Spatio-Temporal Evolution Characteristics of Land Consolidation in the Coastal Regions: A Typical Case Study of Lianyungang, China

Abstract

Understanding the spatio-temporal evolution of land consolidation is essential for optimizing regional land resource allocation and mitigating human–land conflicts during socio-economic development. This study introduced a novel framework for analyzing such patterns in China. Utilizing a two-decade (2002–2022) prefecture-level city dataset of land consolidation projects in Lianyungang, Jiangsu Province, we developed a “land consolidation intensity” metric and applied quantitative techniques—including land use transfer matrices, landscape pattern indices, Sankey diagrams, and standard deviation ellipses—to assess spatio-temporal dynamics and centroid shifts. Key findings included: (1) Land consolidation intensity exhibited distinct stages, evolving from initial development to rapid growth and eventual stabilization, closely aligning with national policy shifts. (2) The primary sources for supplemented cultivated land were ponds, rivers, and tidal flats, followed by grassland, construction land, and forest land, with cultivated land consistently dominating the consolidated landscape. (3) Land consolidation projects distribution concentrated in economic and political centers, with a spatial shift from inland western region towards the eastern coastal region. (4) Gray relational analysis identified economic development as the predominant driver, with policy and social factors providing secondary guidance. This research elucidates the spatio-temporal evolution characteristics of land consolidation at the prefecture-level city and demonstrates the utility of the proposed framework for similar analyses, offering insights relevant to national land use planning and policy formulation.

1. Introduction

In recent decades, China has experienced rapid economic growth. The country faces external challenges, such as food insecurity caused by international instability; and internal challenges, such as land use problems stemming from rapid urbanization and a long-standing urban-rural dual system [1], which has led to inefficient allocation of labor, capital, and land resources between urban and rural areas. The continuous expansion of secondary and tertiary industries has increased the demand for fixed assets, land, and labor investment, encroaching on agricultural development space [2,3,4]. Further, practical problems such as ineffective agricultural land planning, a lagging rural infrastructure, and difficulties in increasing farmer incomes need to be addressed [5].

Land consolidation is a key instrument for coordinating urban-rural development [6,7], contributing to the dynamic equilibrium of cultivated land, optimal land resource allocation, increased agricultural fiscal revenue, improved rural infrastructure, and enhanced farmer incomes and living conditions. Collectively, these outcomes help mitigate human-land conflicts in China’s economic development [7,8]. However, most research to date has examined the scale, investment, and benefits of individual projects, with few studies adopting a macro perspective to investigate the evolutionary characteristics and general patterns of land consolidation within a defined spatio-temporal scope.

Coastal regions are vital drivers of China’s economic, social, and cultural development, offering valuable experience accumulated over decades of national growth. Lianyungang, located in the eastern coastal region, typifies the area’s topography and geomorphology, combines both northern and southern cultural traits, and has a moderate level of economic development. This study therefore employs Lianyungang as a case study to examine the spatio-temporal evolution of land consolidation, providing insights to help government agencies optimize the deployment of consolidation initiatives.

Land consolidation originated in medieval Europe [9]; Germany and The Netherlands were the most prominent examples [10]. From the 16th to the 19th centuries, countries such as France, Canada, Russia, South Korea, and Japan also successively practiced land consolidation. Internationally, it is referred to using such terms as “land consolidation”, “land development”, “land reclamation”, and “land readjustment”. Global land consolidation research began to grow significantly in the 1970s and 1980s, with a surge in the 21st century and sustained high publication volumes in recent years. Research has been conducted in 133 countries; China and the United States have played a leading role, followed by the United Kingdom, Canada, Germany, Australia, Brazil, France, and Japan. Internationally, land consolidation research primarily centers on land fragmentation, the development process of land consolidation, its impact on soil, and its benefits and objectives [7,9,11,12].

Currently, most countries have shifted from initial agricultural production goals to supporting rural development in both theory and practice. Land consolidation practices now emphasize rural infrastructure, developing plans that combine the benefits of environment and landscape, and addressing rural ecological degradation. Implementation of land consolidation projects increasingly emphasizes public participation [10,13,14,15,16]. For example, Germany enacted its first land consolidation law in Bavaria in 1886. To adapt to the changes in land use relationships after World War II, Germany promulgated the Federal Land Consolidation Act in 1953, clearly defining the purposes, tasks, methods, organizational structures and the functions, participant rights and obligations, costs, land valuation, ownership adjustment, and accepted outcomes of land consolidation. Germany’s land consolidation focus has shifted from adjusting farmland ecosystems and improving agricultural infrastructure to emphasizing rural infrastructure construction, regional planning, and natural landscape protection.

China has a long history of land development and a well-developed agricultural civilization; it has explored land utilization practices since the Shang Dynasty over 3000 years ago. Many scholars consider the square-field system of the Western Zhou Dynasty to be an early form of land consolidation in China. Through more than 2000 years of feudal society, land consolidation activities like land reclamation (e.g., land clearing, land enclosure, and military farming) continued uninterrupted. This was evidenced by the military farming system during the Qin and Han Dynasties, the land occupation system in the Western Jin Dynasty, and the equal-field system in the Northern Wei, Sui, and Tang Dynasties.

Modern land consolidation in China emerged after the reform and opening-up; the concept was formally introduced in the Land Administration Law of the People’s Republic of China in the 1990s. The 1997 policy document Notice on Further Strengthening Land Management and Effectively Protecting Cultivated Land proposed “implementing a policy of occupying cultivated land with development and reclamation” and “summarizing and disseminating the experiences of land consolidation, with integrated management of farmland, water, roads, forests, and villages implemented to enhance land quality based on the requirements of the overall land use plan. The goal was to increase the area of effective cultivated land and enhance agricultural production conditions and ecological environments.” In 2003, China issued the National Land Development and Consolidation Plan (2001–2010), further segmenting land consolidation, reclamation, and development. The plan provided specific definitions, leading to a rapid growth in theoretical and practical achievements around 2000.

The concept of “land consolidation” was first proposed by Chinese leadership in 2008. The National Land Consolidation Plan (2016–2020) advocated for comprehensively advancing land consolidation using a new development philosophy. This formed the widely accepted definition of land consolidation as a systematic process conducted within a specific region, guided by objectives and purposes defined in an overall land use plan, urban planning processes, and specialized land rehabilitation plans. Land consolidation is conducted using administrative, economic, legal, and engineering construction measures; and involves the comprehensive consolidation and development of farmland, water systems, roads, forests, and villages. This includes the in-depth development of rural residential land that is improperly configured, inefficiently utilized, fragmented, unused, or underutilized. The goal is to enhance the intensive utilization rate and productivity of land, and improve production conditions, living standards, and the ecological environment.

Land consolidation is a government-led, multi-stakeholder, systematic project targeting the rational organization and utilization of land resources. Based on land consolidation activities during different periods, scholars have divided China’s land consolidation into four stages: 1986–1997 (cultivated land consolidation), 1998–2007 (land reclamation and consolidation), 2008–2012 (multi-type consolidation), and 2013-present (comprehensive land consolidation) [7]. To analyze the benefits, Chinese scholars have extensively studied the economic, social, and ecological benefits of land consolidation. Recent research has focused on ecological landscape benefits [7,17,18,19,20,21]. Xu et al. explored the impact of agricultural reclamation on the physicochemical properties of soil in eastern coastal China [22].

The Chinese government has heavily emphasized protecting cultivated land [23] and implementing a compensation system that mitigates cultivated land occupation [6,14,24]. Management practices have achieved positive outcomes in such areas of market accesses, project quality improvement, post-consolidation guardianships, and index exchanges. Cui et al. analyzed the multi-agent governance network structure of land consolidation from the perspective of the “power-interest-trust” relationship [25]. Studies have applied methods such as average nearest neighbor, standard deviation ellipse, kernel density, and geographic detector models to explore spatio-temporal differentiation patterns and the center of gravity migration trajectory of land consolidation [26]. In recent years, Chinese scholars have increasingly recognized that comprehensive land consolidation optimizes the production-living-ecological spatial pattern in terms of spatial quantity, spatial quality, and spatial structural relationships. This is performed by reorganizing spatial structures, reconfiguring elements, and restructuring spatial frameworks.

The focus of land consolidation has shifted over time and across countries, with scholars tailoring their research to national development strategies and examining models, technical systems, and practical approaches. Yet, few studies have synthesized the general patterns underlying the spatio-temporal evolution of land consolidation under policy regulation. In China, coastal regions have accumulated valuable experience through economic and social development since the reform and opening-up, serving as pilot zones for many new land consolidation policies. After two decades of policy-driven practice, new research questions have emerged: What are the spatio-temporal evolution characteristics of land consolidation in China’s coastal regions? What general patterns underlie these characteristics? What are the future trajectories of coastal land consolidation? And how can government agencies scientifically deploy these initiatives?

To address these questions, this study establishes a spatio-temporal evolution analysis framework for land consolidation. It examines characteristics under policy regulation in coastal regions, identifies general patterns, projects future trends, and offers recommendations. Lianyungang, a representative eastern coastal city, is selected as the case study. The paper is structured as follows: Section 2 outlines the framework; Section 3 presents the empirical analysis; Section 4 discusses; and Section 5 conclusions.

2. Materials and Methods

2.1. Research Framework

Human-land relationship theory provides the theoretical foundation for the field of geography. Research has expanded to explore the evolution of human-land relationships, distribution patterns, and intrinsic driving mechanisms. This reveals the interactions between human socio-economic activities and natural geographical environments. Spatial econometrics posits that economic activities and variables are influenced by their inherent characteristics and by their spatial locations and structures. Incorporating spatial effects allows for a more accurate depiction of spatial characteristics, economic patterns, and their evolution, providing a scientific basis for regional planning, resource allocation, and policy design. Advances in GIS technology have expanded the use of spatial econometrics in geography. Land consolidation reflects the human-land relationship as a spatial projection of economic and social dynamics—the process through which human activities shape the land. Multiple factors influence these spatial patterns, underscoring the need for temporal analysis. A framework that integrates temporal and spatial perspectives enables more comprehensive and nuanced descriptions of China’s developmental changes.

Using a timeline as the main axis, this framework analyzes the outcomes of land consolidation at regular time intervals. As shown in Figure 1, the framework combines land consolidation intensity, land use transition matrices, landscape pattern indices, and standard deviation ellipse analysis to characterize spatio-temporal evolution. Gray correlation analysis is integrated with economic, policy, and other relevant factors. This reveals general patterns in the developmental stages of land consolidation, sources of supplemental cultivated land, distribution ranges, migration patterns, and factor correlations.

The framework also projects future center-of-gravity shifts in land consolidation and proposes optimization strategies for adjusting regional consolidation grades and controlling project scale.

2.2. Study Area

The 2023 Lianyungang Statistical Yearbook reports that Lianyungang had a permanent population of 4.6 million as of December 2023. Lianyungang, it is located in the northern part of Jiangsu Province, China, as shown in Figure 2, and lies between 34°07′–35°08′ N latitude and 118°24′–119°55′ E longitude (Lianyungang Overall Land Use Plan, 2006–2020). The study area has a temperate monsoon climate with slight maritime characteristics. The terrain slopes from northwest to southeast, and is divided into four major geomorphic regions: the western hilly area, the central plain area, the eastern coastal area, and the Yuntai Mountain area. Lianyungang covers a land area of 7626 square kilometers and a sea area of 7516 square kilometers; the cultivated land covers 3706 square kilometers, which is 48.59% of the land area.

Among China’s 54 coastal cities, Lianyungang ranks 28th in total economic output and 32nd in per capita gross domestic product (GDP). This reflects a moderate level of economic development. The city’s key industries encompass new pharmaceuticals, advanced materials, renewable energy, high-end equipment manufacturing, petrochemicals and chemical materials, steel production, grain and food processing, photovoltaic materials, and emerging service sectors. The marine economy comprises marine transportation, coastal tourism, fisheries, marine equipment manufacturing, marine biomedicine, marine energy, and seawater utilization. Lianyungang serves as an effective case study owing to its diverse geomorphology, moderate economic standing, and strategic location at the intersection of northern and southern Chinese cultural spheres.

Lianyungang constitutes a typal case for coastal China’s land consolidation evolution due to its:

(1)

Geographically central position within China’s coastal belt;

(2)

Transitional physiography bridging northern and southern landforms;

(3)

Median economic status among peer coastal cities;

(4)

Role as a national policy testing ground.

These attributes collectively render its land consolidation trajectory a microcosm of broader coastal development patterns. The study’s findings thus will reveal empirically generalizable spatio-temporal applicable to coastal regions in China.

2.3. Data Sources

(1)

Land use and cover data are from the China Land Cover Dataset (CLCD) from 2002 to 2022, with a 30 m spatial resolution; the database was developed by Professors Jie Yang and Xin Huang of Wuhan University in China. The overall accuracy of CLCD reached 79.31% based on 5463 visually interpreted samples [27] (http://doi.org/10.5281/zenodo.4417809, accessed on 20 July 2025).

(2)

Digital Elevation Model (DEM) Data were generated using ASTER GDEM V2 data, with a 30 m spatial resolution; from the Geospatial Data Cloud platform (http://www.gscloud.cn/ accessed on 29 June 2024).

(3)

Land consolidation project data for 2002–2022 are from the Lianyungang Natural Resources and Planning Department; data are from provincial, prefectural, and county-level funded land reclamation, development, and consolidation projects. Vector data processing was performed to aggregate vector boundaries of all projects into a single layer.

(4)

Socioeconomic Data, including data on cultivated land area, permanent population, regional GDP, fixed asset investment, and related statistics are from the Lianyungang Statistical Yearbook, annual economic and social development bulletins, the Lianyungang Overall Land Use Plan (2006–2020), and the Lianyungang Territorial Space Master Plan (2021–2035).

2.4. Research Methods

2.4.1. Land Consolidation Intensity

Drawing on the formula for the dynamic degree of land use type changes [28], the concept of land consolidation intensity (D) represents the degree of change in land consolidation scale over a specific period. A higher D value indicates a faster expansion of land consolidation scale. The formula used is as follows.

$$D={\displaystyle \frac{S_b-S_a}{S_a}}\times {\displaystyle \frac{1}{T}}\times 100\%$$

(1)

In Formula (1), D (%) is the dynamic degree of land consolidation; S_a and S_b represent the land consolidation scale (in hectares) at the beginning and end of the study period, respectively; and T indicates the duration of the study period. When T represents 1 year, D indicates the annual change rate of land consolidation scale within the study area. The step size of T in this article is 5.

2.4.2. Land Use Transition Matrix

The land use transition matrix is used to express the mutual transformation relationships between different land use types at the beginning and end of each land use period [29,30]. The spatial analysis functions of professional software, such as ArcGIS 10.8 and Origin 2021, are used to visualize the analysis results, demonstrating the overall changes in land use types and their mutual transformation characteristics within the study area. The formula used is as follows:

$$S_{ij}=\left[\begin{array}{c}{S}_{11 }{S}_{12} \cdots S_{1n}\\ S_{21 }{S}_{22 }\cdots S_{2n}\\ \cdots \cdots \cdots \cdots \\ S_{n1 } S_{n2 }\cdots { S}_{nn}\end{array}\right]$$

(2)

In Formula (2), the subscripts i and j denote the land use types before and after consolidation, respectively. The parameter n represents the total number of land use types both pre- and post-consolidation. S_ij indicates the area undergoing conversion from land use type i to type j.

2.4.3. Landscape Pattern Indices

Landscape pattern indices are quantitative indicators that report changes in landscape patterns and explain the relationships between landscape structure and processes [28]. This study uses Fragstats 4.2.1 software to analyze the landscape patterns of the study area in the years 2002, 2007, 2012, 2017, and 2022. In the model, Total Class Area (CA), Percentage of Landscape (PLAND), and Largest Patch Index (LPI) directly reflect the dominant land types in the study area. Landscape Shape Index (LSI) and Aggregation Index (AI) are used to describe landscape continuity. Patch Density (PD) and Edge Density (ED) reflect the degree of landscape fragmentation in the study area.

2.4.4. Standard Deviation Ellipse

This study applies the spatial distribution analysis method of the standard deviation ellipse [26,29,30,31,32,33,34,35]. The size of the ellipse reflects the degree of concentration in the spatial elements. The center, major and minor axes, and azimuth angle describe the overall spatial distribution of the study object. The analysis focuses on the migration of the spatial distribution center of gravity, changes in spatial distribution shape, and variations in the azimuth angle. This helps analyze the spatial distribution characteristics and trajectories of center of gravity migration of land consolidation. The center of the ellipse is determined by the arithmetic mean center; 0° is set as due north and the direction is determined using the clockwise rotation point. A larger flattening ratio (the difference between the major and minor axes) is associated with more significant data directionality. After determining the center and direction, the standard deviations of the two axes are calculated to generate the ellipse curve. The formula used is as follows:

$${SDE}_x=\sqrt{{\displaystyle \frac{{\sum }_{i=1}^n{\left(x_i-\overline{X}\right)}^2}{n}}} {SDE}_y=\sqrt{{\displaystyle \frac{{\sum }_{i=1}^n{\left(y_i-\overline{Y}\right)}^2}{n}}}$$

(3)

$$\tan \theta ={\displaystyle \frac{\left({\sum }_{i=1}^n{\tilde{x}}_i^2-{\sum }_{i=1}^n{\tilde{y}}_i^2\right)+\sqrt{{\left({\sum }_{i=1}^n{\tilde{x}}_i^2-{\sum }_{i=1}^n{\tilde{y}}_i^2\right)}^2+4{\left(\sum _{i=1}^n{\tilde{x}}_i{\tilde{y}}_i\right)}^2}}{2\sum _{i=1}^n{\tilde{x}}_i{\tilde{y}}_i}}$$

(4)

$${\sigma }_x=\sqrt{2}\sqrt{{\displaystyle \frac{\sum _{i=1}^n{\left({\tilde{x}}_i\mathit{cos}\theta -{\tilde{y}}_i\mathit{sin}\theta \right)}^2}{n}}} {\sigma }_y=\sqrt{2}\sqrt{{\displaystyle \frac{\sum _{i=1}^n{\left({\tilde{x}}_i\mathit{sin}\theta +{\tilde{y}}_i\mathit{cos}\theta \right)}^2}{n}}}$$

(5)

In Formulas (3)–(5), ${SDE}_x$ and ${SDE}_y$ represent the coordinates of the center (centroid) of the Standard Deviational Ellipse (SDE) characterizing the spatial distribution of land consolidation projects. $x_i$ and $y_i$ denote the spatial coordinates of the centroid of the i-th land consolidation project patch. $\overline{X}$ and $\overline{Y}$ represent the mean center coordinates of all patches, and n denotes the total number of patches. The term $\tan \theta$ defines the orientation angle (θ) of the ellipse. ${\sigma }_x$ and ${\sigma }_y$ represent the standard deviations of the coordinate values along the X-axis and Y-axis, respectively. Within the coordinate system, where the X-axis is oriented north–south (with north defined as 0 degrees) and angles increase clockwise from north, the deviation terms ${\tilde{x}}_i$ and ${\tilde{y}}_i$ represent the deviations of each patch centroid from the mean center ($\overline{X}$, $\overline{Y}$) along the X and Y axes. These deviations are used to determine the lengths of the semi-major axis and semi-minor axis of the ellipse. Using the annual mean distribution center of the land consolidation scale as the centroid and the main trend direction of the land consolidation distribution (given by the azimuth angle θ) as the orientation, the distribution ellipse for land consolidation within the study area is constructed based on the standard deviations (${\sigma }_x$ and ${\sigma }_y$) in the X and Y directions, thereby defining its axes.

2.4.5. Gray Correlation Analysis

Gray correlation analysis investigates the relationships between system variables. The method compares the development trends of different factors to quantify the dynamic processes, and calculate the degree of correlation between factors. The method has the following advantages: it effectively analyzes small sample sizes with irregular patterns; it has low computational complexity; it is convenient; and it is consistent when comparing quantitative and qualitative results [36]. The formula used is as follows:

$$G_{0i}\left(k\right)={\displaystyle \frac{{∆}_{min}+{\rho ∆}_{max}}{{∆}_{0i}\left(k\right)+{\rho ∆}_{max}}}$$

(6)

$$r_{0i}={\displaystyle \frac{1}{N}}{\sum }_{k=1}^N{G}_{0i}\left(k\right)$$

(7)

In Formulas (6) and (7): i = 1, 2, …, m, denotes there are m factors, meaning there are m time series. k = 1, 2, …, N, where N represents the temporal length (number of time points) of each sequence. $G_{0i}\left(k\right)$ and $r_{0i}$ represent the gray relational coefficient and gray relational degree, respectively, between the parent sequence {$m_0$} and the sub-sequence {$m_i$} at time point k. These reflect the closeness between the two sequences, satisfying 0 < $G$ ≤ 1 and 0 < r ≤ 1. ${∆}_{min}$ and ${∆}_{max}$ denote the minimum and maximum absolute differences across all compared sequences at all time points. $\rho$ is the distinguishing coefficient, with 0 < $\rho$ < 1. This study uses gray correlation analysis to explore the degree of correlation between land consolidation scale and socio-economic, demographic, and policy factors in the study area. GSTA V7.0 software is used to calculate the correlation between relevant factors and land consolidation scale. A result close to 1 indicates a higher correlation, and a correlation degree above 0.5 confirms the selected indicators are effective.

3. Results

3.1. Evolution Characteristics of Land Consolidation Development Stages in Lianyungang

This study analyzes projects from 2002 to 2022 (Figure 3). Over these 20 years of development, Lianyungang implemented 2888 land consolidation projects of various types, covering a total area of 112,852 ha and supplementing 29,226 ha of cultivated land. These projects contributed substantially to maintaining the dynamic balance of total cultivated land and ensured a stable land supply for development. Formula (1) was used to calculate the land consolidation intensity of the area over the study period using five-year intervals (Figure 4). Between 2002 and 2022, Lianyungang’s land consolidation intensity exhibited an initial increase followed by stabilization.

3.1.1. Initial Development Stage (2002–2007)

The land consolidation intensity from 2002 to 2007 was 130.74, marking the initial development stage. The Land Consolidation Center of Lianyungang City was established in 2002; it was a government agency that organized and implemented land reclamation and development projects to maintain a dynamic balance across the city’s total cultivated land. In 2006, the Chinese government redefined the meaning of land consolidation, expanding it beyond land readjustment with a primary focus on agricultural land. The goal was to improve cultivated land quality and increase the area of effective cultivated land; the broader scope included “coordinating urban and rural economic and social development; building a new type of industrial-agricultural-urban-rural relationship; promoting modern agricultural construction; strengthening the industrial foundation for a socialist new rural development; and fostering sustained increases in farmer incomes, thereby solidifying the economic foundation for socialist new rural development.” This stage saw the nationwide rollout of land consolidation efforts, with Lianyungang beginning to explore region-specific approaches.

3.1.2. Gradual Growth Stage (2007–2012)

The land consolidation intensity from 2007 to 2012 increased to 172.33; this was a 31.81% increase compared to the previous stage. Urbanization in China led rural land to be increasingly occupied. This prompted the government to introduce successive policies that encouraged land consolidation to protect cultivated land and ensure food security. In 2007, the “Notice on Adjusting the Allocation Method of Central Government’s Share of Land Use Fees for New Construction Land” stated that the central government’s share of land use fees for new construction land would be allocated to provinces (autonomous regions, and municipalities) using a factor-based method. Fees were specifically earmarked for expenditures on basic farmland construction and protection, land consolidation, and cultivated land development.

In 2009, the former Ministry of Land and Resources issued “Opinions on Promoting Stable Agricultural Development, Continuous Farmer Income Growth, and Urban-Rural Integration”. It proposed the integration of rural land consolidation with a policy of “Increasing vs. Decreasing Balance of Urban-Rural Construction Land”. This initiative established a new platform for promoting socialist new rural construction and integrated urban-rural development. This elevated rural land consolidation to a national-level strategic deployment. During this stage, land consolidation in Lianyungang entered a period of rapid growth, with a gradual increase in land consolidation intensity.

3.1.3. Rapid Growth Stage (2012–2017)

From 2012 to 2017, land consolidation intensity reached 312.67; this was a 81.44% increase compared to the previous stage. China implemented a new urbanization development strategy, balancing urban land demand with food security. Land consolidation policies shift toward ensuring that both urban and rural areas shared urban economic achievements. With State Council approval, the “National Land Consolidation Plan (2011–2015)” was officially promulgated and implemented in 2012, outlining the main tasks of land consolidation: (1) comprehensively advancing land consolidation; (2) vigorously promoting agricultural land consolidation; (3) standardizing and promoting rural construction land consolidation; (4) conducting urban and industrial-mining construction land consolidation in an orderly way; and (5) accelerating land reclamation.

In 2016, the Ministry of Land and Resources issued the Notice on Utilizing the Increasing vs. Decreasing Balance Policy to Actively Support Poverty Alleviation and Relocation (valid for five years). This guided the flow of surplus quotas under the “Increasing vs. Decreasing Balance” policy and fully realized the differential land value benefits. In the same year, the Jiangsu Provincial Department of Natural Resources issued the Implementation Opinions on Supporting the Province’s Poverty Alleviation and Prosperity Project. It required the intensification of comprehensive land consolidation in economically weak areas. The Shilianghe area in Lianyungang was identified as one of six key poverty alleviation regions in Jiangsu Province, including Donghai County and Ganyu District. Guanyun County and Guannan County in Lianyungang were designated as two of the twelve key assistance counties in the province. As a result, the scale of land consolidation in Lianyungang peaked around 2017.

3.1.4. Stabilization and Decline Stage (2017–2022)

Between 2017 and 2022, the land consolidation intensity decreased to 268.10; this was a 14.25% decrease compared to the previous stage. The Chinese government issued the “Notice of the State Council on Issuing the National Land Planning Outline (2016–2030)” in 2017. This emphasized the control of land development intensity; optimization of spatial development structures in the Bohai Rim, Yangtze River Delta, and Pearl River Delta regions; and strict regulation of development intensity and new construction land supplies. Efforts were made to revitalize construction land and reduce the proportion of industrial land. This led Lianyungang’s land consolidation scale to enter a period of stabilization and then gradual decline.

3.2. Analysis of Land Use Patterns and Landscape Changes in Land Consolidation

3.2.1. Analysis of Land Use Pattern Changes

Based on the land use transition matrix calculations, ArcGIS 10.8 and Origin 2021 software were used to generate the following: a table of land use structure changes within the land consolidation area (

Table 1. Land use structure changes within the land consolidation study area (hm²; %).

Land Use	Year	2002	2007	2012	2017	2022
Cultivated land	Area	70,203.6	69,995.25	69,969.24	69,929.19	69,671.79
	Percentage	88.76	88.5	88.47	88.42	88.09
Forest	Area	3.15	1.62	1.44	2.16	1.35
	Percentage	0.004	0.002	0.0018	0.0027	0.0017
Grassland	Area	36	21.6	14.4	7.65	6.84
	Percentage	0.05	0.03	0.02	0.01	0.01
Water	Area	2335.23	2313.36	1585.8	1268.19	1145.97
	Percentage	2.95	2.92	2.01	1.6	1.45
Construction land	Area	6512.76	6758.91	7519.86	7883.55	8264.79
	Percentage	8.23	8.55	9.51	9.97	10.45
Total		79,090.74	79,090.74	79,090.74	79,090.74	79,090.74

), an interannual change chart (Figure 5), a table presenting the land use transition matrix (

Table 2. Land use transition matrix within the Land consolidation study area (hm²).

Year	Land Use	Grassland	Construction Land	Water	Forest	Cultivated Land	Total
2002–2007	Grassland	19.17	5.22	0.09		11.52	36
	Construction land		6438.15	73.17		1.44	6512.76
	Water		51.03	2108.61		175.59	2335.23
	Forest				1.62	1.53	3.15
	Cultivated land	2.43	264.51	131.49		69,805.17	70,203.6
	Total	21.6	6758.91	2313.36	1.62	69,995.25	79,090.74
2007–2012	Grassland	10.08	2.25			9.27	21.6
	Construction land		6720.48	36.45		1.98	6758.91
	Water		426.24	1431.72		455.4	2313.36
	Forest				1.44	0.18	1.62
	Cultivated land	4.32	370.89	117.63		69,502.41	69,995.25
	Total	14.4	7519.86	1585.8	1.44	69,969.24	79,090.74
2012–2017	Grassland	6.3	5.13	0.09		2.88	14.4
	Construction land		7449.12	49.86		20.88	7519.86
	Water		18.81	854.19	0.09	712.71	1585.8
	Forest				1.17	0.27	1.44
	Cultivated land	1.35	410.49	364.05	0.9	69,192.45	69,969.24
	Total	7.65	7883.55	1268.19	2.16	69,929.19	79,090.74
2017–2022	Grassland	5.94	0.27			1.44	7.65
	Construction land		7861.05	20.97		1.53	7883.55
	Water		24.75	779.4		464.04	1268.19
	Forest				0.81	1.35	2.16
	Cultivated land	0.9	378.72	345.6	0.54	69,203.43	69,929.19
	Total	6.84	8264.79	1145.97	1.35	69,671.79	79,090.74

), a chart of the types of land use transitions (Figure 6), and a spatio-temporal distribution map of transitions (Figure 7). Cultivated land was the dominant land use type during the study period, and remained relatively stable across the total area, accounting for 88.09–88.76% of land use.

However, there were active transitions between cultivated land and other land types, including construction land, ponds, rivers, and tidal flats. This indicated these was a dynamic balance of cultivated land maintained within the area. The proportion of ponds, rivers, and ditches decreased from 2.95% to 1.45%; the proportion of grassland decreased from 0.05% to 0.01%; these primarily transitioned to construction land and cultivated land. The proportion of forest land was relatively low and generally declined, decreasing from 0.0040% to 0.0017%, with all the land transitioning to cultivated land. The proportion of construction land increased from 8.23% to 10.45%, and was mainly converted from cultivated land and small amounts of ponds, rivers, ditches, and grassland. A small amount of construction land was transformed into cultivated land through land reclamation.

In summary, ponds, rivers, tidal flats, and small amounts of grassland, construction land, and forest land within the Lianyungang land consolidation area were the primary sources of supplemental cultivated land. Land consolidation led to changes in land use patterns, gradually forming a new cultivated land use structure and regional spatial pattern.

3.2.2. Analysis of Landscape Pattern Changes

The Total Class Area (CA), Percentage of Landscape (PLAND), and Largest Patch Index (LPI) metrics directly characterize the dominant land-cover types in the study area. Between 2002 and 2022, within the land consolidation areas of Lianyungang, the CA, PLAND, and LPI values for cultivated land consistently ranked highest among all landscape types. This indicates that cultivated land is the dominant landscape and demonstrates that land consolidation is an effective tool for maintaining the dynamic balance of total cultivated land and implementing the “Occupation-Compensation Balance” policy. Changes in landscape pattern indices within the land consolidation project area were quantified using Fragstats 4.2.1software (

Table 3. Changes in landscape pattern indices within the land consolidation project area.

Year	Landscape	CA (hm2)	PLAND	LPI	LSI	AI	PD	ED
2002	Cultivated land	70,203.60	88.7634	6.0941	75.8331	91.5145	11.6461	27.2998
	Construction land	6512.76	8.2345	0.1033	102.5399	62.0429	11.8383	26.5468
	Water	2335.23	2.9526	0.6859	21.5201	87.1478	0.8256	3.4627
	Grassland	36.00	0.0455	0.0035	9.4750	55.3947	0.0961	0.2401
	Forest	3.15	0.0040	0.0006	4.2500	32.7586	0.0202	0.0322
2007	Cultivated land	69,995.25	88.4999	6.0746	76.0368	91.4806	11.7169	27.9499
	Construction land	6758.91	8.5458	0.1086	103.0219	62.5724	12.0065	27.0210
	Water	2313.36	2.9249	0.6894	22.9502	86.2078	0.9344	3.7135
	Grassland	21.60	0.0273	0.0026	8.3548	49.2205	0.0733	0.1650
	Forest	1.62	0.0020	0.0006	3.4444	18.5185	0.0152	0.0205
2012	Cultivated land	69,969.24	88.4670	5.4956	76.7029	91.4018	11.7296	29.5134
	Construction land	7519.86	9.5079	0.2659	102.9499	64.5533	12.1519	28.3607
	Water	1585.80	2.0050	0.1886	28.8571	78.8128	1.1657	4.2316
	Grassland	14.40	0.0182	0.0030	5.6923	58.5034	0.0303	0.0963
	Forest	1.44	0.0018	0.0006	3.3750	20.8333	0.0126	0.0193
2017	Cultivated land	69,929.19	88.4164	5.4508	78.2904	91.2214	11.7549	32.0457
	Construction land	7883.55	9.9677	0.2470	104.2500	64.9916	12.3820	29.2976
	Water	1268.19	1.6035	0.0692	31.9412	73.6473	1.1759	4.3628
	Grassland	2.16	0.0027	0.0007	3.5000	34.2105	0.0152	0.0239
	Forest	7.65	0.0097	0.0032	4.0526	61.5894	0.0164	0.0516
2022	Cultivated land	69,671.79	88.0910	5.4318	79.3136	91.0875	11.6423	33.0786
	Construction land	8264.79	10.4498	0.2375	104.8122	65.5765	12.5893	30.2338
	Water	1145.97	1.4489	0.0642	26.9823	76.7353	0.8636	3.8826
	Grassland	1.35	0.0017	0.0003	3.1250	22.7273	0.0114	0.0178
	Forest	6.84	0.0086	0.0032	3.6111	64.9254	0.0114	0.0444

).

The Landscape Shape Index (LSI) characterizes land shape and spatial distribution. Higher LSI values indicate greater shape complexity and broader spatial coverage. Between 2002 and 2022, within Lianyungang’s land consolidation area, the LSI of cultivated land increased from 75.8331 to 79.3136; construction land LSI rose from 102.5399 to 104.8122; ponds, rivers, and tidal flats LSI increased from 21.5201 to 26.9823; grassland LSI declined from 9.4750 to 3.1250; and forest land LSI fluctuated and decreased from 4.2500 to 3.6111. The data indicate continuous emergence of new cultivated land plots within the project area and an expansion in the spatial distribution of cultivated land. Construction land became increasingly widespread, while the coverage of agricultural water-conservancy structures, such as ditches, expanded due to dredging. In contrast, grassland coverage declined, and forest land distribution fluctuated and contracted, reflecting a compression of ecological space.

The Aggregation Index (AI) reflects the continuity of landscapes; higher values indicate higher continuity. Patch Density (PD) and Edge Density (ED) reflect the degree of landscape fragmentation; higher values indicate greater fragmentation. The calculation results indicated the following for the land consolidation area of Lianyungang from 2002 to 2022.

The AI of cultivated land decreased from 91.5145 to 91.0875, while PD remained almost unchanged, shifting slightly from 11.6461 to 11.6423; and ED increased from 27.2998 to 33.0786. The declining continuity and aggregation of cultivated land indicate that supplemental cultivated land in the study area was often in remote and fragmented plots.

The AI of construction land increased from 62.0429 to 65.5765; the PD rose from 11.8383 to 12.5893; and the ED rose from 26.5468 to 30.2338. The improved continuity and aggregation of construction land indicate that land consolidation encouraged the concentrated settlement of farmers, significantly improving the conveniences of living.

The AI of ponds, rivers, and tidal flats decreased from 87.1478 to 76.7353; the PD initially increased from 0.8256 to 1.1759; and the ED initially increased from 3.4627 to 4.3628. The PD and ED then declined to 0.8636 and 3.8826, respectively, in 2022. The declining continuity, and the initial reduction and then increased water body aggregation, were due to the reclamation of many ponds, rivers, and tidal flats into cultivated land.

The AI of grassland decreased from 55.3947 to 22.7273; the PD declined from 0.0961 to 0.0114; and the ED declined from 0.2401 to 0.0178. The declining continuity and improved aggregation of grassland were due to the conversion of scattered grasslands into cultivated land and construction land, which led to the remaining grasslands becoming more concentrated.

The AI of forest land initially decreased from 32.7586 to 20.8333 before rising to 64.9254. The PD value dropped from 0.0202 to 0.0114, while the ED value first declined from 0.0322 to 0.0193 before increasing to 0.0444. The continuity of forest land improved, but its aggregation fluctuated. This was because land consolidation efforts promoted the formation of a connected forest network within the project area. However, forest land initially decreased before recovering in recent years due to a growing awareness of ecological conservation.

3.3. Analysis of the Center of Gravity Migration in the Spatial Distribution of Land Consolidation

The spatial distribution and center of gravity migration trajectory of land consolidation in Lianyungang from 2002 to 2022 were generated using ArcGIS 10.8 software (Figure 8).

3.3.1. Spatial Distribution Characteristics

The center of the standard deviation ellipse reflecting land consolidation distribution was located in the urban center (Haizhou District) and adjacent areas, with its westernmost point originating from Niushan Town, Donghai County (2005), extending northward to Qingkou Town, Ganyu District (2013), and reaching Yishan Town, Guanyun County (2022) in the south. Haizhou District, Niushan Town, Qingkou Town, and Yishan Town are major population and industrial agglomeration areas within Lianyungang, characterized by high urbanization levels and well-developed infrastructure. The political, economic, and cultural centers of Lianyungang’s central urban area are Donghai County, Ganyu District, and Guanyun County, respectively. Each area possesses a long history and rich cultural heritage. Concentrated land consolidation in these regional political, economic, and cultural centers indicates a strong correlation between land consolidation development and local socio-economic achievements.

3.3.2. Distribution Center of Gravity Migration Trajectory

The center of the standard deviation ellipse of land consolidation spatial distribution generally migrated from the inland western region (Donghai County) to the eastern coastal regions (Xuwei New District, Ganyu District, and Guanyun County). The center migration trajectory reveals that, after moving eastward from Donghai County to the city center in Haizhou, the center diverged into the three directions of northeast, due east, and southeast. The center of gravity of land consolidation distribution in Lianyungang rapidly extended northward and southward along the coastline during its eastward migration. There was a clear development trajectory towards the sea, demonstrating Lianyungang’s strategic shift from a traditional agricultural and rural society to a marine-oriented economy and society.

Following the 12th Five-Year Plan (2011–2015), coastal regions implemented key strategic initiatives outlined by the Central Committee of the Communist Party of China and the State Council. Lianyungang actively adjusted its marine industrial structure, upgraded traditional marine industries, fostered emerging strategic marine sectors, and endeavored to enhance the quality and efficiency of marine economic development. During the 13th Five-Year Plan (2016–2020), the port areas of Ganyu, Xuwei, and Guanhe obtained national approval to operate, and the Lianyungang-Khorgos New Eurasian Land–Sea Transport Corridor became a benchmark demonstration under the Belt and Road Initiative. Lianyungang Port is developing a “one body, two wings” port cluster framework, centered on the main port area of Lianyungang, with the Ganyu, Xuwei, and Guanhe port areas functioning as the wings. The city’s marine economy has steadily expanded, with the gross marine product value approaching one-third of the city’s GDP.

3.3.3. Changes in Spatial Distribution Shape

Analysis of changes in the azimuth angle of the standard deviation ellipse indicates a continuous counterclockwise rotation during the initial, middle, and final periods of the study. In other words, the growth rate in the scale of land consolidation was higher in the eastern region compared to the western region. When examining the change in the ellipse’s axis length, the east–west axis shortened and the north–south axis lengthened. This indicates that the scale of land consolidation was larger in the north–south direction (Guanyun County-Ganyu District) than that in the east–west direction. Temporally, the center migrated first to the northeast and then to the southeast; the axis was longer during the southeast phase compared to during the northeast phase. The migration of the center from Ganyu District in the northeast to Guanyun County in the southeast coincided with the rapid development stage of land consolidation. The rotation of the standard deviation ellipse toward Guanyun County in the southeast benefited from the top-down “targeted poverty alleviation” policy during that stage in Jiangsu Province. The Jiangsu Provincial Department of Natural Resources mandated the thorough implementation of targeted poverty alleviation strategies, integrating land and resource policies to strengthen the support of territorial resources for poverty alleviation efforts. Comprehensive land consolidation was intensified in economically disadvantaged areas, with newly added cultivated land and surplus quotas from the “Increasing vs. Decreasing Balance policy serving as Occupation-Compensation Balance” indicators after verification. These quotas can be traded within the province to ensure that the poverty alleviation benefits of land consolidation projects are realized. Guanyun County, located in eastern Lianyungang, is a poverty-stricken county within Jiangsu Province. Between 2017 and 2022, Guanyun County underwent a rapid expansion in land consolidation scale under the “poverty alleviation” policy.

3.4. Gray Correlation Degree Between Land Consolidation and Its Factors

3.4.1. Indicator Selection

The preceding analysis indicates that the selection of driving factors should be primarily based on economic development, social conditions, and policy impacts. The selection of evaluation indices for land consolidation is based on three dimensions: economic capacity to support consolidation projects, social demand for food security and related infrastructure, and policy imperatives for land development and cultivated land protection. Economic indices serve as proxies for material prerequisites, social indicators reflect bottom-up pressures, and policy metrics represent top-down constraints. Given the feasibility of data acquisition, the study considered the following indices [1,12,26,31,33,37,38,39,40].

(1)

Economic Indicators (Material Foundation). Economic variables reflect the fiscal and productive capacities essential for implementing land consolidation. The combined output of the primary, secondary, and tertiary sectors shapes overall productivity, which in turn affects resource allocation in land consolidation initiatives. Macroeconomic strength—measured through fiscal revenue and GDP—governs the financial feasibility of land improvement projects, while fixed asset investment serves as a critical indicator of capital formation capacity for the infrastructure development required in land consolidation. These economic variables collectively establish the necessary material conditions for effective planning, financing, and implementation of land consolidation programs.

(2)

Social Indicators (Demand & Infrastructure). Social Indicators (Demand & Infrastructure). The social indicators cover two aspects: the supply-demand drive of food security and the support from infrastructure. The permanent resident population serves as the benchmark indicator for land development and land use policy formulation, and also acts as a driving factor for land improvement needs. The effective cropland irrigation area and the total power of agricultural machinery are directly related to the infrastructure support needed by land improvement. The total grain output indirectly reflects the demand for land supply by the population.

(3)

Policy Indicators (Regulatory Constraints). Policy variables quantify institutional governance over land use. Cultivated land protection tasks are mandatory conservation targets from government planning. Area of cultivated land occupied by government-approved construction is trade-offs between development and preservation. These are the frequently mentioned policies in the Chinese government documents. The key way to implement these two policies is through land consolidation.

3.4.2. Calculation Results

Table 4. Ranking of correlations related to land consolidation scale.

Ranking	Index	Degree of Gray Correlation
1	Primary industry output	0.6968
2	Fiscal and tax revenue	0.6764
3	Total fixed asset investments for all of society	0.6563
4	GDP	0.6372
5	Tertiary industry output	0.6333
6	Secondary industry output	0.6225
7	Cultivated land protection tasks	0.5677
8	Total power of agricultural machinery	0.5366
9	Effective irrigation area of cropland	0.5288
10	Total grain output	0.5156
11	Permanent resident population	0.5037
12	Area of cultivated land occupied by government-approved construction	0.5013

presents the ranking of calculation results. The degree of correlation of all selected indices exceeds 0.5, indicating changes in the effectiveness of the selected indices for analyzing the factors influencing the scale of land consolidation. It also indicates that the selection of indices is effective and can be used to analyze the factors influencing changes in the scale of land consolidation. The economic factor correlations all exceed 0.6, highlighting the significant influence of economic development levels on land consolidation implementation. This is identified as the primary influencing factor, followed by policy and social indicators.

4. Discussion

4.1. Feasibility and Applicability of the Research Framework

This study establishes an innovative research framework integrating human-land relationship theory and spatial econometrics to capture spatio-temporal dynamics of land consolidation in a coastal region of China. Through empirical analysis of the case study, the framework demonstrates the following contributions:

This study provides a precise analysis of the spatio-temporal evolution mechanism. By integrating temporal dimensions into spatial change analysis, it quantitatively reveals human-land interactions associated with land consolidation. Unlike conventional approaches that classify land consolidation stages based on project types and contents, this research introduces a Land Consolidation Intensity Index, which objectively identifies developmental phases using the specific periods rate of change in land consolidation scale within the study area. The proposed classification method enables objective stage identification, cross-regional comparison, and detection of nonlinear transition patterns and policy effects, representing a substantial improvement over form- and content-based approaches. The framework integrates spatial analytical techniques—transition matrices, landscape metrics, and standard deviation ellipses—to quantify land use conversions, spatial configuration changes, and centroid migration, with results presented cartographically. Moving beyond the quantitative targets that dominate existing research (e.g., area expansion, abandonment mitigation), it introduces a novel approach to pinpointing the primary sources of cultivated land replenishment from sample data, thereby establishing a unified temporal-spatial research paradigm for land consolidation. In gray relational degree analysis, normative policy language is operationalized into measurable indicators, such as converting farmland protection mandates into explicit protection targets and land development restrictions into approved construction land occupation metrics. This supports evidence-based decision-making through a tripartite classification (economic, social, policy) that identifies the dominant dimension shaping regional land consolidation and highlights gaps between regulatory intent and actual outcomes.

4.2. The Spatio-Temporal Evolution Characteristics of Land Consolidation in the Coastal Regions of China and the General Patterns in China

(1)

Developmental stages division: Land consolidation in the coastal regions of China corresponded to changes in national policy orientation over different periods showing a development trajectory characterized by initial development (2002–2007), gradual growth (2007–2012), and rapid growth (2012–2017). After a period of rapid growth, it reaches a relatively stable state (2017–2022). The development phases identified in this study align closely with the classification by Jian Zhou et al., which defines three periods in China’s land consolidation: the cultivated land expansion period (1986–2007), the multi-type consolidation period (2008–2012), and the comprehensive consolidation period (2013–present). Establishing scientifically defined stages provides a vital basis for policymakers to forecast consolidation trajectories and design rational deployment strategies. Under stable macroeconomic conditions and consistent agricultural land preservation policies, China’s land consolidation sector is likely to remain in a phase of moderate stability.

(2)

Spatial evolution characteristics: From the perspective of distribution characteristics and migration trajectory, the center of gravity of land consolidation distribution was generally concentrated in the regional economic and political centers of Lianyungang. The spatial gravity center of land consolidation activities in Lianyungang migrated from interior western regions to coastal eastern territories during the study period. The findings of this study were consistent with those reported by Guoxin Xu, whose research indicated that land consolidation projects in China were predominantly located in the economically developed eastern regions. Moreover, the spatial gravity center of these projects was observed to have shifted northeastward from the initial benchmark in Hunan Province toward coastal provinces such as Shandong, Jiangsu, and Zhejiang [41]. This spatial shift is in line with China’s national strategic reorientation from land-based agricultural development to ocean-oriented economic growth. It indicates that the changes in the center of migration of land consolidation were significantly influenced by national development strategies and industrial policy orientations.

(3)

Driving factors: Through gray correlation analysis, the research using human factors indicated that the economic development level significantly influenced land consolidation implementation, making it the primary factor. This finding aligns with the research outcomes of most geo-economics scholars. However, diverging from existing studies, this research further reveals that policy and social factors played a secondary, but important, guiding role in land consolidation. The 12-indicator system balances theoretical relevance (covering economic-social-policy trilemma) and empirical feasibility (data availability across coastal regions).

4.3. Future Development Trends and Optimizing Strategies of Land Consolidation in China

Building on the identified patterns, we project two transformational trends. To cope with these trends, local governments require differentiated strategies to align land consolidation initiatives with these macro-trends through scientific deployment.

(1)

Functional transition from economic to multi-dimensional governance is underway, with land consolidation projected to become an integral part of local development planning. In China, its role has expanded beyond cropland protection and food security to serve as a key social instrument for ecological restoration, rural revitalization, and urban-rural integration. Under the “Coastal Zone Protection and Restoration Project”, forthcoming policies for coastal land protection will follow the ecological restoration principle of “protection first, natural restoration as the priority” while balancing socio-economic needs. Multi-functional land consolidation is likely to play a pivotal role in territorial spatial planning, urban-rural benefit distribution, and social security enhancement. Examples include Beihai City’s improvement of farmland ecosystem services through systematic consolidation, Qingdao City’s slope stabilization at 21 coastal sites, and Hainan Province’s rehabilitation of existing construction land, restoring 127 km of coastline in four years. Optimizing spatial functions will require improving agricultural production conditions, guiding rural development, safeguarding farmers’ interests, adjusting urban industrial structures, planning regional spatial layouts, and fostering urban cultural development.

(2)

Spatial realignment driven by industrial relocation is reshaping China’s land consolidation geography. During the study period, the consolidation distribution center shifted coastward, aligning with national strategies along rivers, coastlines, and borders. Now, shifting international dynamics and domestic demands are driving the relocation of capital-, technology-, and labor-intensive industries from eastern coasts to central and western hinterlands, positioning inland regions as new strategic growth frontiers. This westward industrial migration is expected to fundamentally reconfigure consolidation patterns, with inland areas projected to replace coastal zones as primary hotspots. To support this transition, inland and western cities should expand consolidation to accommodate industrial relocation and development, while eastern coastal cities should scale back deployment due to declining industrial advantages, land development saturation, and reduced future demand.

4.4. Limitations and Uncertainties

This study has several limitations in demonstrating the framework’s effectiveness. Although the best available data were used, the 20-year study span resulted in missing records, and inconsistent statistical standards across periods reduced data continuity, potentially influencing gray correlation analysis. Data access restrictions confined the analysis to land consolidation projects within designated study areas, limiting the ability to reflect broader regional trends. The CLCD data may be somewhat inaccurate during vectorization due to interpretation methods or precision limitations, leading to potential errors in the classification of intertidal zones, such as confusion between tidal flats and construction land. The research scope was restricted to spatio-temporal evolution patterns, excluding engineering practices, benefit assessments, and investment mechanisms, which should be incorporated in future extensions. Moreover, the absence of directly comparable spatial trajectory studies currently prevents cross-regional validation, presenting both a constraint and an opportunity for collaborative research.

5. Conclusions

This study proposed a novel framework for analyzing such patterns in China. A “land consolidation intensity” metric was developed, combined with quantitative techniques—land use transition matrices, landscape pattern indices, Sankey diagrams, and standard deviation ellipses—to examine spatio-temporal dynamics and centroid shifts. The Lianyungang case study revealed spatio-temporal patterns of land integration in coastal zones, with the developed framework proving adaptable for regional planning analyses elsewhere.

Land consolidation intensity progressed through distinct stages—initial development, gradual growth, rapid growth, and eventual stabilization—closely mirroring national policy shifts. Project distribution was concentrated in economic and political centers, with a spatial shift from inland western areas toward the eastern coastal region. Land consolidation proved effective in implementing the “Occupation–Compensation Balance” policy for cultivated land. Gray relational analysis indicated that economic development was the primary driver, while policy and social factors played secondary roles.

Building on the identified patterns, we anticipate two transformational trends: a functional shift from economic to multi-dimensional governance and a spatial realignment driven by industrial relocation. Industrial relocation is expected to trigger a westward shift in land consolidation distribution. To address these trends, local governments should adopt differentiated strategies through scientifically informed deployment. For inland and western cities, this means scaling up consolidation to support industrial relocation and development. For eastern coastal cities, it involves gradually reducing project deployment due to declining industrial advantages, saturation from past land development, and projected decreases in land demand. Regardless of prevailing industrial configurations, local governments should implement functional upgrading of land consolidation projects to actively respond to local industrial policy adjustments. These findings provide valuable guidance for Chinese policymakers developing regionally differentiated consolidation strategies and for geographers examining spatial governance in transitional economies.

Despite some limitations, this study provides valuable insights for Chinese policymakers and for the broader field of geography. Future research will expand the current analytical framework by incorporating economic, social, and ecological benefit assessments of land consolidation, thereby broadening the scope of investigation.