Unlocking Natural Capital Through Land Tenure Reform and Spatial Reconfiguration: Evidence from the “Spatial-First” Mode in Nanhai, China

Abstract

Efficiently converting natural capital into economic assets is a critical challenge in urban–rural transformation, yet the interactive mechanism between institutional land reform and physical spatial restructuring remains underexplored. While traditional frameworks emphasize institutional design, this study identifies a “Spatial-First” mechanism where physical reconfiguration serves as a spatial mediator to catalyze property rights breakthroughs. Using an entropy-weighted coupling coordination model, we analyzed policy dynamics in Nanhai District, China, a unique “dual-pilot” zone, from 2020 to 2024. The results indicate a nonlinear leap in the Coupling Coordination Degree (D) from 0.100 to 0.978. We interpret this surge as a policy-driven shock during the intensive pilot phase, where substantive spatial integration (0.719) effectively bypassed high transaction costs inherent in collective tenure, outpacing institutional progress (0.281). However, an Ecological Lag was observed; the disproportionately low weighting of the ecological carrier index (7.09%) suggests that current gains are primarily driven by green industrialization rather than the expansion of absolute ecological stock. This study concludes that while spatial tools can effectively unlock natural capital value in the short term, long-term sustainability necessitates a strategic shift from administrative-led economic efficiency to market-based ecological restoration.

1. Introduction

Natural capital has emerged as a central issue in global sustainable development. International experience demonstrates that clear land property rights and payments for ecosystem services (PES) mechanisms can effectively convert ecological reserves into economic gains, thereby resolving the “ecology versus growth” dilemma commonly faced by developing countries. However, amid rapid urbanization, defining natural capital property rights through institutional design and ensuring their physical implementation via spatial planning remain highly challenging focal points in academia [1]. Current academic debates primarily revolve around two paradigms: institution-led approaches and “spatial optimization.” Traditional literature often emphasizes the “institutions-first” logic, arguing that clear property rights definitions are prerequisites for spatial efficiency. Yet in semi-urbanized areas with highly fragmented land ownership, initial property negotiations often incur prohibitively high transaction costs, stalling institutional reforms. Similar land succession challenges emerge globally, such as pressure on the periphery of Algeria’s Oran metropolitan area and land use evolution in Nairobi’s Upper Hill district, revealing complex interactions between land strategies and urbanization [2,3]. In China, while studies have employed models like entropy analysis and TOPSIS for comprehensive evaluations of urban renewal (e.g., in Shandong Province), few have deeply explored how physical spatial reorganization can conversely drive institutional breakthroughs [4]. This creates a critical research gap: existing theories often overlook the potential role of physical spatial reorganization as a “catalyst” for institutional change amid property rights deadlocks. This paper proposes a “Spatial-First” mechanism to explore how physical space integration reduces transaction costs in defining property rights by creating “established physical facts,” thereby unlocking natural capital value.

This study examines Nanhai District, Foshan City, Guangdong Province, China. As the nation’s sole dual pilot zone for “market entry of rural collective-owned construction land” and “comprehensive land consolidation across entire regions,” Nanhai offers a unique empirical setting. Its core logic lies in using market-based approaches to dismantle urban–rural land barriers while employing comprehensive consolidation tools to restore fragmented natural capital carriers [5]. Nanhai’s practice fundamentally constitutes a coupled experiment of “property rights reform” and “physical spatial restructuring [6].” Accordingly, this study focuses on the critical policy window period from 2020 to 2024, aiming to address the following core questions: How does physical spatial restructuring function as a driving force for institutional change, forming a coupled and coordinated relationship with land reform? What effects and limitations does this “spatial-first” model exhibit in unlocking natural capital value?

The thesis is structured as follows: Section 2 constructs a “institutional-spatial” coupling theoretical framework driven by natural capital; Section 3 introduces the practical context and coupling logic of the South China Sea region; Section 4 outlines the research methodology and indicator system; Section 5 analyzes empirical findings; Section 6 engages in in-depth discussion on the spatial-first pathway and ecological lag; Section 7 summarizes research conclusions and proposes policy recommendations.

2. Core Concepts, Theoretical Foundations, and Research Framework of Natural Capital

The essence of natural capital lies in the economic valuation of ecological assets and the ecological enhancement of economic benefits [7]. Against the backdrop of urban–rural transformation, natural capital is not merely a static resource reserve but a dynamic coupled system involving three core dimensions: “value realization,” “property rights demarcation,” and “spatial allocation.” This study posits that these dimensions do not exist in isolation but form a closed-loop feedback mechanism: spatial allocation (through comprehensive land consolidation) provides the physical platform for property rights definition (via the “three certificates” system), thereby reducing transaction costs during institutional transformation and ultimately driving the value realization of natural capital [8,9].

Unlike the traditional paradigm emphasizing “institutional precedence” (which posits that clear property rights are a prerequisite for spatial efficiency), this paper proposes a “Spatial-First” mechanism. This theoretical framework posits that in semi-urbanized areas characterized by highly fragmented land ownership and high negotiation costs, the physical restructuring of space can serve as a “catalyst” for institutional breakthroughs. By creating “established physical facts”, such as demolishing dilapidated industrial parks, local governments can compel stakeholders to renegotiate benefit allocations, thereby reducing resistance to subsequent property rights reforms [10].

This coupling logic is elaborated in detail in Figure 1. Within this research framework, natural capital serves as the core driving force, guiding the interaction between System A (land tenure reform) and System B (physical spatial restructuring). These two systems achieve synergy through three interrelated dimensions: “objectives-pathways-effects.” Objective Coupling: Land reform pursues the quantification and equitable distribution of property rights, while spatial restructuring aims to optimize resource carriers for enhanced efficiency, both collectively driving the appreciation of natural capital [11]. Path Coupling: Institutional innovations (such as market access policies) clear ownership barriers for spatial remediation, while spatial restructuring (such as contiguous development) provides concrete implementation scenarios for institutional rollout. Effect Coupling: Economic gains from reforms provide financial backing for ecological restoration, while restored spaces offer sustained momentum for green industry clustering.

Theoretically, this framework integrates property rights economics with landscape ecology. Property rights economics achieves coupled allocation of natural and economic capital through market instruments like green certificates; landscape ecology guides connectivity restoration of ecological carriers via the “patch-corridor-matrix” theory, ensuring spatial reorganization transcends mere physical relocation to deliver systematic enhancement of ecological functions. This multidimensional theoretical integration enables the “spatial-first” approach to transcend conventional spatial planning, emerging as a pragmatic strategy for transitioning economies to resolve collective land property rights deadlocks and maximize natural capital value.

3. Practicing Contextual and Coupling Logic in Learning Zones

3.1. Nanhai District: A Laboratory for Land Reform Theory

Located at the heart of the Pearl River Delta (Figure 2), Nanhai District has become a prime observation point for the “spatial priority” mechanism due to its intense land use conflicts. As a manufacturing powerhouse, the district boasts an industrial output exceeding 600 billion CNY. However, this economic success rests upon a highly fragmented collective land system, where collectively owned land constitutes 70% of the total area. “dual pilot zone,” simultaneously serving as a market entry point for collectively owned rural construction land and a comprehensive land consolidation initiative across the region. This unique status enables Nanhai to bypass traditional institutional rigidities. The district faces structural bottlenecks: approximately 8000 hectares (ha) of underutilized, fragmented industrial land (“brownfield assets”) and fragmented ecological spaces comprising only 10% of the total land area. Rigid land ownership and spatial fragmentation make traditional top-down institutional reforms prohibitively costly, compelling a pragmatic “spatial priority” approach to unlock natural capital.

3.2. Three-Dimensional Collaborative Mechanism

The core of the South China Sea model lies in the mutual empowerment between institutional innovation and substantive restructuring. This synergy is achieved through three distinct yet interconnected coupling dimensions.

Objective coupling focuses on the three-dimensional unity of natural capital appreciation, economic growth, and social equity. Nanhai does not treat ecology and growth as a zero-sum game but aligns them through market valuation. In the Guicheng Pingzhou Industrial Park case, land reforms quantified natural capital value to safeguard farmer incomes, while spatial reorganization fostered green industry clustering. Similarly, the Xiqiao Mountain project treated ecological capital as a core asset, achieving a win-win outcome where ecological enhancement directly drove economic returns through increased land premiums.

Path coupling establishes a mechanism where spatial restructuring acts as a battering ram, breaking institutional deadlocks. Traditional “institution-first” approaches often stall due to ownership barriers. Nanhai’s “space-first” logic reverses this: by initiating continuous spatial integration, local governments create an “established physical reality,” thereby reducing resistance to subsequent redefining of property rights. The “Three Tickets” System: This institutional innovation (land, housing, and green tickets) establishes market entry rules, while spatial restructuring provides the implementation context. Xiaoqiao Mountain Case Study: The combined introduction of green bonds and land bonds enabled proceeds from reclaimed land to fund ecological corridor construction. This institutional innovation provided stable financial backing for spatial restoration while simultaneously realizing bond value through enhanced spatial connectivity.

Effect coupling ensures that the economic impacts of reform provide a financial guarantee for natural capital restoration. In Guicheng Pingzhou, coordinated restructuring elevated industrial land value from 22.5 million CNY/ha to 90 million CNY/ha, with market yields rising from 5% to 12%. Crucially, 10% of these collective revenues were reinvested into natural capital, funding a 10 ha community park and increasing ecological coverage to 20%. At Xiqiao Mountain, the integration of green certificates with spatial restoration increased ecological corridor connectivity from 40% to 90% and carbon sequestration capacity by 30%. This successfully converted ecological benefits into economic returns, driving green certificate prices from 20,000 to 35,000 CNY per certificate.

4. Materials and Methods

4.1. Data Sources and Study Period Justification

The investigation adopts a focused observation window spanning 2020 to 2024. While this timeframe is relatively concise, it holds exceptional scientific value for dynamic policy evaluation, as it encompasses the complete lifecycle of Nanhai District’s comprehensive land consolidation pilot, from its inaugural launch in 2020 to the phased acceptance milestone in 2024. During such periods of intense institutional transformation, the high volatility and rapid growth rate of data provide a more direct reflection of policy impact and institutional intensity than long-term historical evolution.

To ensure transparency and international comparability, all land-related metrics originally measured in “mu” (a traditional Chinese unit of area) have been converted to hectares (ha), utilizing the standard conversion factor of 1 ha approx 15 mu. Primary data were collected from authenticated official channels, including: The Nanhai District Bureau of Natural Resources (Annual Reports 2020–2024 and market entry ledgers). The Nanhai District Bureau of Statistics (Statistical Bulletin on National Economic and Social Development). The Bureau of Ecology and Environment (Environmental Status Bulletins). The Bureau of Agriculture and Rural Affairs (Collective land records).

4.2. Indicator System Construction and Theoretical Grounding

Based on the principles of scientific rigor and regional specificity, this study constructs a hierarchical evaluation system comprising 10 tertiary indicators (

Table 1. Evaluation Index System for the Coupling Effect of Land System Reform and Spatial Reconfiguration.

Objective Layer	Primary Indicators	Second-Level Indicators	Tertiary Indicators	Indicator Attribute
Land System Reform and Spatial Restructuring Coupling Synergy Effects	Reform–Restructuring Coupling Effects	Natural Capital Appreciation Effect	Proportion of Ecological Land Use	+
			Carbon Emissions per Unit of GDP	−
			Share of Green Industry Land Use	+
			Length of ecological greenways	+
		Economic coupling effect	Ecological space concentration	+
			Land Market Entry Yield Rate	+
			Industrial Land Value	+
		Social Coupling Effects	Farmers’ Land Income	+
			Living Space Support Rate	+
		Mechanism Coupling Effect	Planning Coordination Rate	+

The symbol “+” indicates a positive indicator, and “−” indicates a negative indicator.

and

Table 2. Overview of Data Sources for Indicator Definitions and Calculation Methods.

Indicator Name	Unit	Indicator Meaning	Calculation Method	Data Source
Planning coordination rate	%	Measure the alignment between the reform pilot boundary and the high-level territorial spatial planning, and reflect the ability of the mechanism to reduce internal friction costs.	${\mathit{R}}_{\mathit{p}}={\displaystyle \frac{{\mathit{A}}_{\mathit{p},\mathit{c}}}{{\mathit{A}}_{\mathit{p}}}}\times \mathbf{100}\mathrm{\%}$	Nanhai District Bureau of Natural Resources (DNRB)
The yield of land entering the market	%	It reflects the excess economic premium brought about by the coupling of property rights definition and spatial allocation, and measures the efficiency of realizing the value of natural capital.	${\mathit{R}}_{\mathit{y}}={\displaystyle \frac{{\mathit{P}}_{\mathit{r}\mathit{a}\mathit{n}\mathit{s}}-{\mathit{P}}_{\mathit{b}\mathit{a}\mathit{s}\mathit{e}}}{{\mathit{P}}_{\mathit{b}\mathit{a}\mathit{s}\mathit{e}}}}\times \mathbf{100}\mathrm{\%}$	Nanhai District DNRB (Market Entry Ledgers)
per capita land income of farmers	CNY/ person	Measuring the degree to which reform benefits give back to social space aims to establish positive feedback from social capital on the maintenance of natural capital.	${\mathit{I}}_{\mathit{f}}={\displaystyle \frac{{\mathit{I}}_{\mathit{t}\mathit{o}\mathit{t}\mathit{a}\mathit{l},\mathit{f}}}{{\mathit{N}}_{\mathit{f}}}}$	Nanhai District Bureau of Agriculture and Rural Affairs (BARA) and Bureau of Statistics (BoS)
It Proportion of ecological land	%	The absolute reserves of physical carriers of natural capital are measured, reflecting the direct contribution of spatial reorganization to physical ecological stocks.	${\mathit{R}}_{\mathit{e}}={\displaystyle \frac{{\mathit{A}}_{\mathit{e}\mathit{c}\mathit{o}}}{{\mathit{A}}_{\mathit{t}\mathit{o}\mathit{t}\mathit{a}\mathit{l}}}}\times \mathbf{100}\mathrm{\%}$	Nanhai District DNRB (Land Use Census Data)
10,000 yuan of GDP carbon emissions	tone/10k CNY	Measure the degree of decoupling between economic growth and ecological loss, reflecting the contribution of green industrial restructuring to reducing the pressure on natural capital.	${\mathit{E}}_{\mathit{c},\mathit{G}}={\displaystyle \frac{{\mathit{C}}_{\mathit{t}\mathit{o}\mathit{t}\mathit{a}\mathit{l}}}{{\mathit{G}}_{\mathit{t}\mathit{o}\mathit{t}\mathit{a}\mathit{l}}}}$	Nanhai District Bureau of Ecology and Environment (BEE) and BoS
The proportion of green industrial land	%	Measure the intensity of the transition of economic space from “brown assets” to sustainable natural capital carriers.	${\mathit{R}}_{\mathit{g},\mathit{i}}={\displaystyle \frac{{\mathit{A}}_{\mathit{g},\mathit{i}}}{{\mathit{A}}_{\mathit{t}\mathit{o}\mathit{t}\mathit{a}\mathit{l},\mathit{i}}}}\times \mathbf{100}\mathrm{\%}$	Nanhai District DNRB (Green Classification Records)
Length of ecological corridor	km	The effect of spatial reorganization on the spatial connectivity of ecosystem service functions was measured.	${\mathit{L}}_{\mathit{g}}=\mathit{M}\mathit{e}\mathit{a}\mathit{s}\mathit{u}\mathit{r}\mathit{e}\mathit{d}\mathit{V}\mathit{a}\mathit{l}\mathit{u}\mathit{e}$	Nanhai District DNRB (Consolidation Planning Data)
Industrial Ha land output value	CNY/ha	It reflects the effect of physical contiguous remediation on economic spatial density.	${\mathit{O}}_{\mathit{i}}={\displaystyle \frac{{\mathit{V}}_{\mathit{i}}}{{\mathit{A}}_{\mathit{t}\mathit{o}\mathit{t}\mathit{a}\mathit{l},\mathit{i}}}}$	Nanhai District BoS and DNRB
Production space agglomeration index	Dimensionless	The degree to which industrial plaque agglomeration is achieved and the cost of fragmentation friction is reduced by physical engineering means is measured.	${\mathit{I}}_{\mathit{a},\mathit{p}}={\displaystyle \frac{{\mathit{P}}_{\mathit{m}\mathit{a}\mathit{x}}}{{\mathit{P}}_{\mathit{t}\mathit{o}\mathit{t}\mathit{a}\mathit{l}}}}$	Nanhai District DNRB (Spatial GIS Analysis)
Living space security rate	%	The ability of spatial restructuring to provide social support is measured, reflecting the support of social capital for the protection of natural capital.	${\mathit{R}}_{\mathit{s}}={\displaystyle \frac{{\mathit{A}}_{\mathit{l}\mathit{i}\mathit{v}\mathit{e}}}{{\mathit{A}}_{\mathit{t}\mathit{o}\mathit{t}\mathit{a}\mathit{l}}}}\times \mathbf{100}\mathrm{\%}$	Nanhai District DNRB and Community Ledgers

Notes: The variables are defined as follows: A_p,c is the area compliant with hierarchical spatial planning within the pilot region (ha); A_p is the total area of the pilot region (ha); P_trans is the market transaction price per hectare (CNY/ha); P_base is the standardized base land price per hectare (CNY/ha); I_total,f is the total income allocated to farmers from land reform (CNY); N_f is the number of covered farmers (person). A_eco is the total ecological land area (ha); A_total is the total regional area (ha); C_total is the total regional carbon emissions (ton); G_total is the total regional GDP (10k CNY); A_g,i is the area of green industrial land (ha); A_total,i is the total area of all industrial land (ha); V_i is the total industrial output value (CNY); P_max is the area of the largest industrial patch within the region (ha); P_total is the total area of all industrial patches within the region (ha); A_live is the total area of social/living space (e.g., community parks) (ha).

). In response to scholarly concerns regarding conceptual justification, each indicator is selected for its theoretical alignment with natural capital governance: Production Space Aggregation Index: This metric quantifies the consolidation of industrial land from fragmented “brownfield” plots into contiguous, efficient zones. Theoretically, spatial aggregation reduces the ecological footprint of scattered industrial activities, thereby safeguarding the integrity of the broader ecological carrier. Living Space Support Rate: This reflects the social feedback mechanism of natural capital value realization. Enhancing social amenities ensures that the economic gains from land reform translate into improved social capital, fostering community-led ecological stewardship. Planning Alignment Rate: This quantitatively measures the consistency between hierarchical spatial plans to prevent resource misallocation and ensure the policy’s physical implementation.

To enhance the external validity of the findings, the indicator framework incorporates comparative perspectives from global land governance studies, including peri-urban pressures in Algeria’s Oran metropolis, land use succession strategies in Nairobi, and entropy-based urban renewal evaluations in Shandong Province.

4.3. Research Methods

4.3.1. Data Processing and Statistical Reliability Validation

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Data Standardization

Due to significant differences in the units of measurement for various indicators, e.g., “farmers’ land income” is measured in “yuan/person” while the “natural capital carrier land use ratio” is measured in “%”, extreme value standardization is employed to eliminate unit interference. Data are mapped to the [0, 1] interval to distinguish between positive and negative indicators. Positive indicators include ecological land use ratio, land market entry yield rate, green industry land use ratio, industrial land output value, farmer land income, and planning coordination rate. The processing formula is as follows:

$${\mathit{Y}}_{\mathit{i}\mathit{j}}={\displaystyle \frac{{\mathit{X}}_{\mathit{i}\mathit{j}}-\mathit{m}\mathit{i}\mathit{n}{\mathit{X}}_{\mathit{i}\mathit{j}}}{{\mathit{m}\mathit{a}\mathit{x}\mathit{X}}_{\mathit{i}\mathit{j}}-\mathit{m}\mathit{i}\mathit{n}{\mathit{X}}_{\mathit{i}\mathit{j}}}}$$

In the formula: ${\mathit{Y}}_{\mathit{i}\mathit{j}}$ represents the standardized value of indicator j in year i; ${\mathit{X}}_{\mathit{i}\mathit{j}}$ denotes the original value of indicator j in year i; ${\mathit{m}\mathit{a}\mathit{x}\mathit{X}}_{\mathit{i}\mathit{j}}$ and $\mathit{m}\mathit{i}\mathit{n}{\mathit{X}}_{\mathit{i}\mathit{j}}$ denote the maximum and minimum values of indicator j during the study period, respectively [12].

The only negative indicator is carbon emissions per unit of GDP, processed as follows:

$${\mathit{Y}}_{\mathit{i}\mathit{j}}={\displaystyle \frac{{\mathit{m}\mathit{a}\mathit{x}\mathit{X}}_{\mathit{i}\mathit{j}}-{\mathit{X}}_{\mathit{i}\mathit{j}}}{{\mathit{m}\mathit{a}\mathit{x}\mathit{X}}_{\mathit{i}\mathit{j}}-\mathit{m}\mathit{i}\mathit{n}{\mathit{X}}_{\mathit{i}\mathit{j}}}}$$

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Statistical Reliability Validation and Determining Indicator Weights

The entropy method is employed to determine the weights of each tertiary indicator [13]. This method reflects the degree of dispersion through the indicator’s “information entropy”—higher dispersion yields lower information entropy, indicating a more significant influence of the indicator on coupling synergistic effects and thus a larger weight. This approach avoids subjective weighting biases. The specific calculation process is as follows: Calculate the proportion of indicator j in year i: ${\mathit{P}}_{\mathit{i}\mathit{j}}$:

$${\mathit{P}}_{\mathit{i}\mathit{j}}{\displaystyle \frac{{\mathit{Y}}_{\mathit{i}\mathit{j}}}{{\sum }_{\mathit{i}=1}^{\mathit{n}}{\mathit{Y}}_{\mathit{i}\mathit{j}}}}$$

In the formula: n = 5 (number of study years); ${\mathit{P}}_{\mathit{i}\mathit{j}}$ represents the standardized value proportion, reflecting the contribution of the indicator in that year to the overall score. Calculate the information entropy ${\mathit{e}}_{\mathit{j}}$ for the jth indicator:

$${\mathit{e}}_{\mathit{j}}=\mathbf{k}{\sum }_{\mathit{i}=1}^{\mathit{n}}{\mathit{P}}_{\mathit{i}\mathit{j}}\mathit{l}\mathit{n}{\mathit{P}}_{\mathit{i}\mathit{j}}$$

In the formula, K = ${\displaystyle \frac{1}{\mathbf{ln}\mathbf{n}}}$ (adjustment coefficient), ensuring ${\mathit{e}}_{\mathit{j}}$ ∈ [0, 1]. If ${\mathit{P}}_{\mathit{i}\mathit{j}}$ = 0, then ln${\mathit{P}}_{\mathit{i}\mathit{j}}$ is treated as 0.

Calculate the coefficient of variation ${\mathbf{g}}_{\mathbf{j}}$ for the jth indicator:

$${\mathbf{g}}_{\mathbf{j}}=1-{\mathit{e}}_{\mathit{j}}$$

The larger the coefficient of variation, the higher the dispersion of the indicator, and the stronger its ability to distinguish coupling synergistic effects.

Calculate the weight for the jth indicator ${\mathbf{W}}_{\mathbf{j}}$.

$${\mathbf{W}}_{\mathbf{j}}={\displaystyle \frac{{\mathbf{g}}_{\mathbf{j}}}{\sum _{\mathbf{j}=1}^{\mathbf{m}}{\mathbf{g}}_{\mathbf{j}}}}$$

In the formula, m = 10 (total number of indicators), and ${\sum }_{\mathbf{j}=1}^{\mathbf{m}}{\mathbf{w}}_{\mathbf{j}}$ = 1.

The results of the 10 indicators calculated using the entropy weight method are shown in

Table 3. Weights of Evaluation Indicators for the Spatial Integration Synergy Effect of Land System Reform.

Third-Level Indicator	Information Value Entropy ej	Coefficient of Variation gj	Weight Coefficient wj	Weight Proportion
Carbon Emissions per Unit of GDP	0.79	0.21	0.11	11.23%
Planning alignment rate	0.81	0.19	0.10	10.01%
Farmers’ Land Income	0.84	0.16	0.08	8.48%
Industrial Land Value	0.81	0.19	0.10	9.96%
Land Market Entry Yield Rate	082	0.18	0.10	9.63%
Green Industry Land Use Ratio	0.78	0.22	0.12	11.77%
Proportion of ecological land use	0.87	0.14	0.07	7.09%
Length of ecological greenways	0.79	0.21	0.11	11.16%
Production Space Aggregation Degree	0.79	0.21	0.11	11.02%

and Figure 3. To address concerns regarding the reliability of weight allocation, this study systematically validated the dispersion and information utility of each indicator prior to determining their weights. The core assumption of the entropy weight method is that indicators with higher variability possess greater information utility and thus make more significant contributions to the evaluation objective. Based on calculations using data from Nanhai District for the period 2020–2024, the verification results are as follows: Significant divergence in information utility: The calculated indicator divergence coefficient g_i ranges from 0.14 to 0.22. This distribution pattern indicates that the selected indicator system does not exhibit steady linear growth but instead captures strong policy-driven signals. Identification of Core Drivers: “Proportion of Green Industrial Land” exhibited the lowest information entropy (0.78) and highest dispersion coefficient (0.22), ultimately receiving the highest weight (11.77%). Statistically, this reflects the indicator’s sharp surge during the pilot period (Dispersion), while logically it demonstrates the intensity of Nanhai District’s administrative measures to forcibly drive industrial spatial transformation. Identification of “Slow Variables”: In contrast, “Proportion of Ecological Land” exhibited the highest information entropy (0.87) and a weight of only 7.09%. This low dispersion validates ecological restoration as a “slow variable,” whose evolutionary pace significantly lags behind the spatial reorganization of industrial land. This aligns statistically with the subsequent finding of “Ecological Lag” in this study. In summary, the allocation of indicator weights not only meets the mathematical requirements of the entropy weight method but also accurately and reliably captures the asymmetric evolutionary characteristic of “spatial restructuring preceding ecological restoration” in the Nanhai model.

The weights for the 10 indicators, calculated via the entropy method, are presented in Table 3 and Figure 3. Among them: Green industrial land use ratio exhibits the lowest information entropy (0.78) and highest variance coefficient (0.22), with a weight of 17.27%, indicating it is the core indicator influencing the “reform-restructuring” coupling synergistic effect in Nanhai District; The proportion of natural capital carrier land use had the highest information entropy (0.87) and the lowest weight (10.39%), reflecting Nanhai District’s current situation of low ecological land base but gradual annual improvement. The Entropy Weight Method assigns higher weights to indicators with greater dispersion. The relatively lower weight of “Proportion of Ecological Land” suggests that while important, the variation in this indicator was less drastic compared to the rapid surge in “Green Industry Land” and “Carbon Efficiency,” which were the primary drivers of the system’s evolution during the pilot phase.

4.3.2. Coupling Coordination Degree Model Construction

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Subsystem Division and Comprehensive Development Index Calculation for Land System Reform and Spatial Reconfiguration

The core of the coupling coordination model involves first calculating the comprehensive development indices for the “Land System Reform Subsystem” and the “Spatial Reconfiguration Subsystem.” Synergy effects are then assessed by evaluating the intensity of interactions between subsystems through coupling degree and the quality of these interactions through coordination degree [14]. Based on the core connotations of “Land System Reform” and “Spatial Reconfiguration,” tertiary indicators are assigned to corresponding subsystems as shown in

Table 4. Division and Core Logic of the Land System Reform Subsystem and the Spatial Reconfiguration Subsystem.

Subsystem Name	Included Tier-3 Indicators (and Entropy Method Weights)	Core Logic
Land System Reform Subsystem	Land Market Entry Yield Rate (${\mathrm{W}}_1$), Farmer Land Income (${\mathrm{W}}_2$), Planning Coordination Rate (${\mathrm{W}}_3$)	Focusing on the core objectives of land system reform: activating land assets, safeguarding farmers’ income, and promoting interdepartmental coordinated reform
Spatial Reconfiguration Subsystem	Proportion of Ecological Land (${\mathrm{W}}_4$), Carbon Emissions per Unit of GDP (${\mathrm{W}}_5$), Proportion of Green Industrial Land (${\mathrm{W}}_6$), Industrial Land Output Value (${\mathrm{W}}_7$), Length of Ecological Greenways (${\mathrm{W}}_8$), Production Space Aggregation Index (${\mathrm{W}}_9$), Living Space Support Rate (${\mathrm{W}}_{10}$)	Focusing on the core objectives of spatial restructuring: optimizing ecological space, enhancing economic space efficiency, improving social space amenities, and strengthening planning coordination

below:

Calculate the comprehensive development index for the land system reform subsystem and spatial restructuring subsystem. Based on entropy-weighted values, employ linear weighting to compute the composite development index for both subsystems, reflecting their overall development level.

Comprehensive Development Index A for the Land System Reform Subsystem: Includes land market entry yield ${\mathbf{W}}_{\mathbf{1}}$ = 0.10, farmers’ land income ${\mathbf{W}}_{\mathbf{2}}$ = 0.08, and planning coordination rate ${\mathbf{W}}_{\mathbf{3}}$ = 0.10. Formula:

$$\mathsf{A}={\sum }_{\mathit{j}=\mathbf{1}}^{\mathbf{3}}{\mathit{W}}_{\mathit{j}}\times {\mathit{Y}}_{\mathit{i}\mathit{j}}=0.10{\mathit{Y}}_{\mathit{i}\mathbf{1}}+0.08{\mathit{Y}}_{\mathit{i}\mathbf{2}}+0.10{\mathit{Y}}_{\mathit{i}\mathbf{3}}$$

Spatial Reconfiguration Subsystem Comprehensive Development Index B: Covers ecological land ratio ${\mathbf{W}}_{\mathbf{4}}$ = 0.07, carbon emissions per unit GDP ${\mathbf{W}}_{\mathbf{5}}$ = 0.11, green industrial land ratio ${\mathbf{W}}_{\mathbf{6}}$ = 0.12, industrial land output value ${\mathbf{W}}_{\mathbf{7}}$ = 0.10, ecological greenway length ${\mathbf{W}}_{\mathbf{8}}$ = 0.11, production space agglomeration ${\mathbf{W}}_{\mathbf{9}}$ = 0.11, and living space support ratio ${\mathbf{W}}_{\mathbf{10}}$ = 0.10. The formula is:

$$\mathsf{B}={\sum }_{\mathit{j}=\mathbf{7}}^{\mathbf{10}}{\mathit{W}}_{\mathit{j}}\times {\mathit{Y}}_{\mathit{i}\mathit{j}}=0.07{\mathit{Y}}_{\mathit{i}\mathbf{4}}+0.11{\mathit{Y}}_{\mathit{i}\mathbf{5}}+0.12{\mathit{Y}}_{\mathit{i}\mathbf{6}}+0.10{\mathit{Y}}_{\mathit{i}\mathbf{7}}+0.11{\mathit{Y}}_{\mathit{i}\mathbf{8}}0.11{\mathit{Y}}_{\mathit{i}\mathbf{9}}+0.10{\mathit{Y}}_{\mathit{i}\mathbf{10}}$$

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Calculation of Coupling Index C, Coordination Index T, and Coupling Coordination Degree D

Coupling Index C Value: The coupling degree reflects the mutual dependence and constraint intensity between the two major subsystems. The formula is:

$$\mathrm{C}={\displaystyle \frac{\mathbf{2}\sqrt{\mathbf{A}\times \mathbf{B}}}{\mathbf{A}+\mathbf{B}}}$$

In the formula: C ∈ [0, 1]; The closer C is to 1, the stronger the interaction between subsystems, e.g., higher linkage between land market entry and industrial spatial integration. The closer C is to 0, the more independent the subsystems tend to be. Calculations show C approaches 1.

Coordination Index T Value: The coordination index reflects the overall development level of the two subsystems, used to correct the “low-level coupling” bias and avoid misjudgment where both systems lag but exhibit high coupling. The formula is:

T = αA + βB

In the formula: α = β = 0.5. Following standard practice in coupling coordination studies, we assign equal weights to both subsystems, assuming that institutional reform and spatial reconfiguration are equally critical to the synergistic development goal. T ∈ [0, 1]; a higher T indicates a higher overall development level of both systems.

Coupling Coordination Degree D Value: Coupling coordination degree serves as the core metric for synergistic effects, integrating coupling intensity and overall level. The formula is:

$$\mathrm{D}=\sqrt{\mathit{C}\times \mathit{T}}$$

In the formula: D ∈ [0, 1]; the closer D approaches 1, the more positive the “reform-integration” synergistic effect. The D value is divided into 10 levels, as detailed in

Table 5. Classification Standards for the D Value of the Coupling Coordination Degree of Land System Reform and Spatial Reconfiguration.

D Value Range	Coordination Level	Coupling Coordination Level	Interpretation of Synergy Effects
[0.0, 0.1)	1	Extreme Mismatch	Subsystems exhibit minimal interaction, with synergistic effects absent
[0.1, 0.2)	2	Severe Dysfunction	Weak subsystem interaction with significant contradictions
[0.2, 0.3)	3	Moderate Dysfunction	Limited subsystem interaction with collaborative shortcomings
[0.3, 0.4)	4	Mild Dysfunction	Preliminary interaction among subsystems, but insufficient coordination
[0.4, 0.5)	5	Borderline Dysfunction	Subsystem interaction has been enhanced, but further optimization is still required
[0.5, 0.6)	6	Barely coordinated	Preliminary coordination effects emerging, requiring consolidation and enhancement
[0.6, 0.7)	7	Primary Coordination	Synergy effect is stable, with overall positive trends
[0.7, 0.8)	8	Intermediate Coordination	Synergy is pronounced, with subsystems interacting favorably
[0.8, 0.9)	9	Good Coordination	Synergistic effects are prominent, supporting the transformation of “Two Mountains”
[0.9, 1.0)	10	High-Quality Coordination	Optimal synergy, highly compatible subsystems

below:

4.3.3. Methodological Reflections and Limitations

Although the Coupling Coordination Degree (CCD) model is widely applied in regional planning research to quantify the level of interaction between systems, it possesses several methodological limitations that require critical consideration when interpreting results.

A measure of alignment rather than absolute performance, the CCD model primarily assesses the “synchronization” or “correlation strength” between subsystems, rather than the absolute level of regional sustainability. In this study, the near-perfect D value of 0.978 in 2024 primarily reflects the high degree of synchronization achieved between administrative rules (institutional system) and engineering objectives (physical system) under strong policy mobilization. This high score may mask absolute lags in certain elements within the system.

Risks of “Low-Level Coordination”: Although the coordination index T was introduced to correct biases from low-level coupling, the model may still be influenced by the predefined indicator system. For instance, if the selected indicators primarily emphasize policy response speed, the results may reflect administrative mobilization capacity rather than the endogenous stability of market mechanisms. Normative Assumption on Weight Allocation: This study sets α = β = 0.5, assuming that institutional and spatial factors are equally important for collaborative objectives. While this setting aligns with industry conventions, it constitutes a normative assumption that may not fully capture the inherent “lag” of institutional change relative to physical infrastructure development.

Sensitivity to external shocks: Since the model assigns weights based on data dispersion, its results exhibit extreme sensitivity to short-term “policy shocks.” This implies that a sudden surge in D values may represent a temporary spike, with its long-term steady state requiring further validation through extended observation periods after administrative interventions are withdrawn.

Through the above reflections, this study aims to remind readers that quantitative findings on coupling coordination should be interpreted qualitatively within specific policy contexts and institutional frameworks, rather than being regarded as the ultimate indicator of perfect regional governance.

5. Empirical Findings and Analysis

5.1. Evaluation of Subsystem Development Levels: Validating the Evolutionary Characteristics of “Space First”

This study reveals the evolutionary trajectories of various evaluation indicators through standardized processing of empirical data from Nanhai District between 2020 and 2024 (Figure 4). Results indicate that during the pilot policy window, positive indicators, such as the proportion of green industrial land and per capita land income for farmers, exhibited pronounced nonlinear growth patterns. Notably, the carbon emissions per 10,000 yuan of GDP indicator achieved a full-value leap from its 2020 baseline state, demonstrating that administrative interventions exerted significant short-term momentum for the green transformation of production spaces.

Further analysis of the composite development indices for the land system reform subsystem (A) and the physical spatial restructuring subsystem (B) (

Table 6. Comprehensive Development Index A of the Land System Reform Subsystem and Comprehensive Development Index B of the Spatial Integration Subsystem.

Year	Land System Reform Subsystem Index A	Spatial Integration Subsystem Index B	Difference Between A and B	Subsystem Development Trend Analysis
2020	0.000	0.000	0.000	Both systems are in their initial stages with weak collaborative foundations
2021	0.096	0.115	−0.019	The spatial integration subsystem is slightly ahead, while the reform has started somewhat slower
2022	0.238	0.292	−0.054	Accelerated growth in spatial integration (increase in green industrial land)
2023	0.301	0.387	−0.086	Widening Gap Between Two Systems: Spatial integration benefits from comprehensive land consolidation reform but constrained by ownership negotiations
2024	0.281	0.719	−0.438	Both systems reach relatively high levels, with spatial integration advantages remaining stable

) reveals significant asynchrony between the two systems. From 2020 to 2024, the spatial restructuring index (B) rapidly climbed from 0.000 to 0.719, while the institutional reform index (A) only increased to 0.281. The gap between the two (B-A) widened to 0.438 by 2024.

This empirical finding strongly supports the “spatial-first” mechanism proposed in this paper. In semi-urbanized areas characterized by highly fragmented collective land ownership and extremely high property rights negotiation costs, comprehensive institutional reforms (System A) are often constrained by protracted negotiations over interest distribution, exhibiting significant lag. In contrast, physical spatial reorganization (System B)—leveraged through “comprehensive regional land consolidation”—creates “established physical facts” by demolishing contiguous inefficient industrial parks and constructing ecological corridors. This approach effectively circumvents initial institutional gridlock, becoming the core driver for unlocking natural capital value.

The comprehensive development indices of both subsystems show a consistent upward trend over the years. From 2020 to 2024, the A value increased from 0.000 to 0.281, while the B value rose from 0.000 to 0.719, reflecting the dual achievements of deepening land system reform and optimizing spatial restructuring in the Nanhai District region. The comprehensive development index B of the spatial restructuring subsystem consistently outperformed the comprehensive development index A of the land system reform subsystem, with a gap reaching 0.438 by 2024. Based on current development patterns, spatial integration primarily benefited from the “Comprehensive Land Improvement Across the Region” pilot program, such as ecological corridor construction and green industrial land supply. In contrast, reforms were constrained by factors like complex collective land ownership and profit distribution negotiations, resulting in relatively slower growth.

5.2. Evolutionary Dynamics of Coupling Coordination: A Qualitative Interpretation of Policy-Driven Shocks

The coupled interaction between land reform and spatial restructuring underwent a leapfrog evolution during the study period. The coupling coordination degree (D) rapidly surged from severe imbalance (0.100) in 2020 to high-quality coordination (0.978) in 2024. Regarding the dramatic surge in coupling coordination over four years (Figure 5 &

Table 7. Calculation results of coupling degree (C), coordination index (T), and coupling coordination degree (D).

Year	Coupling Degree (C)	Coordination Index (T)	Coupling Coordination Degree (D)	Coordination Level	Degree of Coupling Coordination	Synergy Effect Stages
2020	1.000	0.010	0.100	2	Severe Dysfunction	Initial Synergy Phase (No Interaction)
2021	0.948	0.245	0.482	5	Near Dysfunction	Preliminary Synergy Phase (Low Interaction)
2022	0.949	0.596	0.752	8	Intermediate Coordination	Stable Coordination Phase (Positive Interaction)
2023	0.955	0.764	0.854	9	Well-coordinated	Highly Efficient Synergy Phase (Prominent Effects)
2024	0.999	0.957	0.978	10	High-Quality Coordination	Optimal Synergy Phase (Highly Compatible)

), this study posits that this does not represent a natural trajectory of systemic evolution but should be defined as a strong “policy-driven shock.” This period coincided with the intensive implementation phase of the “National Pilot Program for Comprehensive Land Improvement Across All Regions.” The exceptionally high coordination index (D = 0.978) reflects the strong alignment between administrative assessment targets (institutional dimension) and physical project progress (spatial dimension) within a specific policy window.

Analysis of the coupling degree (C) and coordination index (T) reveals that while the mutual dependency intensity (C) between the two subsystems consistently remained high (close to 1.000), the substantial improvement in coordination levels was primarily attributable to the explosive growth of the coordination index (T) from 0.010 to 0.957. This finding reveals the phased characteristics of the South China Sea model: through administrative measures such as the “three certificates” system, the government forcibly bridged the gap caused by institutional lag in the short term, achieving rapid resonance between administrative rules and spatial governance [15].

However, this coordination model, reliant on high-intensity administrative mobilization, also signals potential risks. On one hand, the weighting of indicators reveals a pronounced “ecological lag” phenomenon—the weight assigned to absolute ecological space indicators (7.09%) is significantly lower than that of efficiency metrics such as green industrial output value. This indicates that current coordination is primarily driven by “economic greening” rather than substantive growth in ecological capital. On the other hand, the current high-quality coordination remains a “policy dividend-driven” form of synergy. Once the overwhelming administrative pressure of the pilot phase subsides, the true challenge for future natural capital governance will be transforming this externally driven coordination into an endogenous steady state guided by market mechanisms.

The trends in coupling degree (C), coordination index (T), and coupling coordination degree (D) from 2020 to 2024 are shown in Figure 5 below:

6. Discussion

This study identifies a “spatial-first” pathway, wherein physical spatial restructuring significantly precedes institutional reform in terms of scale (B > A). This approach is not only a pragmatic choice for reducing transaction costs but also reflects a deeper trade-off between institutional path dependency and governance capacity in land governance within transition economies [16]. From the perspective of institutional path dependency, Nanhai District has long been constrained by fragmented ownership and the entrenched path of collective economies [17]. The traditional logic of institutional precedence often stalls due to entanglement in complex interest conflicts. In contrast, the physical fait accompli strategy observed in this study represents the government leveraging its formidable mobilization capabilities and spatial intervention authority to forcibly sever the stickiness of old institutional pathways. This logic of using spatial reshaping to drive institutional change demonstrates local governments’ remarkable governance resilience in resolving Node B dilemmas, yet it also implies excessive reliance on administrative power when institutional innovation lacks endogenous momentum. From a regional political economy perspective, this “spatial-first” approach can be viewed as local governments creating administrative rents in the natural capital arena: by physically integrating contiguous spaces, governments enhance land asset liquidity, thereby securing green premiums in regional competition. This explains why spatial subsystems respond far more rapidly than institutional frameworks in defining rules [18].

At the model evaluation level, the coupling coordination degree (D) surged from 0.100 in 2020 to 0.978 in 2024—a phenomenon that warrants rigorous critical assessment. As noted by Qiao, Z et al. (2025) in their study on urban renewal in Shandong Province, comprehensive evaluations based on the entropy weight method exhibit extreme sensitivity to policy-driven indicators such as the proportion of green industries and carbon emission intensity [4]. During Nanhai District’s pilot window, these metrics experienced abrupt, stair-step increases under administrative directives. Since the entropy weight method tends to assign higher weights to indicators with greater dispersion, this policy-driven shock may have statistically generated an illusion of near-perfect coordination—a “statistical illusion.” This high alignment essentially reflects a peak in administrative efficiency rather than a steady state of spontaneous system evolution. The model’s excessive capture of policy fluctuations cautions us to be vigilant about potential administrative coercion-driven alignment biases when interpreting high coordination levels. Future research should incorporate more resilient approaches like stochastic frontier analysis or nonlinear lag models to validate the system’s true collaborative capacity after administrative support tapers off.

When viewed within a broader international context, the Nanhai model reveals significant divergence from global practices in addressing urban–rural pressures. Nemouchi, H. (2023), in their study of Algeria’s Oran metropolitan area, demonstrate that passive, unguided spatial evolution often results in net losses of natural capital, with institutional responses perpetually operating in a fragmented, reactive mode [2]. In contrast, the South China Sea region’s “spatial prioritization” demonstrates a proactive interventionist logic. Furthermore, unlike the “streamlined urban development strategy” proposed by Nguah, E., & K’Akumu, O. (2024) [3] for Nairobi, the South China Sea model does not rely solely on market-driven land succession. Instead, it achieves precise administrative control over land transfers through its “three-voucher” system. While market forces in the Kenyan case enabled land use conversion, they demonstrated significant failures in supplying ecological public goods [3]. The Nanhai model, by physically reconfiguring ecological corridors, demonstrates that government-led spatial intermediation can more effectively secure ecological foundations than pure market mechanisms. This international comparison further confirms that in semi-urbanized regions lacking mature land markets, spatial restructuring as a physical institution serves as a necessary transitional step for unlocking natural capital.

However, this study’s empirical claims regarding ecological transformation still face the inherent paradox of ecological lag. Despite relatively high overall system coordination, the improvement in the ecological land use ratio indicator (with a weight of only 7.09%) during the observation period lagged significantly behind economic indicators such as industrial output value [19]. This reveals that the current coordination essentially reflects economic greening rather than in situ ecological restoration. Given the Pearl River Delta’s exceptionally high land opportunity costs, the appreciation of natural capital manifests primarily through enhanced green industrial efficiency (e.g., reduced carbon emissions per ten thousand yuan of GDP) rather than absolute expansion of ecological reserves. This finding corrects traditional ecological urbanism perspectives that assume spatial optimization automatically increases ecological reserves. Under real-world political-economic constraints, physical space integration often prioritizes economic density, while the lag in ecological indicators reflects a deep financial misalignment between “conservation and development.” Future policy focus should therefore extend beyond mere physical space restructuring (“replacing old industries with new ones”) to refine non-market premium recovery mechanisms for ecological services—such as through “green certificates.” This would endow physical ecosystem restoration with institutional incentives commensurate with economic growth [20].

Finally, the practical significance of this “spatial-first” approach lies in its provision of a replicable “conflict deferral” template for densely populated, globally semi-urbanized areas with complex property rights. By rapidly integrating the physical layer (System B) to reduce initial friction in property rights demarcation (System A), it creates a buffer period for institutional innovation. However, it must be clearly recognized that this model essentially represents an administrative force “compressing” market principles across time and space. When the policy dividend period ends, the transition from administrative alignment to endogenous market coordination will determine whether the South China Sea model can evolve from a policy showcase into an institutional evergreen. Future research should further explore the marginal utility shift points between “spatial-first” and “institutional-first” approaches under varying governance capacities over extended cycles, thereby constructing a more universally applicable theory for global natural capital governance.

7. Conclusions

This study deconstructs the synergistic evolution of “institutional reform and spatial restructuring” in Nanhai District from 2020 to 2024, distilling a universally applicable “Spatial-First” governance model. Unlike traditional institutional economics emphasizing a linear path of securing rights before facilitating transfers, this study demonstrates that in semi-urbanized areas characterized by highly fragmented property rights and extremely high negotiation costs, forcibly integrating physical space to create a physical fait accompli serves as an effective alternative pathway to break institutional inertia and reduce transaction costs associated with unlocking natural capital. This finding corrects the romanticized assumption in Ecological Urbanism that spatial optimization automatically induces institutional change, underscoring the necessity of administrative mobilization as the adhesive between space and institutions. The theoretical contribution lies in constructing a spatial intermediation model that elucidates how physical remediation reduces initial friction in property rights demarcation, thereby securing a policy buffer period for natural capital to transition from dormant assets to economic assets.

Empirical findings reveal that the surge in coupling coordination degree (D) is not a natural evolution toward system stability, but rather a policy-driven shock resulting from high-intensity administrative mobilization. This nonlinear growth validates that under national pilot program pressure, local governments can forcibly bridge the evolutionary gap between spatial and institutional dimensions through administrative directives. However, the ecological lag paradox revealed in the indicator weight distribution—where economic efficiency indicators outperform absolute ecological stock indicators—reminds us that the realization of natural capital value remains efficiency-driven. This insight offers significant implications for global natural capital governance theory: while physical spatial integration can achieve green value-added growth, absolute ecological restoration will continue to face severe financial exclusion in the absence of non-market ecological value pricing mechanisms (such as “green certificate” systems).

Regarding the global applicability of the “South China Sea Model,” this study adopts a cautious stance. Comparisons with Algeria’s passive evolution model in the Oran metropolitan area and Kenya’s market-driven model in Nairobi reveal that the South China Sea Model’s success is highly dependent on local governments’ exceptional spatial governance capabilities and specific collective land legal contexts. This model offers a reference path for high-density areas in the Global South facing similar ownership deadlocks: when institutional reforms stall, prioritizing the spatial integration of public assets can activate the natural capital value chain. However, scaling this model requires robust planning coordination mechanisms; otherwise, purely physical interventions may generate new spatial injustices.

This study still has certain limitations. First, as the 2020–2024 period coincided with the critical phase of pilot implementation, the observed high coupling exhibits distinct characteristics of policy dividends. Its endogenous stability following the tapering of administrative mobilization remains to be verified. Second, the quantification of deep ecological indicators such as biodiversity within the evaluation system is constrained by data precision, failing to fully capture the micro-level degradation of natural capital quality. Future research should focus on long-term policy regression analysis to explore how the “spatial-first” approach can transition smoothly to market-driven coordination after pilot programs conclude. Additionally, comparative evidence from more cross-national micro-level cases should be incorporated to refine measurements of the marginal contribution rate of physical space to institutional innovation under varying governance levels. This will provide more resilient empirical support for global eco-urbanism and spatial planning theory.