Research on Spatial Coupling Coordination of Population Shrinkage and Land Use Efficiency from the Human–Land Relationship Perspective: Case Study of Zhejiang Province, China

Abstract

It has long been assumed that population shrinkage signifies a decline, potentially leading to decreases in both economic performance and land use efficiency (LUE). To provide a foundation for understanding the coupling coordination of the human–land relationship, this study explores the actual relationship between population change and LUE in the population-shrinking regions of Zhejiang Province. A multivariate remote sensing spatial data platform was developed for the period 2010–2020, and the shrinkage rate calculation method was applied, while LUE was assessed using the Super-SBM model. The coupling coordination algorithm was then employed to identify the different types of human–land relationships and their spatial distribution. The findings reveal that the county unit is the main spatial unit of population shrinkage in Zhejiang Province, and that the LUE changes in the province show significant spatial differentiation characteristics. Meanwhile, there are four types of human–land linkages in the shrinkage areas of Zhejiang Province, with no instances of a severe lack of coordination. Targeted strategies for each human–land relationship type are proposed to mitigate unreasonable urban expansion, shift the incremental development pattern, and reduce land resource waste.

1. Introduction

German scholars first introduced the concept of “shrinkage” [1], with subsequent research primarily using the degree and extent of population decline as indicators [2,3,4]. In the context of global shrinkage, the “smart shrinkage” response model has become a mainstream trend [5], emphasizing increased land use efficiency through shrinkage planning to achieve sustainable social, economic, and environmental development [6]. Recognizing the true relationship between shrinkage and land use efficiency (LUE) and promoting human–land adaptation has thus become a crucial research topic.

Scholars have investigated the relationship between shrinkage and the various influencing factors, including economic, social, and spatial elements [7,8,9]. It has been found that the mechanisms and features of population decline vary significantly across regions and scales [10,11,12]. Some researchers have even suggested that shrinkage is more pronounced in developed regions, which may lead to unbalanced economic growth [13]. Other scholars have identified the drivers of population shrinkage in developed regions from economic, social, and ecological perspectives. However, most studies and methodologies in China have focused on less developed regions, such as Northeast China. Population shrinkage has often been assumed to indicate decay [3,14], leading to structural aging, economic decline, and idle or wasted land [15,16], often accompanied by an imbalanced human–land relationship. Despite the widespread belief that developed regions are characterized by intensive economic activity and high LUE, recent studies show that shrinkage is also occurring in these areas. The adaptation of human–land relationships in shrinking developed regions presents unique mechanisms. Therefore, targeted research on the true human–land relationship in developed regions has become an urgent priority.

Land use efficiency (LUE) refers to the optimal allocation of resources, with the lowest input costs, to generate the best possible economic, social, and environmental outputs [17], and is used to measure the level of land resource utilization [18]. Inputs include land resources, labor, and capital [19], while outputs encompass economic, social, and environmental effects [20]. Methodologies for assessing LUE are continuously evolving. Data envelopment analysis (DEA) handles multiple inputs and outputs [21], but it neglects non-expected outputs, such as pollution. Tone developed the non-expected output model based on the Slacks-Based Measure (SBM) of slacks variables [22]. The Super-SBM model, an extension, allows for the inclusion of multiple non-expected output indicators and addresses the issue of slackness in inputs or outputs, making it the most widely used LUE measure today.

Shrinkage and LUE exhibit a non-linear relationship [23] and a long-range feedback chain [24], driven by the joint action and interaction of economic, social, and natural conditions [25]. This makes land issues in shrinking areas more severe than in growing areas. On one hand, studies have demonstrated a two-way causal relationship between LUE and economic development [26,27], with shrinkage often leading to economic decline [28], which in turn results in idle land and changes in land use structure [29], further decreasing LUE [30]. On the other hand, land affected by shrinkage creates new demands on the population [31]. If factors such as labor and capital cannot be continuously and adequately invested in the irrationally expanding land, this will exacerbate land inefficiency and emptiness.

However, shrinkage does not always result in a decline in LUE [32]. In some cases, shrinkage can have a positive effect on LUE [33]. For instance, Kroll found that shrinkage in eastern Germany did not coincide with a decrease in land consumption [34]. It has been shown that rational land use can significantly promote regional economic development, which in turn encourages population inflow [35], producing agglomeration effects in both the population and economy [36]. Population growth is also a factor that enhances LUE [37]. China’s economic development is geographically diverse [38]. In developed regions experiencing shrinkage, the following question arises: can LUE improve despite population shrinkage? If this issue is not addressed promptly, continued development-oriented planning may disrupt the balance of human–land relationships. Moreover, because land use adjustments often experience time lags, many areas may continue the uncoordinated trend of increasing construction land, leading to a paradoxical phenomenon of shrinkage and spatial expansion, further exacerbating resource wastage [25]. Therefore, the government must identify the true impacts of shrinkage on LUE and formulate adaptive policies based on contextual factors to achieve a balanced and rational land use strategy.

This study aims to provide a basis for planning adjustments to achieve a balanced human–land relationship and address the rational allocation of land resources. The objectives of the study are as follows:

(1)

Explore the coupling coordination relationship between shrinkage and LUE and illustrate its spatial heterogeneity.

(2)

Illustrate the actual distribution of human–land coupling coordination, reveal the unbalanced spatial pattern, and analyze the reasons of the difference match types.

(3)

Propose adjustment strategies for resource allocation and provide scientific basis for the future adaptive planning.

Zhejiang Province, located on the southeastern coast (Figure 1), plays a pivotal role in China’s development. It has historically been one of the most economically developed and densely populated regions. Over the past decade, Zhejiang has consistently ranked among the top five provinces in GDP per capita. Shrinkage in Zhejiang Province exhibits both spatial and temporal variability, with a “growth-shrinkage” phenomenon coexisting. Between 2000 and 2020, 24 counties experienced shrinkage, six of which had shrinkage rates exceeding 20%. The government has increasingly focused on addressing this issue. Some scholars have already measured the LUE at the scale of county administrative divisions in Zhejiang Province, but the adjustment of human–land relations needs to be based on a more detailed and cross-administrative division LUE characterization [39]. An in-depth study of the relationship between shrinkage and LUE can provide valuable insights for resource allocation and shrinkage planning in both developed regions and Zhejiang itself.

2. Methods and Materials

2.1. Research Material

The GDP growth rates of counties with higher population shrinkage rates were lower from 2000 to 2010. However, the increase in the population shrinkage rate of counties from 2010 to 2020 follows the same trend as GDP growth, contrary to the traditional understanding that “population shrinkage is accompanied by economic downturn”. So, the period from 2010 to 2020 was selected for the study.

A database was constructed by integrating remote sensing data with socio-economic data at the kilometer grid scale (

Table 1. Data sources (drawn by authors).

Data Categories		Indicator	Datatype	Year	Data Sources	Original Resolution
Economic		GDP: Gross Domestic Product (10,000 Yuan/km2)	grid	2010/2020	The Resource and Environmental Science and Data Centre (RESDC) of the Chinese Academy of Sciences	1 km
		LPBE: Local Public Budget Expenditure (10,000 Yuan)	statistics	2010/2020	Statistical yearbooks and social gazettes	-
		NTL: Nighttime Light (NW/cm2/sr)	grid	2010/2020	[40]	1 km
		DIU: Disposable Income of Urban Residents (10,000 Yuan)	statistics	2010/2020	Statistical yearbooks and social gazettes	-
Social		POP: Population	statistics	2010/2020	The Sixth and Seventh Population Censuses	-
		LFP: Labor Force Population (people/km2)	statistics	2010/2020	Statistical yearbooks and social gazettes	-
		POI: Point of Interest	shapefile	2010/2020	Gaode Map 2024	-
Environment	Land use	BUA: Built-up Land Area (km2)	shapefile	2010/2020	[41]	30 m
		BH: Building Height (m)	grid	2010	Pengcheng Laboratory (pcl.ac.cn)	30 m
			grid	2020	[42]	10 m
		Administrative boundary data	shapefile	2020	Chinese National Administration of Surveying: bzdt.ch.mnr.gov.cn	-
		DEM: Digital Elevation Model	grid		China National Earth System Science Data Sharing Service Platform: www.geodata.cn; accessed on 1 May 2024.	30 m
	Ecological environment	GSA: Green Space Area (km2)	shapefile	2012	Gaode Map 2024	-
			shapefile	2020	Open Street Map	-
		CO2 emission: Carbon Dioxide Emission (ppm)	grid	2010/2020	EDGAR-Global Greenhouse Gas Emissions EDGAR-The Emissions Database for Global Atmospheric Research (europa.eu); accessed on 1 May 2024.	10 km
		PM2.5 concentration: The concentration of Particulate Matter 2.5 (mg/m3)	grid	2010/2020	[43]	1 km
		LST: Land Surface Temperature (K)	grid	2010/2020	[44]	1 km

). Most research on shrinkage and land use efficiency (LUE) is conducted at the administrative division scale, which often lacks spatial precision. However, the actual distribution of population and other relevant factors does not adhere to administrative boundaries but spans across them. Therefore, it is essential to use geographic information data to accurately represent the geospatial distribution of human–land relationships. This study utilizes three types of data: economic, social, and environmental.

The initial data selection is based on two theoretical frameworks (Figure 2): Smart Shrinkage Theory and Land Use Theory. Both theories emphasize key influencing factors related to the economy, society, and environment. Smart Shrinkage Theory focuses on the integration of these three dimensions—economy, society, and environment [6]. LUE, on the other hand, evaluates the extent of comprehensive land resource utilization in a region, encompassing the combined effects of economic, social, and environmental factors.

• Economic Data: GDP raster data are published by the Resource and Environmental Science and Data Centre (RESDC) of the Chinese Academy of Sciences. The nighttime light data are the corrected China-wide DMSP-OLS data, obtained by integrating DMSP-OLS and SNPP-VIIRS. Data on the disposable income of urban residents and local public budget expenditures are sourced from statistical yearbooks and social gazettes.
• Social Data: Population data are derived from the Sixth and Seventh Population Censuses. It is important to note that the population data used in this study are from the actual census data of the Sixth and Seventh Chinese Population Censuses, rather than model projections. The data were visualized by dividing the impermeable ground into 1 km × 1 km grids, with population statistics evenly distributed across the grid cells. This method ensures the most accurate population distribution, with population changes calculated for each unit grid. The POI (Point of Interest) data were collected through Gaode Map 2024.
• Environment Data: Firstly, land use data: Land cover data helps determine the extent of construction land and provides insights into land area inputs. These data are sourced from the China Multi-Period Land Use Remote Sensing Monitoring Dataset (CNLUCC). Building height data characterize construction output, which is closely related to urban expansion. The 2010 building height data are from Pengcheng Laboratory, while the 2020 building height data are provided by the GC3S team, which utilized multi-source Earth observation data and machine learning techniques to create a 10 m resolution building height dataset for China. The Digital Elevation Model (DEM) data were obtained from the National Platform for Earth System Science Data Sharing Service, and the 2020 administrative division data were sourced from the Standard Map Service System of the National Bureau of Surveying, Mapping, and Geographic Information of China (NBMSGI). Secondly, ecological environment data: The earliest green space area data in China, available up to 2012, were sourced from Gaode Maps, while the 2020 green space area data were obtained from OpenStreetMap. CO₂ data indicate carbon emissions, PM2.5 data reflect urban pollution levels, and temperature data assess the urban heat island effect. The PM2.5 data were sourced from the China High Air Pollutants dataset (CHAP), and temperature data were obtained from the National Tibetan Plateau Data Centre (NTPDC).

During pre-analysis, the indicator factors were tested for covariance. After all raster data were re-projected to the WGS-84 geographic coordinate system, they were resampled to 1 km resolution to ensure consistency in subsequent analyses. For the economic and social data, densities were first calculated and then evenly distributed across all impervious surfaces of urban, rural, industrial, mining, and residential land, based on the 1 km resolution grid.

2.2. Methodology Framework

This study proposes a measurement framework for human–land relationships (Figure 3). Population shrinkage rates are calculated using data from China’s Sixth [45] and Seventh [46] National Population Censuses. LUE is obtained through multivariate data synthesis, and the coupling coordination relationship between population change and LUE change is calculated to assess the mismatch that occurred in geographic locations. Finally, spatial correlation analyses of LUE and the coupling coordination index (CCI) are conducted to reveal the spatial heterogeneity pattern of the human–land relationships.

2.3. Methods

2.3.1. Identification of Population Shrinkage

This study represents the shrinkage data on a 1 km × 1 km grid of impervious surfaces. The formula is expressed as follows:

$$S_i=\left(X_{in}-X_{im}\right)/X_{in}\times 100\%$$

(1)

where S_i indicates the shrinkage rate of spatial region i in the n–m examined years, X_in and X_im indicate the size of the resident population of spatial region i in the n and m years, and S_i > 0 indicates that the population of spatial region i is shrinking in the n–m examined years.

2.3.2. Calculation of LUE Based on the Super-SBM Model

The non-expected output Super-SBM model is used to calculate the LUE using MATLAB 2020 software, and the indicators are selected from “inputs, expected outputs and non-expected outputs” [47] (

Table 2. Indicators for measuring LUE (drawn by authors).

Indicator Type	Indicator	Meaning of the Indicator
inputs	BUA: Built-up Land Area	Land input
	LPBE: Local Public Budget Expenditures	Capital input
	LFP: Labor Force Population	Labor input
expected output	GDP: Gross Domestic Product	Economic output
	DIU: Disposable Income of Urban Residents
	NTL: Nighttime Light
	GSA: Green Space Area	Ecological output
	Number of POI	Social output
	Number of medical facility POI
	Number of educational and cultural facilities POI
	Number of catering and shopping POI
	BH: Building Height	Land use output
non-expected output	CO2 emission	Environmental pollution output
	PM2.5 concentration
	LST: Land Surface Temperature	Environmental heat island effect output

), reflecting the ability of land to provide a high-quality and sustainable use of life. Currently, relevant studies have carried out preliminary measurements of land use efficiency in Zhejiang Province from the scale of county administrative divisions, and their results show that the LUE in Zhejiang Province has changed from a south–high and north–low spatial distribution to an east–high and center–high spatial distribution during the ten years [39].

It is assumed that the efficiency DMU_j (j = 1, 2, …, n) of n decision-making units is to be measured, ρ is the LUE value to be measured (when ρ < 1, the city’s LUE is in an ineffective state), and each DMU has m input factors, which are denoted by X_i = (x_1k, x_2k, …, x_mk). Each DMU has q₁ expected outputs, denoted by Y_r = (Y_1k, Y_2k, …, B_qk), and each DMU has q₂ undesired outputs, denoted by B_t = (B_1k, B_2k, …, B_pk), i.e., m, q₁, and q₂ are the number of input elements, expected outputs, and non-expected output elements, respectively. $S_i^{-}$, $S_r^{+}$, and $S_t^{b-}$ are the slack variables corresponding to input, expected output, and non-expected output variables, respectively, and λ_j is the constraint. The Super-SBM model with non-expected outputs is as follows [21,48,49]:

$$\min \rho ={\displaystyle \frac{1+\frac{1}{m}{\displaystyle {\sum }_{i=1}^m{s}_i^{-}/x_{ik}}}{1-\frac{1}{q_1+q_2}\left({\displaystyle {\sum }_{r=1}^{q1}{s}_r^{+}/y_{rk}+{\displaystyle {\sum }_{t=1}^{q2}{s}_t^{b-}/b_{rk}}}\right)}}$$

(2)

$$s.t.\left\{\begin{array}{c}{\displaystyle {\sum }_{j=1,j\ne k}^n{x}_{ij}{\lambda }_j-s_i^{-}}\le x_{ik}\\ {\displaystyle {\sum }_{j=1,j\ne k}^n{y}_{rj}{\lambda }_j+s_r^{+}}\ge y_{rk}\\ {\displaystyle {\sum }_{j=1,j\ne k}^n{b}_{tj}{\lambda }_j+s_t^{b+}}\ge b_{tk}\\ {\lambda }_j,s^{-},s^{+}\ge 0\\ i=1,2,\cdots ,m;r=1,2,\cdots ,q\\ t=1,2,\cdots ,p;j=1,2,\cdots ,n\left(j\ne k\right)\end{array}\right.$$

(3)

Firstly, the multi-source data were unified by a 1 km × 1 km scale fishing grid, and after extracting the data of each grid, the input, output, and non-desired output were distinguished based on the above model and the evaluation index system. Then, MATLAB 2020 software was applied and brought into the Super-SBM model for calculation to obtain the LUE value of each 1 km × 1 km grid. Finally, ArcGIS was used to visualize the distribution of LUE and the change.

2.3.3. Measurement of the Coupling Coordination Index for Human–Land Relationships

The changes in “population” and “land use rate” in each 1 km × 1 km fishing net cell were organized into an excel table, and MATLAB was used to obtain the coupling coordination index (CCI) of each cell.

Firstly, the coupling degree C is calculated, where U₁ is the change in population size and U₂ is the change in LUE. The calculation formula is as follows:

$$C=\sqrt{{\displaystyle \frac{U_1{U}_2}{{\left(\frac{U_1+U_2}{2}\right)}^2}}}={\displaystyle \frac{2\sqrt{U_1{U}_2}}{U_1+U_2}}$$

(4)

The degree of coordination T is then calculated, with a and b as weights, generally chosen as a = b = 0.5, and the formula is as follows:

$$T=a\times U_1+b\times U_2$$

(5)

Finally, the coupling coordination degree D, is calculated as follows:

$$T=a\times U_1+b\times U_2$$

(6)

D takes the value of [0, 1]; a larger value of D indicates that the development level of the two systems is more coordinated, and vice versa indicates that the degree of coordination between the two systems is low. C takes the value of [0, 1]; a larger value of C indicates that the coupling state of the two systems is better, whereas a smaller value of C indicates that the coupling state of the two systems is worse and it will tend to disordered development. After completing the calculation, the coupling coordination index data of each cell are imported into ArcGIS for visualization.

Regarding the delineation of the different coordination levels, we use the natural breakpoint method to portray the data features in fine segments, which more accurately responds to the unique territorialized characteristics of the dataset than the usual same interval as a segment.

2.3.4. Spatial Auto-Correlation Analysis

The Global Moran’s Index (Moran’s I) is used to assess the overall aggregation effect of the indicators, determining whether the data are spatially clustered. Moran’s I ranges between −1 and +1; when Moran’s I is greater than 0, it indicates a positive spatial correlation, with larger values reflecting stronger spatial correlation.

$$I={\displaystyle \frac{n{\displaystyle {\sum }_{i=1}^n{\displaystyle {\sum }_{j=1}^n{w}_{ij}\left(x_i-\overline{x}\right)\left(x_j-\overline{x}\right)}}}{{\displaystyle {\sum }_{i=1}^n{\displaystyle {\sum }_{j=1}^n{w}_{ij}{\left(x_i-\overline{x}\right)}^2}}}}$$

(7)

$$Ii={\displaystyle \frac{\left(x_i-\overline{x}\right)}{{\displaystyle {\sum }_{i=1}^n}{\left(x_i-\overline{x}\right)}^2}}{\displaystyle \sum _{j=1}^n{\omega }_{ij}\left(x_i-\overline{x}\right)}$$

(8)

where I is the global Moran index, Ii is the local Moran index, n is the number of cities in the study area, w_ij is the spatial weight value, and x_i, x_j denote the CCI values of city i and city j, respectively.

Using GeoDa 1.22 software, local Moran’s I analysis was performed. If the pseudo p-value is significantly less than 0.05, Moran’s I can be considered significant and valid. The LISA aggregation map is generated for areas that pass the significance test at the given level of significance.

3. Results

3.1. Subsection Population Shrinkage Situation

During the period from 2010 to 2020, areas in Zhejiang Province that have experienced population changes can be categorized into five types, with the highest percentage of mild growth types (75.92%), followed by mild shrinkage types (19.11%), and a very small percentage of moderate growth (3.47%), substantial growth (0.87%), and severe shrinkage (0.63%) types. No population shrinkage was observed at the provincial or municipal levels. However, counties were the primary spatial units of shrinkage, with 10 counties experiencing population decline (Figure 4). Four distinct types of shrinkage patterns were identified:

• Concentrated in economic centers along the north plain region and eastern coastal region, such as areas around the urban centers of Hangzhou, Ningbo, and Wenzhou, which are economically well-developed. For example, Xiacheng District of Hangzhou, Yinzhou District, and Shengzhou District around Ningbo, Ouhai District, Longwan District, and Cangnan District around Wenzhou.
• In economic centers near inland plains, such as areas around Jinhua City, for example, Longyou District around Jinhua.
• Proximity to Shanghai, the most economically developed city, where the northern plains along the border with Jiangsu Province and Shanghai Municipality exhibit a nearly continuous zone of shrinkage, for example, Pinghu District.
• Areas with unique geographical characteristics, such as some islands, for example, Shengsi District.

3.2. Land Use Situation

All multi-source data were consistent according to the 1 km × 1 km fishing grid (Figure 5). The LUE was calculated by the Super-SBM model and visualized in ArcGIS based on MATLAB to obtain the change in LUE from 2010 to 2020 (Figure 6).

Changes in land use efficiency in Zhejiang Province can be classified into five categories, with the percentage of area from high to low being, respectively, a mild decrease (51.59%), a mild increase (28.76%), a moderate increase (12.65%), a significant decrease (4.67%), and a significant increase (2.33%). The results indicate that among the three economic center areas, Hangzhou and Wenzhou, located in the coastal region, experienced a more significant increase in land use efficiency (LUE). The peripheries of these areas show declining LUE, reflecting the siphoning of resources from the economic centers to the surrounding regions, alongside the strengthening of factor circulation and the deepening labor division within the region. Ningbo, located in the eastern coastal area, did not experience an increase in LUE; however, its neighboring areas showed a general improvement in LUE.

The LUE of the southwestern mountainous areas exhibited a significant decline due to geographical limitations, resulting in a lack of driving forces and a weak development foundation. Additionally, these shrinking areas experience a reinforced Matthew effect due to their proximity to economic centers. Over time, population migration to regional centers with better development prospects leads to the shrinkage of areas that cannot meet the demands for a high quality of life. This phenomenon is likely to result in an increasing risk of shrinkage and a decline in economic development in the future.

The LUE of the southeastern coastal areas shows an overall increase, facilitated by the construction of the Jinhua–Taizhou Railway, which breaks physical constraints on synergistic development (Figure 6). As a result, a “LUE rising belt” perpendicular to the mountain range was formed in the central “Taizhou-Jinhua” area. This area is connected to the plains, where land resources are more accessible and better utilized compared to other mountainous regions.

Some areas, however, have seen either a decline or moderate increase in LUE due to favorable ecological environments, including mountains and water bodies. These areas are constrained by topographical factors and ecological protection policies. For example, large natural wetlands and water source protection zones in the northwestern region, important islands in the eastern region, and mountainous areas in the southwestern region with high forest coverage have been restricted by stringent ecological and environmental policies, limiting land use and economic development.

3.3. Coupling Coordination Types of Human–Land Relationships

Between 2010 and 2020, Zhejiang Province exhibited a “core coordination-boundary differentiation” pattern in its human–land relationships, peaking in coordination levels within the four economic hubs of Hangzhou, Ningbo, Wenzhou, and Jinhua. These coordinated relationships extended along major transportation arteries while displaying fluctuations at administrative jurisdictional boundaries, with additional disparities observed across distinct ecological zones. Based on the natural breakpoint method, five types of human–land relationships appeared in Zhejiang Province, which are highly coordinated (4.76%), moderately coordinated (26.13%), poorly coordinated (57.71%), uncoordinated (10.09%), and severely uncoordinated (1.31%). However, four types are identified in the shrinkage areas: highly coordinated, moderately coordinated, poorly coordinated, and uncoordinated (

Table 3. Types of human–land relationships (drawn by authors).

Population Change	LUE Change	Human–Land Relationship	Districts or Countries
Population shrinkage	LUE grows faster	Highly coordinated	Wenzhou: Lucheng District, Ouhai District, Longwan District, Cangnan County
		Moderately coordinated	Ningbo: Yinzhou District Quzhou: Longyou County
	LUE grows and falls in parallel	Poorly coordinated	Hangzhou: Chun’an County, Xiucheng District Jiaxing: Pinghu
	LUE falls mostly	Uncoordinated	Zhoushan: Shengsi County Shaoxing: Shengzhou
Population growth	LUE falls mostly	Severely uncoordinated	South-West Mountain area administrative division junction
	LUE falls mostly	Poorly coordinated	most area
	LUE grows faster	Moderately coordinated	Three regional centers, and the central areas connected to them (Xianju County, Yongkang County, Wucheng County)

). Notably, there are no instances of severely uncoordinated human–land relationships (Figure 7). This finding challenges the common belief that “shrinkage implies decline in land use and an imbalance in the human-land relationship”.

Firstly, shrinkage areas with highly coordinated human–land relationships are predominantly located in the southeastern coastal areas (marked by red circles), exhibiting relatively rapid growth in land use efficiency. In particular, Wenzhou exhibits a highly coordinated human–land relationship, while Yinzhou District of Ningbo shows a moderately coordinated relationship. This suggests that, in these shrinkage areas (highlighted by red circles in Figure 7), population decline is a natural tendency that helps regulate the balance of the human–land relationship, rather than implying decay. This trend even indicates a more refined and sustainable development of LUE. Similarly, Longyou County in central Quzhou, located near the economically developed city of Jinhua, also showed no decline in LUE, despite experiencing population shrinkage.

Secondly, population shrinkage areas with moderately coordinated human–land relationships are concentrated around the central economic hubs of Zhejiang Province and the eastern coastal economic zones, while those with poorly coordinated relationships cluster around the northeastern provincial capital region (marked by green circles), demonstrating concurrent growth and decline in land use efficiency. For instance, the core of the Hangzhou metropolitan area in the northern plains exhibits a fragmented shrinkage pattern. In Xiacheng District, a core urban area with a stronger economic and industrial foundation, the population has shrunk, leading to a partial decline in LUE and a low level of coordination in the human–land relationship. This is attributed to a lag in development policies, where population decline has not been promptly addressed by corresponding policy measures. Additionally, this region has high space costs and certain barriers to policy responsiveness. Furthermore, the Qiandao Lake area in the northwestern region is constrained by ecological protection policies, limiting LUE development and the evolution of the human–land relationship. The northeastern part, close to Shanghai, presents a dual challenge: while it benefits from the metropolitan area’s radiation and forms a regional synergistic linkage, its resources are often siphoned off by Shanghai. As a result, LUE and the human–land relationship in this area do not achieve their optimal state.

Finally, population shrinkage areas with uncoordinated human–land relationships are located in the eastern offshore islands and Shengzhou City of Shaoxing (marked by blue circles in Figure 7), where land use efficiency predominantly shows a declining trend, reflecting the close relationship between population shrinkage and decline in the general sense. For example, Shengsi County in Zhoushan City, an island in the east, offers better development opportunities but remains physically isolated, resulting in higher LUE and better human–land coordination. In contrast, less accessible islands are poorly developed. Most shrinkage areas in the eastern part of Shengzhou City exhibit a decaying human–land relationship due to their reliance on traditional development models, ineffective industrial transformation, weak support for factors of production, and failure to integrate effectively into the regional development framework, resulting in low land use efficiency. While Shengzhou’s traditional industries, like textiles, had a competitive edge a decade ago, the decline of these industries, under pressure from high-tech sectors, has contributed to shrinkage and lower LUE.

Additionally, severely uncoordinated human–land relationships are only concentrated in the southwestern mountainous areas, where the population continues to grow. These regions, often located at the boundaries of administrative divisions (e.g., between Deqing County and Nanxun, Deqing and Yuhang, Xiaoshan and Shangyu, and Shengzhou and Zhuji), are still in the early stages of development, with weak economic and industrial foundations, leading to a significant decline in LUE. Conversely, the three Zhejiang provincial regional centers and their connected areas (Xianju County, Yongkang, and Wucheng) display moderately coordinated human–land relationships. Here, as the population grows, LUE increases more rapidly, gradually creating a spatial “core-edge” differentiation. This is due to a strong economic and industrial base and an emerging trend of agglomeration. Additionally, the opening of the Wenzhou–Jinhua high-speed rail and the Taizhou–Jinhua railway has improved the region’s infrastructure, breaking the isolation of mountainous areas and facilitating the flow of regional resources and interaction, thus forming a “people and land coordination” belt running vertically along the mountain ranges.

3.4. Spatial Auto-Correlation

The Moran indices of population change, LUE, and the coupling coordination degree of the human–land relationship were 0.332, 0.255, and 0.242, respectively (Figure 8). All three spatial analyses passed the Monte Carlo test, and the overall spatial pattern shows a positive correlation. Notably, the Hangzhou area in the north and the Wenzhou area on the southeast coast exhibit a high aggregation of population change values, meaning that where population change is faster, neighboring areas also experience faster population changes. In contrast, the Ningbo area on the eastern coast and the areas extending inland along the southeastern coast show a low degree of spatial aggregation, meaning that slower population change in one area is mirrored by slower changes in neighboring areas.

The spatial aggregation of LUE increase closely mirrors the aggregation of the coupling degree of the human–land relationship. High aggregation is observed around Hangzhou City in the north, Wenzhou City on the southeast coast, and areas along railways and highways. This suggests that in regions with better coupling of human–land relationships and faster LUE growth, surrounding areas also show improved coupling and faster LUE growth. Conversely, spatial aggregation is low in the southwestern mountainous areas, where regions with poorer human–land relationship coupling and slower LUE growth are surrounded by similar areas with weak coupling and slower LUE.

4. Discussion

4.1. Reliability Test for LUE

The LUE distribution maps calculated in this study were compared with the more recent literature. Notably, Chen [4039] examined the spatial and temporal evolution of LUE in 69 counties in Zhejiang from 2007 to 2018, finding a spatial differentiation characterized by “high LUE in the east and low LUE in the west”. High-efficiency areas are mainly located in the center, surrounded by medium-efficiency areas at the metropolitan edges, while low-efficiency areas are found in the southwestern and northern parts of Zhejiang. This pattern closely mirrors the results of the current study (Figure 9).

4.2. Advantages and Limitation

The strengths of this study are as follows: First, it identifies that the coupling coordination between shrinkage and land use in developed regions differs from the common belief that “shrinkage implies LUE decay”. Second, it uses multifaceted and integrated remote sensing data to accurately depict the spatial distribution of human–land relationships, overcoming the limitations of administrative boundaries, offering high spatial resolution, and addressing the shortcomings of statistical data.

There is potential for further development in this area. In the future, more accurate data integration and year-by-year analyses could establish the trajectory of temporal changes in human–land relationships. Additionally, distinguishing between urban and rural areas could enable more detailed observations and analyses, helping to understand the interaction between urban and rural population flows and land resources.

4.3. Future Development Directions for Human–Land Relationships

The study area of this study is “Urban Studies”, so we address the direction and guidance of response in resource adjustment and planning layout for each type of human–land relationship in the context of population shrinkage in order to achieve a balanced and rational utilization of urban land.

Conventional development-centric planning paradigms demonstrate inherent limitations, necessitating urgent exploration of adaptive governance systems grounded in authentic human–land relationship coupling coordination to address resource allocation imbalances under population shrinkage. Oriented by shrinkage-responsive planning objectives, this approach prioritizes “adaptability” and “optimal fit” zoning governance over mere quantitative growth targets. Within this framework, governance aims to precisely identify problematic resource allocation areas, diagnose their causal typologies, and implement differentiated intervention strategies. Establishing zonal warning systems and tiered management protocols enables dynamic monitoring of human–land relationship infrastructure, with the “monitoring-early warning-response-feedback” mechanism constituting the critical operational spine.

The early warning component serves as the pivotal evaluation node, empowering decision-makers to identify equilibrium states and implement balancing interventions in real time, thereby ensuring equitable regional resource distribution and utilization efficiency. Through continuous tracking of human–land relationship dynamics, this mechanism facilitates timely policy calibration and spatial strategy adjustments, preventing resource overload or redundancy while promoting sustainable utilization.

The proposed four-phase mechanism operates through (1) supply–demand relationship monitoring; (2) coupling coordination analysis categorizing regions into five-tier warning zones (red, orange, yellow, blue, green); (3) tiered governance measures guiding development activities; and (4) feedback loops integrating outcome evaluations with population–land use efficiency tracking. This cyclical process achieves dynamic governance optimization, steering balanced resource allocation across spatial–temporal dimensions.

4.3.1. Human–Land Relationship Coupling Coordination Warning Grading

“Early warning” typically refers to issuing alerts prior to crises by monitoring and analyzing factors that may induce disasters. The early warning mechanism identifies resource supply–demand imbalances through tracking input–output indicators of land use efficiency, thereby preemptively signaling warnings to ensure rational resource allocation and mitigate issues arising from mismanagement. Resource and environmental carrying capacity consists of two interacting systems: System I encompasses human production and living activities (the anthropogenic subject), while System II comprises natural resource and environmental elements (the natural object). Their interaction defines the resource–environment carrying capacity framework.

Integrated with human–land relationship coupling coordination analysis, this framework reveals conceptual congruence: land carrying capacity embodies the interactive relationship between “population” and “land use efficiency”. This dynamic reflects whether land use efficiency satisfies regional population demands, forming the basis for human–land relationship planning and optimization.

Consequently, adopting the warning classification from resource–environment carrying capacity studies and synthesizing preceding analyses, we categorize human–land relationship warnings into five tiers corresponding to specific response protocols (

Table 4. Warning typology–issue–response framework for classified governance of human–land relationships in Zhejiang province (drawn by authors).

Warning Type	D Range	Current Proportion	Key Issues	Suggestion	Response Type
Red Warning	D ≥ 0.71	1.31%	Supply and demand imbalance: severely uncoordinated human–land relationships	Keynote	Population growth areas: promoting development (concentrated in population-growing areas along southwestern mountainous administrative boundaries)
Orange Warning	0.66 ≤ D < 0.70	10.09%	Inefficient utilization: uncoordinated human–land relationships		Population shrinkage areas: implementing shrinkage planning (e.g., Shengsi County, Shengzhou City)
					Population growth areas:overcoming waste and irrational expansion, reasonably utilizing stock
Yellow Warning	0.62 ≤ D < 0.65	57.71%	Low human–land relationship coordination	Priority	Population shrinkage areas: transforming development models (e.g., Yinzhou District, Longyou County)
					Population growth areas: improving efficiency
Blue Warning	0.55 ≤ D < 0.61	26.13%	Moderately coordinated human–land relationships.	Prevent	Population shrinkage areas: stable resources, timely updates (e.g., Xianju County, Yongkang, Wucheng)
					Population growth areas: ensuring stability of development (e.g., Hangzhou–Jiaxing–Huzhou metropolitan area)
Green Warning	D ≤ 0.54	4.76%	Steady state equilibrium: highly coordinated human–land relationships	Monitor	Population shrinkage areas: maintain the status quo, conduct regular evaluations, monitor learning, and summarize experience (e.g., Yinzhou District, Longyou County)

). The red warning zone denotes severe human–land relationship dissonance, orange indicates significant dissonance, yellow represents low coordination, blue signifies moderate (favorable) coordination, and green designates high coordination levels.

4.3.2. Define the Types of Warning Units for Differentiated “Classification-Zoning” Guidance

The preceding analysis reveals that Zhejiang Province exhibits a “core coordination-boundary differentiation” spatial pattern in its human–land relationships. The observed phenomena of low land use efficiency and human–land imbalances in population-shrinking areas can be primarily attributed to three systemic factors: delayed policy adjustments, weak economic–industrial foundations, and geospatial constraints. To optimize resource allocation aligned with actual demographic demands, the implementation of hierarchical governance guidance across spatially delineated warning zones proves essential.

• Specifically, red warning zones represent areas with severely uncoordinated human–land relationships and low resource allocation efficiency, predominantly located in population-growing regions along administrative boundaries in southwestern mountainous areas. For these zones, development-oriented planning should be intensified, leveraging local industrial strengths through government policies to attract high-end talent and cultivate regional specialty economies, thereby enhancing spatial utilization efficiency.
• Orange warning zones indicate uncoordinated human–land relationships, exclusively situated in population-shrinking regions such as Shengsi County in Zhoushan’s eastern offshore islands and Shengzhou City, Shaoxing. These zones require measures to reduce land waste and curb irrational urban expansion, including establishing urban growth boundaries and implementing urban renewal projects. Urban planning should integrate both development-oriented and shrinkage-responsive strategies based on local economic foundations and demographic profiles to address developmental imbalances. Resource allocation should be rationally adjusted to ensure broader coverage and accessibility.
• Yellow warning zones represent areas with low human–land relationship coordination. For population-shrinking yellow zones concentrated in Yinzhou District, Ningbo (eastern Zhejiang’s economic hub) and Longyou County, Quzhou (western Zhejiang, adjacent to Jinhua’s central economic zone), priority must be given to transforming development models to address resource supply imbalances and resolve inefficient spatial configuration or facility utilization in resource-abundant subregions. Implementing shrinkage-responsive planning with optimized intra-zone resource allocation is critical to prevent waste and low-efficiency land use, as exemplified by population-shrinking areas near the northeastern economic hubs of Hangzhou, Shaoxing, and Jiaxing. For population-growing yellow zones, development-focused strategies should enhance land use efficiency through improved resource utilization and facility layout adjustments.
• Blue warning zones indicate moderately coordinated human–land relationships. Population-shrinking blue zones clustered in the Hangzhou–Jiaxing–Huzhou metropolitan area should stabilize existing resource operations while strategically optimizing land functions to meet future demands. These areas must capitalize on their differentiated industrial strengths and regional synergies to drive multi-layered metropolitan development. Population-growing blue zones, primarily located in three regional centers and their connected central areas (e.g., Xianju County, Yongkang, and Wucheng), require consolidation of current development models to ensure stability.
• Green warning zones denote highly coordinated human–land relationships, exclusively found in population-shrinking regions such as Wenzhou (Lucheng, Ouhai, Longwan, and Cangnan districts). Governance here focuses on maintaining existing resource allocations, periodically assessing facility performance, and implementing targeted optimizations. Sustainable development necessitates maximizing existing facility efficiency while avoiding overdevelopment. The population shrinkage observed in Wenzhou’s southeastern economic hub reflects a natural equilibrium in urban maturation. Planning should align with this developmental phase by refining successful models. For these balanced, high-efficiency, and economically advanced zones, persisting with “growth-acceleration” urban strategies would precipitate spatial resource waste.

5. Conclusions

This study explores the coupling coordination between shrinkage and LUE in Zhejiang Province, characterizing the spatial distribution of different human–land relationship types. It provides a valuable reference for adjusting human–land relationships in the context of shrinkage and offers guidance for land and resource allocation.

From 2010 to 2020, some shrinkage areas in Zhejiang saw a rise in LUE, indicating a positive coupling relationship between shrinkage and LUE. In developed regions, shrinkage coincides with the advantages of new productivity, high-tech industries, and economic foundations. Even with population decline, LUE—which reflects economic, social, and environmental development—remained on an upward trajectory. This suggests that shrinkage is a natural and healthy part of the urban development process.

There are distinct spatial distribution patterns of human–land relationships across Zhejiang Province. The process of economic development is complex, and each region’s development history exhibits temporal and spatial variability. In the future, decision-making authorities should abandon the “development-first” mentality and adopt a perspective of coordinated human–land development. They should design response strategies to population shrinkage based on regional variations in human–land relationships, promoting sustainable development.

The integrated planning governance framework proposed in this study—encompassing population shrinkage identification, land use efficiency measurement, human–land coupling analysis, and differentiated warning–response governance—demonstrates critical relevance for regions across all developmental tiers. For other developed regions, the strategies proposed in this shrinkage can help them break out of the growthist development paradigm and prevent over-investment and misallocation of public resources. Regarding undeveloped regions, this research investigates the coupled relationship between population shrinkage and land use efficiency and the reasons for the variability in the types of human–land relationships, and proposes corresponding strategies for adjusting land and resource allocation, including economic, social, and environmental aspects. These mechanisms and planning strategies can also provide a reference for solving the problem of recession caused by population shrinkage in underdeveloped regions; for example, ecological or social benefits can be used to compensate for the economic recession caused by population shrinkage. Within this process, it is critical to recognize that the physical characteristics of land use—particularly topographic and geological conditions—serve as fundamental determinants in land use adjustment decision-making. Only when such adjustments align with constraining geographical parameters can human–land relationship modifications yield positive outcomes, and this requires concurrent implementation of context-sensitive policy guidance.