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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]

This paper investigates the economic development within the Guangdong-Hong Kong-Macao Greater Bay Area from two perspectives--spatial pattern and influencing factors--to promote coordinated development across the area. This paper employs Moran's I test and local Getis-Ord G statistic from spatial statistics. Furthermore, it constructs a hierarchical spatial econometric model to facilitate empirical investigation. It is found that the overall economic development of the Guangdong-Hong Kong-Macao Greater Bay Area exhibits a "mountain-shaped" spatial pattern of the high-level homogeneous regions with "high-high correlation" and the low-level homogeneous regions with "low-low correlation." The internal difference in economic density is moderate, with an obvious trend of decrease year by year. Economic density shows a significant spatial positive correlation, with the expansion of the scope of areas exhibiting "high-high correlation." The differences in economic density between hotspots and sub-hotspots have decreased, but the economic density of cold spots has failed to keep up with the development of other regions. The difference in factor input density among the influencing factors explains most of the differences in economic density among different regions. The results from the R&D capital investment coefficient indicate that in recent years, the effect of investments in urban scientific and technological innovation factors has been more extensive and uniform among the regions under its jurisdiction, but the spatial spillover effect of innovation factors at both layers is not significantly positive. Apart from the city's location within the Greater Bay Area, the relative location of the jurisdictions within the city equally influences the economic development configuration of the Greater Bay Area. Although economic density in regions adjacent to cities outside the Greater Bay Area is notably lower than in other regions, their growth rate and production efficiency remain on par with other regions. T-test and model results underscore the rapid development of the areas encircling the bay. The coefficient of location dummy variables in areas adjacent to cities in the Greater Bay Area varies among cities. At a particular factor input density, some cities have higher output efficiency in areas contiguous to cities in the Greater Bay Area. This study uniquely adopts low-level city jurisdictions and high-level cities to shape a two-tiered hierarchical dataset with nested geographic units. This innovative approach fully leverages insights from distinct layers, delving into spatial interdependence and interplay across layers. This paper aims to explore the spatial pattern and influencing factors steering economic development in the Guangdong-Hong Kong-Macao Greater Bay Area. In doing so, it aims to identify problems and present pertinent policy recommendations. [ABSTRACT FROM AUTHOR]

As one of the four major bay areas in the world, the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is a highly integrated mega urban agglomeration and its unparalleled urbanization has induced prominent land contradictions between humans and nature, which hinders its sustainability and has become the primary concern in this region. In this paper, we probed the historical characteristics of land use and land cover change (LUCC) in the GBA from 2005 to 2015, and forecasted its future land use pattern for 2030, 2050, and 2070, using a cellular automata–Markov (CA–Markov) model, under three typical tailored scenarios, i.e., urban development (UD), cropland protection (CP), and ecology security (ES), for land use optimization. The major findings are as follows: (1) The encroachments of build-up land on the other land uses under rapid urbanization accounted for the leading forces of LUCCs in the past decade. Accordingly, the urban sprawl was up to 1441.73 km2 (23.47%), with cropland, forest land, and water areas reduced by 570.77 km2 (4.38%), 526.05 km2 (1.76%), and 429.89 km2 (10.88%), respectively. (2) Based on the validated CA–Markov model, significant differences are found in future land use patterns under multiple scenarios, with the discrepancy magnified over time and driven by different orientations. (3) Through comprehensive comparisons and tradeoffs, the ES scenario mode seems optimal for the GBA in the next decades, which optimizes the balance between socio-economic development and ecological protection. These results serve as an early warning for future land problems and can be applied to land use management and policy formulation to promote the sustainable development of the GBA. [ABSTRACT FROM AUTHOR]

In the face of an increasingly complex and unstable external development environment, enhancing economic resilience has become a key task in the construction of the Guangdong-Hong Kong-Macao Greater Bay Area. This paper analyzes the changes in the economic resilience of the Guangdong-Hong Kong- Macao Greater Bay Area and its constituent cities after the 2008 global financial crisis by virtue of the regional economic resilience assessment method proposed by Martin et al. It constructs an economic connection network for the Guangdong-Hong Kong- Macao Greater Bay Area using the data from corporate headquarters and branches of A-share listed companies to analyze its impact on the economic resilience of the Area. The study reveals the three following conclusions. Firstly, the economic resilience of the Guangdong-Hong Kong-Macao Greater Bay Area generally outperforms the national average level, seeing a rapid boost over the recent years and exceeding the level witnessed during the 2008 financial crisis. However, there are marked disparities in the economic resilience of various cities within the Greater Bay Area, with Shenzhen, Guangzhou, and Dongguan emerging as the most robust in this regard. Secondly, the economic connection network has a positive impact on the economic resilience of the cities in the Guangdong-Hong Kong-Macao Greater Bay Area. Specifically, there is a positive correlation between a city's economic resilience and its centrality in the economic connection network. Such centrality exerts a positive spillover effect on the economic resilience of surrounding cities. Thirdly, from the perspective of industry-specific networks, circulation and service industry networks are more conducive to improving the economic resilience of a city. Given the significant role of the economic connection network in shaping regional and urban economic resilience, it is imperative for the Guangdong-Hong Kong-Macao Greater Bay Area to prioritize ensuring economic development security and enhancing economic resilience, promote the development of the economic connection network, and enhance the network centrality of its constituent cities. This can improve the economic resilience of itself and its constituent cities in an effective manner. [ABSTRACT FROM AUTHOR]

The Guangdong-Hong Kong-Macao Greater Bay Area comprises a regional economy encompassing nine cities located in the Pearl River Delta region, in addition to the two special administrative regions of Hong Kong and Macao. The process of regional economic integration within the Guangdong-Hong Kong- Macao Greater Bay Area traces its origins back to the commencement of China's reform and opening up policies in 1978. It evolves from an initially spontaneous collaborative model, where front-end commercial activities are paired with rear-end manufacturing facilities, to a deliberate form of collaboration underpinned by institutional interventions. This paper examines the fundamental characteristics of the regional economic integration within the Guangdong-Hong Kong-Macao Greater Bay Area. It specifically delves into an extensive analysis of the construction and development of this area. The discussion encompasses four significant theoretical and practical facets that shape the progress of the Greater Bay Area's construction. These aspects include the synchronization of market integration within the Greater Bay Area, the harmonization of institutional frameworks, the convergence of the eastern and western sections of the Pearl River Estuary, and the amalgamation of regional innovations. Furthermore, the paper engages in an exploration of strategies designed to propel the advancement of the Greater Bay Area's development. These strategies encompass collaborative initiatives, such as the joint development of the Shenzhen-Hong Kong adjacent region, the cooperative advancement of Hengqin Island by Zhuhai and Macao, the approach to integrating adjacent areas, and the interlock strategy aiming to connect the two pivotal urban centers of Guangzhou and Shenzhen. [ABSTRACT FROM AUTHOR]

The construction of the Guangdong-Hong Kong-Macao Greater Bay Area (the Greater Bay Area) has injected new impetus into the development of the tourism economy and created new emerging tourism industries. Therefore, many tourism planning needs to be formulated, which generates new requirements for tourism planners in terms of quantity and quality of tourism planning. Different stakeholders have different views on the needs of tourism planners. From the perspective of different stakeholders, what are the factors affecting the training needs of tourism planners? There is little research on the training needs of tourism planners from the perspective of stakeholders. Most of the existing research on the training needs of tourism planners derive only from the perspective of students or the ones from supply and demand, rather than from multiple stakeholders. This paper aims to construct a conceptual framework identifying factors affecting the training needs of tourism planners from stakeholders' perspective. A qualitative method (semi-structured interviews) is applied in this study to investigate the factors affecting the supply and demand of tourism planners in the Great Bay Area. The results show educators focus on market demand, working competence and teacher resources. Enterprises are concerned about the tourism policy, market demand, educational requirements and working competence. Students focus on employment motivation and working competence. Market demand is a common concern of educators and enterprises. Working competence is a factor of common interest to all stakeholders. This paper also proposes theoretical and practical implications for the government, tourism planning companies, and tourism universities and students. [ABSTRACT FROM AUTHOR]

Regional tourism with urban agglomeration as the spatial carrier has become one of the flow characteristics of many tourists. Tourists visiting multiple cities at one time will certainly gain more value than visiting one city, but they will also pay more costs, thus affecting their emotions and willingness to travel. But the mechanism and theoretical boundaries have not been clarified. Based on the cognitive–affective–behavioral attitude model and the customer-delivered-value model, this paper takes the tourism destination tourists of the Guangdong–Hong Kong–Macao Greater Bay Area urban agglomeration as samples to explore the influence mechanism of tourists' perceived functional value, social value and knowledge value on positive emotion and travel intention, and to test the moderating mediating effect of monetary cost, energy cost and psychological cost on positive emotion. Social value and knowledge value positively affect travel intention. Functional value, knowledge value and social value positively affect positive emotion, and positive emotion positively affect travel intention. Positive emotion plays a complete mediating role between functional value, social value and travel intention, and plays a partial mediating role between knowledge value and travel intention. Monetary cost, energy cost and psychological cost respectively moderate the mediating effect of positive emotion between functional value and travel intention. Psychological cost moderates the mediating effect of positive emotion between social value and travel intention. Monetary cost and psychological cost respectively moderate the mediating effect of positive emotion between knowledge value and travel intention. Differences in the impact of consumers' perceived value at different levels on positive emotion and travel intentions. It opens up the differences in the effects of the interaction of different levels of perceived value and different types of perceived cost on positive emotion. From an empirical point of view, it is proved that the tourism boutique line design based on multicity destination combination in China is feasible as a method to promote tourism integration and regional tourism brand building. It also puts forward the management enlightenment of regional tourism integration and regional tourism brand building from the perspectives of enterprises, front-line service personnel, residents and government. [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]

At present, most existing studies on the urban building height are limited to a single city, and large-scale urban building height data are still lacking. Based on the Google Earth Engine platform, this paper first integrates radar remote sensing, optical remote sensing and other multi-source remote sensing data to retrieve the average heights of buildings at the scale of 500 m x 500 m in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in 2015. The average heights of buildings from 1985 to 2014 in GBA are then reconstructed by considering the processes of urban expansion, urban renewal and greening restoration. On this basis,the temporal and spatial evolution characteristics of urban building heights from 1985 to 2015 are analyzed. The results show that the distribution pattern of building heights in GBA in 2015 corresponds well with the pattern of regional economic development. The cities in GBA exhibit four growth patterns of urban average building heights from 1985 to 2015, namely, low growth with high base (Hong Kong and Macao),high growth with medium base (Shenzhen),low growth with medium base (Guangzhou, Zhuhai, Jiangmen and Zhaoqing), and high growth with low base (Foshan, Dongguan, Zhongshan and Huizhou). The evolution of urban structure in the built-up area can be divided into three types: upward expansion (Dongguan, Foshan, Shenzhen, Zhongshan and Macao), upward and outward expansion (Guangzhou and Hong Kong) and outward expansion (Jiangmen, Huizhou, Zhaoqing and Zhuhai). The reconstruction method of urban building height data proposed in this paper can be used to analyze the temporal and spatial distribution and growth patterns of urban building heights in different cities in the GBA, as well as help to understand the urban development process in the GBA, and provide references for other city studies. [ABSTRACT FROM AUTHOR]

Guangdong-Hong Kong-Macao Greater Bay Area is one of the regions with the highest degree of openness and the strongest economic vitality in China. In this paper, the remote sensing ecological index (RSEI) model is used to calculate the Ecological Traffic footprint of the Greater Bay area of Guangdong-Hong Kong-Macao, and the remote sensing data of the Greater Bay area of Guangdong-Hong Kong-Macao is used to analyse and evaluate the development and evolution of Ecological Traffic in the Greater Bay area of Guangdong-Hong Kong-Macao. The results show that the ecological traffic environment of the Greater Bay area of Guangdong-Hong Kong-Macao is good, and it is getting better. The poor areas are mainly concentrated in the central region, which is also improving. [ABSTRACT FROM AUTHOR]

Sci-tech innovation is an important measure for the Guangdong– Hong Kong–Macao Greater Bay Area to achieve high-quality economic development. theoretically, there is a coupling coordination relationship between sci-tech innovation and high-quality economic development, and coordinating the relationship between the two is beneficial to building the Guangdong– Hong Kong–Macao Greater Bay Area into an international innovation and technology hub and promoting high-quality economic development. At present, the Guangdong–Hong Kong–Macao Greater Bay Area has seen a year-on-year increase in both the level of sci-tech innovation and the level of high-quality economic development, with a sound development trend. Its coupling coordination degree between sci-tech innovation and high-quality economic development has steadily improved from a slightly uncoordinated stage to a primarily coordinated stage, but the overall degree of coordination is still not high, with some gaps from realizing the excellently coordinated stage. From the perspective of spatial distribution, the internal development of the Guangdong-Hong Kong–Macao Greater Bay Area is unbalanced, with obvious differences in development level and coupling coordination degree among cities. e convergence analysis shows that the differences in the coupling coordination degree among the cities in the Guangdong Hong Kong–Macao Greater Bay Area have stabilized after narrowing during the study period. However, the difference among the cities with higher coupling coordination degrees tends to widen in recent years. therefore, necessary measures should be taken to promote the coordination between sci-tech innovation and high-quality economic development in the Guangdong–Hong Kong–Macao Greater Bay Area. [ABSTRACT FROM AUTHOR]

When considering the economic growth of the Greater Bay Area, digital transformation stands out as a crucial catalyst. Given its significance, it is imperative to delve into both the theoretical and empirical aspects of how digital transformation affects the region's position in the global value chain. Theoretical hypotheses are put forward regarding the impact and mechanisms of digital transformation on the global value chain position of the Greater Bay Area based on a global value chain perspective. A three-tier nested input–output table that incorporates the Greater Bay Area is constructed, and trade decomposition and global value chain position measurement models specific to the area are developed for analyzing its current state in terms of global value chain position. Empirical testing was conducted to examine how digital transformation impacts this position. Digital transformation will significantly enhance the position of the Greater Bay Area in the division of global value chains, mainly through enhancing technological innovation transformation capabilities and value added by exports from the Greater Bay Area. Digital transformation within the Greater Bay Area has had a more substantial positive impact than the digital transformation in other provinces in China and digital transformation from foreign sources. In terms of city heterogeneity, it is evident that the global value chain from Hong Kong and Macao has experienced a more significant impact from digital transformation compared to cities in the Pearl River Delta. Therefore, the Greater Bay Area should increase the use of both digital transformation and differentiated use of digital transformation. PRD cities should actively learn from Hong Kong and Macao's forms of digital construction and promote the Greater Bay Area's global value chain status. [ABSTRACT FROM AUTHOR]

Exploring the evolution of economic spatial correlation and its network effect has theoretical significance of the economic spatial cooperation pattern in the Guangdong-Hong Kong-Macao Greater Bay Area In this paper, the modified gravity model, social network analysis, and OLS regression model were used to examine the evolution characteristics regarding the economic spatial network structure and its effect in the Guangdong-Hong Kong-Macao Greater Bay Area. It is found as follows. 1) The strength of economic connection in the Guangdong-Hong Kong-Macao Greater Bay Area had been continuously increased, driven by four cores in space and economic radiating at the edge became stable. Additionally, the potential energy of economic connection consistently rose, the network density and network relationships of economic network structure fluctuated, but dynamically increased. The network hierarchy varied but increased overall, the network efficiency showed a "V" shaped structure with an initial decline but an overall increase later. 2) The degree centrality of network steadily improved, and the spatial structure gradually transformed from a multi-point agglomeration to a bipolar confrontation. Additionally, the closeness centrality was highly unbalanced and varied greatly, and the betweenness centrality increased significantly. The control force of Shenzhen dominated economic connection role. 3) The augment of the network density and hierarchy as well as the decrease of network efficiency can effectively improve the economic connection intensity of the Guangdong-Hong Kong-Macao Greater Bay Area The degree centrality, betweenness centrality, and closeness centrality are also able to enhance the economic connection strengths in the Greater Bay Area. [ABSTRACT FROM AUTHOR]

• A method based on Neural Network is proposed for provincial-level CO 2 emissions estimation. • An autoencoder is proposed to downscale the provincial-level CO 2 emissions to city-level. • A complete city-level CO 2 emissions inventory of GBA and surrounding cities is complied. • Spatial dependence and agglomeration characteristics of CO 2 emissions are analyzed. • Spatio-temporal heterogeneity of factors affecting CO 2 emissions is investigated. Significant attention has been given to the issue of CO 2 emissions worldwide, especially for China as the largest emitter. Cities, as the main carriers of China's economic development, account for up to 85 % of CO 2 emissions from energy use in the country. The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) and surrounding cities play important leading roles in low-carbon development. However, compiling city-level CO 2 emissions is challenging due to the limited availability of data. In this paper, a new idea is proposed by using SSA-BP for estimating provincial-level CO 2 emissions and downscaling provincial-level CO 2 emissions to city-level CO 2 emissions by reconstructing missing values of city-level CO 2 emissions with an autoencoder. This study compiles a city-level CO 2 emissions inventory and conducts a spatio-temporal analysis of CO 2 emissions in the GBA and surrounding cities. The results show that CO 2 emissions exhibit a spatial distribution pattern with spatial dependence. Furthermore, according to the analysis of spatio-temporal heterogeneity, GDP influenced CO 2 emissions the most within the five influencing factors, and the influences of GDP, population, and energy intensity were mainly positive, while the influence of patents was mainly negative, and that of trade was both positive and negative. [ABSTRACT FROM AUTHOR]

The aim of this study is to comprehensively evaluate the economic network patterns and their influencing factors of the Guangdong–Hong Kong–Macao Greater Bay Area (GHMGBA) in China, considering population scale, economic development level, and land-resource endowment. By employing a modified gravity model and a social-network method, we quantitatively analyzed urban agglomeration integrity indices, such as network density, edge–core structure, cohesive-subgroup index, and urban individual index (e.g., centrality degree) of this region, encompassing nine cities in Guangdong Province and two special administrative regions. The results revealed significant changes in the economic network patterns within the GHMGBA over time. Furthermore, the quadratic assignment procedure correlation analysis index was used to identify the various factors affecting the strength of the economic interaction. The findings demonstrated an annual increase in the strength of economic interaction between cities and regions within the GHMGBA over the past 20 years, along with the emergence of a polycentric economic development pattern. The results also suggest that the spatial location and level of economic development are key determinants influencing the strength of economic linkages in this area. This study supports the conclusion that deepening exchanges and cooperation among core cities, bolstering economic development in sub-core cities, and facilitating the construction of an integrated regional transportation network will expedite the process of economic integration. [ABSTRACT FROM AUTHOR]

This paper applies statistical and synthetic analysis methods to study the characteristics of the three types of tropical cyclone (TC) that landed in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from 1981 to 2018 and the reasons for the differences of TC-induced wind and precipitation. The results show that there are interdecadal changes in the frequency and intensity of the landfalling TCs in the GBA, with decreased frequency but increased intensity in the 2010s. The TCs that landed in the west of the Pearl River Estuary (PRE) have the highest frequency and the strongest intensity during landing, which bring the strongest winds; the TCs that landed in the PRE have the lowest frequency and the shortest duration after landing, which cause the strongest precipitation; the TCs that landed in the east of the PRE have the longest duration on the land. This study shows that near the center of the TCs that landed in the PRE, there is a weak anomalous cyclonic shear compared with the ones that landed in the west of the PRE. It is a confluence area of anomalous north wind and anomalous southwest wind, with better water vapor convergence and dynamic rising conditions, which is conducive to the formation of heavy precipitation. Compared with the TCs that landed in the PRE and in its east, there is a closed positive anomalous center of pressure gradient in the northwest center of the TCs that landed in its west, resulting in higher wind speeds in the west of the PRE. The characteristics of the three types of TCs in the GBA are highly related to TC-induced damage. In the future, the GBA needs to focus on preparing for TCs landing in its west. Zhuhai, Jiangmen and Huizhou are key cities to guard against TCs. The results of this study provide foundations for effective management and reduction of TC disaster risks in the future development of the GBA. [ABSTRACT FROM AUTHOR]

[Display omitted] • Groundwater vulnerability zonation investigation is conducted for GHM Greater Bay Area. • Three different perspectives are comprehensively considered in this study. • Groundwater risk, function value and contamination status are superposed using the AHP method. • Quantification and visualization of groundwater contamination prevention regionalization. • The AHP-DRASTICH model is an effective method to evaluate the groundwater risk in shallow aquifers. The groundwater contamination assessment and prevention regionalization are an important part of groundwater investigation, which is of great significance to groundwater environmental management and contamination control. Although the Guangdong-Hong Kong-Macao Greater Bay Area (GHM Greater Bay Area) has faced significant groundwater contamination since 2020, there's been no investigation into its vulnerability zonation. In this paper, groundwater in a typical area of south China was taken as the research object. We analyzed groundwater contamination risk by considering both the groundwater vulnerability and the load from contamination sources. Using the analytic hierarchy process (AHP) method, we superposed the groundwater risk, function value and contamination status, and realized the quantification and visualization of groundwater contamination prevention regionalization. Based on the DRASTIC model, the zones of low, moderate, high, and very high vulnerability are respectively 14.2 %, 24.28 %, 37.67 %, and 20.45 % of the study area. Results show that the groundwater vulnerability grade in the area is III-IV, which means it is easy to be polluted. The functional value of groundwater in the south-central part of the region is relatively high. Most of the areas with high prevention and control levels are located in plain areas and the Piedmont plain area with an area of 460 km2. The secondary control levels (key protection area) are mainly located in the middle, southwest, and northeast of the plain, with an area of 6944 square kilometers. Consequently, the ideas and contamination prevention zone maps might provide a scientific foundation for long-term planning and management and groundwater conservation in the Guangdong-Hong Kong-Macao Greater Bay Area and peripheral zones. [ABSTRACT FROM AUTHOR]

As the population and industries continue to gather in cities, such cities face acute shocks caused by various natural disasters and high pressure caused by human interference. The means of accurately assessing and improving the level of urban resilience (UR) to cope with disturbances and shocks, enhance regional resilience, and improve sustainable urban development has become a hot topic. In this study, we constructed an UR assessment framework using "social-economic-institutional-ecological-engineering" to explore the dynamic evolution of spatial and temporal patterns in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) of China, to reveal the main factors influencing the evolution of this spatial pattern and the strength and interaction mechanisms among such influencing factors and to dynamically simulate the spatial and temporal evolution characteristics in the next 16 years. The results show that from 2000 to 2019, the difference in UR level in the GBA is obvious. Hong Kong and Macao are obviously ahead of other cities, and the comprehensive resilience level of Guangzhou and Shenzhen cities in the mainland is developing rapidly. The difference in per capita consumption capacity, industrial structure optimization, foreign trade vitality, infrastructure support and other factors are the main causes of the spatial differentiation of UR in the GBA. In the future, the UR level of the GBA urban agglomeration will show an overall upwards trend. The three development poles of Guangzhou-Foshan, Shenzhen-Hong Kong, and Zhuhai-Macao have obvious advantages in spatial distribution. The results of the study can enrich regional UR research, and also provide theoretical references to facilitate the high-quality development of the area. [Display omitted] • We constructed a "social-economic-institutional-ecological-engineering" urban resilience assessment framework. • The paper developed an evaluation system for the urban resilience to reveal its spatial and temporal evolution patterns. • The main factors influencing the evolution of the spatial patterns of urban resilience is investigated. • Our results provide a basis for the development of comprehensive management framework for urban resilient. [ABSTRACT FROM AUTHOR]

Understanding the economic impact of COVID-19 is the foundation for formulating targeted policies promoting economic recovery. This study uses panel data of the county economy in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) from 2017 to 2022. Firstly, the evolution characteristics of the economic structure in the GBA were analyzed using the standard deviation ellipse, geographical concentration, and spatial autocorrelation methods. Then, we revealed the changes in various economic indicators. Finally, a spatial Durbin model was constructed to study the factors affecting economic growth and spatial spillover effects in different periods. The results reveal that the economic distribution in the GBA presents a "core–edge" structure. The FDI, consumption, and exports of the Greater Bay Area fluctuate greatly, while investment growth is relatively stable. There is a significant spatial spillover effect in the county economy of the GBA. Investment, consumption, exports, labor, and innovation all have significant positive effects on economic growth, with investment having the greatest impact, while FDI has a significant negative impact. The impact of COVID-19 on the economy of the GBA is mainly reflected in the weakening of spatial spillovers, the strengthening of economic agglomeration, the decline in factor growth, and the change in the driving effect of factors on the economy. These findings can provide a reference for formulating targeted economic development policies. [ABSTRACT FROM AUTHOR]

This paper makes a systematic analysis on the development characteristics and evolution process of the built-up areas, as well as the substructure of the network connectivity and the functional division, of core cities in the Guangdong-Hong Kong-Macao region from the dual perspectives of "space of places" and "space of flows." It does so by using data of satellite remote sensing and industrial census to describe the spatial development trend and to establish an urban connectivity network based on the headquarter-branch data of enterprises. It also reveals the development differences within and outside of the Bay Area in Guangdong Province. The research shows that, firstly, since the new century, the integration of the Bay Area has become more obvious for Guangdong-Hong Kong-Macao region, as it has developed in a highly-concentrated way around the Bay Area of the Pearl River Estuary. Secondly, Guangdong, Shenzhen, Hong Kong, and Macao exhibited a more obvious regional division of labor, and their functions have been increasingly complementary to each other. Thirdly, a regional hub of "two sectors" - linking outside to the world city network and driving internal development of regional hinterland has been formed. Finally, based on the conclusion of the empirical research, development and planning implications of the Bay Area are discussed. [ABSTRACT FROM AUTHOR]

As one of the world's four Greater Bay Areas, the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) faces many environmental challenges along with rapid economic development, causing significant degradation of aquatic ecosystems. However, there is limited knowledge on long-term environmental changes (i.e., >50 years), and restoration of the degraded aquatic ecosystems in the GBA has become increasingly difficult. This study selects a typical inland water body, the Miaotan Reservoir, from Huizhou City in the GBA, to explore long-term changes in water and the eco-environment over the past 70 years and to provide some restoration and management strategies for degrading aquatic ecosystems in the region. We collected a sediment core from the reservoir center and established an age–depth profile by integrating 210Pb and 137Cs dating. We then set up high-resolution diatom community succession stratigraphy and multiple indicators (grain size, element, geochemical and social indicators) as responses to environmental changes in the reservoir. Our results show that significant changes have occurred in the ecosystem and environment of the Miaotan Reservoir and its catchment over the past 70 years. The diatom community underwent a gradual transition from absolute dominance of the mesotrophic species Aulacoseira granulata to dominance of the eutrophic species Nicizschia gracilis, Nicizschia palea and Achanathes sp., indicating the onset of water quality degradation and ecosystem changes in the 1990s due to eutrophication. The RDA (Redundancy analysis) results demonstrate that exogenous pollutant inputs into the Miaotan Reservoir resulting from agricultural activities over the period led to serious environmental changes, e.g., toxic algal bloom and heavy metal pollution. This study enriches our understanding of long-term environmental changes in inland lakes and reservoirs in South China and provides insights into the restoration and management of aquatic ecosystems in the GBA. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) pose a significant threat in terms of wind-induced damage and storm risk to the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) of China. In this research, annual power dissipation index (PDI) and storm surge and wave destructive potential (SDP) index from June to November were used to estimate the damage caused by the TC events in the buffer zone of the GBA. The regime shifts in 1993 and 2013 were identified through the Bayesian changepoint detection in six TC datasets. The TC-induced damage during 1994–2012 (the low-damage period) was weaker than that in 1977–1993 and 2013–2020 (the high-damage periods). The intensity and size of stronger TCs are the dominant factors responsible for the interdecadal changes in the TC damage. This may be explained by the influences of sea surface temperature (SST), surface latent heat flux and mid-level relative humidity. During high-damage periods, TCs can extract more energy from the ocean, leading to increased TC intensity and larger size. Conversely, during low-damage periods, TCs undergo a decrease in energy intake, resulting in reduced TC power and smaller size. The variations in the SST are relative to the Luzon Strait transport. In addition, the reduction in TC translation speed is unfavorable for the development of TCs in low-damage periods. Further research suggested that mid-level steering flow affects the TC movement velocity. The results offer valuable insights into the variations in the TC-induced damage in the GBA. [ABSTRACT FROM AUTHOR]

Macroalgae provide food for microbial, meio- and macro-faunal communities in coastal ecosystems, thus mediating nutrient dynamics and functions in these ecosystems. Because of this vital role, it is important to clarify physiological information about macroalgae as it reflects their growth potential in the field. In this study, we examined the biomass, pigment content, and photosynthetic O2 evolution rate versus irradiance curves of 18 macroalgal species from the intertidal zone of the Guangdong–Hong Kong–Macao Greater Bay Area, China, and investigated their photosynthetic patterns in relation to phyla characteristics, morphology, and growth locations. The results showed that green algae had the highest maximum photosynthetic O2 evolution rate (Pmax), light utilization efficiency (α), and dark respiration (Rd) among the three macroalgal phyla; the sheet-like macroalgal species had the highest Pmax, α, and Rd among the four morphological categories. The macroalgal species in the upper intertidal zone showed higher Pmax and α and lower saturation irradiance (EK) and compensation irradiance (EC) than those species in the lower intertidal location. The PCA results showed that the biomass of sheet-like macroalgal species was positively correlated with factor PC1 (50.34%), and that of finely branched species was negatively correlated with factor PC2 (25.17%). In addition, our results indicate that the light absorption and utilization capabilities of macroalgae could determine whether they could dominate the intertidal zone and that their photosynthetic characteristics could be used as a potential indicator of their biomass distribution in the Greater Bay Area. [ABSTRACT FROM AUTHOR]

Based on the theory of planned behavior, this study considers entrepreneurial traits and divides the entrepreneurial environment into policy environment, economic environment, and educational and cultural environment. Using SPSS 23.0 and Amos 24.0 softwares, a structural equation model of entrepreneurial intentions for university students originating from Hong Kong and Macao is constructed. A questionnaire survey method is used to collect data from 689 Hong Kong and Macao students in Chinese mainland universities to explore the factors influencing their entrepreneurial intentions and the ranges of their entrepreneurial intentions based on demographic variables. The results show that entrepreneurial attitude, subjective norms, perceived behavioral control, entrepreneurial traits, and entrepreneurial environment have a significant positive impact on the entrepreneurial intentions of Hong Kong and Macao students in the mainland universities. Male students have a higher level of entrepreneurial intentions than female students. Hong Kong students are more likely to have entrepreneurial intentions than Macao students, and students in Guangdong-Hong Kong-Macao Greater Bay Area (briefly, Greater Bay Area) universities are more likely to have such intentions than those from non-Greater Bay Area universities. Students majoring in science, engineering and medicine show a lower likelihood of intension than those majoring in humanities and social sciences. To comprehensively improve the entrepreneurial intentions of Hong Kong and Macao students and enable them to better shoulder their responsibilities in sharing the development dividends of the Greater Bay Area, it is necessary to start from the entrepreneurial environment and from the perspective of cultivating innovative talents. A support framework that integrates a training system, an ecological system, and a resource system is proposed to support students from Hong Kong and Macao in entrepreneurial development in the mainland. [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]

Relative contributions of greenhouse gases (GHGs) concentration and land use/cover (LULC) change induced by urbanization on future temperature over the Guangdong-Hong Kong-Macao Greater Bay Area in China under different climate scenarios are investigated in this study. The Weather Research and Forecasting model is used to downscale the future mean and extreme temperature using the Representative Concentration Pathways (RCP) 4.5&8.5 simulations from the Community Earth System Model. Results show that GHGs concentration is the key dominate factor for future mean and extreme temperature over the Greater Bay Area in the next 10–30 years, LULC change (urbanization) has relative slight effect on the future regional temperature. On the urban local scale, the LULC change in urban area has an obvious warming effect on urban local temperature under future different GHGs concentration scenarios. The regional-averaged annual mean surface air temperature (SAT) of urban area will increase 0.51 °C (0.73 °C), 0.83 °C (1.17 °C) and 1.26 °C (1.74 °C) resulting from the combined contributions of urbanization and GHGs forcing for the year of 2030, 2040, and 2050 under RCP4.5(RCP8.5) scenario, respectively. The contributions of the GHGs forcing are 0.33 °C (0.53 °C), 0.66 °C (1.00 °C) and 1.12 °C (1.58 °C), the urbanization forcing are 0.18 °C (0.20 °C), 0.17 °C (0.17 °C) and 0.14 °C (0.16 °C). Same as regional-averaged annual mean SAT, for the extreme high- and low-temperature, the contribution of GHGs forcing is more important than that of the urbanization forcing. The extreme temperatures are more sensitively to GHGs concentration under RCP8.5 scenario than that under RCP4.5, especially for the extreme low-temperature. GHGs are the most important factor for dominating the future mean and extreme temperature of the Greater Bay Area. [ABSTRACT FROM AUTHOR]

Accurately grasping the structural characteristics and influencing factors of green development spatial association are significant for green coordinated development and ecological civilization construction in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). This study evaluates the GBA's green development performance from 2015 to 2019 based on duality theory, and uses social network analysis to explore the structural characteristics and evolution of the green development spatial association network, and then uses the exponential random graph model to reveal the influencing factors of network formation. we find that: (1) the GBA's green development is steady. Its spatial association network became increasingly complex, and tends to be tight. (2) As important hubs, Guangzhou, Shenzhen, and Hong Kong have the dominant positions in the GBA's green development spatial association network. Huizhou, Jiangmen, Zhaoqing, and Macao are at the edge of the network, and their interoperability with other cities is relatively weak. (3) Four subgroups exist in the GBA during different periods, with obvious gradient characteristics between them, and the multilevel transmission mechanism of the green development network gradually forms. (4) Economic development and urbanization level, ecological environment endowment, and geographical, institutional, and industrial proximity all have significant impacts on the formation of the GBA's green development spatial association network. [ABSTRACT FROM AUTHOR]

The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is one of the most economically developed regions in China. To support the highly developed socioeconomical activities, the water demand in the GBA is in large amount and outstrips supply. Therefore, water resources in the GBA are under serious pressure. In this study, the supply, utilization, treatment, and reuse of water in the GBA were surveyed. Most cities in the GBA, face severe water shortage with per capita water resources being lower than the threshold of severe water scarcity (500 m³). During 2011-2020, the water consumption per 10,000 yuan of gross domestic product (GDP) of the GBA, decreased by 58%, showing an increased water utilization efficiency. The water quality was consecutively increased in recent years in the GBA with pollution-induced water shortage decreased and most drinking water sources meeting the Class III water quality standard. The utilization rate of reclaimed water varies greatly within the region, which is closely related to the degree of economic development and the amount of water resources. Besides, reclaimed water is mainly used for recharging water environment in the GBA, with unrealized potential in the industrial and municipal fields. It has been reached a consensus that water shortage would be one critical factor limiting the sustainable development of the GBA. To solve all facing challenges, the following strategies are proposed: 1) design urban development paths based on water resources; 2) promote the use of reclaimed water and other non-conventional water resources; 3) promote the construction of sponge cities. [ABSTRACT FROM AUTHOR]

• Carbon emissions of Guangdong cities were estimated. • The new-type urbanization levels were calculated by Principal Component Analysis. • Spatial Lag Model (SLM) and Spatial Error Model (SEM) were employed. • There are spatial clusters of carbon emissions in the study area. • The new-type urbanization has negative impact on carbon emission growth. The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) has an important strategic position in the overall development of China. Low-carbon development and the new-type urbanization are significant parts of the world-class greater bay blueprint. However, there is still insufficient literature about the carbon emission estimation and spatial analysis of the region. Based on the latest city-level data, this paper not only estimated the carbon emission and the new-type urbanization level of Guangdong cities but also applied spatial analysis of their carbon emissions, carbon emission per capita, and carbon intensity. It also used spatial data in the STIRPAT model to investigate the relationship between carbon emission and population, affluence, and the new-type urbanization. The results show that in 2017 Guangdong province emitted 603.92 million tonnes of CO 2. Of all the cities Guangzhou emerges as the biggest emitter, followed by Shaoguan and Zhanjiang which are industrial cities. There are spatial clustering effects among these cities and the GBA cities are the low carbon intensity clusters. The new-type urbanization level (NUL) has negative impact on carbon emission and the GBA cities have higher NUL than the others. Targeted policy implications are proposed based on the findings of the study. [ABSTRACT FROM AUTHOR]

Understanding the driving factors and assessing the risk of rainstorm waterlogging are crucial in the sustainable development of urban agglomerations. Few studies have focused on rainstorm waterlogging at the scale of urban agglomeration areas. We used the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) of China as a case study. Kernel density estimation (KDE) and spatial autocorrelation analysis were applied to study the spatial distribution characteristics of rainstorm waterlogging spots during 2013–2017. A geographical detector (GD) and geographically weighted regression (GWR) were used to discuss the driving mechanism of rainstorm waterlogging by considering eight driving factors: impervious surface ratio (ISR), mean shape index of impervious surface (Shape_MN), aggregation index of impervious surface (AI), fractional vegetation cover (FVC), elevation, slope, river density, and river distance. The risk of rainstorm waterlogging was assessed using GWR based on principal component analysis (PCA). The results show that the spatial distribution of rainstorm waterlogging in the GBA has the characteristics of multicenter clustering. Land cover characteristic factors are the most important factors influencing rainstorm waterlogging in the GBA and most of the cities within the GBA. The rainstorm waterlogging density increases when ISR, Shape_MN, and AI increase, while it decreases when FVC, elevation, slope, and river distance increase. There is no obvious change rule between rainstorm waterlogging and river density. All of the driving factors enhance the impacts on rainstorm waterlogging through their interactions. The relationships between rainstorm waterlogging and the driving factors have obvious spatial differences because of the differences in the dominant factors affecting rainstorm waterlogging in different spatial positions. Furthermore, the result of the risk assessment of rainstorm waterlogging indicates that the southwest area of Guangzhou and the central area of Shenzhen have the highest risks of rainstorm waterlogging in GBA. These results may provide references for rainstorm waterlogging mitigation through urban renewal planning in urban agglomeration areas. [ABSTRACT FROM AUTHOR]

In this study, a compound method using modified Boeing Fuel Flow Method 2 (BFFM2) and an updated First Order Approximation V3.0 (FOA3.0) method deploying the ICAO Time-in-Mode (TIM) was used to produce a more reliable aircraft emission inventory for the Guangdong–Hong Kong–Macao Greater Bay Area (GBA). The results show that compared with the International Standard Atmosphere (ISA) conditions, the total emission of nitrogen oxides(NOx) decreased by 17.7%, while carbon monoxide(CO) and hydrocarbons(HC) emissions increased by 11.2%. We confirmed that taxiing is the phase in which an aircraft emits the most pollutants. These pollutant emissions will decrease by 0.3% to 3.9% if the taxiing time is reduced by 1 minute. Furthermore, the impact of reducing taxi-out time on emissions is more significant than that of reducing the taxi-in time. Taking the total aircraft emission factors as the main performance indicators, Hong Kong International Airport (VHHH) contributes the most to the total emissions of the GBA, while the Zhuhai airport(ZGSD) contributes the least. The contribution of an individual airport to the total emissions of the GBA is mainly determined by the proportion of Boeing B77L, B77W, and B744. [ABSTRACT FROM AUTHOR]