolition area lacked multi-factor consideration, which may lead to various doubts and even conflicts [65]. Under the constraints of incomplete primary data, CA-based models, coupled with multiple impact factors based on geospatial data and remote sensing data, could help to identify areas that need to be demolished and can be reclaimed efficiently.

5. Conclusions

Over the past 40 years, China has undergone tremendous urbanization of land and population in conjunction with economic prosperity. The implementation of the IVDB policy has played an important role in the process of urbanization. However, research on the impact of the IVDB policy on urban expansion is relatively scarce, which leads to theoretical and methodological challenges. This research aimed to fill these gaps with hopes of integrating the IVDB policy into an urban expansion simulation and revealing the process of urban land growth with bottom-line constraints, such as permanent prime farmland and ecological conservation red lines.
Combined with the implementation of the IVDB policy, urban land expansion can be divided into two situations and realized through a three-step simulation. The results of the study area, Fenghua District of Ningbo City, show that under the premise that 35% of the village land undergoes an urbanization process, the transformation of village land into urbanized land in the first situation is concentrated in the northern and western parts of Fenghua District, which is reflected in two spatial expansion trends: transformation along the existing urban land and the traffic routes. In the second situation, the spatial differences in the conversion of village land to arable land or other agricultural land are relatively small in Fenghua District, and the degree of concentration of arable land is significantly increased after reclamation. The northern part of Fenghua District could meet the demand for urban construction land with the help of transfer quotas “produced” by other areas.
This study makes an important contribution to the process of urban land expansion simulation in rapidly urbanizing areas of China, and further application should consider the following: Firstly, urban expansion is a product of human initiative combined with objective laws, but due to limited data, existing planning factors in Ningbo City and Fenghua District, such as major projects and platforms and the planning of traffic roads, have not been included as driving factors of urban land expansion. Impact factors focusing on urban planning should be added in future studies. Secondly, this study only considered the expansion process under the dynamic balance of the total amount of urban and rural construction land and did not take into account the moderate increase in the scale of construction land. It is not rigorous enough to predict the scale of village land undergoing the urbanization process. The internal relationship between urban construction land quotas assigned by the superior governments and transfer quotas needs to be further understood to optimize the simulation result.

Author Contributions

Conceptualization, Y.J., Q.Z. and Q.L.; methodology, Y.C.; software, J.D.; formal analysis, Y.J. and C.Z.; investigation, Y.J. and J.D.; resources, X.H.; formal analysis, C.Z.; data curation, X.H.; writing—original draft preparation, Y.J.; project administration, Q.Z. and Q.L.; writing—review and editing, Q.Z. and Q.L.; visualization, C.Z.; supervision, Q.Z. and Q.L.; funding acquisition, Y.J. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Youth Foundation for Humanities and Social Science Research of the Ministry of Education (22YJC630049), the Special Funding Project of Shaanxi Province Postdoctoral (2023BSHTBZZ26), the Basic Research Program of Natural Science of Shaanxi Province (2022JQ-747), and the Startup Foundation of Northwest A&F University (2452021012).

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy requirements of the participants.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Lewis, S.L.; Maslin, M.A. Defining the Anthropocene. Nature 2015, 519, 171–180. [Google Scholar] [CrossRef] [PubMed]
  2. Ruan, L.; He, T.; Xiao, W.; Chen, W.; Lu, D.; Liu, S. Measuring the coupling of built-up land intensity and use efficiency: An example of the Yangtze River Delta urban agglomeration. Sustain. Cities Soc. 2022, 87, 104224. [Google Scholar] [CrossRef]
  3. Sun, Y.; Liu, D.; Wang, P. Urban simulation incorporating coordination relationships of multiple ecosystem services. Sustain. Cities Soc. 2022, 76, 103432. [Google Scholar] [CrossRef]
  4. Deng, X.; Huang, J.; Rozelle, S.; Zhang, J.; Li, Z. Impact of urbanization on cultivated land changes in China. Land Use Policy 2015, 45, 1–7. [Google Scholar] [CrossRef]
  5. Peng, J.; Zhao, S.; Dong, J.; Liu, Y.; Meersmans, J.; Li, H.; Wu, J. Applying ant colony algorithm to identify ecological security patterns in megacities. Environ. Modell. Softw. 2019, 117, 214–222. [Google Scholar] [CrossRef]
  6. Liu, X.; Wei, M.; Zeng, J. Simulating urban growth scenarios based on ecological security pattern: A case study in Quanzhou, China. Int. J. Environ. Res. Public Health 2020, 17, 7282. [Google Scholar] [CrossRef] [PubMed]
  7. Bai, X.; McPhearson, T.; Cleugh, H.; Nagendra, H.; Tong, X.; Zhu, T.; Zhu, Y.G. Linking urbanization and the environment: Conceptual and empirical advances. Annu. Rev. Environ. Resour. 2017, 42, 215–240. [Google Scholar] [CrossRef]
  8. Jin, Y.; Zhang, B.; Zhang, H.; Tan, L.; Ma, J. The scale and revenue of the land-use balance quota in Zhejiang province: Based on the inverted U-shaped curve. Land 2022, 11, 1743. [Google Scholar] [CrossRef]
  9. Liu, W. Can Community-based concentration revitalise the upland villages? A case comparison of two villages in Chongqing, southwestern China. Habitat Int. 2018, 77, 153–166. [Google Scholar] [CrossRef]
  10. Li, Y.; Liu, Y.; Long, H.; Cui, W. Community-based rural residential land consolidation and allocation can help to revitalize hollowed villages in traditional agricultural areas of China: Evidence from Dancheng county, Henan province. Land Use Policy 2014, 39, 188–198. [Google Scholar] [CrossRef]
  11. Feng, R.; Wang, K. The direct and lag effects of administrative division adjustment on urban expansion patterns in Chinese mega-urban agglomerations. Land Use Policy 2022, 112, 105805. [Google Scholar] [CrossRef]
  12. Li, Y.; Pu, S.; Zhao, Y.; Yao, J.; Qian, K. Refractive-index-matched coupling generated by magnetic fluid around cladding removed multimode optical fiber. Opt. Fiber Technol. 2020, 59, 102326. [Google Scholar] [CrossRef]
  13. Cheng, M.; Wu, S.; Zeng, C.; Yu, X.; Wang, J. Can economic growth and urban greenness achieve positive synergies during rapid urbanization in China? Ecol. Indic. 2023, 150, 110250. [Google Scholar] [CrossRef]
  14. Dang, Y.; Zhao, Z.; Kong, X.; Lei, M.; Liao, Y.; Xie, Z.; Song, W. Discerning the process of cultivated land governance transition in China since the reform and opening-up—Based on the multiple streams framework. Land Use Policy 2023, 133, 106844. [Google Scholar] [CrossRef]
  15. Feng, W.; Liu, Y.; Qu, L. Effect of land-centered urbanization on rural development: A regional analysis in China. Land Use Policy 2019, 87, 104072. [Google Scholar] [CrossRef]
  16. Liu, Y.; Li, J.; Yang, Y. Strategic adjustment of land use policy under the economic transformation. Land Use Policy 2018, 74, 5–14. [Google Scholar] [CrossRef]
  17. Zhou, Y.; Li, X.; Liu, Y. Cultivated land protection and rational use in China. Land Use Policy 2021, 106, 105454. [Google Scholar] [CrossRef]
  18. Bryan, B.; Gao, L.; Ye, Y. China’s response to a national land-system sustainability emergency. Nature 2018, 559, 193–204. [Google Scholar] [CrossRef]
  19. He, Q.; Zeng, C.; Xie, P.; Tan, S.; Wu, J. Comparison of urban growth patterns and changes between three urban agglomerations in China and three metropolises in the USA from 1995 to 2015. Sustain. Cities Soc. 2019, 50, 101649. [Google Scholar] [CrossRef]
  20. Song, P.; Qiu, Z.; Jin, X. Zhejiang: Linking increase and decrease to promote the optimal layout of urban and rural space. China Land 2020, 10, 55–57. (In Chinese) [Google Scholar]
  21. Long, H.; Li, Y.; Liu, Y.; Woods, M.; Zou, J. Accelerated restructuring in rural China fueled by ‘Increasing vs. Decreasing Balance’ land-use policy for dealing with hollowed villages. Land Use Policy 2012, 29, 11–22. [Google Scholar] [CrossRef]
  22. Ministry of Land and Resources of China (MLRC). Number 138 Document: Management Measure of ‘Increasing vs. Decreasing Balance’ Land Use Policy in Pilot Areas; MLRC: Beijing, China, 2008.
  23. Gao, W.; De Vries, W.T.; Zhao, Q. Understanding rural resettlement paths under the increasing versus decreasing balance land use policy in China. Land Use Policy 2021, 103, 105325. [Google Scholar] [CrossRef]
  24. Yep, R.; Forrest, R. Elevating the peasants into high-rise apartments: The land bill system in Chongqing as a solution for land conflicts in China? J. Rural Stud. 2016, 47, 474–484. [Google Scholar] [CrossRef]
  25. Yu, D.; Yanxu, L.; Bojie, F. Urban growth simulation guided by ecological constraints in Beijing City: Methods and Implications for Spatial Planning. J. Environ. Manag. 2019, 243, 402–410. [Google Scholar] [CrossRef] [PubMed]
  26. Wang, J.; Li, Y.; Wang, Q.; Cheong, K.C. Urban–rural construction land replacement for more sustainable land use and regional development in China: Policies and practices. Land 2019, 8, 171. [Google Scholar] [CrossRef]
  27. Tian, L.; Guo, X.; Yin, W. From urban sprawl to land consolidation in suburban Shanghai under the Backdrop of increasing versus decreasing balance policy: A perspective of property rights transfer. Urban Stud. 2017, 54, 878–896. [Google Scholar] [CrossRef]
  28. Yin, C.; Meng, F.; Yang, X.; Yang, F.; Fu, P.; Yao, G.; Chen, R. Spatio-temporal evolution of urban built-up areas and analysis of driving factors —A Comparison of typical cities in north and south China. Land Use Policy 2022, 117, 106114. [Google Scholar] [CrossRef]
  29. Liu, X.; Liang, X.; Li, X.; Xu, X.; Ou, J.; Chen, Y.; Li, S.; Wang, S.; Pei, F. A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects. Landsc. Urban Plan. 2017, 168, 94–116. [Google Scholar] [CrossRef]
  30. Verburg, P.H.; Soepboer, W.; Veldkamp, A.; Limpiada, R.; Espaldon, V.; Mastura, S.S.A. Modeling the spatial dynamics of regional land Use: The CLUE-S model. Environ. Manag. 2002, 30, 391–405. [Google Scholar] [CrossRef]
  31. Pijanowski, B.C.; Alexandridis, K.T.; Müller, D. Modelling urbanization patterns in two diverse regions of the world. J. Land Use Sci. 2006, 1, 83–108. [Google Scholar] [CrossRef]
  32. Dietzel, C.; Clarke, K.C. Toward optimal calibration of the SLEUTH land use change model. Trans. GIS 2007, 11, 29–45. [Google Scholar] [CrossRef]
  33. Sohl, T.L.; Sayler, K.L.; Drummond, M.A.; Loveland, T.R. The FORE-SCE model: A Practical approach for projecting land cover change using scenario-based modeling. J. Land Use Sci. 2007, 2, 103–126. [Google Scholar] [CrossRef]
  34. Liang, X.; Guan, Q.; Clarke, K.C.; Liu, S.; Wang, B.; Yao, Y. Understanding the drivers of sustainable land expansion using a patch-generating land use simulation (PLUS) model: A case study in Wuhan, China. Comput. Environ. Urban. 2021, 85, 101569. [Google Scholar] [CrossRef]
  35. Yang, D.; Luan, W.; Li, Y.; Zhang, Z.; Tian, C. Multi-scenario simulation of land use and land cover based on shared socioeconomic pathways: The case of coastal special economic zones in China. J. Environ. Manag. 2023, 335, 117536. [Google Scholar] [CrossRef] [PubMed]
  36. Huang, X.; Wang, H.; Xiao, F. Simulating urban growth affected by national and regional land use policies: Case study from Wuhan, China. Land Use Policy 2022, 112, 105850. [Google Scholar] [CrossRef]
  37. Li, L.; Huang, X.; Yang, H. Scenario-based urban growth simulation by incorporating ecological-agricultural-urban suitability into a future land use simulation model. Cities 2023, 137, 104334. [Google Scholar] [CrossRef]
  38. Zhang, Y.; Yu, P.; Tian, Y.; Chen, H.; Chen, Y. Exploring the impact of integrated spatial function zones on land use dynamics and ecosystem services tradeoffs based on a future land use simulation (FLUS) model. Ecol. Indic. 2023, 150, 110246. [Google Scholar] [CrossRef]
  39. Liang, X.; Liu, X.; Li, D.; Zhao, H.; Chen, G. Urban Growth simulation by incorporating planning policies into a CA-based future land-use simulation model. Int. J. Geogr. Inf. Sci. 2018, 32, 2294–2316. [Google Scholar] [CrossRef]
  40. Liu, X.; Wei, M.; Li, Z.; Zeng, J. Multi-scenario simulation of urban growth boundaries with an ESP-FLUS Model: A Case study of the Min Delta Region, China. Ecol. Indic. 2022, 135, 108538. [Google Scholar] [CrossRef]
  41. Xu, T.; Gao, J.; Li, Y. Machine learning-assisted evaluation of land use policies and plans in a rapidly urbanizing district in Chongqing, China. Land Use Policy 2019, 87, 104030. [Google Scholar] [CrossRef]
  42. Assaf, C.; Adams, C.; Ferreira, F.F.; França, H. Land use and cover modeling as a tool for analyzing nature conservation policies—A case study of Juréia-Itatins. Land Use Policy 2021, 100, 104895. [Google Scholar] [CrossRef]
  43. Li, J.; Fang, W.; Wang, T.; Qureshi, S.; Alatalo, J.; Bai, Y. Correlations between socioeconomic drivers and indicators of urban expansion: Evidence from the heavily urbanised Shanghai Metropolitan Area, China. Sustainability 2017, 9, 1199. [Google Scholar] [CrossRef]
  44. Huang, Z.; Wei, Y.; He, C.; Li, H. Urban land expansion under economic transition in China: A multi-level modeling analysis. Habitat Int. 2015, 47, 69–82. [Google Scholar] [CrossRef]
  45. Yang, X.; Bai, Y.; Che, L.; Qiao, F.; Xie, L. Incorporating ecological constraints into urban growth boundaries: A Case study of ecologically fragile areas in the Upper Yellow River. Ecol. Indic. 2021, 124, 107436. [Google Scholar] [CrossRef]
  46. Chen, H.; Wang, X.; Guo, Z. The transition of China’s urban planning: Thoughts on increment planning, inventory planning and reduction planning. Mod. Urban Res. 2015, 09, 44–48. (In Chinese) [Google Scholar]
  47. Wang, C.; Zhu, G.; Xiang, Z.; Liang, J. Research on the transformation of territorial space planning mode from incremental expansion to stock adjustment: Based on the practical exploration of high-intensity development areas in the Pearl River Delta. China Land Sci. 2021, 35, 1–11. (In Chinese) [Google Scholar]
  48. Yang, Y.; Feng, Z.; Zhao, Y.; You, Z. Coordination between urban land expansion and population growth in China. Geogr. Res. 2013, 32, 1668–1678. (In Chinese) [Google Scholar]
  49. Cunha, E.R.D.; Santos, C.A.G.; Silva, R.M.D.; Bacani, V.M.; Pott, A. Future scenarios based on a CA-Markov land use and land cover simulation model for a tropical humid basin in the Cerrado/Atlantic forest ecotone of Brazil. Land Use Policy 2021, 101, 105141. [Google Scholar] [CrossRef]
  50. Openshaw, S. Neural network, genetic, and fuzzy logic models of spatial interaction. Environ. Plann. A 1998, 30, 1857–1872. [Google Scholar] [CrossRef]
  51. Li, X.; Yeh, A.G.-O. Neural-network-based cellular automata for simulating multiple land use changes using GIS. Int. J. Geogr. Inf. Sci. 2002, 16, 323–343. [Google Scholar] [CrossRef]
  52. Lin, Y.; Chu, H.; Wu, C.; Verburg, P.H. Predictive ability of logistic regression, auto-logistic regression and neural network models in empirical land-use change modeling—A case study. Int. J. Geogr. Inf. Sci. 2011, 25, 65–87. [Google Scholar] [CrossRef]
  53. Liao, J.; Shao, G.; Wang, C.; Tang, L.; Huang, Q.; Qiu, Q. Urban sprawl scenario simulations based on cellular automata and ordered weighted averaging ecological constraints. Ecol. Indic. 2019, 107, 105572. [Google Scholar] [CrossRef]
  54. Lv, J.; Wang, Y.; Liang, X.; Yao, Y.; Ma, T.; Guan, Q. Simulating urban expansion by incorporating an integrated gravitational field model into a demand-driven random forest-cellular automata model. Cities 2021, 109, 103044. [Google Scholar] [CrossRef]
  55. Yang, J.; Xie, P.; Xi, J.C.; Ge, Q.S.; Li, X.M.; Ma, Z.D. LUCC simulation based on the cellular automata simulation: A case study of Dalian economic and technological development zone. Acta Geogr. Sin. 2015, 70, 461–475. (In Chinese) [Google Scholar]
  56. Pontius, R.G.; Boersma, W.; Castella, J.C.; Clarke, K.; De Nijs, T.; Dietzel, C.; Duan, Z.; Fotsing, E.; Goldstein, N.; Kok, K. Comparing the input, output, and validation maps for several models of land change. Ann. Reg. Sci. 2008, 42, 11–37. [Google Scholar] [CrossRef]
  57. Chen, Y.; Li, X.; Liu, X.; Ai, B. Modeling urban land-use dynamics in a fast developing city using the modified logistic cellular automaton with a patch-based simulation strategy. Int. J. Geogr. Inf. Sci. 2014, 28, 234–255. [Google Scholar] [CrossRef]
  58. Domingo, D.; Palka, G.; Hersperger, A.M. Effect of zoning plans on urban land-use change: A multi-scenario simulation for supporting sustainable urban growth. Sustain. Cities Soc. 2021, 69, 102833. [Google Scholar] [CrossRef]
  59. Li, Y.; Ma, Q.; Song, Y.; Han, H. Bringing Conservation Priorities into Urban Growth Simulation: An Integrated Model and Applied Case Study of Hangzhou, China. Resour. Conserv. 2019, 140, 324–337. [Google Scholar] [CrossRef]
  60. Li, F.; Wang, L.; Chen, Z.; Clarke, K.C.; Li, M.; Jiang, P. Extending the SLEUTH model to integrate habitat quality into urban growth simulation. J. Environ. Manag. 2018, 217, 486–498. [Google Scholar] [CrossRef]
  61. Liu, P.; Ravenscroft, N. Collective Action in implementing top-down land policy: The case of Chengdu, China. Land Use Policy 2017, 65, 45–52. [Google Scholar] [CrossRef]
  62. Wang, B.; Li, F.; Feng, S.; Shen, T. Transfer of development rights, farmland preservation, and economic growth: A case study of Chongqing’s land quotas trading program. Land Use Policy 2020, 95, 104611. [Google Scholar] [CrossRef]
  63. Qu, Y.; Jiang, G.; Li, Z.; Tian, Y.; Wei, S. Understanding rural land use transition and regional consolidation implications in China. Land Use Policy 2019, 82, 742–753. [Google Scholar] [CrossRef]
  64. Ding, L.; Wu, Q.; Li, Y. Study of the landless peasants’ welfare changes during new urbanization. China Popul. Resour. Environ. 2017, 27, 163–169. (In Chinese) [Google Scholar]
  65. Li, Y.; Li, Y.; Fan, P.; Long, H. Impacts of land consolidation on rural human–environment system in typical watershed of the Loess Plateau and implications for rural development policy. Land Use Policy 2019, 86, 339–350. [Google Scholar]
Land 12 02099 g001
Figure 1. The study area.
Figure 1. The study area.
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Figure 3. Methodological framework.
Figure 3. Methodological framework.
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Figure 4. The driving factors of urban expansion in Fenghua District.
Figure 4. The driving factors of urban expansion in Fenghua District.
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Figure 5. The restricted area in the process of urban expansion simulation.
Figure 5. The restricted area in the process of urban expansion simulation.
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Figure 6. The simulation results of urban land expansion in Fenghua District (model: FLUS). Notes: (a) indicates spatial pattern of land use in 2017, (b) indicates the first step of village land convert into urban land directly in 2035, (c) indicates the second step of village land convert into arable land and other agricultural land in 2035, (d) indicates the third step of arable land and other agricultural land convert into urban land in 2035.
Figure 6. The simulation results of urban land expansion in Fenghua District (model: FLUS). Notes: (a) indicates spatial pattern of land use in 2017, (b) indicates the first step of village land convert into urban land directly in 2035, (c) indicates the second step of village land convert into arable land and other agricultural land in 2035, (d) indicates the third step of arable land and other agricultural land convert into urban land in 2035.
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Figure 7. Spatial heterogeneity in the first step of urban land expansion. Notes: CH—Chunhu Street, DY—Dayan Town, FQ—Fangqiao Street, JK—Jiangkou Street, JP—Jinping Street, QC—Qiucun Town, ST—Shangtian Street, SD—Songdai Town, XW—Xiwu Street, XK—Xikou Town, XWM—Xiaowangmiao Street, YL—Yuelin Street.
Figure 7. Spatial heterogeneity in the first step of urban land expansion. Notes: CH—Chunhu Street, DY—Dayan Town, FQ—Fangqiao Street, JK—Jiangkou Street, JP—Jinping Street, QC—Qiucun Town, ST—Shangtian Street, SD—Songdai Town, XW—Xiwu Street, XK—Xikou Town, XWM—Xiaowangmiao Street, YL—Yuelin Street.
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Figure 8. Expansion near the urban land and traffic roads of Xiwu Street from 2017 to 2035. Notes: (a1,a2) represent spatial pattern of land use in 2017, (b1,b2) represent spatial pattern of land use of the first step of simulation in 2035.
Figure 8. Expansion near the urban land and traffic roads of Xiwu Street from 2017 to 2035. Notes: (a1,a2) represent spatial pattern of land use in 2017, (b1,b2) represent spatial pattern of land use of the first step of simulation in 2035.
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Figure 9. Arable land concentration after reclamation in Dayan Town from 2017 to 2035. Notes: (a1,a2) represent spatial pattern of land use in 2017, (b1,b2) represent spatial pattern of land use of the second step of simulation in 2035.
Figure 9. Arable land concentration after reclamation in Dayan Town from 2017 to 2035. Notes: (a1,a2) represent spatial pattern of land use in 2017, (b1,b2) represent spatial pattern of land use of the second step of simulation in 2035.
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Figure 10. Spatial heterogeneity of land types for village land reclamation.
Figure 10. Spatial heterogeneity of land types for village land reclamation.
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Figure 11. Spatial heterogeneity in the third step of urban expansion simulation.
Figure 11. Spatial heterogeneity in the third step of urban expansion simulation.
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Figure 12. Spatial heterogeneity of urban land stock expansion.
Figure 12. Spatial heterogeneity of urban land stock expansion.
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Figure 13. The simulation results of the urban land expansion (model: PLUS).
Figure 13. The simulation results of the urban land expansion (model: PLUS).
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Table 1. Data information and sources.
Table 1. Data information and sources.
CategoryVariable NameData SourceData Type
Administrative divisionFenghua administrative divisionNRPBVector
Land use and spatial planningLand use data (2017)NRPBVector
Land use data (2010/2020)RESDC (https://www.resdc.cn/, accessed on 12 April 2023).Raster (30 m)
Permanent prime farmlandNRPBVector
ecological conservation red lineNRPBVector
Fenghua District Master Plan of 2020NRPBText and picture
IVDB dataOnline Supervision System (https://zjgg.mnr.gov.cn/, accessed on 20 November 2021).Text
Natural environmentDEMGDC (https://www.gscloud.cn/, accessed on 25 March 2023).Raster (30 m)
RiverNRPBVector
Socio-economic informationRailway landNRPBVector
Highway landNRPBVector
Population densityRESDC (https://www.resdc.cn/, accessed on 12 April 2023).Raster (1000 m)
GDP densityRESDC (https://www.resdc.cn/, accessed on 12 April 2023).Raster (1000 m)
POIs location (commerce, industry, hospital, school, etc).Gaode Map API (https://lbs.amap.com/, accessed on 15 December 2017).Vector
Table 2. The corresponding relationship between different land types of Fenghua District.
Table 2. The corresponding relationship between different land types of Fenghua District.
Three Categories Land UseLand Type for Simulation
(km2)		Land Classification of Land Use Change Survey
Agricultural landArable land (258.29)Paddy fields, dry land
Other agricultural land (845.58)Woodland, meadows, reservoir pits
Construction landVillage land (79.21)Rural settlements
Urban land (44.14)Urban land, other construction land
Unused landWater bodies (50.50)Canals, lakes, tidal flats, bare land
Table 3. The scale of different land use types and the conversion amount in Fenghua District.
Table 3. The scale of different land use types and the conversion amount in Fenghua District.
Year20172035
Land Use Types with Transition
Situation 1Step 1Village land converted into urban land directly —11.09
Situation 2Step 2Village land converted into arable land—15.80
Village land converted into other agricultural land—0.83
Step 3Arable land converted into urban land —15.64
Other agricultural land converted into urban land—0.16
Urban land 44.1471.03
Village land 79.251.48
Arable land 258.29258.45
Other agricultural land845.58846.25
Table 4. Comparison of urban land expansion simulation studies.
Table 4. Comparison of urban land expansion simulation studies.
Number Study AreaHighlightsReference
1Coastal special economic zones in ChinaLULC simulation based on shared socio-economic pathways[35]
2Jiangsu Province, ChinaExploring the impact of ecological–agricultural–urban suitability on LULC simulation[37]
3Wuhan, ChinaEffect of spatial functional zones on LULC simulation[38]
4The Min Delta region, ChinaBringing ecological security patterns into urban growth[40]
5Upper Yellow River, ChinaIncorporating ecological constraints into urban expansion[45]
6Madrid, Barcelona, Valencia, and Zaragoza Spanish Functional Urban AreasImpact of zoning plans on urban land use change for the purpose of sustainable growth[58]
7Hangzhou, ChinaIntegration of conservation priorities into urban land growth model[59]
8Beijing, ChinaConsidering the ecological constraints in the process of urban growth simulation[25]
9Changzhou, ChinaIncorporating habitat quality into urban land growth[60]
Table 5. A comparison of simulation accuracy.
Table 5. A comparison of simulation accuracy.
Situation 1Situation 2
FLUS model vs. PLUS model OAKappaFOMOAKappaFOM
0.9890.9780.3610.9870.9740.623
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Jin, Y.; Ding, J.; Chen, Y.; Zhang, C.; Hou, X.; Zhang, Q.; Liu, Q. Urban Land Expansion Simulation Considering the Increasing versus Decreasing Balance Policy: A Case Study in Fenghua, China. Land 2023, 12, 2099. https://doi.org/10.3390/land12122099

AMA Style

Jin Y, Ding J, Chen Y, Zhang C, Hou X, Zhang Q, Liu Q. Urban Land Expansion Simulation Considering the Increasing versus Decreasing Balance Policy: A Case Study in Fenghua, China. Land. 2023; 12(12):2099. https://doi.org/10.3390/land12122099

Chicago/Turabian Style

Jin, Yaya, Jiahe Ding, Yue Chen, Chaozheng Zhang, Xianhui Hou, Qianqian Zhang, and Qiankun Liu. 2023. "Urban Land Expansion Simulation Considering the Increasing versus Decreasing Balance Policy: A Case Study in Fenghua, China" Land 12, no. 12: 2099. https://doi.org/10.3390/land12122099

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