Home Spatio-temporal analysis of the driving factors of urban land use expansion in China: A study of the Yangtze River Delta region
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Spatio-temporal analysis of the driving factors of urban land use expansion in China: A study of the Yangtze River Delta region

  • Yue Wu , Zexu Han , Auwalu Faisal Koko EMAIL logo and Siyuan Zhang
Published/Copyright: April 24, 2024
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Open Geosciences
From the journal Open Geosciences Volume 16 Issue 1

Abstract

Urban land use dynamics play a key role in the sustainable development of rapidly urbanizing regions, such as the Yangtze River Delta (YRD) in China. In this study, we employed geospatial and statistical methods comprising remotely sensed data, Geographic Information Systems, and the Spatial Durbin model to examine the complex changes in urban land uses. These techniques allowed for a broader assessment of the evolving urban landscape, emphasizing the significance of considering spatial relationships and socioeconomic panel data in the study area. The result indicated a substantial increase in built-up land within the YRD, rising from 6.83% in 2000 to 12.29% in 2020. This growth predominantly occurred at the expense of agricultural land, forests, and water bodies, with agricultural areas contributing over 90.2% to the built-up land expansion. The eastern cities experienced a more noticeable urban expansion compared to the western cities. The findings revealed a positive spatial spillover effect among neighboring cities, indicating a significant spatial clustering of built-up land. Population and urbanization emerged as primary drivers influencing both local and neighboring built-up land expansions. However, economic development, fixed asset investment, and transportation networks influenced the local areas of the YRD region but acted as inhibitors for the growth of neighboring areas. The result also suggests that industrial structures effectively curb local built-up land expansion without adversely affecting neighboring areas. These findings contribute to the existing knowledge by providing a wider understanding of land uses within the YRD region and valuable policy recommendations for sustainable urban planning in similar rapidly urbanizing areas.

Keywords: land-use changes; driving factors; spatial autocorrelation; spatial Durbin model; Yangtze River Delta

1 Introduction

Land serves as a fundamental natural asset that underpins economic development at the local, regional, and global levels [1,2]. The constant alteration in global land use and land cover (LULC) has consistently drawn significant interest in geographical [3,4,5], ecological [6,7,8], and environmental research [9,10,11]. This is primarily due to the continuous alterations in urban land uses driven by socioeconomic development. As one of the largest developing countries, China’s economic transformation since the country’s reform and opening up in 1978 has resulted in rapid urbanization and substantial changes in land use [12,13]. Over the years, China’s urbanization rate (UBR) has grown significantly, escalating from 17.92% in 1978 to approximately 65.2% in 2022. The built-up area, which measured about 7,000 km2 in 1978, has expanded to 63,700 km2 in 2022, i.e., approximately nine times its previous size. This surge in urban growth has given rise to many environmental challenges, encompassing food security, social conflicts, and ecological degradation. Consequently, these issues have garnered substantial attention from the government and scholars alike. Studies utilizing satellite remote sensing and land use statistics reveal noteworthy shifts in China’s regional land use structure. Over the past 40 years, a significant portion of farmland, forested areas, and water bodies has been infringed upon by expanding urban built-up land [12,13,14]. Therefore, studies on land-use patterns and spatial factors driving land-use interactions are vital in mitigating the negative impacts of alterations in LULC.

Numerous scholars have utilized several remote sensing data and geospatial technologies to conduct thorough investigations and studies on China’s LULC changes. This research spans multiple scales, encompassing the national [15,16,17], regional [18,19,20,21], and city levels [22,23,24]. In addition to analyzing changes in land use and making forecasts, substantial studies have delved into the root causes of these transformations, focusing on the drivers behind the built-up area expansion. The primary determinants of urban land use expansion are divided into two categories: natural and socioeconomic forces [25]. Natural variables such as soil, climate, relief, hydrology, and other environmental components significantly determine the pattern, direction, and intensity of urban land expansion. However, it is important to note that these factors exert gradual and lasting impacts that may not be immediately apparent. Other socioeconomic factors, such as population (POP) growth [26,27], economic development [28,29], investment [30,31], industrial structure (STR) [32,33], and infrastructure enhancements [34,35], stand out as crucial contributors to the rapid transformations in land use patterns. Similarly, scholars have employed various analytical methods, including econometric regression analysis and correlation analysis, to examine the specific impacts of these socioeconomic factors on changes in land use [36,37,38,39].

However, there are significant limitations in existing land use studies. First, these studies often lack continuity in the data used, typically relying on selecting a specific time point during the research period. This approach involves collecting and organizing natural and socioeconomic data at that particular moment and analyzing their influence on land use changes. The utilization of the spatial Durbin model (SDM) that employs long-term panel data for analysis is rarely employed. Second, insufficient consideration is given to the spatial spillover impact. Urban land use changes are influenced by a multitude of factors that not only impact the local area but also have positive or negative effects on the surrounding adjacent regions. However, only a few studies have considered and analyzed the impact of spatial variables on neighboring areas. Wang [40] examined the direct and indirect influence of regional economic growth on urban land development in China. Yang and Luxin’s research unveiled a pronounced positive spatial spillover phenomenon within the urbanization of land in Chinese cities. The heightened urbanization level in local urban areas triggers a corresponding upswing in urbanization levels within the neighboring cities [41]. Likewise, Yang and Zhang’s research revealed a progressive spatial spillover influence in the context of spatial competitiveness in China’s urban expansion [42]. In a comprehensive study of the multifaceted interplay between urbanization and regional growth in the Beijing-Tianjin-Hebei region, Tang et al. [43] employed spatial economic models to examine the inherent spatial variable influencing urban land expansion. However, most of these studies focus on the national level and have not addressed the issues facing China’s Yangtze River Delta (YRD).

The aim of this present study is to provide a comprehensive understanding of the land use alterations in the YRD and their implications. The study examined the driving mechanisms influencing built-up land expansion to identify spatial spillover effects and fill the existing gap in the literature. The research contributes to urban planning by providing strategic policy recommendations that seek to ensure the preservation of natural land resources in the face of urban expansion. The study’s significance lies in its focus on a vital region within China, providing a broader perspective for informed urban planning strategies. The study contributes to existing literature through its comprehensive approach, which combines both empirical findings with practical policy recommendations for sustainable urban development in similar areas facing rapid urbanization.

2 Materials and methods

2.1 Study area

The study area encompasses the lower parts of the Yangtze River in China, bordered to the east by the Yellow and East China Seas (Figure 1). The YRD region spans across four provinces comprising Shanghai (SH), Jiangsu (JS), Zhejiang (ZJ), and Anhui (AH). The region is vital at both national and global levels due to the following:

  1. Geographical and economic factors

    The YRD region, covering approximately 350,000 km2, plays a crucial role in China’s economic landscape. The region has 41 cities and a POP of over 235 million, contributing significantly to the country’s demographic composition. In 2022, the region reported a Gross Domestic Product (GDP) of 29 trillion yuan, representing over 24.5% of China’s total economic output.

  2. Demographic pressure and urbanization

    Despite its economic growth, the YRD faces various challenges, such as land resource scarcity, environmental degradation, and rapid POP growth. The POP density, standing at 656 people per square kilometer, is 4.5 times higher than the national average. This demographic pressure is intensified by urbanization, resulting in the increased demand for land that poses threats to agricultural areas and ecological stability.

  3. Major cities and economic activities

    The YRD region is characterized by vibrant cities, each contributing uniquely to the economic landscape. Shanghai, as a global financial hub, stands out for its financial activities, while other cities stand out as major hubs for manufacturing, trade, and technological innovation.

  4. Geomorphic, climatic, and hydrological factors

    The topography, climate, and water systems within the YRD region also play a key role in the study area’s land use patterns. The region has a typical subtropical monsoon climate and the highest density of river network in China, with an average length of 4.8–6.7 km/km2.

Figure 1 Map of the YRD region in China.
Figure 1

Map of the YRD region in China.

These factors contribute to the crucial need for planning land utilization and sustainable use of natural resources within China’s YRD region.

2.2 Data sources and variable selection

We utilized China’s annual classified land cover products (CLCD), which are freely accessible in the Zenodo database, to acquire remote sensing data for the years 2000–2020. The socioeconomic data for the study area were primarily sourced from the Chinese City Statistical Yearbook. When specific data were unavailable, missing information was complemented with data extracted from the statistical yearbooks of Shanghai, Jiangsu, Zhejiang, and Anhui. Based on an extensive literature review, GDP, POP, STR, UBR, Fixed Asset Investment (INV), and Road Traffic Mileage (RTM) were chosen as variables for the entire study period, i.e., 2000–2020. Table 1 shows the statistical data of the variables.

Table 1

Description of the study’s spatial variables

Variable Description Number Min. Max. AVG STD
BUA Urban and rural built-up area (km2) 861 63.30 2715.05 823.17 545.28
GDP Annual GDP (108 yuan) 861 60.05 38963.30 2744.82 4258.60
POP POP total at the end of the year (104 people) 861 68.48 2488.22 521.75 362.85
STR The proportion of tertiary to secondary industries’ value added (%) 861 0.42 2.79 0.93 0.31
UBR The percentage of the region’s inhabitants residing in urban areas (%) 861 18.20 89.60 53.99 15.21
INV Total investment in fixed assets (108 yuan) 861 9.13 8837.80 1508.05 1672.00
RTM Total mileage of regional road (km) 861 386.00 25084.00 8979.67 5012.70

2.3 Methods

We structured the study into three main sections: data collection and processing, analysis of changes in built-up land expansion, and inquiry into the driving factors behind this urban expansion. Figure 2 highlights the framework of this study.

Figure 2 Methodological flowchart of the research.
Figure 2

Methodological flowchart of the research.

To begin with, we categorized the original CLCD data into nine groups. Using Geographic information system (GIS), we reclassified these data into six broader categories: built-up land, agricultural land, forest land, grassland, water bodies, and barren land. We then established the yearly land use dataset within the YRD through resampling at a 100 m × 100 m resolution. In addition, we obtained the study area’s data from the Chinese City Statistical Yearbook to construct an economic panel dataset for each province within the YRD. In the second section, we analyzed the changes in land use and the expansion of built-up land within the YRD over the period spanning from 2000 to 2020. This analysis includes examining the overall changes, land use transfer matrix, spatial distribution, and the intensity of built-up land expansion, among other aspects. Finally, we examined the factors driving the growth of built-up land within the YRD. To achieve this, we conducted a spatial autocorrelation test to ascertain the efficacy of the spatial econometric model. We employed the three most utilized spatial econometric models (i.e., spatial lag model SLM; spatial error model, SEM; and SDM) using two distinct spatial weight matrices, followed by a series of model tests to determine the most appropriate model for the study’s objectives. We further analyzed the driving mechanism influencing built-up area expansion, with a particular emphasis on identifying spatial spillover effects.

2.3.1 Change dynamics of land uses

Similar to the research of Li et al. [44] and Yang er al. [5], we utilized a Markov transition matrix to analyze quantitative changes in land use. In addition, we determined the periodic variation of individual LULC classes within the study area. The Markov transition matrix indicates the present state and area proportion of LULC classes during a specific period. The transition matrix presents the evolution of LULC classes using equation (1).

(1) S ij = S 11 S 12 … S 1 j S 21 S 22 … S 2 j … … … … S i 1 S i 2 … S ij ,

where S ij is the area of land use transition from class i to j; and i and j signify the different classes of land uses.

In this study, we determined the intensity of built-up area expansion during the different time nodes using equation (2).

(2) K = U t 2 − U t 1 U t 1 × 1 T ,

where K is the intensity of built-up expansion, U t 1 and U t 2 signify the area of built-up land at the initial and final time of a specific period, and T indicates the year difference between the initial and final period.

2.3.2 Establishment of the spatial weight matrix

The analysis of spatial econometric models was based on creating the spatial weight matrix. To achieve a reliable regression, we developed two distinct matrixes. This matrix comprises the queen contiguity and the geographic distance weight matrix, denoted using W 1 and W 2 , respectively.

− W 1 : Queen contiguity weight

The queen contiguity weight, as introduced by Berry et al. [45], belongs to the group of first order contiguity weights and utilizes principles akin to those in chess. The spatial weight matrix element is set to 1 when regions sharing an edge or vertex are considered neighbors; otherwise, it is set to 0. The queen contiguity weight was computed using equation (3).

(3) W 1 = 1 , j ∈ N ( i ) 0 , j ∉ N ( i ) ,

where N ( i ) signifies the neighborhood set of region i.

− W 2 : Geographic distance weight

The first principle of geographical law establishes the vital connection between objects, emphasizing that objects close to each other are closer in proximity than distant ones [46]. Therefore, when using the spatial weight matrix, it is not appropriate to rely solely on the conditions of the geographical contiguity. The degree of spatial correlation is usually influenced by the proximity of spatial units [47,48]. We utilized equation (4) to compute the spatial weight matrix’s inverse distance.

(4) W 2 = 1 / d ij γ , i ≠ j 0 , i = j ,

where d ij denote the distance between region (i) and (j) geometric center, with γ being fixed at 1 or 2. In this study, we set the value of γ to 1.

2.3.3 Assessment of spatial autocorrelation

A critical step in determining the spatial dependability of variables is the spatial autocorrelation test. The tests determine whether implementing spatial economic models in regression analysis is necessary [49]. We employed Moran’s I Index to examine the global spatial autocorrelation of the YRD region. The Moran’s I Index has values ranging from –1 to 1. The values between 0 and 1 signify a positive correlation between variables, while values between –1 and 0 signify a negative correlation. Similarly, an index of zero (0) indicates the non-correlation of land use variables over the entire region. We computed the Moran’s I index using equation (5).

(5) I = ∑ i = 1 n ∑ j = 1 n W ij ( x i − x ̅ ) ( x j − x ̅ ) S 2 ∑ i = 1 n ∑ j = 1 n W ij ,

where I signifies the Moran’s I Index; x i is the observed variables in each region; x ̅ signifies the variables’ mean; S 2 is the variance of the variables and W ij is the matrix of the spatial weight.

2.3.4 Spatial econometric model

Spatial econometric models are vital in research studies dealing with spatial data that exhibit autocorrelation among variables. The three categories of the spatial econometric model comprise the SLM, (SEM, and SDM. The fundamental difference between these models is their unique spatial correlation approaches.

In the SLM, spatial correlation is evident within the model’s dependent variables [50]. It is computed using equation (6).

(6) y = ρ Wy + X β + ε , ε ∼ N ( 0 , σ 2 I ) ,

where y is the explained variable, i.e., BUA; ρ signifies the coefficient of the spatial autoregression; Wy indicates the explained variable’s spatial lag term; X signifies the independent variable’s matrix, having the potential factors influencing land use changes (i.e., POP, GDP, STR, UBR, INV, RTM); β is the coefficient of regression; ε defines the random error term; and W signifies the spatial weight matrix.

In the SEM, spatial correlation is attributed to the random error term, representing unobservable influencing factors [51]. It is calculated using equation (7).

(7) y = X β + u , u = λ Wu + ε , ε ∼ N ( 0 , σ 2 I ) ,

where Wu is the error term of the spatial lag, and λ is the spatial error coefficient, indicating the influence of the errors, i.e., neighborhood regions on local regions. Other symbols have the same meanings as above.

The SDM adopts a more comprehensive approach by recognizing the spatial correlation present in the dependent variable and the bivariate spatial relationship between the dependent and independent variables [52]. It is computed using equation (8).

(8) y = ρ Wy + X β + WX θ + ε , ε ∼ N ( 0 , σ 2 I ) ,

where θ is the lag spatial parameter, representing the immediate impact of the explanatory variables of neighboring areas on the explained variables, other symbols have the same meanings as above.

2.3.5 Selection and validation of model

To address the impact of varying data dimensions and mitigate heteroscedasticity, we employed natural logarithms as variables. We used the maximum likelihood estimation method for our regression analysis. The choice of spatial econometric model is an essential part of spatial econometric modeling and a critical step of empirical analysis [53]. We examined a series of tests to identify the most suitable analytical model. We employed the LaGrange Multiplier (LM), robust LM, and Hausman tests to validate our selection of the most appropriate model. We utilized the capabilities of the “R” programming language to run all these tests simultaneously.

3 Results

3.1 Dynamics of land use changes

There was a notable growth in the extent of built-up land within the YRD, expanding from 24076.54 km² in 2000 to 43312.45 km² in 2020. This expansion coincided with a simultaneous decrease in farmland, which contracted by 15774.33 km². Table 2 presents the quantitative data of the different classes of land use and their change dynamics. The spatial distribution of the study area’s LULC classes are shown in Figure 3.

Table 2

Distribution of LULC classes in YRD from 2000 to 2020 (km² and percent)

Sl no. LULC classes 2000 2005 2010 2015 2020
Area (km²)
1. Agric. land 197998.62 191679.05 185997.85 183119.21 182224.29
2. Forest areas 107912.59 109055.03 109263.32 106232.75 104980.87
3. Grass land 184.33 157.3 164.65 107.39 50.97
4. Water bodies 22137.17 23065.64 23122.77 22766.69 21755.88
5. Barren land 19.43 9.62 7.026 4.8456 4.22
6. Built-up areas 24076.54 28362.04 33773.064 40097.79 43312.45
352328.68 352328.68 352328.68 352328.68 352328.68
Area (%)
1. Agric. land 56.1971 54.4035 52.7910 51.9740 51.7200
2. Forest areas 30.6284 30.9526 31.0118 30.1516 29.7963
3. Grass land 0.0523 0.04465 0.04673 0.0305 0.0145
4. Water bodies 6.2831 6.5466 6.5628 6.4618 6.1749
5. Barren land 0.0055 0.0028 0.0020 0.0014 0.0012
6. Built-up areas 6.8335 8.04987 9.5857 11.3808 12.2932
100.0000 100.0000 100.0000 100.0000 100.0000
Figure 3 Spatio-temporal pattern of land uses. (a) 2000, (b) 2005, (c) 2010, (d) 2015, and (e) 2020.
Figure 3

Spatio-temporal pattern of land uses. (a) 2000, (b) 2005, (c) 2010, (d) 2015, and (e) 2020.

In 2000, agricultural areas were the most prevailing land use, having approximately 56.20% of the study area’s total LULC, followed by forest areas (30.63%), built-up areas (6.83%), water bodies (6.28%), grassland (0.05%), and barren land (0.01%). During the past 20 years, i.e., 2000–2020, built-up areas were the only land cover class that significantly expanded, increasing from 6.83 to 12.29%. Meanwhile, agricultural land, forest areas, barren land, grassland, and water bodies declined considerably during the same period. The study’s findings revealed that the area occupied by agricultural land declined by 7.97%, forest areas by 2.72%, and waterbodies by 1.72%.

3.2 Transition of LULC classes

The various alterations and changes in land uses during the different periods are presented using the Sankey Diagram in Figure 4.

Figure 4 Sankey diagram of land use transfer between (a) 2000 and 2005, (b) 2005 and 2010, (c) 2010 and 2015, (d) 2015 and 2020, and (e) 2000 and 2020.
Figure 4

Sankey diagram of land use transfer between (a) 2000 and 2005, (b) 2005 and 2010, (c) 2010 and 2015, (d) 2015 and 2020, and (e) 2000 and 2020.

Despite the several transitions across all classes of land uses within the YRD region, the magnitude of these changes varied considerably. Over the entire study period, i.e., between 2000 and 2020, the alteration of agricultural land was the main contributor to built-up land development, accounting for over 90.2% of the study area’s urban growth. Forest land and water bodies contributed 577.23 and 1354.85 km2 to the increase in built-up land, representing 2.9% and 6.8%, respectively. The three land uses contributed 99.9% of the total inflow into built-up land. At the same time, built-up land is more difficult to transform to other land uses, as 96.93% remained unaltered over the study period. Forest land exhibited a similar trend, with 94.00% of its area remaining unchanged, followed by agricultural land (87.44%), water bodies (80.43%), grassland (19.68%), and barren land (3.29%). Most forest land was transformed into agricultural and built-up land, with an area of approximately 5848.85 and 577.23 km², respectively. Water bodies also witnessed significant changes, transforming 2938.79 and 1354.85 km² into agricultural and built-up land, respectively.

3.3 Pattern of built-up land expansion

This section provided a comprehensive comparison and analysis of the urban expansion in various cities within the YRD region, focusing on different study periods. Urban expansion is the variation in the extent of built-up land over two distinct timeframes, quantified as the difference between the area at time 2 and time 1 using the (a) 20 years period and (b) interval of 5 years.

Considering the entire study period in Figure 5(a), the built-up land expansion of the YRD exhibited higher development in the east with reduced urban growth in the west. The most significant expansion is in the eastern coastal areas, comprising Hangzhou, Shanghai, Suzhou Jiangsu (JS), Nantong, and Ningbo. Flat terrain, dense POPs, and advanced economic development characterize these areas. Areas with the least expansion were Zhoushan, Lishui, Huangshan, and Chizhou. These regions are situated on islands or mountainous areas, where land resources for development are relatively limited. Their economic development has been comparatively slow, substantially restraining the scale of urban growth and expansion.

Figure 5 The pattern of built-up area (km2) expansion from 2000 to 2020. (a) Over the 20 years period and (b) over an interval of 5 years.
Figure 5

The pattern of built-up area (km2) expansion from 2000 to 2020. (a) Over the 20 years period and (b) over an interval of 5 years.

Over a distinct time interval of 5 years, the built-up land expansion of the YRD region indicated notable temporal disparities, as shown in Figure 5(b). From 2000 to 2005, the most remarkable growth was observed in areas such as Shanghai, the southern part of Jiangsu, and the northern part of Zhejiang. Equally, cities within Anhui Province exhibited a notably more gradual expansion rate in built-up land compared to the other three provinces. Between 2006 and 2010, there was a significant uptick in the northern regions of Jiangsu, encompassing cities such as Xuzhou and Lianyungang. A similar trend was also evident in Anhui Province, particularly in cities like Hefei and Fuyang. From 2011 to 2015, built-up land expansion across the entire Jiangsu Province accelerated notably, surpassing the rates in Zhejiang and Anhui during the same period. This period manifests the peak phase of urban development within the YRD region. Between 2016 and 2020, the expansion declined in most areas except Suzhou (JS), Nantong, Yancheng, and Hefei.

3.4 Intensity of built-up land expansion

The result indicates that the intensity of built-up land expansion is higher in the central areas compared to the northern and southern regions, and the coastal areas exhibit higher expansion intensity than the inland regions, as shown in Figure 6(a). Particularly in the southern parts of Jiangsu and northern fragments of Zhejiang, cities like Suzhou (JS), Jiaxing, and Huzhou stand out with the highest expansion intensity. In contrast, in the northern parts of Anhui and Jiangsu, the expansion intensity is notably lower than in other parts of YRD. Similarly, various regions display significant differences in built-up land expansion intensity during different periods, with inland areas gradually experiencing an increase in intensity over time, as presented in Figure 6(b).

Figure 6 The intensity of built-up area (km2) expansion from 2000 to 2020. (a) Over the 20 years period and (b) over an interval of 5 years.
Figure 6

The intensity of built-up area (km2) expansion from 2000 to 2020. (a) Over the 20 years period and (b) over an interval of 5 years.

From 2000 to 2005, the areas with the highest expansion intensity included Suzhou (JS), Jiaxing, Huzhou, and Jinhua. Between 2006 and 2010, new areas in Anhui Province, such as Xuancheng, Wuhu, and Chizhou, exhibited a relatively high intensity of urban expansion. From 2011 to 2015, the expansion intensity in cities like Nantong and Taizhou in Jiangsu Province significantly increased, while from 2016 to 2020, the expansion intensity in cities like Lishui in Zhejiang and Hefei and Anqing in Anhui rapidly escalated. The underlying factors contributing to these trends may be multifaceted. The coastal regions typically enjoy more accessible economic resources and development opportunities in their early stages, resulting in heightened expansion intensity. On the other hand, inland regions may have implemented more attractive policies and development plans, actively attracting industries and influencing the intensity of urban expansion. Further research into the causes of these differences will help us better understand the region’s urban expansion patterns.

3.5 Spatial autocorrelation

As illustrated in Table 3, which presented the spatial autocorrelation result determined using Moran’s I index, all the study’s data successfully passed the significance test. The Moran’s I index for built-up land during the 2000–2010 period declined from 0.3608 to 0.2309, and during the 2010–2020 period, it initially rose and then decreased, ultimately reaching 0.2466 in 2020. This outcome underscores a noteworthy positive spatial correlation in built-up land within the YRD. Developing a spatial econometric regression model is required to understand the drivers of the land use alteration.

Table 3

Moran’s I index of built-up land between 2000 and 2020

Sl no. Year Moran’s I Z-Score p-Value
1. 2000 0.3608 4.0656 0.000
2. 2005 0.2652 3.0389 0.000
3. 2010 0.2309 2.6729 0.000
4. 2015 0.2542 2.8929 0.000
5. 2020 0.2466 2.8092 0.002

3.6 Result of model validation

The result of the LM and the robust LM tests presented in Table 4 indicates that both passed the results at a 1% significance level. This outcome suggests the concurrent presence of the spatial lag and the SEM, denoted using SLM and SEM. We further evaluated other spatial econometric models, and the SDM was found to be the most appropriate option, as previously established [54,55,56]. Meanwhile, a Wald test was conducted with a 1% significance threshold. The result rejected the null hypothesis that the SDM could be simplified into a SLM or a SEM. Therefore, the SDM was utilized in this study. It is imperative to acknowledge that regional disparities, resulting from geographical location and policy changes, were noted in the panel data utilized for most cities in the YRD region. To address this, the fixed effect model was chosen over the random-effects model. The Hausman test also validated the fixed effect model’s superiority. This approach allowed us to systematically evaluate the need for spatial components in our model, contributing to a more robust and accurate representation of the underlying drivers of the study area’s change dynamics in land use.

Table 4

Results of the spatial panel model selection method

Sl no. Test Statistic p-Value
1. LM-lag 409.98 0.000
2. LM-error 254.85 0.000
3. RLM-lag 238.69 0.000
4. RLM-error 83.559 0.000
5. Wald spatial lag 210.91 0.000
6. Wald spatial error 224.12 0.000
7. Hausman 76.742 0.000

Note: LMs are presented using LM-lag and LM-error, while the robust LM test is signified using robust LM-lag and robust LM-error.

To further validate our result, we performed a multiple regression analysis on the variables of SDM using two spatial weight matrices. The findings are presented in Table 5. Despite the inconsistencies in the estimated coefficients, the results revealed that the sign and statistical significance were unchanged. This outcome not only confirmed the reliability of our results but also highlighted the robustness of our findings. The spatial autoregressive coefficient ρ in the SDM’s regression analyses passed the 1% significance test weight W 1 and W 2. This suggests that the urban growth and expansion of the YRD positively impacted the development of neighboring areas. Specifically, when local built-up areas increased by 1%, neighboring regions also experienced growth of approximately 0.4994%. This result aligns with the previous studies of He et al. [57] and Deng et al. [58], which demonstrated a similar trend of urban land use alteration. We utilized the Geographic Distance Weight, i.e., W 2, for the study’s further analysis due to its better explanatory capability, having a larger R-square and a lower absolute value, i.e., logLik.

Table 5

Results of the multiple regression analysis using the SLM, SEM, and SDM

Variables SLM SEM SDM
W 1 W 2 W 1 W 2 W 1 W 2
ln GDP 0.1501*** 0.0789* 0.4992*** 0.3995*** 0.4394*** 0.5183***
(3.0495) (19.6014) (11.8379) (9.0108) (9.5408) (12.7499)
ln POP 1.0969*** 1.0796*** 0.4705*** 0.5883*** 0.4842*** 0.4576***
(23.3616) (24.4047) (11.1720) (12.9841) (9.9522) (10.7124)
ln STR −0.2823*** −0.3056*** −0.2406*** −0.3147*** −0.2436*** −0.3250***
(−6.2518) (−7.1291) (−7.7644) (−9.3537) (−7.1574) (−10.2227)
ln UBR 0.0034* 0.0027 0.0019 0.0006 0.0024 0.1793***
(1.8798) (1.5720) (1.5893) (0.4703) (0.0358) (2.8970)
ln INV 0.0288 0.0651* 0.1667*** 0.1593*** 0.1195*** 0.1222***
(0.7019) (1.6673) (4.9442) (4.7129) (3.4919) (4.0724)
ln RTM 0.0021 0.0143 0.1085*** 0.0495* 0.1069*** 0.1004***
(0.0623) (0.4559) (4.4242) (1.8123) (4.1423) (4.1026)
ρ 0.4495*** 0.5766*** 0.6251*** 0.4994***
(19.024) (22.981) (22.806) (14.258)
λ 0.8698*** 0.9183***
(59.079) (82.808)
w. l GDP −0.8948*** −0.9185***
(−11.6280) (−9.2496)
w. l POP 0.5779*** 0.8757***
(6.5788) (7.4550)
w. l STR 0.0253 0.2727**
(0.2964) (2.9539)
w. l UBR 0.1474 1.5757***
(0.9193) (7.4435)
w. l INV −0.2201*** −0.5138***
(−3.5419) (−7.8906)
w. l RTM −0.2920*** −0.3156***
(−5.7792) (−5.1778)
R-squared 0.8479 0.8629 0.6443 0.6618 0.9177 0.9277
logLik −234.4553 −193.0844 86.9540 0.8821

Note: ***,**, and * indicate that the results are significant at the 0.01, 0.05, and 0.1 levels, respectively, with T-statistics in parentheses.

3.7 Spatial effect result

In the SDM, the coefficients of explanatory variables and spatial lag term in the regression results could not directly represent the magnitude of this contribution. Therefore, we modified the variables in Table 5 to depict the direct and indirect effects, as shown in Table 6, utilizing the partial differential decomposition approach [55].

Table 6

Statistical information of the direct, indirect, and total effects of variables

Variables Direct effect Indirect effect Total effect
GDP 0.4637*** −1.2630*** -0.7993
POP 0.5491*** 2.1144*** 2.6635***
STR −0.3158*** 0.2114 −0.1044
UBR 0.3174*** 3.1889*** 3.5063***
INV 0.0846*** −0.8671*** −0.7825
RTM 0.0780*** −0.5086*** −0.4306

Note: ***,**, and * indicate that the results are significant at the 0.01, 0.05, and 0.1 levels.

The local influence was depicted using the direct effect, illustrating how driving factors influenced local built-up area expansion. The indirect effects were defined using the spatial spillover effect, which showed how local factors influenced the urban expansion of neighboring areas. The sum of the direct and indirect effects represents the study area’s total effect.

4 Discussion

Over the past two decades, rapid urbanization has not only functioned as a potent catalyst for socioeconomic progress in the YRD region but has also encroached significantly upon agricultural land, leading to a range of ecological and environmental challenges. These challenges have had a profound impact on the long-term prospects for both socio and economic growth in urban and rural areas, underscoring the need for an in-depth study to understand the mechanisms driving the expansion of built-up areas in the YRD region.

The findings of this study revealed that the demand for built-up land increases as the economy of the YRD region prospers. Economic prosperity is often accompanied by increased infrastructure development, industrial expansion, and residential projects, which require substantial land resources [59,60]. However, the impact of economic development on the growth and expansion of urban land in neighboring areas can be different. For every 1% increase in the local GDP, local built-up land increases by 0.4637%. On the other hand, built-up land in neighboring areas decreased by about 1.2630%. This can be attributed to the siphoning effect in economic development, where the prosperity of one region attracts additional resources and investments. However, this reduces investments in neighboring regions when resources are limited.

The rapid growth of the POP significantly impacts the expansion of built-up land in both local and neighboring areas. The results revealed that for every 1% increase in regional POP, this led to increases in built-up area of 0.5491 and 2.1144% in the local and neighboring areas, respectively. Land is crucial for people’s livelihoods and economic activities, but its finite resources limit it. As the POP increases, it becomes necessary to expand urban built-up land or establish new towns to accommodate the growing POP’s housing and employment needs [61]. The expansion of developed land in adjacent regions is also encouraged by POP growth, as some individuals may opt to relocate to nearby areas, resulting in an overall rise in POP across the entire region.

The STR coefficient serves as a restriction on the expansion of local built-up land. As the coefficient of the STR increased by 1%, the local built-up area decreased by 0.3158%. An increase in the proportion of the tertiary sector compared to the secondary sector reduces the amount of land allocated for construction [32]. The tertiary sector primarily comprises service industries such as finance, education, healthcare, consultancy, and information technology. These sectors typically do not require large plots of land or physical factories for their operations and often generate higher added value. Conversely, the secondary sector encompasses manufacturing and industrial activities that require extensive land for factories, warehouses, and production facilities, leading to increased land resource consumption.

The surge in urbanization serves as a positive catalyst for the expansion of built-up areas. A 1% increase in the UBR corresponds to a growth of 0.3174% in the local built-up area and 3.1889% in neighboring areas. This indicates that more and more people are moving to urban areas for better job prospects, education, healthcare, and living conditions, resulting in a higher demand for housing, commercial spaces, and infrastructure.

Fixed asset investment is also vital for driving urban expansion. For every 1% increase in fixed asset investment, the local built-up area increased by 0.0846%, whereas the neighboring built-up area decreased by 0.8671%. On the one hand, infrastructure development, real estate projects, industrial facilities, and other construction activities require significant land consumption. These investments result in more job opportunities due to infrastructural development, real estate, and other manufacturing schemes, attracting a larger workforce and stimulating economic growth. However, an increase in fixed asset investment in one area can reduce the investment appeal of neighboring regions, intensifying competition and potentially decreasing the influx of investments, which could slow down their built-up land expansion.

Finally, the role of RTM in driving built-up land expansion is significant. For every 1% increase in the local RTM, built-up land expands by 0.0780%. This is probably because a well-designed transportation infrastructure improves accessibility and connectivity, making it easier for people to travel to and from the region [35]. This leads to increased POP mobility and economic development, which drives built-up land expansion. However, a well-developed road transportation system can also make the local area more attractive to investors and businesses, potentially leading to a loss of investment appeal in neighboring regions, which might have a negative impact, i.e., a 0.5086% reduction in built-up land.

4.1 Policy implications

Based on the above findings, we proposed the following policy recommendations, which may be helpful to both the YRD region and other urban areas facing similar trends of urbanization.

  1. The expedited transformation and development of the STR: Tertiary sector development can effectively mitigate the transition between arable and built-up land. Our analysis shows that promoting the growth of the tertiary industry decelerates the expansion of built-up land in the YRD and enhances land use efficiency. Therefore, city administrators should proactively steer the transformation and growth of the conventional secondary industry, thereby fostering a profound realignment of an industrial framework responsive to resource and environmental constraints. This will also propel the expansion of the tertiary sector.

  2. Policymakers should adopt a holistic approach that transcends administrative boundaries, considering the overall progress of the region and not just individual areas. It is essential to exploit each region’s unique strengths, promote industrial land allocation, and facilitate a unified relationship between coastal and inland areas, thereby fostering a more balanced and coordinated trajectory of regional development.

  3. The study advocates for controlled and prudent built-up land expansion in areas with favorable urban development. Major metropolises should take the lead in driving economic growth and serve as development zones. In smaller and mid-sized cities, development strategies should be tailored to local circumstances and control unplanned built-up land expansion. In areas with exceptionally delicate ecological environments, stringent protection measures must be enforced to prevent large scale construction and development that could cause irreparable environmental harm.

  4. Local governments should enforce strict safeguards for farmland while systematically conserving and revitalizing natural land resources. It is crucial to advocate for the principles of ecosystem integrity and enhanced construction land utilization efficiency, which seeks to increase the efficiency of land utilization while reducing encroachment on arable land, forests, and water bodies. Additionally, it is crucial to heighten oversight and administration of land resources to ensure comprehensive protection in quantity, quality, and ecological vitality. This holistic approach would help ensure the well-being and sustainable advancement of the economy and society.

5 Conclusion

The research highlighted the crucial need to consider spatial effects and interactions within the neighboring regions of the YRD region in understanding the major driving factors of land use changes. Policymakers, urban planners, and researchers can benefit from understanding these findings, which extend beyond the immediate spatial context, influencing considerations related to natural land management, economic growth, and other pertinent factors in the realm of urban planning and management. Future studies may consider detailed analysis at a smaller scale to capture the spatial variations within cities and neighborhoods. This could provide more insights into the factors influencing urban land use at a local level.

5.1 Key findings and implications

  1. Substantial urban growth

    The built-up area in the YRD significantly increased from 24076.54 km² in 2000 to 43312.45 km² in 2020, with total land area rising from 6.83 to 12.29%. Concurrently, agricultural areas experienced a notable decline, emphasizing the transformative impact on land use over the past two decades.

  2. Agricultural area transition

    The transition of agricultural areas was the primary driver, contributing over 90.2% to overall urban growth. Forestland and water bodies also played roles, emphasizing the importance of understanding these dynamics for policymakers and planners dealing with the evolving landscape.

  3. Spatial variation in urban growth

    The study revealed higher growth in built-up land towards the eastern areas of the YRD, particularly in Shanghai, Jiangsu, and Zhejiang. Notable expansion intensity was observed in Suzhou, Jiaxing, and Huzhou. However, rapid growth in inland areas, especially from 2016 to 2020, highlighted the significance of considering regional variations.

  4. Spatial agglomeration and spillover effect

    In the YRD region, the spatial distribution of built-up land exhibits a significant spatial agglomeration and a noticeable positive spatial spillover effect. This suggests that the growth of built-up land between neighboring cities is not independent but forms a mutually reinforcing relationship through spatial transmission. Also, different driving factors may have different impacts on the growth of built-up land in local and neighboring areas. Therefore, it is necessary to incorporate spatial effects into the study of built-up land expansion to reveal its underlying mechanisms.

  1. Funding information: The authors state no funding involved.

  2. Author contributions: Y.W. designed the study, and Z.H. and A.F.K. carried them out. Z.H. and A.F.K. collected the data and analyzed the results. S.Z. formatted the final manuscript.

  3. Conflict of interest: The authors state no conflict of interest.

  4. Data availability statement: The data utilized in this study can be obtained from China’s Statistical Yearbook, Geographic Information Bureau, and Resource and Environment Science and Data Centre.

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Received: 2023-12-29
Revised: 2024-01-24
Accepted: 2024-01-29
Published Online: 2024-04-24

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  65. Usability of PPGIS tools exemplified by geodiscussion – a tool for public participation in shaping public space
  66. Efficient development technology of Upper Paleozoic Lower Shihezi tight sandstone gas reservoir in northeastern Ordos Basin
  67. Assessment of soil resources of agricultural landscapes in Turkestan region of the Republic of Kazakhstan based on agrochemical indexes
  68. Evaluating the impact of DEM interpolation algorithms on relief index for soil resource management
  69. Petrogenetic relationship between plutonic and subvolcanic rocks in the Jurassic Shuikoushan complex, South China
  70. A novel workflow for shale lithology identification – A case study in the Gulong Depression, Songliao Basin, China
  71. Characteristics and main controlling factors of dolomite reservoirs in Fei-3 Member of Feixianguan Formation of Lower Triassic, Puguang area
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  75. Structural features and tectonic activity of the Weihe Fault, central China
  76. Application of the wavelet transform and Hilbert–Huang transform in stratigraphic sequence division of Jurassic Shaximiao Formation in Southwest Sichuan Basin
  77. Structural detachment influences the shale gas preservation in the Wufeng-Longmaxi Formation, Northern Guizhou Province
  78. Distribution law of Chang 7 Member tight oil in the western Ordos Basin based on geological, logging and numerical simulation techniques
  79. Evaluation of alteration in the geothermal province west of Cappadocia, Türkiye: Mineralogical, petrographical, geochemical, and remote sensing data
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  85. Hydrogeological mapping of fracture networks using earth observation data to improve rainfall–runoff modeling in arid mountains, Saudi Arabia
  86. Petrography and geochemistry of pegmatite and leucogranite of Ntega-Marangara area, Burundi, in relation to rare metal mineralisation
  87. Prediction of formation fracture pressure based on reinforcement learning and XGBoost
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  92. Sedimentary processes and patterns in deposits corresponding to freshwater lake-facies of hyperpycnal flow – An experimental study based on flume depositional simulations
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  97. Formation mechanism and oil-bearing properties of gravity flow sand body of Chang 63 sub-member of Yanchang Formation in Huaqing area, Ordos Basin
  98. Diagenesis of marine-continental transitional shale from the Upper Permian Longtan Formation in southern Sichuan Basin, China
  99. Vertical high-velocity structures and seismic activity in western Shandong Rise, China: Case study inspired by double-difference seismic tomography
  100. Spatial coupling relationship between metamorphic core complex and gold deposits: Constraints from geophysical electromagnetics
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  102. Research on spatial correlation structure of war heritage based on field theory. A case study of Jinzhai County, China
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  104. Magnetic data interpretation: Implication for structure and hydrocarbon potentiality at Delta Wadi Diit, Southeastern Egypt
  105. Deeply buried clastic rock diagenesis evolution mechanism of Dongdaohaizi sag in the center of Junggar fault basin, Northwest China
  106. Application of LS-RAPID to simulate the motion of two contrasting landslides triggered by earthquakes
  107. The new insight of tectonic setting in Sunda–Banda transition zone using tomography seismic. Case study: 7.1 M deep earthquake 29 August 2023
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  112. Study on the influence mechanism of land use on carbon storage under multiple scenarios: A case study of Wenzhou
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  117. Predicting stability factors for rotational failures in earth slopes and embankments using artificial intelligence techniques
  118. Origin of Late Cretaceous A-type granitoids in South China: Response to the rollback and retreat of the Paleo-Pacific plate
  119. Modification of dolomitization on reservoir spaces in reef–shoal complex: A case study of Permian Changxing Formation, Sichuan Basin, SW China
  120. Geological characteristics of the Daduhe gold belt, western Sichuan, China: Implications for exploration
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  122. Enhancing the total-field magnetic anomaly using the normalized source strength
  123. Shear wave velocity profiling of Riyadh City, Saudi Arabia, utilizing the multi-channel analysis of surface waves method
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  126. Detection of seepage zones in artificial levees: A case study at the Körös River, Hungary
  127. Tight sandstone fluid detection technology based on multi-wave seismic data
  128. Characteristics and control techniques of soft rock tunnel lining cracks in high geo-stress environments: Case study of Wushaoling tunnel group
  129. Influence of pore structure characteristics on the Permian Shan-1 reservoir in Longdong, Southwest Ordos Basin, China
  130. Study on sedimentary model of Shanxi Formation – Lower Shihezi Formation in Da 17 well area of Daniudi gas field, Ordos Basin
  131. Multi-scenario territorial spatial simulation and dynamic changes: A case study of Jilin Province in China from 1985 to 2030
  132. Review Articles
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  135. Communication
  136. Essential questions in earth and geosciences according to large language models
  137. Erratum
  138. Erratum to “Random forest and artificial neural network-based tsunami forests classification using data fusion of Sentinel-2 and Airbus Vision-1 satellites: A case study of Garhi Chandan, Pakistan”
  139. Special Issue: Natural Resources and Environmental Risks: Towards a Sustainable Future - Part I
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  141. Exploring environmental awareness, knowledge, and safety: A comparative study among students in Montenegro and North Macedonia
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  143. Application of remote sensing in monitoring land degradation: A case study of Stanari municipality (Bosnia and Herzegovina)
  144. Optimizing agricultural land use: A GIS-based assessment of suitability in the Sana River Basin, Bosnia and Herzegovina
  145. Assessing risk-prone areas in the Kratovska Reka catchment (North Macedonia) by integrating advanced geospatial analytics and flash flood potential index
  146. Analysis of the intensity of erosive processes and state of vegetation cover in the zone of influence of the Kolubara Mining Basin
  147. GIS-based spatial modeling of landslide susceptibility using BWM-LSI: A case study – city of Smederevo (Serbia)
  148. Geospatial modeling of wildfire susceptibility on a national scale in Montenegro: A comparative evaluation of F-AHP and FR methodologies
  149. Geosite assessment as the first step for the development of canyoning activities in North Montenegro
  150. Urban geoheritage and degradation risk assessment of the Sokograd fortress (Sokobanja, Eastern Serbia)
  151. Multi-hazard modeling of erosion and landslide susceptibility at the national scale in the example of North Macedonia
  152. Understanding seismic hazard resilience in Montenegro: A qualitative analysis of community preparedness and response capabilities
  153. Forest soil CO2 emission in Quercus robur level II monitoring site
  154. Characterization of glomalin proteins in soil: A potential indicator of erosion intensity
  155. Power of Terroir: Case study of Grašac at the Fruška Gora wine region (North Serbia)
  156. Special Issue: Geospatial and Environmental Dynamics - Part I
  157. Qualitative insights into cultural heritage protection in Serbia: Addressing legal and institutional gaps for disaster risk resilience
https://doi.org/10.1515/geo-2022-0609
Keywords for this article
land-use changes; driving factors; spatial autocorrelation; spatial Durbin model; Yangtze River Delta
Creative Commons
BY 4.0

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  86. Petrography and geochemistry of pegmatite and leucogranite of Ntega-Marangara area, Burundi, in relation to rare metal mineralisation
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  88. Hazard zonation for potential earthquake-induced landslide in the eastern East Kunlun fault zone
  89. Monitoring water infiltration in multiple layers of sandstone coal mining model with cracks using ERT
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  93. Study on time-dependent injectability evaluation of mudstone considering the self-healing effect
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  96. Spatiotemporal variation pattern and spatial coupling relationship between NDVI and LST in Mu Us Sandy Land
  97. Formation mechanism and oil-bearing properties of gravity flow sand body of Chang 63 sub-member of Yanchang Formation in Huaqing area, Ordos Basin
  98. Diagenesis of marine-continental transitional shale from the Upper Permian Longtan Formation in southern Sichuan Basin, China
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  103. Formation mechanisms of Qiaoba-Zhongdu Danxia landforms in southwestern Sichuan Province, China
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  106. Application of LS-RAPID to simulate the motion of two contrasting landslides triggered by earthquakes
  107. The new insight of tectonic setting in Sunda–Banda transition zone using tomography seismic. Case study: 7.1 M deep earthquake 29 August 2023
  108. The critical role of c and φ in ensuring stability: A study on rockfill dams
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  110. Extreme hydroclimatic events and response of vegetation in the eastern QTP since 10 ka
  111. Spatial–temporal effect of sea–land gradient on landscape pattern and ecological risk in the coastal zone: A case study of Dalian City
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  119. Modification of dolomitization on reservoir spaces in reef–shoal complex: A case study of Permian Changxing Formation, Sichuan Basin, SW China
  120. Geological characteristics of the Daduhe gold belt, western Sichuan, China: Implications for exploration
  121. Rock physics model for deep coal-bed methane reservoir based on equivalent medium theory: A case study of Carboniferous-Permian in Eastern Ordos Basin
  122. Enhancing the total-field magnetic anomaly using the normalized source strength
  123. Shear wave velocity profiling of Riyadh City, Saudi Arabia, utilizing the multi-channel analysis of surface waves method
  124. Effect of coal facies on pore structure heterogeneity of coal measures: Quantitative characterization and comparative study
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  126. Detection of seepage zones in artificial levees: A case study at the Körös River, Hungary
  127. Tight sandstone fluid detection technology based on multi-wave seismic data
  128. Characteristics and control techniques of soft rock tunnel lining cracks in high geo-stress environments: Case study of Wushaoling tunnel group
  129. Influence of pore structure characteristics on the Permian Shan-1 reservoir in Longdong, Southwest Ordos Basin, China
  130. Study on sedimentary model of Shanxi Formation – Lower Shihezi Formation in Da 17 well area of Daniudi gas field, Ordos Basin
  131. Multi-scenario territorial spatial simulation and dynamic changes: A case study of Jilin Province in China from 1985 to 2030
  132. Review Articles
  133. Major ascidian species with negative impacts on bivalve aquaculture: Current knowledge and future research aims
  134. Prediction and assessment of meteorological drought in southwest China using long short-term memory model
  135. Communication
  136. Essential questions in earth and geosciences according to large language models
  137. Erratum
  138. Erratum to “Random forest and artificial neural network-based tsunami forests classification using data fusion of Sentinel-2 and Airbus Vision-1 satellites: A case study of Garhi Chandan, Pakistan”
  139. Special Issue: Natural Resources and Environmental Risks: Towards a Sustainable Future - Part I
  140. Spatial-temporal and trend analysis of traffic accidents in AP Vojvodina (North Serbia)
  141. Exploring environmental awareness, knowledge, and safety: A comparative study among students in Montenegro and North Macedonia
  142. Determinants influencing tourists’ willingness to visit Türkiye – Impact of earthquake hazards on Serbian visitors’ preferences
  143. Application of remote sensing in monitoring land degradation: A case study of Stanari municipality (Bosnia and Herzegovina)
  144. Optimizing agricultural land use: A GIS-based assessment of suitability in the Sana River Basin, Bosnia and Herzegovina
  145. Assessing risk-prone areas in the Kratovska Reka catchment (North Macedonia) by integrating advanced geospatial analytics and flash flood potential index
  146. Analysis of the intensity of erosive processes and state of vegetation cover in the zone of influence of the Kolubara Mining Basin
  147. GIS-based spatial modeling of landslide susceptibility using BWM-LSI: A case study – city of Smederevo (Serbia)
  148. Geospatial modeling of wildfire susceptibility on a national scale in Montenegro: A comparative evaluation of F-AHP and FR methodologies
  149. Geosite assessment as the first step for the development of canyoning activities in North Montenegro
  150. Urban geoheritage and degradation risk assessment of the Sokograd fortress (Sokobanja, Eastern Serbia)
  151. Multi-hazard modeling of erosion and landslide susceptibility at the national scale in the example of North Macedonia
  152. Understanding seismic hazard resilience in Montenegro: A qualitative analysis of community preparedness and response capabilities
  153. Forest soil CO2 emission in Quercus robur level II monitoring site
  154. Characterization of glomalin proteins in soil: A potential indicator of erosion intensity
  155. Power of Terroir: Case study of Grašac at the Fruška Gora wine region (North Serbia)
  156. Special Issue: Geospatial and Environmental Dynamics - Part I
  157. Qualitative insights into cultural heritage protection in Serbia: Addressing legal and institutional gaps for disaster risk resilience
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