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Regional Sustainability >

2024 , Vol. 5 >Issue 3: 100161

DOI: https://doi.org/10.1016/j.regsus.2024.100161

Full Length Article

What are the underlying causes and dynamics of land use conflicts in metropolitan junction areas? A case study of the central Chengdu- Chongqing region in China

  • TIAN Junfeng a ,
  • WANG Binyan , b, * ,
  • QIU Cheng b ,
  • WANG Shijun c
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  • aSchool of Public Policy and Administration, Chongqing University, Chongqing, 400044, China
  • bSchool of Architecture and Urban Planning, Chongqing University, Chongqing, 400044, China
  • cSchool of Geographical Sciences, Northeast Normal University, Changchun, 130024, China
* E-mail address: (WANG Binyan).

Received date: 2024-01-31

  Revised date: 2024-06-30

  Accepted date: 2024-08-22

  Online published: 2025-08-14

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Abstract

Land use conflicts (LUCs), as a spatial manifestation of the conflicts in the human-land relationships, have a profound impact on regional sustainable development. For China’s metropolitan junction areas (MJAs), the existence of “administrative district economies” has made the issue of LUCs more prominent. Based on a case study of the central Chengdu-Chongqing region, we conducted an exploratory spatial data analysis of the evolutionary process of regional LUCs. Furthermore, structural equation modeling was utilized to analyze the dynamic mechanism of LUCs in MJAs, with a particular emphasis on exploring the influences of administrative boundary. The results showed that from 2010 to 2020, LUCs in the central Chengdu-Chongqing region continued to worsen, and the spatial process conflict and spatial structure conflict indices increased by more than 30.0%. The intensification of LUCs in the central Chengdu-Chongqing region from 2010 to 2020 was mainly the result of the deterioration of conflicts in evaluation units with low conflict levels. LUCs in China’s metropolitan areas generally presented a circular gradient distribution, weakening from the core to the periphery, but there were some strong isolated conflict zones in the outer regions. LUCs in China’s MJAs were the result of interactions among multiple factors, e.g., natural environment, socio-economic development, policy and institutional processes, and administrative boundary effects. Administrative boundary affected the flow of socio-economic elements, changing the supply-and-demand competition of stakeholders for land resources, consequently exerting an indirect influence on LUCs. This study advances the theory of the dynamic mechanism of LUCs, and provides theoretical support for the governance of these conflicts in transboundary areas.

Key words： Land use conflicts; Urban agglomerations; Administrative boundary; Land resources; Structural equation modeling (SEM); Central Chengdu-Chongqing region

Cite this article

TIAN Junfeng , WANG Binyan , QIU Cheng , WANG Shijun . What are the underlying causes and dynamics of land use conflicts in metropolitan junction areas? A case study of the central Chengdu- Chongqing region in China[J]. Regional Sustainability, 2024 , 5(3) : 100161 . DOI: 10.1016/j.regsus.2024.100161

1. Introduction

Since the Anthropocene began, human-land relationships have become increasingly tense, and competition for limited land resources has become fiercer, with land use conflicts (LUCs) becoming a worldwide problem (Chen et al., 2024). LUCs occur when there is competition and conflict between stakeholders over rights and interests regarding land use patterns and structures during the process of utilization. Previous research on LUCs has mainly focused on four aspects: (1) the concept of LUCs (Campbell et al., 2000; Von der Dunk et al., 2011) and basic theories of LUCs (Grimble and Wellard, 1997; Lin and Li, 2016; Adhami et al., 2018; Dietz and Engels, 2020); (2) the identification, evaluation, and classification of LUCs from the perspectives of sociology, ecology, economics, and geography (Jiang et al., 2020; Bao et al., 2021; Cui et al., 2021; Dong et al., 2021; Zuo et al., 2022; Niu et al., 2023); (3) the factors that influence and drive the mechanism of LUCs (Henderson, 2005; Cieslak, 2019; Bekele et al., 2022; Han et al., 2023; Mo et al., 2023); and (4) the theoretical methods and implementation of LUC governance (Shapira et al., 2019; Aghmashhadi et al., 2022; You et al., 2022; Li et al., 2023b).
Previous studies of LUCs had several deficiencies. First, the research largely focused on an empirical analysis of different types of conflict at different scales (Zhou et al., 2019; Bao et al., 2021; Cui et al., 2021). Although a basic theoretical system of LUCs covering rudimentary theories of cognition, governance, and principles that control their spatio-temporal evolution was initially developed (Long et al., 2011; Ngaruiya et al., 2016; Zou et al., 2019), the existing theoretical studies of the development mechanism of LUCs are relatively weak. Second, empirical work has mainly focused on the direct impacts of natural environment, economy, policy, and institutional environment on LUCs, while ignoring the indirect impacts. Some studies have indicated that the influence of policy and institutional environment on LUCs is indirect and dependent; i.e., it mainly affects LUCs indirectly by regulating the regional socio-economic development process (Campbell et al., 2000). This research is theoretical and qualitative, with a lack of empirical studies. Third, although it is widely believed that LUCs are the product of interactions among economic, policy, and social driving forces, and are the result of relationships among natural ecological processes, human activities, and different land use modes (Henderson, 2005; Yu et al., 2015; Cieslak, 2019; Su and Wang, 2022), existing studies have not explored these multiple interactions (this is largely due to the lack of attention to the indirect effects of influencing factors, as noted above). Therefore, it is necessary to further explore the direct and indirect effects of the key influencing factors on LUCs, and to systematically analyze these multi-factor interactions to construct a theoretical framework and better understand the dynamic mechanisms involved.
Since the 1970s, with the decentralization of global production and the centralization of management, urban agglomerations have become important spatial carriers in global and national economies. Therefore, the construction of urban agglomerations has received much attention from the governments of developing countries. The aim of urban agglomeration construction in developing countries, specifically in China, is to realize integrated and efficient development through intra-regional spatial coupling. This has been achieved by the construction and growth of metropolitan junction areas (MJAs), which are geographical spaces at the junction of several metropolitan zones. MJAs in China are concentrated regions where problems such as low land use efficiency, spatial governance disorders, and spatial conflicts occur (Yu et al., 2019). This is related to the “border exclusion” dilemma that occurs under the leadership of local government departmentalism (Yang and Li, 2013). Administrative boundary is the cause of spatial governance problems in these regions. Although some studies have focused on the relationship between administrative boundary and regional land use and land cover change, most have explored national border areas (Bekele et al., 2022; Wang and Xiao, 2023). A previous study has indicated that cross-border infrastructure construction and frequent cross-border population flows in national border areas influence the process of land use changes in border areas, exacerbating LUCs (Xiao et al., 2021). However, the causal relationship between administrative boundary and LUCs is still poorly understood, especially for non-national administrative boundary.
Therefore, in this study, we asked the following questions. What is the impact of administrative boundary on LUCs? Does it stimulate or inhibit the formation and development of LUCs? Are the effects of administrative boundary manifested as direct influences, indirect influences, or both? Through which factors do indirect influences act on LUCs, and what dynamic mechanism is formed? The answers to these questions will enable the effective governance of LUCs in MJAs, and promote the high-quality development of urban agglomerations.
The aims of this study were to: (1) construct a theoretical framework of the dynamic mechanism of LUCs in MJAs based on the theory of human-land relationships; (2) establish an evaluation index system of LUCs to analyze the spatiotemporal process and characteristics of regional LUCs in the central Chengdu-Chongqing region of China; and (3) systematically examine the impacts of multiple factors on regional LUCs caused by administrative boundary to better understand the dynamic mechanism of LUCs in MJAs.
This study made two main contributions to the field of research. First, it revealed the spatial characteristics and patterns of regional LUCs in urban agglomerations, suggesting that they do not display a uniform spatial circular structure, but a non-uniform circular structure. This finding significantly enhances our understanding of the spatial laws governing LUCs. Second, this study contributed significantly to our understanding of the mechanism of non-state boundaries in LUCs. While researchers have conducted a preliminary investigation into the impact of state boundaries on LUCs (Bekele et al., 2022; Wang and Xiao et al., 2023), there remains a limited understanding of the influence of non-state boundaries. This study helped us to understand the mechanism and internal logic behind the impact of administrative boundary on LUCs.

2. Theoretical framework

As a spatial resource, land resources are of limited quantity and have multiple utilization modes and spillover functions. The utilization of land resources by humans originates from the diversified needs of survival, production, and development to meet the various goals of protecting natural environment and realizing economic and social benefits (Fig. 1). For different subjects, e.g., governments, farmers, enterprises, and citizens, land development behaviors are based on different interests in the available land spaces (Fig. 1). The suitability of land for these diverse purposes leads to spatial competition over land resources. The scarcity of land resources intensifies competition and forms LUCs. The natural characteristics and location of land control the suitability and scarcity of land resources.
Fig. 1. Interpretation framework for land use conflicts (LUCs) in metropolitan junction areas (MJAs).
During different stages of socio-economic development, and in the presence of various socio-economic factors, land use resources and the objectives of different stakeholders change. This leads to fluctuations in land use demand that affect the competition over land resources and generate LUCs. Additionally, space is not blocked and isolated. Any region can influence the function of land and the corresponding goals and behaviors of land resource utilization through the spatial spillover effects of resources and the environment, economy, and public service facilities. Therefore, the spillover of spatial functions intensifies the conflicts between spatial development and utilization.
For MJAs, the existence of administrative boundary makes the mechanism of LUCs more complicated. Administrative boundary can directly impact LUCs in MJAs by inhibiting land use efficiency, thereby highlighting the scarcity of land resources and exacerbating LUCs (Fig. 1). Market integration in the process of regional integration forms a larger unified market, which provides an endogenous driving force to promote the optimal allocation of production factors and the improvement of urban land use efficiency (Long, 2021; Zhao et al., 2021). However, the existence of administrative boundary can hinder the process of market integration, thus restricting the improvement of land use efficiency, stimulating the process of land spatial expansion, and intensifying LUCs.
Administrative boundary may also have indirect effects on LUCs in MJAs. By restricting the flow of socio-economic factors, administrative boundary shapes the spatial spillover process and indirectly influences LUCs. The key to the efficient allocation of land production factors in urban agglomerations lies in the formation of complementarity. The complementarity of land use is determined by the relationship between supply and demand, and is realized through the spatial spillover effect caused by factor flows (Zou et al., 2021). The core edge theory in the study of Friedman (1966) considers that regions can be divided into a core and edges in a spatial economic structure. The core has distinct advantages over the edges because it is an agglomeration of socio-economic factors. There is a polarization-diffusion effect between the core and the edges. In the early stage of regional integration, a polarization effect occurs inside urban agglomerations. Land use in the core area tends to be strained, and LUCs arise. The marginal area develops slowly, and LUCs are not significant. Administrative boundary, as a barrier formed by administrative powers, has a screening effect (Zhao et al., 2021). It impedes the flow of factors and further influences the land use optimization process, resulting in poor land use complementarity in regions along administrative boundary. LUCs may further intensify. However, the restriction of factor flows created by administrative boundary may also lead to the lagging development of border areas, weak demand for productive space expansion (Wang et al., 2020), and weakened LUCs.
With regional integration, there is a diffusion effect in urban agglomerations. Socio-economic elements flow from the core of the metropolis to the periphery. Due to the resulting expansion, excessive agglomeration occurs in some of the marginal areas, which intensifies human-land conflicts. The increase in non-land inputs optimizes the regional land use structure and improves the overall utilization efficiency. In the process of regional integration, along with the construction of traffic corridors (e.g., expressways and high-speed railways), administrative boundary has a mediating effect, the trans-regional flow of socio-economic elements becomes possible, the complementarity of land use in border areas is enhanced, and LUCs may weaken. However, cross-border channel construction and policy-driven cooperative development in border areas may lead to new conflicts in agriculture, natural areas, and construction space. The strengthening of function spillover under strengthening factor flows also allows for the emergence and deterioration of LUCs.

3. Study area and methodology

3.1. Study area

The central Chengdu-Chongqing region is located at the junction of Chengdu City in Sichuan Province and Chongqing Municipality, western China. The region covers Ziyang (including Yanjiang District, Anyue County, and Lezhi County), Suining (including Chuanshan District, Anju District, Shehong City, Pengxi County, and Daying County), and Guang’an (including Guang’an District, Qianfeng District, Yuechi County, Wusheng County, Linshui County, and Huaying City) cities in Sichuan Province, and Yongchuan, Dazu, Rongchang, Tongnan, Tongliang, Bishan, Beibei, and Yubei districts in Chongqing Municipality (Fig. 2).
Fig. 2. Overview of the study area. Note that the figure is based on the standard map (No. GS(2024)0650) of the Map Service Center (https://www.tianditu.gov.cn/) marked by the Ministry of Natural Resources of the People’s Republic of China, and the standard map used in this study has not been modified.
The Chengdu and Chongqing metropolitan areas jointly constitute the Chengdu-Chongqing urban agglomeration. China’s National New Urbanization Plan (2014-2020) defines this area as an urban agglomeration in central and western China. It is similar to other urban agglomerations in central and western China in terms of its level of socio-economic development. In 2021, the growth rate of gross domestic product (GDP) for the central Chengdu-Chongqing region was 8.3%, which was much higher than the urban agglomeration average (6.7%) (Chongqing Municipal Bureau of Statistics, 2022; Sichuan Provincial Bureau of Statistics, 2022). Due to the rapid economic development and the special location at the intersection of urban areas, the central Chengdu-Chongqing region is characterized by extensive construction land coverage, inefficient and unsustainable land use practices, and evident conflicts in land utilization. The Chengdu and Chongqing metropolitan areas are adjacent to each other, but the two areas have displayed fierce competition in the establishment of industry and transport networks throughout their long-term development. Administrative boundary has a significant impact on the regional economy and social connections. Therefore, the central Chengdu-Chongqing region is a typical MJA whose LUCs and strong administrative boundary make it a useful case study of the dynamic process of LUCs in such regions.

3.2. Research methods

3.2.1. Construction of land use conflict (LUC) evaluation system

The LUC index was the dependent variable. Multiple identification methods based on various attributes of LUCs have been developed in the fields of sociology, ecology, economics, and geography. The application of a geographical perspective is of great significance in identifying conflict areas and evaluating conflict intensity. Starting from a geographical perspective, a comprehensive index of spatial conflict was constructed to measure LUCs with a linear evaluation model. Following Bao et al. (2021), we divided the LUC index into spatial type conflict, spatial process conflict, and spatial structure conflict indices, and then constructed an evaluation index system (Table 1).
Table 1 Evaluation index of land use conflicts (LUCs) in metropolitan junction areas (MJAs).
Target layer Rule layer Index layer Index description Attribute Weight
LUCs Spatial type conflict Development intensity index $D I=\frac{S_{c} / S}{I}$,
where DI is the construction land development intensity index; Sc is the construction space area (hm2); S is the total area of the evaluation unit (hm2); and I is the development intensity threshold of the evaluation unit.		 + 0.260
Agricultural retention index $A R=\frac{S_{a}}{G} \times 100 \%$,
where AR is the agricultural retention index (%); Sa is the agricultural space area (hm2); and G is the minimum agricultural control standard values.		 - 0.032
Spatial process conflict Construction occupancy index $A E C=\frac{S_{c e}+S_{a e}}{S_{a}+S_{e}}$,
where AEC is the construction occupancy index; Sce is the area of ecological space occupied by construction space during 2000–2010 or 2010–2020 (hm2); Sae is the area of agricultural space occupied by construction space (hm2); Sa is the agricultural space area (hm2); and Se is the initial ecological space area (hm2).		 + 0.316
Agricultural occupancy index $E A=\frac{S_{e a}}{S_{e}}$,
where EA is the agricultural occupancy index; Sea is the area of ecological space occupied by agricultural space during 2000–2010 or 2010–2020 (hm2); and Se is the initial ecological space area (hm2).		 + 0.064
Spatial structure conflict Construction space fragmentation index $I{{F}_{c}}=\frac{{{N}_{c}}}{S}$,
where IFc is the spatial fragmentation index of construction space; Nc is the number of construction land patches in the evaluation unit; and S is the total area of the evaluation unit (hm2).		 + 0.128
Agricultural space fragmentation index $I F_{a}=\frac{N_{a}}{S}$,
where IFa is the spatial fragmentation index of agricultural space; Na is the number of agricultural space patches in the evaluation unit; and S is the total area of the evaluation unit (hm2).		 + 0.107
Natural space fragmentation index $I{{F}_{e}}=\frac{{{N}_{e}}}{S}$,
where IFe is the spatial fragmentation index of natural space; Ne is the number of natural space patches in the evaluation unit; and S is the total area of the evaluation unit (hm2).		 + 0.090

Note: In this study, we determined I according to China’s main functional areas. The national key development zone was set to 0.30, the provincial key development zone was set to 0.25, the main agricultural product production zone was set to 0.15, and the natural function zone was set to 0.10. We determined G based on the per capita cultivated land warning line of 530 m2/person and the population size determined by the United Nations (Min et al., 2018). The symbol “+” signifies a positive correlation between the growth of this index and the occurrence of LUCs, with a negative correlation in the opposite direction. The symbol “-” indicates that the growth of this index is inversely correlated with the occurrence of LUCs.

3.2.2. Exploratory spatial data analysis model

We used the exploratory spatial data analysis model to examine the spatial patterns and characteristics of LUCs in the central Chengdu-Chongqing region using the global and local Moran’s I. The specific formula for the exploratory spatial data analysis model is described by Tian et al. (2020).
While the global Moran’s I reveals the overall spatial dependence and spatial heterogeneity of geographical factors, the local Moran’s I reflects the spatial distribution characteristics of geographical factors within the local scope. The specific formula is described by Lyu et al. (2019).
The spatial correlation patterns can be divided into four categories where the attribute values of the study area and surrounding area are: (1) both high (high-high, HH): a positive spatial correlation; (2) both low (low-low, LL): a positive spatial correlation; (3) the high-value region is surrounded by the low-value region (high-low, HL): a negative spatial correlation; and (4) the low-value region is surrounded by the high-value region (low-high, LH): a negative spatial correlation (Lyu et al., 2019).

3.2.3. Variables

Based on the theoretical construction and a systematic summary of previous studies (Henderson, 2005; Brown and Raymond, 2014; Cieslak, 2019; Dietz and Engels, 2020), we constructed an index system of factors influencing LUCs from four aspects as the independent variables: natural environmental conditions, socio-economic factors, policy and institutional environment, and administrative boundary effect. Specific indices are shown in Table 2. In terms of the policy and institutional environment, three indices (fixed-asset investment, grain production function, and ecological protection function) were used to quantitatively reflect the impacts of urban construction, cultivated land protection and agricultural development, and environmental protection policies on regional LUCs (Zhou et al., 2021). We measured the indices of grain production function and ecological protection function for each research unit according to the main functional zone planning of Sichuan Province and Chongqing Municipality.
Table 2 Indices of the driving factors of regional LUCs.
Factor setting Variable Definition
Natural environmental conditions Mean slope Average slope per study unit (%)
Mean elevation Average elevation per study unit (m)
Socio-economic factors Per capita gross domestic product (GDP) (CNY)
Output value of primary industry (CNY)
Output value of secondary industry (CNY)
Output value of tertiary industry (CNY)
Population size Population per unit (persons)
Urbanization rate Ratio of unit urban population to total population (%)
Density of high-grade road Ratio of the sum of length of unit national highway and provincial highway to unit area (km/hm2)
Density of low-grade road Ratio of the sum of length of unit county road and township road to unit area (km/hm2)
Policy and institutional environment Fixed-asset investment Investment scale of fixed assets per unit (CNY)
Grain production function Importance of food production in each unit
Ecological protection function Importance of eco-environmental protection in each unit
Administrative boundary Boundary effect
Based on Wang et al. (2020), to capture the administrative boundary effect, we constructed the “boundary effect” index and expressed it as a dummy variable. After consulting relevant experts, from 2000 to 2010, each “administrative boundary” was coded 1 for towns (villages) located at the border of non-provincial cities (districts), 3 for towns (villages) located at the border of cities (districts), and 4 for towns (villages) located in the border area between Chengdu City and Chongqing Municipality. As an important center of growth in western China, the Chinese Central Government has promoted the Chengdu-Chongqing region as an example of regional coordinated development.
The Chinese Central Government adopted a series of policy measures to promote regional integration. In March 2011, the State Council of China adopted “Master Plan for the Construction of the Chengdu-Chongqing Economic Circle”, which defined Guang’an City and Tongnan District in the central Chengdu-Chongqing region as Sichuan-Chongqing cooperation demonstration zone (Cheng et al., 2020) to break the administrative boundary effect and accelerate the development of adjacent areas. Therefore, we expected that the administrative boundary effect in the central Chengdu-Chongqing region had weakened during 2010-2020 and would be more obvious in the Sichuan-Chongqing cooperation demonstration zone. Administrative boundary of towns in Guang’an City and Tongnan District within the Sichuan-Chongqing cooperation demonstration zone from 2010 to 2020 was therefore coded 3, towns (villages) at the border of cities (districts) were coded 2, and towns (villages) at the border of non-provincial cities (district) were coded 1.

3.2.4. Ordinary least squares (OLS) and simultaneous equation models

An OLS regression was used to explore the possible factors influencing regional LUCs. The analysis was conducted using SPSS version 23.0 (IBM, New York, the United States). We hypothesized that administrative boundary had both direct and indirect effects on LUCs in MJAs (Fig. 3). Therefore, structural equation modeling (SEM) was introduced to test the effects of administrative boundary on LUCs.
Fig. 3. Initial model of structural equation modeling (SEM). GDP, gross domestic product.
SEM can quantify the direct, indirect, and total effects of independent variables on dependent variables (Wang et al., 2020). A factor analysis was used to detect the effects of administrative boundary on LUCs. A path analysis was applied to determine whether administrative boundary had direct or indirect effects (or both) on LUCs. All SEM and corresponding tests were conducted using SPSS Amos software (Amos 24, IBM, New York, the United States).
We used the ratio of chi-square to degrees of freedom (CMIN/DF), goodness-of-fit index (GFI), adjusted goodness-of-fit index (AGFI), root mean square error of approximation (RMSEA), and other parameters to test and estimate the goodness of fit of the model (Wang et al., 2020). All parameters in 2010 and 2020 passed the test and met the requirements of the saturation model, and the model fitting effect was good (Table 3).
Table 3 Main parameters used in structural equation modeling (SEM).
Parameter 2010 2020 Recommended value
CMIN/DF 1.296 1.560 <3.000
GFI 0.997 0.985 >0.900
AGFI 0.983 0.966 >0.900
RMSEA 0.032 0.042 <0.080

Note: CMIN/DF, the ratio of chi-square to degrees of freedom; GFI, goodness-of-fit index; AGFI, adjusted goodness-of-fit index; RMSEA, root mean square error of approximation.

3.3. Data sources

Multi-source data, including spatial, statistical, topographic, and land use data, were used to measure the degree of LUCs and analyze the influencing factors (Table 4). All spatial data were defined in the Albers Equal-Area Conic projection and processed in ArcGIS 10.5 (Environmental Systems Research Institute (ESRI), Redlands, the United States). To match the border data for towns and townships and further improve the research accuracy, the average economic data at the county level were used as a proxy for towns and townships. The land use data included 30 m raster data for 2000, 2010, and 2020. The land use types in the study area included cultivated land, forest, grassland, shrubland, wetland, water body, and built-up land. Cultivated land and grassland constituted agricultural space; forest, shrubland, wetland, and water body constituted natural spaces; and built-up land was the urban area. The 546 towns and streets in 10 cities (districts) constituted the basic units of the analysis.
Table 4 Data sources and their descriptions.
Data name Data description Data source
Per capita GDP Statistical data (township as the basic unit) Chongqing Municipal Bureau of Statistics (2022); Sichuan Provincial Bureau of Statistics (2022)
Output value of primary industry
Output value of secondary industry
Output value of tertiary industry
Population size
Fixed-asset investment
Land use data Grid; 30 m×30 m GlobeLand30 (http://www.globallandcover.com)
Elevation data and slope data Grid; 90 m×90 m Spatial information alliance (http://srtm.csi.cgiar.org/selection/inputCoord.asp)
Road data Vector; line Data Center for Resources and Environmental Sciences, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (http://www.resdc.cn)

4. Results

4.1. Characteristics and spatial process of LUCs

4.1.1. Characteristics of LUCs

During the study period, LUCs in the central Chengdu-Chongqing region intensified (Fig. 4a). Spatial process conflict and spatial structure conflict increased by more than 30.0%, indicating a significant worsening. The results based on the coefficient of variation (Fig. 4b) showed a significant polarization of spatial process conflict and spatial structure conflict. Combined with the change in the average value, the intensification of LUCs in the Chengdu-Chongqing region from 2010 to 2020 was mainly the result of the deterioration of conflicts in evaluation units with low conflict levels.
Fig. 4. Evolution of LUCs in the central Chengdu-Chongqing region in 2010 and 2020. (a), average value of LUC index; (b) coefficient of variation of LUC index.

4.1.2. Spatial process of LUCs

Using the exploratory spatial data analysis model, the spatial characteristics of regional LUCs were accurately depicted. The results showed that, in 2010 and 2020, the global Moran’s I of LUCs and each LUC index type were greater than 0.000, indicating that LUCs in the central Chengdu-Chongqing region presented a spatial agglomeration feature (Table 5).
Table 5 Global Moran’s I of LUCs.
Year Index LUCs STC SPC SSC
2010 Moran’s I 0.623*** 0.587*** 0.614*** 0.513***
Z-value 23.070 20.902 22.386 18.879
2020 Moran’s I 0.616*** 0.637*** 0.462*** 0.556***
Z-value 22.223 22.039 16.418 19.798

Note: Z-value, the value was obtained by dividing the Moran’s I by its standard deviation and used to determine whether the Moran’s I is significant; STC, spatial type conflict; SPC, spatial process conflict; SSC, spatial structure conflict; ***, statistical significance at the P≤0.01 level.

The local spatial autocorrelation results showed that LUCs in 2010 and 2020 presented significant spatial agglomeration characteristics and were mainly positively spatially correlated. The LL units accounted for the highest proportion of evaluation units and were concentrated in the central and eastern parts of the region. These units were mainly distributed along the borders of province and city (district). In 2020, the scope of LL units in Hechuan and Yubei districts was greatly reduced (Fig. 5). The HH units also accounted for a high proportion of evaluation units and were concentrated in Yubei District. In 2020, the number of HH units increased, and their spatial scope expanded significantly (Fig. 5).
Fig. 5. Spatial agglomeration characteristics of LUCs in 2010 (a) and 2020 (b). Note that the figure is based on the standard map (No. GS(2024)0650) of the Map Service Center (https://www.tianditu.gov.cn/) marked by the Ministry of Natural Resources of the People’s Republic of China, and the standard map used in this study has not been modified. HH, the attribute values of the study region and surrounding region are both high; HL, the high-value region is surrounded by the low-value region; LH, the low-value region is surrounded by the high-value region; LL, the attribute values of the study region and surrounding region are both low.
For spatial type conflicts, the spatial agglomeration characteristics in 2010 and 2020 were relatively stable, with a positive spatial correlation. The LL units were mainly concentrated in the central junction between Ziyang City of Sichuan Province and Tongnan District of Chongqing Municipality, and between Guang’an City of Sichuan Province and Yubei District of Chongqing Municipality (Fig. 6). The proportion of HH units was also high, with a concentration in Yubei District, and the range expanded over time.
Fig. 6. Spatial agglomeration characteristics of spatial type conflict, spatial process conflict, and spatial structure conflict in 2010 and 2020. (a), spatial type conflict in 2010; (b), spatial process conflict in 2010; (c), spatial structure conflict in 2010; (d), spatial type conflict in 2020; (e), spatial process conflict in 2020; (f), spatial structure conflict in 2020. Note that the figure is based on the standard map (No. GS(2024)0650) of the Map Service Center (https://www.tianditu.gov.cn/) marked by the Ministry of Natural Resources of the People’s Republic of China, and the standard map used in this study has not been modified.
A positive spatial correlation was also presented for spatial process conflict. In 2010, the LL units were concentrated in the eastern part of the region, while the HH units were concentrated in Yubei District (Fig. 6b). In 2020, the number of LL units in the region decreased and their range was reduced. They were only concentrated in the border zone between Dazu District of Chongqing Municipality and Anyue County of Sichuan Province, the border zone between Hechuan and Yubei districts, and the border zone between Tongliang and Bishan districts (Fig. 6e). The HH units were still concentrated in Yubei District, and their spatial range expanded (Fig. 6e).
Spatial structure conflict exhibited a positive spatial correlation in 2010 and 2020, and almost all units with a significant performance were the LL units (Fig. 6c and f). In 2010, the LL units were concentrated in Lezhi and Anyue counties in Ziyang City, Tongnan and Hechuan districts in Chongqing Municipality, and Wusheng County in Guang’an City (Fig. 6c). In 2020, the LL units were mainly concentrated in the border zone between Lezhi and Anyue counties (Fig. 6f).

4.2. Factors influencing LUCs

4.2.1. Regression results

Table 6 presents the analysis results of the factors affecting LUCs. The adjusted R2 values of the OLS model in 2010 and 2020 were 0.344 and 0.432, respectively. The output value of primary industry and the mean elevation in 2010 had significant negative impacts on LUCs (P≤0.01). The output value of secondary industry had a positive and significant (P≤0.01) impact on LUCs, and the influence of the mean slope was also positive and significant (P≤0.05). Fixed-asset investment was negatively associated with LUCs (P≤0.10).
Table 6 Regression coefficients between LUCs and influencing factors in central Chengdu-Chongqing region.
Variable 2010 2020
Output value of primary industry -0.319***
Output value of secondary industry 0.924*** -0.404**
Output value of tertiary industry 0.334*
Per capita GDP 0.303***
Population size 0.433***
Fixed-asset investment -0.268*
Density of high-grade road 0.264***
Mean slope 0.127** 0.176***
Mean elevation -0.208*** -0.107*
Administrative boundary -0.064 -0.067
Durbin-Watson 1.590 1.317
F-statistic 23.187*** 29.104***
Adjusted R2 0.344 0.432

Note: Durbin-Watson is an important statistic used to test whether there is an autocorrelation (sequence correlation) phenomenon in the residual term of regression analysis. ***, statistical significance at the P≤0.01 level; **, statistical significance at the P≤0.05 level; *, statistical significance at the P≤0.10 level.

Compared with 2010, the factors influencing LUCs in 2020 were different. The effect of the output value of secondary industry on LUCs changed from positive to negative (Table 6). The output value of tertiary industry had a positive impact on LUCs (P≤0.10). Per capita GDP, population size, and the density of high-grade road were positive correlated with LUCs (P≤0.01). Among the natural environmental factors, mean slope and mean elevation still had significant influences on LUCs (positive and negative, respectively).

4.2.2. Results of the structural equation modeling (SEM) analysis

The results of the SEM analysis confirmed that administrative boundary had no direct effect on LUCs during the study period, but did have a significant indirect effect (Fig. 7). Indirect effects included the influences of administrative boundary on the medium (socio-economic elements) and the impact of the medium on LUCs. Administrative boundary played an important but not decisive role in the formation and development of LUCs in the MJA (Fig. 7).
Fig. 7. SEM results of the impacts of administrative boundary on LUCs. (a), results of SEM in 2010; (b), results of SEM in 2020. In Figure 7a, e1 and e2 represent the residuals of the output value of the secondary industry and LUC index, respectively. In Figure 7b, e1, e2, e3, and e4 represent the residuals of population size, per capita GDP, the density of high-grade road, and LUC index, respectively. The value above the arrow is the regression coefficient. ** and *** indicate statistical significance at the P≤0.05 and P≤0.01 levels, respectively.
The mediums between administrative boundary and LUCs differed by period. In 2010, administrative boundary affected regional LUCs by influencing the output value of secondary industry, which had a negative impact (Fig 7a), thus weakening LUCs. At the beginning of the 21st century, the Chengdu-Chongqing urban agglomeration was in the initial stage of integration. Its socio-economic development was strongly polarized, and the socio-economic elements, such as industry, population, and investment, flowed to the “dual core” of the main urban areas of Chongqing Municipality and Chengdu City, resulting in the strengthening of the core-periphery structure. The central Chengdu-Chongqing region is located in the area connecting Chengdu City and Chongqing Municipality and has been significantly affected by the polarization effect of the “dual core”. Since the administrative division and administrative-level adjustments of Chongqing Municipality in 1997, significant administrative barrier effects have formed between Sichuan Province and Chongqing Municipality. Chengdu City and Chongqing Municipality have developed a fierce competition in their industrial and transportation network development. Under the shielding of administrative boundary, socio-economic elements (e.g., industry and population) flowed toward the core of the administrative region, and a “central collapse” phenomenon occurred. Therefore, with industry as the main driving force of regional urban spatial expansion, administrative boundary drove the flow of socio-economic elements to the core area of the administrative region under a strong screening effect, weakened the industrial development of the central Chengdu-Chongqing region, reduced regional demand for production space, restricted urban spatial expansion, and weakened LUCs.
In 2020, administrative boundary affected LUCs by influencing population size, per capita GDP, and the density of high-grade road (Fig. 7b). It had a negative impact on all three parameters, and thus restrained LUCs. As the main axis of the dual-core connection between Chengdu City and Chongqing Municipality, the central Chengdu-Chongqing region was the main area affected by the regional integration policy. The strengthening of factor flows promoted the development of some towns with advantageous locations in the region, and the differentiated agglomeration of economic and population factors led to a surge in the demand for construction space in some cities, which has further intensified human-land conflicts. Under the influence of regional integration policies, the existing administrative barriers in the central Chengdu-Chongqing region weakened but have not been completely removed, and administrative boundary has entered the stage of cross-border channel construction. Administrative boundary still presents a limited screening effect, which is manifested as a restriction on cross-border factor flows. Through these restrictions, the flow of factors has been hindered, socio-economic development of the border area has been restricted, land demand has been inhibited, and LUCs have been weakened.

4.3. Summary of the dynamic mechanism

Through an empirical study of the central Chengdu-Chongqing region, we demonstrated the formation and evolution of LUCs in an MJA. The identified LUCs were the result of conflicts and interactions between land supply and demand in natural environment, economic factors, policies and institutional systems, and administrative boundary which was closely related to the finite, multi-use, and spatial spillover of land resources. The LUCs in MJAs arise through the different land use objectives of multiple stakeholders. The conflict is a consequence of the land supply and demand caused by the finite nature of land resources and their multiple uses (Fig. 8). Land uses by multiple stakeholders based on their different interests create competition for land resources, intensify the conflicts between land supply and demand, and form several types of LUCs, i.e., spatial type, spatial structure, and spatial process conflicts.
Fig. 8. Mechanism of LUCs in the central Chengdu-Chongqing region.
Natural environmental and location factors, represented by terrain and traffic conditions, are the basic variables determining the direct influences of multi-use and finite factors on the supply of land resources. The demand for land resources depends on the land use objectives of different stakeholders, and is closely related to the regional socio-economic development characteristics. Changes in the regional economic growth and evolution of the industrial structure, as well as the population growth and contraction processes, led to changes in stakeholders’ objectives and demand for land use. This in turn influenced land supply and demand and the extent of LUCs. The interplay of policy and institutional factors, including environmental protection policies and government investment, along with favorable natural location conditions, collaboratively influences the demand for land resources. This, in turn, leads to the reconstruction of the relationship between land supply and demand, ultimately impacting LUCs.
Administrative boundary plays an important role in the formation and evolution of LUCs in MJAs, and can indirectly affect regional LUCs by acting on socio-economic factors. The socio-economic development of MJAs is significantly affected by a “polarization-diffusion” effect. This effect triggers the flow of socio-economic factors and influences regional socio-economic development. Administrative boundary has a screening effect in the process of urban agglomeration development. In the initial stage of regional integration (Stage 1 in Fig. 9), the strong screening effect of administrative boundary restricted the flow of socio-economic factors within the metropolitan area and blocked the flow of factors and function spillover across the region. The strong polarization process within the metropolitan area concentrated the socio-economic elements in the core area, and promoted the acceleration of socio-economic development. This intensified the conflicts among the land use objectives of multiple stakeholders in the metropolitan core, highlighted the imbalance between land supply and demand, and intensified LUCs. Under administrative boundary screening and regional polarization effects, LUCs in the metropolitan area presented a “core-periphery” feature and were eased, while conflicts in the border areas were suppressed (Fig. 9).
Fig. 9. Spatial evolution of LUCs in MJAs.
With the development of urban agglomeration, the supply of land resources became increasingly tight, which intensified the overall human-land relationship and worsened LUCs in metropolitan junction areas (Fig. 9). The maturation of urban agglomeration weakened the internal polarization effect and accelerated the development of the outer regions of the metropolitan area. To promote regional integrated development, policies implemented by the government, such as the construction of traffic channels and pilot zones for coordinated development, weakened the polarization of the core area and promoted the flow of socio-economic factors from the core to the peripheral regions with advantageous geographical conditions for development, accelerating socio-economic development in these regions and enhancing the spillover of spatial functions. As a result, the LUCs between the interests of different stakeholders were intensified, and LUCs increased. Administrative boundary still presented a screening effect, but its intensity was weakened (Stage 2 in Fig. 9). The screening effect was mainly manifested as an obstacle to the flow of cross-border factors that continuously emerged and strengthened. Therefore, it maintained an inhibitory effect on LUCs in MJAs.

5. Discussion

5.1. Theoretical implications

This study supplemented previous research in two main ways. First, it improved our understanding of the spatio-temporal evolution of LUCs in MJAs during the process of urban agglomeration integration. It also improved our understanding of the spatio-temporal evolution of LUCs in MJAs within urban agglomerations. Zhou and Peng (2012) considered the Changsha-Zhuzhou-Xiangtan urban agglomeration in China and argued that the intensity of spatial conflict within urban agglomerations presented an inverted “U-shaped” change with the development of urban agglomerations. Through an empirical study of the central Chengdu-Chongqing region, we found that internal LUCs in urban agglomerations were strengthened as the agglomerations developed. This was consistent with Zhou and Peng (2012). Long et al. (2011) argued that differences in regional land resource utilization patterns and the degree of competition caused LUCs in China to display an “urban-suburban-rural” gradient pattern of gradually weakening from urban core areas to the periphery. Taking Suzhou-Wuxi-Changzhou region as an example, Qiu et al. (2022) discovered that LUCs within China’s urban spatial zoning exhibited higher intensity in the core areas compared to the fringe and suburb zones. The intensive and severe conflict was concentrated in core areas due to very intense human activities. Li et al. (2023a) also found that LUCs in Changzhou City exhibited a circular pattern around urban areas. Generally, this study found that LUCs in China’s metropolitan areas presented a circular gradient distribution, weakening from the core to the periphery. However, with the weakening of regional development polarization and the appearance of diffusion effects, changes in the spatial structure of socio-economic factor flows will lead to the excessive agglomeration of socio-economic elements in regions with advantages in the periphery, which intensifies human-land conflicts. Therefore, LUCs in metropolitan areas do not have strict gradient distribution characteristics, but there are some strong isolated conflict zones in the outer regions.
Second, this study provided examples that help with understanding the influence of non-state administrative boundary on regional LUCs, and investigated the mechanism by which administrative borders affect regional LUCs. As the frontier of cooperation and competition between countries and regional governments, border areas are undergoing rapid and extensive land use and land cover changes, and administrative boundary plays a vital role in this process. Xiao et al. (2021) reported that during geopolitical cooperation, the boundary effect, which is characterized by openness and connectivity along national borders, plays a significant role in attracting the migration and aggregation of socio-economic elements of population and industry to border areas. As a result, large-scale land acquisition for construction overlaps with the original forest and agricultural spaces, causing new LUCs and intensifying existing conflicts (Xiao et al., 2021; Le Bivic and Idt, 2023; Wang and Xiao, 2023). For non-state administrative boundary, such as provincial and municipal areas, existing studies have mainly focused on the effects of administrative boundary on land use efficiency, urban land structure, and urban land expansion (Wang et al., 2020; Wu et al., 2020). Currently, there are divergent perspectives regarding the impacts of administrative boundary on regional LUCs. A previous study has argued that the presence of administrative boundary hampers the enhancement of land use efficiency (Wu et al., 2020). This is compounded by the influence of the “administrative district economy”, leading to chaotic land development and extensive land use in the regions where administrative boundary is located, thereby exacerbating LUCs (Tang et al., 2013; Wu et al., 2020). However, other studies have argued that administrative boundary impedes the spatial expansion of construction land by constraining the flow of socio-economic elements, thereby exerting a restraining influence on LUCs (Wang et al., 2020). This study considered MJAs and found that, as typical non-state border areas, they differed from national border areas, with a weak intensity of LUCs. This was due to the screening effect of administrative boundary on the flow of socio-economic factors, which was consistent with the study conducted by Wang et al. (2020). It was also found that the establishment of traffic channels to facilitate regional integration can attenuate the screening effect imposed by administrative boundary. However, rather than alleviating regional LUCs, it actually exacerbated such conflicts. This was because the establishment of transportation channels led to enhanced regional traffic accessibility, thereby generating a spillover effect that accelerated urban construction land expansion and exacerbated LUCs among various stakeholders. This was consistent with Wu et al. (2020). Furthermore, this study proved that administrative boundary can affect regional LUCs through complex interactions with multiple factors, such as socio-economics, policy systems, and environmental conditions. The findings of this study not only complemented existing research on administrative boundary, but also provided insights into the underlying mechanisms through which different administrative boundaries influence land use and land cover change. The findings of this study also extended the theory of the dynamic mechanism of LUCs.

5.2. Practical implications

The intensities of LUCs in the central Chengdu-Chongqing region were low, but they have worsened with the interactions among administrative boundary, natural environment, location, socio-economic conditions, and public policy and institutions. In the process of realizing sustainable development, a major challenge for MJAs such as the central Chengdu-Chongqing region is how to effectively control and restrain LUCs while promoting the integration process.
To address this challenge, the government should use planning as its main policy tool to strengthen the spatial governance of MJAs. First, in terms of spatial planning, while strengthening the coordinated development of cross-border regions, it is necessary to scientifically set the urban development boundary, delineate the “red line” for ecological protection, alleviate the conflicts in human-land relationships, and curb LUCs using planning restrictions and guidance. Second, it is necessary to strengthen the effectiveness of regional spatial governance through the establishment of cross-administrative district management institutions. It is important to establish special management organizations across administrative regions at the national level and in major urban agglomerations (Fang and Yu, 2020). The management function of these organizations should be strengthened in addition to the coordination function. Third, the effectiveness of the market in the regional governance of metropolitan areas should be strengthened by promoting regional market integration. Emphasis should be placed on accelerating the market-oriented reform of land factors. By establishing a dynamic management platform and a technology trading platform for regional land use indices, the exchanges between peripheral and core areas, and different regions in the peripheral areas in terms of land development and utilization technology, as well as the increase-decrease linkage of built-up land should be promoted to realize the reallocation of spatial resources and enhance the complementarity of land use.

5.3. Limitations and future directions

This study had two main limitations. First, although the central Chengdu-Chongqing region is representative of other MJAs in China in terms of its socio-economic development and regional integration, the intermediary effect of administrative boundary was not fully explored due to the relatively immature development of the Chengdu-Chongqing urban agglomeration. Therefore, additional long-term tracking studies and comparative analyses should be conducted for mature urban agglomerations (e.g., the Beijing-Tianjin-Hebei urban agglomeration and Yangtze Delta urban agglomeration) to more accurately explain the processes and mechanisms by which administrative boundary impacts on LUCs. Second, while LUC is spatially manifested as a conflict between the quantity and structure of different types of land use, its essence lies in the dynamics between conflicting land values and divergent concepts of land use among various stakeholders (Milczarek-Andrzejewska et al., 2018). These dynamics indirectly influence the macro regional land use structure by shaping the land development and utilization behavior of stakeholders at the micro level. Therefore, to achieve a comprehensive understanding of LUCs, it is imperative to conduct systematic research encompassing both macro and micro perspectives. This study preliminarily explored the dynamic mechanism of LUCs in MJAs from the macro-regional scale, without considering the various stakeholder interests in relation to land resource use in the region. This would require systematic research at a micro-scale, as well as interactive research at both the macro- and micro-scales. This will be the main direction of our future research.

6. Conclusions

Through an exploratory spatial data analysis and SEM, and by taking the central Chengdu-Chongqing region as an example, we explored the evolutionary characteristics and spatial patterns of LUCs in China’s MJAs, and revealed the dynamic mechanism involved. This study supplemented the theoretical framework of the dynamic mechanism of LUCs, and provided theoretical support for the governance of these conflicts in transboundary areas.
The findings indicated that LUCs in the central Chengdu-Chongqing region escalated during 2010-2020, which was primarily attributed to the exacerbation of conflicts within low-conflict evaluation units. Among the three subtypes of conflicts, the deterioration characteristics of spatial process conflict and spatial structure conflict were found to be most significant. The LUCs in the central Chengdu-Chongqing region exhibited characteristics of spatial agglomeration. The high-conflict units were primarily concentrated in urbanized regions, whereas the low-conflict units were predominantly concentrated in inter-provincial border areas. In terms of the dynamic mechanism, the LUCs in MJAs, exemplified by the central Chengdu-Chongqing region, emerged as a result of multiple factors encompassing natural environment, socio-economic development, policy and institutional processes, and administrative boundary. The evolutionary process of LUCs in MJAs was predominantly influenced by administrative boundary, with the primary manifestation being the indirect impact on regional LUCs. The screening effect of administrative boundary affected the process of socio-economic development by hindering the flow of socio-economic factors, thus weakening the demand for land use and inhibiting LUCs. This study constructed a theoretical framework of the LUCs mechanism, which supplemented the theoretical framework of the dynamic mechanism of LUCs. This study also provided an important theoretical and practical reference for achieving the sustainable development of spatial resources in border and cross-border regions.

Authorship contribution statement

TIAN Junfeng: methodology, writing - original draft, and funding acquisition; WANG Binyan: conceptualization, writing - reviewing & editing, and funding acquisition; QIU Cheng: data curation and visualization; and WANG Shijun: investigation and project administration. All authors approved the manuscript.

Declaration of conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This research was funded by the National Natural Science Foundation of China (42101264; 42101200), the Postdoctoral Fellowship Program of China Postdoctoral Science Foundation (CPSF) (GZC20233314), the Chongqing Natural Science Foundation, China (cstc2021jcyj-msxmX0811), and the Fundamental Research Funds for the Central Universities, China (2023CDSKXYGG006; 2024CDJXY014).

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