Measurement and Scenario Simulation of Territorial Space Conflicts Under the Orientation of Carbon Neutrality in Jiangsu Province, China

Abstract

Measuring and simulating territorial space conflicts (TSCs) for the achievement of carbon neutrality is of critical significance for formulating regional sustainable utilization of territorial resources that are inherently green and low-carbon. This study develops a TSC evaluation framework: “conflict identification–scenario simulation–carbon effect assessment”. Focusing on Jiangsu Province, we clarify the evolutionary mechanism of TSCs under carbon neutrality goals, providing a scientific basis for high-quality regional development and low-carbon spatial governance. Results show that Jiangsu’s average TSC level was categorized as “strong conflict” (0.66) during 2005–2020. For 2030, four scenarios (natural development, economic priority, ecological protection, low-carbon development) project TSCs shifting from scattered to point-like distribution, concentrating in key core areas. Corresponding projected average carbon neutrality indices are 1.10, 1.11, 1.33, and 1.11, respectively. Under the low-carbon scenario, grid units with serious TSCs decreased by 4.53% compared to 2020—higher than natural development and economic priority scenarios, but lower than the ecological protection scenario (12.45%). Consequently, the low-carbon development scenario can optimally mitigate land use conflicts while maintaining carbon balance. This research provides robust data support for Jiangsu’s sustainable coordinated development and informs efficient land use and regional ecological security.

1. Introduction

Global climate change and carbon emissions have become one of the most critical challenges in sustainable development in the 21st century [1]. In response to this challenge, China officially declared the “Dual Carbon” objectives in 2020, aiming to reach the peak of carbon emissions by 2030 and achieve carbon neutrality by 2060. This commitment indicates a fundamental shift in the national development strategy towards a green and low-carbon transformation [2]. In this context, territorial space, as the primary connection between human activities and natural ecosystems, has seen the optimization of its structure and the coordination of its functions emerge as key approaches for achieving carbon neutrality [3]. TSCs refers to the spatial incoordination arising from resource competition and contradictions between ecological conservation and economic development among different land use types, functions, and stakeholders [4]. Such conflicts not only jeopardize the sustainable utilization of land resources but may also reduce the carbon sequestration capacity of ecosystems and potentially exacerbate carbon emissions [5]. Consequently, identification of TSCs and the simulation of carbon reduction effects under diverse development scenarios are of great significance for sustainable economic development, the improvement of land use efficiency, and the attainment of carbon neutrality in support of SDGs 8 and 15.

Early studies on TSCs have transitioned from concentrating on conflicts among land-use stakeholders to those between humans and the environment [6], indicating that intense human development activities and fierce competition for spatial resources have led to apparent TSCs [7,8]. In recent years, in tandem with the global pursuit of carbon neutrality objectives, scholars commonly hold the view that carbon balance serves as a crucial regulatory mechanism for addressing the issue of territorial space [9]. Existing studies mainly focus on the identification and assessment of territorial spatial conflicts under carbon emission constraints [10,11], the driving mechanisms of territorial spatial conflicts within the context of carbon neutrality [12,13], and the scenario simulation and regulation of territorial spatial conflicts are conducted as well [14,15]. Despite the remarkable advancements in the research on territorial spatial conflict and carbon neutrality, three key gaps remain. First, the majority of studies address territorial spatial conflict and carbon neutrality goals independently, lacking an integrated analytical framework that integrates conflict identification with the spatial dynamics of carbon sinks and sources [16]. Second, the quantitative indicators for spatial conflict are relatively simplistic, frequently relying on the area ratios of land use types or landscape metrics, which are insufficient to capture the subtleties of “spatial relationships” and “functional synergies” [17]. In addition, relevant research in this area remains scarce [18]. Third, scenario simulation studies primarily concentrate on single dimensions, such as urban sprawl or ecological preservation [19]. Distinct from prior research overly emphasizing the antagonistic relationship between economic development and ecological protection, this study highlights a low-carbon development model of economic and ecological symbiosis. This work will undoubtedly yield new scientific questions, namely, what is the relationship between territorial space utilization and low-carbon development; and how to achieve a dual victory in “conflict alleviation” and “carbon sequestration improvement” through spatial optimization?

To address these research gaps, this study presents an integrated territorial spatial analysis framework that encompasses “conflict identification–scenario simulation–carbon effect assessment,” this research endeavors to systematically delineate the spatiotemporal differentiation characteristics of territorial spatial conflicts, simulate their evolutionary trajectories under diverse development strategies, and evaluate their potential impacts on carbon balance. This study contributes significantly to the field through its exploration of the drivers behind territorial spatial conflicts in the pursuit of carbon neutrality, and providing a framework for sustainable regional development and low-carbon spatial governance. Practically, the findings provide theoretical support and actionable guidance for territorial spatial optimization and carbon neutrality pathways in Jiangsu Province and analogous regions.

2. Materials and Methods

2.1. Study Area

Jiangsu Province is located in the central coastal area of China, spanning the lower reaches of the Yangtze River and the Huai River. It has a total land area of 107,200 Km², and water bodies constitute 16.9% of its total territory. The terrain is mainly flat, composed of plains, water bodies, and low hills. Jiangsu is characterized by a high-density population and a high level of urbanization. In 2020, its permanent resident population reached 84.748 million, with a population density of 792 persons per square kilometer and an urbanization rate of 73.4%. This has facilitated the formation of a well-coordinated, high-density urban network consisting of large, medium, and small cities, which is gradually integrating into Yangtze River Delta urban agglomeration. In the same year, the provincial GDP reached 10.27 trillion yuan, with a per capita GDP of 121,200 yuan, ranking first among all provincial-level administrative regions in China.

As a developed coastal province in eastern China, Jiangsu has witnessed rapid urbanization and intensive land utilization, resulting in conspicuous conflicts among ecological, urban, and agricultural spaces. A crucial challenge for Jiangsu, and for China at large, in attaining carbon neutrality lies in how to optimize territorial space to augment carbon sequestration and mitigate carbon source intensity while maintaining economic growth. In recent years, Jiangsu has clearly embraced the strategic tenet of “ecological priority and green development” in its territorial spatial planning. Nevertheless, conflicts over territorial space endure amidst rapid urbanization and industrialization. These conflicts not only endanger regional ecological security and carbon sink functions but also impede the modernization of territorial spatial governance systems. As an economically developed province marked by high carbon emissions and severe spatial conflicts, Jiangsu represents a pivotal case for researching the identification and scenario simulation of territorial spatial conflicts within the context of carbon neutrality, presenting substantial theoretical and practical implications. Figure 1 presents the study area.

2.2. Data Source and Processing

The dataset employed in this study is primarily divided into two categories: spatial data (encompassing meteorological data, land use data, topographic data, cultivated land quality data, nighttime remote-sensing imagery, and transportation road data) and statistical/textual data (such as the Jiangsu Statistical Yearbook (2001–2020) and various specialized plans. The sources and details are presented in

Table 1. Data types and sources.

Data Type	Data	Data Sources
Land use data	Land use data	Chinese Academy of Sciences Resource and Environmental Sciences and Data Center (https://www.resdc.cn/Default.aspx)
Socioeconomic factors	Density of population	Chinese Academy of Sciences Resource and Environmental Sciences and Data Center
	GDP
	Night lighting	Harvard dataverse (https://data.harvard.edu/dataverse (accessed on 6 January 2026))
Distance factor	Distance from the highway	Open Street Map (https://osgeo.cn/map/)
	Distance from the first-class road
	Distance from the secondary road
	Distance to the river
	Distance from the railway
	Distance from the city government	National Basic Geographic Information Center (https://www.ngcc.cn/)
	Distance from the county government
Natural factor	Soil erosion	Chinese Academy of Sciences Resource and Environmental Sciences and Data Center
	DEM
	Aspect	Generated by DEM
	Slope gradient
	Annual mean temperature	National Earth System Science Data Center
	Annual precipitation

). Specifically, land use data is classified into six categories according to the research objectives: cropland, forest, grassland, water, build-up land, and unused land.

2.3. Methods

2.3.1. Carbon Emission Calculation

Cultivated land and construction land serve as the primary carbon sources associated with land use [20]. In this study, the carbon emissions from cultivated land were calculated by utilizing the direct carbon emission coefficient method. This method directly calculates carbon emissions by multiplying the area of a particular land use type by its corresponding carbon emission coefficient. Based on the actual situation of Jiangsu Province and referring to existing research [21], the carbon emission coefficient for cultivated land (E_crop) was ascertained to be 0.422 tons/ha. Regarding construction land, the indirect carbon emission coefficient method was adopted. Given the incomplete energy statistics in certain counties within the study area, and taking into account that the values of the secondary and tertiary industries mainly originate from construction land, the energy consumption per unit of GDP can more precisely reflect energy utilization. Therefore, the carbon emissions from construction land (E_build) in Jiangsu Province were indirectly calculated by using the values of the secondary and tertiary industries and the energy consumption per unit of GDP, referring to the processing methods of existing studies [22,23]. The calculation formula is as follows:

E_build = GDP_2,3 × H × K

In this equation, E_build represents the carbon emissions of construction land; GDP₂ and GDP₃ denote the output values of the secondary and tertiary industries respectively; H signifies the energy consumption per unit of GDP; and K stands for the standard coal equivalent coefficient. In this research, the total carbon emissions from cultivated land and construction land are regarded as the carbon emissions associated with territorial space utilization (E_crop + E_build).

2.3.2. Carbon Storage Assessment

The InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) model (v3.17.1) employs carbon density data and land use maps specific to each land use type to evaluate carbon storage in individual units. Its carbon storage and sequestration module assesses the current regional carbon storage and the projected carbon sequestration over a period by incorporating four carbon pools: above-ground biomass (C_above), below-ground biomass (C_below) [24], soil (C_soil) [25], and dead organic matter (C_dead), in accordance with the current land use patterns. The formula for calculating the total carbon sequestration in the carbon storage and sequestration services is presented as follows:

$${\mathrm{C}}_{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}}={\mathrm{C}}_{\mathrm{a}\mathrm{b}\mathrm{o}\mathrm{v}\mathrm{e}}+{\mathrm{C}}_{\mathrm{b}\mathrm{e}\mathrm{l}\mathrm{o}\mathrm{w}}+{\mathrm{C}}_{\mathrm{s}\mathrm{o}\mathrm{i}\mathrm{l}}+{\mathrm{C}}_{\mathrm{d}\mathrm{e}\mathrm{a}\mathrm{d}}$$

Owing to the scarcity of experimental data, extant studies generally compute the carbon sequestration of above-ground biomass, below-ground biomass, soil, and dead organic matter by utilizing unit coefficients. Subsequently, these computed values are multiplied by the area of each land use type to quantify the regional carbon sequestration per grid unit. As a result, the carbon density coefficients for diverse land use types in the study area mainly draw on previous research [26] (

Table 2. Carbon density settings for different land use types (tons/ha).

Land Use Type	${\mathit{C}}_{\mathit{a}\mathit{b}\mathit{o}\mathit{v}\mathit{e}}$	${\mathit{C}}_{\mathit{b}\mathit{e}\mathit{l}\mathit{o}\mathit{w}}$	${\mathit{C}}_{\mathit{s}\mathit{o}\mathit{i}\mathit{l}}$	${\mathit{C}}_{\mathit{d}\mathit{e}\mathit{a}\mathit{d}}$
Cropland	4.75	0.745	33.51	0
Forest	49.60	24.97	128.67	1.99
Grassland	20.38	15.59	48.29	18.74
Water	2.45	0.62	80.11	0.10
Build-up land	4.83	2.17	6.37	0.58
Unused land	1.83	0.01	11.53	0.01

). This approach was employed to evaluate the carbon storage and sequestration services in Jiangsu Province.

2.3.3. Carbon Neutrality Index

The Carbon Neutrality Index (CNI) assesses a region’s capability to achieve equilibrium between carbon sequestration and emissions. It is computed as the ratio of the region’s carbon uptake to its carbon emissions, and the formula is presented as follows:

$$\mathrm{C}\mathrm{N}\mathrm{I}={\displaystyle \frac{{\mathrm{C}\mathrm{A}}_{\mathrm{L}}/\mathrm{C}\mathrm{A}}{{\mathrm{E}}_{\mathrm{L}}/\mathrm{E}}}$$

CA_L and CA denote the carbon absorption of the L-th grid and the entire Jiangsu province respectively, while E_L and E represent the carbon emissions of the L-th grid and the entire Jiangsu province, respectively. A higher CNI value indicates a higher carbon-absorption ecological carrying coefficient, suggesting a stronger carbon sequestration capacity of the ecosystem. Conversely, a lower CNI value implies a weaker carbon sequestration capacity.

2.3.4. Spatial Conflict Measurement Model

Landscape ecology theory contends that alterations in land use intensity induced by human activities reverberate within ecosystems via the evolution of landscape [27] patterns. When natural factors or human interventions impinge upon terrestrial ecosystems, the spatial configuration of landscape patches undergoes corresponding adjustments, thereby influencing the spatial distribution characteristics of land use conflicts. The ecological risk level stands as a pivotal indicator for gauging the intensity of spatial land use conflicts. Human activities involved in the development and utilization of territorial space can induce alterations in the landscape pattern. Moreover, when land use conflicts are acute, the ecological risk level of the landscape is elevated [28]. Consequently, this study utilizes ecological risk levels to characterize the intensity of land-use conflicts [29]. Considering the three attributes of land systems, namely complexity, vulnerability, and stability, this study selects three indices: the spatial complexity index (CI), the spatial vulnerability index (FI), and the spatial stability index (SI) to assess the conflict levels within regional spatial units [30]. This model has been extensively applied in land use conflict research and has attained recognition [31].

To preclude the fragmentation of regional spatial units, a 5 km × 5 km grid was chosen as the evaluation unit after taking into consideration the research scope, scale, spatial resolution, spatial patch status, and data type. The spatial conflict composite index (SCCI) was expressed as

SCCI = CI + FI − SI

In this formula, CI represents the spatial complexity index, FI denotes the spatial vulnerability index, and SI signifies the spatial stability index.

(1)

Complexity Index (CI)

During the process of urbanization, land use patterns tend to become increasingly intricate. The Area-Weighted Mean Fragmentation Dimension (AWMPFD) index is employed to quantify the landscape complexity resulting from natural or anthropogenic factors. This index functions as a spatial complexity indicator (CI) for the identification of risk sources. A higher value of CI implies more irregular or convoluted boundaries, which reflects a greater vulnerability to external disturbances. The formula for CI is presented as follows:

$$\mathrm{C}\mathrm{I}=\mathrm{A}\mathrm{W}\mathrm{M}\mathrm{P}\mathrm{F}\mathrm{D}=\sum _{\mathrm{i}=1}^{\mathrm{m}}\sum _{\mathrm{j}=1}^{\mathrm{n}}\left[{\displaystyle \frac{2\ln \left(0.25{\mathrm{P}}_{\mathrm{i}\mathrm{j}}\right)}{\mathrm{L}\mathrm{n}{\mathrm{a}}_{\mathrm{i}\mathrm{j}}}}\left({\displaystyle \frac{{\mathrm{a}}_{\mathrm{i}\mathrm{j}}}{\mathrm{A}}}\right)\right]$$

where ${\mathrm{P}}_{\mathrm{i}\mathrm{j}}$ represents the perimeter of the j-th patch of the i-th land use type; $a_{ij}$ denotes the area of the j-th patch of the i-th land use type; A signifies the total area of the spatial unit; m represents the total number of patches in the grid; and n denotes the number of land use types.

(2)

Spatial Vulnerability Index (FI)

The Fragmentation Index (FI) predominantly reflects the capacity of landscape patches to withstand external disturbances, serving as a risk receptor. Lower values of FI signify a greater resistance to external disturbances and, in turn, a reduced intensity of conflicts. The FI is formulated as follows:

$$\mathrm{F}\mathrm{I}=\sum _{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{F}}_{\mathrm{i}}\times {\displaystyle \frac{{\mathrm{a}}_{\mathrm{i}}}{\mathrm{A}}}$$

In this formula, Fi represents the landscape vulnerability index for the i-th category, a_i represents the total area of the i-th land type, A is the total area of the spatial unit, and n indicates the quantity of land use types. Based on the land use alterations in the study area and referring to the coefficients from existing research [32], the vulnerability indices of unused land, forest, grassland, farmland, water bodies, and construction land are assigned values of 1, 2, 2, 3, 4, and 5, respectively.

(3)

Space Stability Index (SI)

Landscape stability serves to characterize the stability of individual landscapes, which is manifested in the evolution of landscape patches resulting from human activities and natural geographical processes. Patch Density (PD) is frequently employed to denote the level of regional landscape fragmentation, which represents the risk effect. A higher PD value implies a greater degree of landscape fragmentation, lower land stability, and more intense conflicts per unit area [33]. Drawing upon the prevalent methodologies adopted by scholars [34], this study utilizes the PD index to negatively characterize landscape stability. The SI is expressed as

SI = 1 − PD, PD = n_i/A

In this formula, n_i is the total number of all land use type patches within a spatial unit i, A is the area of the unit. SI is derived subsequent to the normalizing PD [29], which denotes the index of TSCs. A larger value of SI indicates a stronger conflict. Moreover, the study used ArcGIS 10.8 and applied equal interval classification to classify the TSCs into five class [14]: weak conflict (0–0.2), general conflict (0.2–0.4), moderate conflict (0.4–0.6), strong conflict (0.6–0.8), and serious conflict (0.8–1.0).

2.3.5. Markov-PLUS Model and Multi-Scenario Settings

The PLUS (Patch-generating Land Use Simulation, v1.0) model is a simulation framework designed to forecast future land use changes based on existing land use patterns. It commences by superimposing land use data from two time periods. Subsequently, it extracts image elements representing change states from the later dataset to demarcate the altered areas for each land use type. Employing the Random Forest algorithm, the model conducts an analysis of the relationships between land use types and multiple driving factors, thereby revealing their development potential [35]. This methodology dynamically integrates spatial factors with geographic units to simulate land use changes, resulting in high-resolution spatial distribution models. Using a 5-year interval, the PLUS model’s Markov Chain is applied to predict the land use demands under the natural growth scenario for the years 2030. By analyzing land use data from 2005, 2010, and 2015, the model generates expansion maps for the periods 2005–2010 and 2010–2015. These maps are then input into the LEAS module to compute the growth potential for each land use type [31]. The CARS module then integrates the 2015 land use data and potential values to simulate the 2020 land use patterns [15]. After validating the accuracy through comparison with the actual 2020 data, the model utilizes the 2020 data and the 2010–2020 potential values to predict the 2030 land use patterns (as shown in

Table 3. Land use demand forecast in Jiangsu province in 2030.

Type	Cropland	Forest	Grassland	Water	Build-Up Land	Unused Land
Natural development	6,084,879	294,214	129,669	1,537,924	2,260,946	9336
Ecological protection	6,176,749	297,818	131,173	1,539,105	2,162,763	9362
Economic priority	6,023,744	292,703	129,055	1,537,228	2,324,922	9318
Low-carbon development	6,146,222	296,395	130,632	1,541,126	2,193,237	9358

).

(1)

Factors Driving Land-Use Change

Land-use change is affected by socioeconomic, natural environmental, and transportation location factors. In this study, taking into account the basic development requirements and data availability of Jiangsu Province, 16 driving factors were identified to evaluate the land-use suitability probability in the study area (Table 1). Owing to policy or regional constraints, certain areas may be restricted from undergoing conversion. Each grid cell is assigned a value: 0 denotes prohibited land-type conversion, while 1 indicates permitted conversion.

(2)

Scenario Setting

Sustainable territorial development requires a holistic approach that balances socioeconomic progress, farmland preservation, and ecological conservation. Based on Jiangsu Province’s actual conditions and using 2030 (the target year for Jiangsu’s territorial spatial planning) as the simulation benchmark, four scenarios were established: (1) Natural Development Scenario (Nd): Based on the land use change patterns from 2005 to 2020, this scenario follows the principle of “everything remains unchanged,” where each land use type is set to evolve according to natural trends. Additionally, the Nd scenario serves as the foundation for other scenario simulations. (2) Economic Priority Scenario (Ed): Prioritizing maximum economic returns, this scenario accelerates urban-rural integration, expands urban infrastructure, and further increases urbanization rates. (3) Ecological Protection Scenario (Ep): Aiming to preserve critical ecological spaces and maximize environmental benefits, this scenario strengthens ecological land conservation and moderately controls urban-rural expansion. (4) Low-Carbon Development Scenario (Cd): Under macroeconomic constraints, this scenario maintains farmland integrity and protects key ecological zones while balancing economic and ecological goals. In GDP structure, it maintains primary industry growth, reduces secondary industry expansion, and boosts tertiary industry development, steering the national economy toward low-carbon, high-quality growth.

(3)

Transfer Matrix

The transition matrix delineates the conversion regulations among diverse land use types. The specific regulation is as follows: when a certain land type is eligible for conversion to another land type, the corresponding matrix value is designated as 1; when conversion is prohibited, the matrix value is set to 0 (

Table 4. Land Use Transition Options for Each Scenario.

2020–2030	Natural Development Scenario						Economic Priority Scenario						Ecological Protection Scenario						Low-Carbon Development Scenario
	a	b	c	d	e	f	a	b	c	d	e	f	a	b	c	d	e	f	a	b	c	d	e	f
a	1	1	1	1	1	1	1	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	0	1
b	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	0	0	0	0	1	1	0	0	0
c	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	0	0	0	1	1	1	1	0	1
d	0	0	0	1	0	0	0	0	0	1	1	0	0	1	1	1	0	0	0	0	0	1	0	0
e	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0
f	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	1

Note: a, b, c, d, e, and f denote cropland, forest, grassland, water, build-up land, and unused land, respectively. In the matrix, rows signify transfers out, whereas columns represent transfers in.

).

(4)

Domain weights

$$X^{*}={\displaystyle \frac{X-X_{min}}{X_{max}-X_{min}}}$$

In this formula, $X^{*}$ represents the standardized deviation value. $X$ denotes the change area of each land category between two periods of land use data; $X_{max}$ and $X_{min}$ represent the maximum and the minimum value of the area change among all land categories, respectively (

Table 5. Domain weights.

Type	Cropland	Forest	Grassland	Water	Build-Up Land	Unused Land
Natural development	0.01	0.56	0.69	0.83	1.00	0.61
Ecological protection	1.00	0.60	0.66	0.75	0.01	0.60
Economic priority	0.01	0.51	0.56	0.61	1.00	0.53
Low-carbon development	0.94	0.65	0.79	1.00	0.01	0.67

).

(5)

Accuracy Verification

The Kappa coefficient is a commonly employed approach for evaluating validation accuracy, and its calculation formula is as follows [36]:

$$Kappa={\displaystyle \frac{P_a-P_b}{1-P_b}}$$

In this formula, P_a represents the weight of the correctly simulated grids, whereas P_b denotes the pre-set weight of the correctly simulated grids. The value of 1 signifies the weight of the correctly simulated grids under ideal simulation circumstances. The Kappa coefficient ranges between 0 and 1, and higher values suggest greater simulation precision. To simulate the land conditions in 2020, the simulated outcomes were compared with the actual 2020 data by utilizing the Kappa coefficient, with a sampling rate of 5%.

3. Results

3.1. Characteristics of Territorial Spatial Change

3.1.1. Spatio-Temporal Variations in the Utilization of Territorial Space

Figure 2 indicates that Jiangsu Province witnessed the most substantial loss of arable land in 2005, 2010, 2015, and 2020, characterized by a uniform spatial distribution. From 2005 to 2010, extensive areas of farmland in southern and northern Jiangsu were transformed into construction land, leading to severe non-agriculturalization of arable land. Water bodies are primarily concentrated in southern and central Jiangsu, with minor distributions in northern Jiangsu. Construction land demonstrates a high degree of concentration in city centers, presenting a multi-point expansion trend.

To conduct a more in-depth analysis of the changes in territorial space, this study analyzed the land use proportions (

Table 6. Area Ratio of Territorial Space Types from 2005 to 2020/%.

Year	Cropland	Forest	Grassland	Water	Build-Up Land	Unused Land
2005	73.12	1.87	0.03	13.06	11.93	0.003
2010	70.99	1.81	0.04	12.96	14.19	0.003
2015	68.58	1.63	0.01	12.64	17.13	0.002
2020	68.27	1.57	0.00	11.62	18.53	0.001

). The areas of cropland, forest land, and water areas have been continuously decreasing, while the grassland area initially increased and then decreased. The area of construction land has shown continuous growth, with the highest growth rate of 21% occurring between 2010 and 2015. Furthermore, from 2015 to 2020, the cultivated land area decreased by 0.5%, in contrast to a 3% decrease from 2010 to 2015. The rate of cultivated land loss has witnessed a substantial decline. Remarkably, during the same time frame, the areas of grassland and water bodies continued to experience an escalating rate of reduction, suggesting that the additional construction land from 2015 to 2020 primarily originated from grassland and water areas. Owing to the stringent farmland protection policy in China, the area of farmland has shown a tendency towards stability. However, the rapid development of the social economy has occurred at the cost of ecological land, such as grassland and water areas.

3.1.2. Multi-Scenario Simulation of Territorial Space Utilization

Leveraging the 2005 baseline data and the PLUS model, we conducted a simulation of the land use layout within the study area under natural evolution scenarios in 2020 (Figure 3). Upon comparing the simulation results with the 2020 land use status, the Kappa coefficient was found to be 0.823, and the overall accuracy reached 0.898. A Kappa value greater than 0.8 implies satisfactory simulation performance and high precision, indicating that the model meets the requisite accuracy standards. Subsequently, based on the 2020 land use status map and the corresponding quantitative and criterion controls, this study further optimized the land spatial structure and layout of the study area under different development scenarios in 2030. The outcomes are presented in Figure 4 and

Table 7. Territorial spatial structure of Jiangsu Province under different scenarios from 2020 to 2030.

Land Use Type/104 ha	Status in 2020	Territorial Spatial Structure Under Different Scenarios in 2030
		Nd	Ed	Ep	Cd
Cropland	624.762	608.488	602.374	617.675	614.622
Forest	30.662	29.421	29.270	29.782	29.640
Grassland	10.634	12.628	12.668	12.884	12.766
Water	149.282	153.792	153.723	153.911	154.113
Build-up land	215.098	226.095	232.492	216.276	219.324
Unused land	1.258	1.273	1.170	1.169	1.233

Note: Nd, Ed, Ep and Cd represent the natural development scenario, the economic priority scenario, the ecological protection priority scenario and the low-carbon development scenario respectively.

.

The Nd scenario, grounded in the 2020 land use data of Jiangsu Province, simulates the structural layout by leveraging the development potential of various land use categories from 2005 to 2020. Under this natural scenario, it is projected that by 2030, the areas of cultivated land and forest land in Jiangsu Province will decline. The significant conversions among cultivated land, forest land, water bodies, and construction land are the primary forms of land transformation. Land use expansion is primarily driven by the growth of construction land, with the increase in water area playing a secondary role. After simulating the 2030 Nd scenario using the 2005–2020 development potential data, the land use structure undergoes adjustments, resulting in an increase in unused land compared to the situation in 2020. In comparison with 2020, the Nd scenario leads to a reduction of 162,741 hectares in the cultivated land area and an increase of 109,963 hectares in the construction land area.

Under the economic development priority scenario, where the maximization of economic benefits is the primary objective, the process of urbanization accelerates. This scenario is characterized by a sharp decrease in arable land and a substantial increase in land use reduction. Construction land is mainly sourced from farmland and forested areas. It exhibits a distinct trend of population concentration and clustered urban development, along with enhanced urban-rural connectivity, which facilitates future socio-economic development and exchanges. The arable land area in this scenario exceeds the grain security target of Jiangsu Province, ensuring regional food security while reserving sufficient space for agricultural production. However, this approach entails extensive deforestation, posing significant ecological risks. It is crucial to address the issue of achieving sustainable development that integrates social, economic, and ecological goals.

Additionality, ecological sources were first identified by integrating ecological importance and landscape connectivity, which were then taken as limiting factors [37], and four future scenarios were established for 2030 using the PLUS model. The ecological functional zones served as areas having restricted ecological conditions, and the four future scenarios were coupled into the corresponding functional zones to optimize the land-use structure in 2030. Under the natural evolution, economic benefit priority, and ecological benefit priority scenarios, the avoidance rates are 80%, 60%, and 100% respectively.

3.2. The Spatio-Temporal Evolution Characteristics of TSCs

The spatial conflict model was employed to evaluate territorial spatial conflicts in Jiangsu Province (

Table 8. Results of the Conflict Index of Territorial Space Utilization from 2005 to 2020.

Conflict Level	Threshold Interval	Space Unit Percentage/%
		2005	2010	2015	2020
Weak conflict	0–0.2	4.79	1.68	1.32	1.18
General conflict	0.2–0.4	7.01	3.38	5.31	5.34
Moderate conflict	0.4–0.6	29.00	15.75	12.99	11.76
Strong conflict	0.6–0.8	53.05	62.45	62.94	62.85
Serious conflict	0.8–1	6.15	16.73	17.44	18.87

). ArcGIS 10.8 was utilized to visualize the conflict levels within the study area. The mean conflict levels in 2005, 2010, 2015, and 2020 were 0.60, 0.68, 0.68, and 0.69 respectively, all of which were categorized as strong conflict levels. The proportions of grid units at the strong conflict level accounted for 53.05%, 62.45%, 62.94%, and 62.85% of the study area respectively. In 2005, moderate and strong conflict levels were predominant; subsequently, the proportion of strong conflict levels gradually increased. From 2010 to 2020, strong conflict levels remained the most widespread, while moderate conflict levels decreased. Compared with 2005, the quantity of grid units at the strong conflict level increased by 559 in 2020, whereas that at the weak conflict level decreased by only 160.

From 2005 to 2020, the proportion of general conflict grid units initially declined and then gradually ascended. The proportion of moderate conflict grid units exhibited a continuous downward trend, while that of strong conflict grid units rose steadily until 2015, reached its peak in 2015, and then declined. Notably, the proportion of strong conflict grid units in the study area increased rapidly before 2010 and then stabilized at a moderate rate, indicating serious spatial conflicts in the overall land use of Jiangsu Province. Evidently, taking 2010 as the inflection point, all five conflict trends have witnessed substantial alterations. Simultaneously, the growth rate of the proportion of seriously conflicting grid units has decelerated, suggesting that TSCs in Jiangsu Province have been effectively managed.

From 2005 to 2020, the intensity of spatial conflicts in southern Jiangsu Province (southern Suzhou), southeastern Jiangsu (southeastern Nantong), northwestern Jiangsu (eastern Yancheng), and northern Jiangsu (northern Lianyungang) was relatively low (Figure 5). Nevertheless, the central urban areas of Wuxi, Suzhou, and Changzhou, along with the southern part of Lianyungang, demonstrated higher degrees of territorial spatial conflicts. After the transition from general to strong conflict levels, the intensity gradually escalated, while the spatial units within weak conflict levels gradually contracted and the conflict-prone areas expanded. The primary cause of this phenomenon was the increasingly intricate urban land use and the intensified competition for land resources in recent years. Driven by the policy trend of the integrated development of multiple territorial spatial plans, spatial conflicts gradually became uncontrollable. Conversely, the peripheral areas of towns far from the central urban areas had a greater proportion of water bodies and farmland, with relatively less human interference and milder spatial conflicts. Notably, in terms of spatial distribution, the regions with intense territorial spatial conflicts in Jiangsu Province gradually shifted from southern Jiangsu to northern Jiangsu from 2005 to 2020, a change that was particularly conspicuous between 2005 and 2010. However, after 2015, the changes in seriously conflicted areas became less prominent, indicating that the intensity of territorial spatial conflicts in the study area had been effectively curbed, owing to the multiple policies proposed by China for the coordinated development of economic growth and ecological protection. Additionally, the land use conflict levels along the eastern coastline of Jiangsu Province were frequently high, with most areas classified as strong or serious conflicts, suggesting that Jiangsu Province might have encountered significant functional and structural conflicts in land use due to long-term maritime changes.

3.3. Multi-Scenario Simulation of TSCs

Under the scenarios of Nd, Ed, Ep, and Cd in 2030, the average conflict levels in territorial space were 0.66, 0.65, 0.66, and 0.67 respectively (

Table 9. Measurement results of the conflict index of territorial space utilization under different scenarios in 2030.

Conflict Level	Threshold Interval	Space Unit Percentage/%
		Nd	Ed	Ep	Cd
Weak conflict	0–0.2	4.42	4.49	5.10	1.56
General conflict	0.2–0.4	5.15	4.81	5.15	5.49
Moderate conflict	0.4–0.6	16.44	15.94	16.35	18.39
Strong conflict	0.6–0.8	55.95	54.43	56.63	54.66
Serious conflict	0.8–1	18.03	20.32	16.76	19.89

). In comparison with the situation in 2020, under the Nd scenario, construction land expanded steadily and uniformly around existing construction areas. The proportions of grid units with weak and moderate conflicts increased by 275% and 39.8% respectively. Conversely, the proportions of grid units with strong and serious conflicts decreased by 10.98% and 4.44% respectively, presenting a trend of “three decreases and two increases” across the five conflict levels. In contrast, the other three scenarios remained relatively stable. Under the economic development scenario, construction land expanded significantly with strong agglomeration effects. This increased the likelihood of the conversion of farmland, forest land, and grassland into construction land, thereby intensifying the conflicts in territorial space utilization. The number of grid units with serious conflicts in such scenario increased by 12.7% compared to the Nd scenario. In the Ep scenario, governments at all levels prioritized the ecological protection policies and strictly restricted the conversion of farmland, grassland, forest land, and water areas into construction land. This scenario exhibited the lowest proportion of grid units with serious conflicts, with the quantity of grid units decreased by 17.5% compared to the Ed scenario. The Cd scenario maintained a stable land use structure and conflict level by balancing economic growth and ecological protection, resulting in a moderate conflict level among all scenarios.

The weakly conflicted zones are predominantly situated in the northeastern and eastern parts of the study area (Figure 6), mainly in regions characterized by favorable ecological conditions and low land development intensity. These areas manifest relatively mild territorial spatial conflicts and a stable spatial distribution. In contrast to the period from 2005 to 2020, the territorial spatial conflicts in the northern and eastern coastal regions of Jiangsu Province have been significantly alleviated, with the conflict epicenter shifting to northern Jiangsu. The distribution pattern has evolved from a scattered one to a concentrated distribution centered around several core areas. This suggests that the overlap between natural environmental constraints (e.g., topography) and territorial spatial conflicts has diminished, while socio-economic development factors have become concentrated in certain core regions, resulting in intensified territorial spatial conflicts in these areas.

3.4. Evaluation of the CNI and the Effect of Spatial Conflict Resolution Under Different Scenarios

The carbon neutrality index under four scenarios (Figure 7) was computed and classified into five levels using the natural breakpoint method in ArcGIS 10.8. The analysis indicates that the low-value carbon neutrality zones are primarily concentrated in Nanjing, the provincial capital of Jiangsu, as well as economically developed cities in southern Jiangsu, such as Suzhou, Changzhou, and Wuxi. Xuzhou in northwestern Jiangsu also exhibits a cluster of low carbon neutrality indices. The high-value carbon neutrality zones under all four scenarios are mainly located in central Jiangsu and the north-central regions with a higher density of arable land. The average carbon neutrality indices for Jiangsu under the Nd, Ed, Ep, and Cd scenarios are 1.10, 1.11, 1.33, and 1.11, respectively. The Ep scenario shows the highest proportion of high-value zones (4th and 5th levels) at 11.75%, exceeding Nd scenario by 81.18%. The Ed scenario demonstrates the highest proportion of low-value zones (1st and 2nd levels) at 77.12%, while the lowest proportion of high-value zones is 6.42%. The Cd scenario has a proportion of low-value zones at 77.01%, with the proportion of high-value zones at 6.44%.

Through a comparison of the intensity of TSCs and CNI across four scenarios in 2030, this study evaluates the efficacy of conflict resolution in diverse land use approaches. In Nd scenario, the total carbon emissions in Jiangsu Province reached 242,369,023,325.18 tons, with carbon storage amounting to 521,675,665.742 tons. This scenario exhibited a 4.33% reduction in the levels of serious spatial conflict compared to the situation in 2020. In Ed scenario, the total emissions were 320,968,867,309.5010 tons, and the carbon storage was 519,624,364.677 tons. Serious spatial conflicts accounted for 20.32% of the grid units. In Ep scenario, emissions were recorded at 232,384,158,601.873 tons, and carbon storage was 525,348,154.311 tons, with serious conflicts representing 16.76% of the grid units. In Cd scenario, emissions soared to 269,051,583,823.694 tons, and carbon storage increased to 524,228,154.311 tons. Compared to Nd scenario, this entailed an increase of 266,825,604.98514 tons in emissions and a rise of 25,524,672.39 tons in carbon storage. The ratio of serious conflicts increased by 5.42% from the 2020 levels, surpassing both Nd and Ep scenarios, yet remaining lower than 7.7% in Ed scenario. The Cd scenario attains economic growth with higher levels of carbon neutrality and improved spatial utilization conflicts, demonstrating superior coordination effectiveness compared to other scenarios.

Overall, land use strategies under the ecological priority scenario and the Cd scenario can effectively mitigate the degree of TSCs in Jiangsu Province. Nevertheless, the ecological priority scenario may impose substantial constraints on traditional economic activities or infrastructure development, which is impractical for Jiangsu, a region with concentrated economic activities and population. In contrast, the Cd scenario effectively reduces carbon emissions and environmental pressure while balancing economic growth. By integrating ecological conservation with economic development, this approach enhances carbon neutrality capabilities and improves the living environment, rendering it the optimal option for the future coordination and resolution of territorial space utilization conflicts.

4. Discussion

4.1. Key Findings and Mechanism Analysis

This study systematically characterized the spatiotemporal evolutionary features of territorial spatial conflicts in Jiangsu Province under the carbon-neutrality objective and simulated the evolutionary trends of these conflicts under multiple scenarios using the Markov-PLUS model. Three key findings were derived from the analysis.

Firstly, the TSCs in Jiangsu Province remained at a relatively high level from 2005 to 2020. The average conflict index increased from 0.60 to 0.69, both values falling within the “strong conflict” category. Previous study has also verified that the overall territorial space conflict in Jiangsu Province is relatively prominent at the county-level scale [38]. Specifically, TSCs in regions experiencing rapid urbanization tend to be more pronounced. The proportion of grid units classified as “strong conflict” or “serious conflict” rose from 59.2% to 81.72%, indicating intensifying competition among urban construction land expansion, agricultural land protection, and ecological space conservation. This phenomenon is closely associated with Jiangsu’s high population density (792 people/km²) and rapid urbanization (73.4% urbanization rate in 2020). The continuous expansion of construction land has encroached upon large areas of cultivated land and ecological space, resulting in fragmented landscape patterns and increased spatial complexity.

Secondly, the spatial pattern of TSCs has undergone a significant transformation, with the conflict core gradually shifting from southern Jiangsu to northern Jiangsu. Southern Jiangsu, represented by cities such as Suzhou, Wuxi, and Changzhou, features high-level economic development and intensive land use. The early-stage rapid urbanization in this region triggered serious conflicts among construction land, cultivated land, and ecological land. However, the implementation of ecological protection policies (e.g., the Yangtze River Economic Belt Development Strategy) in recent years has effectively curbed the intensity of conflicts in southern Jiangsu. In contrast, northern Jiangsu, as a key area for industrial transfer and urban expansion, has witnessed a sharp increase in the demand for construction land, leading to the gradual migration of high-conflict areas to this region. Existing studies have also confirmed the changing characteristics of this conflict and provided explanations from the perspectives of the changes in population density and GDP [38]. Additionally, the coastal areas in eastern Jiangsu have long maintained high conflict levels, which can be attributed to functional overlaps and structural contradictions in land use during coastal zone development. Evidently, unlike previous studies that reported the continuous concentration of conflicts in the core urban areas [39], this study identified a shift in Jiangsu’s conflict core to northern Jiangsu. This discrepancy mainly stems from differences in regional development strategies. Jiangsu’s “Northern Development Strategy” has promoted the transfer of industries and populations to northern Jiangsu, altering the spatial pattern of conflicts.

Thirdly, different scenarios have distinct impacts on the evolution of territorial spatial conflicts and carbon balance. Among the four scenarios, the “ecological protection priority” scenario had the lowest proportion of “serious conflict” grid units (16.76%) and the highest average carbon neutrality index (1.33), indicating that enhanced ecological space protection can effectively alleviate spatial conflicts and improve carbon sequestration capacity [40]. Nevertheless, this scenario may restrict economic development to some extent. The “economic development priority” scenario, on the contrary, led to the most serious spatial conflicts (20.32% of grid units classified as “serious conflict”) and the lowest carbon-neutrality index (1.11). This is because the large-scale conversion of cultivated land and forest land to construction land not only intensifies spatial competition but also reduces the ecosystem’s carbon sequestration capacity. The Cd scenario achieved a balance between economic development and ecological protection, with a moderate conflict level (19.89% of “serious conflict” grid units) and a stable carbon-neutrality index (1.11). Undoubtedly, the Cd scenario has advantages in reducing the conversion probability of ecological and agricultural land to construction land while reserving appropriate space for economic development among the four scenarios, which emerges as the preferable approach for coordinating spatial conflicts and carbon-neutrality goals in Jiangsu Province.

Furthermore, regarding the scenario simulation results, Wu (2025) used the SD-CA model to simulate territorial spatial conflicts in Beijing and found that ecological protection scenarios can effectively mitigate conflicts, which is consistent with the conclusions of this study [41]. Notably, this study further integrated the carbon-neutrality index into the evaluation system, revealing that Ep scenarios not only reduce conflicts but also significantly enhance carbon sequestration capacity, thus enriching the research perspective on scenario effect evaluation. Surprisingly, unlike existing studies that focus more on single goals such as food security or ecological protection [42], this study found that the Cd scenario, which balances multiple goals, is more feasible in Jiangsu, an economically developed region. This difference arises from varying resource endowments and development stages. Jiangsu has a higher level of economic development and a stronger demand for coordinated growth, while the Yellow River Basin prioritizes food security and ecological restoration [43]. Additionally, this study integrated carbon emission calculation, InVEST model-based carbon storage assessment, and a spatial conflict measurement model to construct a comprehensive analytical framework of “carbon effect-spatial conflict-scenario simulation.” Compared with the single spatial conflict identification methods used in previous studies [44,45], this framework more comprehensively captures the interaction mechanism between territorial spatial conflicts and carbon balance, aligning with the research trend of integrating carbon neutrality goals into territorial spatial governance.

4.2. Main Application and Reflection of the Research Results

The findings of this study provide significant practical guidance for territorial spatial governance and the implementation of carbon neutrality in Jiangsu Province and other economically developed regions with similar characteristics. Firstly, in the aspect of spatial planning optimization, differentiated governance strategies should be formulated according to the spatial pattern of territorial spatial conflicts. For high-conflict areas in southern Jiangsu (e.g., Suzhou, Wuxi) and coastal regions, the emphasis should be placed on optimizing the existing land use structure, promoting the intensive utilization of construction land, and restoring ecological spaces (e.g., wetlands, forests) to enhance carbon sequestration capacity. For emerging high-conflict areas in northern Jiangsu (e.g., Lianyungang, Yancheng), it is necessary to strictly control the scale of construction land expansion. Meanwhile, ecological protection red lines and permanent basic farmland should be delineated to avoid replicating the high-intensity development model of southern Jiangsu.

Secondly, in policy formulation, it is advisable to establish a territorial spatial governance system oriented towards low-carbon development. On one hand, rigid constraints, such as the “dual control” of construction land expansion and carbon emissions, should be formulated, and the carbon-neutrality index should be incorporated into the evaluation system for territorial spatial planning. On the other hand, incentive policies should be introduced to encourage the conversion of low-efficiency construction land into ecological land and support carbon sequestration projects (e.g., afforestation, wetland restoration). The result provides a novel approach for a more in-depth exploration of the correlation between land use change and ecosystem service value [46].

Thirdly, in the context of regional coordinated development, efforts should be made to promote the optimal allocation of land resources across administrative boundaries. Jiangsu Province should strengthen the coordination of territorial spatial planning among southern, central, and northern regions, guide the rational transfer of industries from southern to northern Jiangsu, and avoid unregulated competition for construction land. Simultaneously, the carbon sequestration capacity of different regions should be integrated into the inter-regional ecological compensation mechanism, encouraging southern Jiangsu to provide ecological compensation to northern Jiangsu for preserving ecological space and promoting the coordinated development of the economy, society, and environment across the province.

4.3. Research Limitations and Future Directions

Nevertheless, this study exhibits several limitations that necessitate improvement in future research endeavors. Firstly, regarding the selection of indicators for spatial conflict measurement, this study constructed a conflict index based on spatial complexity, vulnerability, and stability. However, disparities in land use policies among different cities may result in variations in the evolution of spatial conflicts [47]; thus, it failed to comprehensively account for the influence of social factors such as the perspectives of social groups [48] and the intensity of policy implementation. The evaluation system needs to be more complete in future studies. Secondly, in the carbon balance assessment, the carbon emission calculations in this study predominantly focused on cultivated land and construction land, while overlooking emissions from other land use types (e.g., transportation land, industrial land). Moreover, the carbon density parameters were derived from existing studies, lacking field-measured data specific to Jiangsu Province, which may potentially affect the accuracy of carbon storage assessments. In subsequent research endeavors, it is imperative to obtain more precise greenhouse gas emission data [49] and broaden the accounting scope of carbon emission intensity. This will enhance the comprehensiveness and precision of carbon balance assessments. Thirdly, in scenario setting, this study considered four basic scenarios: Nd, Ed, Ep, and Cd. Nonetheless, it did not incorporate more refined scenarios [50] (e.g., different carbon emission reduction targets, policy combinations). For example, the impact of carbon tax policies and renewable energy development on the evolution of territorial spatial conflicts could be further explored [51]. Additionally, the current scenario simulations only project up to 2030. Future research could extend the simulation period to 2060 (China’s carbon neutrality target year) to better reflect the long-term evolutionary trends of spatial conflicts under the carbon neutrality goal. Fourth, in terms of the research scope, this study concentrated on the provincial scale of Jiangsu Province, lacking in-depth analysis at the county levels. The types and characteristics of territorial spatial conflicts may vary substantially across cities. Future research could conduct case studies on typical cities (e.g., Suzhou, Nanjing, Lianyungang) to propose more targeted governance strategies. Furthermore, comparative studies with other economically developed provinces (e.g., Guangdong, Zhejiang) could be carried out to explore the universal patterns and regional differences in the evolution of territorial spatial conflicts under the carbon neutrality target, providing more comprehensive theoretical support for national territorial spatial governance.

In conclusion, we will address the aforementioned limitations, further refine the research framework and methodologies, and deepen investigations into the coordination mechanisms between territorial spatial conflicts and carbon neutrality. We will also strengthen the integration of qualitative and quantitative research, combining policy analysis, field investigations, and model simulations to offer more scientific and operable decision-making support for regional sustainable development.

5. Conclusions

Unlike existing studies focusing predominantly on static identification and evaluation of TSCs, this research explores the influence mechanism of the evolution of TSCs on carbon neutrality under diverse scenarios. The research framework we constructed is highly suitable for revealing the evolution mechanism of regional spatial conflicts under carbon neutrality conditions and their impact on regional carbon balance. The findings showed that the average level of territorial space conflict in Jiangsu Province was categorized at a strong conflict level (0.66) during the period from 2005 to 2020. Under the four scenarios projected for 2030, namely Nd, Ed, Ep, and Cd, TSCs are anticipated to transition from a scattered distribution pattern to a more point-like pattern, converging around a few core areas. The projected average carbon neutrality indices of Jiangsu Province under these scenarios are 1.10, 1.11, 1.33, and 1.11 respectively. In the Cd scenario, the quantity of grid units with serious TSCs decreased by 4.53% compared to 2020. This reduction is greater than those under the Nd and Ep scenarios but smaller than that under the Ep scenario (12.45%). Surprisingly, the Cd scenario can most effectively mitigate land use conflicts while simultaneously maintaining carbon balance.

This study provides essential data support for the sustainable and coordinated development of Jiangsu Province, and holds substantial implications for the efficient utilization of land resources and regional ecological security. Furthermore, the proposed regulatory strategies offer references for the optimization of the territorial spatial layout in similar regions in China. Simultaneously, they also offer potential solutions for low-carbon governance in other countries or regions worldwide.