Search

The concept of symbiotic and integrated development represents the adaptive response of Macao tourism to the necessity of diversified development. It is the inevitable path for Macao tourism to achieve high-quality sustainable development. In accordance with the principles of the symbiosis theory, this paper presents an analytical framework and employs Qualitative Comparative Analysis (QCA) to examine the intricate causal processes that simultaneously influence the symbiosis integration development of Macao tourism. The results of the empirical test demonstrate that: (1) The development of Macao tourism symbiosis and integration is influenced by five factors: external policy (EP), regional integration (RI), new digital technology (DT), gaming industry (GI), and cultural element (CE); (2) There are three configuration paths for Macao's tourism symbiosis, integration, and high-quality development, including an industrial synergy mode, innovation-leading mode, and value-sharing mode; (3) There are two configuration paths formed by the non-high-quality symbiotic integration of Macao tourism, namely, the government absence type and the market failure type; (4) RI and GI are the core conditions for the symbiotic and integrated development of Macao tourism, and there is a certain substitution relationship between EP, DT, and CE. This study advances the academic understanding of the asymmetric causal relationship between various factors and the symbiotic integration of tourism. It enhances the application of the symbiotic theory in the practise of tourism and reveals differences in the path of Macao tourism development under the combination of multiple factors. The findings provide a theoretical basis for the Macao government to optimise the tourism spatial layout and enhance tourism sustainability, build a multi-level "tourism+" symbiotic industrial structure, and establish a modern tourism governance system. [ABSTRACT FROM AUTHOR]

High-quality development is the foremost priority for comprehensively building a modern socialist country, and accelerating the establishment of a new development pattern is the strategic foundation for promoting high-quality development. Currently, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is focused on building a modern, globally competitive industrial system, advancing towards the goal of becoming a world-class bay area and a globally leading city cluster. This article discusses in depth the progress and achievements of the GBA in technological innovation, examines associated problems and challenges, and based on this, elucidates the significance of high-level forums in promoting technological innovation and development in the region. and proposes policy recommendations for promoting technological innovation and development through high-level forums in the Greater Bay Area. [ABSTRACT FROM AUTHOR]

Nowadays, China has expanded its partnerships with the participating countries in broader areas, with various cooperation platforms and projects being steadily implemented. These fruitful and productive collaborations have not only created tangible benefits for these countries, but have also contributed to promoting economic globalization, tackling global development challenges and improving global governance systems. The Guangdong-Hong Kong-Macao Greater Bay Area (GBA), as a crucial node of the Belt and Road Initiative (BRI), plays an important role in driving forward the BRI technological innovation agenda. This article explores the progresses of the GBA in BRI technological innovation practices, analyzes its advantages and challenges in this regard, and proposes pathways and measures for transforming the GBA into a leading hub of technological innovation under the BRI, for the purpose of advancing the leading role of the region in the initiative and contributing wisdom and power to technological innovation and cooperation worldwide. [ABSTRACT FROM AUTHOR]

Rapid morphological and socioeconomic changes have accelerated the urbanization process and urban land use transformation in China. Megacities comprise clusters of urban cities and exhibit both newly formed and well-developed urban land use development beyond administrative boundaries. It is necessary to distinguish the changing effects of spatial-varying driving factors on newly formed urban land uses from well-developed built-up areas in megacities. This study proposed a multi-spatial urbanization framework to quantify region-level socioeconomics, cluster-level ecological morphologies, and grid-level urban functional morphologies. A three-level Bayesian hierarchical model was developed to investigate the impacts of multi-spatial driving factors on urban land use transformation in megacities. The study period focused on the urbanization process between 2000 and 2018 in Guangdong–Hong Kong–Macao Greater Bay Area (GBA). Results revealed that compared with well-developed urban built-up land, changing impacts of three-level driving factors in urban land use transformation could be captured based on the proposed Bayesian hierarchical model. The region-level total population was associated with increasing possibilities in forming new residential land than the well-developed ones in 35 districts/counties/cities in GBA. Cluster-level ecological attributes with higher proportion, lower edge density of urban built areas, and lower-degree ecological complexity showed increasing probability on newly formed industrial and public land. Grid-level urban functional factors including public transportation density and shopping/dining distribution exhibited significantly decreasing probability (coefficients: −2.12 to −0.51) on contributing newly formed land uses compared with the well-developed areas, whereas business/industry distribution represented higher (coefficients: 0.99 and 0.15) and lower probabilities (coefficient: −0.22) of forming industrial/public land and residential land separately. This research shows a new attempt to distinguish multi-spatial morphological and socioeconomic effects in urban land use transformation in megacities.

Solid waste disposal and management have become a global problem, which is particularly tricky in China with its large population and rapid urbanization. This study focused on the disposal status of multi-source solid waste as well as the park management of some typical cities of China. Firstly, the main technical methods for solid waste disposal were summarized as follows: landfill, incineration, anaerobic digestion and aerobic compost. Secondly, the network analysis method was applied to seek an optimized method for solid waste disposal and management. Thirdly, typical demonstration parks for solid waste disposal and management were analyzed to study their respective operating modes and strategies for synergistic development in terms of resources, environment and economy and to explore the sustainable development potential of the Guangdong–Hong Kong–Macao operating mode. The results showed that the collaborative disposal and recycling of solid waste are important for cities (especially megacities) to achieve resource conservation and environmental protection. The NIMBY effect and environmental pollution risks caused by decentralized construction could be reduced through the construction of circular industrial parks. Advanced technologies and the national policy for solid waste disposal and recycling in industrial parks of small–medium cities, large–medium cities and megacities were systematically analyzed so as to explore a self-operating management mode of industrial parks. Finally, reasonable suggestions, such as sharing, saving and cycling and propaganda education, as well as green and low-carbon solutions were put forward for solid waste disposal and management in typical industrial parks, effectively resolving the contradiction between economic development and environmental protection so as to help urban sustainable development. [ABSTRACT FROM AUTHOR]

Rapid morphological and socioeconomic changes have accelerated the urbanization process and urban land use transformation in China. Megacities comprise clusters of urban cities and exhibit both newly formed and well-developed urban land use development beyond administrative boundaries. It is necessary to distinguish the changing effects of spatial-varying driving factors on newly formed urban land uses from well-developed built-up areas in megacities. This study proposed a multi-spatial urbanization framework to quantify region-level socioeconomics, cluster-level ecological morphologies, and grid-level urban functional morphologies. A three-level Bayesian hierarchical model was developed to investigate the impacts of multi-spatial driving factors on urban land use transformation in megacities. The study period focused on the urbanization process between 2000 and 2018 in Guangdong–Hong Kong–Macao Greater Bay Area (GBA). Results revealed that compared with well-developed urban built-up land, changing impacts of three-level driving factors in urban land use transformation could be captured based on the proposed Bayesian hierarchical model. The region-level total population was associated with increasing possibilities in forming new residential land than the well-developed ones in 35 districts/counties/cities in GBA. Cluster-level ecological attributes with higher proportion, lower edge density of urban built areas, and lower-degree ecological complexity showed increasing probability on newly formed industrial and public land. Grid-level urban functional factors including public transportation density and shopping/dining distribution exhibited significantly decreasing probability (coefficients: −2.12 to −0.51) on contributing newly formed land uses compared with the well-developed areas, whereas business/industry distribution represented higher (coefficients: 0.99 and 0.15) and lower probabilities (coefficient: −0.22) of forming industrial/public land and residential land separately. This research shows a new attempt to distinguish multi-spatial morphological and socioeconomic effects in urban land use transformation in megacities. [ABSTRACT FROM AUTHOR]

Accurate and reliable mangrove datasets are essential for the protection and management of mangrove ecosystems. Therefore, the evaluation of the current mangrove datasets and understanding the differences among them are critical. This study takes the Guangxi Beibu Gulf (GBG) and Guangdong-Hong Kong-Macao Greater Bay Area (GBA) as the study areas and analyzes the agreement and accuracy of eight mangrove datasets from 2000 to 2020 using area comparison, spatial agreement analysis, and absolute accuracy evaluation. The results show that; 1) significant differences exist in mangrove area and spatial distribution among the different mangrove datasets, with the percentage of high agreement areas ranging from 10% to 42%. 2) The overall accuracy of the evaluated mangrove datasets ranges from 56.2% to 95.6%, and the classification accuracy of mangrove datasets in inland areas is lower than the overall level. 3) There are regional differences in the quality of mangrove datasets, with the agreement and accuracy of mangrove datasets in the GBG being greater than those in the GBA. 4) Fine-scale mangrove mapping based on high-resolution remote sensing images, such as Sentinel, and global mangrove mapping based on the Google Earth Engine (GEE) cloud platform should be emphasized in the future. The findings of this study can provide guidance for data users to select appropriate mangrove datasets and a reference for future mangrove mapping research.

To investigate the potential impact of emission reduction measures on ozone (O3) formation under the carbon neutrality target, we examined the changes in O3 concentration and their sensitivity to various parameters in the urban and suburban areas of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). In this study, we used the Weather Research and Forecasting (WRF), the Sparse Matrix Operator Kernel Emissions (SMOKE) and the Community Multi-scale Air Quality Modeling system (CMAQ) air quality model to simulate O3 formation in three key years of 2020, 2030 and 2060, based on the Ambitious-pollution-Neutral-goal scenario data from the Dynamic Projection for Emissions in China (DPEC) model. The decoupled direct method (DDM) module embedded in CMAQ was used to calculate the first-order sensitivity coefficients of O3 to nitrogen oxides (SO3_NOx) and volatile organic compounds (SO3_VOC). The results show several important trends in the O3 concentrations and sensitivity. (1) For the changes in O3 concentrations, in terms of different seasons, the O3 concentration in the GBA region shows an increasing trend in winter in both 2030 and 2060 compared to 2020. In terms of different cities, the O3 concentration in Shenzhen shows a significant increasing trend compared to the other cities. (2) For changes in O3 sensitivity, SO3_NOx shows an increasing trend, with the negative area declining and the positive area increasing. In 2030, the negative absolute value of SO3_NOx is reduced, indicating that the NOx titration effect will be weakened. In 2060, SO3_NOx becomes positive in most areas of the GBA region. For SO3_VOC, the future scenario shows positive values throughout the study area for all years, but a decreasing trend. [ABSTRACT FROM AUTHOR]

With the increase in global economic integration, high-quality economic development (HQED) has become a common goal of all countries. Based on these five development concepts, this paper uses the Gini coefficient, trend surface analysis, geographically weighted regression (GWR), the entropy weighting method, and standard deviation ellipse analysis to study the spatio-temporal pattern and driving mechanism of HQED in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). This paper examines the spatial and temporal patterns and driving mechanisms of HQED in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) based on five development concepts. The study revealed that (1) HQED is on the rise overall, but at the same time, it highlights the uneven development of multiple dimensions, especially in terms of significant differences in innovation, openness, and sharing. (2) HQED shows a clear center-periphery structure, with Guangzhou, Shenzhen, and Hong Kong as the core high-value areas; the growth rate of HQED capacity in the internal areas is significantly greater than that in the external areas, and HQED is prominent in the cities around Guangzhou, Shenzhen, and Hong Kong. (3) Factors such as agglomeration level, human capital, foreign investment, infrastructure development, financial and environmental protection expenditures, and financial inputs, and scientific, and technological inputs have a significant positive effect on HQED, and their interactions are further strengthened. This study reveals the importance of the realization of HQED in the GBA and the promotion of the overall development of the region.

Effective monitoring of human activity in urban areas is essential for social stability and urban development. Traditional monitoring methods include wearable devices, survey sensor networks, and satellite remote sensing, which may be affected by privacy and weather conditions. Ambient seismic noise recorded by seismometers contains rich information about human activity and exhibits significant temporal and spatial variations, which provides valuable insights into social mobility. In this study, we investigated the correlation between human activity and ambient seismic noise in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) using the data recorded by 138 seismometers. Our results indicate that ambient seismic noise produced by human activity in the GBA is mainly concentrated between 2 and 20 Hz. The spatial distribution of ambient seismic noise exhibits a strong correlation with population and economy. Our results show that the analysis of ambient seismic noise can reveal the spatial and temporal impacts of different factors on human activity in the GBA, such as day and night, holidays, weather changes, national policies, and the coronavirus disease 2019 (COVID-19) pandemic. Furthermore, the analysis of 12-year-long ambient seismic noise at the Hong Kong seismic station shows a close connection between long-term changes in ambient seismic noise and local social development. This study suggests that the analysis of ambient seismic noise represents a novel method to gather critical information about human activity. Seismometers, which are widely deployed worldwide, have great potential as innovative tools for sensing human activity. [ABSTRACT FROM AUTHOR]

Over the past four decades, urban expansions driven by rapid economic development and climate change have exerted profound impacts on land-use dynamics in the Guangdong–Hong Kong–Macao Greater Bay Area. However, our understanding of the interplay of different urban landscape patterns in this rapid urbanization zone remains limited. This study examines remote sensing data from 1999, 2009, and 2019 to analyze the spatial heterogeneity of the urbanization impact on landscape patterns, as well as driving forces, in Zhuhai City, in the western part of the Great Bay. The findings reveal that land-use changes in Zhuhai City are primarily characterized by the conversion of cultivated land and water areas into construction land and forest land, particularly in the western part of the city. These changes may result from rapid urbanization, leading to increased fragmentation and spatial aggregation of landscape patterns. Landscape pattern changes in Zhuhai City were related to both socio-economic development and natural environment change. The urbanization has promoted the agricultural transformation with a loss of cultivated land and water areas. Additionally, this study suggests that the coordination of land use along with the protection and construction of the ecological environment is urgently needed to achieve sustainable development in the region. [ABSTRACT FROM AUTHOR]

The Guangdong-Hong Kong-Macao Greater Bay Area (the Greater Bay Area, the GBA) aims to facilitate in-depth cooperation among the Guangdong, Hong Kong, and Macao regions to create a world-class bay area-based urban agglomeration. This endeavor seeks to guide China's economy toward high-quality development and comprehensive opening-up. This paper examines the challenges and obstacles faced by Guangdong, Hong Kong, and Macao regarding regional coordinated development, thus putting forward the following standpoints. It proposes enhancing infrastructure interconnectivity to establish the GBA 1-hour Express Traffic Circle, thereby driving the construction of a world-class international shipping and logistics hub. Furthermore, the paper advocates for establishing an industrial collaborative development system in the GBA, jointly creating a pilot zone for industrial transformation and upgrading, and further advancing market integration. Creating an international innovation corridor and innovation center for science and technology (S&T) industry is also essential. This initiative would center around critical sectors, improving S&T innovation chain, and building a talent cooperation demonstration zone. The paper also emphasizes the establishment of a transport and logistics hub for the Belt and Road Initiative, along with the joint development of a cultural, economic, and trade exchange platform under the "Belt and Road" framework. Leveraging the comparative advantages of the three regions, the paper underscores the importance of innovative collaboration models, fostering a mutually beneficial and complementary mechanism for synergistic development. These approaches aim to realize the integrated and innovative development of the GBA. [ABSTRACT FROM AUTHOR]

Power transition is the top priority in energy transition. All existing power transition paths have been studied under the same system; thus far, no basic research has investigated what paths are involved and how they cooperate with each other under the interaction of different systems. Taking the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), featuring a "one country, two systems" approach, as an example, this research identified and quantified the best path for the GBA's power transition and explored the mode of cooperation during the power transition among the three regions under the interaction of different systems. The results showed that a combination of multiple low-carbon technologies is the best option for the GBA's deep power transition, which can be characterized by the following components: "gas increase, nuclear increase, coal guarantee, and low proportion of renewable energy". In this scenario, the GBA can achieve a carbon peak of 167 million tons of CO2 in 2023. Before 2030, the GBA needs to first develop class H gas power, photovoltaic power and nuclear power while phasing out subcritical and below thermal power cogeneration, and subcritical and below coal power. After 2030, a significant increase will be needed in the installed capacity of distributed gas power to replace some class E and F gas power units. Distributed rooftop PV power generation will be the mainstream method of renewable energy generation. Power generation through waste incineration can also provide a prominent contribution to urban biomass power. Under the interaction of different systems, breaking the technical barriers among the three regions would represent a breakthrough for establishing a cooperative power transition. A "one primary system, two auxiliary systems" theoretical framework of cooperation is proposed, and the scope of its application is revealed. This study can provide a case reference for the establishment of a win–win cooperation mechanism for energy transition in different countries. [ABSTRACT FROM AUTHOR]

As one of the most densely populated and rapidly growing metropolitan areas worldwide, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) of China has been developing extensive metro networks to relieve the escalating traffic congestion inner cities and to shorten public transport time inter cities. Metro construction possibly triggers ground deformation, which may result in damage to the tunnel, pipeline, and ground structures. Fast and efficient monitoring ground deformation along metro lines is vital not only to the metro itself but also to these structures. This study provides a comprehensive investigation of ground deformation along 67 metro lines within the GBA, based on multi-temporal synthetic aperture radar interferometry (MT-InSAR) by using Sentinel-1 from March 2017 to February 2022. The results reveal that deformation velocity in this area ranges from –39.4 mm/year to 14.2 mm/year, which is mainly caused by the tunnel excavation, adjacent excavation and construction, and unfavorable geological conditions. Furthermore, risk levels have been evaluated based on the InSAR-derived results with the proposed median absolute deviation heatmap clustering method. The risky area accounts for 16.31% (∼205.24 km2) of the whole study area in the GBA, among which 13.76%, 2.16%, and 0.39% are classified as three risk levels, with mean deformation velocities of –2.2 mm/year, –3.4 mm/year, and –6.5 mm/year, respectively. This study presents a comprehensive megalopolitan-scale ground deformation monitoring, for the first time, along metro lines in six cities of the GBA, which provides important insights into future metro line constructing, operating and planning.

Wetland ecosystems contain rich natural resources and vital ecological functions, and the investigation of spatial and temporal evolution characteristics of wetlands and their driving factors is critical for the management and conservation of wetlands. This study aimed to explore the spatial and temporal dynamics of wetlands in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) from 1976 to 2019 using multi-source remote sensing data (DISP KH-9, Landsat, and GaoFen-1), combing with the object-based classification method and landscape invasion index, and further analyze the driving forces affecting the spatial and temporal evolution of wetlands. The results showed that: (1) The total area of wetlands in the GBA showed a trend to first increase and then stabilize from 1976 to 2019. (2) The rapid development of aquaculture led to a continuous increase in aquaculture ponds and offshore aquaculture and a flat change in the middle and late stages, the area of mangroves declined substantially before 2000 and has gradually recovered since then, the invasion of various types of wetlands by built-up land is increasing, and wetlands are becoming increasingly fragmented. (3) The wetland changes in the GBA are the result of a combination of natural factors and human activities. Environmental conditions represent the basis for wetland dynamics, while the population, socio-economics, and policies are important drivers of wetland evolution. The findings will be beneficial to the understanding of wetland dynamic changes in the GBA over the past 40 years, and helpful to the scientific management and sustainable development of wetlands. [ABSTRACT FROM AUTHOR]

Effective monitoring of human activity in urban areas is essential for social stability and urban development. Traditional monitoring methods include wearable devices, survey sensor networks, and satellite remote sensing, which may be affected by privacy and weather conditions. Ambient seismic noise recorded by seismometers contains rich information about human activity and exhibits significant temporal and spatial variations, which provides valuable insights into social mobility. In this study, we investigated the correlation between human activity and ambient seismic noise in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) using the data recorded by 138 seismometers. Our results indicate that ambient seismic noise produced by human activity in the GBA is mainly concentrated between 2 and 20 Hz. The spatial distribution of ambient seismic noise exhibits a strong correlation with population and economy. Our results show that the analysis of ambient seismic noise can reveal the spatial and temporal impacts of different factors on human activity in the GBA, such as day and night, holidays, weather changes, national policies, and the coronavirus disease 2019 (COVID-19) pandemic. Furthermore, the analysis of 12-year-long ambient seismic noise at the Hong Kong seismic station shows a close connection between long-term changes in ambient seismic noise and local social development. This study suggests that the analysis of ambient seismic noise represents a novel method to gather critical information about human activity. Seismometers, which are widely deployed worldwide, have great potential as innovative tools for sensing human activity.

Disasters caused by extreme precipitation under global warming are anticipated to have a strong negative impact on urban construction and social security. In this study, daily grid precipitation datasets of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) for the period 1961–2018 were extracted to explore the temporal and spatial characteristics of extreme precipitation by using regression analysis, moving average and kriging interpolation. The frequency and intensity indices showed an increasing trend, whereas a decreasing trend was found for the persistence indices, which indicates that GBA tends to slowly become wetter. The mean values of extreme precipitation indices (EPIs) in GBA generally increased from west to east and from north to south. Except for the indices of consecutive wet days and consecutive dry days, other EPIs showed an upward trend in most regions, especially in coastal cities where floods are more likely to occur. Principal component analysis and regression analysis showed that the correlations between the EPIs mostly passed the 0.05 significance test, which suggests that they had a good indicator of extreme precipitation in GBA. This study provides a theoretical basis for extreme precipitation disaster prevention and control within the urban agglomerations of the GBA. HIGHLIGHTS Nine indices were applied to spatio-temporally analyze extreme precipitation trends.; Time-series analysis and spatial interpolation were used to examine the characteristics of extreme precipitation in the GBA.; Except for CDD and CWD, the other seven EPIs had an increasing tendency.; Coastal regions generally showed a stronger upward trend of extreme precipitation.;

Strategic coupling is a hot field in the research of global production networks. The existing literature mostly consists of studies from the perspective of countries, regions, and enterprises, and relatively lacks the content for describing global–local strategic coupling networks and their evolution path with industries as carriers. The GBA is a bridgehead for China to participate in the global production network. Based on the systematic construction of the strategic coupling network analysis framework of global–local knowledge spillovers, this paper, taking the biopharmaceutical industry of GBA as an example, depicts the evolution process of its coupling network nodes, structures, and networks from 1990 to 2019 by using system analysis, social network analysis, and other methods, and analyzes node centrality, network structure, coupling paths, and their influencing factors. We found that the strategic coupling of global–local knowledge spillover is typically a networked structure, with the network organization presenting a multi-element sub-system hierarchical state. The overall network structure, with knowledge spillover as the carrier, shows obvious phased differences, having gone through three path stages from exploratory to expanding to stable. The path selection shows a spatial progression and a temporal sequence. The main factors affecting the path selection of the coupling network are the bargaining power of regional knowledge, behavioral subjects' will, and multi-dimensional proximity. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has seriously affected China's macroeconomy, industrial transformation, and high-quality development. Research on economic patterns and urban network systems can provide a reference for healthy development of the regional economic system. The evolution of the economic pattern and urban network system of Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from 2010 to 2020 is investigated using methods (e.g., the gravity center model, the gravitational force model, social network analysis, and geographic information system). (1) The gravity center of gross domestic product (GDP) of the GBA is located in Nansha district, Guangzhou, with a skewing direction northwest-east-northwest and a movement rate of “large-small-large.” The center of import and export and the center of consumption show a “zigzagging migration” in which the center of investment shows an “irregular (random) migration”. (2) The economic connection degree of cities in the GBA exhibits a high ascending velocity, and the whole area tends to be mature, with a significant effect of spatial proximity. With the steady increase in network density, there is significant polarization of network centrality in the region. The four major cohesive subgroups have been relatively stable and consistent with the degree of geographic proximity of the cities. The center-periphery structure is more significant, in which the core area is extended to the cities on the east coast of the Pearl River Estuary, thus forming the core cluster of “Hong Kong-Shenzhen-Guangzhou-Dongguan.” In this study, the evolution of economic patterns and urban network systems in the GBA over the past decade is analyzed using multiple methods (i.e., gravity model, urban network system analysis, and geographic information system) based on urban socioeconomic data by starting from various spatial elements (e.g., “points, lines, and networks”) to gain insights into and optimize research on regional economic development after the COVID-19 pandemic.

With the continuous improvement in the integration of urban agglomeration, a multi-functional, socialized, and complex dynamic system, effective prevention and control of emergent public health events have become increasingly important. Based on the Public-Health Vulnerability-Assessment-System of Urban Agglomeration (PVUA), the temporal and spatial differentiation characteristics of vulnerability in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) for the period of 2015-2019 are explored, and the vulnerable cities to public health events are identified in this area. The results can be summarized as follows: (1) The overall vulnerability to public health events in GBA decreases in the investigated period. (2) In the temporal dimension, accompanied by social and economic development, the sensitivity to public health events increases in GBA, and the coping capacity change from stable fluctuation to rapid improvement. (3) From the spatial dimension, the sensitivity level in GBA is low in the west, relatively high in the middle, and high in the southeast; the coping capacity is high in the southeast and low in the northwest; the collaborative governance capacity presents a spatial pattern of being low in the south and high in the north. (4) In the period of study, the vulnerability to public health events in Guangzhou and Jiangmen is stable at the lowest level, while that in Zhaoqing, Foshan, and Hong Kong SAR (Special Administrative Region) gradually reduces; the vulnerability in Shenzhen, Zhuhai, and Dongguan is fluctuating, and that in Huizhou, Zhongshan, and Macao SAR is continually maintained at a higher and the highest level.

Collaborative innovation can promote scientific productivity and the development of clean technology and thus has a great potential in constraining the ecological footprint. However, current studies on the impact of collaborative innovation on ecological footprint are insufficient, and results remain controversial. To better understand these impacts, this paper took Guangdong-Hong Kong-Macao Greater Bay Area of China as a case, estimated the ecological footprint at the municipal level from 2008 to 2018, measured collaborative innovation both from four dimensions and from a composite approach, then applied threshold regression models to compare the impact of collaborative innovation on the ecological footprint across different economic intervals. The findings showed that: the ecological footprint of the Greater Bay Area displayed an overall upward trend with prominent spatial heterogeneity. The impact of collaborative innovation on the ecological footprint presented a double-threshold effect when examined with different indicators. Among which, the flow of scientific personnel and capital boosted the ecological footprint, which intensified with economic development, while collaboration in technology exerted significant inhibitory effects on ecological footprint, and the influence of inter-city knowledge collaboration was limited. Overall, collaborative innovation inhibited ecological footprint when measured by a composite index. This might inspire policymakers to adopt sustainable strategies depending on the type of collaborative innovation and the economic status of the city to constrain growth of the ecological footprint, thus minimizing the pressures of human activities on the environment and moving towards a more carbon neutral society. [ABSTRACT FROM AUTHOR]

Hong Kong has an ambitious carbon neutral goal to meet by 2050. Achieving this goal requires a departure from a traditional city-scale centralised, fossil fuel-based energy infrastructure to a more decentralised, locally-generated renewable energy (RE) while expanding the regional intercity smart grid system to accommodate RE import in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). Such energy transitions inevitably bring new social challenges, but how Hong Kong citizens perceive such transitions in the GBA context is not well studied. This study draws on quantitative and qualitative data derived from an online deliberative poll (DP) (N = 174) on smart energy transitions. We have four key findings. Firstly, citizens showed a low level of trust in the national, provincial, and city governments whilst a high level of trust towards the incumbent electricity companies. Secondly, citizens showed distrust to the governments, suspecting that the genuine motives of the governments were to prioritise regional RE import over local RE production. Thirdly, citizens raised concerns over five types of risks (price volatility risks, energy reliability risks, cost overrun risks, data privacy risks, and environmental risks) that contributed to new sources of public distrust in governments' competence. Fourthly, the public distrust toward multilevel governments was found to be underpinned by demographic factors (age group and family size) and a sociopolitical context of recent social movements against government policies. Our findings suggest that policymakers in the GBA need to give sufficient attention to enhancing public trust, and thereby the policy legitimacy of regional smart energy transitions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Guangdong University of Technology is the property of Journal of Guangdong University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Currently, urban crises are spreading, even tending to be magnified along the urban networks. Improving urban network resilience can effectively reduce the loss and cope with sudden disasters. Based on the dimensions of regional resilience and the framework of urban network, a new evaluation system of network resilience, including economic, social, and engineering networks, was established to assess the network resilience of the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) from a structural perspective. We analyzed the spatial characteristics and influencing factors of network resilience using social network analysis and quadratic assignment procedure. The results were as follows: (1) regional difference was biggest in GBA's economic network strength while smallest in its transportation network strength, and the east bank of the Pearl River represented an extremely resilient connection axis; (2) the structures of network resilience and its subsystems were heterogeneous, and the connection paths of network resilience were more heterogeneous and diversified than those of the subsystems; (3) network resilience presented an obvious core–edge structure, and the spatial correlation and spillover effect between blocks were substantial; and (4) geographical proximity, as well as differences in economic development, urban agglomeration, and market development, had a significant impact on network resilience. This study provides a more systematic approach to evaluate the regional network resilience, and the results provide references for the construction of bay areas in developing countries. [ABSTRACT FROM AUTHOR]

In the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), a series of natural environmental, economic, and social issues have emerged sequentially in the process of rapid economic and social development. Therefore, for the sustainable development of the GBA, how to closely integrate nature protection with economic and social development to improve the sustainable development level of the social-economic-natural compound ecosystem, and realize the coordinated development of the system is particularly important. Based on the perspective of complex network and the theory of compound ecosystem, this study proposes a set of sustainable development evaluation model based on complex network modeling to evaluate the sustainable development level of compound ecosystem in GBA from 2014 to 2018, and further analyze the coupling coordination degree. The major findings include: 1) For the sustainable development in the GBA, the development of the natural subsystem is an important foundation, and the synchronous development of the social and economic subsystems are the main driving force. 2) The sustainable development level in the GBA shows an overall steady upward trend; the average level of the compound ecosystem’s coupling coordination development is in a “good” state, and it shows an evident upward trend. 3) Whether it is within the GBA or the GBA and its surrounding regions, there are problems of unbalanced and insufficient regional development. Policy recommendations include increasing the emphasis on the sustainable development of the natural subsystem, promoting the coordinated development of the economic, social, and natural subsystems, and promoting the balanced development of cities within the GBA, as well as the GBA and surrounding regions.

Copyright of Journal of Shenzhen University Science & Engineering is the property of Editorial Department of Journal of Shenzhen University Science & Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The longest cross-sea bridge worldwide, i.e., the Hong Kong-Zhuhai-Macao Bridge (HZMB), opened in October 2018, integrating nine cities and two special administrative regions of China's Guangdong-Hong Kong-Macao Greater Bay Area (GBA). The GBA is one of the regions with the most active economic vitality in China and has an important strategic position in the overall national development. Using full-sample freeway toll data of the GBA, this paper proposed a new structured evaluation methodology that combines the node degree, traffic volume, and topological and flow field-theory (e.g., agglomeration and distribution) to explore the hub city ranking and evolution in this area. The data period we selected to analyze is immediately before and after the opening of the HZMB, and we assess the counties' centrality changes in the GBA by analyzing these data. The findings reported in this paper can reflect the traffic hub evaluation process and provide more macroscopic intercity transportation views to the government.

Against the background of globalization and informatization, innovation is the primary driving force for regional economic and social development. Urban agglomerations are the main body of regional participation in global competition, and promoting the construction of the Guangdong-Hong Kong-Macao Greater Bay Area is an important strategy for China's regional economic development. Aimed at the differences in location advantages among cities in the Guangdong-Hong Kong-Macao Greater Bay Area, based on the theory of innovation chain, we developed a three-stage model of "knowledge innovation-scientific research innovation-product innovation". A three-stage DEA model was used to measure the innovation efficiency of cities in the Greater Bay Area at different stages, and two progressive two-dimensional matrices are constructed to locate the innovation development of cities according to the efficiency value. The results show the following: ① The overall innovation efficiency of the Greater Bay Area urban agglomerations gradually decreased in the process from knowledge innovation and scientific research innovation to product innovation, and the innovation efficiency among cities was unbalanced. ② Shenzhen, Guangzhou, and Hong Kong all performed well in the whole innovation stage, while other cities in the Greater Bay Area showed weakness in innovation at different stages. Based on this, this paper puts forward relevant countermeasures and suggestions for promoting and optimizing collaborative innovation in the Greater Bay Area taking into account factor flow, industrial structure, and innovation network of urban agglomerations. [ABSTRACT FROM AUTHOR]

Wetland ecosystems contain rich natural resources and vital ecological functions, and the investigation of spatial and temporal evolution characteristics of wetlands and their driving factors is critical for the management and conservation of wetlands. This study aimed to explore the spatial and temporal dynamics of wetlands in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) from 1976 to 2019 using multi-source remote sensing data (DISP KH-9, Landsat, and GaoFen-1), combing with the object-based classification method and landscape invasion index, and further analyze the driving forces affecting the spatial and temporal evolution of wetlands. The results showed that: (1) The total area of wetlands in the GBA showed a trend to first increase and then stabilize from 1976 to 2019. (2) The rapid development of aquaculture led to a continuous increase in aquaculture ponds and offshore aquaculture and a flat change in the middle and late stages, the area of mangroves declined substantially before 2000 and has gradually recovered since then, the invasion of various types of wetlands by built-up land is increasing, and wetlands are becoming increasingly fragmented. (3) The wetland changes in the GBA are the result of a combination of natural factors and human activities. Environmental conditions represent the basis for wetland dynamics, while the population, socio-economics, and policies are important drivers of wetland evolution. The findings will be beneficial to the understanding of wetland dynamic changes in the GBA over the past 40 years, and helpful to the scientific management and sustainable development of wetlands.

Coastal wetlands play a vital role in coastline protection. Coastal landscape of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) has changed drastically. In order to explore the process and law of coastal landscape dynamics in GBA, in this research, multi-temporal Landsat images and proposed hybrid object-based image analysis and multi-level decision tree classification were applied to monitor the changing trends and current status of the coastal landscape in GBA from 1988 to 2018. There were 5818.58 km2 of natural wetlands in 2018, including mangroves (39.62 km2), tidal flats (73.64 km2), rivers (161.65 km2), and shallow seas (5543.67 km2). Due to the influence of anthropogenic activities, natural wetlands showed a decreasing trend, while artificial wetlands and non-wetlands have been increasing. Results indicated that the landscape in the study area showed a trend of fragmentation, the degree of landscape aggregation weakened, the dominant patches were scattered, the shape became more and more complex, and the landscape heterogeneity increased. Then, a future land use simulation model was used to simulate the coastal landscape change trends of GBA in 2028 under four scenarios. The mangrove has the fastest growth rate of 1.47 km2/a under wetland protection scenario. The tidal flat and paddy field would increase under scenarios of the wetland protection and farmland protection, respectively. The construction land has the fastest growth rate under the economic development scenario and would be 47.56 km2/a. Woodland, dry land, and culture ponds are further transformed into other landscape types, while rivers, shallow seas, and reservoirs/ponds have no obvious change.

As climate change intensifies, extreme weather events, especially the coastal flooding is becoming increasingly severe. Carbon emissions exacerbate climate change, leading to increased frequency and intensity of extreme precipitation, which may result in flooding. This study focuses on the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), a coastal city cluster in China, and adopts the scenario analysis approach to evaluate flood risks in the different carbon mitigation scenario from the perspectives of dangerousness, and adaption ability. The result shows that the Huizhou in the east of the GBA and the Zhaoqing in the northwest have the highest level of flood disaster risk. However, the central region of the GBA (Guangzhou, Dongguan, Zhongshan, and Foshan) is very vulnerable to disaster-inducing environments and hazard-affected bodies, which means that the flooding risk and the impact of such risk in the GBA display regional differentiation characteristics and the spatial patterns of them are different. According to the carbon neutrality scenario, the flood risk in the GBA peaks in 2030, and decreases during 2030–2090. In contrast, under the uneven development and high carbon pathways, the risk peaks in 2060, and the overall risk by 2090 is roughly the same as that in 2060. In addition, the regions with the highest flood disaster risk are Guangzhou, Foshan, Dongguan and Zhongshan along the Pearl River estuary. Overall, it shows that areas with higher levels of urbanization and more developed economies will face higher flooding risks. The "dangerousness-adaption ability" structure can reflect the spatial distribution pattern of flood disaster risks, identify the priority areas for flood prevention and control, and is conducive to risk management. At the same time, the research results show that ecological protection have a positive effect on reducing flood risk. In addition, corresponding policy recommendations for pre-disaster, mid-disaster and post-disaster have been proposed, providing references for flood risk management in the GBA. [ABSTRACT FROM AUTHOR]

China has experienced rapid urbanizations with dramatic land cover changes since 1978. Forest loss is one of land cover changes, and it induces various eco-environmental degradation issues. As one of China's hotspot regions, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) has undergone a dramatic urban expansion. To better understand forest dynamics and protect forest ecosystem, revealing the processes, patterns and underlying drivers of forest loss is essential. This study focused on the spatiotemporal evolution and potential driving factors of forest loss in the GBA at regional and city level. The Landsat time-series images from 1987 to 2017 were used to derive forest, and landscape metrics and geographic information system (GIS) were applied to implement further spatial analysis. The results showed that: 1) 14.86% of the total urban growth area of the GBA was obtained from the forest loss in 1987–2017; meanwhile, the forest loss area of the GBA reached 4040.6 km2, of which 25.60% (1034.42 km2) was converted to urban land; 2) the percentages of forest loss to urban land in Dongguan (19.14%), Guangzhou (18.35%) and Shenzhen (15.81%) were higher than those in other cities; 3) the forest became increasingly fragmented from 1987–2007, and then the fragmentation decreased from 2007 to 2017); 4) the landscape responses to forest changes varied with the scale; and 5) some forest loss to urban regions moved from low-elevation and gentle-slope terrains to higher-elevation and steep-slope terrains over time, especially in Shenzhen and Hong Kong. Urbanization and industrialization greatly drove forest loss and fragmentation, and, notably, hillside urban land expansion may have contributed to hillside forest loss. The findings will help policy makers in maintaining the stability of forest ecosystems, and provide some new insights into forest management and conservation. [ABSTRACT FROM AUTHOR]

With the increase in global economic integration, high-quality economic development (HQED) has become a common goal of all countries. Based on these five development concepts, this paper uses the Gini coefficient, trend surface analysis, geographically weighted regression (GWR), the entropy weighting method, and standard deviation ellipse analysis to study the spatio-temporal pattern and driving mechanism of HQED in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). This paper examines the spatial and temporal patterns and driving mechanisms of HQED in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) based on five development concepts. The study revealed that (1) HQED is on the rise overall, but at the same time, it highlights the uneven development of multiple dimensions, especially in terms of significant differences in innovation, openness, and sharing. (2) HQED shows a clear center-periphery structure, with Guangzhou, Shenzhen, and Hong Kong as the core high-value areas; the growth rate of HQED capacity in the internal areas is significantly greater than that in the external areas, and HQED is prominent in the cities around Guangzhou, Shenzhen, and Hong Kong. (3) Factors such as agglomeration level, human capital, foreign investment, infrastructure development, financial and environmental protection expenditures, and financial inputs, and scientific, and technological inputs have a significant positive effect on HQED, and their interactions are further strengthened. This study reveals the importance of the realization of HQED in the GBA and the promotion of the overall development of the region., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author(s).)

The article applies a three-stage Slacks-Based Measure-Data Envelopment Analysis (SBM-DEA) pattern to examine the relationship between energy consumption and unfavorable CO2 emissions on green sustainable development, for the 11 cities of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) during 2010–2016, by going through various violated factors and stochastic disturbance. Labor, capital and energy resource are chosen as input variables, while GDP and CO2 emission as output variables. During the three phases consisting of the SBM-DEA model (first stage and third stage) and SFA analysis (second stage), CO2 emission is considered as an unfavorable outcome, while stochastic statistical disturbances and external environmental influences are identified. The results show that the average efficiency of the GBA cities is 0.708, with only Shenzhen, Macao SAR and Hong Kong SAR having an efficiency of 1 during the whole study period. Based on the findings, suggestions are made for the GBA cities’ sustainable development aspects.

We investigate the evolution of the container port system of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), better known as Pearl River Delta (PRD). We analyze the economic drivers that over the years have shaped port development in one of the world's most dynamic regions, embracing three of the world's busiest container ports: Shenzhen, Hong Kong and Guangzhou. Three industry concentration methodologies are employed, each with its own distinct advantages: Concentration Ratios; the Herfindahl-Hirschman Index; and Dynamic Shift-Share Analysis (DSSA). Especially through the latter methodology, DSSA, -used here for the first time in the analysis of the evolution of 'port systems'- we explain not only the shifts in market shares among the three 'giants', but also the underlying economic forces responsible for these shifts and for the relocation of economic activity in the hinterlands of those ports. We analyze the foreland and hinterland strategies of the ports, pursued as a result of rising inter-port competition and fuzzy, intertwined, hinterlands. The paper argues for the need of a more system-wide coordination and collaboration among ports, aiming to avoid overcapacity; duplication of scarce resources; low return on investment and, in general, wasteful competition. It is hoped that our analysis and ensuing recommendations will help other countries, port policymakers and stakeholders, to better understand, and thus exploit, the economic levers which shape the evolution of ports in proximity. Complementarity, existing or targeted, is a necessary condition for effective port cooperation between neighboring ports (Stamatovic et al., 2018). In addition to the development of complementary port functions and possibly the sharing of equipment and services (e.g., tugboats; pilot organizations; port community systems, management knowhow, etc.), an interesting manifestation of complementarity is port regionalization. Here, our recommendations are: a) Hong Kong pursues foreland-based regionalization. It is therefore necessary to consolidate and enhance its status as a leading international shipping center and port cluster and support the development of high-end maritime services. Guangzhou, on the other hand, develops hinterland-based regionalization. It is therefore necessary to enhance its role in domestic trades, further improve its connectivity with other seaports, and improve further its integrated transport network of inland waterways, railroads and highways. Finally, Shenzhen pursues a regionalization model between that of Hong Kong and Guangzhou, advancing both foreland- and hinterland-based regionalization at the same time. Here, our advice to Shenzhen is to spearhead an institutional dialogue with all system-ports, aiming at enhancing the overall international competitiveness of the container port system of the GBA, capitalizing on the region's world-class advantages. [ABSTRACT FROM AUTHOR]

The identification of ecosystem types is important in ecological environmental assessment. However, due to cloud and rain and complex land cover characteristics, commonly used ecosystem identification methods have always lacked accuracy in subtropical urban agglomerations. In this study, China's Guangdong-Hong Kong-Macao Greater Bay Area (GBA) was taken as a study area, and the Sentinel-1 and Sentinel-2 data were used as the fusion of active and passive remote sensing data with time series data to distinguish typical ecosystem types in subtropical urban agglomerations. Our results showed the following: (1) The importance of different features varies widely in different types of ecosystems. For grassland and arable land, two specific texture features (VV_dvar and VH_diss) are most important; in forest and mangrove areas, synthetic-aperture radar (SAR) data for the months of October and September are most important. (2) The use of active time series remote sensing data can significantly improve the classification accuracy by 3.33%, while passive time series remote sensing data improves by 4.76%. When they are integrated, accuracy is further improved, reaching a level of 84.29%. (3) Time series passive data (NDVI) serve best to distinguish grassland from arable land, while time series active data (SAR data) are best able to distinguish mangrove from forest. The integration of active and passive time series data also improves precision in distinguishing vegetation ecosystem types, such as forest, mangrove, arable land, and, especially, grassland, where the accuracy increased by 21.88%. By obtaining real-time and more accurate land cover type change information, this study could better serve regional change detection and ecosystem service function assessment at different scales, thereby supporting decision makers in urban agglomerations.

The COVID-19 pandemic has seriously affected China's macroeconomy, industrial transformation, and high-quality development. Research on economic patterns and urban network systems can provide a reference for healthy development of the regional economic system. The evolution of the economic pattern and urban network system of Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from 2010 to 2020 is investigated using methods (e.g., the gravity center model, the gravitational force model, social network analysis, and geographic information system). (1) The gravity center of gross domestic product (GDP) of the GBA is located in Nansha district, Guangzhou, with a skewing direction northwest-east-northwest and a movement rate of "large-small-large." The center of import and export and the center of consumption show a "zigzagging migration" in which the center of investment shows an "irregular (random) migration". (2) The economic connection degree of cities in the GBA exhibits a high ascending velocity, and the whole area tends to be mature, with a significant effect of spatial proximity. With the steady increase in network density, there is significant polarization of network centrality in the region. The four major cohesive subgroups have been relatively stable and consistent with the degree of geographic proximity of the cities. The center-periphery structure is more significant, in which the core area is extended to the cities on the east coast of the Pearl River Estuary, thus forming the core cluster of "Hong Kong-Shenzhen-Guangzhou-Dongguan." In this study, the evolution of economic patterns and urban network systems in the GBA over the past decade is analyzed using multiple methods (i.e., gravity model, urban network system analysis, and geographic information system) based on urban socioeconomic data by starting from various spatial elements (e.g., "points, lines, and networks") to gain insights into and optimize research on regional economic development after the COVID-19 pandemic., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Tang, Chen, Zhang and Sun.)

With the continuous improvement in the integration of urban agglomeration, a multi-functional, socialized, and complex dynamic system, effective prevention and control of emergent public health events have become increasingly important. Based on the Public-Health Vulnerability-Assessment-System of Urban Agglomeration (PVUA), the temporal and spatial differentiation characteristics of vulnerability in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) for the period of 2015-2019 are explored, and the vulnerable cities to public health events are identified in this area. The results can be summarized as follows: (1) The overall vulnerability to public health events in GBA decreases in the investigated period. (2) In the temporal dimension, accompanied by social and economic development, the sensitivity to public health events increases in GBA, and the coping capacity change from stable fluctuation to rapid improvement. (3) From the spatial dimension, the sensitivity level in GBA is low in the west, relatively high in the middle, and high in the southeast; the coping capacity is high in the southeast and low in the northwest; the collaborative governance capacity presents a spatial pattern of being low in the south and high in the north. (4) In the period of study, the vulnerability to public health events in Guangzhou and Jiangmen is stable at the lowest level, while that in Zhaoqing, Foshan, and Hong Kong SAR (Special Administrative Region) gradually reduces; the vulnerability in Shenzhen, Zhuhai, and Dongguan is fluctuating, and that in Huizhou, Zhongshan, and Macao SAR is continually maintained at a higher and the highest level., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Cui, Chen, Shen, Zhang, Liu and Zhou.)

The article applies a three-stage Slacks-Based Measure-Data Envelopment Analysis (SBM-DEA) pattern to examine the relationship between energy consumption and unfavorable CO2 emissions on green sustainable development, for the 11 cities of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) during 2010–2016, by going through various violated factors and stochastic disturbance. Labor, capital and energy resource are chosen as input variables, while GDP and CO2 emission as output variables. During the three phases consisting of the SBM-DEA model (first stage and third stage) and SFA analysis (second stage), CO2 emission is considered as an unfavorable outcome, while stochastic statistical disturbances and external environmental influences are identified. The results show that the average efficiency of the GBA cities is 0.708, with only Shenzhen, Macao SAR and Hong Kong SAR having an efficiency of 1 during the whole study period. Based on the findings, suggestions are made for the GBA cities' sustainable development aspects. [ABSTRACT FROM AUTHOR]