Ecological Priority-Oriented Performance Evaluation of Land Use Functions and Zoning Governance by Entropy–Catastrophe Progression Model

Abstract

As land use performance undergoes abrupt shifts due to the transition from growth-centric to ecology-focused development, traditional evaluation methods often overlook the catastrophe characteristics of urban complex functions in the process of system evolution, resulting in land governance strategies being unable to adjust rapidly to adapt to regional transformation. To address this limitation, this study develops an ecological priority-oriented performance evaluation system for land use Production–Living–Ecological (PLE) Functions and introduces the Entropy–Catastrophe Progression model to conduct comprehensive measurement and obstacle diagnosis of land use PLE function performance in the Yangtze River Economic Belt of Hubei Province, a typical region, thereby proposing differentiated control strategies. The results show the following: (1) The Entropy–Catastrophe Progression Model can accurately measure the spatiotemporal evolution of land use PLE function performance during the development transition period. (2) The average value of land use PLE function performance presents a fluctuating upward trend, increasing from 0.812 (Poor level) in 2014 to 0.924 (Good level) in 2023. (3) Significant spatial disparities are observed, exhibiting a gradient decrease from provincial capital centers, provincial sub-centers, and ecological economic belts to metropolitan areas. (4) The key obstacles restricting performance improvement include a weak foundation for high-quality tertiary industries, insufficient intensity in environmental purification, and an inadequate supply of high-level living services. These can be addressed by dividing high-quality service optimization zones, green industry enhancement zones, and ecology–economy synergy zones, and establishing differentiated governance mechanisms to improve land use PLE function performance. This study provides theoretical guidance and empirical support for optimizing pathways for urban–rural land use and management.

1. Introduction

Since the reform and opening-up, China has achieved remarkable progress in both social and economic fields. However, this progress has been accompanied by the intensification of urban challenges, including population pressure, spatial imbalances, and environmental pollution [1,2]. Against this backdrop, issues of land use conflict and dysfunction have become increasingly prominent [3]. In fact, the central government has consistently underscored the necessity of developing a territorial spatial pattern characterized by intensive and efficient production space, livable and moderate living space, and beautiful and ecological space. Furthermore, the central government has proposed the overarching goal of establishing a spatial governance system that encompasses spatial planning, use control, and differentiated performance appraisal. The objective of spatial governance is to achieve efficiency improvement and spatial justice [4], and the premise of efficiency improvement is fundamentally predicated on performance management and the optimization of functions [5]. As a critical resource for human survival and development, the functions of land can be categorized into production functions, living functions, and ecological functions [6], which can effectively integrate national spatial planning concerning the Production–Living–Ecological framework and the Regional Transformation and Development Strategy. Therefore, leveraging the advantages of land use in PLE functions, and accurately assessing and optimizing the performance of limited land resources, are essential for enhancing spatial governance capabilities [7].

In recent years, performance management has gained significant attention as an innovative approach within the framework of the new public management movement [8]. It has been defined in relation to system operation effectiveness, particularly following the US Benchmarking Research Report (1997), which characterized performance as the process of achieving predetermined goals. In land management, performance encompasses both the processes and outcomes of spatial governance, reflecting the effectiveness of institutional arrangements for spatial resource utilization. It is widely accepted that land use functions represent the external manifestation of internal structural elements, which are characterized by their comprehensive, diverse, and spatial attributes [9]. Currently, scholars from multiple disciplines have examined the functional characteristics of urban areas from diverse perspectives. Economists contend that urbanization should enhance a city’s diverse functions, particularly agglomeration and marketization, thereby directing resources such as land, population, industry, and capital towards urban centers [10,11]. Geographers contend that there exists a reciprocal relationship between spatial structure and economic performance; specifically, the spatial structure influences economic performance, while economic performance, in turn, prompts adjustments in spatial utilization behaviors and the optimization of functional structures [12,13]. From the standpoint of urban planning, it was posited that the complementary nature of land use functions facilitates a high degree of specialization and close coordination in the city’s operations. The efficient performance of urban functions depends on a rational spatial form [14,15]. Consequently, the land use performance of the city exhibits different patterns based on Production–Living–Ecological space [16]. Barbosa et al. constructed the “urban structure–spatial justice” coupling model, arguing that the polycentric structure was more conducive to equitable growth [4]. Emphasizing the concept of urban ecological priority development is a manifestation of the value of spatial justice. Therefore urban ecological space is becoming increasingly important. As Ahern pioneered the spatial connotation of Green Infrastructure as a “multi-scale network structure” [17], Green Infrastructure serves as a critical link between natural and urban systems, with literature focusing on its definitional evolution, functional synergies, and practical applications. Grabowski et al. proposed that Green Infrastructure could achieve ecological–social functional synergy to meet environmental and human needs by analyzed U.S. urban planning documents [18]. Belmeziti et al. empirically demonstrated that the components of urban green spaces directly influence the urban services [19]. Young synthesized Green Infrastructure planning practices from major U.S. cities, highlighting three strategies including multi-scale coordination, interdepartmental collaboration, and public participation [20]. Parker et al. showed that public Green Infrastructure enhanced urban livability through a systematic quantitative review [21]. PLE spaces are the result of the synergistic interaction between natural systems and socioeconomic systems [22,23]. Thus, green space is so important in urban land functions that we must take into account the value concept of ecological priority in conducting land use performance evaluation. In this context, researchers have explored various aspects, including the development of theoretical frameworks [24,25], the construction of classification systems [26], the identification of functions [27], and the optimization of patterns [28]. Notably, the differentiated arrangement of PLE spaces is fundamentally a consequence of the implementation of land use regulatory systems. Assessing the performance of these PLE spaces can characterize and evaluate the modernization level of land spatial governance. From a benefit-oriented perspective, scholars have studied the evaluation systems and characteristics of land use performance through various methodologies, such as the weighted factor method [29] and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) [30]. These studies provide comprehensive guidance for the management practices of land use performance. However, diverse foundational endowments and the multi-suitability of land use contribute to the diversification of spatial functions and the imbalance in development levels. Amidst the transformation of value concepts, the internal structure and functions of land are likely to undergo significant changes, giving rise to differentiated external performance indicators. Nevertheless, existing research on land use performance evaluation often overlooks the systematic mutations brought about by a shift in value-based objectives.

Currently, China attaches great importance to ecological conservation. The 20th National Congress of the Communist Party of China highlighted the nation’s commitment to prioritizing ecological protection, conserving resources, and pursuing green and low-carbon development. Notably, the 19th National Congress of the Communist Party of China proposed accelerating reforms to establish an ecological civilization system and the vision of constructing a Beautiful China. Xi Jinping, General Secretary of the Communist Party of China Central Committee, has consistently reiterated the necessity for China to adopt a path of ecological priority and green development. The transition to a green development pattern necessitates industrial restructuring. Consequently, adapting to these transformations will likely require adjustments in spatial functions. The foundation of land use performance is rooted in the land element–structure–function system, which is further influenced by the complex interplay of social, economic, and ecological systems. Thus, it is imperative to apply the methods of Evolving Systems Theory to elucidate the production mechanisms and measurement systems of land use multifunctional performance. This study accordingly advocates developing a framework to evaluate the performance of land use PLE functions, guided by the principle of ecological prioritization in the transition toward green development. Additionally, we employ Catastrophe Models to analyze performance characteristics and identify potential obstacles. Targeted policy recommendations are proposed to promote the coordinated and sustainable development of land use space alongside the economy, society, and ecology. This research aims to provide methodological support for the modernization of land use spatial governance systems.

In comparison to the existing literature, this study presents three key contributions and innovations as follows: Firstly, it innovatively introduces a nonlinear Entropy–Catastrophe Progression Model to evaluate land use performance under the ecological priority orientation, addressing the limitation of traditional evaluation methods that neglect the nonlinear “catastrophe–feedback” relationships within land use systems. Secondly, starting from the “space–structure–function” perspective, it constructs an evaluation system for land use Production–Living–Ecological function performance under the ecological priority orientation, providing a theoretical analytical framework for accurately characterizing land use performance during the transition period. Thirdly, from three dimensions including spatial optimization, potential exploration, and intensive and economical utilization, it proposes differentiated governance paths and potential policy optimization directions, offering a reference for promoting the coordinated development of land use and regional development.

2. Methods and Data

2.1. Study Methods

2.1.1. The Analytical Framework

Land use spaces possess distinct public attributes, with their functional optimization inherently linked to public governance tools. Incorporating performance management to examine land use spatial issues facilitates the exploration of sustainable pathways through the lens of public governance. Performance encompasses both value rationality and tool attributes [8]. Value rationality involves acting based on individual beliefs and needs, while tool attributes focus on using and incidental consequences as the basis for decision-making [31]. Drawing on the experience of developed countries, ecological justice with green infrastructure at its core has gradually become the goal pursued by urban development [4,21], thereby expanding the multi-functional attributes of urban space [14,19]. As China commits to green development, balancing ecological protection with economic growth has become an urgent challenge in land governance. A sound ecological environment serves as the foundation for production and living, as well as a vital source of material for economic development. Ecological prioritization entails emphasizing ecological protection amidst economic growth. Promoting high-quality, sustainable economic development requires identifying resource endowments, development stages, and efficiency characteristics to manage and guide these processes effectively. Land, as the foundation and essential component of urban and rural space, has internal structure, external dynamics, and management modes that are intricately linked to the goals of ecological civilization. Green development fundamentally requires that land resource utilization and management embody an orientation toward ecological justice. It means that while supporting economic production, we must enhance the environmental sustainability of land, protect arable land, prioritize social equity, and emphasize differentiated and refined development. As a core tool for measuring the efficiency and sustainability of land resource allocation, the value orientation of Land Use Performance Evaluation directly affects the fairness and long-term viability of regional development. Ecological Justice, as the intersection of environmental ethics and social equity, provides in-depth value-rational guidance for Land Use Performance Evaluation, promoting the transformation of the evaluation system from “efficiency first” to “equal emphasis on social equity and green development”. By emphasizing the fair allocation of ecological resources, Ecological Justice requires that land performance evaluation should balance the spatial layout of economic, social, and ecological aspects, with particular consideration given to the fairness of social services and the inclusiveness of ecological services. To this end, ecological function indicators such as the scale of urban green infrastructure, greening level, proportion of wetlands and water areas, energy consumption level, and sewage treatment need to be incorporated into the evaluation system to reflect the guiding role of eco-priority indicators. In short, the concept of eco-priority provides a value anchor for indicator selection and weight allocation, while model indicators provide path guidance and a correction tool for ecological justice. In the New Era, the value rationality of land use functional performance management is fundamentally oriented towards the pursuit of ecological justice within land use processes. This paradigm emphasizes a transition towards high-quality economic development, environmental sustainability, and social equity. Instrumental rationality is manifested in the selection of specific methods, optimization pathways, and management mechanisms. This involves developing a performance evaluation index system based on an Element–Space–Structure–Function framework, which reflects spatial functions—economic production, social life, and ecological protection. Additionally, we utilize the Entropy–Catastrophe Model to address the limitation of macro performance assessments in objectively capturing the impacts of systemic transitions. Furthermore, this assessment is integrated with an obstacle diagnosis model for regional zoning, thereby facilitating the establishment of a differentiated performance management mechanism. The ultimate objective is to achieve the most efficient optimization and management strategies within the constraints of specific resource endowments. Specifically, elemental data, such as the scale of various land types, can be acquired by extracting current land patch in the land use data set. Data on Production–Living–Ecological spaces can be obtained by integrating land use data and urban construction statistical yearbooks of various cities. The functional performance indicators are obtained by collecting data from the statistical yearbooks of these cities. For detailed sources of each indicator, please refer to the section “Research Scope and Data Sources”. The overall research framework is illustrated in Figure 1.

2.1.2. Development of a Performance Evaluation Index System

Cities dominated by commercial, industrial, and residential functions tend to prioritize economic and social advancement, with a strong emphasis on economic growth. In light of the public’s desire for an improved life and a high-quality environment, the functional value of land is subject to change. The development of the index system is guided by System Theory—specifically its elements–structure–function framework—and informed by mechanisms inherent in China’s land use control system. At the micro level, performance is shaped by diverse land use elements, including commercial, industrial, residential, ecological, and public service lands. At the meso level, this performance manifests as economic production, social life, and ecological provision. At the macro level, these interactions ultimately give rise to production, living, and ecological (PLE) spaces. Accordingly, we develop a performance evaluation index system for land use PLE functions, with specific quantitative indicators derived from the three dimensions of production, living, and ecology. Based on the studies by Fu et al. [25] and Lu et al. [32], and in accordance with Systematic Principle (covering the full dimensions of PLE functions), Scientific Principle (connotation matching and logical rigor), and Operational Principle (conciseness, clarity, and easy accessibility of data), this study has constructed the PLE functions indicator system, as illustrated in

Table 1. The performance evaluation index system of land use PLE functions.

Element Space	Function Layer	Criterion Layer	Index Layer	Attribute	Calculation Method	Unit	Weight
Production space	Economic Production (A1)	Funds Input (B1)	Fixed Asset Investment per Unit Area (C1)	+	Fixed assets investment/LA	RMB MM/km2	0.0417
			Construction and Maintenance Funds per Unit Area (C2)	+	Construction and maintenance funds/LA	RMB MM/km2	0.0496
		Industrial Production (B2)	Industrial Land Ratio(C3)	+	Industrial land area/LA	%	0.0337
			Industrial Output per Unit Area (C4)	+	Industrial output value/LA	RMB MM/km2	0.0542
		Commercial Production (B3)	Commercial Land Ratio (C5)	+	Commercial land area/LA	%	0.0475
			Tertiary Industry Output per Unit Area (C6)	+	Tertiary industry output value/LA	RMB MM/km2	0.0781
Living space	Living Services (A2)	Housing Security (B4)	Residential land Ratio(C7)	+	Residential land area/LA	%	0.0391
			Residents’ Income (C8)	+	The per capita disposable income of residents	RMB	0.0575
			Population Urbanization Rate (C9)	+	Permanent urban population/total population	%	0.0399
		Public Services (B5)	Public Service Land Ratio (C10)	−	Public administration and service land area/LA	%	0.0300
			Bed Density in Healthcare Institutions (C11)	+	The quantity of beds in healthcare institutions/LA	pcs./km2	0.0569
			Employment per Unit Area (C12)	+	Employment in the secondary and tertiary sectors/LA	pcs./km2	0.0257
			Traffic Land Ratio (C13)	+	The area of road traffic facilities/LA	%	0.0400
		Educational Services (B6)	Students per Unit Area (C14)	+	Enrolled students/LA	10,000 persons/km2	0.0449
			Availability of Books per Unit Area (C15)	+	Public library holdings/LA	unit/km2	0.0574
Ecological Space	Ecological Protection (A3)	Greening Resources (B7)	Greening Degree (C16)	+	Green land area/LA	%	0.0566
			Cultivated Land Ratio (C17)	+	Cultivated land at year-end/LA	%	0.0434
			Water Area Ratio (C18)	+	Water area/LA	%	0.0450
		Friendly Environment (B8)	Energy Consumption per Unit Area (C19)	−	Energy consumption values/LA	ton of standard coal/m2	0.0403
			Sewage Treatment Capacity per Unit Area (C20)	+	Total volume of treated sewage discharge/LA	cubic meter/hectare	0.0606
			Household Waste Treatment Capacity per Unit Area (C21)	+	Total volume of treated household waste/LA	ton/hectare	0.0579

Note: The “LA” in the Calculation method column refers to the total land area. The “+” in the Character column means a positive index, and “−” means a negative index.

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2.1.3. Entropy–Catastrophe Progression Model

Catastrophe Theory is a mathematical discipline established by the French mathematician René Thom in 1975 [33]. This theory is characterized by classifying critical points based on a systematic potential function, analyzing discontinuous features in proximity to various critical points, and describing and predicting qualitative changes arising from continuity disruptions. As a comprehensive evaluation technique derived from Catastrophe Theory, the Catastrophe Progression Method has been widely applied in areas such as resource performance evaluation [34], complex system assessment [35], and other multi-objective comprehensive evaluations [36]. Compared with Entropy-TOPSIS, DEA, or Coordination Coupling Models, Entropy–Catastrophe Progression Model has certain advantages in addressing the measurement of abrupt change characteristics of land use performance under conceptual transformation, as detailed in

Table 2. Comparison of Relevant Models.

Model Types	Theoretical Basis	Core Advantages	Main Limitations
Entropy–Catastrophe Progression Model [37]	Information Entropy Theory and Mutation Theory (Mutation Series Model)	1. Integration of Objective Weighting and Nonlinear Processing: The entropy method avoids subjective weight biases, while the mutation series model handles nonlinear relationships between indicators using normalization formulas (e.g., cusp mutation, swallowtail mutation) without preset weights, requiring only clarification of indicator priority rankings. 2. Hierarchical Evaluation Logic: Decomposes complex systems into “target layer-criterion layer-indicator layer,” suitable for multi-dimensional, multi-level comprehensive evaluation. 3. Strong Result Interpretability: Intuitively reflects the contribution of indicators at all levels to the comprehensive result through membership values, facilitating the identification of shortcomings.	1. Indicator Ranking Sensitivity: The classification of indicator hierarchical rankings (e.g., “main control indicators” vs. “secondary indicators”) directly affects mutation normalization results. 2. Normalization Formula Dependence: Different mutation types (cusp, swallowtail, butterfly mutations) require multiple mutation models for normalization formulas.
Entropy-TOPSIS [30]	Information Entropy Theory (Entropy Method) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)	1. Intuitive Relative Ranking: Quantifies relative superiority by calculating distances between evaluation objects and “positive/negative ideal solutions,” with results easy to compare. 2. Strong Data Adaptability: Handles indicators with different dimensions and magnitudes.	1. Ideal Solution Dependence: The selection of positive/negative ideal solutions is subjective, and deviations in ideal solutions may amplify evaluation errors. 2. Lack of Absolute Scoring: Only outputs relative rankings and cannot provide absolute superiority grades for evaluation objects.
DEA (Data Envelopment Analysis) [37]	Linear Programming Theory and Production Economics	1. No Preset Production Function: Directly constructs production frontiers using input–output data to evaluate the relative efficiency of Decision-Making Units (DMUs), avoiding subjective weight interference. 2. Multi-Input Multi-Output Processing: Excels at analyzing “multi-input–multi-output” systems (e.g., industrial production, public service efficiency).	1. Relative Efficiency Limitation: Only evaluates “relative efficiency” of DMUs (compared to the optimal unit in the sample) and cannot derive absolute efficiency values. The reliability decreases when the sample size is small. 2. Radial and Angular Limitations: Traditional DEA (e.g., CCR model) only considers radial improvements (proportional changes in inputs/outputs) and struggles with undesirable outputs (e.g., pollutants). Extreme values may distort the production frontier, affecting overall evaluation.
Coordination-Coupling Model [38]	Systems Theory and Synergetics	1. Focus on Inter-System Interactions: Based on systems theory and synergetics, it quantifies the interdependence intensity (coupling degree) and coordinated development level (coordination degree) of multiple subsystems (e.g., economic-ecological systems, social-environmental systems). 2. Visualization of Dynamic Coordination Relationships: Intuitively presents inter-system synergy through coupling-coordination grades (e.g., “extreme imbalance-high-quality coordination”).	1. Strong Weight Dependence: The determination of subsystem indicator weights (e.g., entropy method, AHP) directly affects coupling and coordination results, with subjective weighting prone to bias; 2. Lack of Coordination Mechanisms: Relies entirely on data evaluation, easily obscuring the essence of coordination (e.g., high coupling does not equate to high coordination).

.

As an ethical framework for measuring the fairness of resource allocation and the rationality of responsibility sharing, the combination of ecological justice and the entropy-mutation model (as a quantitative analysis tool) essentially lies in transforming abstract “fairness and justice” into an operational and early-warning systematic evaluation process. The core function of the entropy-mutation model is to objectively assign weights through the entropy method (eliminating subjective bias of indicators) and identify critical points via mutation theory (judging system stability), where these “critical points” must be based on the “bottom-line requirements” of ecological justice. The destruction of ecological justice is often not gradual but undergoes a “mutation” at a certain threshold (e.g., sharp changes in energy consumption, breaching the red line of cultivated land protection, etc.). Through topological structures such as cusp mutation and swallowtail mutation, the mutation model identifies the critical points where the ecological justice system shifts from “stable fairness” to “unbalanced injustice,” quantitatively pinpointing the “weak links” of ecological justice in a certain region, thereby guiding policy intervention and formulating differentiated regulatory strategies. In general, the Entropy–Catastrophe Progression method should take into account the relative importance of each evaluation index. The entropy method serves as an objective weighting technique, wherein the utility value of index information entropy is determined by a judgment equation constructed from the index values, specifically the index weights. The weights derived from the entropy method quantify the significance of each index, thereby mitigating the subjectivity inherent in manual weighting and enhancing the objectivity of the research findings. The detailed calculation steps are provided as follows.

$$\left\{\begin{array}{c}{w}_i={\displaystyle \frac{1-H_i}{m-{\displaystyle {\displaystyle \sum }_{i=1}^m}{H}_i}}\\ H_i={\displaystyle \frac{{\displaystyle {\displaystyle \sum }_{j=1}^n}{f}_{ij}\ln f_{ij}}{\ln n}}\\ f_{ij}={\displaystyle \frac{r_{ij}}{{\displaystyle {\displaystyle \sum }_{j=1}^n}{r}_{ij}}}\end{array}\right.$$

(1)

In these equations, $r_{ij}$ represents the standardized data matrix, while $H_i$ represents the entropy of the $ith$ indicator. $f_{ij}$ represents the proportion of the standardized value of the $ith$ indicator in region $j$. In addition, when $f_{ij}=0$, $f_{ij}\ln f_{ij}=0$. $w_i$ represents the weight assigned to the $ith$ indicator.

According to Catastrophe Theory, four primary catastrophe models are typically applicable when the number of control variables does not exceed four (

Table 3. Common potential functions and normalized formulas of catastrophe models.

Catastrophe Model	Variables	Potential Functions	Normalization Formula
Fold Catastrophe	1	$f\left(x\right)=x^3+ax$	$x_a=\sqrt{a}$
Cusp Catastrophe	2	$f\left(x\right)=1/4x^4+1/2a{x}^2+bx$	$x_a=\sqrt{a}$, $x_b=\sqrt[3]{b}$, where $w_a>w_b$
Swallowtail Catastrophe	3	$f\left(x\right)=1/5x^5+1/3a{x}^3+1/2b{x}^2+cx$	$x_a=\sqrt{a}$, $x_b=\sqrt[3]{b}$, $x_c=\sqrt[4]{c}$, where $w_a>w_b>w_c$
Butterfly Catastrophe	4	$f\left(x\right)=1/6x^6+1/4a{x}^4+1/3b{x}^3+1/2c{x}^2+dx$	$x_a=\sqrt{a}$, $x_b=\sqrt[3]{b}$, $x_c=\sqrt[4]{c}$, $x_d=\sqrt[5]{d}$, where $w_a>w_b>w_c>w_d$

). These models utilize a normalized formula for evaluation. The state variables of the system can be ascertained incrementally, following the principles of non-complementary and complementary. Given the complementarity of the underlying indexes and the non-substitutability of the three spatial functions within the factor layer, the state variables in both the criterion layer and the index layer are calculated as the average of the corresponding catastrophe progression values. In contrast, the state variables of the function layer are established based on the principle of selecting the maximum from the minimum values.

Accordingly, an Entropy–Catastrophe Model was constructed to evaluate the land use PLE performance (Figure 2). In the Entropy–Catastrophe method, the appropriate catastrophe model for an upper-layer index is determined by the number of lower-layer indexes it encompasses. For instance, the “Economic Production” dimension, measured by three indexes (Funds Input, Industrial Production, and Commercial Production), is calculated using the Swallowtail Catastrophe model. Similarly, the “Commercial Production” index, which consists of two sub-indexes, is evaluated with the Cusp Catastrophe model.

2.1.4. Evaluation Criteria

Since the final evaluation scores derived from the Entropy–Catastrophe Progression Model tend to cluster within a high value range, it is essential to establish grading criteria to interpret these results. With reference to the relevant literature [39], the performance evaluation standards are classified into five grades: Excellent, Good, Middle, Low, and Poor. The corresponding value ranges for each performance grade are defined in

Table 4. Five grades of the comprehensive performance of land use PLE functions.

Level	Excellent	Good	Middle	Low	Poor
Evaluation Standards	0.96~1	0.92~0.96	0.88~0.92	0.84~0.88	0~0.84

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2.1.5. Obstacle Diagnosis Model

The primary objective of performance management is to identify the factors that impede performance levels, categorize regions based on their development stage, and develop targeted strategies and policies to enhance regional spatial functions. To this end, an Obstacle Diagnosis Model is proposed to ascertain the barriers constraining comprehensive land use performance. The model is formulated as follows:

$$\left\{\begin{array}{l}{O}_j={\displaystyle \frac{I_j\times W_j}{{\displaystyle \sum _{j=1}^m\left(I_j\times W_j\right)}}}\times 100\%\\ I_j=1-y_j\\ U_i={\displaystyle \sum _{j=i}^n{O}_{ij}}\end{array}\right.$$

(2)

In the model,$O_j$ represents the obstacle degree of indicator $j$ relative to the overall target. $I_j$ represents the gap between indicator $j$ and the optimal index. $W_j$ represents the weight of indicator $j$ to the overall objective. $y_j$ represents the normalized value of indicator $j$, and $m$ is the total number of indicators. $U_j$ represents the obstacle degree of the $ith$ criterion layer in relation to the target. $n$ is the number of indicators encompassed within each criterion layer.

2.2. Research Scope and Data Sources

This study selects Hubei Province as the case study area, a region known as the “Crossroads of Nine Provinces”. It is located in central China within the middle reaches of the Yangtze River. In 2023, the province’s Gross Domestic Product (GDP) reached 5.58 trillion yuan, with a permanent resident population of 58.38 million. The province comprises sixteen cities, including Wuhan, Huangshi, Shiyan, Yichang, Xiangyang, Ezhou, Jingmen, Xiaogan, Jingzhou, Huanggang, Xianning, Suizhou, Enshi, Xiantao, Qianjiang, Tianmen (Figure 3). It has a geographical advantage of connecting the east and the west, as well as the south and the north, and is an important support area for national strategies like the Rise in Central China and the Yangtze Economic Belt initiative. Hubei is endowed with abundant ecological resources, featuring the longest corridor of the Yangtze River main stream at 1061 km. It hosts the Three Gorges Dam and serves as a critical water source for the South-to-North Water Diversion Project. It contains 755 natural lakes exceeding 100 acres in size, ranking third in China for its total number of freshwater lakes. The province also encompasses National Key Ecological Function Areas, such as the Qinba, Three Gorges Reservoir, Wuling, and Dabie Mountain areas. With a forest coverage rate of 42.4%, the province surpasses the national average by approximately 20 percentage points. Currently designated as a pilot area for ecological prioritization, Hubei is exploring a path of coordinated development between new urbanization and the ecological economy. The economic transformation necessitates a shift in land use spatial governance from extensive models to more refined approaches. This transition may intensify existing land use conflicts, thereby necessitating adjustments in land use functions, patterns, and governance strategies.

The data for this analysis were primarily obtained from land use statistics for each city and construction land data for Hubei Province, as published in the China Urban Construction Statistical Yearbook from 2014 to 2024. And socio-economic data for cities within Hubei were sourced from the Hubei Statistical Yearbook for the corresponding period. It should be noted that the extensive forest land in the Shennongjia Forestry District was excluded from consideration in this analysis. The data sources and details are as follows (

Table 5. Data sources and explanations.

Data Type	Data Name	Indicator	Year (s)	Data Accuracy	Source
Land use data	Land use	Land use area; The area of road traffic facilities; Green land area; Water area	2013–2023	30 m	https://www.resdc.cn/ URL (accessed on 13 November 2025)
Social, economic, and ecological data	Hubei Provincial and Every City’s Statistical Yearbook	Fixed assets investment; Industrial output value; The per capita disposable income of residents; Permanent urban population; The quantity of beds in healthcare institutions; Employment in the secondary and tertiary sectors; Enrolled students; Cultivated land at year-end; Energy consumption values; Total volume of treated sewage discharge; Total volume of treated household waste	2013–2023	Every city	https://tjj.hubei.gov.cn/tjsj/sjkscx/tjnj/qstjnj/index.shtml URL (accessed on 13 November 2025)
	China Urban Construction Statistical Yearbook	Construction and maintenance funds; Industrial land area; Commercial land area; Residential land area; Public administration and service land area; The area of road traffic facilities; Green land area		Every city	http://www.tjnjw.com/hangyefb/c/ URL (accessed on 13 November 2025)

).

3. Results Analysis

3.1. Analysis of the Multifunctional Performance Results

Figure 4 presents the comprehensive performance values of land use spaces in Hubei Province from 2014 to 2023, as evaluated by the Entropy–Catastrophe Progression Model. The key findings are as follows:

Time-series analysis revealed an upward trend in the comprehensive performance. The average performance of land use PLE functions for all cities increased from 0.812 in 2014 to 0.924 in 2023. The comprehensive performance moved dramatically, shifting from the “Poor” to the “Good” level. A comparative analysis of selected years indicates that the comprehensive performance of certain cities experienced a temporary decline in 2017, which was followed by an increase in 2018. That same year, several national ministries jointly issued the “Eco-environmental Protection Plan for the Yangtze River Economic Belt”, outlining a strategy of ecological priority and green development. Accordingly, Hubei Province intensified the implementation of ecological conservation measures, including the “Grain for Green” and “Returning Farmland to Lakes” programs, and increased investment in ecological protection. Analysis of pertinent indicators reveals that Hubei has been actively pursuing economic development while also prioritizing investment in and protection of the regional environment. The GDP of the province grew from 2737.92 billion yuan in 2014 to 5580.36 billion yuan in 2023—its economic strength has been continuously rising. Meanwhile, energy consumption per unit of GDP fell from 0.60 to 0.30 tonnes of standard coal per 10,000 yuan. The share of secondary industry declined from 46.6% to 36.2%, while that of the tertiary industry rose from 41.5% to 54.7%. These shifts reflect a gradual improvement in the ecological environment and a distinct optimization of the industrial structure. Furthermore, land’s economic output and its intensification level have gradually improved, which has significantly contributed to the enhancement of land use spatial performance. Overall, the comprehensive performance of land use PLE functions in Hubei Province has improved, though the growth rate was relatively moderate, and it has not yet reached an optimal level. Most cities have shown an upward trend in land use performance, with enhanced stability in high-performance (including Excellent level and Good level) ratings, such as the number of cities with Excellent level and Good level ratings increased in 2023 compared to 2014. The results indicate that land use has shifted from “short-term economic priority” to “long-term ecological sustainability,” reflecting the protection of ecological rights and interests of future generations (progress in intergenerational justice).

Spatially, significant disparities exist in the performance of land use PLE functions, exhibiting a gradient-decreasing pattern from the provincial capital center, through provincial sub-centers and ecological economic belts, to satellite cities. Moreover, the internal imbalance of comprehensive performance within the Wuhan Metropolitan Area is more pronounced than in the eco-tourism region of northwest Hubei, as indicated by its higher standard deviation. This spatial pattern corresponds to the industrialization and urbanization gradients across Hubei Province. In terms of performance, Wuhan significantly outperforms other regions, progressing from a moderate level in 2014 to an excellent level by 2023, reflecting substantial improvement over the decade. As the provincial capital and central city, Wuhan’s distinct advantages in land policy, resource allocation, and locational conditions enhance its internal spatial utility. Following Wuhan, cities like Xiangyang and Yichang demonstrate intermediate performance levels. These cities have shown substantial improvement, progressing from a “Poor” grade in 2014 to a “Good” grade in 2023. As sub-central urban hubs in Hubei, these agglomerations play a pivotal role in the Hanjiang Ecological Economic Belt. However, compared to Wuhan, their development stages and models remain in the early stages, indicating their substantial potential for future economic and ecological advancement. The third tier includes Huanggang, Xiaogan, Jingmen, Shiyan, Jingzhou, and Huangshi, where performance improved from “Poor” in 2014 to “Middle” in 2023. This area represents a key zone and resource characteristic region within the Hubei Ecological Economic Belt. More proactive industrial policies, social services commensurate with city scales, and a focus on food security and ecological protection facilitate the coordinated development of PLE spaces here. Finally, the comprehensive performance of Ezhou, Tianmen, Xiantao, Xianning, Suizhou, Enshi, and Qianjiang is relatively Poor. While resource-endowed, the siphon effect produced by certain smaller cities in proximity to Wuhan is significant, which impedes the effective expansion of their spatial functions in land use. This situation suggests that, under the guidance of ecological prioritization, it is essential to explore how to effectively allocate resources and achieve coordinated regional development in the formation of urban agglomerations. From the perspective of spatial performance results, with the gradual deepening of the concept of ecological priority, the gaps in land use performance among cities have gradually narrowed. In particular, cities relying on ecological resources, such as ecological economic zones, have made progress in capital investment, resource utilization, and development of ecological industries, and ecological coordination in the overall region has gradually moved towards balance.

From the perspective of ecological justice, the implementation and promotion of policies prioritizing ecological considerations have significantly enhanced ecological spatial performance across cities in Hubei Province. This improvement has gradually aligned with economic and social functions. Construction land provides the fundamental spatial framework for urban economic development, and its scale serves as a direct indicator of the state of spatial functions. Consequently, when considering the scale of construction land and the level of economic development across various regions, the spatial performance of the city presents stage-specific characteristics. The relationship between the scale of urban construction land and the stage of economic development can be described by an S-shaped logistic curve. Throughout this evolution, the functions of land use space transition from a state of “high coupling and low coordination” to “low coupling and low coordination”, ultimately evolving into a state of “high coupling and high coordination” (Figure 5). This transformation mirrors the hierarchy of fundamental human needs. Initially, in early development stages, the primary focus is on survival needs. Economic production efficiency is often prioritized at the expense of ecological integrity, social services, and equity. As society and the economy advance, there is a noticeable shift towards a greater emphasis on quality social services and ecological amenities, with increasing focus on spiritual needs, social justice, and ecological justice. Furthermore, this evolution illustrates a transition in urban development paradigms, moving from an economic priority to a social priority, and ultimately to an ecological priority. The goal is to achieve a harmonious balance among these three dimensions.

3.2. Obstacle Diagnosis and Zone Optimization

3.2.1. Obstacle Factor Diagnosis

Based on Equation (2), the obstacle degree of the indicator layer of the land use PLE functional performance for each region in Hubei Province in 2023 was calculated, with the top five factors in each region presented in descending order (

Table 6. Obstacle degree of the main indicator layer of land use PLE functional performance.

Region	Obstacle Factors and Obstacle Degree (%)					Region	Obstacle Factors and Obstacle Degree (%)
Wuhan	C11	C19	C17	C10	C16	Jingzhou	C6	C20	C4	C8	C15
	18.46	15.63	14.40	12.99	9.43		15.70	10.35	7.91	7.65	7.01
Huangshi	C6	C21	C20	C11	C17	Huanggang	C6	C20	C8	C15	C14
	14.44	8.06	7.24	6.76	6.40		12.34	10.24	9.20	8.98	7.71
Shiyan	C6	C8	C4	C20	C21	Xianning	C6	C20	C8	C15	C2
	15.04	8.48	7.50	6.99	6.86		13.24	10.92	8.57	7.44	6.40
Yichang	C6	C20	C11	C14	C21	Suizhou	C6	C20	C8	C15	C16
	14.66	11.69	7.90	7.78	7.27		13.76	8.94	8.56	7.49	7.49
Xiangyang	C6	C20	C21	C8	C11	Enshi	C6	C20	C8	C15	C16
	15.62	10.06	8.18	7.51	7.35		11.50	9.65	9.06	8.43	7.05
Ezhou	C6	C20	C16	C11	C21	Xiantao	C6	C15	C20	C11	C8
	14.41	10.48	7.48	7.42	7.38		13.91	9.23	8.77	7.77	7.66
Jingmen	C6	C20	C21	C8	C14	Qianjiang	C6	C20	C15	C21	C11
	12.91	9.55	8.18	7.15	7.10		13.51	10.33	8.49	7.42	7.30
Xiaogan	C6	C20	C8	C15	C21	Tianmen	C6	C20	C15	C8	C21
	14.44	8.27	7.54	6.69	6.50		13.49	11.45	9.79	9.59	6.18

). The results indicate that the inadequate foundation of a high-quality tertiary industry is the primary obstacle to improving land use PLE functional performance. Except for Wuhan, the primary obstacle to the comprehensive performance of other regions is Tertiary Industry Output per Unit Area (C₆). The development level of the tertiary industry is a critical metric for regional economic quality. Enhancing tertiary industry output requires a robust industrial foundation for support. From an economic structural perspective, many regions in Hubei Province predominantly prioritize the secondary industry, including sectors such as automobile manufacturing and mining, resulting in a relative deficiency in the tertiary industry. Secondly, the insufficient capacity for high-intensity environmental purification is the secondary factor restricting the comprehensive performance. The obstacle factor rankings across regions show that Sewage Treatment Capacity per Unit Area (C₂₀), Household Waste Treatment Capacity per Unit Area (C₂₁), and Energy Consumption per Unit Area (C₁₉) are common secondary obstacles. These factors are significant indicators of regional environmental friendliness and ecological purification capacity. Therefore, guided by the principle of ecological priority, these cities should enhance land allocation for sewage and waste management, while simultaneously reducing land-related energy consumption. Thirdly, the inadequate provision of high-quality life services is another significant obstacle to current comprehensive performance. Analysis of regional obstacles reveals that Bed Density in Healthcare Institutions (C₁₁), Residents’ Income (C₈), and Availability of Books per Unit Area (C₁₅) are critical factors that hinder the enhancement of comprehensive performance This points to persistent disparities in income, access to healthcare, and the attainment of high-quality living standards among residents across regions, underscoring the need for further improvements.

3.2.2. Differentiated Zoning Regulatory Strategies

The fundamental principle of ecological prioritization is to formulate targeted management strategies that account for the specific resource and environmental constraints of each region, thereby achieving precise improvements. Accordingly, the constraint strength of each criterion layer on the overall objective was calculated based on Equation (2). This calculation is integrated with the categorization of barrier factors affecting each index’s contribution to comprehensive performance, as well as the functional positioning and resource endowment of each region. Consequently, various management types are delineated as follows (Figure 6):

Ecosystem Service Optimization Zone. This zone, which includes Wuhan, is designed to enhance the quality of public services and the supply of ecological resources to improve its comprehensive performance. As a central city in Hubei Province, Wuhan demonstrates a high level of economic development and urbanization, marked by extensive construction land and elevated utilization intensity, with significant comprehensive performance of land use. Its primary obstacles are the inadequate public service space and green resource provision capacity. For this purpose, this region should implement supply side structural reforms in conjunction with an ecological priority strategy, leveraging its advantages in regional industrial layout to optimize the land supply structure. The objectives are to promote high-quality industries, foster ecological development, and enhance social services. This entails appropriately regulating construction land scale, revitalizing existing land stock, and promoting urban renewal. Additionally, spatial planning should be prioritized to enhance public service levels and promote three-dimensional development. Simultaneously, it is essential to enhance the industrial entry threshold in accordance with standards for land-intensive use. This involves reinforcing land supply for green and environmentally protective industries, as well as bolstering the ecological restoration and greening functions of land use spaces. Furthermore, it is necessary to decelerate the pace of urban expansion, improve the quality of urbanization, and ensure that land use spaces perform optimally, thereby achieving a coordinated multifunctionality of land use to the greatest extent possible.

Green Industry Strengthening Zone. This zone comprises Yichang, Xiangyang, Huanggang, Jingzhou, Jingmen, Xiaogan, and Shiyan. Its objective is to optimize the land use structure and enhance the supply of land for green industries. Located within the Yangtze River and Hanjiang Ecological Economic Belts, this region exhibits moderate levels of economic development, urbanization, and comprehensive performance. However, it possesses distinct resource advantages. Yichang and Xiangyang are recognized as provincial sub-central cities with a rich cultural heritage, presenting significant potential for development. The primary obstacles are an underdeveloped high-quality tertiary industry and a lack of environmental purification capacity. Therefore, this region should leverage its leading industries to optimize spatial structure. Firstly, there should be an increase in capital investment in urban construction, with a focus on revitalizing existing land stock. This can be achieved by linking urban and rural construction land adjustments to stimulate the land market. Additionally, it is essential to strategically augment the supply of new construction land to facilitate industrial transfer. Concurrently, it is imperative to leverage local resource endowments, including ecological tourism and mineral resources, to enhance ecological functions and foster the development of green industries. Secondly, educational services and cultural cultivation should be progressively strengthened. Furthermore, mechanisms for land revenue sharing and distribution should be improved. While concentrating urban populations, efforts should be made to reduce public management costs through the reform of administrative examination and approval processes. Moreover, talent attraction policies need to be enhanced. The ultimate goal is to establish model cities within the Yangtze River Economic Belt and the Hanjiang Ecological Economic Belt.

Ecological and Economic Collaborative Zone. This zone, including Ezhou, Huangshi, Xianning, Suizhou, Xiantao, Qianjiang, Tianmen, and Enshi, aims to advance economic development while concurrently ensuring ecological protection, leveraging its unique resource endowments. This region is characterized by limited urban construction land, a less developed economy, and relatively lower comprehensive performance. However, it possesses a distinctive geographical location and development positioning, with relatively abundant ecological resources that offer potential for performance improvement. The primary obstacles to development in the region include a weak industrial foundation, inadequate social security, and limited environmental purification capacity. The region should enhance the construction of infrastructure services, adopt a balanced land use approach that reconciles economic development with ecological protection, and improve the efficiency of urban space utilization. And the supply of new construction land should be moderately increased to support economic growth. Emphasis should be placed on the replacement of urban and rural construction land, in alignment with the satellite city strategy. A complementary cooperation strategy should be implemented to strengthen the region’s capacity for industrial undertaking and functional diversion, thereby addressing the developmental needs of urbanization and industrialization while minimizing associated costs. Simultaneously, land use input–output efficiency must be enhanced with the primary objective of developing an ecological and green economy. This requires striking a balance between preserving traditional industries and pursuing the industrial upgrading required for the ecological economy, thereby improving comprehensive performance while fostering multi-functional coordination.

4. Conclusions, Discussion, and Policy Implications

4.1. Conclusions

The comprehensive performance of land use functions in Hubei Province exhibited a fluctuating but overall upward trend. The average performance of PLE functions rose from 0.812 in 2014 to 0.924 in 2023, indicating a transition from “Poor” to “Good” level. Concurrently, significant spatial disparities in the functional performance across different regions were observed, demonstrating a gradient-decreasing pattern from provincial capital centers to provincial sub-centers, the ecological economic belts, and finally to satellite cities. The average performance gradually decreased from 0.946 to 0.86. Furthermore, the functional performance within the urban ecological economic circle was more balanced than that in the Wuhan Metropolitan Area. The evolution of spatial functions follows a trajectory from a state of high coupling with low coordination to one of high coupling with high coordination.

A fundamental aspect of ecological prioritization is predicated on regional disparities in resources and environmental constraints. This necessitates the formulation of differentiated governance strategies for precise development. Through an analysis of obstacles, it has been identified that the main factors limiting the enhancement of land use performance in Hubei Province include inadequate high-intensity environmental purification efforts (with an average obstacle degree of 19.24%), weak foundation in high-quality tertiary industries (with an average obstacle degree of 15.61%), and deficiency in high-level life service provision (with an average obstacle degree of 13.99%). Accordingly, in order to improve these shortcomings, we propose delineating specific areas designated as the Ecosystem Service Optimization Zone, Green Industry Strengthening Zone, and Ecological and Economic Collaborative Zone. Additionally, a differentiated management mechanism should be established to enhance regional land use performance.

Land use functions exhibit complexity, comprehensiveness, and systematic characteristics. Adhering to the “Element–Structure–Space–Function” running logic, we develop a multi-functional performance evaluation system and for land use PLE functions, guided by ecological priorities. Additionally, we innovatively introduce a nonlinear Entropy–Catastrophe Progression Model to evaluate land use performance of the Yangtze River Economic Belt. This method employs control variables and state variables to describe and predict the qualitative changes and abrupt transition processes associated with continuity interruptions. It effectively identifies key factors and indicators of spatio-temporal mutation points, thereby minimizing the subjectivity inherent in artificial empowerment. As a result, the research findings possess a high objectivity and reference value. Consequently, the application of the Entropy–Catastrophe Progression model to examine land use PLE functional performance during the transitional period of development concepts addresses the limitations of traditional methodologies in studying mutation characteristics within system evolution. This approach more objectively elucidates the evolutionary patterns of land use functional performance.

4.2. Discussion

Land use performance management and control is a systematic project. Starting from the concept of ecological priority, this paper has further explored the theoretical system, indicator system, and methodological system of land use performance management and control. However, constrained by data availability, it is difficult to fully reflect the comprehensive effects of urban land use in indicator selection, especially ecological background indicators such as biodiversity, carbon emissions, and land degradation, which have not been directly reflected. To ensure the continuity of the research, we will conduct further discussions on research scale, indicator selection, and other aspects in the future.

4.3. Policy Implications

Under the concept of ecological priority, urban land use performance exhibits “wave-like” growth, indicating that ecological priority policies may reduce the land use performance level of some cities in the short term. This is mainly due to the lag in adjustment caused by the dependence on land use patterns amid regional development transformation, showing a certain “pain period”. However, with the deepening of the concept of ecological priority, land use begins to take the initiative to transform and gradually adapt to regional development, ultimately promoting the improvement of comprehensive land use performance. Moreover, this improvement rate is more prominent in cities with strong dependence on ecological resources, thereby narrowing the gap between cities, and the spatial pattern gradually tends to be stable and coordinated. Therefore, the following policy implications are proposed:

Optimize the “urban–rural–regional” spatial layout based on spatial justice to address resource imbalance. Establish an “urban–rural equivalence” land revenue sharing mechanism, carry out comprehensive land consolidation across the entire region, promote the market entry reform of rural collective operational construction land through benefit sharing, continue to implement the “two-way linkage” of urban–rural construction land indicators, and facilitate the flow of spatial resources from low-efficiency areas to high-efficiency areas with revenue compensation to disadvantaged areas.

Promote the synergy of “land–industry–ecology” through ecological modernization to enhance green efficiency. Implement the “ecological redevelopment” project for stock land, increase coordinated financial input to cities along the Yangtze River, while strengthening the empowerment of “ecological technological innovation” in land use, and carry out dynamic performance early warning for underperforming indicators. In concentrated agricultural areas such as the Jianghan Plain, promote ecological agricultural models such as “rice–fish co-culture” and “crop rotation and fallow”. In areas rich in ecological resources, including ecological land such as forest land and wetlands in “GEP (Gross Ecosystem Product) accounting”, develop industries such as under-forest economy and ecological health and wellness, ensuring that “protectors” receive economic rewards.

Driving coordinated spatial development through “regional differentiation”. For the Ecological Service Optimization Zone, Green Industry Intensification Zone, and Ecological-Economic Coordination Zone, respectively, formulate differentiated policies that coordinate comprehensive service efficiency, land space, factor input, and industrial development. In accordance with the principles of strictly controlling increments, revitalizing stock land, and tapping potential, establish a dual-indicator regulatory mechanism for “GDP per unit land area and carbon emission intensity” to promote the coordination of multiple land functions. In particular, give full play to the advantages of resources such as eco-tourism and minerals, and strengthen the development of green industries and ecological functions.