Spatial–Temporal Evolution and Driving Mechanism of Territorial Space Conflicts in Rapid Urbanization Areas from the Perspective of Suitability: An Empirical Study of Jinan City, China

Abstract

The precise identification of territorial space conflicts (TSCs) and their driving mechanisms is key to enhancing spatial security governance. Taking Jinan City as a case study, this research evaluates territorial space suitability across production, living, and ecological dimensions, proposes an empirical TSC identification model, and employs GeoDetector to analyze spatiotemporal evolution patterns and driving mechanisms. The results indicated that (1) from 2000 to 2020, significant spatial heterogeneity characterized the suitability of production–living–ecological spaces in Jinan City. High suitability zones of production and living space expanded in the northern plain along the Yellow River and central piedmont plain, respectively, while those of ecological space contracted in the southern mountainous and hilly areas. (2) Significant spatiotemporal variations in territorial space conflicts (TSCs) were observed in Jinan City over the past two decades. Intense conflicts dominated production–living and production–ecological space interactions, while moderate conflicts were prevalent in living–ecological and production–living–ecological space interactions. Production–living space conflict zones expanded, living–ecological space conflict zones contracted, and production–ecological and production–living–ecological space conflict zones showed consistent expansion trends. (3) The spatiotemporal evolution of territorial space conflicts is jointly driven by the natural environment, geographical location, social economy, and regional policies. The interaction of driving factors exhibited significant dual-factor and nonlineal enhancement effects. Finally, this study provides some scientific references for the comprehensive management and pattern optimization of territorial space in Jinan City.

1. Introduction

Since the 21st century, the rapid advancement of China’s ecological civilization, urbanization, and industrialization has significantly altered land use structures through the interaction and coupling of regional human and natural geographical environments. Consequently, there is enormous pressure and numerous challenges in ensuring the sustainable utilization of territorial space for production, living, and ecological functions [1,2,3,4]. In this new era of integrating ecological civilization into national land and space development, the coordinated development of production, living, and ecological spaces has been emphasized in both the 14th Five-Year Plan and the 20th National Congress of the Communist Party of China. However, effectively addressing functional spatial conflicts among these three aspects and achieving sound decision-making in territorial space planning has become an urgent issue that must be resolved [5,6]. Diagnosing and regulating territorial space conflicts, based on evaluating the production, living, and ecological functions of territorial space, serve as the foundation for optimizing the development and protection patterns of territorial space [4,7,8]. This approach also acts as a crucial lever in achieving sustainable development within urban social ecosystems. Therefore, investigating the spatial–temporal evolution and driving mechanisms of territorial space conflicts from the perspective of production–living–ecology suitability is essential for advancing high-quality regional development.

As a spatial carrier of human activities and natural resources, national land space is a complex system with multiple interacting elements nested at various scales, serving diverse functions, including production, living, and ecological functions [4,9]. The function of production, living, and ecological space provides the necessary material resources, living security, and ecological support for the development of human society. The limited supply and infinite demand for human social development are the root causes of space conflicts [8,10]. Space conflicts stem from land use conflicts and are closely related to them. However, space conflicts better reflect the relationships between people, between people and the land, and among different land areas. Additionally, the research objects in this context are more micro and comprehensive [11,12]. Territorial spatial conflicts can be understood as a geographical phenomenon resulting from the spillover of spatial resources and their functions in the interaction between humans and the environment. Their essence lies in the external conflicts arising from competition among multiple actors over environmental resources [13,14].

Changes in production, living, and ecological space conflicts in rapidly urbanized areas reflect the evolving interactions and relational dynamics between human systems and the natural environment, providing a novel perspective for integrated research on coupled human–environment systems [7,15,16]. The academic community is currently investigating conflicts of production, living, and ecological space from diverse perspectives, methods, objects, and regions, with a primary focus on four key aspects. (1) Research on the identification and diagnosis of spatial conflict. Presently, there are two main types of conflict identification methods in the academic community. One is the comprehensive index method, which primarily employs the Pressure–State–Response (PSR) model, the Pythagorean Fuzzy Conflict Information (PFCI) system, and the landscape pattern ecological index to construct a spatial conflict comprehensive index model to reflect the intensity of conflicts [17,18,19,20]. The second is the multi-objective evaluation method, which develops a conflict evaluation system consisting of three modules: natural, social, and policy [20,21,22]. Spatial conflict identification is widely regarded as an essential method due to its efficacy in accurately pinpointing the locations of conflicts. Notably, analytical techniques such as F-H analysis, actor analysis, and ecosystem services assessments have demonstrated positive outcomes in spatial conflict identification research [23,24,25]. (2) Research on spatial pattern differences in spatial conflict: At the macro, meso, and micro scales, researchers have primarily investigated the spatial–temporal patterns and distribution disparities of existing or potential spatial conflicts across various geographic levels, including national, provincial, municipal, county, and specialized regions. This exploration has employed methodologies such as PSR models, landscape pattern analysis, and suitability evaluations, alongside techniques like PLUS, FLUS, and CLUE-S [26,27,28,29,30]. (3) Influencing factors and mechanisms of spatial conflict: Earlier analyses of conflicts predominantly focused on singular perspectives, such as land resource characteristics, systemic issues, and ownership. However, as research has advanced, the emphasis has gradually shifted toward understanding the multifaceted influences on the evolution of spatial conflicts, including natural conditions, demographic shifts, and economic activities [31,32,33,34]. (4) Mitigation and regulation of spatial conflict: Scholars have primarily approached the regulation of spatial conflict through strategies such as land space planning or utilization and land management practices. A significant focus has been on developing differentiated mediation strategies based on the intensity of the conflict [5,35]. Moreover, advancements in remote sensing (RS), geographic information systems (GIS), and artificial intelligence have positioned graph theory methods, spatial planning, and multi-scenario simulation as pivotal techniques for conflict mitigation [15,36,37]. Despite notable achievements in the current academic research on spatial conflicts, there remains a paucity of studies interpreting the connotations of territorial spatial conflict within the framework of urban social–ecological systems. Meanwhile, the theoretical system and analytical framework for understanding the spatial–temporal differentiation and driving mechanisms of territorial spatial function conflicts based on multifunctional evaluation are still relatively weak, and the evaluation indicators are relatively limited in their comprehensiveness and systematic nature [23,28].

Jinan City, located in the lower reaches of the Yellow River, serves as an important hub integrating production, living, and ecological functions, and represents a typical region experiencing rapid urbanization. However, the city faces the dual challenges of balancing ecological preservation with economic development. Rapid urbanization has led to excessive resource consumption and disordered spatial function utilization, contributing to the degradation of social–ecological systems. Therefore, it is imperative to scientifically identify the spatial–temporal patterns and driving mechanisms underlying territorial space conflicts in Jinan City to enable the comprehensive management and pattern optimization of territorial space. This study aims to (1) analyze the conceptual connotation of territorial space conflicts from the perspective of suitability; (2) evaluate the suitability of production, living, and ecological space to identify the types and intensity of territorial space, and their spatial distribution patterns; and (3) examine the effects of the diverse driving factors and driving mechanisms of spatial–temporal evolution in territorial space conflicts.

2. Study Area and Research Framework

2.1. Study Area

Jinan City is located in central Shandong Province, within the lower reaches of the Yellow River between latitudes 36°02′–37°54′ N and longitudes 116°21′–117°93′ E. Geographically, it is bordered by Mount Taishan to the south and traversed by the Yellow River to the north, occupying a transitional zone between the hilly terrain of central–southern Shandong Province and the expansive plains of the northwest (Figure 1). Jinan City presents a diverse and complex topographical profile, characterized by a distinct elevation gradient decreasing from south to north. Its landscape encompasses low mountains, rolling hills, mountain-fringed sloping plains, and extensive alluvial plains formed by Yellow River sedimentation. Elevation across the territory ranges widely from 1 to 970 m, with a north–south topographical relief of approximately 1100 m. Prominent peaks include Changchengling, Paomaling, Tizishan, and Heiniuzhai, which constitute the southern topographical barrier. Jinan experiences a warm temperate continental climate with distinct seasonal variations. The average annual sunshine duration is 2481.1 h, while mean annual temperature ranges from 13.5 °C to 15.5 °C. The annual accumulated temperature ≥ 10 °C reaches 4575.3 °C, supporting a frost-free period of approximately 230 days. Average annual precipitation varies between 650 mm and 700 mm. As of the end of 2023, Jinan City has a total population of 9.4371 million, with an urbanization rate of 74.31%. The regional GDP reaches 1.28 trillion yuan.

2.2. Research Framework

Territorial space is conceptualized as a social–ecological system (SES) formed by the deep integration of human activities and the natural environment. When establishing an evaluation system of territorial space suitability, it is essential to consider not only the resource attributes but also the diverse needs and roles of human societies in achieving functional suitability. Consequently, this study focuses on exploring the multifunctional suitability of territorial space as its primary objective. It proposes a theoretical framework for investigating conflicts of production, living, and ecological space through the logical flow of “conflict identification and diagnosis—spatial–temporal evolution—driving mechanism” (Figure 2).

3. Data Sources and Methods

3.1. Data Sources and Processing

The primary data utilized in this research include statistical data, land use and soil survey data, meteorological records, transportation data, Gaode Map points of interest (POI), remote sensing image, terrain data, and other geographic information layers for Jinan City (

Table 1. Data sources.

Type		From	Source and Description
Remote sensing data	Landsat-TM imaging	30 m	Geospatial data cloud (http://www.gscloud.cn) (accessed on 2 March 2025)
	Landsat-OLI imaging	15 m
Terrain data	DEM	30 m	The Resource and Environmental Science and Data Center (http://www.resdc.cn/) (accessed on 2 March 2025)
Meteorological data	Mean annual temperature, average annual precipitation	1 km	The China Meteorological Data Network (http://data.cma.gov.cn/) (accessed on 20 March 2024)
Environmental data	PM2.5	1 km	The National Data Center for Earth System Sciences (https://www.geodata.cn/main/) (accessed on 20 March 2024)
Soil data	Soil texture, soil organic carbon content	500 m	The Chinese dataset in the World Soil Database (http://www.cgiar-csi.org) (accessed on 20 May 2024)
Vegetation data	Rain erosion force factor	1 km	The European Soil Data Centre (https://esdac.jrc.ec.europa.eu/) (accessed on 1 April 2025)
	NDVI, NPP	250 m	The US National Geophysical Data Center (https://www.ngdc.noaa.gov/) (accessed on 20 April 2024)
Traffic data	Railway, expressway, national road, provincial road, county road, township road	-	Shandong Province basic geographic information database, network data vectoring
Water system data	River surface, rural settlements	-	1:1 million national basic geographic database
Amap POI	POI data	-	Amap API interface query and download (including hotels, hospitals, banks, gas stations, schools, libraries, bus stations, parks, amusement parks, and natural scenic spots)
Socioeconomic statistics	Population, labor force quantity, GDP, etc.	-	statistical yearbook of Jinan City in 2001–2021

). We employed ArcGIS 10.8 software to perform data preprocessing tasks, including geometric calibration, spatial masking, and format conversion, which enabled the integration of all datasets into a unified WGS1984-Albers coordinate system with a spatial resolution of 30 m × 30 m. Consequently, this process established a foundational geodatabase for evaluating the production, living, and ecological suitability of territorial space in Jinan City, providing essential data support for this study.

3.2. Conceptual Connotation of Territorial Space Conflicts

The suitability of production, living, and ecological space serves as a critical foundation for sustainable land resource utilization. Conflicts arising from these three suitability dimensions, rooted in territorial space and shaped by disparities in other resource endowments, demonstrate clear systemic attributes. Territorial space conflicts and their comprehensive management constitute a complex process influenced by multiple interconnected systems, including land resources, population dynamics, economic structures, social institutions, and ecological environments [21]. Furthermore, this framework can be viewed as a complex social–ecological system comprising diverse elements and multiple functions. It is a composite system characterized by exceptional comprehensiveness, interactivity, and complexity, arising from the mutual influences and interactions between the social subsystem (SOs) and economic subsystem (ESs)—encompassing human socioeconomic activities and their organizational methods—and the ecological subsystem, which includes water, soil, air, and organisms [38]. The developmental trajectory of social–ecological systems is governed by three interrelated subsystems, including the social subsystem, economic system, and natural ecological subsystem (NEs). Interactions within the social subsystems and economic subsystems exerting pressures on the natural ecological subsystem act as a crucial endogenous driving force in the multi-suitability conflicts of territorial space [39,40,41].

Based on the connotations of production–living–ecological space suitability and the structure of social–ecological systems, territorial space conflicts can be understood as the competition and mutually exclusive relationships among the production, living, and ecological functions of social–ecological systems caused by the multi-suitability of resources and environments within a specific region. This competition results in tangible contradictions, such as mismatched national land spatial patterns, structural imbalances, and chaotic land use, ultimately leading to spatial competition and rights conflicts between humans and land. The study identifies four types of territorial space conflicts (Figure 3), including production–living suitability conflicts (PLSCs), production–ecological suitability conflicts (PESCs), living–ecological suitability conflicts (LESCs), and production–living–ecology suitability conflicts (PLESCs). The formation of territorial space conflicts adheres to a logical progression of “source–catalysis–growth– conflict”. The multi-suitability of territorial space constitutes the fundamental source of conflict formation, while the interactions between social–ecological subsystems act as catalysts for conflict escalation, which provides a conducive environment for the proliferation of territorial space conflicts.

3.3. Methods

3.3.1. Construction of Suitability Evaluation Model for Territorial Space

(1)

Theoretical framework for suitability evaluation of production–living–ecological space

Territorial space constitutes a complex socio-ecological system integrating natural environment elements, human activities, and functional processes, serving as the fundamental spatial carrier supporting human survival and sustainable development. This multi-functionality arises from the intertwined evolution of natural and human systems, encompassing both the inherent characteristics of the land and its suitability for meeting human needs [7,9]. The sustainable development of territorial space is manifested through the synergistic interactions of diverse functional attributes. Among these, the production function plays a key role in optimizing the diverse functionalities of territorial space, aiming to meet human needs while unlocking the industrial and agricultural economic potential of land resources. The living function embodies the ultimate goal of human land use, aiming to create livable environments that optimize resource utilization while ensuring land stability and human security. As a fundamental prerequisite for sustaining production and livelihoods, the ecological function strives for harmonious coexistence between man and nature, reflecting the ecological service capacity of territorial space resources. The Jinan City section of the Yellow River, an above-ground river, exerts a profound influence on the urban socio-ecological system of Jinan City through its unique hydrological characteristics and geographical location. The suitability is the basis of its territorial space function. Therefore, the suitability evaluation of production–living–ecological space in Jinan City should be carried out from the aspects of natural environment, socioeconomy, and regional policies. The conceptual framework guiding the construction of the production–living–ecological space suitability evaluation index system is presented in Figure 4.

The suitability of production space can be characterized by indicators such as natural production potential, spatial location conditions, industrial and agricultural productivity, and land use policies. The living space suitability is defined by indicators reflecting regional livability, spatial location conditions, economic and social development, and regional protection policies. The ecological space suitability is assessed through indicators including landscape ecological foundation, spatial ecological context, land ecological potential, and ecological protection policies. Natural factors—such as soil organic matter content, soil texture, climate, terrain, water resources, and landscape ecology—constitute the fundamental determinants of human production and living activities, thereby shaping the underlying conditions for land use. Geographical location is intricately linked to variables including rivers, roads, urban settlements, and ecological resources, reflecting the spatial driving forces behind land utilization. Socioeconomic indicators are selected primarily based on their direct relevance to production, living, and ecological functions, or their capacity to reflect the functional status of land. These encompass industrial and agricultural production, environmental governance, ecosystem functions, and economic development, effectively capturing the socioeconomic driving forces behind land use. Policies related to farmland protection, nature reserve planning, and agricultural subsidies are among the few quantifiable indicators that delineate the prioritization of land use in regional policies.

(2)

Evaluation index systems of production–living–ecological space suitability

Based on the theoretical framework outlined above, this study identified fifty-one key indicators related to production–living–ecological suitability, each exerting varying degrees of influence. The indicator selection process prioritized scientific validity, systematicity, rationality, and data availability. Guided by expert consultations in the field, the analytic hierarchy process (AHP) and YAAHP V12.13 software were employed to construct pairwise comparison matrices for the evaluation indices of production–living–ecological suitability. Consistency checks were performed on these judgment matrices to ensure reliability. The consistency test (CR value) results of production, living, and ecological space suitability are 0.031, 0.086, and 0.030, respectively. This shows that the analytic hierarchy process (AHP) is suitable for the weight determination. Subsequently, the importance of each indicator layer relative to the target layer was calculated, with the average weight assigned as the indicator weight for the corresponding layer.

The evaluation index system of production space suitability encompasses six dimensions, including land resources, spatial location, population size, economic scale, agricultural production, and agricultural policies (

Table 2. Evaluation index system and weight of production space suitability.

Standard Layer		Index Layer	Factor Classification and Score					Weight
			9	7	5	3	1
Natural environment	Land resources	LUP	Construction land	Cultivated land	Forest, grassland	Unused land	Water area	0.187
		SOCC	≥1.30	0.86–1.30	0.65–0.86	0.49–0.65	<0.49	0.052
		SST	Ball clay	–	Sandy soil	–	No soil	0.037
		TI	<0.28	0.28–0.55	0.55–0.82	0.82–1.11	>1.11	0.049
Geographical location	Spatial location	DFW/km	<1.2	1.2–2.5	2.5–4.0	4.0–6.0	>6.0	0.058
		DFCVR/km	<0.5	0.5–1.5	1.50–3.0	3.0–4.5	>4.5	0.041
		DFV/km	<0.5	0.5–1.0	1.0–1.5	1.5–2.1	>2.1	0.050
		CT/km	<0.15	0.15–0.5	0.5–1.0	1.0–2.2	>2.2	0.045
Socioeconomy	Population size	UR/%	<25	25–40	40–55	60–70	>70	0.079
		LR (person/km2)	>600	300–500	200–300	100–200	<100	0.040
	Economic scale	CAI/%	>10	7–10	4–7	2–4	<2	0.052
		GDP/100 million	>1200	900–1200	400–800	200–400	<200	0.075
	Agricultural production	TPAM/104 kW	>65	50–65	25–50	15–25	<15	0.049
		GS/t	>1	0.88–1	0.7–0.88	0.5–0.7	<0.5	0.083
Regional policy	Agricultural policy	AS/100 million	>1300	800–1300	250–800	100–250	<100	0.050
		BFPA	From 2000 to 2020, the stable cultivated land value is high suitability; otherwise, it is not appropriate					0.053

).

• Land resources determine the scope of land utilization, reflecting regional soil fertility, cultivated land quality, and overall production capacity. Therefore, the dynamic intensity of land conversion for industrial and agricultural production is characterized by land use type (LUP), soil organic carbon content (SOCC), surface soil texture (SST), and topographic index (TI) [26].
• Spatial location indicates the frequency and efficiency of information and material circulation between regions and proximity to essential production factors, such as water bodies, roads, and cultivated land facilitates industrial and agricultural production. To quantify this, we selected indicators including distance from water bodies (DFW), distance from county and township roads (DFCR), distance from villages (DFV), and cultivation distance (CT) [42,43].
• Population size is characterized by urbanization rate (UR) and labor resources (LR) [40]. Higher values in these metrics suggest a more substantial role for population in supporting employment and a stronger influence on production suitability.
• Economic scale and non-agricultural production relate to the impact of regional production efficiency on the industrial and agricultural economy, primarily evaluating levels of economic and social development as well as food security. This is characterized by four indicators, including comparative advantage index (CAI), gross domestic product (GDP), total power of agricultural machinery (TPAM), and grain supply (GS) [21,44].
• Agricultural policies are evaluated based on their effects on land use suitability, with agricultural subsidies (AS) and basic farmland protection areas (BFPAs) serving as critical indicators.

The evaluation index system of living space suitability encompasses eight core dimensions, including land resources, geological hazards, hydro-meteorological resources, spatial location, leisure environment, living convenience, economic development level, and ecological protection policies (

Table 3. Evaluation index system and weight of living space suitability.

Standard Layer		Index Layer	Factor Classification and Score					Weight
			9	7	5	3	1
Natural environment	Land resources	LUP	Construction land	Cultivated land	Unused land	Forest, grassland	Water area	0.111
		TI	<0.28	0.28–0.55	0.55–0.82	0.82–1.11	>1.11	0.048
	Geological hazards	BBC	≥250	180–250	120–180	60–120	<60	0.053
		GHR	≥0.35	0.35–0.46	0.46–0.54	0.54–0.62	<0.62	0.053
	Hydro- meteorological resource	AAT/°C	>13.7	13.3–13.7	12.8–13.3	12.3–12.8	<12.3	0.035
		TWR/104 m3	≥250	180–250	120–180	60–120	<60	0.053
Geographical location	Spatial location	DFYR/km	<5	5–9	9–12	12–15	>15	0.034
		DFR/km	<1.0	1.0–2.4	2.4–4.0	4.0–6.5	>6.5	0.054
		DFVT/km	<0.3	0.3–0.8	0.8–1.4	1.4–2.3	>2.3	0.065
Socio- economy	Leisure environment	PCGSA/m2	>15	13–15	11–13	9–11	<9	0.053
		TSFD	High	Higher	Medium	Lower	Low	0.035
	Living convenience	PD/(person·km2)	>609	235–609	81–235	22–81	<22	0.065
		RND	>0.9	0.6–0.9	0.3–0.6	0.1–0.3	<0.1	0.036
		PID	High	Higher	Medium	Lower	Low	0.081
		LCC	High	Higher	Medium	Lower	Low	0.069
	Economic development level	PGDP/yuan	>2.3	1.4–2.3	0.8–1.4	0.3–0.8	<0.3	0.076
Regional policy	Policies of ecological protection	FWPA	With 500 m as the bandwidth, outward buffer has 5 levels, respectively, assigned to 9, 7, 5, 3, 1, divided into core area, protected area, buffer area, edge area, and no protected area					0.055
		WSPA	According to the land use data from 2000 to 2020, it is divided into primary water source and secondary water source					0.045

).

• Land resources measure the suitability of land for residential and construction purposes, quantified using indicators such as land use planning (LUP) and topographic index (TI).
• Geological hazards assess their potential impact on land use planning and human settlement safety, represented by basic bearing capacity (BBC) and geological hazard risk (GHR) [2,15].
• Hydro-meteorological resources reflect the capacity of regional water availability, temperature, and humidity to support human habitation, evaluated through total water resources (TWRs) and annual average temperature (AAT) [7].
• Spatial location emphasizes the efficiency of material and information exchange among regions, measured by metrics such as distance from the Yellow River (DFYR), distance from roads (DFR), and distance from villages and towns (DFVT) [29,30].
• Leisure environment is a fundamental component of urban ecosystems related to the quality of urban living. It serves as a crucial indicator of human–land harmony and residents’ well-being, evaluated by per capita green space area (PCGSA) and tourism service facility density (TSFD).
• Living convenience refers to the spatial carrying capacity, accessibility of public services, and daily living security provided by urban infrastructure. This dimension includes indicators such as population density (PD), road network density (RND), public infrastructure density (PID), and living carrying capacity (LCC) [14,18,45].
• Economic development level reflects residents’ material living standards, typically represented by per capita gross domestic product (PGDP).
• Policies of ecological protection guide regional governmental efforts to convert land into ecological reserves or preserve existing ecological areas, measured by indicators including forest area, wetland park areas (FWP), and water source protection areas (WSPAs).

The evaluation index system of ecological space suitability comprises five aspects, including land resources, spatial location, environmental comfort, ecosystem service functions, and environmental protection policies (

Table 4. Evaluation index system and weight of ecological space suitability.

Standard Layer		Index Layer	Factor Classification and Score					Weight
			9	7	5	3	1
Natural environment	Land resources	LUP	Forest land	Grassland	Cultivated land, water area	Unused land	Construction land	0.174
		LS	>1.1	1.05–1.1	1.02–1.03	1.0–1.02	<1.0	0.051
		LF	<9	9–18	18–27	27–45	>45	0.056
		SES	<0.11	0.11–0.27	0.27–0.38	0.38–0.51	>40.51	0.045
Geographical location	Spatial location	DFW/km	<1.2	1.2–2.4	2.4–4.0	4.0–6.0	>6.0	0.042
		DFR/km	>6.5	4.5–6.5	2.4–4.0	1.0–2.4	<1.0	0.037
		DFCL/km	>3.0	2.0–3.0	1.0–2.0	0.5–1.0	<0.5	0.045
		DFES/km	<0.8	0.8–2.2	2.2–4.0	4.0–6.5	>6.5	0.048
Socioeconomy	Environmental comfort	MPM2.5	Low	Lower	Medium	Higher	High	0.045
		PCGSA/m2	>15	13–15	11–13	9–11	<9	0.056
		NDVI	>0.72	0.66–0.72	0.58–0.66	0.45–0.58	<0.45	0.036
	Ecosystem service functions	HQ	The model of InVEST 3.16.1 is employed to perform calculations based on the set parameters					0.076
		WY						0.056
		CS						0.065
		SC						0.066
Regional policy	Policies of ecological protection	EC	Ecological compensation will be implemented in the ecological engineering area of Jinan City. A value of 9 is assigned to areas with ecological compensation, while a value of 1 is assigned to areas without.					0.034
		ER	The nature reserve is buffered outward with a bandwidth of 500 m for 5 levels, which are assigned 9, 7, 5, 3, and 1, respectively, and divided into core area, protected area, buffer area, marginal area, and no protected area					0.068

).

• Land resources are characterized by indicators such as LUP, landscape stability (LS), landscape fragmentation (LF), and soil erosion sensitivity (SES). These metrics quantify the potential of ecological and environmental disturbances in the region [15,34], thereby reflecting the sensitivity of the local ecological environment.
• Spatial location is evaluated using metrics such as distance from water areas (DFW), distance from roads (DFR), distance from construction land (DFCL), and distance from ecological sources (DFES). These indicators assess how natural and anthropogenic spatial patterns influence land ecological suitability [12,13,17].
• Environmental comfort is represented by indicators including mean PM2.5 (MPM2.5), per capita green space area (PCGSA), and Normalized Difference Vegetation Index (NDVI). These metrics characterize the environmental conditions that shape livable habitats and support high-quality human settlement.
• Ecosystem service functions are evaluated by indicators such as habitat quality (HQ), water yield (WY), soil conservation (SC), and carbon storage (CS). These metrics assess the adaptive capacity and regulatory functions of terrestrial ecosystems, as well as the positive externalities derived from their structural and operational attributes [7].
• Policies of ecological protection are reflected in indicators such as ecological compensation (EC) and nature reserve (NR). These metrics indicate governmental commitments to environmental conservation and enhancement, ecological equilibrium maintenance, and regional ecological security assurance.

(3)

Scores of production–living–ecological space suitability

To directly reflect the suitability scores of production–living–ecological space in Jinan City, a weighted sum model is employed to calculate the scores for each evaluation unit. The formula is as follows [7]:

$$\left\{\begin{array}{l}FPi={\displaystyle \sum (w_{ij}^P\times f_{ij}^P)}\\ FLi={\displaystyle \sum (w_{ij}^L\times f_{ij}^L)}\\ FEi={\displaystyle \sum (w_{ij}^E\times f_{ij}^E)}\end{array}\right.$$

(1)

where F_Pi, F_Li, and F_Ei represent the score of the i-th evaluation unit’s production, living, and ecological space suitability, respectively. The larger the value is, the higher the suitability is. $f_{ij}^P$, $f_{ij}^L$, and $f_{ij}^E$ are the standardized values of the j-th suitability indicators for production, living, and ecological space in the i-th evaluation unit, respectively. $w_{ij}^P$, $w_{ij}^L$, and $w_{ij}^E$ are the weights of the j-th suitability evaluation index for production, living, and ecological space in the i-th evaluation unit, respectively. According to the suitability score of the evaluation unit, the natural breaks are used to divide the suitability of production, living, and ecological space into three grades such as high, moderate, and low suitability (

Table 5. Grades of production, living, and ecological space suitability in Jinan City.

Suitability Grade	Production Space Suitability	Living Space Suitability	Ecological Space Suitability
Low suitability	[0, 0.48]	(0.48, 0.69]	(0.69, 1]
Moderate suitability	[0, 0.36]	(0.36, 0.56]	(0.56, 1]
High suitability	[0, 0.37]	(0.37, 0.59]	(0.59, 1]

).

3.3.2. Identification and Diagnosis of Territorial Space Suitability Conflicts

This research builds on the evaluation results of production–living–ecology space suitability, drawing upon existing studies [7,11] to establish a multi-dimensional territorial space conflicts identification model. The model classifies territorial space conflicts into two types, including dual suitability conflicts (i.e., production–living suitability conflicts, production–ecological suitability conflicts, and living–ecological suitability conflicts; Figure 5a) and multi-suitability conflict (i.e., production–living–ecological suitability conflict; Figure 5b). The labels L, M, and H represent low, moderate, and high values of the evaluation results for production, living, and ecological space suitability, respectively. Utilizing ArcGIS 10.8 software, overlapping analysis was conducted to classify TSCs into five grades, including no conflict, mild conflict, moderate conflict, strong conflict, and violent conflict (Figure 5).

3.3.3. Identification of Driving Factors for Spatial–Temporal Evolution of TSCs

(1)

Driving factors of spatial–temporal evolution of TSCs

Considering the actual conditions of Jinan City and adhering to the principles of data availability, rationality, and comprehensiveness, 21 indicators across four dimensions, including natural environment, geographical location, socioeconomic factors, and regional policy, were selected as explanatory variables to analyze the driving mechanisms of Jinan City (

Table 6. Driving factors of spatial–temporal evolution of TSCs in Jinan City.

Driving Factors		Variables	Variable Interpretation (Units)
Natural environment factors	Climatic condition	Average annual temperature (AAT)	Average annual air temperature of the grid units (°C)
		Average annual precipitation (AAP)	Average annual precipitation of the grid units (mm)
	Vegetation condition	Vegetation index (VI)	Vegetation index of the grid cell
	Terrain condition	Elevation (ELE)	Average elevation of the grid cell (m)
		Slope (SLO)	Average slope of the grid cell (°)
Geographical location factors	Natural location	Distance from the river (DR)	Geometric center of the grid cell is the closest distance to the river (km)
	Traffic location	Distance from the road (DFR)	The geometric center of the grid unit is the nearest distance from the expressway, railway, national road, provincial road, and other main roads (km)
	Economic location	Distance from the county center (DFCC)	The shortest distance between the geometric center of the grid unit and the county center (km)
Socioeconomic factors	Population size	Population density (PD)	Total population/total land area, statistical yearbook data (person/km2)
		Urbanization level (UL)	Urban population/rural population, statistical yearbook data (%)
	Agricultural production	Land reclamation rate (LRR)	Farmland area/total land area, statistical yearbook data (%)
		Level of farming mechanization (LFM)	Using agricultural mechanization, statistical yearbook data (10,000 kW)
		Grain yield (GY)	Total amount of grain produced by cultivated land, statistical yearbook data (kg)
	Urban construction	Road network density (RND)	Road traffic mileage/total land area, statistical yearbook data (km/km2)
		Proportion of construction land (PCL)	Construction land/total land area, statistical yearbook data (%)
	Economic development	Proportion of secondary and tertiary industries (PSTI)	Output value of the second and third industries/GDP, statistical yearbook data (%)
		Average fixed assets investment (AFAI)	Investment in fixed assets of the whole society/total land area, statistical book data/(10,000 yuan/km2)
		Per capita GDP (PGDP)	Regional GDP/total population, statistical yearbook data (RMB)
		Per capita disposable income of rural residents (PDIR)	Per capita net income of rural residents, statistical yearbook data (yuan)
Regional policy factors		Cultivated land non-grain policy (CLGP)	The sum of the various protected areas in the county area (hm2)
		Basic farmland protection policy (BFPP)	The stable cultivated land value from 2000 to 2020 is 1, otherwise it is 0

).

(2)

GeoDetector model

The factor detector and interaction detector modules of the GeoDetector model are used to identify the driving factors influencing the spatial–temporal evolution of TSCs. The calculation formula is as follows [15,46].

$$q=1-{\displaystyle \frac{1}{n{\sigma }^2}}{\displaystyle {\sum }_{h=1}^L{n}_h\times {\sigma }_h^2}$$

(2)

In the formula, n and σ² denote the total number of units and variance of the sample, respectively; n_h and ${\sigma }_{\mathrm{h}}^2$ represent the number of units and variance within stratum h, respectively; and L refers to the total number of driving factors. The q-statistic quantifies the explanatory power of each driving factor, with a value range of [0, 1]. The larger q value indicates a stronger influence of driving factor X on the spatial–temporal evolution of TSCs. Specifically, q = 1 indicates that the spatial–temporal evolution of the TSCs is entirely determined by X, whereas q = 0 indicates no statistical association between X and the spatial–temporal evolution of the TSCs. Interaction detection further examines the combined effects of driving factors, identifying five interaction types, including nonlinear attenuation, single-factor nonlinear attenuation, dual-factor enhancement, independent, and nonlinear enhancement.

4. Results and Analysis

4.1. Spatial–Temporal Evolution of Territorial Space Suitability

Mathematical and statistical methods are employed to summarize the change characteristics of territorial space suitability areas in Jinan City from 2000 to 2020 (

Table 7. Area and proportion of territorial space suitability in Jinan City from 2000 to 2020.

Production Suitability	2000		2010		2020		2000–2020
	Area/km2	Proportion/%	Area/km2	Proportion/%	Area/km2	Proportion/%	Change Area/km2	Change Rate/%
Low	2587.21	25.31	2342.29	22.91	2878.53	28.16	291.32	11.26
Moderate	5362.44	52.46	3555.51	34.79	3429.42	33.54	−1933.02	−36.05
High	2272.70	22.23	4324.55	42.30	3914.40	38.30	1641.7	72.23
Living Suitability	2000		2010		2020		2000–2020
	Area/km2	Proportion/%	Area/km2	Proportion/%	Area/km2	Proportion/%	Change Area/km2	Change Rate/%
Low	2758.31	26.98	2552.81	24.97	2459.34	24.06	−298.96	−10.84
Moderate	5531.17	54.11	5484.58	53.66	5483.69	53.64	−47.48	−0.86
High	1932.88	18.91	2184.96	21.37	2279.32	22.30	346.45	17.92
Ecological Suitability	2000		2010		2020		2000–2020
	Area/km2	Proportion/%	Area/km2	Proportion/%	Area/km2	Proportion/%	Change Area/km2	Change Rate/%
Low	1069.02	10.46	1455.38	14.24	2582.65	25.26	1513.63	141.59
Moderate	5881.73	57.54	6065.46	59.34	5757.04	56.32	−124.70	−2.12
High	3271.60	32.00	2701.51	26.43	1882.67	18.42	−1388.93	−42.45

). As illustrated in Figure 5, significant differences exist in the spatial–temporal evolution patterns of production, living, and ecological space suitability in Jinan City. Notably, the proportion of low suitability areas for production space increased from 25.31% to 28.16% during 2000–2020, resulting in an area expansion of 291.32 km². Conversely, moderate suitability areas experienced a continuous decline, with an overall decrease of 1933.02 km², equating to a reduction of 36.05%. In contrast, high suitability areas increased by a factor of 0.72, with an average annual growth of 82.08 km². Specifically, low suitability areas of production space are clustered in a block-like spatial distribution in the central and southern parts of the study area, encompassing forest land, grassland, and other ecological land. The moderate suitability areas are primarily situated in the economically developed and highly urbanized central region. High suitability areas are concentrated in the northern plain along the Yellow River, where abundant production materials and labor resources contribute to robust production capacity. Overall, the spatial distribution of production space suitability in Jinan City exhibits a high concentration in the north and a low concentration in the south, revealing a pronounced spatial gradient and block features. Additionally, the spatial connectivity is robust, demonstrating clear continuity in spatial production activities, which aligns closely with the status of industrial and agricultural production as well as the density of the road network throughout the city.

From 2000 to 2020, there was a consistent decline in the areas characterized by low suitability and moderate suitability of living space, while regions with high living suitability experienced substantial growth. Specifically, low suitability areas decreased by 298.96 km², while moderate suitability areas diminished by 47.48 km². In contrast, high suitability areas consistently expanded over the past two decades, increasing by 346.45 km² and reaching a significant proportion of 17.92%. From a spatial distribution perspective, high suitability areas of living space are predominantly located within the central mountainous plain belt, which provides favorable opportunities for integrating commercial, medical, educational, and cultural activities. Moderate suitability areas are primarily found in the northern part of the research region, exhibiting a planar distribution pattern surrounding the high suitability areas in the central piedmont plain. Farmers in this area, motivated by the need for food and economic viability, are compelled to convert various land types into agricultural land to improve their livelihoods. Low suitability areas predominantly exist in the southern mountainous and hilly regions at the intersection of Licheng District, Zhangqiu County, and Laiwu District. Due to topographical and geomorphological constraints, these areas exhibit limited livability functions. Overall, the spatial distribution characteristics of living space suitability are high in the center piedmont plain and low in the surrounding areas, demonstrating a regular decreasing trend from the center to the outer edges. This spatial pattern bears resemblance to that of the production space suitability.

As illustrated in Figure 6g–i, the areas with low ecological suitability values have seen a remarkable increase from 2000 to 2020, with a growth factor of up to 1.42 times. This expansion predominantly occurred in densely populated urban regions within the central piedmont plain, including Lixia District, Shizhong District, Huaiyin District, Zhangqiu County, and surrounding counties and districts. The primary driver of this trend is the extensive growth of urban and rural residential areas, which have encroached upon arable land, forests, and grasslands, thereby compromising their ecological space suitability. Notably, the moderate suitability areas experienced a slight decline, with a reduction of 2.12%. These areas are primarily situated in the agricultural regions of the northern plain along the Yellow River, where the ecological service functions of farmland and water bodies are somewhat lower than those of forest and grassland vegetation, positioning them as an intermediary zone for ecological space suitability. Furthermore, high suitability areas have undergone a substantial reduction, decreasing by 1388.89 km², or 42.45%. These high suitability areas are primarily located in the southern hilly and mountainous parts of the study area, characterized by rich ecological land, such as mountain forests and grasslands, which exhibit strong ecological functions. Overall, the spatial distribution of ecological space suitability exhibits a distinct gradient pattern, with high values concentrated in the southern mountainous areas and low values dominating the northern plain along the Yellow River. This represents the exact opposite spatial distribution trend to that of production space suitability.

4.2. Spatial–Temporal Evolution of Territorial Space Conflicts

4.2.1. Spatial–Temporal Evolution of Territorial Space Dual Suitability Conflicts

By applying a three-dimensional suitability conflict recognition and diagnosis model to analyze the types and intensities of TSCs from 2000 to 2020, a significant disparity in the intensity of dual suitability conflicts is clearly evident (Figure 7).

The figure depicts notable spatial–temporal changes in the PLFC in Jinan City from 2000 to 2020. The regions experiencing violent and strong conflicts have progressively shifted from the central plain to the northern plain, while the moderate conflict zones have expanded westward, resulting in a decrease in areas with no conflict. In 2000, the PLFC was predominantly characterized by moderate and strong conflicts, accounting for 34.45% and 27.19% of the study area, respectively, and were mainly distributed in the central piedmont plain. By 2020, the extent of areas with strong and violent conflicts further increased, with increments of 0.25 and 0.68 times, respectively, becoming more concentrated in the northern plain along the Yellow River. Meanwhile, regions with no conflict and mild conflict are increasingly clustered in the southern mountainous and hilly areas of Changqing District and Laiwu District. It is clear that the northern plain area of Jinan City is rich in resources and exhibits a high capacity for economic development, characterized by a significant overlap between production and living functions, thus indicating potential risks associated with conflict.

From 2000 to 2020, the no-conflict areas within the PEFC framework in Jinan City were predominantly located in the central piedmont plain. In contrast, mild to moderate conflicts were sporadically distributed across the southern mountainous and hilly areas. Locations experiencing strong and violent conflicts generally remained stable, characterized by an encroachment of the economically developed central plain into the agriculturally advantageous northern plain. Remarkably, the no-conflict areas constituted a mere 0.77% of the total study area in 2000. Conversely, mild and moderate conflict areas dominated, covering 3501.49 km² and 3582.84 km², respectively. These regions exhibited a staggered distribution in the central piedmont plain due to stable industrial production activities and minimal environmental disturbances. By 2020, the zones of mild and violent conflict further retreated toward the northern plain along the Yellow River, decreasing by 247.48 km² and 282.37 km², respectively. In contrast, the strong conflict areas expanded, experiencing a growth rate of 64.38%. Influenced by population density and agricultural production activities, high-conflict spatial clusters emerged in Shanghe County and Zhangqiu District.

Throughout the same period, the area designated as mild conflict within the LEFC framework ranked first, primarily concentrated in regions with agricultural advantages. Following this, moderate and strong conflicts showed a tendency to expand outward from the central urban area, while violent and no-conflict areas ranked last, gradually retreating toward the southern mountainous regions. In 2000, the area of moderate conflict was 4210.42 km², largely situated in the agricultural lands of central towns and surrounding villages in the northern plain region. The no-conflict and violent conflict areas comprised the smallest proportions, accounting for 0.034% and 0.56% of the total area in Jinan City, respectively. By 2020, the mild conflict areas were primarily concentrated in mountainous regions and human habitation zones in the central and western parts of the city, characterized by lower ecological demands and reduced levels of conflict. The areas of moderate and strong conflict experienced reductions of 203.42 km² and 797.86 km², respectively, predominantly along both sides of the Yellow River and transportation routes. Due to the influence of the transportation infrastructure, nearby ecological spaces are at risk of transformation into production or residential areas, posing significant potential for conflict.

4.2.2. Spatial–Temporal Evolution of Territorial Space Multi-Suitability Conflicts

The spatial analysis module of ArcGIS 10.8 was utilized to integrate and classify 27 territorial space suitability levels, generating a spatial distribution map delineating multi-suitability territorial space conflicts in Jinan City (Figure 8).

According to Figure 8, moderate conflict areas of the PLESC framework predominated from 2000 to 2020. In contrast, mild and violent conflicts exhibited a consistent decline, gradually receding toward the southern mountainous and hilly areas. Conversely, areas of moderate and strong conflict increased by 6.48% and 4.84%, respectively, progressively expanding into urban built-up areas. Notably, in 2000, the area characterized by a mild conflict level was 2532.53 km², primarily located in the low mountains, hills, or significant water sources where land development and utilization are challenging or restricted, indicating a relatively weak overall level of conflict.

The regions experiencing significant conflict are predominantly situated in economically developed urban areas and residential zones in the northern plain along the Yellow River. Intensive human land development in these areas has led to elevated conflict levels. By 2020, there was a marked decrease of 24.80% in the proportion of areas with no conflict and a reduction of 13.55% in areas classified as experiencing mild conflict. Human production activities have exacerbated the degradation of ecological land, resulting in a notable reduction in its overall extent. Concurrently, areas experiencing moderate conflict have expanded to cover 6139.32 km², predominantly distributed across low-lying agricultural plains. This expansion manifests as a complex interplay of production, residential, and ecological land use. Areas designated as strong and violent conflict constitute 10.36% of the total, with a tendency to concentrate in the central piedmont plain of economically advanced counties. Overall, the distribution of the PLESC exhibits spatial differentiation, characterized by the patterns of low mountains, hilly terrains, central piedmont plain, and plain along the Yellow River. The spatial heterogeneity of conflict intensity underscores varying demands for resource endowments across different suitability classifications.

4.3. Driving Mechanism of Spatial–Temporal Evolution of Territorial Space Conflicts

4.3.1. Detection Result of Driving Factors

The detection results of the driving factors by GeoDetector reveal significant correlations between natural environment, geographical location, socioeconomic factors, and regional policies, all of which influence the spatial–temporal evolution of TSCs in Jinan City (

Table 8. Detection q-values of driving factors from 2000 to 2020.

Driving Factors		Index	2000		2010		2020
			q-Value	Rank	q-Value	Rank	q-Value	Rank
Natural environmental factors	Climatic condition	AAT	0.403 ***	3	0.295 **	3	0.237 ***	15
		AAP	0.144 ***	9	0.260 ***	10	0.390 ***	9
	Vegetation condition	VI	0.476 ***	1	0.446 ***	1	0.404 ***	7
	Terrain condition	ELE	0.435 ***	2	0.392 ***	2	0.309 ***	12
		SLO	0.166 ***	5	0.137 ***	16	0.095 ***	17
Geographical location factors	Natural location	DR	0.093 ***	19	0.113 **	18	0.047 ***	18
	Traffic location	DFR	0.029 *	21	0.113 ***	19	0.015 ***	21
	Economic location	DFCC	0.117 ***	16	0.105 ***	20	0.033 **	19
Socioeconomic factors	Population size	PD	0.154 ***	8	0.244 ***	11	0.025 ***	20
		UR	0.142 ***	11	0.217 ***	14	0.392 ***	8
	Agricultural production	LRR	0.143 ***	10	0.295 **	4	0.419 ***	5
		LFM	0.129 ***	13	0.279 ***	6	0.426 ***	4
		GY	0.123 ***	15	0.226 **	13	0.441 ***	1
	Urban construction	RND	0.097 **	18	0.053 ***	21	0.251 ***	14
		PCL	0.126 ***	14	0.265 ***	8	0.279 **	13
	Economic development	PSTI	0.133 ***	12	0.275 ***	7	0.432 ***	3
		PFAI	0.112 ***	17	0.114 ***	17	0.414 ***	6
		PGDP	0.077 ***	20	0.285 ***	5	0.319 ***	11
		PDIR	0.157 ***	7	0.229 ***	12	0.325 ***	10
Regional policy factors		CLGP	0.161 ***	6	0.165 ***	15	0.436 ***	2
		BFPP	0.239 ***	4	0.261 ***	9	0.181 ***	16

Note: ***, **, and * are significant at 0.01, 0.05, and 0.10 levels, respectively.

).

(1)

Natural environmental factors

The influence of natural environmental factors on TSFC in Jinan from 2000 to 2020 has been predominantly significant. From 2000 to 2010, ELE, SLO, and AAT had the most pronounced impact on TSCs. In 2000, their q-values were recorded at 0.476, 0.435, and 0.403, respectively. By 2010, these values had changed to 0.446, 0.392, and 0.295, respectively. The plain areas generally offer a favorable natural environment and socioeconomic context. However, human activities can significantly alter surface landscapes, contributing to urban heat island effects, which heighten the risk of conflict. By 2020, the impact of AAP on conflict had intensified, while the effects of ELE and VI on TSCs remained significant. Changes in climate and geomorphic conditions have substantially transformed land landscape patterns, exacerbating TSCs.

(2)

Geographical location factors

The explanatory power of the DFCC in 2000, 2010, and 2020 was 0.117, 0.105, and 0.033, respectively, indicating that proximity to road networks correlates positively with conflict intensity. Additionally, the explanatory power of the DFR was recorded at 0.029, 0.113, and 0.015 over the same years. As transportation infrastructure has improved in Jinan City, the density of the road network has expanded, resulting in an increased intensity of conflicts near these roads. Conversely, the q-value of the DR declined from 0.093 in 2000 to 0.047 in 2020, attributed to the degradation of riparian wetland ecosystems due to urban development and industrial activities along riverbanks. This environmental degradation has exerted a significant impact, although the implementation of ecological protection policies has subsequently mitigated this influence.

(3)

Socioeconomic factors

The explanatory power of GY and PSTI regarding functional conflict in Jinan City from 2000 to 2020 has shown a progressive increase. By 2020, their respective q-values reached 0.441 and 0.432, signifying that the intensification of demands for arable and industrial land has heightened the conflict between production and living functions. Furthermore, the level of economic development has also emerged as a contributing factor exacerbating territorial spatial function conflicts. In 2020, the explanatory power of PGDP, PDIR, and AFAI saw notable increases, with q-values recorded at 0.319, 0.325, and 0.414, respectively. The ongoing expansion of industrial facilities continues to encroach upon agricultural land in urban fringe areas, thereby intensifying conflicts between different land use functions. As society progresses, socioeconomic factors have increasingly become the primary driving force behind the spatial–temporal variations in TSCs, further amplifying their impact.

(4)

Regional policy factors

The explanatory power of the CLGP from 2000 to 2020 was 0.161, 0.165, and 0.436, respectively, ranking it second in terms of its driving force. This suggests that urbanization has accelerated the depletion of arable land, transformed land use structures, and, to some extent, influenced the severity of conflicts. Furthermore, the BFPP demonstrates significant explanatory power at confidence levels exceeding 1%, with q-values of 0.239, 0.261, and 0.181 for the respective years. In areas designated for basic farmland protection, the predominant land use types primarily consist of agricultural fields characterized by a relatively stable and homogeneous land use structure, resulting in increased conflicts between production and residential functions.

4.3.2. Interaction Detection of Driving Factors

Interactive detection was conducted to calculate q-values that quantify the relative influence of each driving factor. The results demonstrated distinct temporal variations in factor interaction patterns across the three periods, with interactions exhibiting either double-factor enhancement or nonlinear amplification effects (Figure 9).

In 2000, the impacts of ELE∩BFPP, ELE∩RND, and ELE∩PSTI on TSCs ranked among the top three, with explanatory powers of 0.554, 0.552, and 0.550, respectively. Conversely, the combinations of AFAI∩DR, PGDP∩AFAI, and PGDP∩DR ranked lowest, with respective impacts of 0.116, 0.120, and 0.120. This indicates that natural environmental factors and geographical location exert a significant driving force on the changes in the spatial–temporal evolution of TSCs in Jinan City, while the influence of the economic development level remains relatively weak.

By 2010, the explanatory power of ELE∩PD was the highest at 0.559, followed closely by ELE∩GY and ELE∩AAP, with explanatory powers of 0.558, 0.557, and 0.549, respectively. This suggests that the interaction between natural and socioeconomic factors synergistically enhances the spatial–temporal differentiation of the TSCs in Jinan City.

By 2020, ELE∩GY exhibited the strongest explanatory power, ranking first, indicating that terrain conditions and agricultural production were the primary driving factors behind the spatial–temporal evolution of the TSCs in Jinan City. Notably, the interactions among SLO∩GY, ELE∩PSTI, and ELE∩UL exhibited q-values of 0.607, 0.604, and 0.595, respectively, underscoring that the interplay between natural environments and socioeconomic factors collectively drives the spatial–temporal evolution of the TSCs. Additionally, the explanatory power of the PD∩BFPP interaction was the lowest, at only 0.197. Meanwhile, the driving forces of AAT, ELE, SLO, GY, UL, PSTI, and AFAI were relatively significant, and, with the ongoing development of the economy and society, the influence of non-agricultural production factors is rising.

4.3.3. Driving Mechanism of Spatial–Temporal Evolution for the TSCs

The spatial–temporal evolution of TSCs in Jinan City arises from the complex interactions of multiple factors, including natural environment, geographical location, socioeconomic conditions, and regional policies. These factors operate synergistically across temporal and spatial dimensions, collectively shaping the dynamic processes and heterogeneous patterns of TSCs (Figure 10).

The natural environment serves as the foundational determinant of the spatial–temporal evolution of TSCs. Attributes such as soil fertility, hydrological regimes, and climatic conditions continuously modulate the suitability of territorial spaces for production, living, and ecological space. Addressing emerging environmental challenges—including air pollution reduction, green space preservation, soil degradation mitigation, and water source conservation—is therefore a critical prerequisite for the comprehensive management of TSCs.

Geographic location significantly influences the spatial–temporal evolution of TSCs. Transportation networks, river systems, and topographic features contribute to environmental fragmentation, thereby exacerbating competition among diverse land use types. To mitigate such conflicts, future strategies should prioritize upgrading transportation infrastructure and optimizing water resource allocation systems, with the aim of reducing spatial friction and enhancing land use efficiency.

Socioeconomic development emerges as the primary driver of TSC evolution. Urbanization, industrial expansion, and population growth amplify the competition for land resources, particularly between productive and ecological space. Strategies aimed at increasing household incomes, promoting intensive land use, and expanding green employment opportunities can effectively mediate and reduce conflict intensity. Regional policies serve as macro-regulatory instruments to address spatial–temporal disparities in TSCs. By adjusting incentives for production, living, and ecological spaces (i.e., zoning regulations and ecological compensation mechanisms), regional policies can guide sustainable land use practices and mitigate conflict escalation.

Finally, achieving the sustainable development goals of Jinan City depends on the integrated management of these interacting drivers, which are characterized by intensive production spaces, livable residential areas, and resilient ecological environments. Balancing the trade-offs between competing land uses requires cross-sectoral coordination and adaptive governance strategies tailored to the city’s evolving spatial–temporal dynamics.

5. Discussion

5.1. Evolution Characteristics of Territorial Space Conflicts

Scientifically elucidating the mechanisms underlying the spatial–temporal evolution of the TSCs is not only essential for optimizing the pattern of territorial space but also serves as a foundational basis for resolving human–land conflicts. This research clarifies the conceptual framework of territorial space conflicts from the perspective of social ecosystems, which evaluates territorial space suitability across production, living, and ecological dimensions, proposes an empirical TSC identification model, and employs GeoDetector to analyze the spatial–temporal evolution patterns and driving mechanisms in Jinan City over the past two decades. The decline in the production and ecological space functions of Jinan City is primarily attributed to the extensive conversion of farmland for urban construction and industrial development [47,48]. In contrast, gradual improvements in urban transportation, energy, water conservancy, communication, and green infrastructure have driven a progressive enhancement of living functions, consistent with prior research findings [49].

At present, the process of urbanization has led to the imbalance of territorial space types and proportions, which has intensified the conflicts of territorial space, as evidenced by Chen et al. (2024) [47]. Specifically, the rapid expansion of urban construction space in the central piedmont plain has been continuously encroaching on cultivated land, where the conflict of production–living space expanded significantly. In contrast, the intensity of production–ecological space conflict and living–ecological space conflict has diminished, with high-intensity areas of these conflict types concentrated in the northern plain along the Yellow River and southern mountainous regions. Overall, the intensity of territorial space conflicts is consistent with the level of economic development, and the conflict intensity in the plain area is higher than that in the southern mountainous and hilly areas. This disparity may be attributable to the differences in economic development levels, geographical locations, and natural resource endowments between urban and rural areas, leading to significant spatial variations in conflict intensity across different regions [46,50]. Therefore, to mitigate the territorial space conflicts of the central piedmont plain and northern plain along the Yellow River, a multi-dimensional strategy integrating spatial governance, ecological restoration, and industrial optimization was proposed. The “Three Zones and Three Lines” control system was strictly enforced to alleviate the conflicts of production–living–ecological spaces. The implementation of the main functional area strategy ensured food security in the northern plain along the Yellow River. In addition, Jinan City, based on the ecological protection and high-quality development strategy of the Yellow River, promoted ecological restoration projects to implement ecological protection along both banks of the Yellow River, and strengthened the important ecological barrier of the southern mountainous areas.

5.2. Driving Factors of Territorial Space Conflicts Evolution

Territorial space encompasses both material elements and socio-ecological processes, with functional conflicts consistently driven by the interaction between the natural environment and human activities including natural condition, geographical location, socioeconomic dynamics, and regional policies (Figure 11). Our investigation reveals that the initial climatic and topographical conditions have significantly influenced the spatial–temporal differentiation of TSCs in Jinan City [21,40]. However, with societal development, the contributions of population size, agricultural productivity, and economic growth to the spatial–temporal evolution of TSCs in Jinan City have become increasingly pronounced, consistent with the findings of Cui et al. (2021) [50].

As urbanization progresses, territorial space conflicts have emerged as a pervasive and inevitable phenomenon [51]. Exploring the driving factors and driving mechanisms of the spatial–temporal evolution of TSCs is crucial for achieving the coordinated development of human–land relationships. The spatial–temporal evolution pattern of territorial space conflicts is shaped by a combination of natural and anthropogenic factors (Figure 11). Fundamentally, natural ecological components, including climate, vegetation, topography, and biodiversity, serve as the foundational basis for human activities [22,33,52]. Concurrently, anthropogenic factors, including population growth or migration, economic development, and urbanization, have significantly altered the terrestrial environment [6,8,47]. Consequently, human society has developed diverse demands for the production, habitation, and ecological functions of territorial space. Thus, the interplay and integration of the natural environment and human activities forms the basis of territorial space conflicts. Similarly, the production, habitation, and ecological functions of land provide essential resources, labor, and ecosystem services to human society [53,54,55]. Meanwhile, the human socioeconomic system exerts both positive and negative externalities on land development, utilization, and conservation. The reciprocal interaction between humans and land shapes the formation of territorial spaces. Therefore, when selecting indicators for the driving factors of the spatial–temporal differentiation of TSCs, it is essential to consider not only the beneficial effects of land resources on the economy and society but also the adverse consequences resulting from land use processes [4,56,57]. In fact, the factors influencing the spatial–temporal evolution of TSCs identified from the natural environment, geographical location, socioeconomic condition, and regional policy have been recognized by numerous scholars [14,19,20,49,57].

5.3. Limitations and Future Research Prospect

An index evaluation system was developed to assess the production, living, and ecological space suitability in Jinan City. Additionally, a multi-dimensional suitability conflict of territorial space identification model was employed to diagnose four distinct types of conflicts arising from production–living–ecological space suitability. The driving mechanisms of the spatial–temporal evolution of TSCs were further explored. This approach partially mitigated the limitations of a single measurement model in capturing the characteristics and intensity of territorial space conflicts, while elucidating the mechanisms of territorial space conflict evolution in watershed cities. With the increasing contradiction between human activities and land use, the identification of production–living–ecological space conflicts from the perspective of suitability has become a reliable and effective strategy with far-reaching implications. The research should provide scientific reference for the optimization and planning of the future territorial space pattern in Jinan City, and also offer technical support for promoting sustainable development in rapid urbanization regions. It should be noted that significant variations exist in the geographical conditions and suitability zoning in rapidly urbanizing areas. Further in-depth research is required to determine how to adopt different evaluation index systems based on regional differences and suitability types.

Despite the valuable insights generated, this research has notable limitations. Due to the constraints related to fundamental data and research methodologies, the quantification of key social security dimensions, such as education and healthcare, was ignored in the selection of suitability evaluation indicators. Therefore, future studies should prioritize compiling a comprehensive suite of socioeconomic, environmental, and policy indicators to enable rigorous assessment of the TSCs. To investigate the relationships between the spatial–temporal evolution of the TSCs and diverse natural, social, and policy factors, the GeoDetector model was employed. While the geographic detector effectively analyzes the spatial explanatory power of multiple factors, including natural environmental factors and human activities, determining the specific positive or negative impacts of each driving factor remains challenging. Future research should consider integrating methodologies such as random forests and structural equation modeling to enable a more nuanced analysis of the factors contributing to conflict generation.

6. Conclusions

Taking Jinan City as a case study, this research evaluates territorial space suitability across production, living, and ecological dimensions, proposes an empirical identification model of territorial space conflicts, and employs GeoDetector to analyze the spatial–temporal evolution patterns and their driving mechanisms. The main conclusions are as follows.

(1)

Significant spatial heterogeneity characterized the suitability of production–living– ecological space in Jinan City from 2000 to 2020. High suitability zones of production and living space expanded in the northern plain along the Yellow River and central piedmont plain, respectively, while high suitability zones of ecological space contracted in the southern mountainous and hilly areas.

(2)

Significant spatial–temporal variations in territorial space conflicts (TSCs) were observed in Jinan City over the past two decades. Intense conflicts dominated production–living and production–ecological space interactions, while moderate conflicts were prevalent in living–ecological and production–living–ecological space interactions. Production–living space conflict zones expanded in the northern plain along the Yellow River, while living–ecological space conflict zones contracted in the northern plain along the Yellow River and central piedmont plain. Moreover, production–ecological and production–living–ecological space conflict zones showed consistent expansion trends.

(3)

The spatial–temporal evolution of territorial space conflicts is jointly driven by natural environment, geographical location, social economy, and regional policies. The influence of natural environmental factors gradually weakened, while the driving force of socioeconomic factors significantly strengthened. The interaction of driving factors exhibited significant dual-factor and nonlinear enhancement effects.