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Original research article, simulation and prediction of land use in urban agglomerations based on the plus model: a case study of the pearl river delta, china.

• 1 Institute of Data Science and Agricultural Economics, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
• 2 School of Public Administration, Guangzhou University, Guangzhou, China
• 3 Institute of Rural Revitalization, Guangzhou University, Guangzhou, China

The Pearl River Delta (PRD) is a highly urbanized region in China that faces significant challenges in land use management. These challenges include the decrease in agricultural and ecological land resulting from rapid urbanization, the effectiveness of government governance, and the trajectory of development, all of which warrant careful research examination. Moreover, existing studies on land use in the PRD predominantly rely on static historical analysis, lacking a multi-scenario simulation approach. This study examines land use in PRD using a Patch-Generating Simulation (PLUS), from 1985 to 2020 to address this gap. Three scenarios were established to simulate potential land use outcomes in the PRD by 2030: spontaneous change, cropland protection, and ecological protection. The findings reveal that cropland, forest, and impervious surfaces are the dominant land use types in the PRD. From 1985 to 2020, the proportion of cropland decreased from 37.63% to 27.40%, with most conversions occurring to impervious surfaces and forest land. The proportion of impervious surfaces increased from 1.81% to 12.57%, primarily from conversions of cropland, forest, and water bodies. Economic development, population growth, accessibility, climatic factors, and topographic conditions were shown to be the primary determinants of land use in the PRD. Modelling results suggest that under the spontaneous change scenario, cropland and ecological land decrease, while impervious surfaces expand significantly, threatening cropland preservation and ecological construction. However, under the cropland protection scenario, the conversion rate of cropland to other land types can be effectively controlled, contributing to efficient preservation. Under the ecological protection scenario, impervious infrastructure encroachment on ecological land can be mitigated, but cropland protection is limited. The study proposes cropland protection and ecological priority policies to optimize the structure of land use, enhance efficiency, and offer policy guidance for the efficient utilization of land resources and the preservation of the ecological environment in the PRD.

1 Introduction

The land is a key element in nature and the most basic natural resource and material basis for human survival and development ( Kozak et al., 2017 ; Wang et al., 2023a ). Human activities transform different attributes of land to meet the requirements of social development, especially in key urban agglomerations, where rapid urban development is seriously crowding out other land uses, threatening food security and ecological safety. This phenomenon has received considerable attention from researchers in various countries and regions. Urban agglomerations are focal areas for land use/land cover change. However, the patterns and processes of land use change in urban agglomerations differ significantly from those in non-urban areas ( Chen et al., 2020 ). With higher population densities and greater influence of economic and social factors, urban agglomerations experience more pronounced land use changes compared to non-urban regions ( Yu et al., 2019 ). Therefore, it is crucial to use satellite imagery, population census data, and statistical models to analyze the dynamic changes in land use within urban agglomerations. In particular, it is essential to unveil the primary drivers that shape land use change in these areas.

Pearl River Delta (PRD) is one of the most populous urban agglomerations in China, with relatively scarce resources, rapid economic development, and high ecological and environmental pressure ( Ouyang et al., 2021 ). In the context of rapid economic development and accelerated urbanisation, the PRD has experienced dramatic land use, leading to a series of problems such as a reduction in cropland area, ecosystem degradation, and biodiversity loss ( Jiao et al., 2019 ; Li et al., 2023 ). Therefore, researching land use and its impact mechanisms in the PRD has great theoretical significance and practical value.

In the 1990s, the International Geosphere-Biosphere Programme and the International Human Dimensions Programme on Global Environmental Change jointly proposed the land use programme, making land use a hot topic and a Frontier in the natural and social sciences ( Li et al., 2012 ). Previous studies have identified economic development, population growth and climate change as the main drivers of land use and explored their effects on natural ecosystems ( Cai et al., 2011 ; Liu et al., 2019 ; Himes et al., 2020 ). These studies have also revealed that land use can have hidden and adverse impacts on ecosystem services, such as biodiversity, climate regulation and hydrological regulation, leading to various ecological problems ( Yu et al., 2010 ; Li et al., 2017 ; Pellissier et al., 2017 ). To support land use planning and ecological protection, some studies have conducted multi-scenario simulations and analyses of land use under different conditions ( Liu et al., 2020 ; Cabral et al., 2021 ). For instance, Li et al. (2022) used land use images to examine land use change in the PRD, simulated land use under different scenarios and evaluated the corresponding ecosystem service values. This study can help us better understand the environmental consequences of land use choices and formulate more sustainable land use policies.

Model-based analysis methods are very effective for land use studies as they can provide information on the trends and changes in land use in terms of quantity and spatial patterns ( Jiao et al., 2019 ; Sun et al., 2022 ). However, different quantitative research methods are suitable for different conditions and problems, and they have some differences in the issues they address. Firstly, evaluation models of land use are mainly used to assess land use over a specific period. These models quantify the current situation and characteristics of land use, such as the distribution, area and spatial pattern of land use types, by collecting land use data and relevant indicators, such as the use/cover change transfer matrix, land use dynamics and so on ( Chang et al., 2022 ; Yang et al., 2022 ). Secondly, causal identification models of land use are used to infer the causes and mechanisms of land use change by analysing the correlation and influence of land use and drivers, such as system dynamics models and regression analysis ( He et al., 2022 ; Jiang et al., 2023 ). Thirdly, predictive models of land use are used to forecast future trends in land use change. These models are based on historical land use data, drivers and trend analysis, etc. , and they use mathematical-statistical models or machine learning algorithms to predict future land use changes, such as Markov chain models, cellular automata models and machine learning models ( Genga et al., 2017 ; Huang et al., 2022 ; Wang et al., 2023a ). However, it has been shown that these models can hardly combine the advantages of both quantitative and spatial pattern prediction ( Liang et al., 2021 ).

The model that integrates quantitative and spatial pattern prediction can reveal more information about the trend of land use change, thus providing more comprehensive guidance for total land use control and local governance and facilitating accurate land use management ( Jiao et al., 2019 ; Liu et al., 2023 ). The Patch-Generating Simulation (PLUS) is a raster-based CA model ( Liang et al., 2021 ), which can explore the drivers of land expansion and predict the patch-level evolution of land-use landscapes ( Zhang et al., 2022 ; Wang et al., 2023b ). This model has several advantages. Firstly, it can consider not only the quantity and spatial distribution of land use types but also the shape and size of land use patches, thus reflecting the spatial pattern of land use better. Secondly, it can analyse the interactions and competition between different land-use types in an integrated way by introducing multiple drivers and constraints, as well as adaptive inertia and competition mechanisms, thus simulating the processes and outcomes of land-use change more accurately. This model can help us better understand the impacts of land use on ecosystems and provide a scientific basis for sustainable land use and environmental protection.

The PRD is strategically positioned within China’s territorial spatial development pattern, and is facing the challenge of rapid urbanization and its consequent impact on land use changes, which poses a threat to its sustainable development. To comprehensively understand the evolving trends and devise effective strategies for regulating land use changes in urban agglomeration areas, this study employs a multi-perspective analysis and simulation approach based on the case of the PRD. Specifically, the PLUS model is utilized, which combines the quantitative predictive capabilities of Markov models with the spatial pattern predictive advantages of meta-cellular automata. This model enables an examination of the spatial and temporal dynamics of land use changes and their driving forces in the PRD from 1985 to 2020. It also sets up three scenarios of inertial development, Cropland protection and ecological priority to predict the land use situation in the PRD in 2030. The paper aims to answer the following questions: What are the trends and factors of land use change in the PRD? What are the future land use trends of urban agglomerations under different scenarios? The projections are intended to provide a reference for the optimal allocation of land resources and a rational land use structure in the PRD. This study applies the PLUS model to reveal the main factors affecting the land use change processes and outcomes in urban agglomerations, and provides decision support for optimizing the allocation and rational utilization of land resources in the Pearl River Delta region.

The remaining sections of the paper are structured as follows: Section 2 provides an overview of the study area and data sources. Section 3 outlines the research methodology employed in this study. Section 4 presents a detailed analysis of the obtained results. Section 5 engages in a comprehensive discussion of the findings. Finally, Section 7 draws conclusions based on the findings and contributions of this paper.

2 Study area and data sources

2.1 study area.

The PRD urban agglomeration is one of China’s most significant economic regions and ranks among the largest urban clusters globally ( Ouyang et al., 2021 ; Li et al., 2023 ). Characterized by a subtropical monsoonal climate, this area benefits from abundant rainfall and well-established water systems. With a robust industrial base, efficient transportation networks, high population density, and remarkable economic vitality, it holds a crucial strategic position as a Frontier of China’s reform and opening-up policies. As the economy continues to flourish, the land use patterns within the PRD have been undergoing rapid transformations. In comparison to other prominent urban agglomerations in China, such as the Beijing-Tianjin-Hebei region and the Yangtze River Delta, the PRD exhibits a greater diversity of land use types and faces more pronounced ecological and environmental pressures and challenges. Consequently, the land use changes occurring in this region bear significant implications for both China’s and the world’s socio-economic development and ecological conservation efforts ( Figure 1 ).

FIGURE 1 . Study area.

The PRD is located between 112°57′-114°48′E and 21°48′-23°56′N. It covers nine prefecture-level cities and is bordered by the mountains of northern Guangdong to the north, the South China Sea to the south, the border between Fujian, Guangdong and Jiangxi to the east and the Xijiang River basin to the west. The PRD is mainly a plain region, with plains accounting for 70% of the total area. Its topography is high in the north and low in the south, with a low centre and high sides. The western, northern and eastern parts are surrounded by hilly mountains, forming a natural barrier. The southern coastline of the PRD has many islands and is 1,059 km long.

2.2 Data sources and pre-processing

This paper uses land use data, socio-economic data and natural conditions data ( Table 1 ). The land use data are from the CLCD dataset, which was published by Yang Jie and Huang Xin of Wuhan University in Earth System Science Data ( Yang and Huang, 2021 ). This dataset has the advantage of providing 30 m annual land use classification results for 30 years, which is a higher temporal resolution than other products such as GLC_FCS30, Global30, AGLC2000_2015, FROM-GLC10, ESA10 and ESRI10. The dataset divides the land into nine categories: Cropland, Forest, Shrub, Grassland, Water, Sonw/Ice, Barren, Impervious and Wetland. The DEM data is from SRTM ( http://srtm.csi.cgiar.org/ ) and the slope data is derived from DEM. The data on soil type, GDP, population, annual mean temperature and annual precipitation are from the CAS Data Centre for Resource and Environmental Sciences ( https://www.resdc.cn/ ). The data on railways and other transport features are from OSM ( https://www.openstreetmap.org/ ).

TABLE 1 . Data information.

3 Research methodology

Our research methodology has two main parts ( Figure 2 ). The first part is a historical analysis of land use in PRD from 1985 to 2020. We use descriptive statistics, land use transfer matrix, and land use dynamic attitudes to analyse the spatial and temporal patterns of land use. The second part is a future simulation of land use using the PLUS model. The year 2030 marks the deadline for the United Nations Sustainable Development Goals, making it a crucial milestone that many studies focus on. With reference to relevant research ( Feng et al., 2020 ; Floreano and de Moraes, 2021 ; Rahnama, 2021 ), we examine the impact and driving effects of future variables on land use and compare three scenarios of possible land use change by 2030.

FIGURE 2 . Analytical framework.

3.1 Study area

3.1.1 land use transfer matrix.

The land use transfer matrix reflects the values and direction of transfer of land use types in a region over the study period. The calculation formula is as follows:

Where n is the number of land classes, i , j i , j = 1,2 , … , n are the land use type at the beginning and end of the land transfer in the study period respectively, S ij is the amount of area converted from i land use type to j land use type.

3.1.2 Land use dynamic attitudes

Land use dynamics quantifies the magnitude and rate of change occurring in each category ( Yang et al., 2022 ), facilitating the study of the overall change and structural dynamics of land use types, with the single land use dynamic attitude as follows:

Where K i denotes the single land use dynamic attitude of i land use type during the study period, U ai and U bi are the area at the beginning and end of the study period for i land use type, and T denotes the study period.

The expression for the integrated land use dynamic attitude is

Where S denotes the integrated land use dynamic attitude of i land use type during the study period, U ai and U bi are the area at the beginning and end of the study period for i land use type, and T denotes the study period.

3.2 Scenario setting

To investigate the land use changes in the PRD under different development objectives and concerning relevant studies ( Wang et al., 2023a ), this paper sets up three scenarios: the inertial development scenario, the cropland protection scenario and the ecological protection scenario. These scenarios are used to predict the spatial pattern of land use in the PRD in 2030. In the ecological protection scenario, the conversion of forest, shrubland, grassland and water to other land types is restricted. The cost of each scenario is shown in Table 2 .

TABLE 2 . Matrix of land use conversion costs by scenario.

4 Analysis of results

4.1 analysis of spatial and temporal patterns of land use in the prd, 4.1.1 analysis of spatial distribution characteristics of land use.

The PRD primarily encompasses a flat region situated in the lower reaches of the Pearl River. Its topography exhibits higher elevations in the north and lower elevations in the south, resulting in a concave shape with elevated sides. These geographical features significantly influence its land use characteristics (refer to Figure 3 ). As of 2020, the dominant land use types in the PRD are cropland (27.40%), forest (55.07%) and construction land (12.57%), while grassland, shrub and barren are less common. Urban built-up land is mainly concentrated along the Pearl River Estuary and the Pearl River, forming large and medium-sized cities such as Guangzhou, Shenzhen, Dongguan, Foshan and Zhongshan. The hilly and mountainous areas are the main areas of ecological space, with forests, water bodies and wetlands occupying most of the land. Forests are mainly distributed in the green ecological barrier areas such as Rangke Mountain-Dinghu Mountain in Zhaoqing, Gudou Mountain in Jiangmen and Xiangtou Mountain-Lufushan in Huizhou. Water bodies are mainly distributed in the coastal and inland lakes in the east. Wetlands are mainly distributed in the Pearl River estuary and coastal mudflats.

FIGURE 3 . Land use distribution in the PRD, 1985–2020. (A) 1985. (B) 1990. (C) 1995. (D) 2000. (E) 2005. (F) 2010. (G) 2015. (H) 2020.

The PRD has experienced significant changes in land use types. The area of cropland decreased from 37.63% in 1985 to 27.40% in 2020, a decrease of 10.23%. The area share of forest and shrub also decreased slightly, while the area share of grassland, water and barren decreased as well. The area share of impervious increased from 1.81% in 1985 to 12.57% in 2020, an increase of 10.76%, indicating the rapid urbanisation of the PRD, which leads to a reduction in the amount of cropland and natural habitats, which could have adverse impacts on the ecology ( Jiao et al., 2019 ).

4.1.2 Analysis of land use dynamics

Table 3 shows the changes in the various land use types over time. The area share of cropland decreases, while the area share of impervious increases, indicating the acceleration of urbanisation in the PRD. The area share of forest and shrub decreases slightly, while the area share of grassland, water and barren decreases as well. The integrated land use dynamics in the PRD increase overall, and the changes are relatively stable over time, which may indicate the diversification and balanced development of land use types in the PRD.

TABLE 3 . Dynamics Attitudes of land use in the PRD by period 1985–2020 (Unit: %).

4.1.3 Analysis of land use transfer

Table 4 shows the main trend of land use transfer in the PRD is the shift from cropland to urban building land and ecological space, reflecting the dual needs of urbanisation and ecological protection. The main features of land use transfer in the PRD by specific types are: Cropland has the largest reduction, with a decrease of about 8,039.44 km 2 and a transfer out of 39.13%, mainly to impervious and forest. This indicates that cropland has been used for urban and construction development or through natural succession in the past decades. Barren has the largest proportion of transfer out, with 96.82% of transfer in, mainly to water and impervious. Impervious has the smallest proportion of transfer out and the largest increase, with an increase of about 5,909.39 km 2 . This reflects the accelerated urbanisation and construction activity, with many cropland and other land use types being converted to impervious, leading to a decrease in the ecosystem service of grain production in this region ( Liu et al., 2019 ). Barren has the largest proportion of transfers in, with 97.82% of transfers in, mainly from cropland and water. The forest is the relatively stable type, with less change, mainly within the natural ecological space, reflecting the role of natural succession and anthropogenic disturbance.

TABLE 4 . PRD Land Use Type Transfer Matrix 1985–2020 (Unit: km 2 ).

4.2 Analysis of the drivers of land use in the PRD based on the PLUS model

Land use change is driven by various factors, including distance from tertiary roads, temperature, and economic development ( Figure 4 ). These drivers can affect the suitability of farmland for agricultural production, the growth and development of crops, and the shift in land use from farming to urbanization. Similarly, changes in forest areas may be attributed to population growth, topographic conditions, and soil types. Shrubland is also impacted by population growth, economic development, and urbanization. Grasslands, on the other hand, may experience a reduction in area due to population growth and changes in precipitation and temperature. Waters are influenced by topographic conditions and changes in temperature and precipitation, while barren areas are affected by accessibility, population growth, and transport and logistics development. Impervious areas are impacted by precipitation, temperature, and topography.

FIGURE 4 . Contribution analysis of driving factors. (A) Cropland. (B) Forest. (C) Shrub. (D) Grassland. (E) Water. (F) Barren. (G) Impervious.

4.3 Analysis of land use in the PRD under multiple scenario modelling

4.3.1 plus model accuracy validation.

To evaluate the performance of the PLUS model, we used the 2010 land use data to simulate the land use spatial distribution in 2020 and compared it with the actual land use data in 2020 ( Figure 4 ). We also sampled the actual land use data, constructed a confusion matrix between the simulated and actual images in 2020, and calculated the overall accuracy and Kappa coefficient. Figure 5 shows that the PLUS model simulation has a high degree of similarity with the actual distribution. However, due to the small area and sample size of Grassland and Barren in the study area, their simulation accuracy is relatively low. Nevertheless, the overall spatial simulation accuracy is high, with an overall accuracy of 0.902052 and a Kappa coefficient of 0.837767. These results indicate that the PLUS model meets the simulation requirements and is suitable for simulating the land use spatial distribution in the PRD.

FIGURE 5 . Spl comparison of simulated and actual land use in the PRD in 2020. (A) Acture land use. (B) Simulated land use.

4.3.2 Multi-scenario simulation analysis

We used the 2020 land use data of the study area as the initial value and applied the CARS module of the PLUS model to simulate the land use in 2030. The CARS module was based on the projected area results of the three scenarios, the land use cost transfer matrix and the neighbourhood weights. We obtained the simulation results of the 2030 land use ( Figure 6 ) and the projected area table ( Table 5 ) for the study area.

FIGURE 6 . Spatial distribution of land use simulations in the PRD under the three scenarios. (A) Spontaneous change. (B) Cropland protection. (C) Ecological protection.

TABLE 5 . Projected land use area under three scenarios for the PRD in 2030 (Unit: km 2 ).

Under the Spontaneous change scenario, the impervious area of the PRD increases by 560.68–7,423.35 km 2 in 2030, expanding from the urban centre to the periphery. Cropland decreases by 56.31 km 2 compared to 2020, mainly converting to impervious. This scenario poses a serious threat to cropland protection and ecological construction in the study area.

Under the Cropland protection scenario, the impervious area of the PRD increases by 0.65–6,863.32 km 2 in 2030, which is the lowest expansion among the three scenarios. This scenario effectively controls the growth of impervious areas, but reduces forest by 493.29 km 2 , shrub by 0.57 km 2 and grassland by 9.38 km 2 compared to 2020. This scenario also prevents the conversion of cropland to other land types, leading to efficient protection of cropland and food security.

Under the Ecological protection scenario, the forest area of the PRD increases by 62.37–30,121.79 km 2 in 2030; shrub increases by 2.06 km 2 ; grassland decreases by 7.88 km 2 , which is the smallest decrease among the three scenarios; cropland remains unchanged; water remains unchanged; and impervious increases slightly. This scenario reduces the occupation of ecological land by impervious infrastructure and limits the expansion of impervious areas to some extent, which is beneficial for ecological construction and economic development. However, this scenario does not protect cropland effectively, which may compromise food security in PRD.

The three scenarios reflect the different development orientations of the study area. The economic development scenario aims to accelerate the urbanisation and infrastructure construction in the PRD, resulting in a greater expansion and agglomeration of impervious areas than the other two scenarios. The cropland protection scenario aims to implement the cropland protection system and strengthen food security, resulting in a smaller reduction of cropland and a smaller loss of ecological land than the other two scenarios. It also controls the expansion of impervious areas effectively. The ecological protection scenario aims to consolidate and strengthen ecological protection and restoration efforts, resulting in a more compact distribution of ecological land than the other two scenarios. It also improves the encroachment of impervious areas on ecological land. In any scenario, impervious area expansion is inevitable due to urbanisation and socio-economic progress in the study area. It also shows that in the PRD region, more attention should be paid to the protection of ecological environment while developing the economy ( Li et al., 2017 ).

5 Discussion

Global land use change is confronted with multiple challenges and trends, including population growth, economic globalization, urbanization, agricultural expansion, forestry degradation, desertification, and land degradation, among others ( Long et al., 2021 ). These challenges and trends interact with each other, forming a complex land use system that requires comprehensive analysis and management. In order to achieve the Sustainable Development Goals (SDGs), it is necessary to coordinate land use policies and actions across different scales and sectors, striking a balance between the supply and demand of ecosystem services, protecting and restoring natural capital, and promoting social equity and economic benefits ( Zhou et al., 2022 ; Wei et al., 2023 ).

Human activities are the direct drivers of land use change, which are also constrained by natural factors and influenced by socio-economic factors ( Zhou et al., 2015 ; Huang et al., 2019 ; Msofe et al., 2019 ). The demand for a greener environment and the implementation of ecological protection policies are the major drivers for restoring regional ecosystems ( Wang et al., 2012 ; He et al., 2020 ; Qiu et al., 2022 ). The PRD is one of the most affluent and dynamic economic regions in China, as well as one of the most densely populated areas. Urbanisation in the PRD is driven by various factors such as economic development, policy support and social demand, leading to changes in land use/cover and ecology ( Liu et al., 2019 ; Zhou et al., 2019 ). This study analyses the historical changes in land use/cover in the PRD and explores their evolutionary process. It then selects a high-precision model to simulate future land use scenarios. The results of this study can provide decision support for land use planning in the PRD. Most of the converted cropland was transformed into the impervious area and a small part was transformed into a forest under the ‘Food for Green’ policy. The impervious area expanded along the existing built-up areas ( Jiao et al., 2019 ).

The future land use pattern of the PRD will be influenced by multiple factors, such as economic development, environmental protection and agricultural production. Economic growth drives urbanisation, which increases the demand for impervious areas and may reduce cropland, degrade the environment and threaten food security ( Jiao et al., 2019 ; Li et al., 2020 ). The PRD was historically an important food production region in China, with agriculture dominating the land use pattern until the 1980s. The rapid urbanisation and industrialisation of the PRD changed the cropland pattern significantly ( Jiao et al., 2019 ; Zhou et al., 2019 ). Cropland is an important source of food production, and its conservation and rational use are crucial for the sustainable development of agriculture in the PRD. The rapid urbanisation and industrialisation in the PRD also reduced and fragmented the ecological land, such as forest, shrub and grassland, causing ecosystem degradation, atmospheric pollution and water stress ( Li et al., 2020 ; Li et al., 2023 ). In the future, the PRD needs to optimise its industrial and energy structures, improve land use efficiency, control the scale of impervious areas, and protect cropland and ecological land.

The urban agglomeration represented by the PRD region faces the daunting task of striking a balance between urbanization and sustainability. This challenge arises from the continuous expansion of impervious areas, which leads to the reduction of cropland and ecological land. To promote sustainable development in urban agglomeration, the government needs to control the growth of impervious areas and protect cropland and ecological land, which are essential for a liveable and sustainable ecological environment. Therefore, it is necessary to conduct land use modelling and scenario analysis based on different development objectives (e.g., regional economy, agricultural production and ecological protection). This approach can simulate different future land use structures and provide multiple perspectives for regional planning decision-makers. Through this approach, we can better predict and assess the trends of land use change in urban agglomeration and provide feasible policy recommendations to balance the different objectives and ensure sustainable urban development in the future.

6 Conclusion

This paper analyses the spatial and temporal changes in land use in the PRD from 1985 to 2020 and uses the PLUS model to analyse the drivers of land use expansion for each land type, validate the land use simulation and project the land use under different scenarios for 2030. The main conclusions are.

(1) The main land use types in the PRD are cropland, forest and impervious area, followed by grassland, shrub and barren. The area of cropland decreases from 37.63% in 1985 to 27.40% in 2020, while the area of impervious area increases from 1.81% in 1985 to 12.57% in 2020. The proportion of cropland and natural landscape land decreases as the urbanisation level increases.

(2) The main trend of land use transfer in the PRD is the conversion of cropland to the impervious area and ecological space, reflecting the dual needs of urbanisation and ecological protection. Cropland is the type with the largest decrease, with a reduction of about 8,039.44 km 2 and a transfer out rate of 39.13%, mainly to impervious areas and forest. Impervious area is the type with the smallest transfer out rate and the largest increase, with an expansion of approximately 5,909.39 km 2 and a transfer in the rate of 86.11%, mainly from cropland, forest and water.

(3) According to the driving force analysis, economic development, population growth, accessibility, climatic factors, and topographical conditions are the main drivers of land use change in the PRD. For cropland, the main drivers of change include distance from tertiary roads, temperature, and gross regional product. Population growth, topography, and soil type are the main drivers of forest change, while population growth and gross regional product are the main drivers of shrubland change. Population numbers, precipitation, and temperature drive grassland change, while topography, temperature, and precipitation are the main drivers of water change. For barren land, distance to rail stations and airports and population growth are significant drivers, while precipitation, temperature, and topography affect impervious surfaces.

(4) The PLUS model simulation meets the requirements with an overall accuracy of 0.902052 and a Kappa coefficient of 0.837767. In the Spontaneous Change scenario, there is a significant decrease in cropland and ecological land, while impervious areas expand by 560.68 km 2 from the urban center to the periphery. This expansion poses a severe threat to cropland protection and ecological construction. The Cropland protection scenario prevents cropland conversion to other types, increasing cropland area by 504.87 km 2 compared to 2020 and ensuring food security efficiently. The Ecological protection scenario limits the occupation of ecological land by impervious infrastructure and other areas, restricting the expansion of impervious areas to some extent. The forest area increases by 62.37 km 2 , promoting ecological construction and economic development. However, this scenario may not effectively protect cropland, potentially compromising food security in PRD.

This study analyzes the historical land use in the PRD and uses the PLUS model to simulate future land use structures under different scenarios. The PLUS model integrates the differential impacts of road networks on land use changes and controllable conversion rates under different scenarios. The simulation results reveal the spatial patterns of regional land use changes under different scenarios and propose effective regulatory pathways. However, this study has certain limitations. Firstly, the parameter settings of the PLUS model are subjective. Furthermore, the transfer cost matrix and neighborhood weight values are adjusted based on the existing land use structure and previous research experience in the study area, which may introduce errors.

Future research should explore a more objective and quantitative method for parameterization. When assessing the impact of land use changes on the ecological environment, a multidimensional, multiscale, and multi-indicator approach should be adopted to comprehensively reflect the impact of land use changes on ecosystem services, biodiversity, carbon emissions, and other aspects. In formulating land use planning and management strategies, the impact of land use changes on regional sustainable development under different scenarios should be fully considered to balance the relationship between urbanization, agricultural production, and ecological protection, and achieve the optimal allocation and rational utilization of land resources.

Data availability statement

The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.

Author contributions

JG: Writing–original draft, Writing–review and editing. HD: Writing–review and editing. YS: Writing–original draft, Writing–review and editing. YZ: Writing–review and editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was funded by the Science and Technology Innovation Capacity Building Project of Beijing Academy of Agriculture and Forestry Sciences, grant number KJCX20230501; the Collaborative Innovation Platform Construction Project of Beijing Academy of Agriculture and Forestry Sciences, grant number KJCX201913; the Guangdong Provincial Philosophy and Social Science Planning Project, grant number GD23XGL067.

Acknowledgments

We thank the journal editor and reviewers.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

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Cabral, P., Campos, F. S., David, J., and Caser, U. (2021). Disentangling ecosystem services perception by stakeholders: an integrative assessment based on land cover. Ecol. Indic. 126, 107660. doi:10.1016/j.ecolind.2021.107660

CrossRef Full Text | Google Scholar

Cai, Y. P., Huang, G. H., Wang, X., Li, G. C., and Tan, Q. (2011). An inexact programming approach for supporting ecologically sustainable water supply with the consideration of uncertain water demand by ecosystems. Stoch. Environ. Res. Risk Assess. 25 (5), 721–735. doi:10.1007/s00477-011-0477-5

Chang, X. Q., Xing, Y. Q., Wang, J. Q., Yang, H., and Gong, W. S. (2022). Effects of land use and cover change (LUCC) on terrestrial carbon stocks in China between 2000 and 2018. Resour. Conservation Recycl. 182, 106333. doi:10.1016/j.resconrec.2022.106333

Chen, K. Q., Long, H. L., Liao, L. W., Tu, S. S., and Li, T. T. (2020). Land use transitions and urban-rural integrated development: theoretical framework and China's evidence. Land Use Policy 92, 104465. doi:10.1016/j.landusepol.2020.104465

Feng, Y. J., Chen, S. R., Tong, X. H., Lei, Z. K., Gao, C., and Wang, J. F. (2020). Modeling changes in China's 2000-2030 carbon stock caused by land use change. J. Clean. Prod. 252, 119659. doi:10.1016/j.jclepro.2019.119659

Floreano, I. X., and de Moraes, L. A. F. (2021). Land use/land cover (LULC) analysis (2009-2019) with google Earth engine and 2030 prediction using markov-CA in the rondonia state, Brazil. Environ. Monit. Assess. 193 (4), 239. doi:10.1007/s10661-021-09016-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Genga, B., Zhengb, X. Q., and Fua, M. C. (2017). Scenario analysis of sustainable intensive land use based on SD model. Sustain. Cities Soc. 29, 193–202. doi:10.1016/j.scs.2016.12.013

He, J., Shi, X. Y., Fu, Y. J., and Yuan, Y. (2020). Evaluation and simulation of the impact of land use change on ecosystem services trade-offs in ecological restoration areas, China. Land Use Policy 99, 105020. doi:10.1016/j.landusepol.2020.105020

He, S. T., Wang, D. J., Zhao, P., Chen, W. L., Li, Y., Chen, X. Q., et al. (2022). Dynamic simulation of debris flow waste-shoal land use based on an integrated system dynamics-geographic information systems model. Land Degrad. Dev. 33 (12), 2062–2075. doi:10.1002/ldr.4298

Himes, A., Puettmann, K., and Muraca, B. (2020). Trade-offs between ecosystem services along gradients of tree species diversity and values. Ecosyst. Serv. 44, 101133. doi:10.1016/j.ecoser.2020.101133

Huang, L. S., Wang, B., Niu, X., Gao, P., and Song, Q. F. (2019). Changes in ecosystem services and an analysis of driving factors for China's Natural Forest Conservation Program. Ecol. Evol. 9 (7), 3700–3716. doi:10.1002/ece3.4925

Huang, Z. H., Li, X. J., Du, H. Q., Mao, F. J., Han, N., Fan, W. L., et al. (2022). Simulating future LUCC by coupling climate change and human effects based on multi-phase remote sensing data. Remote Sens. 14 (7), 1698. doi:10.3390/rs14071698

Jiang, R. Y., Xie, C. K., Man, Z. H., Afshari, A., and Che, S. Q. (2023). LCZ method is more effective than traditional LUCC method in interpreting the relationship between urban landscape and atmospheric particles. Sci. Total Environ. 869, 161677. doi:10.1016/j.scitotenv.2023.161677

Jiao, M. Y., Hu, M. M., and Xia, B. C. (2019). Spatiotemporal dynamic simulation of land-use and landscape-pattern in the Pearl River Delta, China. Sustain. Cities Soc. 49, 101581. doi:10.1016/j.scs.2019.101581

Kozak, J., Gimmi, U., Houet, T., and Bolliger, J. (2017). Current practices and challenges for modelling past and future land use and land cover changes in mountainous regions. Reg. Environ. Change 17 (8), 2187–2191. doi:10.1007/s10113-017-1217-2

Li, B. W., Yang, Z. F., Cai, Y. P., Xie, Y. L., Guo, H. J., Wang, Y. Y., et al. (2022). Prediction and valuation of ecosystem service based on land use/land cover change: a case study of the Pearl River Delta. Ecol. Eng. 179, 106612. doi:10.1016/j.ecoleng.2022.106612

Li, L., Huang, X. J., Wu, D. F., and Yang, H. (2023). Construction of ecological security pattern adapting to future land use change in Pearl River Delta, China. Appl. Geogr. 154, 102946. doi:10.1016/j.apgeog.2023.102946

Li, Q., Zhang, X. F., Liu, Q. F., Liu, Y., Ding, Y., and Zhang, Q. (2017). Impact of land use intensity on ecosystem services: an example from the agro-pastoral ecotone of central inner Mongolia. Sustainability 9 (6), 1030. doi:10.3390/su9061030

Li, R. P., Guan, Q. F., and Merchant, J. (2012). A geospatial modeling framework for assessing biofuels-related land-use and land-cover change. Agric. Ecosyst. Environ. 161, 17–26. doi:10.1016/j.agee.2012.07.014

Li, Z. T., Li, M., and Xia, B. C. (2020). Spatio-temporal dynamics of ecological security pattern of the Pearl River Delta urban agglomeration based on LUCC simulation. Ecol. Indic. 114, 106319. doi:10.1016/j.ecolind.2020.106319

Liang, X., Guan, Q. F., Clarke, K. C., Liu, S. S., Wang, B. Y., and Yao, Y. (2021). Understanding the drivers of sustainable land expansion using a patch-generating land use simulation (PLUS) model: a case study in Wuhan, China. Comput. Environ. Urban Syst. 85, 101569. doi:10.1016/j.compenvurbsys.2020.101569

Liu, M. H., Chen, H. Y., Qi, L. I., and Chen, C. (2023). LUCC simulation based on RF-CNN-LSTM-CA model with high-quality seed selection iterative algorithm. Appl. Sci-Basel. 13 (6), 3407. doi:10.3390/app13063407

Liu, W., Zhan, J. Y., Zhao, F., Yan, H. M., Zhang, F., and Wei, X. Q. (2019). Impacts of urbanization-induced land-use changes on ecosystem services: a case study of the Pearl River Delta Metropolitan Region, China. Ecol. Indic. 98, 228–238. doi:10.1016/j.ecolind.2018.10.054

Liu, Y. B., Hou, X. Y., Li, X. W., Song, B. Y., and Wang, C. (2020). Assessing and predicting changes in ecosystem service values based on land use/cover change in the Bohai Rim coastal zone. Ecol. Indic. 56, 117, 106004. doi:10.1016/j.ecolind.2019.106004

Long, H. L., Zhang, Y. N., Ma, L., and Tu, S. S. (2021). Land use transitions: progress, challenges and prospects. Land 10 (9), 903. doi:10.3390/land10090903

Msofe, N. K., Sheng, L. X., and Lyimo, J. (2019). Land use change trends and their driving forces in the kilombero valley floodplain, southeastern Tanzania. Sustainability 11 (2), 505. doi:10.3390/su11020505

Ouyang, Y. R., Cai, Y. P., Xie, Y. L., Yue, W. C., and Guo, H. J. (2021). Multi-scale simulation and dynamic coordination evaluation of water-energy-food and economy for the Pearl River Delta city cluster in China. Ecol. Indic. 130, 108155. doi:10.1016/j.ecolind.2021.108155

Pellissier, V., Mimet, A., Fontaine, C., Svenning, J. C., and Couvet, D. (2017). Relative importance of the land-use composition and intensity for the bird community composition in anthropogenic landscapes. Ecol. Evol. 7 (24), 10513–10535. doi:10.1002/ece3.3534

Qiu, S. J., Peng, J., Zheng, H. N., Xu, Z. H., and Meersmans, J. (2022). How can massive ecological restoration programs interplay with social-ecological systems? A review of research in the South China karst region. Sci. Total Environ. 807, 150723. doi:10.1016/j.scitotenv.2021.150723

Rahnama, M. R. (2021). Forecasting land-use changes in Mashhad Metropolitan area using Cellular Automata and Markov chain model for 2016-2030. Sustain. Cities Soc. 64, 102548. doi:10.1016/j.scs.2020.102548

Sun, Y., Du, H. Y., Liu, B. Y., Kanchanaroek, Y., Zhang, J. F., and Zhang, P. (2022). Evolutionary game analysis for grassland degradation management, considering the livelihood differentiation of herders. Land 11 (10), 1776. doi:10.3390/land11101776

Wang, J., Chen, Y. Q., Shao, X. M., Zhang, Y. Y., and Cao, Y. G. (2012). Land-use changes and policy dimension driving forces in China: present, trend and future. Land Use Policy 29 (4), 737–749. doi:10.1016/j.landusepol.2011.11.010

Wang, J. B., Wu, Y. F., and Gou, A. P. (2023b). Habitat quality evolution characteristics and multi-scenario prediction in Shenzhen based on PLUS and InVEST models. Front. Environ. Sci. 11, 11. doi:10.3389/fenvs.2023.1146347

Wang, Q. Z., Guan, Q. Y., Sun, Y. F., Du, Q. Q., Xiao, X., Luo, H. P., et al. (2023a). Simulation of future land use/cover change (LUCC) in typical watersheds of arid regions under multiple scenarios. J. Environ. Manag. 335, 117543. doi:10.1016/j.jenvman.2023.117543

Wei, C. J., Meng, J. J., Zhu, L. K., and Han, Z. Y. (2023). Assessing progress towards sustainable development goals for Chinese urban land use: a new cloud model approach. J. Environ. Manag. 326, 116826. doi:10.1016/j.jenvman.2022.116826

Yang, H. F., Zhong, X. N., Deng, S. Q., and Nie, S. N. (2022). Impact of LUCC on landscape pattern in the Yangtze River basin during 2001-2019. Ecol. Inf. 69, 101631. doi:10.1016/j.ecoinf.2022.101631

Yang, J., and Huang, X. (2021). The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019. Earth Syst. Sci. Data 13 (8), 3907–3925. doi:10.5194/essd-13-3907-2021

Yu, G. M., Zeng, Q., Yang, S., Hu, L. M., Lin, X. W., Che, Y., et al. (2010). On the intensity and type transition of land use at the basin scale using RS/GIS: a case study of the Hanjiang River Basin. Environ. Monit. Assess. 160 (1-4), 169–179. doi:10.1007/s10661-008-0666-y

Yu, J. Q., Zhou, K. L., and Yang, S. L. (2019). Land use efficiency and influencing factors of urban agglomerations in China. Land Use Policy 88, 104143. doi:10.1016/j.landusepol.2019.104143

Zhang, J. B., Zhu, H. R., Zhang, P. Y., Song, Y. P., Zhang, Y., Li, Y. Y., et al. (2022). Construction of gi network based on mspa and PLUS model in the main urban area of zhengzhou: a case study. Front. Environ. Sci. 10, 10. doi:10.3389/fenvs.2022.878656

Zhou, M., Ma, Y. X., Tu, J. T., and Wang, M. C. (2022). SDG-oriented multi-scenario sustainable land-use simulation under the background of urban expansion. Environ. Sci. Pollut. Res. 29 (48), 72797–72818. doi:10.1007/s11356-022-20904-9

Zhou, R. B., Lin, M. Z., Gong, J. Z., and Wu, Z. (2019). Spatiotemporal heterogeneity and influencing mechanism of ecosystem services in the Pearl River Delta from the perspective of LUCC. J. Geogr. Sci. 29 (5), 831–845. doi:10.1007/s11442-019-1631-0

Zhou, S., Huang, Y. F., Yu, B. F., and Wang, G. Q. (2015). Effects of human activities on the eco-environment in the middle Heihe River Basin based on an extended environmental Kuznets curve model. Ecol. Eng. 76, 14–26. doi:10.1016/j.ecoleng.2014.04.020

Keywords: land use, PLUS model, multiple scenarios, policy simulation, the Pearl River Delta

Citation: Gong J, Du H, Sun Y and Zhan Y (2023) Simulation and prediction of land use in urban agglomerations based on the PLUS model: a case study of the Pearl River Delta, China. Front. Environ. Sci. 11:1306187. doi: 10.3389/fenvs.2023.1306187

Received: 03 October 2023; Accepted: 03 November 2023; Published: 16 November 2023.

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Copyright © 2023 Gong, Du, Sun and Zhan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Yong Sun, [email protected] ; Hongyan Du, [email protected]

This article is part of the Research Topic

Realization of Ecological Product Value, Land Use Change and Environment

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• Published: 15 November 2016

Industrial upgrade and economic governance in the Pearl River Delta—a case study of Dongguan city

• Zhifeng Wang 1 ,
• Xiaoming Xu 1 &
• Zhiqing Liang 1  

China Finance and Economic Review volume  4 , Article number:  17 ( 2016 ) Cite this article

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The Pearl River Delta, one of the main regions of China’s export-oriented economy, has benefited from its traditional economic structure for three decades which in turn appeared to hinder further economic development recently. To advance industrial restructuring and upgrading, the governments have promulgated policies and appropriated special funds to optimize the industrial structure of the Pearl River Delta. Dongguan is a famous export-oriented and manufacturing city in China. Taking Dongguan city as a case, and applying the method of Difference-in-Difference (DID), using time-series data from 1997 to 2014, this article analyzes the policy benefits of Dongguan’s industrial transformation and upgrading, aiming to not only examine the effectiveness of industrial upgrades and the effects of economic governance in the Pearl River Delta but provide some reference to other export-oriented regions of China.

Since the Reform and Opening up, China’s coastal areas represented by the Pearl River Delta focused on manufacturing activities. In the implementation of export-oriented economic policies, many positive advances have been achieved, including the development and structure of the economy, the level of openness, and the degree of foreign trade in these regions, which are important engines of economic growth in China. However, the economic model of high manufacturing dependence resulted in the industry’s low value-added, resource waste, and environmental pollution.

The economic structure of export-oriented regions has changed dramatically, both domestically and internationally. To meet the economic problems and challenges, the governments have promulgated policies and appropriated special funds to support the implementation of industrial restructuring and upgrading. The Guangdong provincial government made the strategy of “double transference” and a series of supporting industrial policies to promote industrial restructuring and implemented economic governance policies with industrial upgrading as the core. How did these government-led industrial policies’ effectiveness and whether the objective of optimizing industrial structure is achieved? These problems are a “hot topic” of current academic research.

Dongguan city, known as the “world factory,” which earlier faced the challenge of transforming economic governance and industry, is a typical representative of the Pearl River Delta. Research on Dongguan will help evaluate the effectiveness of industrial upgrading policy in the Pearl River Delta and will offer guidance to government policy making.

Literature review

Long-term dependence on an export-oriented economy and resource consumption makes industrial transformation imminent in the Pearl River Delta. In recent years, studies on industrial transformation and the evaluation of policy effectiveness in the Pearl River Delta and Dongguan have emerged and can be reviewed from the following aspects:

Research on industrial transformation of the Pearl River Delta and Dongguan

The existing research on the industrial transformation of the Pearl River Delta region can be divided into three levels: the first is the succession among three industries. For example, Zhao Lingling ( 2011 ) found that the proportion of the tertiary industry is gradually increasing, but the industrial structure distribution is not balanced in terms of each city. The second is the industrial restructuring inside the industry sector, such as an upgrade of manufacturing from the labor-intensive model to the technology-intensive one. Wu Hanxian ( 2011 ) explored the process of undertaking the labor-intensive manufacturing aspect of Zhanjiang city, Shanwei city, and Meizhou city, which are less-developed cities surrounding the Pearl River Delta region. Importantly, through the microlevel of enterprise industrial upgrading, Weigen and Ran ( 2010 ) researched the global value chain (GVC) model of three kinds of enterprises in the Pearl River Delta and their impact on industrial transformation and revealed the micro-mechanism of the transformation dilemma. Dongguan’s economy is oriented towards and developed by the market where the “Bottom to Up” model creates the economic miracle of Dongguan. Faced with the domestic and international pressures, Wu Xiaofeng ( 2007 ) thought that it was necessary for Dongguan to design a “Top to Down” model to sustain its economic development. From the view of changes in the organizational form and ownership structure, Lai Wenfeng ( 2007 ) stated that Taiwan-funded enterprises focus on Processing Industries and Compensation Trade in Dongguan has been existed as unincorporated entities, which resulted in interest conflicts between local government and enterprises. A lower dependence on the original path is necessary for Dongguan to smoothly advance and upgrade industrial transformation. Qiu Weinian and Tang Xiuxian ( 2011 ) divided Dongguan’s economic development process into four steps: initial stage, rapid development stage, promotion stage, and upgrade stage. Their research then explored various reasons for industrial transformation in Dongguan that gave careful consideration to the impact of complicated economic factors on the development path.

Policy evaluation of industrial policy and government governance

There are a number of branches of research on the evaluation of industrial policy within China and abroad. Melissa Liew and White ( 2007 ) studied how to use the Petri net model to empirically analyze industrial policy by using an algebraic method. Takatoshi Ito ( 1992 ) explored the role of industrial policy and structure in Japan and found that industrial policy can improve the production efficiency of manufacturing. Tybout ( 1992 ), through the empirical analysis of government policies, found that industrial policy is invalid and that governments’ intervention will hinder industrial development. More recent research by Zhiyong and Xuemei ( 2014 ) used Pearl River Delta panel data of the treatment group, and Yangtze River Delta, the control group, to analyze the net effect of “Double Transformation” strategy in the Pearl River Delta. This research showed that industrial policy has, to a certain degree, hindered its development. They also found that opening up markets are conducive to the development of secondary industries but not to overall industrial upgrade. Kang Lingxiang ( 2014 ) studied the responses of local enterprises to various industrial restructuring policies using a comparative analysis method and described the effect of the current industrial transformation policy. Compared with enterprises’ behavior without government intervention, the article explained the impact that government-led industrial transformation will bring on the enterprises.

All the abovementioned studies provide a basis for the research of upgraded industrial transformation and policy evaluation in the Pearl River Delta, especially in Dongguan. As a whole, this research is still in the initial stage. For example, the majority of the existing research is qualitative that is not deep enough to enable concrete recommendations, while the quantitative studies are inadequately short. In addition, the object in the existing industrial transformation research is relatively simple, limited to an economic region or a city, with the result being that the comparative research of two regions is relatively insufficient.

Through analyzing the evolution of industrial structure in Dongguan, this paper evaluates industrial policy, explores the economic structure and governance efficiency of the Pearl River Delta, and enriches the relevant research in the following areas: (1) makes a comprehensive analysis of the industrial transformation policy of Dongguan and its impact on the industrial structure of the region leading to a deeper understanding about the quality and quantity of the industry transformation; (2) uses the Difference-in-Difference (DID) model to quantitatively analyze the implementation effectiveness of the related industrial transformation policies in Dongguan that improves the existing research; and (3) contributes to further research in that most of the export-oriented regions of China are in a critical period of industrial upgrade, just like Dongguan.

Impact of industrial upgrade policy on its industrial structure in Dongguan

Many problems have emerged from the original industrial pattern due to today’s complex global economic situation, which appeared to have hindered further development of Dongguan city. In 2006, responding positively to the call of the industrial transformation of Guangdong Province, Dongguan’s municipal government implemented the double-transformation of economy and society industry strategy. In the Government Work Report, Dongguan’s municipal government underlined the adjustment and optimization of the economic structure, the utilization of high technology to drive the transformation of traditional industries, and the improvement of the competitiveness of traditional manufacturing, which guided a series of following supporting policies. The implementation of this industrial policy has brought significant impacts on Dongguan. Thus, Dongguan city has stepped into the stage of industrial transformation.

Dongguan has achieved a rapid increase of economic strength during the past three decades, from a single manufacturing-based industry structure to a diversified industry structure. In the early days of the reform and opening up, agriculture was Dongguan’s premier industry. It also accounted for almost half of the proportion. With the capital input from the “Processing Industries and Compensation Trade” and the Joint-Venture enterprises, the manufacturing industry has developed rapidly while the primary industry declines year by year. We can see from Fig.  1 that the second industry accounted for approximately 57.3% of GDP in Dongguan in 2006. Subsequently, as the industrial restructuring policies conducted, the proportion of the second industry began to decline, and in contrast, that of tertiary industry aggrandized gradually, exceeding the second industry’s share of production in 2012 by a more-than-half ratio. Since then, tertiary is the pillar industry in Dongguan.

Shares of three industries in GDP of Dongguan 1978–2013. Source: Dongguan Statistical Yearbook (2014)

Dongguan accelerated its industrialization since the “Processing Industries and Compensation Trade” policy. This, however, has evoked great controversy in the process of economic development due to the resulting reduced taxes from low value-added activities and unclear property rights caused by the absence of enterprises’ independent legal entities. In order to advance industrial restructuring, Dongguan’s municipal government encouraged transformation to the Joint-Venture enterprises. As can be seen from Fig.  2 , both “Processing Industries and Compensation Trade” enterprises and foreign-funded enterprises showed a rising trend in the actual utilization of foreign investment before 2004. In 2005, the amount of foreign capital used by the latter decreased significantly, influenced by the macro-economic environment while that of the former was relatively unchanged. But since 2006, as a consequence of government promotion, utilization of foreign capital in Dongguan city has changed greatly. Figure  2 shows the upward trend in foreign direct investment, mainly due to the rapid increase of the latter’s utilization of foreign capital despite the tremendous reduction of the processing industries and compensation trade. Therefore, processing industries and compensation trade have gradually been replaced by the Joint-Venture enterprises, which is the trend of the industrial transformation in Dongguan.

The actual utilization of foreign investment of Dongguan (ten thousand dollars) 1995–2013. Source: Dongguan Statistical Yearbook (2014)

Availability evaluation of the industrial upgrade policy of Dongguan

Industrial upgrading and transformation inevitably brings economic benefit changes. This paper uses the DID model to analyze the effect of related industrial restructuring policies since 2006. We regress the data from 1997 to 2014, collecting from Guangdong Statistical Yearbook, to analyze the policy benefits of Dongguan’s industrial transformation and upgrading. The primary goal of Dongguan’s industrial transformation is to optimize the traditional manufacturing industry. Therefore, this paper mainly analyzes the change of the secondary industry.

Measures of variables and explanations of data are as follows: (1) This paper uses PCET (per capita tax of above Designed Size Enterprises), which reflects the economic benefits of the enterprises and their contributions to the tax, to measure the effectiveness of industrial upgrading. (2) We use the growth rate of GDP (GDP_R) to measure the level of economic development that can affect the direction of industrial upgrading. (3) Due to the obvious interaction between the secondary and the tertiary industries, we use the added value of tertiary industry (ThirdZ) as a control variable. (4) The amount of foreign direct investment (FDI), a control variable, measures the extent of the openness of a region, which is crucial to industrial upgrading. (5) Total social fixed-asset investment has an influence on the manufacturability and productivity of the enterprises, considering its logarithm (LIFA) as a control variable.

Results and Discussion

Direction model.

This paper uses the advance coefficient of industrial structure ( E 2 ) to measure the advanced degree of the secondary industry’s growth with respect to overall economy’s system. Our formula is as follows:

We use α 2 to express the ratio of the secondary industry’s share in the GDP (present value) of each reporting period and that of base period (1997), use R t to show the average growth rate of the economic system in the same period of secondary industry, and we use n to represent the year number.

The year 2006 is the dividing line. The dummy variable, YEAR06, equals 0 from 1997 to 2005 and is 1 from 2006 to 2012. The formula of the model is then:

The regression results are shown in Table  1 . The significant level is relatively low, due to the small sample size, but we can still observe regularity.

The coefficient of YEAR06 is positive, being significant at the level of 5%, which indicates that the secondary industry has developed at a relatively high speed since the policies of industrial transformation were promulgated.

The coefficient of GDP is negative and that of GDP_R is positive, being significant at the level of 1%. These data indicate that the growth rate of GDP slows down with the present increase of GDP in Dongguan. The secondary industry has realized advanced development when GDP maintained rapid growth; the growth rate of GDP decreased, and at the meantime, the development of the secondary industry witnessed the relative sloth. ThirdZ is not significant at the level of 10%, which indicates that there is not a large correlation between the growth of the third industry and the relative growth rate of the secondary industry. A possibility may be that the slowdown of the relative speed of development of the second industry is likely to be the inevitable result of GDP growth, rather than government policy.

The coefficient of FDI is positive and significant at the level of 10%, which indicates that there is a positive correlation between foreign direct investment and the relative growth rate of the secondary industry. That is to say, opening up promotes the propulsion of industrial restructuring and upgrading. The coefficient of LIFA is positive, being significant at the level of 1%, which indicates that the total investment in fixed assets caused the secondary industry to develop rapidly.

Benefit model

It is the main goal for industrial transformation and upgrade to improve production, as well as management, profits, and taxes of the enterprise. Therefore, we select PCET as the dependent variable in the benefits model. The formula is as follows:

From Table  1 , it can be seen that the coefficient of YEAR06 is positive and it has a significance level of 5%. PECT has improved since the implementation of the industrial transformation policy, which indicates that there is a positive correlation between enterprise benefits and the promotion of industrial policies.

The coefficient of the GDP_R is negative but is not significant at the level of 10%, indicating that it has little relationship with the growth of PECT, which probably lies in the gradual maturity of the enterprise production (and other reasons) while its correlation with the regional GDP is weak.

The coefficient of LFDI is positive, being significant at the level of 5%, which shows that foreign direct investment and industrial enterprise efficiency are positively correlated. To some extent, it indicates that the autonomy of industrial enterprises in Dongguan is poor and that their dependence on export-oriented economy remains high.

The coefficient of LIFA is negative, being significant at the level of 10%, indicating that business efficiency reduces as the fixed-asset investment of society increases, which may be due to the lag of fixed-asset investment, meaning that a large amount of capital investment results in reduced profits. Another possibility may be that the government mainly assisted small- and medium-sized enterprises, which in recent years are not included in the range of PCET’s enterprises.

Availability evaluation of industrial upgrade policies of the Pearl River Delta

Based on our research into the policy evaluation of the industrial transformation and upgrade of Dongguan, this paper chooses other eight cities of the Pearl River Delta as the control group. Those cities are strongly similar to Dongguan in terms of location characteristics, development stage, and growth pattern, so we can explore the status of industrial transformation of those eight cities and make a comparison with that of Dongguan.

We can see from Table  2 , that different from Dongguan, the government-led industrial transformation in the Pearl River Delta does not have a positive enough effect in terms of direction and effectiveness, while the market-led one had an improved impact. The whole society’s fixed-asset investment makes the efficiency of industrial transformation in the Pearl River Delta increase but reduces it for Dongguan. This may be because in the process of industrial restructuring, the Pearl River Delta focused on improving the second industry investment, the independent R & D capability, and the product added-value of the second industry. While Dongguan was dedicated to the introduction of investment and to the vigorous development of the third industry (such as modern logistics, e-commerce, exhibition services, cultural and creative services, and other service industries). Whether it is in the Pearl River Delta or in Dongguan, the data reveal that the slowdown of the second industry is caused by a slowdown in GDP growth. The fixed-asset investment of society can make the relative growth rate of the second industry increase quickly, and the increase of foreign direct investment improves the efficiency of industrial transformation.

Conclusions

Comprehensive analysis shows the following: (1) Since 2006, the Dongguan municipal government-led industrial transformation has achieved certain effectiveness. In the support of various policies, the efficiency of industrial enterprises increased. However, the direction of the industrial upgrading is not significant. In contrast, industrial restructuring of the Pearl River Delta relied more on market-driven industrial transformation; (2) The slowdown of the relative speed of development of the second industry is likely to be the inevitable result of GDP growth, rather than government policy; (3) Whether it is in the Pearl River Delta or in Dongguan, the level of opening up can promote not only the development of the second industry but also industrial upgrading, which shows the Pearl River Delta still relies highly on foreign capital and technology to promote industrial restructuring. (4) Fixed-asset investment of society increased the industrial transformation benefit of the Pearl River Delta. But for Dongguan, the whole social fixed-asset investment accelerated the relative development of the second industry and decreased the efficiency of industrial transformation. The results from our analysis reveal that government-led industrial policy plays a role in the upgrading of industrial structure, but knowledge of its effectiveness will be further advanced with investigations that combine different policy objectives and the specific area, with the indispensable consideration of the important effect of market mechanisms on industrial transformation.

Hanxian W (2011) Study on the role of government in the industries transformation in Guangdong province. Dissertation Doctoral of South China University of Technology

Ito T (1991) The Japanese Economy. The Japanese Economy, vol 1. The MIT Press

Liew M, White LB (2007) Concurrent Temporal Planning Using Timed Petri Nets - Policy Evaluation. Australian Joint Conference on Advances in Artificial Intelligence 4830: 727–731. Springer-Verlag

Lingling Z (2011) Study on the industrial transformation of the Pearl River Delta. Academic Research Journal 8:71–75

Google Scholar  

Lingxiang K (2014) Research on the industrial policy of China's local government and local industrial transformation. Dissertation Doctoral of Capital University of Economics and Business

Tybout JR (1992) Linking trade and productivity: new research directions. World Bank Econ Rev 6(2):189–211.

Article   Google Scholar  

Weigen D, Ran W (2010) The relation between harnessing the global value chain and the transition of the export-type economic industries. Academic Research Journal 1:56–62

Weinian Q, Xiuxian T (2011) Research on the industry transformation and upgrading path in Dongguan city. Economic Research Guide 23:85–86

Wenfeng L (2007) Institutional analysis of Taiwan-funded enterprises in Dongguan. Zhujiang Economy 194(10):19–26.

Xiaofeng W (2007) Study on the transformation of economic development stages and patterns in Dongguan. The era of economic and Trade Affairs. 1Z:76–77

Zhiyong W, Xuemei C (2014) Policy availability of the industrial upgrade. Urban Development Studies 21(9):69–76

Download references

Acknowledgements

This paper is supported by National Natural Science Foundation, an innovation study of local governance in externally oriented regions: logic, route choice, and simulation of system dynamics (71373295); Central University of Finance and Economics Major Research Task of Fostering Project; and the Fundamental Research Funds for the Central Universities (14ZZD006) (NKZXA1406).

Authors’ contributions

ZW conceived the framework and ideas of the entire study and wrote the first and last main sections of this article. XX wrote the second and third main section. ZL wrote the fourth section. All authors read and approved the final manuscript.

Authors’ information

Zhifeng Wang is professor and doctoral supervisor of the School of Management Science and Engineering at Central University of Finance and Economics. His research areas include urban economy, public policy, and regional economics. He has been in charge of many national-level research projects.

Xiaoming Xu and Zhiqing Liang are master students of the School of Management Science and Engineering at Central University of Finance and Economics. Their research areas include urban economy and regional economics.

Competing interests

The authors declare that they have no competing interests.

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Wang, Z., Xu, X. & Liang, Z. Industrial upgrade and economic governance in the Pearl River Delta—a case study of Dongguan city. China Financ. and Econ. Rev. 4 , 17 (2016). https://doi.org/10.1186/s40589-016-0043-x

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Assessment of groundwater sustainable development considering geo-environment stability and ecological environment: a case study in the Pearl River Delta, China

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• Published: 22 October 2021
• Volume 29 , pages 18010–18035, ( 2022 )

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• Peng Huang 1 ,
• Chuanming Ma   ORCID: orcid.org/0000-0001-8233-8352 1 &
• Aiguo Zhou 1  

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Groundwater resources have an important impact on the geo-environment and ecological environment. The exploitation of groundwater resources may induce geo-environmental issues and has a negative impact on the ecological environment. The assessment of groundwater sustainable development can provide reasonable suggestions for the management of groundwater resources in coastal cities. In this study, an assessment method for groundwater sustainable development based on the resource supply function, geo-environment stability function, and ecological environment function was provided. Considering the groundwater quantity and quality; the vulnerability of karst collapse, land subsidence, and seawater intrusion; and the distribution of groundwater-dependent ecosystems (GDEs) and soil erosion, the groundwater in the Pearl River Delta was divided into concentrated groundwater supply area (21.97%) and decentralized groundwater supply area (48.22%), ecological protection area (20.77%), vulnerable geo-environment area (8.94%), and unsuitable to exploit groundwater area (0.10%). ROC curve and single-indicator sensitivity analysis were applied in the assessment of geo-environment vulnerability, and the results showed that the VW-AHP model effectively adjusted the weights of the indicators so that the assessment results were more in line with the actual situation in the Pearl River Delta, and the accuracy of the VW-AHP model was higher than that of the AHP model. This study provides a scientific basis for groundwater management in the Pearl River Delta and an example for the assessment of groundwater sustainable development in coastal cities.

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Achour Y, Boumezbeur A, Hadji R, Chouabbi A, Cavaleiro V, Bendaoud E (2017) Landslide susceptibility mapping using analytic hierarchy process and information value methods along a highway road section in Constantine. Algeria Arab J Geosci 10(8):194. https://doi.org/10.1007/s12517-017-2980-6

Article   Google Scholar  

Adiat KAN, Nawawi MNM, Abdullah K (2012) Assessing the accuracy of GIS-based elementary multi criteria decision analysis as a spatial prediction tool - a case of predicting potential zones of sustainable groundwater resources. J Hydrol 440:75–89. https://doi.org/10.1016/j.jhydrol.2012.03.028

Aghdam IN, Pradhan B, Panahi M (2017) Landslide susceptibility assessment using a novel hybrid model of statistical bivariate methods (FR and WOE) and adaptive neuro-fuzzy inference system (ANFIS) at southern Zagros Mountains in Iran. Environ Earth Sci 76(6):237. https://doi.org/10.1007/s12665-017-6558-0

Allafta H, Opp C, Patra S (2021) Identification of groundwater potential zones using remote sensing and GIS techniques: a case study of the Shatt Al-Arab Basin. Remote Sens 13(1):112. https://doi.org/10.3390/rs13010112

Alley WM, Reily TE, Franke OL (1999) Sustainability of ground-water resources U. S. Geological Survey Circular. United States Geological Survey, Denver , p 1186

Google Scholar  

Almouctar MAS, Wu YP, Kumar A, Zhao FB, Mambu KJ, Sadek M (2021) Spatiotemporal analysis of vegetation cover changes around surface water based on NDVI: a case study in Korama basin, Southern Zinder. Niger Applied Water Science 11(1):4. https://doi.org/10.1007/s13201-020-01332-x

Al-Zu’bi Y, Shatanawi M, Al-Jayoussi O, Al-Kharabsheh A (2002) Application of decision support system for sustainable management of water resources in the Azraq Basin – Jordan. Water International 27(4):532–541. https://doi.org/10.1080/02508060208687040

Amano H, Iwasaki Y (2020) Land cover classification by integrating NDVI time series and GIS data to evaluate water circulation in Aso Caldera, Japan. Int J Environ Res Public Health 17(18):6605. https://doi.org/10.3390/ijerph17186605

Amiri V, Nakhaei M, Lak R, Kholghi M (2016) Investigating the salinization and freshening processes of coastal groundwater resources in Urmia aquifer. NW Iran Environmental Monitoring and Assessment 188(4):233. https://doi.org/10.1007/s10661-016-5231-5

Arabameri A, Saha S, Roy J, Tiefenbacher JP, Cerda A, Biggs T, Pradhan B, Ngo PTT, Collins AL (2020) A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility. Science of Total Environment 726:138595. https://doi.org/10.1016/j.scitotenv.2020.138595

Article   CAS   Google Scholar  

Asefi H, Zhang Y, Lim S, Maghrebi M, Shahparvari S (2020) A multi-criteria decision support framework for municipal solid waste landfill siting: a case study of New South Wales (Australia). Environ Monit Assess 192(11):682. https://doi.org/10.1007/s10661-020-08565-y

Aslan V, Celik R (2021) Integrated GIS-based multi-criteria analysis for groundwater potential mapping in the Euphrates’s Sub-Basin, Harran Basin. Turkey Sustainability 13(13):7375. https://doi.org/10.3390/su13137375

Ayed B, Jmal I, Sahal S, Bouri S (2018) The seawater intrusion assessment in coastal aquifers using GALDIT method and groundwater quality index: the Djeffara of Medenine coastal aquifer (Southeastern Tunisia). Ababian Journal of Geosiences 11(20):609. https://doi.org/10.1007/s12517-018-3966-8

Bao JD, Zhang JD, Li F, Liu CY, Shi SP (2016) Social benefits of the mine occupational health and safety management systems of mines in China and Sweden based on a fuzzy analytic hierarchy process: a comparative study. Journal of Intelligent Fuzzy Systems 31(6):3113–3120. https://doi.org/10.3233/JIFS-169197

Bekesi G, Hodges S (2006) The protection of groundwater dependent ecosystems in Otago. New Zealand Hydrogeology Journal 14(8):1696–1701. https://doi.org/10.1007/s10040-006-0062-z

Besikci EB, Kececi T, Arslan O, Turan O (2016) An application of fuzzy-AHP to ship operational energy efficiency measures. Ocean Eng 121:392–402. https://doi.org/10.1016/j.oceaneng.2016.05.031

Bian JM, Liu CH, Zhang ZZ, Wang R, Gao Y (2016) Hydro geochemical characteristics and health risk evaluation of nitrate in groundwater. Pol J Environ Stud 25(2):521–527. https://doi.org/10.15244/pjoes/61113

Bordbar M, Neshat A, Javadi S (2019) Modification of the GALDIT framework using statistical and entropy models to assess coastal aquifer vulnerability. Hydrological Sciences Journal-Journal Des Sciences Hydrological 64(9):1117–1128. https://doi.org/10.1080/02626667.2019.1620951

Bordbar M, Neshat A, Javadi S, Pradhan B, Aghamohammadi H (2020) Meta-heuristic algorithms in optimizing GALDIT framework: a comparative study for coastal aquifer vulnerability assessment. J Hydrol 585:124768. https://doi.org/10.1016/j.jhydrol.2020.124768

Bui NT, Kawamura A, Bui DD, Amaguchi H, Bui DD, Truong NT, Nakagawa K (2018) Social sustainability assessment of groundwater resources: a case study of Hanoi. Vietnam Ecological Indicators 93:1034–1042. https://doi.org/10.1016/j.ecolind.2018.06.005

Bui NT, Kawamura A, Bui DD, Amaguchi H, Bui DD, Truong NT, Do HHT, Nguyen CT (2019) Groundwater sustainability assessment framework: a demonstration of environmental sustainability index for Hanoi. Vietnam Journal of Environmental Management 241:479–487. https://doi.org/10.1016/j.jenvman.2019.02.117

C. G. S., (1979) Synthetic hydrogeological map Republic of China. F-49 series scale 1:200,000. Guangzhou Geological Survey, Guangzhou (in Chinese)

C. G. S., (1981) Synthetic hydrogeological map Republic of China. F-50 series scale 1:200,000. Guangzhou Geological Survey, Guangzhou (in Chinese)

C. G. S., (2006) Technical requirements for the evaluation and zoning of groundwater function. China Geological Survey, Beijing

C. G. S., (2012) Handbook of hydrogeology. Geological Publishing House, Beijing (in Chinese)

Calligaris C, Devoto S, Galve JP, Zini L, Perez-Pena JV (2017) Integration of multi-criteria and nearest neighbour analysis with kernel density functions for improving sinkhole susceptibility models: the case study of Enemonzo (NE Italy). Int J Speleol 46(2):191–204. https://doi.org/10.5038/1827-806X.46.2.2099

Chang SW, Chung IM, Kim MG, Tolera M, Koh GW (2019) Application of GALDIT in assessing the seawater intrusion vulnerability of Jeju Island, South Korea. Water 11(9):1824. https://doi.org/10.3390/w11091824

Chen J (2012) Assessment of groundwater sustainability using multi-indicators in the Hohhot Plain. Chinese Academy of Geological Sciences, Beijing (in Chinese)

Chen J, Zhang YL, Chen ZY, Nie ZL (2015) Improving assessment of groundwater sustainability with analytic hierarchy process and information entropy method: a case study of the Hohhot Plain, China. Environmental Earth Sciences 72:2353–2363. https://doi.org/10.1007/s12665-014-3583-0

Chen W, Han HX, Huang B, Huang QL, Fu XD (2017) Variable-weighted linear combination model for landslide susceptibility mapping: case study in the Shennongjia Forestry District. China Isprs International Journal of Geo-Information 6(11):347. https://doi.org/10.3390/ijgi6110347

Chen T, Xia J, Zou L, Hong S (2020) Quantifying the influences of natural factors and human activities on NDVI changes in the Hanjiang River Basin. China Remote Sensing 12(22):3780. https://doi.org/10.3390/rs12223780

Cunliffe AM, Assmann JJ, Daskalova GN, Kerby JT, Myers-Smith IH (2020) Aboveground biomass corresponds strongly with drone-derived canopy height but weakly with greenness (NDVI) in a shrub tundra landscape. Environ Res Lett 15(12):125004. https://doi.org/10.1088/1748-9326/aba470

Daneshvar MRM (2014) Landslide susceptibility zonation using analytical hierarchy process and GIS for the Bojnurd region, northeast of Iran. Landslides 11(6):1079–1091. https://doi.org/10.1007/s10346-013-0458-5

Das S (2018) Geographic information system and AHP-based flood hazard zonation of Vaitarna basin, Maharashtra. India Arabian Journal of Geosciences 11(19):576. https://doi.org/10.1007/s12517-018-3933-4

Das Gupta A, Onta PR (1997) Sustainable groundwater resources development. Hydrol Sci J 42(4):565–582. https://doi.org/10.1080/02626669709492054

De Marinis P, Sali G (2020) Participatory analytic hierarchy process for resource allocation in agricultural development projects. Eval Program Plann 80:101793. https://doi.org/10.1016/j.evalprogplan.2020.101793

De Carvalho SCP, Carden KJ, Armitage NP (2009) Application of a sustainability index for integrated urban water management in Southern African cities: case study comparison - Maputo and Hermanus. Water SA 35(2):144–151

De Waele J, Gutierrez F, Parise M, Plan L (2011) Geomorphology and natural hazards in karst areas: a review. Geomorphology 134(1–2):1–8. https://doi.org/10.1016/j.geomorph.2011.08.001

Dilekoglu MF, Aslan V (2021) Determination of groundwater potential distribution of Ceylanpinar plain (Turkey) in Upper Mesopotamia by using geographical information techniques and Fuzzy-AHP with MCDM. Water Supply, Early Access. https://doi.org/10.2166/ws.2021.268

Ding N (2018) Evaluation of groundwater resources and analysis of exploitation potential in Chahar Youyiqianqi-Jining District. China University of Geosciences, Beijing ( (in Chinese) )

Domazatovic F, Sijeg A, Loncar N, Maric I (2019) Development of automated multicriteria GIS analysis of gully erosion susceptibility. Appl Geogr 112:102083. https://doi.org/10.1016/j.apgeog.2019.102083

Dong HG, Huang CS, Chen W, Zhang HX, Zhi BF, Zhao XW (2012) The controlling factors of environment geology in the Pearl River Delta economic zone and an analysis of existing problems. Geology in China 39(2):539–550 (in Chinese)

Drobinina E, Kovaleva T, Koriakina A (2020) The local variation of the overlying soils geotechnical properties in the karst susceptibility assessment. Carbonates Evaporites 35(3):79. https://doi.org/10.1007/s13146-020-00615-3

Du GL, Zhang YS, Yang ZH, Guo CB, Yao X, Sun DY (2019) Landslide susceptibility mapping in the region of eastern Himalayan syntaxis, Tibetan Plateau, China: a comparison between analytical hierarchy process information value and logistic regression-information value methods. Bull Eng Geol Env 78(6):4201–4215. https://doi.org/10.1007/s10064-018-1393-4

Gabrielsen P, Bosch P (2003) Environmental indicators: typology and use in reporting. E. E. A. Internal Working Paper. Copenhagen: European Environmental Agency

Ebrahimy H, Feizizadeh B, Salmani S, Azadi H (2020) A comparative study of land subsidence susceptibility mapping of Tasuj plane, Iran, using boosted regression tree, random forest and classification and regression tree methods. Environmental Earth Sciences 79(10):223. https://doi.org/10.1007/s12665-020-08953-0

Elmahdy SI, Marghany MM, Mohamed MM (2016) Application of a weighted spatial probability model in GIS to analyse landslides in Penang Island. Malaysia Geomatics Natural Hazards & Risk 7(1):345–359. https://doi.org/10.1080/19475705.2014.904825

Erostate M, Huneau F, Garel E, Ghiotti S, Vystavna Y, Garrido M, Pasqualini V (2020) Groundwater dependent ecosystems in coastal Mediterranean regions: characterization, challenges and management for their protection. Water Res 172:115461. https://doi.org/10.1016/j.watres.2019.115461

Fadhillah MF, Achmad AR, Lee CW (2020) Integration of InSAR time-series data and gis to assess land subsidence along subway lines in the Seoul Metropolitan Area. South Korea Remote Sensing 12(21):3505. https://doi.org/10.3390/rs12213505

Fidelibus MD, Gutierrez F, Spilotro G (2011) Human-induced hydrogeological changes and sinkholes in the coastal gypsum karst of Lesina Marina area (Foggia Province, Italy). Eng Geol 118(1–2):1–19. https://doi.org/10.1016/j.enggeo.2010.12.003

G. H. S. T., (1981) Regional hydrogeological survey report. Guangdong Hydrogeological Second Team, Guangzhou

Gao L, Ma CM, Wang QX, Zhou AG (2019) Sustainable use zoning of land resources considering ecological and geological problems in Pearl River Delta Economic Zone. China Scientific Reports 9:16052. https://doi.org/10.1038/s41598-019-52355-7

Gholami V, Yousefi Z, Rostami HZ (2010) Modeling of ground water salinity on the Caspian Southern Coasts. Water Resour Manage 24(7):1415–1424. https://doi.org/10.1007/s11269-009-9506-2

Gugulothu S, Dhakate R, Sreedhar K, Ramesh A, Saxena PR (2020) Geophysical and hydrochemical studies for sustainable development of groundwater resources in northwestern part of Telangana State. India Journal of Earth System Science 129(1):202. https://doi.org/10.1007/s12040-020-01452-7

Guo X, Ma CM, Hu XJ, Yan W (2019) Application of groundwater functional zoning to coastal groundwater management: a case study in the plain area of Weifang City. China Environmental Earth Sciences 78(16):525. https://doi.org/10.1007/s12665-019-8534-3

Gupta A, Castro-Fresno D, Lastra-Gonzalez P, Rodriguez-Hernandez J (2021) Selection of fibers to improve porous asphalt mixtures using multi-criteria analysis. Construction and Building Materials 266(Part A):121198. https://doi.org/10.1016/j.conbuildmat.2020.121198

Hakim WL, Achmad AR, Eom J, Lee CW (2020a) Land subsidence measurement of jakarta coastal area using time series interferometry with Sentinel-1 SAR data. J Coastal Res 102(sp1):75–81. https://doi.org/10.2112/SI102-010.1

Hakim WL, Achmad AR, Lee CW (2020b) Land subsidence susceptibility mapping in Jakarta using functional and meta-ensemble machine learning algorithm based on time-series InSAR data. Remote Sensing 12(21):3627. https://doi.org/10.3390/rs12213627

Hallal DD, Khelfi ME, Zahouani S, Benamghar A, Haddad O, Ammari A, Lobo-Ferreira JP (2019) Application of the GALDIT method combined with geostatistics at the Bouteldja aquifer (Algeria). Environmental Earth Sciences 78(1):22. https://doi.org/10.1007/s12665-018-8005-2

He W, Zhou J, Cheng H, Wang LY, Wei K, Wang WF, Li XH (2012) Validation of origins of tea samples using partial least squares analysis and Euclidean distance method with near-infrared spectroscopy data. Spectrochim Acta Part A Mol Biomol Spectrosc 86:399–404. https://doi.org/10.1016/j.saa.2011.10.056

He KQ, Jia YY, Wang B, Wang RL, Luo HL (2013) Comprehensive fuzzy evaluation model and evaluation of the karst collapse susceptibility in Zaozhuang Region. China Natural Hazards 68(2):613–629. https://doi.org/10.1007/s11069-013-0653-7

Hosseini SM, Parizi E, Ataie-Ashtiani B, Simmons CT (2019) Assessment of sustainable groundwater resources management using integrated environmental index: case studies across Iran. Sci Total Environ 676:792–810. https://doi.org/10.1016/j.scitotenv.2019.04.257

Hou JM, Su C, Wang WS (2008) AHP methodology for partner selection in collaborative design. International Symposium on Computer Science and Computational Technology 2:674–677. https://doi.org/10.1109/ISCSCT.2008.359

Hou QX, Sun JC, Jing JH, Liu CY, Zhang Y (2018) A regional scale investigation on groundwater arsenic in different types of aquifers in the Pearl River Delta. China Geofluids 2018:2471295. https://doi.org/10.1155/2018/3471295

Huang ZG, Zong YQ, Zhang WQ (2004) Coastal inundation due to sea level rise in the Pearl River Delta China. Natural Hazards 33(2):247–264. https://doi.org/10.1023/B:NHAZ.0000037038.18814.b0

Huang GX, Liu CY, Sun JC, Zhang M, Jing JH, Li LP (2018) A regional scale investigation on factors controlling the groundwater chemistry of various aquifers in a rapidly urbanized area: a case study of the Pearl River Delta. Sci Total Environ 625:510–518. https://doi.org/10.1016/j.scitotenv.2017.12.322

Huang F, Chunyu XZ, Zhang DR, Chen X, Ochoa CG (2020) A framework to assess the impact of ecological water conveyance on groundwater-dependent terrestrial ecosystems in arid inland river basins. Sci Total Environ 709:136155. https://doi.org/10.1016/j.scitotenv.2019.136155

I. H. E. G, (2010) Investigation and evaluation report on groundwater pollution in the Pearl River Delta. Shijiazhuang, Institute of Hydrogeology and Environmental Geology Chinese Academy of Geological Sciences (in Chinese)

Janssen R (1992) Multiobjective decision support for environmental management. Kluwer Academic Publishers, Dordrecht

Book   Google Scholar  

Javanbarg MB, Scawthorn C, Kiyono J, Shahbodaghkhan B (2012) Fuzzy AHP-based multicriteria decision making systems using particle swarm optimization. Expert Syst Appl 39(1):960–966. https://doi.org/10.1016/j.eswa.2011.07.095

Javidan N, Kavian A, Pourghasemi HR, Conoscenti C, Jafarian Z, Rodrigo-Comino J (2021) Evaluation of multi-hazard map produced using MaxEnt machine learning technique. Sci Rep 11(1):6496. https://doi.org/10.1038/s41598-021-85862-7

Jha MK, Chowdary VM, Chowdhury A (2010) Groundwater assessment in Salboni Block, West Bengal (India) using remote sensing, geographical information system and multi-criteria decision analysis techniques. Hydrogeolgy Journal 18(7):1713–1728. https://doi.org/10.1007/s10040-010-0631-z

Jhariya DC, Kumar T, Gobinath M, Diwan P, Kishore N (2016) Assessment of groundwater potential zone using remote sensing, GIS and multi criteria decision analysis techniques. J Geol Soc India 88(4):481–492. https://doi.org/10.1007/s12594-016-0511-9

Jia YF, Xi., B. D., Jiang, Y. H., Guo, H. M., Yang, Y., Lian, X. Y., Han, S. B., (2018) Distribution, formation and human-induced evolution of geogenic contaminated groundwater in China: a review. Sci Total Environ 643:967–993. https://doi.org/10.1016/j.scitotenv.2018.06.201

Jia C, Zhang YW, Han JM, Xu XQ (2017) Susceptibility area regionalization of land subsidence based on extenics theory. Cluster Computing-the Journal of Networks Software Tools and Applications 20(1):53–66. https://doi.org/10.1007/s10586-016-0720-4

Jia XY, O’Connor D, Hou DY, Jin YL, Li GH, Zheng CM, Ok YS, Tsang DCW, Lu J (2019) Groundwater depletion and contamination: spatial distribution of groundwater resources sustainability in China. Sci Total Environ 672:551–562. https://doi.org/10.1016/j.scitotenv.2019.03.457

Jiang XZ, Lei MT, Zhao HQ (2019) Review of the advanced monitoring technology of groundwater-air pressure (enclosed potentiometric) for karst collapse studies. Envrionmental Earth Sciences 78(24):701. https://doi.org/10.1007/s12665-019-8716-z

Jothibasu A, Anbazhagan S (2018) Hydrogeological assessment of the groundwater aquifers for sustainability state and development planning. Environmental Earth Sciences 77(3):88. https://doi.org/10.1007/s12665-017-7211-7

Kang MG, Lee GM (2011) Multicriteria evaluation of water resources sustainability in the context of watershed management. J Am Water Resour Assoc 47(4):813–827. https://doi.org/10.1111/j.1752-1688.2011.00559.x

Karamouz M, Teymori J, Rahimi R, Olyaei MA, Mohammadpour P (2018) The impact of artificial groundwater recharge on water resources sustainability. World Environmental and Water Resources Congress 92–102

Kazakis N, Spiliotis M, Voudouris K, Pliakas FK, Papadopoulos B (2018) A fuzzy multicriteria categorization of the GALDIT method to assess seawater intrusion vulnerability of coastal aquifers. Sci Total Environ 621:524–534. https://doi.org/10.1016/j.scitotenv.2017.11.235

Kazakis N, Busico G, Colombani N, Mastrocicco M, Pavlou A, Voudouris K (2019) GALDIT-SUSI a modified method to account for surface water bodies in the assessment of aquifer vulnerability to seawater intrusion. J Environ Manage 235:257–265. https://doi.org/10.1016/j.jenvman.2019.01.069

Kumar R, Anbalagan R (2015) Landslide susceptibility zonation in part of Tehri reservoir region using frequency ratio, fuzzy logic and GIS. J Earth Syst Sci 124(2):431–448. https://doi.org/10.1007/s12040-015-0536-2

Kumru M, Kumru Y (2014) Analytic hierarchy process application in selecting the mode of transport for a logistics company. J Adv Transp 48(8):947–999. https://doi.org/10.1002/atr.1240

Langat PK, Kumar L, Koech R (2020) GIS-based geomorphometric analysis for potential applications in reversing land and biosystem degradation. Environ Monit Assess 192(10):668. https://doi.org/10.1007/s10661-020-08640-4

Lee GKL, Chan EHW (2008) The analytic hierarchy process (AHP) approach for assessment of urban renewal proposals. Soc Indic Res 89(1):155–168. https://doi.org/10.1007/s11205-007-9228-x

Li WB, Fan XM, Huang FM, Chen W, Hong HY, Huang JS, Guo ZZ (2020a) Uncertainties analysis of collapse susceptibility prediction based on remote sensing and GIS: influences of different data-based models and connections between collapses and environmental factors. Remote Sensing 12(24):4134. https://doi.org/10.3390/rs12244134

Li X, Tang CY, Cao YJ, Li D (2020b) A multiple isotope (H, O, N, C and S) approach to elucidate the hydrochemical evolution of shallow groundwater in a rapidly urbanized area of the Pearl River Delta. China Science Og the Total Environment 724:137930. https://doi.org/10.1016/j.scitotenv.2020.137930

Li ZY, Wang YC, Olgun CG, Yang SQ, Jiao QL, Wang MT (2020c) Risk assessment of water inrush caused by karst cave in tunnels based on reliability and GA-BP neural network. Geomat Nat Haz Risk 11(1):1212–1232. https://doi.org/10.1080/19475705.2020.1785956

Lin CJ, Zhang M, Zhou ZQ, Li LP, Shi SS, Chen YX, Dai WJ (2020) A new quantitative method for risk assessment of water inrush in karst tunnels based on variable weight function and improved cloud model. Tunn Undergr Space Technol 95:103136. https://doi.org/10.1016/j.tust.2019.103136

Liu SL, Li WP (2019) Indicators sensitivity analysis for environmental engineering geological patterns caused by underground coal mining with integrating variable weight theory and improved matter-element extension model. Sci Total Environ 686:606–618. https://doi.org/10.1016/j.scitotenv.2019.04.393

Liu F, Huang GX, Sun JC, Jing JH, Zhang Y (2014) Distribution of arsenic in shallow aquifers of Guangzhou region, China: natural and anthropogenic impacts. Water Qual Res J Can 49(4):354–371. https://doi.org/10.2166/wqrjc.2014.014

Liu F, Sun JC, Wang J, Zhang Y (2017) Groundwater acidification in shallow aquifers in Pearl River Delta, China: distribution, factors, and effects. Geochem J 51(4):373–384. https://doi.org/10.2343/geochemj.2.0476

Liu BJ, Peng SH, Liao YY, Long WL (2018) The causes and impacts of water resources crises in the Pearl River Delta. J Clean Prod 177:413–425. https://doi.org/10.1016/j.jclepro.2017.12.203

Lucchese LV, de Oliveira GG, Pedrollo OC (2021) Investigation of the influence of nonoccurrence sampling on landslide susceptibility assessment using Artificial Neural Networks. CATENA 198:105067. https://doi.org/10.1016/j.catena.2020.105067

Luoma S, Okkonen J, Korkka-Niemi K (2017) Comparison of the AVI, modified SINTACS and GALDIT vulnerability methods under future climate-change scenarios for a shallow low-lying coastal aquifer in southern Finland. Hydrogeol J 25(1):203–222. https://doi.org/10.1007/s10040-016-1471-2

M. L. R., (2017a) Guidelines for the assessment of zones of groundwater overdraft (GB/T 34968–2017). Ministry of Water Resources of the People’s Republic of China, Beijing (in Chinese)

M. L. R., (2017b) Standard for groundwater quality (GB/T 14848–2017). Ministry of Land and Resources of the People’s Republic of China, Beijing (in Chinese)

M. W. R., (2005) Groundwater functional zoning outline. Ministry of Water Resources of the People’s Republic of China, Beijing (in Chinese)

Ma X (2018) Groundwater resources evaluation of the ZhenBeiPu area in Yinchuan of Ningxia. China University of Geosciences, Beijing (in Chinese)

Machiwal D, Rangi N, Sharma A (2015) Integrated knowledge- and data-driven approaches for groundwater potential zoning using GIS and multi-criteria decision making techniques on hard-rock terrain of Ahar catchment, Rajasthan. India Environmental Earth Sciences 73(4):1871–1892. https://doi.org/10.1007/s12665-014-3544-7

Mahrez B, Klebingat S, Houha B, Houria B (2018) GIS-based GALDIT method for vulnerability assessment to seawater intrusion of the Quaternary coastal Collo aquifer (NE-Algeria). Arab J Geosci 11(4):71. https://doi.org/10.1007/s12517-018-3400-2

Majidipour F, Bagher SMB, Taheri K, Fathollahi J, Missimer TM (2021) Index-based groundwater sustainability assessment in the socio-economic context: a case study in the Western Iran. Environ Manage 67(4):648–666. https://doi.org/10.1007/s00267-021-01424-7

Manca D, Brambilla S (2011) A methodology based on the Analytic Hierarchy Process for the quantitative assessment of emergency preparedness and response in road tunnels. Transp Policy 18(5):657–664. https://doi.org/10.1016/j.tranpol.2010.12.003

Mandal U, Sahoo S, Munusamy SB, Dhar A, Panda SN, Kar A, Mishra PK (2016) Delineation of groundwater potential zones of coastal groundwater basin using multi-criteria decision making technique. Water Resour Manage 30(12):4293–4310. https://doi.org/10.1007/s11269-016-1421-8

Moghaddam HK, Jafari F, Javadi S (2017) Vulnerability evaluation of a coastal aquifer via GALDIT model and comparison with DRASTIC index using quality parameters. Hydrological Sciences Journal-Journal Des Sciences Hydrologiques 62(1):137–146. https://doi.org/10.1080/02626667.2015.1080827

Mohammady M, Pourghasemi HR, Amiri M (2019) Land subsidence susceptibility assessment using random forest machine learning algorithm. Environmental Earth Sciences 78(16):503. https://doi.org/10.1007/s12665-019-8518-3

Mueller TG, Pusuluri NB, Mathias KK, Cornelius PL, Barnhisel RI, Shearer SA (2001) Map quality for ordinary kriging and inverse distance weighted interpolation. Soil Sci Soc Am J 68(6):2042–2047. https://doi.org/10.2136/sssaj2004.2042

Munch Z, Conrad J (2007) Remote sensing and GIS based determination of groundwater dependent ecosystems in the Western Cape. South Africa Hydrogeology Journal 15(1):19–28. https://doi.org/10.1007/s10040-006-0125-1

Myronidis D, Papageorgoiu C, Theophanous S (2016) Landslide susceptibility mapping based on landslide history and analytic hierarchy process (AHP). Nat Hazards 81(1):245–263. https://doi.org/10.1007/s11069-015-2075-1

Najafi Z, Pourghasemi HR, Ghanbarian G, Shamsi SRF (2020) Land-subsidence susceptibility zonation using remote sensing, GIS, and probability models in a Google Earth Engine platform. Environmental Earth Sciences 79(21):491. https://doi.org/10.1007/s12665-020-09238-2

Narimani R, Jun C, Shahzad S, Oh J, Park K (2021) Application of a novel hybrid method for flood susceptibility mapping with satellite images: a case study of Seoul. Korea Remote Sensing 13(14):2786. https://doi.org/10.3390/rs13142786

Nouri D, Sabour MR, GhanbarzadehLak M (2018) Industrial solid waste management through the application of multi-criteria decision-making analysis: a case study of Shamsabad industrial complexes. J Mater Cycles Waste Manage 20(1):43–58. https://doi.org/10.1007/s10163-016-0544-6

Oh HJ, Syifa M, Lee CW, Lee S (2019) Land subsidence susceptibility mapping using Bayesian, functional, and meta-ensemble machine learning models. Applied Sciences-Basel 9(6):1248. https://doi.org/10.3390/app9061248

Ozdemir A (2016) Sinkhole susceptibility mapping using logistic regression in KarapA +/- nar (Konya, Turkey). Bull Eng Geol Env 75(2):681–707. https://doi.org/10.1007/s10064-015-0778-x

Pamucar D, Stevic Z, Zavadskas EK (2018) Integration of interval rough AHP and interval rough MABAC methods for evaluating university web pages. Appl Soft Comput 67:141–163. https://doi.org/10.1016/j.asoc.2018.02.057

Panahi MR, Mousavi SM, Rahimzadegan M (2017) Delineation of groundwater potential zones using remote sensing, GIS, and AHP technique in Tehran-Karaj plain. Iran Environmental Earth Sciences 76(23):792. https://doi.org/10.1007/s12665-017-7126-3

Pandey VP, Shrestha S, Chapagain SK, Kazama F (2011) A framework for measuring groundwater sustainability. Environ Sci Policy 14(4):396–407. https://doi.org/10.1016/j.envsci.2011.03.008

Paz MC, Alcala FJ, Medeiros A, Martinez-Pagan P, Perez-Cuevas J, Ribeiro L (2020) Integrated MASW and ERT imaging for geological definition of an unconfined alluvial aquifer sustaining a coastal groundwater-dependent ecosystem in Southwest Portugal. Applied Sciences-Basel 10(17):5905. https://doi.org/10.3390/app10175905

Peng J, Peng FL (2018) A GIS-based evaluation method of underground space resources for urban spatial planning: part 1 methodology. Tunn Undergr Space Technol 74:82–95. https://doi.org/10.1016/j.tust.2018.01.002

Pires A, Morato J, Peixoto H, Botero V, Zuluaga L, Figueroa A (2017) Sustainability assessment of indicators for integrated water resources management. Sci Total Environ 578:139–147. https://doi.org/10.1016/j.scitotenv.2016.10.217

Podvezko V (2009) Application of AHP technique. J Bus Econ Manag 10(2):181–189. https://doi.org/10.3846/1611-1699.2009.10.181-189

Pournader M, Ahmadi H, Feiznia S, Karimi H, Peirovan HR (2018) Spatial prediction of soil erosion susceptibility: an evaluation of the maximum entropy model. Earth Sci Inf 11(3):389–401. https://doi.org/10.1007/s12145-018-0338-6

Quan HC, Lee BG (2012) GIS-based landslide susceptibility mapping using analytic hierarchy process and artificial neural network in Jeju (Korea). KSCE J Civ Eng 16(7):1258–1266. https://doi.org/10.1007/s12205-012-1242-0

Rachid G, Alameddine I, Abou Najm M, Qian S, El-Fadel M (2021) Dynamic Bayesian networks to assess anthropogenic and climatic drivers of saltwater intrusion: a decision support tool toward improved management. Intergrated Environmental Assessment and Management 17(1):202–220. https://doi.org/10.1002/ieam.4355

Raghavendra SN, Deka PC (2015) Sustainable development and management of groundwater resources in mining affected areas: a review. Procedia Earth and Planetary Science 11:598–604. https://doi.org/10.1016/j.proeps.2015.06.061

Rahmati O, Falah F, Naghibi S, A., Biggs, T., Soltani, M., Deo, R. C, Cerda, A., Mohammadi, F., Bui D. T., (2019) Land subsidence modelling using tree-based machine learning algorithms. Science of Total Environment 672:239–252. https://doi.org/10.1016/j.scitotenv.2019.03.496

Recinos N, Kallioras A, Pliakas F, Schuth C (2015) Application of GALDIT index to assess the intrinsic vulnerability to seawater intrusion of coastal granular aquifers. Environmetal Earth Sciences 73(3):1017–1032. https://doi.org/10.1007/s12665-014-3452-x

Reddy BP, Kelly MP, Thokala P, Walters SJ, Duenas A (2014) Prioritising public health guidance topics in the National Institute for Health and Care Excellence using the Analytic Hierarchy Process. Public Health 128(10):896–903. https://doi.org/10.1016/j.puhe.2014.07.003

Roy J, Saha S (2021) Integration of artificial intelligence with meta classifiers for the gully erosion susceptibility assessment in Hinglo river basin. Eastern India Advances in Space Research 67(1):316–333. https://doi.org/10.1016/j.asr.2020.10.013

Saaty TL (1980) The analytic hierarchy process: planning, priority setting, resource allocation. McGraw-Hill, New York

Salles LD, Galvao P, Bastos LRB, Pereira RGFD, da Purificacao CGC, Laureano FV (2018) Evaluation of susceptibility for terrain collapse and subsidence in karst areas, municipality of Iraquara, Chapada Diamantina (BA). Brazil Environmental Earth Sciences 77(16):593. https://doi.org/10.1007/s12665-018-7769-8

Sallwey J, Valverde JPB, Lopez FV, Junghanns R, Stefan C (2019) Suitability maps for managed aquifer recharge: a review of multi-criteria decision analysis studies. Environmental Review 27(2):138–150. https://doi.org/10.1139/er-2018-0069

Sangrat W, Thanapongtharm W, Poolkhet C (2020) Identification of risk areas for foot and mouth disease in Thailand using a geographic information system-based multi-criteria decision analysis. Prev Vet Med 185:105183. https://doi.org/10.1016/j.prevetmed.2020.105183

Seenipandi K, Nainarpandian C, Kandathil RK, Sellamuthu S (2019) Seawater intrusion vulnerability in the coastal aquifers of southern India-an appraisal of the GALDIT model, parameters’ sensitivity, and hydrochemical indicators. Environ Sci Pollut Res 26(10):9755–9784. https://doi.org/10.1007/s11356-019-04401-0

Senouci R, Taibi NE, Teodoro AC, Duarte L, Mansour H, Meddah RY (2021) GIS-Based Expert Knowledge for Landslide Susceptibility Mapping (LSM): Case of Mostaganem Coast District. West of Algeria Sustainability 13(2):630. https://doi.org/10.3390/su13020630

Sholagberu AT, Ul Mustafa MR, Yusof KW, Hashim AM, Shah MM, Khan MWA, Isa MH (2019) Multivariate logistic regression model for soil erosion susceptibility assessment under static and dynamic causative factors. Polish Journal of Environmental Studies 28(5):3419–3429. https://doi.org/10.15244/pjoes/91943

Shu BR, Bakker MM, Zhang HH, Li YL, Qin W, Carsjens GJ (2017) Modeling urban expansion by using variable weights logistic cellular automata: a case study of Nanjing, China. Int J Geogr Inf Sci 31(7):1314–1333. https://doi.org/10.1080/13658816.2017.1283505

Singh AP, Bhakaru P (2021) Development of groundwater sustainability index: a case study of western arid region of Rajasthan. India Environment Development and Sustainability 23(2):1844–1868. https://doi.org/10.1007/s10668-020-00654-9

Singh LK, Jha MK, Chowdary VM (2018) Assessing the accuracy of GIS-based Multi-Criteria Decision Analysis approaches for mapping groundwater potential. Ecol Ind 91:24–37. https://doi.org/10.1016/j.ecolind.2018.03.070

Slama T, Sebei A (2020) Spatial and temporal analysis of shallow groundwater quality using GIS, Grombalia aquifer, Northern Tunisia. J Afr Earth Sc 170:103915. https://doi.org/10.1016/j.jafrearsci.2020.103915

Soyaslan II (2020) Assessment of groundwater vulnerability using modified DRASTIC-Analytical Hierarchy Process model in Bucak Basin. Turkey Arabian Journal of Geosciences 13(21):1127. https://doi.org/10.1007/s12517-020-06101-3

Steele K, Carmel Y, Cross J, Wilcox C (2009) Uses and misuses of multicriteria decision analysis (MCDA) in environmental decision making. Risk Anal 29(1):26–33. https://doi.org/10.1111/j.1539-6924.2008.01130.x

Sullivan C (2002) Calculating a water poverty index. World Dev 30(7):1195–1210. https://doi.org/10.1016/S0305-750X(02)00035-9

Sun LW, Ma CM, Li YG (2019) Multiple geo-environmental hazards susceptibility assessment: a case study in Luoning County, Henan Province. China Geomatics Natural Hazards & Risk 10(1):2009–2029. https://doi.org/10.1080/19475705.2019.1658648

Tafreshi GM, Nakhaei M, Lak R (2020) A GIS-based comparative study of hybrid fuzzy-gene expression programming and hybrid fuzzy-artificial neural network for land subsidence susceptibility modeling. Stoch Env Res Risk Assess 34(7):1059–1087. https://doi.org/10.1007/s00477-020-01810-3

Taheri K, Gutierrez F, Mohseni H, Raeisi E, Taheri M (2015) Sinkhole susceptibility mapping using the analytical hierarchy process (AHP) and magnitude-frequency relationships: A case study in Hamadan province. Iran Geomorphology 234:64–79. https://doi.org/10.1016/j.geomorph.2015.01.005

Taheri K, Shahabi H, Chapi K, Shirzadi A, Gutierrez F, Khosravi K (2019) Sinkhole susceptibility mapping: a comparison between Bayes-based machine learning algorithms. Land Degradation Development 30(7):730–745. https://doi.org/10.1002/ldr.3255

Tangestani MH (2004) Landslide susceptibility mapping using the fuzzy gamma approach in a GIS, Kakan catchment area, southwest Iran. Aust J Earth Sci 51(3):439–450. https://doi.org/10.1111/j.1400-0952.2004.01068.x

Trabelsi N, Triki I, Hentati I, Zairi M (2016) Aquifer vulnerability and seawater intrusion risk using GALDIT, GQI(SWI) and GIS: case of a coastal aquifer in Tunisia. Envrionmental Earth Sciences 75(8):669. https://doi.org/10.1007/s12665-016-5459-y

Turk A, Ozkok M (2020) Shipyard location selection based on fuzzy AHP and TOPSIS. Journal of Intelligent & Fuzzy Systems 39(3):4557–4576. https://doi.org/10.3233/JIFS-200522

U. S. G. S., (2020) Land subsidence in the United States. United States Geological Survey, Reston

Unver S, Ergenc I (2021) Safety risk identification and prioritize of forest logging activities using analytic hierarchy process (AHP). Alex Eng J 60(1):1591–1599. https://doi.org/10.1016/j.aej.2020.11.012

Vahidnia MH, Alesheikh AA, Alimohammadi A (2009) Hospital site selection using fuzzy AHP and its derivatives. J Environ Manage 90(10):3048–3056. https://doi.org/10.1016/j.jenvman.2009.04.010

Vaidya OS, Kumar S (2006) Analytic hierarchy process: an overview of applications. European Journal of Opreational Research 169(1):1–29. https://doi.org/10.1016/j.ejor.2004.04.028

Veerappan R, Negi A, Siddan A (2017) Landslide susceptibility mapping and comparison using frequency ratio and analytical hierarchy process in part of NH-58, Uttarakhand, India. Advancing Culture of Living with Landslides. Advances in Landslide Science 2:1081–1091. https://doi.org/10.1007/978-3-319-53498-5_123

Vrba J, Lipponen A (2007) Groundwater resources sustainability indicators, IHP-VI, Series on Groundwater NO. 14. U. N. E. S. C. O., I. A. E. A., I. A. H.

W. C. G. S., (2015) Evaluation report on the resources and environment carrying capacity of the Pearl River Delta. Wuhan Center of Geological Survey, Wuhan (in Chinese)

Wada CA, Pongkijyorasin S, Burnett KM (2020) Mountain-to-sea ecological-resource management: Forested watersheds, coastal aquifers, and groundwater dependent ecosystems. Resource and Energy Economics 59:101146. https://doi.org/10.1016/j.reseneeco.2019.101146

Wang P (1985) Fuzzy set and random set shadow. Beijing Normal University Press, Beijing (in Chinese)

Wang Y, Jiao JJ (2012) Origin of groundwater salinity and hydrogeochemical processes in the confined Quaternary aquifer of the Pearl River Delta, China. J Hydrol 438:112–124. https://doi.org/10.1016/j.jhydrol.2012.03.008

Wang Y, Jiao JJ, Zhang K, Zhou YZ (2016) Enrichment and mechanisms of heavy metal mobility in a coastal quaternary groundwater system of the Pearl River Delta, China. Sci Total Environ 545:493–502. https://doi.org/10.1016/j.scitotenv.2015.12.019

Wang JS, Diao MZ, Yue KH (2017) Optimization on pinch point temperature difference of ORC system based on AHP-entropy method. Energy 141:97–107. https://doi.org/10.1016/j.energy.2017.09.052

Wang H, Jin YN, Tan X (2020) Study on sustainable development of the transnational power grid interconnection projects under diversified risks based on variable weight theory and Bayesian network. Math Probl Eng 2020:5361561. https://doi.org/10.1155/2020/5361561

Wei X, Wu CY (2011) Holocene delta evolution and sequence stratigraphy of the Pearl River Delta in South China. Science China-Earth Sciences 54(10):1523–1541. https://doi.org/10.1007/s11430-011-4238-6

Wei AH, Li D, Zhou YH, Deng Q, Yan LD (2021) A novel combination approach for karst collapse susceptibility assessment using the analytic hierarchy process, catastrophe, and entropy model. Nat Hazards 105(1):405–430. https://doi.org/10.1007/s11069-020-04317-w

Wu CS, Yang SL, Huang SC, Mu JB (2016) Delta changes in the Pearl River estuary and its response to human activities (1954–2008). Quatern Int 392:147–154. https://doi.org/10.1016/j.quaint.2015.04.009

Wu Q, Zhao DK, Wang Y, Shen JJ, Mu WP, Liu HL (2017) Method for assessing coal-floor water-inrush risk based on the variable-weight model and unascertained measure theory. Hydrologeology Journal 25(7):2089–2103. https://doi.org/10.1007/s10040-017-1614-0

Wu YB, Jiang XZ, Guan ZD, Luo WQ, Wang YL (2018) AHP-based evaluation of the karst collapse susceptibility in Tailai Basin, Shandong Province. China Environmental Earth Sciences 77(12):436. https://doi.org/10.1007/s12665-018-7609-x

Wu XJ, Zhang L, Hu BX, Wang Y, Xu ZY (2020) Isotopic and hydrochemical evidence for the salinity origin in the coastal aquifers of the Pearl River Delta, Guangzhou. China Journal of Contaminant Hydrology 235:103732. https://doi.org/10.1016/j.jconhyd.2020.103732

Xu W, Su XS (2019) Challenges and impacts of climate change and human activities on groundwater-dependent ecosystems in arid areas - a case study of the Nalenggele alluvial fan in NW China. J Hydrol 573:376–385. https://doi.org/10.1016/j.jhydrol.2019.03.082

Yadegaridehkordi E, Nasir MHNB, Noor NFBM, Shuib L, Badie N (2018) Predicting the adoption of cloud-based technology using fuzzy analytic hierarchy process and structural equation modelling approaches. Appl Soft Comput 66:77–89. https://doi.org/10.1016/j.asoc.2017.12.051

Yan QY, Zhang MJ, Li W, Qin GY (2020) Risk assessment of new energy vehicle supply chain based on variable weight theory and cloud model: a case study in China. Sustainability 12(8):3150. https://doi.org/10.3390/su12083150

Yang J, Tang ZH, Jiao T, Muhammad AM (2017) Combining AHP and genetic algorithms approaches to modify DRASTIC model to assess groundwater vulnerability: a case study from Jianghan Plain. China Environmental Earth Sciences 76(12):426. https://doi.org/10.1007/s12665-017-6759-6

Yaraghi N, Tabesh P, Guan PQ, Zhuang J (2015) Comparison of AHP and Monte Carlo AHP under different levels of uncertainty. IEEE Trans Eng Manage 62(1):122–132. https://doi.org/10.1109/TEM.2014.2360082

Yilmaz I (2007) GIS based susceptibility mapping of karst depression in gypsum: a case study from Sivas basin. Eng Geol 90(1–2):89–103. https://doi.org/10.1016/j.enggeo.2006.12.004

Yu CS (2002) A GP-AHP method for solving group decision-making fuzzy AHP problems. Comput Oper Res 29(14):1969–2001. https://doi.org/10.1016/S0305-0548(01)00068-5

Zarghami E, Azemati H, Fatourehchi D, Karamloo M (2018) Customizing well-known sustainability assessment tools for Iranian residential buildings using Fuzzy Analytic Hierarchy Process. Build Environ 128:107–128. https://doi.org/10.1016/j.buildenv.2017.11.032

Zhang ZH, Li LR (2005) China resources of China. Sino Maps Press, Beijing (in Chinese)

Zhang QQ, Sun JC, Liu JT, Huang GX, Lu C, Zhang YX (2015) Driving mechanism and sources of groundwater nitrate contamination in the rapidly urbanized region of south China. J Contam Hydrol 182:221–230. https://doi.org/10.1016/j.jconhyd.2015.09.009

Zhang FG, Huang GX, Hou QX, Liu CY, Zhang Y, Zhang Q (2019a) Groundwater quality in the Pearl River Delta after the rapid expansion of industrialization and urbanization: distributions, main impact indicators, and driving forces. J Hydrol 577:124004. https://doi.org/10.1016/j.jhydrol.2019.124004

Zhang J, Wu Q, Mu WP, Du YZ, Tu K (2019b) Integrating the hierarchy-variable-weight model with collaboration-competition theory for assessing coal-floor water-inrush risk. Environmental Earth Sciences 78(6):205. https://doi.org/10.1007/s12665-019-8217-0

Zhang M, Huang GX, Liu GX, Zhang Y, Chen ZY, Wang JC (2020) Distributions and origins of nitrate, nitrite, and ammonium in various aquifers in an urbanized coastal area, south China. J Hydrol 582:124528. https://doi.org/10.1016/j.jhydrol.2019.124528

Zhang EL, Zhuo JM, Hou L, Fu CH, Guo T (2021) Comprehensive annoyance modeling of forklift sound quality based on rank score comparison and multi-fuzzy analytic hierarchy process. Appl Acoust 173:107705. https://doi.org/10.1016/j.apacoust.2020.107705

Zhao W, Gan FP, Meng Y, Zheng ZJ, Liu XM (2018) Application of seismic velocity tomography in investigation of karst collapse hazards, Guangzhou. China Environmental Earth Sciences 77(6):258. https://doi.org/10.1007/s12665-018-7419-1

Zheng GZ, Wang YJ, Li C, Wang X (2020) Real-time quantification of human physiological state in high temperature environments based on variable weight theory. J Therm Biol 89:102531. https://doi.org/10.1016/j.jtherbio.2020.102531

Zhou YX, Wang LY, Liu JR, Li WP, Zheng YJ (2012) Options of sustainable groundwater development in Beijing Plain, China. Phys Chem Earth 47–48:99–113. https://doi.org/10.1016/j.pce.2011.09.001

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Acknowledgements

The authors wish to thank the Wuhan Center of Geological Survey for data curation. The authors also thank Mr. Zuo Liu and Zechen Zhang for their assistance in the analysis and visualization. The authors are grateful to the editor and reviewers for their suggestions.

This work was supported by the Fundamental Research Funds for the Central University, China University of Geosciences (Wuhan) [No. CUGCJ1822].

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Huang, P., Ma, C. & Zhou, A. Assessment of groundwater sustainable development considering geo-environment stability and ecological environment: a case study in the Pearl River Delta, China. Environ Sci Pollut Res 29 , 18010–18035 (2022). https://doi.org/10.1007/s11356-021-16924-6

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DOI : https://doi.org/10.1007/s11356-021-16924-6

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Deposition of ambient particles in the human respiratory system based on single particle analysis: A case study in the Pearl River Delta, China

Affiliations.

• 1 School of Atmospheric Sciences, & Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Sun Yat-sen University, Guangzhou, 510275, PR China; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, PR China; Guangdong Provincial Field Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Guangzhou, 510275, PR China.
• 2 School of Atmospheric Sciences, & Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Sun Yat-sen University, Guangzhou, 510275, PR China; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, PR China.
• 3 Department of Chemical and Biomolecular Engineering, National University of Singapore, 117576, Singapore.
• 4 School of Engineering, Indian Institute of Technology (IIT), Mandi, Kamand, Himachal Pradesh, 175005, India.
• 5 Department of Civil & Environmental Engineering, National University of Singapore, 1 Engineering Drive 2, 117576, Singapore.
• 6 Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Institute for Environmental and Climate Research, Jinan University, Guangzhou, 510632, PR China.
• 7 South China Institute of Environmental Science, MEE, Guangzhou, 510530, PR China.
• 8 Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Institute for Environmental and Climate Research, Jinan University, Guangzhou, 510632, PR China. Electronic address: [email protected].
• PMID: 33862340
• DOI: 10.1016/j.envpol.2021.117056

It is important to evaluate how ambient particles are deposited in the human respiratory system in view of the adverse effects they pose to human health. Traditional methods of investigating human exposure to ambient particles suffer from drawbacks related either to the lack of chemical information from particle number-based measurements or to the poor time resolution of mass-based measurements. To address these issues, in this study, human exposure to ambient particulate matter was investigated using single particle analysis, which provided chemical information with a high time resolution. Based on single particle measurements conducted in the Pearl River Delta, China, nine particle types were identified, and EC (elemental carbon) particles were determined to be the most dominant type of particle. In general, the submicron size mode was dominant in terms of the number concentration for all of the particle types, except for Na-rich and dust particles. On average, around 34% of particles were deposited in the human respiratory system with 13.9%, 7.9%, and 12.6% being distributed in the head, tracheobronchial, and pulmonary regions, respectively. The amount of Na-rich particles deposited was the highest, followed by EC. The overall deposition efficiencies of the Na-rich and dust particles were higher than those of the other particle types due to their higher efficiencies in the head region, which could be caused by the greater sedimentation and impaction rates of larger particles. In the head region, the Na-rich particles made the largest contribution (30.5%) due to their high deposition efficiency, whereas in the tracheobronchial and pulmonary regions, EC made the largest contribution due to its high concentration. In summary, the findings of this initial trial demonstrate the applicability of single particle analysis to the assessment of human exposure to ambient particles and its potential to support traditional methods of analysis.

Keywords: Human exposure; MPPD; Respiratory deposition; SPAMS.

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China floods: four killed in Guangdong sparking concerns over extreme weather defences

Heavy rainstorms in the densely populated Pearl River Delta have left large parts of Guangdong province underwater

Heavy rainstorms that swept across southern China over the weekend killed at least four people as floods swamped cities in the densely populated Pearl River Delta, state media reported.

A search was under way for 10 others missing after record-breaking rains sparked concerns about the region’s defences against bigger deluges induced by extreme weather events.

By Monday, about 110,000 people had been evacuated across the province, while 25,800 people were in emergency shelters, according to Xinhua. In Guangzhou, the capital of Guangdong province, the government said the city had logged a cumulative rainfall of 60.9cm in April, the highest monthly rainfall since record-keeping began in 1959.

The country’s highest-level red rainstorm warning was issued for parts of Guangdong, including the megacity of Shenzhen, the city’s meteorological observatory said. The areas listed were experiencing “heavy to very heavy downpours”, the weather agency said, adding the risk of flash floods was “very high”.

The official Xinhua news agency said three people died in Zhaoqing city while one rescuer died in Shaoguan city. It didn’t provide details about when or how they died. The two cities in Guangdong province are among the worst hit areas of sustained torrential rains that began late last week.

Footage on state broadcaster CCTV showed rescuers in rubber boats evacuating residents from inundated shopping streets and residential areas.

Floods also battered neighbouring Jiangxi province where local media reported 459 people had been evacuated, while rains and floods have affected 1,500 hectares of crops and caused financial losses of more than 41 million yuan ($5.7m).

Guangdong, once dubbed the “factory floor of the world”, is prone to summer floods. Its defences against disruptive floods were severely tested in June 2022 when the province was pounded by the heaviest downpours in six decades. Hundreds of thousands of people were evacuated.

Since Thursday, Guangdong has been battered by unusually heavy , sustained and widespread rainfall, with powerful storms ushering in an earlier-than-normal start to the province’s annual flooding season in May and June.

Over the weekend, waterways in the province overflowed, including in some villages where flood waters reached the second storey of houses after washing out paddy and potato fields.

In Qingyuan, a relatively small city of 4 million, rescuers tackled neck-high waters to extract residents including an elderly lady trapped in waist-deep water in an apartment building.

Others remained on the upper floors of their houses, waiting for the waters to recede as friends delivered food by boat.

Weather events in China have become more intense and unpredictable because of global warming, scientists say, with record-breaking rainfall and drought assailing the world’s second-largest economy, often at the same time.

In Qingyuan residents counted their losses, with one farmer telling Reuters that their rice fields had been “fully flooded”.

“I won’t be making any money this year, I will be making losses,” Huang Jingrong said, estimating his losses at about 100,000 yuan ($13,800).

“What can we do? We won’t get reimbursed for our losses.”

The Associated Press and Reuters contributed to this report

• Extreme weather

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6 Injured In Landslides As China Braces For "Once A Century" Floods After Heavy Rain

Torrential downpours have struck swathes of guangdong province since thursday, swelling waterways in the pearl river delta, china's manufacturing heartland..

Weather alerts are in place across large parts of central Guangdong (File photo)

Landslides in southern China injured at least six people and trapped others, state media reported Sunday, as the region braced for severe floods "seen around once a century".

Torrential downpours across swathes of Guangdong province since Thursday have swollen rivers in the Pearl River Delta and triggered deluges in mountainous areas.

State broadcaster CCTV said Sunday that rains had sparked landslides affecting six villages in the northern Guangdong town of Jiangwan, "causing people to become trapped".

Photographs published by CCTV showed waterfront homes destroyed by a wall of brown mud, and people in fluorescent-coloured ponchos sheltering in a soaked public sports court.

No deaths were immediately reported and the total number of trapped people was not specified.

But CCTV said six people who were "trapped and injured" in the landslides had been airlifted to the nearby city of Shaoguan.

Emergency workers were racing to restore communications to the stricken area "as soon as possible", CCTV said.

It added that more than 80 rescuers were working "through the day and night" to assist people in the disaster zone.

The Pearl River Delta is China's manufacturing heartland and one of the country's most densely populated regions, with Guangdong alone home to around 127 million people.

Aerial footage by CCTV on Sunday showed murky flood-waters lapping close to street level in some towns, leaving riverside promenades and pavilions inundated and a pagoda protruding from the deluge.

Authorities have launched a level-two emergency response in the Pearl River Delta, the second-highest in a four-tier system.

The national weather office imposed weather alerts across central Guangdong and warned of major storms in coastal areas through Sunday evening and into Monday.

Citing the provincial hydrology bureau, CCTV said three monitored locations in the Bei River basin would "experience flooding seen around once a century... due to the impact of heavy precipitation".

Floods of up to 5.8 metres (19 feet) above the warning limit would strike the areas starting early Monday morning, according to CCTV.

Several other monitored tributaries in the basin would endure the kind of floods seen once every 50 years, it said.

There were no initial reports of mass evacuations.

Parts of the neighbouring provinces of Jiangxi and Fujian were also forecast to see severe rainstorms on Sunday evening.

China is no stranger to extreme weather but recent years have seen the country whiplashed by severe floods, grinding droughts and record heat.

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Climate change driven by human-emitted greenhouse gases makes extreme weather events more frequent and intense, and China is the world's biggest emitter.

(Except for the headline, this story has not been edited by NDTV staff and is published from a syndicated feed.)

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