Land Consolidation Potential Assessment by Using the Production–Living–Ecological Space Framework in the Guanzhong Plain, China

Abstract

Land consolidation (LC) is a sustainability-oriented policy tool designed to address land fragmentation, inefficient spatial organization, and ecological degradation in rural areas. This research proposes a Production–Living–Ecological (PLE) spatial utilization efficiency evaluation system, based on an integrated methodological framework combining Principal Component Analysis (PCA), Entropy Weight Method (EWM), Attribute-Weighting Method (AWM), Linear Weighted Sum Method (LWSM), Threshold-Verification Coefficient Method (TVCM), Jenks Natural Breaks (JNB) classification, and the Obstacle Degree Model (ODM). The framework is applied to Qian County, located in the Guanzhong Plain in Shaanxi Province. The results reveal three key findings: (1) PLE efficiency exhibits significant spatial heterogeneity. Production efficiency shows a spatial pattern characterized by high values in the central region that gradually decrease toward the surrounding areas. In contrast, the living efficiency demonstrates higher values in the eastern and western regions, while remaining relatively low in the central area. Moreover, ecological efficiency shows a marked advantage in the northern region, indicating a distinct south–north gradient. (2) Integrated efficiency consolidation potential zones present distinct spatial distributions. Preliminary consolidation zones are primarily located in the western region; priority zones are concentrated in the south; and intensive consolidation zones are clustered in the central and southeastern areas, with sporadic distributions in the west and north. (3) Five primary obstacle factors hinder land use efficiency: intensive utilization of production land (PC1), agricultural land reutilization intensity (PC2), livability of living spaces (PC4), ecological space security (PC7), and ecological space fragmentation (PC8). These findings provide theoretical insights and practical guidance for formulating tar-gated LC strategies, optimizing rural spatial structures, and advancing sustainable development in similar regions.

1. Introduction

Under the dual impetus of rapid urbanization and the rural revitalization strategy in China, optimizing the spatial structure of PLE spaces has become a core pathway to addressing urban–rural disparities and achieving sustainable rural transformation [1,2,3]. As a key grain production base, economic hub, and ecologically vulnerable area in Northwest China, the Guanzhong Plain faces severe land use challenges, including arable land fragmentation [4,5], soil salinization [6], ecological degradation [7], and the deterioration of human settlements [8]. In recent years, systemic risks, such as climate change, continuous population outmigration, and social instability, have increasingly interacted in complex and nonlinear ways within the context of the Anthropocene. These overlapping crises have significantly amplified the vulnerability of regional land systems by undermining the stability of agricultural production, weakening the regenerative capacity of ecosystems, and reducing the functional efficiency of rural living spaces. The ongoing challenges in the Guanzhong Plain represent a typical example of how Anthropocene-related crises unfold at the regional scale [9,10,11,12,13]. Although the Whole-Region Land Consolidation (WRLC) policy introduced under China’s Rural Revitalization Strategy is widely regarded as a key tool for addressing such challenges [14,15,16], the traditional approach centered on resource reallocation has proven insufficient in responding to the governance demands arising from systemic uncertainty. This context underscores the urgency of developing spatial consolidation pathways and a theoretical framework that possesses collaborative adaptation capabilities. Furthermore, both practical outcomes and research results indicate that existing WRLC initiatives have predominantly focused on eastern coastal or urban fringe areas, with a notable lack of targeted consolidation strategies for ecologically sensitive and agriculturally intensive regions, like the Guanzhong area, particularly in terms of the assessment of synergistic PLE functions and the development of differentiated zoning strategies.

A systematic review of international studies on the potential of LC reveals several key issues.

In terms of the research content, existing studies either focus on the evaluation and analysis of a single dimension, such as economic, ecological, or social benefits, or emphasize fragmented discussions of specific spatial units, such as ecological land [17], agricultural land [18], settlements, homesteads, or historical and cultural sites. These studies fail to establish a comprehensive analytical framework that integrates the material space for land use production, the social space for livelihood security, and the natural space for ecological conservation, thereby overlooking the interconnectedness of various spatial elements in LC. Currently, the established PLE evaluation system has preliminarily established a functional-oriented objective evaluation paradigm. However, it generally neglects the collaborative evaluation of economic, social, and ecological efficiency under the compound spatial functions. Moreover, there is a notable lack of systematic consideration of the subjective dimensions, such as farmers’ spatial perceptions and their demands for LC. This research tendency, which objectifies spatial governance subjects, undermines LC policies’ social acceptability and evaluative validity. At the same time, LC is often reduced to a technically oriented development project, failing to reflect its strategic value in addressing systemic risks and enhancing regional resilience. This limitation is particularly evident in the current context of spatial transformation and multi-scalar governance challenges [19]. From a methodological perspective, although the academic community has developed technical approaches based on “3S” technology [20,21] and models such as the Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) [22,23], several remain in practical application. First, the standard system for potential identification is fragmented due to the lack of systematic support from the theoretical framework of PLE space coordination, resulting in a high degree of subjectivity and arbitrariness in determining consolidation thresholds. Second, data collection primarily relies on static sources, such as the Second National Land Survey, leading to significant temporal and spatial lag in the assessment process and making it difficult to capture the dynamic evolution of rural space effectively. Finally, current research paradigms overly depend on spatial morphological interpretation techniques and fail to adequately incorporate farmers’ subjective spatial practices, resulting in an increasingly prominent contradiction between “technical rationality” and “social rationality”. In this context, it is essential to develop an evaluation framework with dynamic adaptability and integrative judgment, supported by multi-scalar theoretical perspectives that combine local knowledge with global governance logics.

In the field of zoning for LC, international scholarship has developed a value-oriented approach that focuses on optimizing ecosystem services and promoting sustainable land use. This approach emphasizes the exploration of zoning logic and methods under a functionalist paradigm. In terms of zoning logic, various indicator systems have been developed, including location potential indicators, hierarchical ecological restoration indicators [24], multi-scale land use evaluation indicators [25], and suitability assessment systems for LC [26]. These frameworks aim to categorize areas for consolidation according to functional typologies and consolidation sequences. However, they lack a problem-oriented zoning framework based on spatial conflict diagnosis, leading to a structural misalignment between “spatial governance demands” and “consolidation path supply”. Methodologically, there is a notable preference for technical rationality, with the widespread use of quantitative tools, such as spatial clustering algorithms [27], machine learning models [28], Self-Organizing Map (SOM) classification networks, Minimum Cumulative Resistance (MCR) models [29], and indicator classification methods [30]. However, these approaches often overlook the collaborative application of sequencing and Obstacle Degree Models for LC, resulting in a misalignment between zoning schemes and the practical demands of consolidation stakeholders. Moreover, the research perspective tends to overfocus on the reconstruction of material spatial forms and fails to integrate social construction elements, such as farmers’ spatial rights, into the zoning standards. This exposes the absence of the subject in spatial governance, as it fails to incorporate the consolidation demands of farmers, the primary stakeholders. Additionally, there is an overreliance on static indicators and technically rational pathways, which overlook the resilience and temporal adjustment mechanisms necessary to address typical “Anthropocene” crises, such as climate disturbances and population decline [31].

The PLE spatial framework, a key component of China’s territorial spatial planning, originates from the “three-functional space” classification system. It emphasizes the coordinated pursuit of three core objectives: supporting agricultural and economic development through production space, improving quality of life through living space, and maintaining ecological functions through ecological space. Widely applied in spatial planning and the “integration of multiple plans” policy practice, this framework has become an important reference for evaluating land use efficiency and LC potential. Compared to traditional, single-function-oriented approaches, the PLE framework inherently integrates agricultural production, human well-being, and ecological restoration, providing a collaborative foundation. This framework offers a spatial governance model with embedded resilience and multi-objective adaptation capabilities to address the systemic challenges in the Anthropocene. The functional synergy emphasized by the PLE framework aligns with the current governance needs of regions characterized by complex vulnerabilities [32]. Meanwhile, international theories, such as multifunctional land use [33], land system science [34], and spatial governance [35], also stress the coordinated configuration of ecological, economic, and social functions, aligning closely with the conceptual foundation and value orientation of the PLE approach. Therefore, this study integrates the PLE framework with international theories on functional synergy in land use, aiming to construct a comprehensive and operational evaluation system for identifying LC potential and zoning strategies. This provides a theoretical basis for optimizing rural spatial systems.

Existing assessments of LC potential often focus on either economic or ecological dimensions, lacking an integrated consideration of PLE functions. Moreover, they tend to overlook farmers’ subjective perceptions and preferences, limiting the effectiveness of targeted and differentiated LC strategies. To address these gaps, this study selects Qian County, a typical agricultural area in Northwest China, as the research site. At the administrative village scale, we integrate multi-source and time-sensitive data to construct a PLE-oriented evaluation system that incorporates both subjective perceptions and objective indicators. Principal Component Analysis (PCA), the EWM, and the Obstacle Degree Model (ODM) are then applied to identify LC potential and define functional zoning.

This study focuses on three main aspects:

(1)

Developing an LC potential evaluation system that integrates both subjective perceptions and objective indicators based on PLE functions;

(2)

Analyzing the spatial differentiation of LC potential and establishing priority levels within the study area;

(3)

Identifying the key constraints across different potential zones and proposing targeted consolidation strategies.

This research addresses critical limitations in existing evaluation frameworks, particularly in terms of indicator coverage, regional adaptability, and the integration of subjective dimensions. Based on empirical analysis in Qian County, this study demonstrates that, under the conditions of multiple systemic risks (polycrisis), LC functions not only as a technical means for resource allocation and spatial optimization but also as a strategic mechanism for enhancing regional resilience (spatial transformation) and promoting collaborative governance (sustainable transition). This approach contributes a more integrated and adaptive perspective to the field of rural LC research, while also providing theoretical support and methodological pathways for developing strategies that can adapt to future multi-risk scenarios [36].

2. Study Area and Research Data

2.1. Study Area

The Guanzhong Plain (33°41′55″ N–35°39′40″ N, 106°42′00″ E–110°35′40″ E) is located in the central part of Shaanxi Province, north of the Qinling Mountains and south of the Beishan Mountains. The region has a terrain that is higher in the west and lower in the east, with a semi-humid climate and distinct seasons. The total area is approximately 56,000 square kilometers. Based on the statistical data from the Shaanxi Provincial Bureau of Statistics and the officially existing administrative divisions, the study area covers five prefecture-level cities (Baoji, Xianyang, Weinan, Tongchuan, and Xi’an) and the Yangling Demonstration Zone, encompassing 54 districts (counties), 616 towns (subdistricts), and 11,248 administrative villages. In recent years, the Guanzhong Plain has undergone significant land use changes, including a reduction and fragmentation of arable land, an increase in rural construction land, inadequate infrastructure and public services, as well as ecological degradation and a decline in ecosystem functions [37]. These challenges are not isolated but result from the intersection of multiple systemic crises, including climate variability, uneven urban–rural development, demographic changes, and resource pressures, highlighting the typical vulnerability and complexity of land systems in the Anthropocene context. These trends not only exacerbate the instability of agricultural production but also weaken the adaptive capacity and regeneration potential of ecological and social systems, severely limiting the sustainable transformation of rural spaces in the region [38]. Therefore, the Guanzhong Plain provides an important case study for exploring inefficient land use and optimization under conditions of compound crises.

This study selects Qian County as a representative case of LC in the Guanzhong Plain region, based on its geographic location, historical land use challenges, urgent ecological restoration needs, and strong policy support. Qian County (34°19′36″ N–34°45′05″ N, 108°00′13″ E–108°24′18″ E) is located in the central part of the Guanzhong Plain, at the transition zone between the southern edge of the Weibei Loess Plateau and the Guanzhong Plain. The total area is 1002.71 square kilometers, encompassing 1 street office, 15 towns, and 173 administrative villages. As one of the key areas for implementing LC and ecological restoration tasks in Shaanxi Province’s Guanzhong Plain [39], Qian County faces typical issues, such as illegal land occupation, land waste, limited reserve land resources, and low development and utilization efficiency. These issues not only constrain the land system’s underlying carrying capacity but also exacerbate its vulnerability to systemic risks in the Anthropocene, such as climate shocks, population outflow, and governance uncertainty [40]. Therefore, there is an urgent need for the scientific identification of consolidation potential and zoning, followed by the development of effective strategies to address the current inefficiency in land use (see Figure 1).

2.2. Data Resources

To construct the spatial utilization efficiency-oriented evaluation system under the PLE framework, multi-source data, such as field survey data, geographic spatial data, and statistical data need to be incorporated. The field survey data include infrastructure quality assessment, resident satisfaction index, and ecological environment assessment.

The spatial data include (1) land use data, comprising 2020 China secondary land classification data, 2021 China soil organic carbon data, and 2021 remote sensing imagery for Qian County; (2) administrative boundary data for Qian County; and (3) transportation network and point of interest (POI) data for Qian County. The statistical data consist of 2021 population data. Detailed information is provided in

Table 1. Main data sources used in this study.

Data Types	Data Sources	Resolution (m)	Collection	Classification	Data Use
Qian County administrative boundary data (county-level, town-level, and village-level administrative divisions)	Scientific data registration and publishing system of geographic remote sensing ecological network (GISRS) (http://gisrs.cn/minindex.html, accessed on 8 January 2025)	-	2020	Vector	Used to clip land use data according to the study area
Land use and cover change (LUCC) secondary land category data of China (based on the national land use classification system (GB/T 21010-2017 [41])	Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (https://www.resdc.cn/, accessed on 10 January 2025)	30	2020	Raster	Used to calculate indicators related to land use types
Soil organic carbon data	Institute of Soil Science, Chinese Academy of Sciences (http://www.issas.cas.cn/, accessed on 12 January 2025)	250	2021	Raster	Used to compute the proportion of soil organic matter content
POI data	Gaode map (https://gaode.com/, accessed on 18 January 2025)	-	2022	Vector	Used to calculate the production building density and industrial-mining built-up land use density
Landsat 8 remote sensing imagery	Geospatial data cloud (https://www.gscloud.cn/, accessed on 10 January 2025)	15	2022	Raster	Used to derive the desertification index
Road network data	Gaode map (https://gaode.com/, accessed on 4 February 2025)	-	2021	Vector	Used to calculate road network density
Population data	Open land map (https://openlandmap.org/, accessed on 11 January 2025)	-	2021	Raster	Used to estimate population density and per capita built-up land area
Field survey data (includes infrastructure satisfaction, resident satisfaction, and ecological environment evaluation data)	Field investigation	-	2024	Excel	Used to assess living and ecological land use efficiency

.

To obtain subjective indicators (infrastructure quality assessment, resident satisfaction index, and ecological environment assessment), three closed-ended questions were designed, each using a five-point Likert scale (1 to 5) to measure satisfaction or perceived quality. In December 2024, a four-member research team conducted household surveys in randomly selected villages across 16 towns (including subdistricts) in Qian County. After excluding invalid responses, 247 valid questionnaires were collected. For each village, the value of a given subjective indicator was calculated as the average score of all valid responses collected within its corresponding town (the questionnaire content is provided in the Supplementary Materials, and the visualized results are shown in Appendix A). Although the questionnaire was not formally validated using standardized psychometric instruments, the use of clear wording, anonymous responses, and consistent data aggregation rules helped minimize subjective bias and ensure the reliability and comparability of the results [42].

3. Methodology

The methodological framework of this study is presented in Figure 2. First, all raw indicators were standardized, and PCA was conducted separately for the production, living, and ecological dimensions to extract principal components for each dimension. Principal component scores were calculated by weighting and summing the standardized indicator values with their corresponding component score coefficients.

Subsequently, the principal component scores were normalized using the min–max method. The EWM was applied to calculate the weights of each principal component based on their information entropy. Then, the normalized component scores for each dimension were weighted by their respective entropy weights to obtain land use efficiency scores and overall comprehensive scores for each dimension.

Thereafter, candidate consolidation zones identified by the Threshold Verification Coefficient Method (TVCM) [43] were classified into preliminary, priority, and intensive consolidation using the Jenks Natural Breaks (JNB) classification method, reflecting varying degrees of intervention urgency.

In the final step, an ODM was constructed based on the standardized component scores and their entropy weights to identify key limiting factors within each consolidation zone.

3.1. PLE-Oriented Evaluation Model for Rural Land Utilization Efficiency

3.1.1. Development of a PLE-Based Evaluation System for LC Potential

This study develops a PLE-based LC potential evaluation system to address the limitations of existing single-criterion evaluation approaches. The system comprises 17 indicators across three functional dimensions: production, living, and ecology. It is designed with a comprehensive consideration of the economic value of land use, social service functions, and ecological environmental protection, while also integrating the regional characteristics of the study area, relevant research findings, and practical needs. In the production dimension, land cultivation rate and soil organic matter content are critical indicators for evaluating agricultural productivity and sustainability in rural areas. The production building density and industrial-mining built-up land use density directly reflect the levels of industrial and agricultural production. They are thus selected as evaluation indicators for the production dimension [44]. The living dimension incorporates population density, per capita built-up land area, and fragmentation of built-up land patches to reflect the intensity of land use and the capacity to support the population, revealing the complexity and stability of land use patterns [45]. The road network density quantifies the accessibility of transportation in living spaces [46]. Subjective indicators, such as infrastructure quality assessment and resident satisfaction index, based on questionnaire data, comprehensively reflect farmers’ willingness and intentions to improve rural living spaces through LC programs. These are therefore considered as evaluation indicators for the living dimension. In the ecology dimension, the proportion of ecological land area and habitat quality index assess the ecosystem function, while the patch density and Shannon diversity index are used to analyze the impacts of land use change on the ecological environment [47]. The desertification index quantifies the degree of land degradation [48]. Additionally, farmers’ evaluations of the ecological environment serve as indicators of willingness and intention for ecological restoration. These, together, form the evaluation system for the ecology dimension (

Table 2. LC potential evaluation indicator system.

Target Level	Indicator Level	Indicator Description	Calculation Method
Production efficiency	Land cultivation rate	Reflects land resource utilization intensity and structure	$\begin{array}{l}\mathrm{Soil}\mathrm{Organic}\mathrm{Matter}\mathrm{Content}\mathrm{Ratio}=\\ {\displaystyle \frac{\mathrm{Soil}\mathrm{Organic}\mathrm{Matter}\mathrm{Content}}{\mathrm{Total}\mathrm{Regional}\mathrm{Land}\mathrm{Area}}}\times 100\%\end{array}$
	Soil organic matter content	Indicates carbon-containing organic compounds in soil	$\begin{array}{l}\mathrm{Soil}\mathrm{Organic}\mathrm{Matter}\mathrm{Content}\mathrm{Ratio}=\hfill \\ {\displaystyle \frac{\mathrm{Soil}\mathrm{Organic}\mathrm{Matter}\mathrm{Content}}{\mathrm{Total}\mathrm{Regional}\mathrm{Land}\mathrm{Area}}}\times 100\%\hfill \end{array}$
	Land use intensity	Measures the human exploitation intensity of land resources	${\mathrm{L}}_{\mathrm{a}}=100\times {\displaystyle {\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{A}}_{\mathrm{i}}}\times {\mathrm{C}}_{\mathrm{i}}$ Where: La—comprehensive land use intensity index. Ai—grading index for the i-th land use intensity level (level 1: unused land/difficult-to-utilize land; level 2: forest land/grassland/water area; level 3: cultivated land/garden plot; level 4: urban and industrial-mining land) Ci—percentage area of the i-th land use intensity classification level n—number of land use intensity classification levels
	Production building density (units/km2)	Indicates secondary/tertiary industry development levels	$\begin{array}{l}\mathrm{Density}\mathrm{of}\mathrm{productive}\mathrm{buildings}=\\ {\displaystyle \frac{\begin{array}{l}(\mathrm{Number}\mathrm{of}\mathrm{Secondary}\mathrm{or}\\ \mathrm{Tertiary}\mathrm{Industry}\mathrm{Buildings})\end{array}}{\mathrm{Regional}\mathrm{Land}\mathrm{Area}}}\end{array}$
	Industrial-mining built-up land use density (units/km2)	Reflects industrial production intensity	$\begin{array}{l}\mathrm{Industrial}\text{-}\mathrm{mining}\mathrm{built}\text{-}\mathrm{up}\\ \mathrm{land}\mathrm{use}\mathrm{density}=\\ {\displaystyle \frac{\mathrm{Industrial}\text{-}\mathrm{Mining}\mathrm{Built}\text{-}\mathrm{up}\mathrm{Sites}}{\mathrm{Regional}\mathrm{Land}\mathrm{Area}}}\end{array}$
Living efficiency	Population density (persons/km2)	Measures population distribution density	$\begin{array}{l}\mathrm{Population}\mathrm{density}=\\ {\displaystyle \frac{\mathrm{Total}\mathrm{Population}}{\mathrm{Regional}\mathrm{Land}\mathrm{Area}}}\end{array}$
	Per capita built-up land area (m2/person)	Evaluates rural land use efficiency	$\begin{array}{l}\mathrm{Per}\mathrm{capita}\mathrm{built}\text{-}\mathrm{up}\mathrm{land}\mathrm{area}=\\ {\displaystyle \frac{\mathrm{Built}\text{-}\mathrm{up}\mathrm{Land}\mathrm{Area}}{\mathrm{Total}\mathrm{Population}}}\end{array}$
	Fragmentation of built-up land patches	Quantifies the spatial fragmentation of construction land	$\begin{array}{l}\mathrm{Fragmentation}\mathrm{built}\text{-}\mathrm{up}\\ \mathrm{land}\mathrm{patches}=\\ {\displaystyle \frac{\mathrm{Number}\mathrm{of}\mathrm{Built}\text{-}\mathrm{up}\mathrm{Land}\mathrm{Patches}}{\mathrm{Regional}\mathrm{Land}\mathrm{Area}}}\end{array}$
	Road network density (km/km2)	Assess transportation infrastructure rationality	$\begin{array}{l}\mathrm{Road}\mathrm{network}\mathrm{density}=\\ {\displaystyle \frac{\mathrm{Total}\mathrm{Road}\mathrm{Length}}{\mathrm{Regional}\mathrm{Land}\mathrm{Area}}}\end{array}$
	Infrastructure quality assessment	Evaluates public facility completeness (1–5 scale)	Subjective rating (1–5 scale)
	Resident satisfaction index	Measures quality of life (1–5 scale)	Subjective rating (1–5 scale)
Ecological efficiency	Proportion of ecological land area (%)	Indicates the proportion of ecologically functional land	$\begin{array}{l}\mathrm{Proportion}\mathrm{of}\mathrm{ecological}\mathrm{land}\mathrm{area}=\\ {\displaystyle \frac{\begin{array}{l}{\mathrm{A}}_{\mathrm{el}}\times (\mathrm{Closed}\mathrm{Forest}\mathrm{Area}+\mathrm{Shrubland}\\ \mathrm{Area}+\mathrm{Open}\mathrm{Forest}\mathrm{Area}+\mathrm{Grassland}\\ \mathrm{Area}+\mathrm{River}\mathrm{Area}+\mathrm{Lake}\left(\mathrm{Nearshore}\right)\\ \mathrm{Area}+\mathrm{Tidal}\mathrm{Flat}\mathrm{Area}+\mathrm{Permanent}\\ \mathrm{Glacier}\mathrm{or}\mathrm{Snowfield}\mathrm{Area}+\mathrm{Swamp}\mathrm{Area}\\ +\mathrm{Sandy}\mathrm{Land}\mathrm{Area}+0.07\times \mathrm{Other}\mathrm{Forest}\mathrm{Area}\\ +0.7\times \mathrm{Reservoir}\mathrm{Area}+0.7\times \mathrm{Paddy}\mathrm{Field}\\ \mathrm{Area}+0.5\times \mathrm{Dryland}\mathrm{Area})\end{array}}{\mathrm{Total}\mathrm{Regional}\mathrm{Land}\mathrm{Area}}}\end{array}$ where: Ael— normalization coefficient of the ecological land area ratio index; reference value: 100.5022
	Habitat quality index	Evaluates ecological environment status	$\begin{array}{l}\mathrm{Habitat}\mathrm{quality}\mathrm{index}=\\ {\displaystyle \frac{\begin{array}{l}\mathrm{Abio}\times (0.35\times \mathrm{Forest}\mathrm{Area}+0.21\times \mathrm{Grassland}\\ \mathrm{Area}+0.28\times \mathrm{Water}\mathrm{Bodies}\mathrm{or}\mathrm{Wetlands}+\\ 0.11\times \mathrm{Cropland}\mathrm{Area}+0.04\times \mathrm{Built}\text{-}\mathrm{up}\mathrm{Land}\\ \mathrm{Area}+0.01\times \mathrm{Unused}\mathrm{Land}\mathrm{Area})\end{array}}{\mathrm{Regional}\mathrm{Land}\mathrm{Area}}}\end{array}$ where: Abio—normalization coefficient for the habitat quality index; reference value: 511.2642131067
	Patch density (units/hm2)	Measures landscape fragmentation	$\begin{array}{l}\mathrm{Patch}\mathrm{density}=\\ {\displaystyle \frac{\mathrm{Number}\mathrm{of}\mathrm{patches}}{\mathrm{Total}\mathrm{area}\mathrm{of}\mathrm{the}\mathrm{patches}}}\end{array}$
	Shannon diversity index	Quantifies landscape diversity	$\mathrm{SHDI}=-\sum {\mathrm{P}}_{\mathrm{i}}{\mathrm{lnP}}_{\mathrm{i}}$ where: Pi Represents the proportion of patch type i in the landscape
	Ecological environment assessment	Subjective evaluation of environmental quality (1–5 scale)	Subjective rating (1–5 scale)
	Desertification index	Assesses land degradation severity	$\begin{array}{l}\mathrm{FVC}={\displaystyle \frac{\mathrm{NDVIveg}-\mathrm{NDVIsoil}}{\mathrm{NDVI}-\mathrm{NDVIsoil}}}\\ \mathrm{Desertification}\mathrm{index}=1-\mathrm{FVC}\end{array}$ where: FVC (Vegetation Cover Fraction)—degree of surface vegetation coverage NDVI—Normalized Difference Vegetation Index of mixed pixels. NDVIsoil—The NDVI value of bare soil pixels represents the NDVI value of pure vegetation pixels 1,2

¹ Inversion models for NDVI and albedo (see Figure A2). ² Desertification difference index (see Figure A3).

).

3.1.2. Indicator Weight Calculation

This study comprehensively applies PCA, the EWM, and the AWM to determine the weights within the PLE-based LC potential evaluation system [49]. PCA [50] is used to transform the original variables into composite factors (principal components), thereby fully utilizing the raw data, eliminating subjective interference, and objectively identifying samples with superior functional characteristics [51]. In this study, PCA is employed to screen variables with high information content and extract principal components that reflect the structure of the original dataset. The EWM is an objective weighting method that determines the weight of each indicator by calculating information entropy, which reflects the degree of uncertainty within the system [52]. In this study, the EWM is used to assign weights to the principal components in the evaluation system. The AWM integrates PCA and the EWM, leveraging the advantages of both to minimize redundancy and interdependence among evaluation indicators. It enhances the efficiency of weight determination for numerical datasets and generates more objective weights while capturing the dispersion characteristics of the indicator data. This ensures high reliability and strong practical applicability [53]. The specific steps are as follows.

Initially, PCA was performed separately for each functional dimension. Based on the results, 16 indicators with the highest information content were retained (see Figure A4, Figure A5 and Figure A6 for the correlation matrices of the production, living, and ecological dimensions). As shown in

Table 3. Kaiser–Meyer–Olkin and Bartlett’s tests.

Metric		Production	Living	Ecological
Kaiser–Meyer–Olkin		0.534	0.612	0.656
Bartlett’s Test	Chi-square	1077.836	208.81	339.544
	d	6	15	15
	level of significance	0.000	0.000	0.000

, the Kaiser–Meyer–Olkin (KMO) values all exceed the recommended threshold of 0.5 [54], and the p-values from Bartlett’s test are less than 0.05, indicating that the indicator data are suitable for factor analysis.

Subsequently, principal components with a cumulative variance contribution rate exceeding 70% were extracted [55]. In the production dimension, two principal components were retained, PC1 and PC2, which represent the intensive utilization of production land and the agricultural land reutilization intensity, respectively. In the living dimension, three components were extracted, PC3, PC4, and PC5, corresponding to living space quality, livability of living space, and residential land use intensity. In the ecological dimension, PC6, PC7, and PC8 reflect ecological foundation quality, ecological space security, and ecological space fragmentation, respectively. The cumulative variance contribution rates for production, living, and ecological dimensions were 96.97%, 75.54%, and 79.09%, respectively, indicating a high level of explanatory power. The main variable loadings for each principal component are presented in

Table 4. Results of Principal Component Analysis.

Objective Layer	Principal Component	Explanation Rate (%)	Variable	Principal Component Load
Production	PC1	59.566	X1 Production building density X2 Industrial-mining built-up land use density	+0.993 +0.994
	PC2	37.406	X3 Land cultivation rate X4 Land use intensity	+0.886 +0.843
Living	PC3	37.962	X5 Population density X6 Fragmentation built-up land patches X7 Road network density	+0.560 +0.779 +0.830
	PC4	20.517	X8 Infrastructure quality assessment X9 Resident satisfaction index	+0.914 +0.851
	PC5	17.061	X5 Population density X10 Per capita built-up land area	−0.589 +0.877
Ecological	PC6	43.207	X11 Proportion of ecological land area X12 Habitat quality index X13 Ecological environment assessment	+0.830 +0.894 +0.538
	PC7	21.319	X14 Shannon diversity index X15 Desertification index	+0.827 +0.890
	PC8	14.558	X16 Patch density X13 Ecological environment assessment	+0.896 −0.578

.

In the subsequent phase, standardized indicator values were used in conjunction with the component score coefficient matrix to compute the linear weighted scores for each principal component. For instance, the score for PC1 was calculated as PC1 = −0.178 × X1 + 0.201 ×X2 + 0.417 × X3 + 0.418 × X4. The score coefficients were grouped by PLE dimensions and are detailed in

Table A1. Component score coefficient matrix for the production dimension.

Variable	PC1	PC2
X1	−0.178	0.595
X2	0.199	0.560
X3	0.417	−0.019
X4	0.418	−0.011

(Production),

Table A2. Component score coefficient matrix for the living dimension.

Variable	PC3	PC4	PC5
X5	0.286	0.111	−0.418
X6	0.530	0.027	0.316
X7	0.504	0.090	−0.078
X8	0.180	0.622	0.020
X9	−0.01	0.528	−0.039

(Living), and

Table A3. Component score coefficient matrix for the ecological dimension.

Variable	PC6	PC7	PC8
X11	0.434	0.014	0.111
X12	0.524	−0.125	0.048
X13	0.348	−0.178	−0.453
X14	−0.062	0.520	−0.008
X15	−0.210	0.642	−0.130

(Ecological).

Ultimately, after calculating the scores for each principal component, weights were assigned to the components to obtain the attribute weights (see

Table 5. Attribute weights of principal components by PLE dimension.

Objective Layer	Principal Component	Weight
Production efficiency	PC1	0.671
	PC2	0.017
Living efficiency	PC3	0.047
	PC4	0.095
	PC5	0.017
Ecological efficiency	PC6	0.020
	PC7	0.128
	PC8	0.005

). The EWM was employed for this purpose, and the corresponding calculation formulas are presented below:

$${\mathrm{P}}_{\mathrm{ij}}={\displaystyle \frac{{\mathrm{x}}_{\mathrm{ij}}}{{\displaystyle {\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{x}}_{\mathrm{ij}}}}}$$

(1)

$${\mathrm{e}}_{\mathrm{j}}=-{\displaystyle \frac{1}{\ln \left(\mathrm{n}\right)}}{\displaystyle {\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{p}}_{\mathrm{ij}}}\ln {\mathrm{p}}_{\mathrm{ij}})$$

(2)

$${\mathrm{W}}_{\mathrm{i}}={\displaystyle \frac{\left(1-{\mathrm{e}}_{\mathrm{i}}\right)}{{\displaystyle {\sum }_{\mathrm{i}=1}^{\mathrm{m}}\left(1-{\mathrm{e}}_{\mathrm{i}}\right)}}}$$

(3)

where ${\mathrm{X}}_{\mathrm{ij}}$ is the standardized value of the indicator; $\mathrm{n}$ is the number of study units; ${\mathrm{e}}_{\mathrm{j}}$ is the information entropy of the indicator; and ${\mathrm{W}}_{\mathrm{j}}$ is the entropy weight of the indicator $\mathrm{j}$.

To ensure consistency across multiple models, the original indicator values were standardized using the z-score method before PCA [56]. After obtaining the principal component scores, min–max normalization was applied to rescale the scores to a 0–1 range, followed by weight assignment using the entropy method. This process ensured the comparability of principal components across different dimensions and facilitated unified analysis in the integrated potential assessment.

3.2. PLE-Oriented Potential Assessment and Zoning for LC

3.2.1. LWSM and TVCM

This study employed LWSM and the TVCM to identify areas suitable for LC. Specifically, the LWSM calculates a comprehensive evaluation value by assigning weights to each indicator and performing a linear combination. It is widely used in multi-criteria evaluation and decision-making contexts [57]. In this study, the LWSM was applied to compute the consolidation potential values for the PLE dimensions. A higher evaluation value indicates greater potential for consolidation within the corresponding spatial unit. The calculation formula is as follows:

$${\mathrm{EF}}_{\mathrm{ik}}={\displaystyle \sum _{\mathrm{j}=1}^{\mathrm{c}}{\mathrm{R}}_{\mathrm{ij}}}\times {\mathrm{W}}_{\mathrm{j}}$$

(4)

The TVCM is a technique used to identify and evaluate efficiency values by comparing the performance of a target unit with that of its higher-level administrative region. A region is considered inefficient, indicating the need for consolidation, if its threshold-verification coefficient is less than 1. A lower coefficient corresponds to lower efficiency. In this study, the TVCM was employed to identify villages that require consolidation. Specifically, when the coefficient is less than 1, the corresponding area is classified as a village in need of consolidation. The calculation formula is as follows:

$${\mathrm{r}}_{\mathrm{ik}}={\displaystyle \frac{{\mathrm{EF}}_{\mathrm{ik}}}{{\mathrm{BEF}}_{\mathrm{k}}}}$$

(5)

$${\mathrm{BEF}}_{\mathrm{k}}={\displaystyle \frac{{\displaystyle \sum _{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{EF}}_{\mathrm{ik}}}}{\mathrm{n}}}$$

(6)

In Formulas (4)–(6), ${\mathrm{R}}_{\mathrm{ij}}$ represents the standardized principal component score; c is the number of principal components for the k-th efficiency type; ${\mathrm{W}}_{\mathrm{j}}$ denotes the principal component weight; ${\mathrm{r}}_{\mathrm{ik}}$ is the pending-verification coefficient of the k-th land use efficiency type in identification region i; $\mathrm{E}{\mathrm{F}}_{\mathrm{i}\mathrm{k}}$ is the k-th indicator land use efficiency value in identification region i, where k indicates the efficiency type; ${\mathrm{BEF}}_{\mathrm{k}}$ is the k-th indicator land use efficiency value in the background region; and n is the total number of identification regions.

3.2.2. JNB Classification

The JNB classification method was employed to divide the regional LC potential into distinct temporal–spatial zones. This method identifies natural inflection and breakpoints within a dataset to group similar values together, thereby maximizing internal homogeneity within each group and maximizing heterogeneity between groups. It also ensures a relatively balanced distribution of values and quantities across the classified groups [58]. In this study, the JNB classification was used to classify LC ZONES in Qian County into three zones: the intensive consolidation zone, priority consolidation zone, and preliminary consolidation zone.

3.3. PLE-Oriented Obstacle Factor Diagnosis in LC

The ODM [59] was employed to identify the dominant factors responsible for low PLE land use efficiency within different consolidation zones. The model evaluates the relative influence of potential constraints by analyzing three key indicators: factor contribution, index deviation, and obstacle degree [60]. A higher obstacle degree indicates a greater hindrance effect of the corresponding principal component on the overall evaluation score. In this study, the standardized principal component scores and attribute weights were first used to calculate the obstacle degree of each component within the consolidation zones. Subsequently, the primary obstacle factors were identified by ranking these components according to their respective obstacle degrees.

The ODM is expressed through the following formulas:

$${\mathrm{D}}_{\mathrm{ij}}=1-{\mathrm{R}}_{\mathrm{ij}}$$

(7)

$${\mathrm{O}}_{\mathrm{ij}}={\displaystyle \frac{{\mathrm{D}}_{\mathrm{ij}}\times {\mathrm{W}}_{\mathrm{j}}}{{\displaystyle {\sum }_{\mathrm{j}=1}^{\mathrm{n}}({\mathrm{D}}_{\mathrm{ij}}\times {\mathrm{W}}_{\mathrm{j}})}}}\times 100\%$$

(8)

$${\mathrm{U}}_{\mathrm{m}}={\displaystyle \sum {\mathrm{O}}_{\mathrm{ij}}}$$

(9)

In Equations (7)–(9), ${\mathrm{D}}_{\mathrm{ij}}$ denotes the deviation degree of principal component scores (i.e., the difference between 1 and the standardized value of individual principal component indicators); ${\mathrm{R}}_{\mathrm{ij}}$ represents the standardized value of principal component scores; ${\mathrm{W}}_{\mathrm{ij}}$ indicates the principal component weight in this study; ${\mathrm{O}}_{\mathrm{ij}}$ stands for the obstacle degree index of principal component scores; and ${\mathrm{U}}_{\mathrm{m}}$ refers to the obstacle degree of the m-th consolidation zone.

4. Results

4.1. Results of the PLE Utilization Efficiency Evaluation

This study applied the JNB classification to classify the utilization efficiency indicators of each dimension into five levels. The evaluation results of production, living, and ecological efficiency were visualized using ArcGIS 10.8, with color gradients from light to dark representing increasing efficiency scores. This approach effectively illustrates the spatial pattern of LC potential in Qian County (see Figure 3, Figure 4, Figure 5 and Figure 6).

• The comprehensive evaluation index of production space utilization efficiency ranges from 0.006 to 0.685, indicating a relatively large span. It exhibits significant spatial heterogeneity, forming a pattern characterized by high values in the central area that gradually decrease outward. The highest and moderately high production efficiency zones are mainly concentrated in the western villages of the CG Subdistrict in central Qian County. At the same time, most other areas fall into the low to moderately low categories. Specifically, the evaluation values of PC1 (land use intensity of production land) show a spatial pattern largely consistent with the overall production efficiency distribution. This is because GG is the political, economic, and cultural center of Qian County, where production-related construction land is relatively extensive and exhibits a concentrated spatial layout in the area. In contrast, PC2 (intensity of agricultural land reuse) shows relatively small distribution differences across the county. Except for lower values observed in Zhoujiahe Village in the west and Yongjiu Village and Qian County Forest Farm in the north, most other areas fall within medium-high to high-value zones. This is mainly because these regions are predominantly mountainous or contain reservoirs, resulting in limited available agricultural land.
• The comprehensive evaluation index of living space utilization efficiency ranges from 0.019 to 0.128, with a relatively narrow value span and limited spatial variation. Overall, it shows higher values in the eastern and western regions, while remaining relatively low in the central area. The highest-value areas are primarily concentrated in villages located in LS, XY, and ZC towns in the western part of Qian County, and in ZG town in the east. In contrast, the lowest-value areas are mainly distributed in villages in FY town (north) and DY town (central region). Specifically, the evaluation values of PC3 (living space quality) show higher values in the central region and lower values in both the northern and southern areas, which is associated with denser road networks and higher fragmentation of built-up land in central areas. The evaluation values of PC4 (livability of living space) show a distribution pattern that is generally consistent with the overall living efficiency index, which is characterized by higher values in the eastern and western regions and lower values in the central area. This reflects the relatively better infrastructure and higher living standards found in the western and eastern villages and towns of Qian County. The evaluation values of PC5 (residential land use intensity) display considerable spatial variation across the county and show a marked advantage in the northern region, indicating a distinct north–south gradient. Most northern areas fall into the moderate to high range, while southern areas are predominantly in the moderate to low range. The highest values are observed in Yongjiu, Xianfeng, Qianling, Taiping, and Chenjiawa villages in the north, and Juzhou Village in the central region. The lowest values are concentrated in Huakou and Dongjie villages in the central region. This pattern is linked to the fact that northern areas are located on hilly and gully terrain with relatively lower population densities than the south, resulting in higher per capita built-up land area in the north. In contrast, the lowest evaluation values occur in areas with both limited built-up land area and higher population density, leading to lower efficiency.
• The comprehensive evaluation index of ecological space utilization efficiency ranges from 0.014 to 0.140, with a relatively narrow value span and limited spatial variation. Overall, it shows a marked advantage in the northern region, indicating a distinct south–north gradient. The areas with the highest and moderately high ecological efficiency scores are primarily concentrated in the northern half of Qian County. In contrast, the lowest and moderately low scores are mostly found in the southern half. PC6 (ecological foundation quality) demonstrates relatively limited variation across the county. Most areas fall into the moderate to high-value range, except for lower scores observed in YY Town in the north, CG Town and Zhongxiang Village in the central area, XL Town in the south, and Sangguan and Sanxing villages in the west. This is largely attributed to the smaller proportions of ecological land types, such as forests, grasslands, and water bodies, in those areas. PC7 (ecological space security) shows a clear spatial trend similar to the composite ecological efficiency score, with higher values in the north and lower values in the south. This reflects higher levels of human disturbance, lower vegetation coverage, and greater land degradation in southern regions. PC8 (ecological space fragmentation) also shows limited spatial variation. Most areas fall into the moderate to high-value range, except for relatively low values observed in XY and CG towns in the central region; XL Town and the villages of Yangzhuang, Tuanjie, and Fengxing in the east; and the Qian County Forest Farm in the north. These areas are characterized by higher ecological fragmentation, which has negatively impacted the local ecological environment.
• The comprehensive evaluation index of integrated space utilization efficiency ranges from 0.091 to 0.854, indicating a relatively wide value span and significant spatial heterogeneity. Overall, it shows a clear trend of higher values in the northern region and lower values in the south, reflecting the integrated spatial characteristics of production, living, and ecological efficiency. Areas with the highest consolidation potential are concentrated in the CG Subdistrict in central Qian County, which aligns with the high-value cluster observed in the production efficiency evaluation. Moderately high values are mainly found in the northern towns of LS and ZG, consistent with the high-value areas of living space utilization efficiency. Influenced by the spatial distribution pattern of ecological efficiency, the overall LC potential also demonstrates a north–south gradient, with higher values in the north. By contrast, the southern region of Qian County shows relatively low scores across all three functional dimensions, resulting in the clustering of low-value zones in this area.

4.2. Consolidation Potential and Zoning Results

This study identified candidate consolidation areas in Qian County’s PLE efficiency using the TVCM and applied the JNB classification to categorize efficiency scores. Lower scores indicate poorer land use performance and higher consolidation potential. According to consolidation urgency, the areas were divided into three zones, intensive, priority, and preliminary, corresponding to low, medium, and high efficiency levels. This classification provides a foundation for developing targeted LC strategies [49] (see Figure 7).

• Production consolidation potential zones. A total of 155 villages were identified within the production consolidation potential zones, accounting for 88.57% of all villages in Qian County. These villages are predominantly distributed across most areas of the county, excluding the central GG region. According to the classification results, 34 villages (19.43%) fall into the preliminary consolidation zone (score range: 0.033–0.042), 70 villages (40.00%) into the priority consolidation zone (0.025–0.032), and 51 villages (29.14%) into the intensive consolidation zone (0.006–0.024). In terms of spatial distribution, preliminary consolidation zones are primarily clustered in XL Town in the southern part of Qian County, an area known for its high-quality Hongxiantao peach industry. Other preliminary zones are scattered across the southern and central regions, with a few found in the north. Priority consolidation zones are dispersed throughout the county but tend to be more concentrated in the north. Intensive consolidation zones are mainly concentrated in contiguous patches in the western and northern parts of the county, while also appearing sporadically in the central and southwestern regions. This pattern is associated with the high overlap of these areas with forest land, grassland, and water bodies, as well as the relatively low proportion of cultivated land and residential construction land, combined with a high degree of land fragmentation.
• Living consolidation potential zones. A total of 88 villages were identified within the living consolidation potential zones, accounting for 50.29% of all villages in Qian County. These villages are primarily distributed in the northern, central, and southeastern parts of the county. Specifically, 36 villages (20.57%) were classified as preliminary consolidation zones (score range: 0.064–0.075), mostly clustered in XL and ML towns in the southern part of Qian County, LY Town in the east, and YY Town in the north. A total of 29 villages (16.57%) were identified as priority consolidation zones (0.043–0.063), mainly located in JC Town in the south and CG Subdistrict in the central part of the county. Meanwhile, 23 villages (13.14%) were designated as intensive consolidation zones (0.019–0.042), predominantly located in FY Town in the northern part of Qian County and DY Town in the central part.
• Ecological consolidation potential zones. A total of 110 villages were identified within the ecological consolidation potential zones, accounting for 62.86% of all villages in Qian County. In terms of both quantity and spatial distribution, ecological efficiency in the northern rural areas of Qian County is significantly higher than in the central and southern regions. Specifically, 30 villages (17.14%) were identified as preliminary consolidation zones (score range: 0.050–0.064), primarily located in the central areas of LC, DY, and YH. Field investigations reveal that these areas suffer from severe soil erosion, which is largely attributed to low vegetation coverage, rugged and fragmented topography, uneven precipitation distribution, and loose soil texture. A total of 42 villages (24.00%) fall within the priority consolidation zone (0.036–0.049), sporadically distributed across the southern part of Qian County. Meanwhile, 38 villages (21.71%) were classified as intensive consolidation zones (0.014–0.035), mainly concentrated in the southern towns of ML, JC, WC, LY, and ZC. This region serves as a key grain-producing area for food security in Qian County. Despite its fertile soils and suitability for cultivation, long-term issues, such as the improper use of plastic mulch and fertilizers, as well as inadequate treatment of livestock and poultry waste, have led to serious non-point and point-source pollution problems in some cultivated lands.
• Integrated consolidation potential zones. A total of 111 villages were identified within the integrated consolidation potential zones, accounting for 64.16% of all villages in Qian County. These zones are mainly distributed across the southern half of the county and in FY Town in the north, indicating considerable spatial disparities in the integrated development of PLE functions between the central area and the northern and southern regions. Preliminary consolidation zones (score range: 0.158–0.181) include 36 villages (32.43%) and are primarily located in the western part of Qian County and FY Town in the north. Priority consolidation zones (0.129–0.158), comprising 43 villages (38.74%), are concentrated in the southern towns of LC, WC, and XL. Intensive consolidation zones (0.091–0.129), with 32 villages (28.83%), are clustered in the central and southeastern areas, with sporadic distribution in the west and north.

4.3. Obstacle Factor Identification Results

To further identify the primary obstacle factors within the production, living, ecological, and integrated consolidation potential zones and to clarify the corresponding consolidation priorities and strategic directions, this study applies the ODM (see Figure 8,

Table A4. Ranking of obstacle factors across production consolidation potential zones.

Production Consolidation Potential Zones	Obstacle Factor Rankings
	1	2
Preliminary consolidation zone	PC1 (0.196)	PC2 (0.078)
Priority consolidation zone	PC1 (0.409)	PC2 (0.240)
Intensive consolidation zone	PC2 (0.597)	PC1 (0.301)

,

Table A5. Ranking of obstacle factors across living consolidation potential zones.

Living Consolidation Potential Zones	Obstacle Factor Rankings
	1	2	3
Preliminary consolidation zone	PC4 (0.241)	PC5 (0.200)	PC3 (0.197)
Priority consolidation zone	PC4 (0.223)	PC3 (0.170)	PC5 (0.159)
Intensive consolidation zone	PC4 (0.246)	PC3 (0.151)	PC5 (0.141)

,

Table A6. Ranking of obstacle factors across ecological consolidation potential zones.

Ecological Consolidation Potential Zones	Obstacle Factor Rankings
	1	2	3
Preliminary consolidation zone	PC7 (0.266)	PC8 (0.254)	PC6 (0.243)
Priority consolidation zone	PC7 (0.218)	PC6 (0.179)	PC8 (0.171)
Intensive consolidation zone	PC7 (0.321)	PC6 (0.212)	PC8 (0.201)

and

Table A7. Ranking of obstacle factors across integrated consolidation potential zones.

Integrated Consolidation Potential Zones	Obstacle Factor Rankings
	1	2	3	4	5	6	7	8
Preliminary consolidation zone	PC8 (0.214)	PC4 (0.213)	PC1 (0.209)	PC3 (0.208)	PC6 (0.205)	PC7 (0.203)	PC5 (0.200)	PC2 (0.195)
Priority consolidation zone	PC7 (0.301)	PC4 (0.264)	PC5 (0.253)	PC3 (0.253)	PC6 (0.252)	PC1 (0.251)	PC8 (0.238)	PC2 (0.11)
Intensive consolidation zone	PC4 (0.265)	PC7 (0.248)	PC5 (0.199)	PC3 (0.191)	PC1 (0.188)	PC8 (0.183)	PC6 (0.177)	PC2 (0.117)

PC1: Intensive utilization of production land; PC2: agricultural land reutilization intensity; PC3: living space quality; PC4: livability of living space; PC5: residential land use intensity; PC6: ecological foundation quality; PC7: ecological space security; PC8: ecological space fragmentation.

). The results indicate that:

• Significant heterogeneity in obstacle factors is observed across different consolidation subzones within the production consolidation potential zones. In the preliminary and priority consolidation zone, the primary obstacle factor impeding the realization of consolidation potential is PC1, with cumulative obstacle degrees of 0.196 and 0.409, respectively. PC1 is predominantly characterized by high loadings on production building density and industrial-mining built-up land use density, with loading coefficients of 0.993 and 0.994. These variables reflect the spatial clustering of structures associated with secondary and tertiary industries. In contrast, the intensive consolidation zone is primarily constrained by PC2, with a cumulative obstacle degree of 0.597. PC2 is mainly associated with the land cultivation rate and land use intensity, bearing loading coefficients of 0.886 and 0.843. These findings underscore the detrimental impact of fragmented land use patterns on production efficiency.
• Significant consistency in obstacle factors is observed across different consolidation subzones within the living consolidation potential zones. In the preliminary consolidation zone, priority consolidation zone, and intensive consolidation zone, the primary obstacle factor constraining the realization of living consolidation potential is PC4, with cumulative obstacle degrees of 0.241, 0.223, and 0.246, respectively. PC4 is primarily characterized by high loadings on the infrastructure quality assessment and resident satisfaction index, with loading coefficients of 0.914 and 0.851, respectively. These variables capture the adverse effects of deficient infrastructure and low levels of resident well-being on the overall efficiency of living space. Field investigations indicate that the preliminary consolidation zone is significantly impacted by challenges, such as inadequate household waste management and lagging infrastructure development, particularly odor pollution resulting from the open-air dumping of domestic waste. In addition, villages in XY Town identified as part of the preliminary consolidation zone, along with rural settlements in central and northern Qian County designated as priority and intensive consolidation zones, are predominantly located in narrow valley corridors or on relatively flat marginal slopes of gullies within the loess hilly region. These areas commonly experience transportation constraints and suffer from critical shortages in infrastructure provision, including wastewater treatment facilities, gas supply systems, and healthcare services, which have become key barriers to the enhancement of living space efficiency.
• A high degree of consistency in obstacle factors is also evident across different consolidation subzones within the ecological consolidation potential zones. In the preliminary consolidation zone, priority consolidation zone, and intensive consolidation zone, the primary obstacle factor impeding the improvement of ecological consolidation potential is PC7, with cumulative obstacle degrees of 0.266, 0.218, and 0.321, respectively. PC7 is mainly defined by high loadings on the Shannon diversity index and the desertification index, with loading coefficients of 0.827 and 0.890, respectively. These variables reflect the negative impact of land use type diversity and land degradation levels on the overall efficiency of the ecological dimension.
• Notable differences in obstacle factors are observed across different consolidation subzones within the integrated consolidation potential zones. In the preliminary consolidation zone, the primary obstacle factor constraining the realization of integrated consolidation potential is PC8, with a cumulative obstacle degree of 0.241. In the priority consolidation zone, the primary obstacle factor is PC7, with a cumulative obstacle degree of 0.301. For the intensive consolidation zone, the primary obstacle factor is PC4, with a cumulative obstacle degree of 0.265. PC8 is primarily associated with patch density and ecological environment assessment, bearing loading coefficients of +0.896 and −0.578, respectively. After the normalization of negatively oriented indicators, these results reflect the adverse impacts of land use fragmentation and degraded ecological conditions on PLE spatial utilization efficiency.

These findings indicate that higher obstacle degrees help identify the most urgent constraints in LC. This provides a scientific basis for formulating targeted strategies and plays a significant role in improving land use efficiency and promoting the precise allocation of consolidation resources [61].

5. Discussion

This study takes Qian County in Shaanxi Province as a case study area and establishes an evaluation framework for LC potential based on the PLE space theory. This theory emphasizes the coordinated development of production, living, and ecological functions, providing a theoretical foundation for assessing consolidation potential and optimizing spatial structure [62]. From the perspective of PLE spatial functions, this study develops a comprehensive evaluation and zoning system that reveals the spatial patterns of land use efficiency, the characteristics of consolidation zoning, and key limiting factors. These findings offer both theoretical and methodological support for improving the efficient allocation of land resources and restructuring rural space.

5.1. The Impact of the PLE Space Utilization Efficiency on the Potential and Zoning of LC

5.1.1. Impact of Production Efficiency on the Potential and Zoning of LC

Production efficiency is a key factor influencing the potential and spatial zoning of LC. It is primarily reflected in the density and utilization level of productive and industrial buildings. A higher consolidation potential not only indicates a weaker production base, but also reveals the degree of coordination between land use structures and the industrial system. The inadequate distribution or low efficiency of productive facilities often limits consolidation potential, constraining improvements in land functionality and resource allocation. Additionally, the distribution of cultivated land and the overall intensity of land use significantly affect the feasibility and potential of production-oriented consolidation. In areas with low proportions of arable land or severe land fragmentation, land use efficiency tends to be low, further diminishing overall production performance. In summary, production efficiency reflects a region’s industrial support capacity, land use intensity, and resource allocation efficiency, making it a critical basis for assessing development potential and formulating consolidation strategies. Improving production efficiency is essential for achieving rational land use and coordinated urban–rural spatial development.

5.1.2. Impact of Living Efficiency on the Potential and Zoning of LC

Living efficiency has a significant impact on both the potential and zoning of LC, particularly in terms of infrastructure quality and resident satisfaction. Well-developed infrastructure directly influences the quality of life, while subjective satisfaction reflects residents’ responses to the living environment and its improvement potential. The spatial differentiation of living consolidation zones indicates that living efficiency is constrained by factors such as natural conditions, settlement patterns, and service capacity, resulting in varying consolidation potential across regions. In the northern hilly and gully areas, as well as parts of the central and southeastern regions, complex terrain and infrastructure deficiencies further reduce the overall potential for living space improvement. The level of living efficiency not only determines the urgency of consolidation but also serves as a key indicator for assessing whether improvements in living conditions can be achieved. Therefore, in LC zoning and potential evaluation, living efficiency should be treated as a core metric to ensure that improvements in living quality are integrated with spatial optimization in a coordinated and effective manner.

5.1.3. Impact of Ecological Efficiency on the Potential and Zoning of LC

Ecological efficiency is a core variable for assessing the potential and rationality of LC zoning. It is primarily reflected through land use diversity (measured by the Shannon diversity index) and the degree of land degradation (indicated by the desertification index). The health of an ecosystem directly determines both the urgency of consolidation and the potential effectiveness of interventions.

In Qian County, ecological efficiency shows significant spatial variation. In the central region, severe soil erosion is the main driver of ecological vulnerability, while in the southern areas, despite a solid agricultural foundation, inappropriate agricultural practices have led to cultivated land pollution, weakening the region’s ecological carrying capacity. These regional differences in ecological consolidation potential reveal the uneven functional status of ecosystems across the country. Enhancing ecological efficiency is a fundamental driver of sustainable land use and spatial optimization. Therefore, ecological efficiency should be considered a key dimension in LC zoning. A systematic evaluation of ecological foundations, environmental pressures, and ecological risks is essential to guide spatial reconfiguration and environmental improvement, ultimately supporting the development of a high-quality, resilient national ecological security pattern.

5.2. Suggestions for the LC in Qian County

In the context of the Anthropocene, Qian County faces challenges such as agricultural decline, ecological degradation, population outflow, and fragmented spatial governance. These issues not only pose a challenge to land use efficiency, but also serve as a systematic test of regional resilience and adaptability [63]. In response, the zoning and consolidation approach proposed in this study aims to enhance the collaborative functions of the “PLE” space and its system adaptability, exploring a comprehensive consolidation model with dynamic adaptability and responsiveness to local contexts. This model is intended to provide sustainable governance references for similar regions. Addressing the different combinations of barrier factors in various land and zoning types in Qian County, this study proposes tailored consolidation strategies to improve underutilized lands, integrating agricultural, construction, and ecological land for holistic consolidation [64,65]. Based on regional characteristics, these strategies focus on optimizing overall functions and enhancing system resilience.

5.2.1. Measures to Enhance Production Space Utilization Efficiency

In the preliminary consolidation zones, land improvement strategies should be tailored to local village conditions, focusing on increasing the density of productive buildings and enhancing industrial functions. This can be achieved by promoting cooperatives, mutual aid organizations, or agricultural extension service centers to help farmers adopt modern agricultural technologies. A coordinated mechanism involving government subsidies and village-level organizations should be used to optimize agricultural management and improve land use efficiency. For industrial development, agricultural authorities should collaborate with relevant departments to establish pilot demonstration projects, particularly targeting the development of secondary and tertiary industries centered on agricultural processing. Taking the Hongxiantao peach as an example, leading enterprises should be introduced to develop a regional brand, and “e-commerce plus direct supply” models can be used to increase value-added and land productivity. Implementation timelines and phased goals should be clearly defined, with strengthened process monitoring.

The priority consolidation zones should focus on improving land use efficiency and reclamation rates. Incentive policies, such as tax reductions and subsidies, are recommended to support land leveling, soil improvement, and vegetation restoration [66]. These measures aim to restore and enhance agricultural productivity.

In the intensive consolidation zones where cultivated and construction land are highly fragmented, the implementation of policies linking urban and rural land use should be strengthened. A dedicated land integration fund should be established to support the acquisition and consolidation of scattered land parcels. High-standard farmland construction should be promoted based on functional zoning and local agricultural characteristics. The development of agricultural processing and light industry should be encouraged [67], supported by entrepreneurship programs and skills training to attract and cultivate rural talent. A regular monitoring system should be established to track production performance and industrial development, ensuring sustained growth in regional productivity and consolidation potential.

5.2.2. Measures to Enhance Living Space Utilization Efficiency

In the preliminary consolidation zones, which are scattered throughout Qian County, the basic living functions are relatively intact, but infrastructure weaknesses are widespread. These include low population density, limited resident satisfaction, damaged roads, and underdeveloped waste management systems. It is recommended that the government increase fiscal investment and establish dedicated funding to address infrastructure deficiencies, prioritizing the improvement of village roads, water supply, and electricity facilities [68]. Meanwhile, a waste management system combining garbage classification with centralized collection should be promoted. An appropriate number of waste bins and collection vehicles should be provided to ensure efficient operation and prevent environmental pollution. Village committees should take the lead in mobilizing residents through local self-governance mechanisms, encouraging active participation in road construction and maintenance. Low-cost and high-efficiency solutions should be adopted to enable sustainable rural infrastructure improvement and enhance the living environment.

In the priority consolidation zones, problems such as fragmented construction land and severe rural depopulation are prominent, and the living functions require urgent enhancement. Whole-village relocation and the development of centralized residential areas are recommended, supported by LC policies to promote rational allocation and transfer of homestead land [69,70], thereby improving land use efficiency. The government should formulate specific compensation and resettlement policies to protect residents’ rights and provide essential public services, including education, healthcare, and cultural facilities, to improve living convenience and service accessibility.

In the intensive consolidation zones, where the population density is low, construction land is highly fragmented, and resident satisfaction is poor, living functions are the weakest. Key areas such as Fengyang Town should be prioritized as pilot sites for residential integration and improvement of housing conditions. Diversified industrial projects and vocational skills training should be introduced to expand employment opportunities and increase rural income. Comprehensive rural infrastructure upgrades should also be implemented, including improvements to roads, electricity, water supply, and waste and sewage treatment systems. These efforts aim to create a clean, well-equipped rural environment and significantly enhance residents’ quality of life and overall living efficiency.

5.2.3. Measures to Enhance Ecological Space Utilization Efficiency

In the preliminary consolidation zones, mainly located in the central and southern parts of Qian County, improving ecological efficiency depends largely on enhancing residents’ perception of the ecological environment, especially air quality. Although the county has made significant progress in air pollution control in recent years, such as reducing industrial emissions, upgrading coal-fired boilers, controlling vehicle exhaust, and managing dust, additional efforts are needed to sustain and strengthen these achievements. Clean heating technologies, such as water-heated brick beds [71], should be promoted by local conditions and supported by targeted financial subsidies to reduce coal-related pollution. Furthermore, the reuse and management of straw resources should be improved, and agricultural waste recycling systems should be established to reduce secondary pollution caused by agricultural activities and enhance perceived ecological quality.

The priority consolidation zones are relatively spatially concentrated, which facilitates the implementation of systematic ecological interventions. These areas present complex ecological challenges and require integrated strategies. In typical areas such as Xinyang Town, the ecological restoration tasks outlined in the Qian County Spatial Ecological Restoration Plan (2021–2035) should be rigorously implemented. Efforts should focus on conserving and restoring ecosystems such as forests, wetlands, and rivers, establishing well-connected ecological corridors and habitat networks with high biodiversity. Functional nodes, such as forest belts and wetland restoration zones, should be prioritized to enhance system integrity. Additionally, optimizing land use patterns can help improve the stability and self-regulation capacity of ecosystems, thereby supporting the long-term delivery of ecosystem services [72].

The intensive consolidation zones are mostly located in densely populated and heavily built-up areas, such as CG Town and its surroundings. These zones exhibit relatively low potential for large-scale ecological restoration and thus require gradual, small-scale interventions [73]. Measures should prioritize reforestation, natural forest protection, and small watershed management. The promotion of organic fertilizer use and integrated crop–livestock systems should be encouraged through technical training and subsidy programs to improve soil quality and reduce agricultural non-point source pollution. Soil and water conservation projects, such as gully stabilization and terracing, should be implemented progressively to improve environmental conditions and enhance the ecological carrying capacity and restoration potential of the land.

In summary, the “Three Spaces–Three Zones” consolidation approach proposed in this study effectively addresses both the efficiency and quality issues currently faced by Qian County’s land system. It also integrates the concept of adaptive governance under the Anthropocene framework into the strategy design [74]. Moving forward, it is recommended to further advance dynamic consolidation models based on farmers’ behavior, community collaboration mechanisms, and ecosystem service feedback, strengthening the long-term resilience of policies and multi-stakeholder coordination capabilities [75]. This will enable land consolidation to truly become a key driving force for guiding sustainable rural transformation.

5.3. Limitations

Although this study presents theoretical innovations and practical contributions in constructing the evaluation framework and zoning methodology for LC, several limitations remain. One key limitation lies in the reliance on static indicators due to constraints in data availability, which may limit the ability to capture the dynamic evolution of land use systems fully [76]. Another limitation is the absence of simulation or validation regarding the long-term effectiveness of proposed consolidation strategies under future policy scenarios, which affects their applicability in spatial planning practices. Future studies could further explore the application of PLE spatial theory in dynamic land systems by integrating spatiotemporal simulation tools and dynamic data analysis [77]. This would allow for improved monitoring of land use changes and enhance the anticipatory and adaptive capacity of consolidation strategies [78]. Moreover, incorporating multi-agent game theory and performance evaluation mechanisms [79] may contribute to a more comprehensive and robust theoretical framework for LC.

Compared with related studies conducted in eastern coastal regions, the Guanzhong region faces more complex geographical conditions and greater ecological vulnerability in LC practices. This study introduces the PLE theory to establish a consolidation potential identification framework, which highlights the urgency of both ecological protection and the improvement of living spaces in the region. It also addresses the research gap in multidimensional evaluation and strategy formulation in Northwest China. This approach enhances the understanding of rural land system dynamics and provides technical support for differentiated and targeted consolidation efforts. Furthermore, it offers empirical evidence for the application of theories, such as multifunctional land use, spatial governance, and sustainable rural transition, in a localized context. Overall, the findings of this study contribute to optimizing rural spatial structures, improving land resource allocation efficiency [80], and enhancing ecological resilience. Particularly in addressing the complex disturbances in land systems under the Anthropocene context [81], this study provides a more resilient theoretical and practical approach for local sustainable governance. These contributions hold significant theoretical value and practical significance.

6. Conclusions

This study developed a land use efficiency evaluation model guided by the coordinated functions of PLE spaces. It provides a comprehensive depiction of the spatial coupling patterns and differentiation of consolidation potential across the three functional dimensions in Qian County. The results indicate that production space efficiency exhibits a pattern characterized by high values in the central area that gradually decrease toward the periphery. Living space efficiency shows a belt-shaped distribution, with higher levels in the eastern and western regions and relatively lower levels in the central area. Ecological space efficiency demonstrates a distinct north–south gradient, with significantly higher values in the northern region. Overall, comprehensive efficiency largely follows the spatial trend of the ecological dimension.

Based on this, this study delineated the prioritization and potential levels of land consolidation within the PLE functional spaces in Qian County, uncovering the composite demands resulting from the spatial overlap of these functions. Certain areas are subject to dual constraints of low living space efficiency and ecological fragmentation, necessitating coordinated infrastructure enhancement and ecological restoration. Other regions simultaneously experience inefficient agricultural utilization and substandard living environment quality, requiring integrated industrial upgrading and spatial reconfiguration. These results demonstrate that interactions among the PLE functions not only generate a complex land use pattern, but also pose comprehensive coordination challenges for land consolidation planning.

The identification of obstacle factors further revealed the primary limiting mechanisms within each spatial dimension. Production space efficiency is mainly constrained by factors such as the density of productive land use, cultivation rate, and land use intensity. Living space efficiency is influenced by the adequacy of infrastructure and transportation accessibility. Ecological space faces challenges related to ecosystem fragmentation and insufficient environmental security. These findings provide a scientific basis for implementing differentiated consolidation strategies and support the prioritization of interventions and the allocation of resources.

In the context of the Anthropocene, the interwoven challenges of climate change, social transformation, and ecological degradation present complex and systemic risks [82]. Traditional LC approaches centered on engineering logic and resource allocation often fall short in meeting the adaptive demands of spatial governance. By incorporating perspectives of functional coordination and system resilience, this study elevates LC to a theoretical dimension of spatial governance. It expands its role as an institutional tool for addressing complexity. The findings offer empirical support for spatial consolidation strategies in Qian County and similar regions, while also contributing to the localized implementation of sustainable rural transition and spatial transformation theories.