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Change and persistence are often richly entangled facets of landscapes. While many studies use land use/land cover (depicted as polygons) to illustrate landscape evolution, this paper draws on approaches from landscape archaeology to investigate how lines and points can also be used to examine landscape morphologies. The study uses three distinct spatial elements: points (graves), lines (field boundaries), and polygons (land use) to represent landscape transformations and reflect time depth in the landscape. The paper aims to identify the most enduring landscape elements within the region and uncover the underlying mechanisms of persistence. It is suggested that the time depth exhibited in field boundaries surpasses that of land use in this case. Field boundaries provide a useful way to examine agricultural intensification, whereas land use is more sensitive to agricultural commercialization and urbanization. In addition, the Chinese Feng Shui funeral culture emerges as a stabilizing force that encourages landscape persistence. This cultural driver ensures the persistence of field patterns surrounding graves, making these fields the most ancient plots within the study area. In conclusion, representing the time depth of landscapes through linear features and points can serve as an important supplement to the study of landscape change based on land use. [ABSTRACT FROM AUTHOR]

Analysis of the spatial patterns and dynamics of tourism services and facilities is crucial for tourism and land use planning. However, most studies in the spatial analysis of tourism rely on the city- or regional-level data; limited research has used POI (point of interest) data to accurately uncover the spatial distribution of tourism, especially its interactive and coupling relationship with the local economy and population. Based on POI data, this paper, therefore, investigates the spatial patterns and driving forces of tourism services distribution and how tourism space is coupled with the local economy and population in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) of China. The results show the following: (1) Different categories of tourism services (catering, shopping, scenic spots, leisure, and sports) exhibit diverse spatial patterns and agglomerations, but they tend to align with the variables of economic level and population in a grid of 1 km2. (2) The spatial econometric models further reveal that population density, transportation, and hospitality facilities are positively correlated with the spatial distribution of tourism services, but GDP in a grid of 1 km2 shows a weak negative correlation with the POI of tourism services, which may be attributed to the incoordination between GDP and tourism in some areas. (3) The analysis of coupling degree further identifies the areas where tourism services have good interaction/coupling with the local GDP and population density, such that these areas can be viewed as hotspots suitable for tourism promotion. This paper thus offers meaningful policy implications by calling for an optimization of the coupling of tourism services with local social–economic factors in the GBA. [ABSTRACT FROM AUTHOR]

The variation in the urban spatial structure (USS) has profound impacts on carbon emissions. Studying the relationship between the two can provide guidance for carbon neutrality strategies and the construction of low-carbon cities in China. However, there is currently a lack of comparative research on the different regions within a province. In this paper, the spatiotemporal evolution of the USS and carbon emissions, at five-year intervals from 2000 to 2020, is investigated in 21 prefecture-level cities in Guangdong Province, China, and the overall relationship of the USS to carbon emissions and their spatiotemporal variations are analyzed by using a two-way fixed-effects model and a geographically and temporally weighted regression model, respectively. The results show that, first, over the past twenty years, the scale of cities has continued to expand, with increasing continuity and aggregation in the built-up areas, while the complexity and fragmentation of their shapes have gradually decreased. Second, the gap in carbon emissions between the Pearl River Delta and other regions in Guangdong shows a trend of first decreasing and then increasing, with high values concentrated in the Pearl River Delta region and the city of Shantou in the east. Third, compared to socio-economic factors, the USS has a more direct and pronounced impact on carbon emissions. Urban expansion and the increased complexity of land patches promote carbon emissions, whereas improving urban spatial continuity and compactness can reduce carbon emissions. Fourth, the dominant spatial structure indicators of carbon emissions differ among the regions of eastern, western, and northern Guangdong and the Pearl River Delta. This study proposes spatial optimization strategies for the low-carbon development of cities in Guangdong Province, providing a new perspective for integrating urban layout and emission reduction policies. [ABSTRACT FROM AUTHOR]

In the context of global environmental changes, the frequency of various disasters and extreme events is increasing, and enhancing urban resilience has become an important guide for current urban development. Previous studies have mainly focused on changes in urban resilience, with less consideration for the impact of spatial spillover effects on urban resilience. Therefore, this paper aims to analyze the temporal and spatial evolution of urban resilience and its spatial spillover effects in Guangdong from 2012 to 2020 based on the urban resilience assessment model, the Getis-Ord Gi* model, and the improved Capello model. The results are as follows: Affected by COVID-19, the urban resilience of Guangdong Province declined from 2019 to 2020, and 42.86% of the cities demonstrated negative growth in their urban resilience. Urban resilience exhibited significant spatially non-equilibrium characteristics among different cities. The urban resilience of the cities in eastern, northern, and western Guangdong, which are the "collapse zone" of urban resilience, was lower than 0.229 from 2012 to 2020. The intensity of urban resilience spillover in Guangdong presented a typical three-level circle structure of "core-subcore-periphery", which decreased from the core circle to the surrounding circle. COVID-19 caused a 27.21% decrease in the total urban resilience spillover in Guangdong from 2019 to 2020. Finally, we identified critical driving factors of urban resilience using the optimal-parameters geographical detector model. This study can provide a scientific reference for the Chinese government to build resilient cities and improve sustainable urban development. [ABSTRACT FROM AUTHOR]

Against the backdrop of severe global warming, the low-carbon city pilot policy, with carbon emission reduction as its main objective, is an important initiative for China to fulfil its international commitment to carbon emission reduction and promote a green and low-carbon development strategy. However, none of the literature has yet evaluated whether the pilot low-carbon city policy promotes urban land use efficiency as a policy effect. In view of this, this paper measures urban land use efficiency from a low-carbon perspective using a global reference super-efficiency SBM model based on data from 186 prefecture-level cities in China from 2005–2017, and subsequently constructs a difference-in-differences method to test the true impact of low-carbon city pilot policies on urban land use efficiency and carbon emissions, and uses a propensity score matching method to test its robustness. It is found that: (1) the average level of urban land use efficiency in China is low and on a downward trend; (2) overall, cities are predominantly low-efficiency cities, with only the high-efficiency cities in Guangdong Province showing spatial agglomeration; and (3) the low-carbon city pilot policy reduces carbon emissions while also negatively affecting urban land use efficiency. Accordingly, this paper puts forward corresponding policy recommendations. [ABSTRACT FROM AUTHOR]

Catalyzed by global change and human activities, social and ecosystems are constantly under increasingly dynamic transformations. The Guangdong–Hong Kong–Macau Greater Bay Area (GBA), bordering the South China Sea and located in the Pearl River Delta Plain, is a typical region of complex SESs with rapid socioeconomic development but severe ecosystem degradation. Therefore, based on the relevant data of 11 GBA cities from 2010–2020, this paper constructs an indicator system for assessing land–sea SESs by extracting from three aspects: exposure, sensitivity, and adaptive capacity. Through the construction of a vulnerability assessment indicator system, via the explicit spatial vulnerability indicator calculation model, and vulnerability factor diagnostic model, this study comprehensively analyzes vulnerability levels, spatiotemporal evolution, and SES vulnerability factors. The study found that, since 2010, the SES vulnerability of the GBA has shown an overall trend of alleviation, and the overall geographical distribution of classified vulnerability levels is rather concentrated, with cities around the Pearl River Estuary relatively less vulnerable. The augmentation of per-capita fiscal expenditure, per-capita gross regional product, and decent air quality rate are the critical ingredients to remedy the vulnerability in the GBA. [ABSTRACT FROM AUTHOR]

Coastal areas are usually considered as pioneering areas for economic development and reform due to their unique geographical locations and ecological conditions. Correspondingly, rapid urbanization in coastal urban agglomerations has resulted in population concentration and land use/cover change (LUCC), leading to the decline of habitat quality and biodiversity. However, few studies have quantitatively explored the impacts of urban agglomeration expansion in coastal zones on habitat quality. Taking the Guangdong-Hong Kong-Macao-Great Bay Area (GBA) as a case study, we applied the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model to assess the habitat quality based on land use data obtained from remote sensing images from 1980 to 2020 and developed a geographically weighted regression model to quantitatively analyze the response of habitat quality to urbanization. The results showed that (1) LUCC in the GBA was dramatic from 1980 to 2020, dominated by the shift from various land use types to construction land, which led to increased fragmentation; (2) the overall habitat quality showed a decreasing trend, characterized by low values in the central part and high values in the surrounding area; (3) population and construction land such urbanization elements had a more significantly negative effect on habitat quality changes, while the relationships among slope, road distance, and habitat quality changes were complex. Based on above analysis, this paper suggests that future land management in the GBA should develop in the direction of intensification, refinement, and regional integration. [ABSTRACT FROM AUTHOR]

Based on the equivalent factor table of terrestrial ESV in China in 2010, this paper proposes a regionalized and interannual ESV correction method. Taking southern Guangzhou as the study area, which is located in the geometric center of the Guangdong–Hong Kong–Macao Greater Bay Area (GHM GBA), this study evaluated the ecosystem service equivalent value (ESEV) and ecosystem service value (ESV) and their changes in 2004, 2010 and 2016 and carried out grid processing, trend analysis and global/local spatial autocorrelation of the ecosystem service density (ESD) in the study area by using the Natural Resources Integrated Database of Guangdong Province (NRIDB). The results of data processing and spatial analysis computed in this study were published as Web services through the NRIDB and displayed in the visualization front-end. The results showed that (1) during 2004–2016, the urbanization process in the study area led to the expansion of built-up land, which occupied a large amount of ecological land and resulted in a decreasing trend of ESV. (2) Benefiting from socioeconomic development, the standard value volume of the ESEV (D value) kept increasing, which resulted in an increasing trend for the total ESV. (3) The spatial autocorrelation analysis results of ESD indicated that the spatial distribution of ESD in the study area had strong spatial agglomeration and positive spatial correlation. With the progress of urbanization and increases in research scale and interval distance, the spatial difference in ESD became larger over time. The research results provide a reference for the coordinated allocation of natural resources and environmental protection in the GHM GBA. [ABSTRACT FROM AUTHOR]

Guangdong Province is an important ecological barrier and the primary pillar of economic development in China. Driven by high-speed urbanization and industrialization, unreasonable land use change in Guangdong Province has exacerbated habitat degradation and loss, seriously affecting habitat quality. Thus, taking Guangdong Province as the study area, this paper quantifies the response of habitat quality on land use change using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model and constructs a contribution index (CI). The following conclusions can be drawn from the results: (1) The habitat quality exhibits a spatial distribution pattern of low quality in plain areas and high quality in hilly and mountainous areas. (2) The annual average habitat quality gradually decreases from 1980 to 2020, with a total decrease of 0.0351 and a reduction rate of 4.83%; (3) The impact of land use change on habitat quality is mainly negative, and the habitat quality mainly decreases by the conversion of forest land to orchards, paddy field to urban land, and forest land to dry land, with CI values of −24.09, −11.67, and −8.04, respectively. Preventing the destruction of natural forests, increasing the diversity of plantation orchards, and rationalizing and mitigating the growth rate of construction land are key to maintaining and improving the habitat quality. [ABSTRACT FROM AUTHOR]

Urbanization brings great enrichment to human production and life, but also has certain environmental impact on the area where the city is located. Many studies have revealed the negative effects of urbanization on the ecological environment of urban or urban agglomerations, especially in the early stage of urbanization, but there are few studies on the impact on the peripheral ecological space environment. Will the peripheral environment be better off with less human interference as people move to cities during urbanization? In order to answer this question, we took the Guangdong-Hong Kong-Macao Greater Bay Area, the most economically dynamic area in China, as an example to explore the relationship between impervious changes of urban agglomerations monitored by remote sensing in the Bay Area and ecological indicators of forest and grassland in Guangdong Province outside the Bay area. The results showed that:(1) in the past 30 years, the area of grassland outside the bay area did not change regularly, while the area of forest decreased year by year. The landscape indices of forest and grassland were gradually fragmented and discrete. Moreover, the distribution of Fraction Vegetation Coverage (FVC) of forest and grassland has changed since before urbanization. (2) Through correlation analysis, it is found that the changes in forest area and the landscape index of forest and grassland are strongly correlated with the development of urbanization in the Greater Bay Area. This shows that the process of urbanization in the Greater Bay Area will have a non-negligible impact on the peripheral environment. In the process of urban development, we should not only focus on the inner city but also consider the outer environment of the city. [ABSTRACT FROM AUTHOR]

In the granite regions of southern China, benggang poses a substantial threat to the ecological environment due to significant soil erosion. This phenomenon also imposes constraints on economic development, necessitating substantial investments in restoration efforts in recent decades. Despite these efforts, there remains a notable gap in comprehensive risk assessment that integrates both the erosion risk and disaster risk associated with benggang. This study focuses on a representative benggang area in Wuhua County, Guangdong province, employing transformer methods and high-resolution imagery to map the spatial pattern of the benggang. The integrated risk of benggang was assessed by combining soil-erosion risk and disaster risk, and cultivated land, residential land, and water bodies were identified as key disaster-affected entities. The machine-learning Segformer model demonstrated high precision, achieving an Intersection over Union (IoU) of 93.17% and an accuracy (Acc) of 96.73%. While the number of large benggang is relatively small, it constitutes the largest area proportion (65.10%); the number of small benggang is more significant (62.40%) despite a smaller area proportion. Prioritization for benggang management is categorized into high, medium, and low priority, accounting for 17.98%, 48.34%, and 33.69%, respectively. These priorities cover areas of 30.27%, 42.40%, and 27.33%, respectively. The findings of this study, which offer benggang management priorities, align with the nature-based solutions approach. Emphasizing the importance of considering costs and benefits comprehensively when formulating treatment plans, this approach contributes to sustainable solutions for addressing the challenges posed by benggang. [ABSTRACT FROM AUTHOR]

The imbalance between the population, transportation, and construction is an important factor affecting coordinated regional development in China. Using transportation land area and population data from 2010, 2015, and 2020 in 121 counties (cities and districts) of Guangdong Province, this study analyzes the spatiotemporal changes in transportation land and population structure. It explores the relationship between transportation land and population size changes using exploratory spatial data analysis (ESDA) and decoupling models. Research suggests the following: (1) From 2010 to 2020, the per capita transportation land in various districts and counties of Guangdong Province showed a hierarchical structure of "low center and high periphery". Conversely, the proportion of transportation land to construction land displayed a "high center and low periphery" characteristic. (2) During the same period, the total amount of transportation land in the peripheral areas of the Pearl River Delta was higher than that in the PRD (the Pearl River Delta) and the gap widened each year. Meanwhile, the proportion of transportation land to construction land in the peripheral areas of the Pearl River Delta was consistently higher than that in the PRD. (3) From 2010 to 2015, the relationship between population growth and transportation land in Guangdong Province showed a hierarchical pattern, while from 2015 to 2020, the pattern became more complex, and regional imbalance intensified. The decoupling results of PRD showed weak decoupling and expanding negative decoupling from 2010 to 2020. The peripheral areas of the Pearl River Delta are characterized by strong negative decoupling and expanding negative decoupling. Research has shown that although Guangdong Province is one of the most developed provinces in China, the mismatch between transportation land and population size in economically underdeveloped areas has become increasingly severe, with changes in the provincial population distribution pattern. The decoupling phenomenon between the transportation land and population is very obvious, and the policy of achieving regional balanced development through transportation infrastructure construction needs to be reviewed. [ABSTRACT FROM AUTHOR]

The rent-free farmland transfer that exists widely in China's rural areas is a topic worthy of attention. Particularly, the regional heterogeneity of its occurrences implies regional cultural heterogeneities. Using local dialects to proxy regional cultural features, this study applies econometric methods to examine the impacts of dialect diversity on rent-free farmland transfers. It also considers possible mechanisms through a mediation analysis, based on a combined two-year rural household survey dataset from the Guangdong and Jiangxi Provinces in 2015 and 2016. Robust estimation results reveal that dialect diversity increases the probability of rent-free farmland transfers at the household and village levels. According to the mediation analysis, dialect diversity influences villages' farmland abandonment, rural farmland market development, and the flexibility of farmland transfer contracts, which further affects rent-free farmland transfer. Rent-free farmland transfer depends on social trust and contracts' self-fulfilling advantages. Therefore, cultural and traditional factors should be taken into consideration, which would form beneficial interactions between the selections of rural farmland institutional arrangement and land rights policy implementations. [ABSTRACT FROM AUTHOR]

Understanding the spatial differences and evolutionary characteristics of urban economy and exploring the impact of industrial agglomeration and industrial proximity on urban economic convergence are the bases for scientifically formulating policies for coordinated regional economic development. This study used QGIS 3.10.10 software and the Theil index to analyze the spatial distribution characteristics and regional disparities of urban economy. Then, a spatial econometric model was constructed to analyze the convergence and influencing factors of Guangdong's urban economy. The results indicate that from 2006 to 2020, Guangdong's urban economy grew rapidly and the degree of economic agglomeration gradually weakened, but its economic pattern always maintained the "Core-Edge" structural feature. The interval disparities between the Pearl River Delta Urban Agglomeration (PRD) and the edge area have always been greater than the intra-regional disparities, so they are main source of disparities in Guangdong. In Guangdong's urban economy, σ-convergence and β-convergence coexist. The conditional β-convergence rate is 0.96~1.53%, and the half-life cycle is 45.4~72.36 years. Compared to the PRD, the economic disparities in the edge area are smaller but the convergence speed is faster and the half-life cycle is shorter. Both industrial agglomeration and industrial proximity have a significant impact on the economic convergence of Guangdong's cities. Among them, industrial agglomeration has a positive impact, while industrial proximity has a negative impact. There is spatial heterogeneity in the impact of industries on economic development. Industrial agglomeration has a positive impact on the overall economic development of Guangdong, but it is not significant within the regions. Industrial proximity has significant negative externalities in the PRD region, and its impact is not significant in the edge area. [ABSTRACT FROM AUTHOR]

The trade-offs between ecosystem services directly affect the quality of the ecological environment and the survival and development of human society, which is of great concern to academia, governments, and non-governmental organizations. Guangdong Province is a strong economic performer in China; hence, we selected it to explore the trade-off and synergy differences between different ecosystem services, and to investigate the mechanisms of their influence in economically developed regions with a large population density. Our results showed three main points: (1) The ecosystem services in Guangdong Province showed clear spatial heterogeneity. In addition, northern Guangdong has high levels of water retention, with a value of 5804.73 × 104 m3/km2 and high values for carbon sequestration and soil retention. Western Guangdong is a functional area for food production, and the Pearl River Delta is an economically developed region with low levels of ecosystem services. (2) Overall, in Guangdong Province, three pairs of ecosystem services, namely water retention–soil retention, carbon sequestration–water retention, and carbon sequestration–soil retention, showed a strong positive correlation and good synergistic relationships. The other three pairs of relationships show strong trade-off effects. (3) The relationships between similar ecosystem services show completely different characteristics in different regions. Carbon sequestration and water retention, carbon sequestration and biodiversity conservation, water retention and biodiversity conservation, and soil retention and biodiversity conservation were mainly manifested in high–high synergies, particularly in northern Guangdong; carbon sequestration and soil retention and water retention and soil retention, primarily manifested synergies; carbon sequestration and food production, water retention and food production, and soil retention and food production mainly manifested as trade-off relationships. [ABSTRACT FROM AUTHOR]

Promoting urban green growth necessitates a dual focus on reducing carbon emissions and fostering high-quality development. However, the body of research on the coupling relationship between high-quality urban development and carbon emissions remains remarkably limited. Taking Guangdong province as an example, this study strived to establish a comprehensive evaluation system for high-quality urban development encompassing economic, societal, and ecological dimensions and further conducted an in-depth examination of the spatiotemporal pattern and driving forces of coupling coordination degree between high-quality urban development and carbon emission intensity during 2000–2017. The coupling coordination degree in Guangdong province has shown continuous growth, transitioning from moderate incoordination to moderate coordination. The coupling coordination degree showed the overall spatial distribution characteristics of "high in southeast and low in others", with Zhuhai, Zhongshan, Foshan, Guangzhou, Dongguan, and Shenzhen as the core. Notably, technological advancement, environmental governance, and economic development emerge as pivotal factors that positively affect carbon emission intensity reduction, environmental quality improvement, and coupling coordination enhancement. This research provides valuable insights for achieving harmonized high-quality development in Guangdong province involving policies of regional differences, industrial competitiveness, and new-type urbanization. [ABSTRACT FROM AUTHOR]

High-speed rail (HSR) has been proven to drive the development of regional tourism. However, few studies paid attention to the impact of HSR on future tourism land use. In this study, points of interest (POI) were used to obtain the locations and types of tourism facilities, and high-resolution remote sensing images helped us determine the spatial extents of the tourism facilities, so that tourism land could be identified. A system dynamics (SD) model was coupled with the patch-generating land use simulation (PLUS) model to simulate future tourism land by considering two scenarios, including HSR-led development (HRD) and the historical development pattern (HD) scenario. The SD model was employed to project the amount of tourism land and determine the HSR effects via empirical analysis. The PLUS model was applied to simulate the spatial distribution of tourism land and model the HSR effect by adopting a planned tourism area as the development area. The study area is Xinxing County in Guangdong Province, China, a county dominated by rural tourism, with the HSR expected to open in 2025. The results revealed that the SD-PLUS model has great advantages in simulating tourism land use change, and the HRD scenario leads to major changes in the sources of tourism land growth. This research provides new insights for policymakers on making countermeasures for the sustainability of tourism and rural revitalization at the county level. [ABSTRACT FROM AUTHOR]

Selecting samples with non-landslide attributes significantly impacts the deep-learning modeling of landslide susceptibility mapping. This study presents a method of information value analysis in order to optimize the selection of negative samples used for machine learning. Recurrent neural network (RNN) has a memory function, so when using an RNN for landslide susceptibility mapping purposes, the input order of the landslide-influencing factors affects the resulting quality of the model. The information value analysis calculates the landslide-influencing factors, determines the input order of data based on the importance of any specific factor in determining the landslide susceptibility, and improves the prediction potential of recurrent neural networks. The simple recurrent unit (SRU), a newly proposed variant of the recurrent neural network, is characterized by possessing a faster processing speed and currently has less application history in landslide susceptibility mapping. This study used recurrent neural networks optimized by information value analysis for landslide susceptibility mapping in Xinhui District, Jiangmen City, Guangdong Province, China. Four models were constructed: the RNN model with optimized negative sample selection, the SRU model with optimized negative sample selection, the RNN model, and the SRU model. The results show that the RNN model with optimized negative sample selection has the best performance in terms of AUC value (0.9280), followed by the SRU model with optimized negative sample selection (0.9057), the RNN model (0.7277), and the SRU model (0.6355). In addition, several objective measures of accuracy (0.8598), recall (0.8302), F1 score (0.8544), Matthews correlation coefficient (0.7206), and the receiver operating characteristic also show that the RNN model performs the best. Therefore, the information value analysis can be used to optimize negative sample selection in landslide sensitivity mapping in order to improve the model's performance; second, SRU is a weaker method than RNN in terms of model performance. [ABSTRACT FROM AUTHOR]

With the arrival of the era of innovative economy, innovation is of great significance for the development of China, and even the world. Based on statistical data and taking 2009, 2014, and 2019 as node years, this study uses the entropy weight method and a modified gravity model to study the pattern and evolution characteristics of China's interprovincial innovation network. The results showed that: 1. The weights of innovation output, innovation input, and innovation environment were 0.253, 0.340, and 0.407, respectively. Currently, innovation output is the most representative of the interprovincial innovation connections, but the weight of innovation environment has been increasing year by year, and its importance is constantly highlighted. 2. The overall spatial structure of China's interprovincial innovation network shows a "core periphery" feature, radiating from coastal provinces such as Jiangsu, Beijing, and Zhejiang to inland provinces, and exhibiting an overall pattern of "strong in the east and weak in the west". 3. In terms of evolutionary characteristics, Guangdong and Jiangsu ranked in the top two in terms of outward innovation scale from 2009 to 2019. The combined total innovation connections of the top five provinces in 2009, 2014, and 2019 accounted for 70.79%, 64.29%, and 64.24%, respectively. Although the phenomenon of uneven innovation connections exists, it has slowed down. In addition, China's interprovincial innovation network is gradually becoming enriched, with the most significant change being the number of innovation links, with a gravity level increasing from four in 2009 to twenty-six in 2019. The interprovincial innovation links continue to strengthen, but the focus has not changed significantly; it is still concentrated in the Bohai Rim region and the Yangtze River Delta region. 4. In terms of maximum gravitational lines, Guangdong Province had the highest number of maximum gravitational lines in 2009, 2014, and 2019, with a total of six. The maximum gravitational line change from 2009 to 2019 took place from 2014 to 2019, transitioning from "Jilin-Liaoning" to "Jilin-Heilongjiang". [ABSTRACT FROM AUTHOR]

Few studies have analyzed the mixed effects of city size and land factors at the macro level on migrant socio-economic integration. On the basis of survey data on migrants in the Pearl River Delta Metropolitan Region (PRDMR), this study developed a system of multidimensional indicators for analyzing the degree of migrant socio-economic integration and factors influencing it. This study demonstrated the following: (1) The overall degree of socio-economic integration of migrants in the PRDMR was low. Factors including city size, hometown landholding, year of birth, education level, gender, and migratory duration exerted effects of varying extents on the degree of the socio-economic integration of migrants. (2) Better job positions were offered and infrastructure was more developed in first-tier cities, so the degree of migrant economic integration was higher, and the sense of identity was stronger in first-tier cities. Given the low housing prices in second-tier cities, migrants therein were more likely to buy a house and achieve family integration, and the degree of their social integration was stronger. (3) In terms of source landholding factors, the degree of socio-economic integration was relatively low among the migrants who owned arable land and homesteads, and who were born outside Guangdong Province. The study tries to measure the socio-economic integration of immigrants more comprehensively and provide reference for the implementation of differentiated socio-economic integration policies and land transfer policies in the immigration and emigration areas. [ABSTRACT FROM AUTHOR]

Accurately identifying the boundary of urban clusters is a crucial aspect of studying the development of urban agglomerations. This process is essential for comprehending and optimizing smart and compact urban development. Existing studies often rely on a single category of data, which can result in coarse identification boundaries, insufficient detail accuracy, and slight discrepancies between the coverage and the actual conditions. To accurately identify the extent of urban clusters, this study proposes and compares the results of three methods for identifying dense urban areas of three major agglomerations in China: Beijing–Tianjin–Hebei, the Yangtze River Delta, and the Guangdong–Hong Kong–Macao Greater Bay Area. The study then integrates the results of these methods to obtain a more effective identification approach. The social economic method involved extracting a density threshold based on the fused nuclear density of socio-economic vitality data, including population, GDP, and POI, while the remote sensing method evaluated feature indices based on remote sensing images, including the density index, continuity index, gradient index, and development index. The traffic network method utilizes land transportation networks and travelling speeds to identify the minimum cost path and delineate the boundary by 20–30 min isochronous circles. The results obtained from the three methods were combined, and hotspots were identified using GIS overlay analysis and spatial autocorrelation analysis. This method integrates the multi-layered information from the previous three methods, which more comprehensively reflects the characteristics and morphology of urban clusters. Finally, the accuracy of each identification result is verified and compared. The results reveal that the average overall accuracy (OA) of the three areas delineated by the first three methods are 57.49%, 30.88%, and 33.74%, respectively. Furthermore, the average Kappa coefficients of these areas are 0.4795, 0.2609, and 0.2770, respectively. After performing data fusion, the resulting average overall accuracy (OA) was 85.34%, and the average Kappa coefficient was 0.7394. These findings suggest that the data fusion method can effectively delineate dense urban areas with greater accuracy than the previous three methods. Additionally, this method can accurately reflect the scope of urban clusters by depicting their overall boundary contour and the distribution of internal details in a more scientific manner. The study proposes a feasible method and path for the identification of urban clusters. It can serve as a starting point for formulating spatial planning policies for urban agglomerations, aiding in precise and scientific control of boundary growth. This can promote the rational allocation of resources and optimization of spatial structure by providing a reliable reference for the optimization of urban agglomeration space and the development of regional spatial policies. [ABSTRACT FROM AUTHOR]

The low–carbon transition of farmland use (LCTFU) is an effective measure to coordinate the development of farmland and the environment to meet China's "dual carbon" and green agricultural transformation goals. We studied the spatial–temporal evolution of the LCTFU and further explored the driving factors of the LCTFU by applying a geographically weighted regression model (GWR) to the coastal cities of Guangdong Province from 2000 to 2020. The results show that (1) temporally, the comprehensive, spatial, functional, and mode transitions of farmland use in coastal areas of Guangdong Province generally declined. The LCTFU level in most counties was low, and the difference in the LCTFU level among counties was narrowing. (2) Spatially, the LCTFU generally followed a high–to–low spatial distribution pattern, with high LCTFU values in the east and west and low values in the center. (3) The hotspots of the comprehensive, spatial, functional, and mode transitions were mainly concentrated in the eastern part of the study area, while the cold spots were in the central region, which is generally consistent with the spatial distribution of high– and low–value areas of the LCTFU. (4) The spatial migration path of the LCTFU migrated from northeast to southwest, with the main body of the standard deviation ellipse in the middle of the study area, displaying a C–shaped spatial pattern with weak expansion. (5) Economic, social, and environmental factors jointly contributed to the spatial–temporal evolution of the LCTFU, with social factors being the strongest driver. [ABSTRACT FROM AUTHOR]

The digital economy plays an important role in the high-quality development of cities, and low-carbon urban development is an integral part of this evolution. Therefore, it is important to explore the impact of the development of the digital economy on urban carbon emissions. By considering 21 cities in Guangdong Province as the object of the research, this study measured the levels of the digital economy from 2011 to 2019 by using an entropy weight-based TOPSIS model, analyzed the spatiotemporal changes, and used geographically and temporally weighted regression to examine the spatiotemporal heterogeneity of the impact of the digital economy on urban carbon emissions. The results showed the following: (1) The development of the digital economy in Guangdong Province in general exhibited a stable trend of growth, and the average level of its development in the 21 cities considered increased by 3.4 times during the study period. (2) The level of development of the digital economy in the Pearl River Delta was significantly higher than that in northern, western, and eastern Guangdong, with Shenzhen being the most developed city in this regard (0.8473), and Shanwei being the least developed (0.0633). (3) The impact of the development of the digital economy on carbon emissions had significant spatiotemporal heterogeneity. The benefits of the development of the digital economy in terms of a reduction in carbon emissions was the most significant in the Pearl River Delta, and regional differences were prominent. (4) The reductions in carbon emissions in cities with a highly developed digital economy were more significant than has been reported in past studies in the area. However, it had a negative effect on the urban carbon emissions in cities with a poorly developed digital economy. The results of this study can be used to guide policies related to the high-quality development of the urban digital economy. [ABSTRACT FROM AUTHOR]

Urban land optimization in urban agglomerations plays an important role in promoting territorial spatial planning to achieve high-quality development, land ecological suitability (LES) is one of the important variables influencing its urbanization and needs to be considered in urban growth simulation and modeling. This research proposed a multi-objective urban land optimization (MULO) model based on the non-dominated sorting genetic algorithm II (NSGA-II) which integrates the LES assessment. MULO starts with LES analysis based on a fuzzy analytical hierarchy process (AHP) and a minimum cumulative resistance (MCR) model. Then, two-step linear regression is used to optimize the quantity structure of built-up land. Finally, suitability and compactness are assigned to NSGA-II as objectives to obtain optimal spatial patterns. Taking the example of the Guangdong–Hong Kong–Macao Greater Bay Area, we found that all the newly added built-up land in 2030 is distributed in peri-urban areas around the original settlements, with approximate clustering in the northern part of Guangzhou and the southern part of Foshan under a balanced development scenario. This study highlights the importance of LES in urban growth modeling, and MULO can provide effective support for the spatial planning of urban agglomerations. [ABSTRACT FROM AUTHOR]

Land fragmentation hinders sustainable development in rural areas by reducing the efficiency of land use, and it could be mitigated by optimum allocation of land resources. However, most land use allocation models address micro-scale interaction, which is not conducive to the arrangement of the specific implementation plan. Facing such issues, this study proposed a village classification method (LUEOVC) that can provide specific optimization strategies for each village according to different optimization objectives. Specifically, we used a multi-objective particle swarm optimization algorithm to find the best land use adjustment strategies under different land use optimization objectives, and the pros and cons of these strategies are based on land use efficiency evaluation. The proposed village classification method can reflect the impact of the optimal allocation of different types of land resources on the land use efficiency of each village. The results of experiments conducted in Xinxing County, Guangdong Province showed that the village-based land use optimization strategy provided in this method can improve the land use efficiency of the cultivated land with the most serious fragmentation in the study area by 0.9%. The method also enables planners to compare the costs and gains under different objections, so as to better help decision-makers in formulating land use optimization strategies for different villages. [ABSTRACT FROM AUTHOR]

In order to enable urban economic development, the use of the right value and asset value of rural collective construction land (RCCL) is increasingly becoming apparent and this market is experiencing rapid development. However, the arrangement of the governance structure of rural shareholding cooperatives (RSCs) can seriously affect the efficiency of collective construction land market transactions, since the governance of RSCs is related to the interests of farmers. Protecting the rights and interests of farmers while improving the governance efficiency of RSCs is a considerable challenge worldwide. To better deal with this challenge, this study used a field survey in Nanhai District, Guangdong Province, China, to estimate how the governance structure of RSCs affect the efficiency of RCCL market transactions. Tobit models were constructed, and the results show that (1) most of the governance functions of RSCs were not separate from the administrative management of the village committees, which leads to low efficiency of RSCs' governance; (2) leaders of rural collective economic organizations played a key role in governance efficiency; (3) from the perspective of collective land property rights, most village shareholders did not have decision-making power or supervisory authority in the RCCL transfers because they could not complete access to transaction information. Furthermore, most villagers felt that the amount of income distributed was unreasonable, and the rights and interests of farmers and village shareholders were not guaranteed by the RSCs. Therefore, we suggest that the Chinese authorities should strengthen their current efforts to construct a more open and fair governance structure of the RSCs and thus improve their market transaction efficiency. Our work provides some insights into ways to improve the governance structure and market transaction efficiency of RSCs, which can further contribute to the development of the RCCL market in other areas of China and worldwide. [ABSTRACT FROM AUTHOR]

With continuous rises in GDP, land cover in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) has undergone a drastic change over the period 1990–2018. In this study, land use in the GBA was divided into six types: farmland, forestland, grassland, wetland, construction land, and unused land. We analyzed changes in spatiotemporal patterns according to region and type by using statistical analysis, spatial clustering, and hotspot analysis, focusing on the spatial characteristics of areas where land-use types changed with high frequency. The high-frequency land use in the GBA has strategic guidance for further urban planning and management. With discussions on urban planning, the natural environment, and social and economic development, we found the following: (1) Urban construction land in the GBA showed a unipolar growth mode, increasing from 5.63% to 14.34% from 1990 to 2018. Accordingly, the degree of urban concentration and contiguity rose continuously. (2) Hotspots with frequent land-use changes were concentrated mainly in areas with economic intensity. (3) Plots with high-frequency land-use changes (Flc > 2) were concentrated primarily in the waters and rivers of the GBA within 10 km of the administrative boundaries of prefecture-level cities. (4) Nearly 80% of the land has been or will be transformed into ecological land over the period 1990–2018. On the basis of these findings, we suggest further improving land-use efficiency, and ecological land damage and the over-occupation of sea space should be avoided while maintaining economic growth. Thus, linking increases and decreases in construction land is an excellent land-consolidation mechanism to transform inefficient urban land into ecological land. [ABSTRACT FROM AUTHOR]

With the acceleration of urbanization in China, the urban surface thermal environment has undergone significant changes. This work aims to calculate the urban thermal comfort index using a temperature and humidity model with the land surface temperature and relative humidity. It also aims to explain the association between the land use/land cover change (LUCC) and urban surface thermal environment of the Beijing–Tianjin–Hebei (BTH) Region, the Guangdong–Hong Kong-Macao Greater Bay Area (GBA) and the Yangtze River Delta (YZD) in 2020, 2015, 2010 and 2005 using geographically weighted regression. The results reveal that (1) the three urban agglomerations have substantial heat island intensity regions, which are clustered and zonally distributed, and the annual average rates of the heat island area growth in the three regions are 1.01%, 1.41% and 1.09%, respectively. (2) Many uncomfortable areas exist in the three urban agglomerations, with an exponential growth trend in summer, and the annual average proportion of the uncomfortable areas in the three regions are 60.8%, 56.8% and 49.4%, respectively. (3) From the spatial point of view, the high-thermal comfort index areas of Beijing, Tianjin, Hebei, Guangdong, Hong Kong and Macao expand to the coast, and the high-index areas of the Yangtze River Delta expand to the inland. In terms of time, the annual distribution of thermal comfort in Beijing–Tianjin–Hebei is discrete, but the annual distribution of thermal comfort in Guangdong, Hong Kong, Macao and the Yangtze River Delta is concentrated. (4) In LUCC, the change intensity in construction land has a remarkable effect on the change in thermal comfort. The areas where the thermal comfort index increases positively correlate with the areas where the construction land increases. This study enriches the research on the impact of LUCC on urban ecological performance, and thus provides the necessary scientific basis for urban environment construction. [ABSTRACT FROM AUTHOR]

Cultivated land fragmentation (CLF) is a key obstacle to agricultural development and has a strong relationship with regional food security and global sustainable development. However, few studies have analyzed the spatio-temporal distribution pattern and evolution characteristics of CLF and the complex interactions among their influencing factors in rapidly developing regions. In this study, first, the GlobeLand30 datasets were used to obtain characteristic parameters of cultivated land in counties in Guangdong Province in 2000, 2010, and 2020. Then, the linear weighted comprehensive evaluation model based on the principal component analysis (PCA) was used to measure the extent of CLF. Finally, the exploratory spatial data analysis (ESDA) was used to analyze the spatio-temporal distribution pattern and evolution characteristics of CLF, and geodetector (GD) and random forest (RF) models were used to explore the factors influencing the spatial difference in CLF. The results showed that the spatial differences in the distribution of cultivated land resources in Guangdong Province are relatively large and the extent of agglomeration is generally low. The extent of CLF on the county scale is mainly medium and higher. The overall spatial distribution shows an increasing trend from the south to the north and from the west to the east, and the spatial distribution pattern with agglomeration and randomness remains relatively stable. From 2000 to 2020, the overall CLF continued to intensify and the evolution of CLF on the county scale mainly increased. The spatial difference in CLF is the result of that based on the natural environment and influenced by factors such as social, economic, and agricultural development. The interaction between influencing factors is very strong, dominated by nonlinear enhancement. The results are of great significance for promoting the intensive and efficient utilization of cultivated land resources and sustainable regional development. [ABSTRACT FROM AUTHOR]

Delineating urban growth boundaries (UGBs) by combining the land-use/land-cover (LULC) change simulation method has become common in recent studies. However, few of the existing studies have integrated multi-source big data to analyze the driving factors of LULC dynamics in the simulation. Moreover, most of previous studies mainly focused on the UGBs delineation in macroscale areas rather than small-scale areas, such as the county area. In this study, taking Xinxing County of Guangdong Province as the study area, we coupled a system dynamics (SD) model and a patch-generating land-use simulation (PLUS) model to propose a framework for the LULC change simulation and UGBs delineation in the county area. Multi-source big data such as points of interest (POIs), night-time light (NTL) data and Tencent user density (TUD) were integrated to analyze the driving forces of LULC change. The validation results indicate that the coupled model received high accuracy both in the land-use demand projection and LULC distribution simulation. The combination of multi-source big data can effectively describe the influence of human socio-economic factors on the expansion of urban land and industrial land. The UGBs delineation results have similar spatial patterns with the LULC change simulation results, which indicates that the proposed UGBs delineation method can effectively transform the LULC simulation results into available UGBs for the county area. It has been proven that the proposed framework in this study is effective for the LULC change simulation and UGBs delineation in the county area, which can provide insight on territorial spatial planning in the county area. [ABSTRACT FROM AUTHOR]

In the context of the increasing frequency of natural disasters caused by climate change in recent years, rational territorial spatial planning must pay attention to production–living–ecological (PLE) risks under climate change scenarios. In this study, a method synthesizing the Box–Cox transformation and area weighted averaging is established for characterizing the PLE risks in China's provinces, which are divided into three zones to cope with PLE risks. Further, targeted strategies from the perspective of the disaster-induced factors and disaster-affected objects are explored for the regions within the different zones. The results show that the regions with a high production risk are mainly distributed in Guangdong, Henan, and Shandong, with an index between 0.80 and 1.00; the regions with a high living risk are concentrated in Jiangsu, Anhui, Guangdong, and Hainan, with an index exceeding 0.72; and the regions with a high ecological risk are concentrated in Guangxi, Ningxia, and Yunnan, with an index exceeding 0.50. The overall PLE risk is high along the southeastern coast, intermediate in central and western China, and low on the Tibetan Plateau. From the A to C zones, the number of risk types and intensity of risks requiring attention gradually decrease. For the category A zone, recommended measures include the construction of disaster risk monitoring and early warning systems for coastal cities and major grain-producing regions, the development of urban ecological protection zones, and the adjustment of economic and energy structures, etc. Production and living risks are central to the category B zone, while ecological and production risks are central to the category C zone. This study can provide theoretical support for China's scientific development of land planning and the realization of a beautiful China. [ABSTRACT FROM AUTHOR]

Accurately identifying the rural production-living-ecological space (PLES) of different islands can help reveal their distinct natural resources and land-use situations, which is significant for the sorted management, subarea utilization, and protection of islands. At present, studies on the PLES of island villages are deficient. For instance, the existing land-use classification system is incomplete; the PLES is poorly identified; and the dominant function of multiple land-use types based on different island geomorphology types is insufficiently investigated. Therefore, a case study was conducted on the island villages of the western sea area of Guangdong Province, based on remote sensing, spatial analysis, and land classification, with field research and the relevant data. In this study, before establishing the PLES system, the islands were classified, including six bedrock islands, 10 sedimentary islands, and one volcanic island. When the PLES system of the island villages was classified, the ecological and utilized areas of the intertidal zone and neritic region should be combined with the island–continent part, and the distinct industrial types should be emphasized, before forming 22 secondary types of PLES. Furthermore, it is found that each island generally has its own dominant space and land-use type. Ecological space (ES) dominates the bedrock islands, and production space (PS) is prominent for sedimentary islands and volcanic islands. Forestland, aquaculture pond, and dryland are the prominent land-use types for bedrock islands, sedimentary islands, and volcanic islands, respectively. The rural residential lands are the main component of living space (LS) in all islands, and the most urban residential lands are distributed on the bedrock islands. The main driving factors for the formation and distribution of island rural PLES are the altitudinal gradient and geomorphic characteristics. The research shows that the main problems of PLES are that the intertidal zones are threatened by aquaculture ponds at various levels, and the development of LS in these islands is generally backward. [ABSTRACT FROM AUTHOR]

The research on the land resources embodied in the inter-regional economic linkages is of great significance for the ecological compensation and sustainable use of the cultivated land. We constructed a model to estimate the agricultural land transfer embodied in inter-regional trade by using the agricultural land footprint model and the multi-regional input–output model. Taking China as an example, using a 2017 multi-regional input–output table and agricultural land and production data, we evaluated the agricultural land footprints embodied in the inter-provincial demand–supply chain in China and explored their characteristics, revealing the balance of income and expenditure that makes up the agricultural land footprint in each region. The results show that: (1) In 2017, China's total agricultural land footprint was 557.63 million ha2. Developed areas such as Shandong, Guangdong, and Jiangsu in the East generally had a greater footprint than the underdeveloped areas in the west. Provinces with more agricultural land do not necessarily have larger agricultural footprints. (2) The Agriculture, Hunting, Forestry, and Fishing industry and the Food, Beverages, and Tobacco industry were the main two sectors that contributed to the agricultural land footprint for each province, accounting for more than 60% of the total agricultural land footprint in China. (3) The embodied agricultural land transfer between regions showed two main directions on the whole: one was from north to south and the other from west to east, reflecting the transfer law of movement from the less developed regions to those that were more developed. [ABSTRACT FROM AUTHOR]

Understanding land use change patterns of rural town settlements (RTSs) is crucial for rural and small-town planning; however, few studies have explored pattern mining approaches to RTS trajectory analysis. In this study, we adopted a novel method by building sequence alignment method (SAM) to detect representative trajectory clusters of land use change of 1158 RTSs in seven waves from 1980 to 2015 in Guangdong, China. The results suggest that there are 10 clusters of RTSs with varying trajectories of land use change, implying their differences in the development processes and underlying socioeconomic, demographical, and institutional factors. A spatial distribution map of RTSs shows that stable cultivated ecological and stable ecologically dominant RTSs are distributed in the northern, eastern, and western parts of Guangdong, whereas stable rural construction and stable mixed construction RTSs are mostly located around the provincial boundary. Notably, 73% of the RTSs that have undergone changes in land use types are located in the Pearl River Delta (PRD), including urbanized and agricultural upgraded RTSs. The analysis presented here summarizes the driving forces of the spatial evolution of RTSs, including the location, landforms, industries, and policy factors. This study provides dynamic policy implications to understand longitudinal and sequential spatial restructuring and regional coordinated development in the fast-growing PRD area. [ABSTRACT FROM AUTHOR]

Rural landscape resources are important ingredients of rural revitalization and modernization in developing countries and regions. Evaluation methods play a crucial role in the development planning, design, transformation, and protection of these resources. However, there has been a lack of research on the evaluation of rural landscape resources, especially from the perspective of rural revitalization. Based on previous evaluation methods and expert consultations on landscape planning and design, we proposed a new approach for evaluating rural landscape resources by establishing a new index system and using the Analytic Hierarchy Process (AHP) and probabilistic linguistic Cloud Model. To demonstrate its applicability and effectiveness, we applied our model to the case of a village (Xiapu) in Guangdong, China, determined the parameter set of its rural landscape resources, and obtained related results showing that the method is practical and can reflect the value of resources objectively and efficiently. Based on this model, further suggestions are provided to improve the design and other utilizations of rural landscape resources. [ABSTRACT FROM AUTHOR]