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

doi:10.1016/j.regsus.2024.100161

Abstract

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

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.

Figures/Tables 15

Fig. 1.

Fig. 2.

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; S_c 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
	Spatial type conflict	Agricultural retention index	$A R=\frac{S_{a}}{G} \times 100 \%$, where AR is the agricultural retention index (%); S_a 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; S_ce is the area of ecological space occupied by construction space during 2000–2010 or 2010–2020 (hm²); S_ae is the area of agricultural space occupied by construction space (hm²); S_a is the agricultural space area (hm²); and S_e is the initial ecological space area (hm²).	+	0.316
	Spatial process conflict	Agricultural occupancy index	$E A=\frac{S_{e a}}{S_{e}}$, where EA is the agricultural occupancy index; S_ea is the area of ecological space occupied by agricultural space during 2000–2010 or 2010–2020 (hm²); and S_e is the initial ecological space area (hm²).	+	0.064
	Spatial structure conflict	Construction space fragmentation index	$I{{F}_{c}}=\frac{{{N}_{c}}}{S}$, where IF_c is the spatial fragmentation index of construction space; N_c 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 IF_a is the spatial fragmentation index of agricultural space; N_a 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 IF_e is the spatial fragmentation index of natural space; N_e is the number of natural space patches in the evaluation unit; and S is the total area of the evaluation unit (hm²).	+	0.090

Table 1

Table 2

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Table 4

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Table 5

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Table 6

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Factor setting	Variable	Definition
Natural environmental conditions	Mean slope	Average slope per study unit (%)
Natural environmental conditions	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

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

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)

Year	Index	LUCs	STC	SPC	SSC
2010	Moran’s I	0.623^***	0.587^***	0.614^***	0.513^***
2010	Z-value	23.070	20.902	22.386	18.879
2020	Moran’s I	0.616^***	0.637^***	0.462^***	0.556^***
2020	Z-value	22.223	22.039	16.418	19.798

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