Guangdong-Hong Kong-Macao Greater Bay Area Research Articles

Evaluation of the urban industrial coupling strategy based on the global production networks theory: A case study of the smart phone industry in the Guangdong-Hong Kong-Macao Greater Bay Area.

In the context of uncertain economic environments urban agglomerations play a crucial role in economic development, reshaping industrial chains and fostering inter-city cooperation. This study employs the Global Production Network (GPN) theory to enhance our understanding of how cities integrate into regions, emphasizing the often-overlooked governmental influence in strategic coupling processes. In examining the evolution of China's smartphone industry within the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) this research categorizes 19,599 smartphone companies into five distinct groups. Through analyzing their spatial distribution and geographical linkage the study identifies four strategic coupling modes based on the localization of assets, considering spatial influence and technological complexity along horizontal and vertical dimensions. Structural and institutional elements within these modes are also explored. The research uncovers unique integration patterns among nine cities in the GBA's mobile industry, revealing distinct spatial clusters rooted in technological, resource and innovation factors. Crucially, local policies play a pivotal role. Cities such as Shenzhen and Dongguan emerge as technology hubs, contrasting with Foshan and Zhongshan, which leverage resource advantages. The spatial impact, contingent on specific assets, underscores the necessity for nuanced top-down coupling methods in regional development. Moreover, the study emphasizes the significance of nurturing innovation links, not only between leading companies but also among midstream and downstream enterprises, enhancing cities' strategic coupling capabilities.

An evaluation model for city-scale construction and demolition waste management effectiveness: A case study in China

The growing generation of construction and demolition waste (CDW) has emerged as a prominent challenge on global environmental agendas. However, the effectiveness of CDW management (CDWM) strategies varies among cities. Existing literature predominantly evaluates the effectiveness of CDWM at the project level, offering a localized perspective that fails to capture a city’s comprehensive CDWM profile. This localized focus has certain limitations. To fill this gap in city-scale evaluations, this study introduces a novel model for assessing CDWM effectiveness at the municipal level. An empirical investigation was conducted across 11 cities within the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) to operationalize this model. The model defines five distinct levels of CDWM effectiveness. Findings indicate that Hong Kong consistently achieves the highest level (level I), while the majority of cities fall within levels III and IV. This pattern suggests that CDWM effectiveness in the GBA is moderately developed, with uneven progress in CDW management outcomes and supporting systems. Essentially, there is a lack of synchronous development of CDWM results and guarantee systems. The proposed evaluation model enriches existing CDWM research field and offers a framework that may inform future studies in other countries.

SDG- and GMAG-oriented analysis of multi scenarios spatiotemporal changes and evaluation of the effectiveness and potential of mangrove forests

ABSTRACT It is crucial to mangrove management by understanding its conservation efforts. This study proposed a spatiotemporal analysis model to evaluate mangrove changes in the Guangxi Beibu Gulf (GBG) and the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), focusing on past, present, and future dynamics. The analysis covered mangrove loss, stability, and gain from 1990 to 2020 and provided projections for 2020 to 2035 across multiple scenarios. Then further assessed the efficacy of past mangrove conservation with the Sustainable Development Goals (SDGs) and the prospective conservation potential with the Global Mangrove Alliance Goal (GMAG) indicators. Findings indicate that by 2020, the GBG and GBA had 81.65 km² and 40.92 km² of mangroves, respectively, with increases of 31.51% and 164.09% since 1990. Projected expansions are anticipated under all three scenarios during 2020- 2035, with the highest growth projected under the trend continuation scenario, followed by ecological restoration and protection, and economic development. While mangrove protection in these regions met SDG targets 6.6.1 and 14.5.1 from 2015 to 2020, the 80% conservation milestone set by GMAG for 2030 was not achieved, necessitating the establishment of nature reserves. The study offers novel insights for sustainable governance of mangrove forests at local, regional, and global levels.

Quantifying high-resolution carbon emissions driven by land use change in the Guangdong-Hong Kong-Macao Greater Bay Area

Accurate research on carbon emission spatial distribution is vital for achieving carbon neutrality. Previous studies suffered from scale effects, demanding enhanced resolution. This study collected and computed land use carbon emission data for the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). It also introduced a novel method to downscale this data into 30 m raster format using night light and NDVI data. The study showed a consistent annual rise in GBA land use carbon emissions, accompanied by increasing disparities. At the city level, emissions in the highest-ranking cities were 58 times higher than those in the lowest-ranking ones. Impervious land carbon emissions held over 90% sway over total carbon emissions, making them the primary driver of land use-related carbon emissions. Remarkably, high-emission zones, such as coastal cities Guangzhou, Foshan, Dongguan, and Hong Kong, expanded outward, forming multi-tiered, high-density carbon areas, giving rise to an “∩” shape pattern. Carbon emission change rates from cropland were modest, characterized by a pattern of fluctuation. Moreover, spatial autocorrelation and K-means cluster analyses uncovered a noticeable spatial correlation among urban land use carbon emissions in the GBA. Consequently, a compelling case exists for enhancing inter-city cooperation to collectively pursue regional emission reduction objectives.

China’s Opening to the World: Models, Stages, and Directions

The article includes four sections: the historical stages of China’s opening-up, the developmental patterns of China’s opening-up, the open policy of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), and the bilateral opening-up developments between China and Russia. Starting with elaboration of the primary processes of China’s opening-up since the era of reform and opening-up, summarizing the distinct historical stages of China’s opening-up and the main features of each developmental phase. Subsequently, it analyzes the characteristics of China’s external development in spatial patterns, and selects two typical cases of China’s external development, namely Hainan Free Trade Port and Chongqing, for further analysis. Furthermore, it meticulously reviews the opening initiatives in key sectors of the GBA’s development and its contributions to China’s high-level external openness. Finally, the article briefly introduces the main measures and outcomes of enhancing bilateral openness between China and Russia under the current international circumstances.

Integrating policy quantification analysis into ecological security pattern construction: A case study of Guangdong–Hong Kong–Macao Greater Bay Area

The construction of an ecological security pattern (ESP) is an important undertaking in ensuring the stability of the ecosystem and the long-term growth of humanity. Existing research, However, existing research often overlooks the quantification of policy implications in ESP development. The objective of this paper was to bridge this gap by integrating policy quantification analysis (PQA) with nature, society and into ESP construction and providing targeted ecological management recommendations. The policy texts were quantified into reusable research data, which served as a foundation for the selection of indicators and the optimization of ecological security patterns, by using machine learning techniques. The methodology “Patch-matrix-corridor” was used to create a spatially correlated ecological security pattern. Through factor analysis and Random Forest (RF), the impact of each indicator on ecological security was thoroughly examined, offering substantial support for policy development and ESP construction planning. The Guangdong-Hong Kong-Macao Greater Bay Area(GBA) was selected as the study area, the study found that: (1) The proposed framework PQA based-ESP could provide a useful eco-management tools. (2) Policy adaptability had a substantial impact on environmental security in the GBA, but the ecosystem service index has the highest impact. (3) Applying the framework, we identified a total of 1,993 km2 of ecological sources, 23 key ecological corridors, 58 general ecological corridors, and 31 ecological nodes. An improved ESP, characterized as “one circle, two bays, three rivers, multiple areas, corridors, and nodes,” was constructed in the GBA. (4) Given the multi-scale analysis, tailored ecological management policy recommendations were proposed for each city in the GBA. The study's findings offer actionable solutions for the international community facing growing ecological challenges.

Pathophysiology characterization of Alzheimer's disease in South China's aging population: for the Greater-Bay-Area Healthy Aging Brain Study (GHABS).

The Guangdong-Hong Kong-Macao Greater-Bay-Area of South China has an 86million population and faces a significant challenge of Alzheimer's disease (AD). However, the characteristics and prevalence of AD in this area are still unclear due to the rarely available community-based neuroimaging AD cohort. Following the standard protocols of the Alzheimer's Disease Neuroimaging Initiative, the Greater-Bay-Area Healthy Aging Brain Study (GHABS) was initiated in 2021. GHABS participants completed clinical assessments, plasma biomarkers, genotyping, magnetic resonance imaging (MRI), β-amyloid (Aβ) positron emission tomography (PET) imaging, and tau PET imaging. The GHABS cohort focuses on pathophysiology characterization and early AD detection in the Guangdong-Hong Kong-Macao Greater Bay Area. In this study, we analyzed plasma Aβ42/Aβ40 (A), p-Tau181 (T), neurofilament light, and GFAP by Simoa in 470 Chinese older adults, and 301, 195, and 70 had MRI, Aβ PET, and tau PET, respectively. Plasma biomarkers, Aβ PET, tau PET, hippocampal volume, and temporal-metaROI cortical thickness were compared between normal control (NC), subjective cognitive decline (SCD), mild cognitive impairment (MCI), and dementia groups, controlling for age, sex, and APOE-ε4. The prevalence of plasma A/T profiles and Aβ PET positivity were also determined in different diagnostic groups. The aims, study design, data collection, and potential applications of GHABS are summarized. SCD individuals had significantly higher plasma p-Tau181 and plasma GFAP than the NC individuals. MCI and dementia patients showed more abnormal changes in all the plasma and neuroimaging biomarkers than NC and SCD individuals. The frequencies of plasma A+/T+ (NC; 5.9%, SCD: 8.2%, MCI: 25.3%, dementia: 64.9%) and Aβ PET positivity (NC: 25.6%, SCD: 22.5%, MCI: 47.7%, dementia: 89.3%) were reported. The GHABS cohort may provide helpful guidance toward designing standard AD community cohorts in South China. This study, for the first time, reported the pathophysiology characterization of plasma biomarkers, Aβ PET, tau PET, hippocampal atrophy, and AD-signature cortical thinning, as well as the prevalence of Aβ PET positivity in the Guangdong-Hong Kong-Macao Greater Bay Area of China. These findings provide novel insights into understanding the characteristics of abnormal AD pathological changes in South China's older population.

Spatial network structure and influencing factors of carbon emission intensity in Guangdong-Hong Kong-Macao greater bay area

Introduction: In response to China’s ambitious dual-carbon goals, this study investigates the spatial correlation and influencing factors of carbon emission intensity within the Guangdong-Hong Kong-Macao Great Bay Area (GBA), a region pivotal for the nation’s energy conservation and emission reduction efforts. Through a comprehensive analysis encompassing the period from 2000 to 2020, this research aims to delineate the spatial dynamics of carbon emissions and identify actionable insights for regional low-carbon development.Methods: Utilizing an integrated methodology comprising spatial autocorrelation analysis, Social Network Analysis (SNA), and the Quadratic Assignment Procedure (QAP), the study analyzes carbon emission data alongside socio-economic variables. These methodologies allow for a nuanced exploration of the spatial correlation structure and the determination of factors influencing carbon emission intensity across the GBA.Results: Findings reveal a cyclical fluctuation in the spatial network of carbon emissions from 2000 to 2020, characterized by varying degrees of cohesion among cities, indicating significant opportunities for spatial optimization. A “core-periphery” pattern emerges, with economically robust cities such as Hong Kong and Macao at the core, and less developed cities like Huizhou and Jiangmen on the periphery. Cities like Guangzhou and Shenzhen play crucial mediator roles. The QAP analysis further identifies six major influencing factors: geographic spatial proximity, economic development level, urbanization rate, industrial configuration, level of technological innovation, and environmental protection efforts, with the latter four having a markedly positive impact on spatial relevance.Discussion: The study’s insights underscore the importance of understanding the spatial dynamics of carbon emissions and the role of socioeconomic factors in shaping these patterns. For policymakers and stakeholders in the GBA, these findings highlight the necessity of targeted intervention strategies that consider both the unique position of cities within the spatial network and the broader socio-economic context. This approach can significantly contribute to achieving China’s dual-carbon objectives, promoting energy conservation, and facilitating the transition to a low-carbon economy.

Changes in Economic Network Patterns and Influencing Factors in the Urban Agglomeration of Guangdong–Hong Kong–Macao Greater Bay Area: A Comprehensive Study

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.

Climate risk assessment and adaption ability in China's coastal urban agglomerations - A case study of Guangdong-Hong Kong-Macao greater bay area

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.

Uncovering the role of urban green infrastructure in carbon neutrality: A novel pathway from the urban green infrastructure and cooling power saving

Cities are crucial in carbon reduction and carbon neutrality. However, the contribution of urban green infrastructure (UGI) to achieve these goals has been underestimated. Here, taking the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) as an example, we develop a methodology incorporating fine-resolution UGI mappings and use the results from 2017 to 2022 to elucidate UGI patterns, trends, and carbon reduction capacity. The results revealed that: 1) Urbanization area increased by scarifying UGI, and the carbon sequestration capacity experienced a decrease and fixed 1.4% carbon emissions in GBA. 2) Based on the climate regulation from UGI, cities in GBA mitigated 29.5% of GBA's carbon emissions. The total carbon reduction from UGI was 193.6 million tons in 2022. 3) By synthesizing global field measurement studies with meta-analysis, we found that the rate of UGI increased by 1%, and the air temperature decreased by 0.01 °C. 4) With consideration of climate changes, we identified that increasing the coverage of UGI is more effective in strengthening the carbon emission reduction capacity than improving UGI quality. This study broadens the capacity of UGI in carbon neutrality, can effectively mitigate urban carbon reduction stress, and provides a novel perspective for urban carbon neutrality in China and elsewhere.

Medical Assistance and Protection in the Guangdong-Hong Kong-Macao Greater Bay Area

Medical Assistance and Protection in the Guangdong-Hong Kong-Macao Greater Bay Area

Mitigating flood impacts on road infrastructure and transportation by using multiple information sources

The transportation disruption caused by flood events is occurring more frequently and intensively due to climate change and rapid urbanization. Understanding the deterioration of road infrastructure and transportation caused by surface water floods is crucial to help city authorities alleviate flood consequences. This study aims to provide a framework to mitigate surface water flood damages in road transportation systems, using the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) as a case study. An integrated framework linking news media analytics and vulnerability assessment is used to assess floods' effect and severity on road infrastructure and transportation. This study first examined historical news media activities toward flood-related transportation damage. Then, the study explored the potential impacts of surface water floods on road infrastructure and transportation via vulnerability assessment and news media analysis, respectively. The findings from this research offer valuable insights for city authorities to enhance flood mitigation strategies in road transportation systems.

Research on the Development of International Functions in the Guangdong-Hong Kong-Macao Greater Bay Area

International function refers to the system of urban functionalities that transcend national boundaries and exert a distributive, radiating, and influential role globally in the fields of economics, hubs, culture, communication, and services. The Guangdong-Hong Kong-Macao Greater Bay Area, one of the most open and economically vibrant regions in China, holds a significant strategic position in the national development framework. The development of international functions in the Greater Bay Area bears considerable strategic value to the nation's advancement. This research analyzes the development of the international functions in the Greater Bay Area from the perspectives of international economy, international hubs, international communication and culture, and international services. It explores new ideas for the development and layout of international functions under the backdrop of international cooperation and exchange, contributing to the refinement of the theoretical system and development models of international functions. Moreover, it offers significant reference value for the construction of international bay urban areas and world-class urban clusters.

Efficiency of green and low-carbon coordinated development for mega urban agglomerations: an empirical study

As a critical engine for national economic growth, mega urban agglomerations have significant scale effects and economic and environmental spillover effects. This paper aims to study the green and low-carbon coordinated development of mega urban agglomerations to evaluate the country’s level of ecological civilization and its green and low-carbon development. The traditional research on green and low-carbon urban development tends to homogenize the redistribution theme, leading to significant errors in spatial allocation. This results in a lower accuracy of spatial distribution calculations for green development. Additionally, the research is constrained by data precision and methodology, making it challenging to measure the spatial differences in green and low-carbon development within urban clusters at the block level. This limitation hinders the ability to conduct detailed studies on the efficiency variations of green and low-carbon development in urban clusters. To achieve this aim, the study adopts the DPSR framework of the economic, resource, social, and ecological environment complex system and focuses on the Guangdong-Hong Kong-Macao Greater Bay Area in China. The study uses the entropy method, Gini coefficient method, and non-expected output super-efficiency SBM model to analyze the spatial effects and development efficiency of green and low-carbon development in this region from 2006 to 2020. The study results indicate that: (1) the overall level of green and low-carbon development in the Greater Bay Area is on the rise, with Shenzhen, Guangzhou, Foshan, and Zhuhai showing more stable development than other cities. Foreign direct investment and fixed asset investment in science and technology have significantly promoted green and low-carbon development. (2) The spatial differences in the region’s level of green and low-carbon development have narrowed trends, mainly due to differences between regions. However, well-developed cities such as Guangzhou and Shenzhen have taken the initiative to lead the development of other cities, fully leveraging their advantages in science and technology, geographical location, and other resources to promote the improvement of the external orientation of other cities. (3) The overall development efficiency of green and low-carbon in the Greater Bay Area is on the rise, with Guangzhou region showing overall stability, and Shenzhen region and Zhuhai region experiencing multiple ups and downs in their development. The three sub-regions show significant differences, but the balance and coordination of development have significantly improved. Finally, this study provides theoretical support for the future green and low-carbon development of urban clusters. It is advantageous for integrating the mainstream policy analysis framework of environmental economics with the complex adaptive systems of urban clusters. The research expands the boundaries of existing theoretical studies and offers new methodological approaches for interdisciplinary research. The study achieves a balance between the opportunity effects of green and low-carbon development and environmental policy constraints in super large urban clusters, effectively enhancing resource utilization efficiency in these clusters.

Heat stress resilience assessment of urban form from physical space dimension: A case study of Guangdong-Hong Kong-Macao Greater Bay Area

Improving urban resilience is crucial to counteract rising high-temperature disasters. This study constructed a framework for a process-based heat stress resilience (HSR_phy) assessment from the urban physical space dimension. Based on the universal thermal climate index (UTCI), the Weather Research and Forecasting (WRF) model, and the Local Climate Zone (LCZ) map generated through WUDAPT, an HSR_phy evaluation method with high spatiotemporal accuracy is proposed. The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) was studied for heat stress resilience patterns in July 2021. Boosted regression tree (BRT) models analyzed LCZ landscape patterns' impacts on HSR_phy. The results indicated that HSR_phy during the daytime was significantly lower than that at night. The HSR_phy of GBA showed the characteristics of low inland and high periphery. Significant differences in HSR_phy existed among most LCZs. LCZA, LCZC, and LCZ9 showed strong HSR_phy, whereas LCZ2, LCZ8, and LCZ7 were opposite. At the 1500 m scale, the Percentage of Landscape (PLAND), Landscape Shape Index (LSI), and Shannon's Diversity Index (SHDI) of LCZ4, LCZ5, and LCZ6 were the key factors affecting HSR_phy. Our research demonstrates how to quantify heat stress resilience and impact factors of urban physical spaces to provide a basis for developing climate-sensitive urban design strategies.

Optimizing flood disaster management: unraveling the role of social organizations in China’s GBA through social network analysis

This study delves into the efficiency and structure of the social organization network involved in flood disaster management in China’s Guangdong-Hong Kong-Macao Greater Bay Area (GBA). Leveraging the global knowledge graph, we analyzed social media data and news reports to identify the pivotal roles and interactions of various social organization types in flood disaster governance. The empirical findings underscore the central role of national public social organizations, evidenced by their high frequency of news coverage and mentions. Meanwhile, International Social Organizations and Local Public Social Organizations exhibit growing influence and participation. As China shifts toward a comprehensive disaster reduction strategy, the study observes increased diversity and decentralization among these organizations, with non-governmental organizations and volunteer groups playing vital roles in resource mobilization, information dissemination, and disaster coordination. Network analysis unveils evolving interaction patterns and co-occurrence frequencies among social organizations, reflecting the evolving roles of different organization types in disaster relief. This research aims to provide actionable insights to enhance the effectiveness of social organizations in disaster management, contributing to the establishment of a more resilient and efficient disaster management framework in the GBA.

Competitiveness without Competition: The Role of Antitrust Law in the Development of the Guangdong-Hong Kong-Macao Greater Bay Area

Abstract The economic integration of the constituent jurisdictions in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) involves an enhanced market connectivity and an asymmetric opening up for trade in goods and services. The development of the GBA presents a unique case study for understanding the role of the competition law and policy in the context of regional economic integration. In contrast to the regulatory experiences of other economic integration projects around the world, such as, for example, the European Union, where harmonization of competition laws and their coordinated enforcement became the norm, the GBA explicitly abandons this approach. The regulatory framework for the GBA development does not envisage common competition rules or policies. Equally, it does not seek to harmonize the existing divergences in competition laws. This article analyses the specifics of the GBA economic integration in the context of the ‘one country, two systems’ principle and explains the reasons behind the distinct role of the competition law and policy in this process.

Dynamics of urban expansion and form changes impacting carbon emissions in the Guangdong-Hong Kong-Macao Greater Bay Area counties

Cities are the main carriers of social and economic development, and they are also important sources of carbon emissions. Therefore, it is essential to explore the impact of urban expansion and form changes on carbon emissions. Here, we attempted to analyzes the relationship between urban expansion and carbon emissions at the county level in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from 1997 to 2017. It further decomposes the driving effects of carbon emissions from multiple factors, and considers the spatial heterogeneity between different urban form changes and driving effects. The results show that: The relationship between urban expansion and carbon emissions in the GBA has gone through three stages from 1997 to 2017, with 2012 as a turning point. Optimization of economic development models and strict protection of the ecological environment can effectively control carbon emissions. After 2012, the economic development effect (GE) and population scale effect (PE) are the driving factors of carbon emissions, while the carbon emission intensity effect (CE) and urban land intensity effect (UE) are the inhibitory factors of carbon emissions. The contribution rate of UE to carbon emission reduction can reach 86 %. The impact of urban form changes on carbon emissions has spatial heterogeneity. The changes in urban form have a significant impact on the carbon emissions of counties in Dongguan and Shenzhen. The increase in fragmentation indirectly promotes carbon emissions. In 2007–2012, the increase in centrality significantly weakened the economic development effect, which is conducive to emission reduction. After 2007, the increase in compactness in counties in the eastern part of the GBA, including Zhongshan and Zhuhai, is not conducive to emission reduction.

Connection patterns and resilience simulation of innovation network in the context of technology transfer: A case study of strategic emerging industries in the Guangdong-Hong Kong-Macao Greater Bay Area

Technology transfer is crucial for the flow and reconfiguration of regional innovation elements. Besides, leveraging network methodologies effectively uncovers regional knowledge exchange and transfer patterns. Combining the “Buzz-Gatekeeper-Pipeline” model, this study introduces a comprehensive analytical framework that utilizes a patent transfer database to characterize innovation network types, identify connection patterns, and enable resilience loss simulation, elucidating regional technology transfer types and connection patterns from a network perspective, delineating the evolution and potential causes of network structural resilience loss, and finally establishing a list of differentiated and hierarchical innovation network management strategies. Focusing on the Guangdong-Hong Kong-Macao Greater Bay Area, this empirical study reveals the following: 1) Technology transfer primarily occurs between different enterprises. However, on average, knowledge products from colleges/universities and research institutions boast relatively high technical contribution efficiency to enterprises. 2) The innovation network is driven by the “Technological Gatekeeper” as the main connector. Four types of innovation communities—isolated, insider, seeker, and networked—are formed based on different combinations of “Community Buzz” and “Regional Pipeline”. 3) A significant positive correlation exists between innovation network connection patterns and network structural resilience. The impact of different types of “Regional Pipelines” on network structural resilience varies significantly.