Search Results (115)

Amid China’s “Manufacturing Power” push, full-chain digital restructuring in the Guangdong–Hong Kong–Macao Greater Bay Area remains hampered by mismatches among technology, organization, and environment. We therefore explored how shop floor actors perceive and shape this Technology–Organization–Environment (TOE) interplay. Semi-structured interviews with frontline operators, maintainers, and supply chain staff from GBA manufacturers were inductively coded, yielding 36 concepts, 10 categories, and 3 core TOE aggregates that were woven into a grounded model. The analysis shows that industrial internet platforms and smart equipment only create value when matched by flexible shop floor structures, cross-department data protocols, and skilled teams; otherwise, data silos, simulation–production deviations, and “buy-but-not-build” procurement stall adoption. Market pressure for customized, short-lead-time products and divergent municipal pilot policies further intensify the TOE balancing act, particularly for SMEs with weak absorptive capacity. By revealing a grassroots “technology-driven → organization-adapted → environment-adjusted” spiral that is moderated by frontline feedback, the study extends the TOE framework to micro-level, regional innovation theory and offers policy–practice levers for differentiated, cross-city manufacturing upgrading. Full article

The ongoing advance of urbanization has increased the need for accurate monitoring of urban green space (UGS). However, existing remote-sensing UGS mapping still struggles with inconsistent data quality, diverse urban forms, and limited cross-city generalization. This study focuses on China’s Guangdong-Hong Kong-Macao Greater Bay Area as its research region, establishing a fully automated UGS mapping framework based on Sentinel-2 time-series imagery and standardized OpenStreetMap (OSM) data. This process achieves UGS mapping at 10 m resolution for 16 cities within the metropolitan area through a dynamic standardized OSM tagging system, a Sentinel-2 satellite image sample generation mechanism integrating spectral and textural features, multidimensional sample quality assessment and weighting strategies, as well as balanced cross-city sampling and weighted SVM classification. The results demonstrate that this method exhibits stable performance across multiple urban environments, achieving an average overall accuracy of approximately 0.83 and an average F1 score of approximately 0.82. The highest recorded F1 score reaches 0.96, highlighting the method’s strong generalization capability under diverse urban conditions. The mapping results reveal significant disparities in UGS distribution within the Guangdong-Hong Kong-Macao Greater Bay Area, reflecting the combined effects of varying urban development patterns and ecological contexts. The unified workflow proposed in this study demonstrates strong applicability in handling heterogeneous urban structures and enhancing cross-regional comparability. It provides consistent, transparent, and reusable foundational data for regional eco-urban planning, urban green infrastructure development, and policy evaluation. Full article

Megacity regions mark a transformative phase of urbanisation, in which interconnected cities undergo land-use and land-cover change (LUCC) that extends beyond administrative boundaries. However, the drivers of cross-boundary LUCC remain insufficiently examined, particularly before the top-down regional integration. The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) provides a clear empirical case, having experienced cross-boundary LUCC prior to its formal designation as a megacity region in 2018. This study builds a Landsat-derived LUCC and driver dataset for the GBA. Global and local spatial autocorrelation (Moran’s I and LISA) are used to characterise spatial structure and clustering, and geographically weighted regression identifies the socio-economic and environmental determinants of built-up expansion over 1980–2018, spanning the pre-reform decade and the post-1990 land-transfer era. Findings reveal that: (1) LUCC in the GBA already exhibited a cross-border, spatially networked expansion pattern before formal regional integration policies at the national level, with built-up area growth extending beyond core cities into decentralised urban nodes. Two prominent cross-border cores and one cross-administrative core emerged, suggesting that regional integration was co-led by market forces and local governments before an institutional framework was established. (2) Although the GBA showed a clear trend towards integrated development, urban expansion was highly uneven. Such spatial disparities were mainly driven by varying socioeconomic and natural factors, including gross domestic product, population growth, real estate investment, water resource proximity, and infrastructure development. These findings enhance understanding of megacity-region dynamics and offer insights from the GBA for cross-border urbanisation and sustainable spatial governance. Full article

As critical ecosystems, coastal zones necessitate the identification of their ecosystem service values, trade-off/synergy patterns, spatiotemporal evolution, and driving factors to inform scientific decision-making for sustainable ecosystem management. This study selected the coastal zone of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) as the research region. By incorporating land-use types such as mangroves, tidal flats, and aquaculture areas, we analyzed land-use changes in 1990, 2000, 2010, and 2020. The InVEST model was employed to quantify six key ecosystem services (ESs): annual water yield, urban stormwater retention, urban flood risk mitigation, soil conservation, coastal blue carbon storage, and habitat quality, while spatial correlations among them were examined. Furthermore, Spearman’s rank correlation coefficient was used to assess trade-offs and synergies between ecosystem services, and redundancy analysis (RDA) combined with the geographically and temporally weighted regression (GTWR) model were applied to identify driving factors and their spatial heterogeneity. The results indicate that: (1) Cultivated land, forest land, impervious surfaces, and water bodies exhibited the most significant changes over the 30-year period; (2) Synergies predominated among most ecosystem services, whereas habitat quality showed trade-offs with others; (3) Among natural drivers, the normalized difference vegetation index (NDVI, positive effect) and evapotranspiration were critical factors. The proportion of impervious surfaces served as a key land-use change driver, and the nighttime light index emerged as a primary socioeconomic factor (negative effect). The impacts of drivers on ecosystem services displayed notable spatial heterogeneity. These findings provide scientific support for managing the supply-demand balance of coastal ecosystem services, rational land development, and sustainable development. Full article

The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is a key economic region in China facing increasing pressure to balance socioeconomic development with environmental protection and energy conservation. This study examines the interrelationships among energy consumption, air pollutants (PM_2.5, NO₂, and SO₂), and carbon dioxide (CO₂) emissions in the GBA from 2000 to 2020. Using spatial correlation matrices and temporo-spatial decoupling analysis, we assess spatial patterns, temporal dynamics, and interactions among these factors. Results show that the GBA has made significant progress in reducing air pollution and carbon emissions. Notably, since 2013, concentrations of PM_2.5, NO₂, and SO₂ have decoupled markedly from energy consumption, reflecting effective pollution control measures. Although CO₂ emissions have decreased more gradually, the trend remains positive, indicating steady advances in carbon management. These findings underscore the need for continued optimization of the energy structure to achieve coordinated control of energy use, air quality, and carbon emissions—essential for promoting sustainable, high-quality development in the region. Full article

Understanding how long-term local climate zone (LCZ) dynamics interact with rapid urbanization and land surface temperature (LST) changes is essential for sustainable planning in megaregion-scale urban clusters. In this paper, we propose a multi-feature local sample transfer method to obtain LCZ maps from 2000 to 2020 in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) and then analyze spatiotemporal changes in LCZs and their impacts on surface thermal environments. Results show the following: (1) The proposed multi-feature local sample transfer approach significantly improves the efficiency of long-term LCZ mapping by greatly reducing the effort required for sample acquisition. (2) The built types (LCZ1–10) increased by 1.34% overall, with large low-rise (LCZ8) showing the greatest expansion (4.72%). The compact low-rise (LCZ3) was the only built type to decline, decreasing by 2.02%. (3) Urbanization has produced a contiguous warming core that expands outward from the central metropolitan zones, thereby promoting the UHI coalescence. (4) Dense trees (LCZA) and large low-rise (LCZ8) exerted the strongest influence on LST. Large low-rise (LCZ8) consistently exhibited the highest warming contribution in Foshan, Zhongshan, and Dongguan. In coastal cities including Shenzhen, Hong Kong, and Macao, the largest LST increases occurred when water (LCZG) areas were converted to bare rock or paved (LCZE) or cs (LCZ1–10). Overall, the results highlight the strong coupling between urbanization and surface heating, providing critical insights for urban climate adaptation and integrated land-use planning in rapidly urbanizing megaregions. Full article

Under the dual pressures of climate change and rapid urbanization, extreme heat events pose growing risks to densely populated megaregions. The Guangdong–Hong Kong–Macao Greater Bay Area (GBA), a densely populated and economically vital region, serves as a critical hotspot for heat risk aggregation. This study develops a high-resolution multi-dimensional framework to assess the spatiotemporal evolution of its heat risk profile from 2000 to 2020. A Heat Risk Index (HRI) integrating heat hazard and vulnerability components to measure potential heat-related impacts is calculated as the product of the Heat Hazard Index (HHI) and Heat Vulnerability Index (HVI) for 1 km grids in GBA. The HHI integrates the frequency of hot days and hot nights. HVI incorporates population density, GDP, remote-sensing nighttime light data, and MODIS-based landscape indicators (e.g., NDVI, NDWI, and NDBI), with weights determined objectively using the static Entropy Weight Method to ensure spatiotemporal comparability. The findings reveal an escalation of heat risk, expanding at an average rate of 342 km² per year (p = 0.008), with the proportion of areas classified as high-risk or above increasing from 21.8% in 2000 to 33.3% in 2020. This trend was characterized by (a) a pronounced asymmetric warming pattern, with nighttime temperatures rising more rapidly than daytime temperatures; (b) high vulnerability dominated by the concentration of population and economic assets, as indicated by high EWM-based weights; and (c) isolated high-risk hotspots (Guangzhou and Hong Kong) in 2000, which have expanded into a high-risk belt across the Pearl River Delta’s industrial heartland, like Foshan seeing their high-risk area expand from 3.4% to 27.0%. By combining remote sensing and socioeconomic data, this study provides a transferable framework that moves beyond coarse-scale assessments to identify specific intra-regional risk hotspots. The resulting high-resolution risk maps offer a quantitative foundation for developing spatially explicit climate adaptation strategies in the GBA and other rapidly urbanizing megaregions. Full article

Dark skies, a vital natural and cultural resource, have been increasingly threatened by light pollution due to rapid urbanization, leading to ecological degradation and biodiversity loss. As a key strategy for sustainable regional development, dark sky parks (DSPs) not only preserve nocturnal environments but also enhance livability by balancing urban expansion and ecological conservation. This study develops a novel framework for evaluating DSP suitability, integrating ecological and socio-economic dimensions, including the resource base (e.g., nighttime light levels, meteorological conditions, and air quality) and development conditions (e.g., population density, transportation accessibility, and tourism infrastructure). Using the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) as a case study, we employ Delphi expert consultation, GIS spatial analysis, and multi-criteria decision-making to identify optimal DSP locations and prioritize conservation zones. Our key findings reveal the following: (1) spatial heterogeneity in suitability, with high-potential zones being concentrated in the GBA’s northeastern, central–western, and southern regions; (2) ecosystem advantages of forests, wetlands, and high-elevation areas for minimizing light pollution; (3) coastal and island regions as ideal DSP sites due to the low light interference and high ecotourism potential. By bridging environmental assessments and spatial planning, this study provides a replicable model for DSP site selection, offering policymakers actionable insights to integrate dark sky preservation into sustainable urban–regional development strategies. Our results underscore the importance of DSPs in fostering ecological resilience, nighttime tourism, and regional livability, contributing to the broader discourse on sustainable landscape planning in high-urbanization contexts. Full article

The rapid growth of construction and demolition waste (CDW) presents significant challenges to sustainable urban development, particularly in densely populated regions, such as the Guangdong–Hong Kong–Macao Greater Bay Area (GBA). Despite substantial disparities in CDW management (CDWM) performance across cities, the key influencing factors and effective strategies remain underexplored, limiting the development of localized and evidence-based CDWM solutions. Therefore, this study formulated three hypotheses concerning the relationships among CDWM performance, city attributes, and governance capacity to identify the key determinants of CDWM outcomes. These hypotheses were tested using clustering and correlation analysis based on data from 11 GBA cities. The study identified three distinct city clusters based on CDW recycling, reuse, and landfill rates. Institutional support and recycling capacity were key determinants shaping CDWM performance. CDW governance capacity acted as a mediator between city attributes and performance outcomes. In addition, the study examined effective strategies and institutional measures adopted by successful GBA cities. By highlighting the importance of institutional and capacity-related factors, this research offers novel empirical insights into CDW governance in rapidly urbanizing contexts. Full article

Dissolved oxygen (DO) is a fundamental water quality parameter that directly determines aquaculture productivity. China contributes 57% of the global aquaculture production, with the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) serving as a key contributor. However, this region faces significant environmental challenges due to increasing intensive stocking densities and outdated management practices, while also grappling with the systematic monitoring limitations of large-scale operations. To address these challenges, in this study, a random forest-based model was developed for DO concentration retrieval (R² = 0.82) using Landsat 8/9 OLI imagery. The Lindeman, Merenda, and Gold (LMG) algorithm was applied to field data collected from four cities—Foshan, Hong Kong, Huizhou, and Zhongshan—to identify key environmental drivers to the changes in DO concentration in these cities. This study also employed satellite imagery from multiple periods to analyze the spatiotemporal distribution and trends of DO concentrations over the past decade, aiming to enhance understanding of DO variability. The results indicate that the average DO concentration in fishponds across the GBA was 7.44 mg/L with a statistically insignificant upward trend. Spatially, the DO levels remained slightly lower than those in other waters. The primary environmental factor influencing DO variations was the pH levels, while the relationship between natural factors such as the temperature and DO concentration was significantly hidden by aquaculture management practices. The further analysis of fishpond water quality parameters across land uses revealed that fishponds with lower DO concentrations (7.293 mg/L) are often located in areas with intensive human intervention, particularly in highly urbanized regions. The approach proposed in this study provides an operational method for large-scale DO monitoring in aquaculture systems, enabling the qualification of anthropogenic influences on water quality dynamics. It also offers scalable solutions for the development of adaptive management strategies, thereby supporting the sustainable management of aquaculture environments. Full article

Green Stormwater Infrastructure (GSI), as a nature-based solution, has gained widespread recognition for its role in mitigating urban flood risks and enhancing resilience. Equitable spatial distribution of GSI remains a pressing challenge, critical to harmonizing urban hydrological systems and maintaining ecological balance. However, the complexity of matching GSI supply with urban demand has limited comprehensive spatial assessments. This study introduces a quantitative framework to identify priority zones for GSI deployment and to evaluate supply–demand dynamics in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) using a coupled coordination simulation model. Clustering and proximity matrix analysis were applied to map spatial relationships across districts and to reveal underlying mismatches. Findings demonstrate significant spatial heterogeneity: over 90% of districts show imbalanced supply–demand coupling. Four spatial clusters were identified based on levels of GSI disparity. Economically advanced urban areas such as Guangzhou and Shenzhen showed high demand, while peripheral regions like Zhaoqing and Huizhou were characterized by oversupply and misaligned allocation. These results provide a systematic understanding of GSI distribution patterns, highlight priority intervention areas, and offer practical guidance for large-scale, equitable GSI planning. Full article

Understanding the historical peak situation and the rules for CO₂ emissions from buildings helps to formulate reasonable building mitigation strategies, accelerating the achievement of the Chinese government’s carbon peak goal. As developed regions, cities in the Guangdong–Hong Kong–Macao Great Bay Area (GBA) provide valuable reference cases. This study quantified the historical building CO₂ emissions of GBA cities and analyzed the contribution of driving factors using the Kaya identity and logarithmic mean Divisia index. Furthermore, we assessed the historical peak situation using the MK trend test method and discussed the reasons behind the inter-city difference in the peak situation shown by the environmental Kuznets curve. The results indicate that the building-related CO₂ emissions of the GBA will slowly increase to 96.90 Mt CO₂ by 2020 and that P&C buildings accounted for a larger proportion of emissions. Emission factors and population made the largest positive and negative contributions, respectively, to this total. At the city level, Guangzhou, Shenzhen, and Hong Kong ranked as the top three sources of building CO₂ emissions. Hong Kong peaked, Dongguan and Macao plateaued, and other cities maintained either slow or quick growth. CO₂ emissions unit area, per capita building CO₂ emissions, and building CO₂ emissions reached a peak in that order. This study provides a valuable reference for formulating a city-level path showing building CO₂ emissions peaks. Full article

The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is one of China’s three major urban agglomerations. Over the past thirty years, the region has undergone intensive economic development and urban expansion, resulting in significant changes in its ecological conditions. Due to the region’s humid and rainy climate, traditional remote sensing ecological indexes (RSEIs) struggle to ensure consistency in long-term ecological quality assessments. To address this, this study developed a unified RSEI (URSEI) model, incorporating optimized data selection, composite index construction, normalization using invariant regions, and multi-temporal principal component analysis. Using Landsat imagery from 1990 to 2020, this study examined the spatiotemporal evolution of ecological quality in the GBA. Building on this, spatial autocorrelation analysis was applied to explore the distribution characteristics of the URSEI, followed by geodetector analysis to investigate its driving factors, including temperature, precipitation, elevation, slope, land use, population density, GDP, and nighttime light. The results indicate that (1) the URSEI effectively mitigates the impact of cloudy and rainy conditions on data consistency, producing seamless ecological quality maps that accurately reflect the region’s ecological evolution; (2) ecological quality showed a “decline-then-improvement” trend during the study period, with the URSEI mean dropping from 0.65 in 1990 to 0.60 in 2000, then rising to 0.63 by 2020. Spatially, ecological quality was higher in the northwest and northeast, and poorer in the central urbanized areas; and (3) in terms of driving mechanisms, nighttime light, GDP, and temperature were the most influential, with the combined effect of “nighttime light + land use” being the primary driver of URSEI spatial heterogeneity. Human-activity-related factors showed the most notable variation in influence over time. Full article

The Guangdong Province is rich in waterways, including those of the Pearl River. The entire watershed of the Pearl River system spans the territory of six provinces. Considering the overarching objective of building a ‘beautiful Bay Area’ under the guidance of Outline Development Plan for the Guangdong-Hong Kong-Macao Greater Bay Area as well as the ecological problems that span over river basins and regions in Guandong Province, public interest litigation is a useful tool in protecting the environment. Analyzing 95 first-instance (trial) judgements handed down in Guangdong Province between 2018 and 2021, we sought to evaluate public interest litigation as a means of safeguarding aquatic ecology in the Greater Bay Area (GBA), China. Cases were categorized for: firstly, their approach to determining the extent of ecological damage; secondly, the procedure used for receiving and auditing restorative compensation; thirdly, the collaboration between the court and government departments in the management and use of restorative compensation; and fourthly, the collaborative ‘public–private’ supervision utilized to monitor the implementation of restorative compensation and actual restoration. Our insights are intended to provide guidance for cooperative opportunities in the large transregional water systems and offshore areas of mainland China. Full article

As an economically active region, the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) faces dual challenges of carbon and air pollution reduction. Existing studies predominantly focus on single pollutants or engineering pathways, lacking systematic analyses of multi-scale synergistic effects. This study investigates the spatiotemporal distributions, driving factors, and synergistic effects of CO₂ and volatile organic compounds (VOCs) at the multi-scale of urban agglomerations, cities, and industries, using global Moran’s index, standard deviational ellipse, logarithmic mean divisa index decomposition model, and Tapio decoupling model. The results show that the average annual growth rate of CO₂ (7.4%) was significantly higher than that of VOCs (4.5%) from 2000 to 2020, and the industrial sector contributed more than 70% of CO₂ and VOC emissions, with the center of gravity of emissions migrating to Dongguan. Industrial energy intensity improvement emerged as the primary mitigation driver, with Guangzhou and Shenzhen demonstrating the highest contribution rates. Additionally, CO₂ and VOC reduction show two-way positive synergy, and the path of “energy intensity enhancement–carbon and pollution reduction” in the industrial sector is effective. Notably, the number of strong decouplings of the economy from CO₂ (11 times) is much higher than the number of strong decouplings of VOCs (3 times), suggesting that the synergy between VOC management and economic transformation needs to be strengthened. This study provides scientific foundations for phased co-reduction targets and energy–industrial structure optimization, proposing regional joint prevention and control policy frameworks. Full article