Evolution and Mechanism of Population and Construction Land Decoupling in China: A Case Study of Shandong Province

Abstract

With the accelerated urbanization in China, the irrational utilization of land resources has triggered a series of ecological challenges. In this context, exploring the decoupling relationship between population and construction land is crucial for achieving land sustainable development. This paper applied Tapio and Geodetector models to Shandong Province, analyzing population–land decoupling evolution and mechanism. The results show the following: (1) Significant spatiotemporal differences exist, with a total of eight decoupling types identified; the main decoupling types of Shandong include strong negative decoupling, expansive negative decoupling, weak decoupling, and strong decoupling. (2) A strong negative decoupling type characterized by “population decline and land expansion” was predominant, though coordination trends are emerging. (3) Weak decoupling townships were primarily influenced by resource factors and transportation; strong decoupling townships were mainly affected by economic activities and transportation; strong negative decoupling townships were closely related to resource factors and economic activities. (4) Multi-factor interactions have a considerable impact on the formation of the population–land decoupling relationship, with natural constraints and economic transformation drivers jointly contributing to diverse decoupling patterns.

1. Introduction

Various forms of human activities have profoundly influenced the earth system. Human processes have gradually become the dominant force driving the changes of the modern surface environment [1], and the population–land relationship has become one of the most crucial relations in the human society. Land resources, as the material basis for human survival and development, are the solid support for the operation of the socio-economic system, while land use is the key link between the human system and the natural system [2]. However, along with the rapid economic growth and accelerated urbanization, irrational development approaches have aggravated the inefficient and wasteful use of land resources, triggering a series of serious challenges such as ecological degradation, food security, etc. [3,4]. In view of this, academics and policymakers have paid more attention to appropriately develop and rationally utilize natural resources. In addition to emphasizing the subjective initiative of “human” as the principal actor in reshaping nature, this perspective also underscores the sustainability of “land” [5,6].

In fact, the international academic community has a mature experience of exploring the population–land relationship, such as coupled human–environment systems [7], coupled human and natural systems [8], and social–ecological systems [9]; also, other relevant topics have been discussed, providing a scientific basis for revealing the interactive and dynamic mechanisms between natural and social elements within the population–land system. Since Mr. Wu Chuanjun [10] put forward the theory of the “population-land relationship territorial system” in 1991, it has become the core proposition of geography research in China and has been widely used to solve practical problems. According to this theory, the population–land system is an open complex formed by the interconnection of two subsystems: the human society and the geographic environment. Based on this theoretical framework, subsequent scholars, taking into account China’s special national conditions, have further divided this construct into the urban and rural territorial system and the urban–rural integration system, respectively [11]. Among them, the core of the urban–rural integration system lies in promoting free flow and optimal allocation between urban and rural areas, ultimately leading to an urban–rural integration and even equivalence.

Urbanization, as a hallmark of modern societal development, interacts with and is reciprocally constrained by the population–land system. The United Nations State of the World’s Cities Report 2022 points out that the proportion of urban population will rise to 68% in 2050 from 56% in 2021, predicting that more than 5 billion people will live in urban areas in the future. The process of urbanization shows two distinctive features: the expansion of construction land and the growth of population. However, the disorderly expansion of built-up land poses dual challenges to high-quality development and environmental protection, threatening sustainable land use at regional and even global scales. For instance, land-use/cover-change studies in rural China indicate that over 80% of construction land expansion has occurred through the occupation of cultivated land [2].

Against the backdrop of China’s long-standing urban–rural dichotomy, urbanization has greatly influenced the evolution of urban–rural territorial system. On the one hand, the urban construction and development have been unprecedentedly active, and the construction land, the carrier for human settlements and city infrastructure, has been growing rapidly; on the other hand, rural China has witnessed a dramatic decline in both resident and registered populations, creating an inverse evolutionary pattern where rural population contraction coincides with the expansion of rural residential land [12]. The persistent urban-centric development philosophy continues to exacerbate the “urban advancement—rural regression nexus”, leaving unresolved the systemic rural issues characterized by rural hollowing, aging and weakening workforce, and environmental degradation [13]. This developmental imbalance underscores the critical need to investigate the coupling mechanisms between urban and rural systemic elements as China’s current urbanization trajectory has yet to achieve effective synergy with the objective of the rural revitalization.

Geographers first conducted a systematic study on the formation of rural settlements in the 1840s [14]. Subsequent scholars cited the scale of construction land to predict population capacity [15], as well as the relationship between population growth and land expansion [16,17], providing methodologies for quantitative study of the population–land system. For example, Orenstein and Hamburg conducted a study on the correlation between population growth and land development at three scales: national, regional, and local [18]. Marshall focused on the coupling relationship between population growth and land scale in urban areas [19]. Johnson systematically investigated the population–land coupling dynamics in rural areas of the United States [20]. Fernández and de la Vega highlighted the contrast in population–land relationships between cities and rural regions from an integrated urban–rural perspective [21]. With the focus on this academic theme, Chinese scholars have also formed diverse research perspectives [22,23]. Coupled coordination research emphasizes the interdependence and dynamic balance between systems, with the core being synchronization and coordination ones, which is commonly quantified by the Coupled Coordination Degree model. Wang Jing et al. [24] carried out a national-scale empirical analysis, pointing out that one third of the provinces in China exhibit serious mismatches in the population–land relations. Tian et al. [25], Shi et al. [26], and Chen et al. [27] systematically analyzed the spatiotemporal evolution and driving mechanism of population–land coordination in different urban agglomerations in China by using hot spot analysis, the spatial Durbin model, and the multi-dimensional index system, respectively. The study of the decoupling theory highlights the asynchrony and non-coordination between systems, which is commonly analyzed by the Tapio model. Zhang Haipeng et al. [28] found that urban and rural population and construction land changes in all cities of Henan Province are in the uncoordinated state. China has yet to observe a coordinated pattern between population growth and land reduction [29]. In analyzing the spatial agglomeration characteristics of population and construction land changes, most scholars apply geostatistics, spatial autocorrelation models, such as the Lorenz Curve and Gini Coefficient [30], super-SBM [31], etc. In addition, some scholars have expanded their research field to low-carbon ecology [32] and the coordination of population, construction land, and economic industry [33]. Coordination measurement has also evolved from the initial single-factor measurement to the current multi-factor coupled coordination measurement and multidimensional establishment of the indicator system [34].

However, in contrast to studies on socioeconomic or ecological issues arising from urban sprawl, research on rural sustainable development remains comparatively limited [5], resulting in insufficient differentiated land management strategies. Most scholars directly apply economic theories to derive the driving mechanism [14], while a few scholars use the spatial Durbin model [26], LMDI model [35], and other empirical methods. The research scales are mainly macro and meso, including national [13,14,18,19], regional [30,31], provincial [12,28,33], and municipal [34], and studies adopting a microscale township perspective remain notably scarce, thereby failing to provide a clearer and more nuanced understanding of the population–land dynamics.

Existing studies universally recognize that the co-evolution of the population–land system results from multifactorial interactions. While the majority of scholars directly apply economic theories to derive driving mechanisms [14], a limited number employ empirical approaches such as spatial Durbin models [26] and LMDI decomposition [35] for quantitative analysis. However, both methodologies fail to explicate how interactive effects among drivers shape population–land relationship evolution. Furthermore, current research predominantly operates at macro (national [18,19] and regional [30,31]) and meso (provincial [12,28,33] and municipal [34]) scales, whereas microscale township-level investigations remain markedly scarce, failing to provide a clearer and more nuanced understanding of the population–land dynamics.

Hence, elucidating the effects of multi-factor interactions on the population–land relationship evolution constitutes the foremost scientific issue of this paper, particularly within township-level microscales. The impact of the interaction of multiple factors on the dependent variable is not merely the simple sum of their individual effects; instead, it generates new outcomes. Such effects may manifest as positive interaction (enhancement), negative interaction (attenuation), or no interaction (independent influence of independent variables on the dependent variable). This paper hypothesizes that the multi-factor interaction effect on the evolution of human–land relationships exhibits a positive interaction effect.

Shandong Province, strategically located at the intersection of the Bohai Rim and Yellow Sea economic circles, connects the economic hinterlands of North and East China. Leveraging its geographical advantages, it has achieved rapid economic development. However, beneath the high-speed urbanization, Shandong faces increasingly acute challenges such as scarce land reserves, unbalanced urban–rural, and regional development, making it a barrier to reconcile its multiple interests of “urban development, agricultural production and ecological protection”. According to the national and local statistical bureau data (http://tjj.shandong.gov.cn/), as of 2023, the urbanization rate of Shandong was 65.53%, while the scale of rural settlement land still retains a large volume. The idle rate of residential land was more than 12%, and some areas exceeded 15%, much higher than the national average level of 8–10%. As a populous province with more than 100 million residents, Shandong still needs to deepen its understanding into the current situation of population–land development to establish a coordinated population–land pattern of the whole province.

In conclusion, the aims of this paper are to analyze the evolution and mechanism of population and land decoupling in Shandong Province at the township-level scale (including both urban areas such as subdistricts and central towns, as well as rural areas such as townships). Based on this, this paper takes Shandong Province as a study area, primarily addressing the following aspects: (1) quantifying the spatiotemporal evolution characteristics of population and construction land in Shandong Province; (2) delineating population–land decoupling types over the past two decades; (3) analyzing the driving mechanisms behind decoupling evolution, particularly the impact of multi-factor interactions on population–land decoupling; (4) proposing differentiated land regulation strategies to support sustainable land development and enhance the coupling of human–land systems in Shandong Province.

2. Materials and Methods

2.1. Study Area

This study takes Shandong Province as the study area. Shandong Province is located on the eastern coast of China and the downstream region of the Yellow River, between latitude 34°22.9′ and 38°24.01′ N and longitude 114°47.5′ and 122°42.3′ E (Figure 1). The territory consists of two parts, the peninsula and the inland part, of which the Shandong Peninsula protrudes out of the Bohai Sea and the Huang Sea. The inland part is bordered by four provinces, Hebei, Henan, Anhui, and Jiangsu, from north to south. There are 16 cities, 136 county-level districts, and 1822 township-level districts in Shandong. The central part of the province is mountainous, the southwest and northwest are flat, and the east is undulating terrain. Mount Tai, located in the middle of the province, has a main peak of 1532.70 m—the highest elevation in Shandong. The Yellow River Delta, generally 2–10 m above the sea level, is the lowest point of land in Shandong.

Shandong is a large economic province on the eastern coast of China, and it held the second-largest urban population in China at the end of 2023 (Figure 2). The per capita disposable income in Shandong Province is nearly 40,000 RMB, higher than the national average. Additionally, while accounting for 7.18% of China’s population, Shandong contributes 7.3% to the national Gross Domestic Product (GDP). Among its regions, the five cities of Rizhao, Weifang, Qingdao, Yantai, and Weihai on the east coast carry 31.95% of the population with 33% of the province’s land area but contribute 42.55% of the province’s GDP, and the degree of economic agglomeration is much higher than that of population agglomeration. However, the situation is reversed in the western part of the province, showing the spatial imbalance between population and economic development.

2.2. Data Sources

The primary data used in this study consist of socio-economic and physical geographic datasets, collected and processed at provincial and township levels. The data of administrative boundaries, land use, and topography were obtained from the Resources and Environmental Sciences Data Platform (RESDP) of the Chinese Academy of Sciences (https://www.resdc.cn/). Among them, the spatial resolution of the land-use data is 30 m, including six first-level land types, which are cropland, woodland, grassland, water body, built-up land (industrial and mining and urban and rural residential areas), and unused land. The topographic data were obtained from the GDEM V3 30 m resolution digital elevation model (https://www.gscloud.cn/). The population and GDP data were obtained from the 1 km resolution raster data; transportation network data were obtained from Openstreet Map (OSM, http://m.osmtools.de/). Climate data mainly consisted of temperature and precipitation raster data with 1 km spatial resolution.

Administrative boundary adjustment data were sourced from Shandong‘s information network, municipal/county government documents, and the China Administrative Division Network (http://www.xzqh.org). Given the frequency of township-level administrative adjustments, this study harmonized statistical and geographic datasets for 2000, 2010, and 2020 using boundary adjustment records, ensuring consistency between them. Adhering to principles of data authenticity and cross-dataset comparability, this study aggregated and consolidated statistical data without segmentation. Specifically, for cases where jurisdictional boundaries remain unchanged (e.g., name changes, township-to-town upgrades, or township-to-subdistrict reorganizations), direct data correspondence is established. For instances involving township splitting, merging, or partial reorganizations, comprehensive data and territorial consolidation is conducted across all affected townships involved in the pre-merger and post-split processes.

2.3. Methodology

2.3.1. Tapio Decoupling Model

Tapio [36] constructed a theoretical framework for decoupling, which has been widely expanded by scholars at home and abroad in recent years. It was used to quantitatively study the intrinsic coupling relationship between two or multiple elements in the fields of carbon emission, economic growth, and energy consumption [37]. In this study, based on the eight types of decoupling proposed by Tapio, the variables were replaced with the number of population and the area of construction land to measure the population–land decoupling relations. The equation was as follows:

$$E={\displaystyle \frac{(L_t-L_0)/L_0}{(P_t-P_0)/P_0}}={\displaystyle \frac{∆\mathrm{L}}{∆\mathrm{P}}}$$

(1)

where E was the elasticity value; $L_t$ and $L_0$ denoted the volume of construction land in the final and initial years, respectively; $P_t$ and $P_0$ denoted the size of the population in the final and initial years, respectively; $∆\mathrm{L}$ was the percentage change in construction land area from the initial to the final year; $∆$P was the percentage change in population from the initial to the final year.

The elasticity coefficient E was used as a measure of the relationship between population and construction land, and E = 1 corresponded to the ideal coupling state. However, in the real situation, the expansion of construction land was not always synchronized with population growth. Based on the empirical research, this paper took 0.8 and 1.2 as the critical values to divide three types of relationships (

Table 1. Definitions and elaborations of the eight decoupling relationships.

Decoupling State		$∆\mathbf{L}$	$∆\mathbf{P}$	Specific Elaboration
Decoupling	Strong decoupling	<0	>0	Construction land decline coupled with population growth signifies the most optimal state of population–land relationship.
	Weak decoupling	≥0	≥0	Both are increasing, and the growth rate of population is relatively faster.
	Recessive decoupling	<0	<0	Both are decreasing, and the decline rate of construction land is relatively faster.
Coupling	Excessive coupling	>0	>0	Both are increasing while maintaining relative synchronization in their rates of growth.
	Recessive coupling	<0	<0	Both are decreasing while maintaining relative synchronization in their rates of decline.
Negative Decoupling	Strong negative decoupling	>0	<0	Construction land expansion coupled with population decline signifies the most undesirable state of population–land relationship.
	Expansive negative decoupling	>0	>0	Both are increasing, and the growth rate of construction land is relatively faster.
	Weak negative decoupling	<0	<0	Both are decreasing, and the decline rate of population is relatively faster.

).

When E value was in the range of 0.8–1.2, it indicated that the scale of population and the construction land presented a coordinated coupling state; when E < 0, if the population increased and the construction land decreased, the land-use efficiency improved, which was the optimal state of the population–land relation, relating to decoupling; on the contrary, the land resources were facing wastage, which was the most undesirable state of population–land relation, relating to negative decoupling (Figure 3).

2.3.2. Geographical Detector

Geographical detector was a model used to detect the causes and mechanisms of the spatial pattern of certain elements and had been widely used in the research of problems related to society, economy, nature, and so on [38,39]. It could not only detect the spatial differentiation characteristics of a single variable but also the heterogeneity of the spatial distribution of two variables so as to discover the possible causal correlation between variables [40]. The equation was as follows:

$$q=1-{\displaystyle \frac{1}{N{\sigma }^2}}\sum _{h=1}^L{N}_h{\sigma }_h^2$$

(2)

where L was the number of partitions of the influencing factors; N and ${\sigma }^2$ denoted the total number of units and variance for the entire study population, respectively; $N_h$ and ${\sigma }_h^2$ denoted the total number of samples (townships) and variance across the entire study area, respectively; the value of q ranged from 0 to 1, where a higher numerical value indicated a stronger influence of the influencing factors on the formation of population–land decoupling.

2.3.3. Hot Spot Analysis

Hot spot analysis was a method for determining the distributional characteristics of local spatial clusters, which was used to measure the clustering relationship between a unit and its neighboring units [41]. In this study, Getis-Ord in ArcGIS version 10.6.1 was invoked to analyze whether the population and construction land were statistically significant high and low value clusters. The equation was as follows:

$$G_i^{*}={\displaystyle \frac{\sum _j^n{w}_{ij}{x}_i}{\sum _j^n{x}_j}}$$

(3)

$$Z={\displaystyle \frac{G_i^{*}-E\left(G_i^{*}\right)}{\sqrt{Var\left(G_i^{*}\right)}}}$$

(4)

where $G_i^{*}$ was the z-score; Z was the significance level of the clustering index; $w_{ij}$ was the spatial weights matrix; $x_i$ and $x_j$ denoted the attribute values of spatial units i and j; $E\left(G_i^{*}\right)$ and $Var\left(G_i^{*}\right)$ denoted the mathematical expectation and variance of $G_i^{*}$.

Higher positive Z-values indicated more significant clustering of hot spots, while lower negative Z-values represented more significant clustering of cold spots. In this study, the confidence level was set to 90% (

Table 2. Getis-Ord $G_i^{*}$ for different confidence levels.

$\mathbf{Getis}-\mathbf{Ord} {\mathit{G}}_{\mathit{i}}^{\mathit{*}}$	Confidence Level
<−2.58 or >+2.58	99%
<−1.96 or >+1.96	95%
<−1.65 or >+1.65	90%

), and the Z-values were classified into seven categories using the natural breakpoint method in ArcGIS [30,42].

3. Results

3.1. Temporal and Spatial Changes in Population and Construction Land in Shandong Province

3.1.1. Temporal Characteristics

Between 2000 and 2020, Shandong exhibited a sustained trend of urban population growth and rural population decline, with the urban population growth rate accelerating from 0.59% to 0.67% and the rural population decline rate intensifying from 0.13% to 0.34% (

Table 3. Population and construction land change in Shandong Province, 2000–2020.

Period	Population				Construction Land
	Urban		Rural		Urban		Rural
	Change Scale	Change Rate	Change Scale	Change Rate	Change Scale	Change Rate	Change Scale	Change Rate
2000–2010	1429.01	0.59	−868.20	−0.13	5270.80	1.68	856.40	0.06
2010–2020	2570.20	0.67	−1941.90	−0.34	848.80	0.10	369.00	0.02

Note: Population scale (10⁴ persons), population change rate (%), construction land scale (km²), and construction land change rate (%).

). Against the backdrop of accelerated urbanization, the urban construction land expanded by 6119.60 km² over the two decades, with a particularly pronounced growth rate of 1.68% during the 2000–2010 period, reflecting the substantial demand for construction land driven by urban development. Notably, despite the rural population decrease, rural residential land area increased by 1225.4 km², indicating a paradoxical phenomenon of land expansion concurrent with rural depopulation.

3.1.2. Spatial Characteristics

From 2000 to 2020, the spatial distribution pattern of the population in Shandong evolved from dispersion to agglomeration, with the hot spots always focusing on the core development axis of Jinan–Qingdao–Linyi and its radiation area (Figure 4). (1) In 2000, population hot spots were widely distributed across Shandong. Economically dominant cities (Jinan, Qingdao, and Weifang) and traditional agricultural regions in southern Shandong emerged as concentration centers due to resource endowments and policy incentives, exhibiting sustained growth trends. During this period, the uneven development of regional economies and disparities in agricultural productivity distribution served as the primary driving forces for the formation of hot spot areas. (2) In 2010, the population further concentrated in central Shandong (e.g., Jinan Metropolitan Area) and the south (Linyi, Zaozhuang, etc.), while sub-cold spots began to appear in northwestern Shandong (Liaocheng and Dezhou) and along the coast of the Peninsula (part of Weihai and Yantai) due to labor outflow or lagging industrial upgrading. This change reflects the impact of industrial gradient transfer in the urbanization process, as well as the challenge of transitioning traditional agriculture to the marine economy in coastal areas. (3) In 2020, population agglomeration patterns stabilized, yet cold spots expanded further in eastern Peninsula and northwestern Shandong. Notably, Jinan’s urban core experienced population decline following functional optimization (e.g., policies to relocate secondary industries and promote tertiary sectors). Simultaneously, suburban areas emerged as new hot spots due to industrial relocation and urban sprawl, creating geographic polarization between hot and cold spots.

The area of construction land in Shandong presents a dual characteristic of the coexistence of hot and cold spots, with spatial expansion (Figure 5). (1) In 2000, hot spots clustered in the northern coastal plain (Binzhou, Dongying, and Weifang’s northern/eastern regions) and across Qingdao, driven by Yellow River Delta land resources and Qingdao’s coastal economic influences. In Jinan’s urban core area, the high level of urbanization and intensive land use have led to a contraction of construction land area, forming cold spots. Meanwhile, the hilly regions of the peninsula exhibit a pattern of sub-cold spots due to topographical constraints and fragmented population distribution. (2) In 2010, the original pattern of cold and hot spots continues, and hot spots are newly formed in the southern part of Linyi due to the deep traditional farming culture and population agglomeration effect of the mountainous and hilly areas, reflecting the resilience of rural development in the southern part of the countryside. (3) In 2020, both the hot and cold spots are significantly expanded, with the hot spots extending to the JiaoWei area and the cold spot areas further expanding in Jinan and the hilly areas. On the one hand, comprehensive land consolidation promotes the intensive utilization of construction land, leading to the expansion of cold spots in certain regions; on the other hand, large-scale agricultural operations and improved infrastructure attract population return or agglomeration, driving the expansion of hot spots.

It is worth noting that the northern coastal region shows hot spots in terms of construction land scale but cold spots in terms of the rate of its change. Dongying and Binzhou, as the central cities of the Yellow River Delta, have a good natural resource base. These cities, shaped by traditional agriculture, feature densely clustered villages and extensive land development patterns, preserving high absolute construction land scales. Around 2010, Shandong implemented a land remediation and ecological constraints policy, and Dongying, relying on the accumulation of capital from the oil industry, has been progressively promoting industrial diversification, with a large shift of population and a slowdown in demand for new construction land. This phenomenon is essentially the outcome of the interaction between existing stock scale and incremental control policies.

3.2. Types of Population–Land Decoupling and Temporal–Spatial Changes in Shandong Province

This paper takes 2010 as the time node and divides two time periods, 2000–2010 and 2010–2020, to systematically analyze the types and temporal–spatial evolution characteristics of population–land decoupling at the township scale in Shandong (Figure 6). The results show that there are eight types of decoupling relationships between population and construction land, with significant spatial disparities and phased evolutionary patterns. Observing from the perspective of changes in population and construction land scale, divergent changes were predominantly characterized by strong negative decoupling (population decline coupled with land expansion), accounting for 36% during 2000–2010 and 48% during 2010–2020. Meanwhile, convergent changes were primarily marked by weak decoupling (concurrent growth of both factors), representing 9% during 2000–2010 and 29% during 2010–2020.

3.2.1. “Population Decline and Land Expansion” Dominate the Relationship

More than 55% of the township units in Shandong have consistently negative decoupling elasticity coefficients over the two decades, with the strong negative decoupling type dominating. The number of such units surged from 664 in the initial study period to 899 in the later period, marking a 35.4% increase. This negative decoupling effect spreads in both spatial and temporal dimensions: From the perspective of spatial patterns, the performance of the strong negative decoupling type is particularly prominent in the Jinan metropolitan area, the dense urban areas of northwestern Shandong, and other areas of accelerated urbanization. This is particularly evident in Qingdao’s coastal economic belt and the Yantai–Weihai urban agglomeration, where developed maritime transport and foreign investment have created a paradox of population overload coexisting with extensive land use. These regions exhibit typical characteristics of urban sprawl coupled with suburban hollowing-out. In suburban rural areas, the rural population continues to outflow, while the inadequate withdrawal mechanism for residential land has resulted in a phenomenon where “people leave, houses stand vacant, but construction land fails to contract”. At the same time, this contradiction has a spatial spillover effect across the province through transportation corridors, economic links, and other channels. From the perspective of evolutionary process, strong negative decoupling and weak negative decoupling have long coexisted. Despite the phenomenon of dual reductions in population and construction land in certain regions, the rate of land contraction (averaging 19%) lags far behind the pace of population loss (averaging 32%). This adjustment mechanism fundamentally reflects a lag in the market-oriented allocation of rural production factors, which fails to effectively alleviate human–land conflicts. Systemic innovation is urgently needed to achieve coupling within the population–land system. Systemic innovation is urgently needed to achieve the coupling state.

3.2.2. Overall Coordination of Population–Land Relationship

This study categorizes the population–land relationship into two major types based on the connotations derived from the eight decoupling relations: “population–land incoordination” (including strong negative decoupling, expansive negative decoupling, and recessive decoupling) and “population–land coordination” (including strong decoupling, weak decoupling, weak negative decoupling, expansive coupling, and recessive coupling) (Figure 7). Over time, the number of townships in Shandong demonstrating coordination increased from 404 to 795, accounting for 43% of the total. This indicates a steady improvement in land intensive utilization, validating the provincial government’s efforts to simultaneously enhance population aggregation effects and land-use efficiency.

In the category of simultaneous population–land growth, expansive negative decoupling has decreased by 80% compared to the previous decade, while weak decoupling has more than tripled (Figure 8). A significant number of townships have transitioned from expansive negative decoupling—characterized by simultaneous growth in both population and construction land but with faster land expansion–to weak decoupling—population growth outpaces land growth. This shift reflects sustained population growth surpassing the expansion rate of construction land. Through policy interventions such as “village consolidation and community integration” and optimized land management, local governments have achieved scientific planning of construction land allocation and strict control over incremental land use. These measures have effectively curbed the previous “sprawling” unplanned expansion of rural residential land, establishing a preliminary virtuous interaction between population growth and land consumption.

Comparative analysis over two periods reveals that the population–land decoupling relationship in Shandong is characterized by significant dynamic evolution. During the rapid urbanization phase from 2000 to 2010, the proportion of uncoordinated townships was as high as 42%, three times that of coordinated ones. But after 2010, with the deepening of the urban–rural integration strategy, there has been a structural expansion of coordinated population–land units. Although the “people decline and land expansion” is still prominent in the core areas of provincial capitals and other major cities, the peripheral townships have initially formed a situation of being surrounded by population–land coordination types. For example, in traditional agricultural regions such as southwestern and northwestern Shandong, policy interventions like rural residential land consolidation and land quota trading mechanisms have elevated the proportion of population–land coordinated regions to 63%. At the same time, regional economic gradient disparities exert profound influences on population–land relationship dynamics. Economically advanced regions like the Jiaodong Peninsula tend to exhibit “economic growth-land expansion” patterns classified as expansive negative decoupling or coupling types, whereas economically lagging areas such as Lunan demonstrate “development lag-resource drainage” characteristics, resulting in recessive decoupling or coupling types. This spatial differentiation necessitates the formulation of differentiated policy regulation mechanisms to optimize resource allocation across regions. Priorities include guiding peripheral metropolitan areas and major agricultural production zones toward sustainable models featuring balanced population growth and intensive land utilization, ultimately achieving a harmonious integration of urban–rural development where human growth and land stability are mutually reinforcing.

3.3. Impact Factor Detection of Population–Land Decoupling

From 2000 to 2020, the main types of population–land decoupling in Shandong Province included weak decoupling, strong decoupling, expansive negative decoupling, and strong negative decoupling. This study applied the geographical detector to conduct detection and analysis of influencing factors for the four main decoupling types mentioned above. Concurrently, collinearity diagnostics were performed on selected influencing factors using IBM SPSS Statistics 22.0. Diagnostic results confirmed that all variables exhibited variance inflation factor (VIF) values below 10 with tolerance levels exceeding 0.1, indicating the absence of multicollinearity among selected influencing factors. The geographical detector analysis further revealed markedly distinct driving forces underlying different decoupling patterns.

First, townships categorized as weak decoupling were predominantly characterized by simultaneous increases in both population and construction land area, with population growth occurring at a comparatively faster rate. The formation of this decoupling type was primarily linked to population density with the resulting increase in congestion [43,44], road density, cropland area, nighttime light index, and slope, all of which demonstrated statistical significance (p < 0.05) (

Table 4. The factor detector results of population–land decoupling in different decoupling types.

	X1	X2	X3	X4	X5	X6	X7	X8	X9
Y1	0.063	0.136 *	0.033	0.101	0.312 **	0.033	0.281 **	0.305 **	0.310 **
Y2	0.005	0.112 *	0.047	0.023	0.155 **	0.071	0.143 **	0.095 *	0.146 **
Y3	0.023	0.028	0.003	0.005	0.027 *	0.02 *	0.015	0.045 **	0.011
Y4	0.002	0.006	0.013	0.018 *	0.04 **	0.008	0.027 **	0.006	0.014

Note: Y₁ is weak decoupling, Y₂ is strong decoupling, Y₃ is expansive negative decoupling, Y₄ is strong negative decoupling, X₁ is DEM, X₂ is slope, X₃ is temperature, X₄ is precipitation, X₅ is population density, X₆ is GDP, X₇ is nighttime light index, X₈ is cropland area, and X₉ is road density. One-way ANOVA is shown (** = p < 0.01; * = p < 0.05), and the grayscale background color is used for highlighting.

). Among these factors, population density, road density, and cropland area exhibited the strongest influence. As illustrated in Figure 9a–e, population density, road density, nighttime light index, and slope showed negative correlations with the weak decoupling index, whereas cropland area displayed a positive correlation. Consequently, within townships of weak decoupling, those with higher population density, more-developed road infrastructure, and smaller cropland areas exhibited lower weak decoupling coefficient values, indicating a more harmonious relationship between population dynamics and land-use changes.

Second, townships classified as strong decoupling were primarily characterized by population growth alongside a reduction in construction land area, representing the most optimal state of the population–land relationship. The formation of this decoupling type was closely associated with indicators such as population density, road density, nighttime light index, slope, and cropland area, all of which demonstrated statistical significance (p < 0.05). Among these factors, population density, road density, and nighttime light index exerted the strongest influence. As shown in Figure 9f–j, population density, road density, nighttime light index, and slope exhibited positive correlations with the strong decoupling index, while cropland area showed a negative correlation. Consequently, among townships of strong decoupling, those with higher population density, more-developed road infrastructure, and more-intense economic activity displayed larger strong decoupling coefficient values, indicating enhanced coordination between population dynamics and land-use changes.

Third, townships identified as expansive negative decoupling were primarily characterized by simultaneous increases in both population and construction land area, with the latter expanding at a relatively faster rate. The formation of this decoupling type was closely correlated with indicators such as population density, GDP, and cropland area, all of which demonstrated statistical significance (p < 0.05). Among these factors, the cropland scale exerted the strongest influence. As illustrated in Figure 9k–m, population density, GDP, and cropland scale showed negative correlations with the expansive negative decoupling index. Within townships of expansive negative decoupling, those with larger arable land areas, higher population density, and more-advanced economic development levels exhibited smaller expansive negative decoupling coefficients. Finally, townships categorized as strong negative decoupling were characterized by population decline alongside an increase in construction land area, representing the least rational population–land change dynamics. The formation of this decoupling type was closely linked to indicators such as precipitation resources, population density, and nighttime light index, all of which passed the significance test (p < 0.05). Among these factors, population density and nighttime light index exhibited the strongest effects. As shown in Figure 9n–p, precipitation resources, population density, and nighttime light index demonstrated negative correlations with the strong negative decoupling index. Within townships of strong negative decoupling, those with more abundant precipitation resources, higher population density, and more-intense economic activity exhibited smaller strong negative decoupling coefficients, indicating a more coordinated relationship between population and land-use changes.

4. Discussion

4.1. The Formation Mechanism of Population–Land Decoupling Under Multi-Factor Interactions

The evolution of population–land relationships resulted from interactions among natural geographical conditions, socioeconomic activities, and policy interventions [45]. This paper identified key influencing factors of decoupling types and employed interaction detector to assess inter-variable impacts, verifying whether combined factors enhance, diminish, or independently affect the explanatory power for the dependent variable. Firstly, the formation of weak-decoupling-type townships were primarily driven by natural constraints and economic transition. As illustrated in Figure 9a, the slope–population density interaction exhibited an explanatory power of 0.465 for the weak decoupling index, while the GDP–population density interaction accounted for 0.494, both demonstrating nonlinear enhancement. Specifically, natural-constraint-driven weak decoupling predominantly occurred in densely populated mountainous townships with steep slopes (>15°). These areas faced inherent limitations due to pronounced topographic relief and restricted developable land reserves, where construction land expansion remained constrained despite population growth [46]. Conversely, economic-transition-driven weak decoupling was concentrated in townships characterized by advanced industrialization and agglomeration of population and industrial factors. These townships exhibited strong demographic attractiveness but achieved high land-use intensification, resulting in decelerated construction land expansion rates [47]. For example, research by Chi and Ho found that in highly developed economic zones of the United States, such as the Southeastern Coast, Washington State, northern Texas, and the southwestern regions, population pressure has surged due to population growth outpacing land expansion [48]. This divergence highlights how differentiated natural and socioeconomic contexts mediate human–land decoupling patterns.

In addition, the formation mechanism of strong-decoupling-type townships was primarily attributed to cropland protection constraints and economic-policy driven [49]. The results indicated that the interaction between cultivated land and population exhibited an explanatory power of 0.299 for the strong decoupling index, while the combined effect of the nighttime light index and road density demonstrated an explanatory power of 0.307, both showing nonlinear enhancement characteristics. The strong decoupling townships under cropland protection constraints were predominantly concentrated in traditional agricultural regions. In the traditional agricultural regions, rural labor forces were liberated through agricultural mechanization and concentrated in township centers. However, construction land was constrained by the protection of permanent prime farmland, hindering its development. Conversely, the strong decoupling townships under economic-policy driven were mainly occurred in suburban zones. Additionally, construction land quotas in such townships were predominantly allocated to industrial parks and road transportation projects due to policy constraints, resulting in insufficient residential land supply in subordinate townships.

Furthermore, the formation of expanding negative-decoupling-type townships were primarily attributed to the multifaceted constraints of topographical conditions, socioeconomic factors, and land resource limitations. The results revealed that the interaction effects between slope and the nighttime light index exhibited an explanatory power of 0.099 for the expanding negative decoupling index, while the interaction between population density and cropland resource endowment demonstrated an explanatory power of 0.121, both demonstrating nonlinear enhancement effects. Such townships were predominantly distributed in areas with relatively flat terrain. Due to comparatively abundant land resources, substantial amounts of cropland were appropriated for construction land development with economic growth and population expansion, while the development patterns were predominantly characterized by low-density spatial configurations. Expanding negative decoupling type was relatively prevalent in urban areas. Research by Liu et al. revealed that between 1985 and 2015, global urban construction land expanded 28 percentage points faster than population growth. This imbalance between construction land expansion and population growth was particularly pronounced in developing countries [50].

Finally, the formation of strong-negative-decoupling-type townships was primarily attributed to developmental constraints [51]. The study demonstrated that the interaction between population and precipitation exhibited an explanatory power of 0.105 for the strong negative decoupling index, while the population–economy interaction demonstrated an explanatory power of 0.039, both manifesting nonlinear enhancement effects. Strong-negative-decoupling-type townships predominantly occurred in traditional agricultural regions. Constrained by multiple factors including demographic resources, water resources, and climatic conditions, these townships exhibited economic underdevelopment that resulted in significant out-migration of labor forces. Concurrently, migrant workers constrained by the urban–rural dual system found it difficult to permanently settle in urban areas, leading to the coexistence of homestead abandonment and spatial expansion in strong-negative-decoupling-type townships. Relevant research indicated that Europe also experienced the phenomenon of decreasing regional populations while construction land expanded, with developing countries such as Bulgaria, Romania, and Lithuania being the most prominent examples [52].

The results of interaction detector analysis revealed that all interactive effects manifested as nonlinear or bifactor enhancement (Figure 10). This further demonstrates that pairwise combinations of influencing factors strengthened explanatory power for the decoupling mechanism of between population growth and construction land expansion.

4.2. Policy Recommendations for Land-Use Management in Townships with Diverse Population–Land Decoupling Types

Under the context of rapid urbanization, population–land contradictions in China became increasingly pronounced, where a prevalent decoupling phenomenon existed between demographic change and land development, while regional development disparities further intensified this conflict. To effectively mitigate population–land contradictions, this study proposed differentiated land-use management policies and recommendations based on having conducted identification and mechanism analysis of population–land decoupling types in Shandong Province. As areas manifesting the most acute population–land contradictions, strong-negative-decoupling-type townships fundamentally faced the issue of inefficient land utilization driven by population outmigration, which exacerbated village hollowization and regional ecological pressures [53]. For this type of township, the urgent actions were given to strictly controlling new construction land quotas while refining exiting mechanisms for inefficient land use. For instance, the “linkage between increment and decrement” policy for construction land was vigorously implemented, incentivizing the reclamation of abandoned homesteads and the retirement of industrial/mining land to exchange for new development quotas. Second, it is recommended that depopulating areas moderately strengthen their ecological functions in future development strategies while establishing cross-regional ecological compensation mechanisms, enabling population-exporting regions to obtain revenues through trading of ecological products.

Townships exhibiting expanding negative decoupling primarily confronted the issue of land expansion outpacing actual demand, resulting in resource wasting and ecological space compression [54]. For this type of township, the primary step was to establish a growth threshold for construction land and rationally regulate the expansion rate of such land areas. For instance, a population-mobility-oriented “population–land linkage” dynamic regulatory mechanism needs to be established. In practical implementation, new land-use quotas were scientifically calculated based on population census data and annual migration monitoring statistics, with comprehensive consideration given to both the increment and structural composition of permanent residents. Meanwhile, subsequent land quotas were appropriately reduced for “low-efficiency land expansion” areas, compelling the tapping into existing land reserves to drive the coordinated development of “population-land-industry” indicators. Regarding future development directions, it was recommended to optimize land-use structure and promote three-dimensional spatial development. For instance, composite utilization of aboveground and underground spaces should be encouraged.

Weak-decoupling-type townships primarily confronted challenges arising from excessive population growth, which manifested in housing shortages, surging pressure on public services, prolonged commuting distances, and compressed ecological spaces [55]. To address these issues, such townships need to adopt strategies centered on intensive land use, stock potential optimization, and structural refinement to establish compact, efficient, and resilient sustainable land management frameworks. Specifically, the primary tasks involved elevating building floor area ratios and enhancing spatial multifunctional utilization to achieve land-use intensification while concurrently implementing urban renewal initiatives and redevelopment of underutilized land to unlock latent land reserves. Regarding long-term development strategies, such townships should appropriately expand land supply allocations for livelihood-oriented sectors including residential, educational, and healthcare purposes to mitigate public service pressures induced by population influx.

The formulation of differentiated land-use management policies essentially involved employing targeted regulation to resolve “human-land decoupling” conflicts, balancing efficiency with equity, and reconciling development with conservation. Centered on population mobility dynamics, these policies integrated regional functional positioning and resource endowments to establish a “categorical guidance–dynamic adjustment–market synergy” framework, ultimately achieving efficient land resource utilization and long-term coordination of human–land relationships.

Additionally, this study has certain limitations: Constrained by township-level data availability, the analysis incorporated only a limited set of driving factors. Future research should integrate household-level determinants to enhance the depth of mechanistic understanding regarding population–land decoupling dynamics.

5. Conclusions

From the perspective of the human–land relationship, this study applied the decoupling model to identify population–land decoupling types at township scale in Shandong Province. This paper further employed the geographical detector to diagnose the core driving factors and interaction mechanisms of four primary decoupling types. The results revealed that the problem of population decline coupled with land expansion has become increasingly prominent in Shandong’s townships, manifested by accelerated rural population loss and continuous construction land expansion. This phenomenon was found to persist due to regional development disparities and resource endowment differences. However, the overall human–land relationship demonstrated a coordination trend over the past two decades, with the number of coordinated townships increasing from 404 to 795, accounting for 43% of total townships. Four predominant decoupling types were identified: weak decoupling, strong decoupling, expansive negative decoupling, and strong negative decoupling. Each decoupling type exhibited distinct driving factors depending on its specific human–land relationship characteristics and development context. Notably, pairwise interactions between factors significantly enhanced the explanatory power of dependent variables, which enabled systematic clarification of the formation mechanisms and critical challenges characterizing distinct decoupling types. Based on these findings, differentiated land-use management policies were proposed to address specific township conditions. This study revealed the evolutionary mechanisms of human–land decoupling relationships at the microscale, theoretically providing microscale empirical support for regional sustainable development theories. Practically, it offered local governments valuable references to optimize land-use management policies, effectively addressing practical challenges of inefficient land resource utilization and accelerated rural hollowing. Meanwhile, the research results have important implications for other regions globally facing similar population–land decoupling situations.