Understanding the characteristics of waterlogging in urban agglomeration is essential for effective waterlogging prevention and management, as well as for promoting sustainable urban development. Previous studies have predominantly focused on the driving mechanisms of waterlogging in urban agglomeration at a single scale, but urban agglomeration space has greater spatio-temporal heterogeneity, it is often difficult to fully reveal such characteristics at a single scale. Consequently, this study endeavors to explore the spatio-temporal evolution characteristics and underlying mechanisms of waterlogging incidents within urban agglomerations by adopting a multi-scale analytical approach. The results indicate that: (1) The waterlogging degree and high-density zones increase in the GBA, and the waterlogging points are spatially polycentric. However, the waterlogging point in Hong Kong is decreasing. (2) The influence of ISP and AI on waterlogging is dominant at all scales, followed by RE and Slope. ISP∩Slope and ISP∩RE are the key interactions for waterlogging. (3) The aggregation of waterlogging decreases with grid scale, and the influence of land cover factors on waterlogging increases with grid scale. Moreover, the findings at the grid scale outperformed those at the watershed scale, indicating that the grid scale is more conducive to the investigation of waterlogging in urban agglomerations. This research broadens our comprehension of the mechanisms behind waterlogging in urban agglomeration and provide references for policy decisions on waterlogging prevention and mitigation within urban agglomerations.

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.

PM2.5 is extremely harmful to the atmospheric environment and human health, and a timely and accurate understanding of PM2.5 with high spatial and temporal resolution plays an important role in the prevention and control of air pollution. Based on multi-angle implementation of atmospheric correction algorithm (MAIAC), 1 km AOD products, ERA5 meteorological data, and pollutant concentrations (CO, O3, NO2, SO2, PM10, and PM2.5) in the Guangdong-Hong Kong-Macao Greater Bay Area during 2015-2020, a geographically and temporally weighted regression model (GTWR), BP neural network model (BPNN), support vector machine regression model (SVR), and random forest model (RF) were established, respectively, to estimate PM2.5 concentration. The results showed that the estimation ability of the RF model was better than that of the BPNN, SVR, and GTWR models. The correlation coefficients of the BPNN, SVR, GTWR, and RF models were 0.922, 0.920, 0.934, and 0.981, respectively. The RMSE values were 7.192, 7.101, 6.385, and 3.670 μg·m-3. The MAE values were 5.482, 5.450, 4.849, and 2.323 μg·m-3, respectively. The RF model had the best effect during winter, followed by that during summer, and again during spring and autumn, with correlation coefficients above 0.976 in the prediction of different seasons. The RF model could be used to predict the PM2.5 concentration in the Greater Bay Area. In terms of time, the daily ρ(PM2.5) of cities in the Greater Bay Area showed a trend of \"decreasing first and then increasing\" in 2021, with the highest values ranging from 65.550 μg·m-3 to 112.780 μg·m-3 and the lowest values ranging from 5.000 μg·m-3 to 7.899 μg·m-3. The monthly average concentration showed a U-shaped distribution, and the concentration began to decrease in January and gradually increased after reaching a trough in June. Seasonally, it was characterized by the highest concentration during winter, the lowest during summer, and the transition during spring and autumn. The annual average ρ(PM2.5) of the Greater Bay Area was 28.868 μg·m-3, which was lower than the secondary concentration limit. Spatially, there was a \"northwest to southeast\" decreasing distribution of PM2.5 in 2021, and the high-pollution areas clustered in the central part of the Greater Bay Area, represented by Foshan. Low concentration areas were mainly distributed in the eastern part of Huizhou, Hong Kong, Macao, Zhuhai, and other coastal areas. The spatial distribution of PM2.5 in different seasons also showed heterogeneity and regionality. The RF model estimated the PM2.5 concentration with high accuracy, which provides a scientific basis for the health risk assessment associated with PM2.5 pollution in the Greater Bay Area.

In the era of experience economy, sports tourism has been emerged as a new tourism form and consumption hot spot. Evaluation of the competitiveness of sports tourism is helpful to accurately grasp the key competitive factors of sports tourism development and enhance regional sports tourism resource development, market expansion and product upgrading. This study adopted entropy-weight TOPSIS model to construct the index system of sports tourism competitiveness of urban agglomerations and investigated the sports tourism competitiveness of 11 cities in Guangdong-Hong Kong-Macao Greater Bay Area from 2016 to 2020. The obtained results showed that sports tourism development in the Greater Bay Area was unbalanced and obviously different. Guangzhou, Shenzhen and Hong Kong are competitive, Macao is average competitive and Dongguan, Jiangmen, Zhuhai, Foshan, Zhongshan, Huizhou and Zhaoqing are less competitive. Based on evolution trend, sports tourism competitiveness in Guangzhou has always been at the forefront of the Greater Bay Area, while those of Hong Kong and Macao have presented a declining trend. Also, sports tourism competitiveness in inland cities has been continuously enhanced and the development focus of sports tourism in Guangdong-Hong Kong-Macao Greater Bay Area has gradually shifted to mainland. The 11 cities of the Greater Bay Area were classified as strong, average and weak areas on the basis of their sports tourism competitiveness scores. Finally, in terms of the overall situation of Guangdong-Hong Kong-Macao Greater Bay Area and specific conditions of various cities, countermeasures and suggestions have been provided for sports tourism resource development, sports tourism service level, government functional departments, event brand building, enterprise integration, etc.

The construction of ecological security pattern (ESP) is of great scientific significance for solving the problem of habitat fragmentation in urban environment. However, previous studies mainly focused on the ESP in land area, leaving the sea area to be ignored. This study took the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) and its offshore area as an example and integrated the land-sea coordination into the construction of ESP based on the minimum resistance model, gravity model, and graph theory centrality. The results showed that there are 171 and 56 ecological sources for land area and offshore area, accounting for 31.46% and 21.51% of total area, respectively. Twenty-four important ecological corridors with a total length of 2738.05 km were identified in GBA, and the width is proposed to be less than 100 m. Moreover, the α, β, and γ index of the ecological network in the study area is 0.19, 1.33, and 0.5, respectively, indicating that the ecological network structure is complex and the connectivity between ecological nodes is good. The ecological restoration area includes 286.6 km2 of ecological pinch points and 140.44 km2 of ecological barrier. The overall ESP of the study area is \"one ring, two belts, and four zones.\" The main body of the area with a superior ecological environment is distributed in a ring-like pattern near the outer edge of the study area, and two belts (important ecological corridor and ecological corridor) are distributed in a network. According to the ecological characteristics, the study area was divided into four zones: ecological preservation areas, ecological restoration areas, limited construction areas, and optimized construction areas. The ESP established herein institute provides a reference for the revision of ecological space control and optimization measures in the GBA. It also provides effective and systematic means to solve ecological problems in the current territorial spatial planning and territorial ecological restoration of coastal urban agglomeration.

Carbon emissions from land-use and land-cover change (together referred to as \'land-use emissions\') are an important way to influence the regional carbon balance. However, due to the limitations and complexity of obtaining carbon emissions data at spatial scales, previous studies rarely reveal the long-term evolution characteristics of regional land-use emissions. Therefore, we propose a method to integrate DMSP/OLS and NPP/VIIRS nighttime light images to calculate land-use emissions over a long time series. The accuracy validation results show that the integrated nighttime light images and land-use emissions have a good fit and can accurately assess the long-term evolution of regional carbon emissions. In addition, by combining the Exploratory Spatial Analysis (ESTDA) model and the Vector Autoregressive Regression (VAR) model, we found significant spatial variation in carbon emissions in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), with the two regional emission centres spreading outwards between 1995 and 2020, with an increase in construction land area of 3445 km2, resulting in 257 million tons (Mt) of carbon emissions over the same period. The rapid increase in emissions from carbon sources is not offset by a correspondingly large amount of carbon sinks, resulting in a serious imbalance. Controlling the intensity of land use, optimizing the structure of land use and promoting the transformation of the industrial structure are now the keys to achieving carbon reduction in the GBA. Our study demonstrates the enormous potential of long-time-series nighttime light data in regional carbon emission research.

The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is one of the most densely built and economically vibrant regions in China, it is of vital importance to study the spatio-temporal heterogeneity and influence mechanisms of its carbon emissions against the backdrop of peaking carbon dioxide emissions and achieving carbon neutrality. However, systematic research on this area is still lacking. Therefore, this study uses spatial autocorrelation, kernel density estimation, and standard deviation ellipses to construct an exploratory spatial data analysis (ESDA) framework to analyze the spatio-temporal evolutionary characteristics of carbon emissions from GBA and combine it with the geographically and temporally weighted regression (GTWR) model to identify the various influencing factors of carbon emissions in GBA and reveal its implications. The results showed that: (1) Between 2009 and 2019, the total carbon emissions in GBA remained stable and gradually decreased. The gap between the carbon emission intensity of the cities narrowed. (2) The GBA urban agglomeration exhibited spatial autocorrelation, but characteristics of the global spatial pattern had not yet formed a steady state. The kernel density of carbon emissions in GBA showed an obvious \"monopolar\" phenomenon. (3) The gravity centre of carbon emissions in GBA was located to the southeast of the geometric centre of the whole region, shifting toward the northwest. (4) Population size, level of economic development and energy intensity have a strong positive contribution to carbon emissions, compared to the level of opening up and industrialization level, which has a weaker impact. There is significant spatial heterogeneity in the distribution of regression coefficients for each factor, and GBA should take full account of the characteristics of different types of cities in terms of carbon emissions and implement targeted emission reduction strategies. Our research provides a comprehensive analytical framework for regional carbon emissions, offering theoretical support for low-carbon development in the GBA.

Wetlands are one of the most productive ecosystems on Earth and are also focused on by the Sustainable Development Goals (SDGs). However, global wetlands have suffered from considerable degradation due to rapid urbanization and climate change. To support wetland protection and SDG reporting, we predicted future wetland changes and assessed land degradation neutrality (LDN) from 2020 to 2035 under four scenarios in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). A simulation model combining random forest (RF), CLUE-S and multi-objective programming (MOP) methods was developed to predict wetland patterns under the natural increase scenario (NIS), economic development scenario (EDS), ecological protection and restoration scenario (ERPS) and harmonious development scenario (HDS). The simulation results indicated that the integration of RF and CLUE-S achieved good simulation accuracy, with OA over 0.86 and kappa indices over 0.79. From 2020 to 2035, the mangrove, tidal flat and agricultural pond increased while the coastal shallow water decreased under all scenarios. The river decreased under NIS and EDS, while increased under ERPS and HDS. The Reservoir decreased under NIS, while increased under the remaining scenarios. Among scenarios, the EDS had the largest built-up land and agricultural pond, and the ERPS had the largest forest and grassland. The HDS was a coordinated scenario that balanced economic development and ecological protection. Its natural wetlands were almost equal to these of ERPS, and its built-up land and cropland were almost equal to these of EDS. Then, the land degradation and SDG 15.3.1 indicators were calculated to support the LDN target. From 2020 to 2035, the ERPS had a smallest gap of 705.51 km2 from the LDN target, following the HDS, EDS and NIS. The SDG 15.3.1 indicator was lowest under the ERPS, with a value of 0.85 %. Our study could offer strong support for urban sustainable development and SDGs reporting.

As one of the most open and economically dynamic regions in China, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is at the forefront of low-carbon development and has an exemplary and leading role for other regions. This study provides a research framework based on the Logarithmic Mean Divisia Index (LMDI) and system dynamics (SD) by first compiling an inventory of CO2 emissions in the GBA and surrounding cities from 2000 to 2019 and then systematically and comprehensively analyzing the driving factors, future trends and policy implications of CO2 emissions in the GBA and surrounding cities. The results show that (a) CO2 emissions in the GBA and surrounding cities grew from 253.39 Mt in 2000 to 627.86 Mt in 2019, with an average annual growth rate of 4.89 %. The per capita CO2 emissions showed a continuous decreasing trend, and the overall carbon intensity of each sector showed a decreasing trend. (b) GDP per capita growth has the greatest effect on CO2 emissions, followed by the number of transport vehicles and population. The negative effects are energy intensity, average output of transportation vehicles, and residential energy intensity, with energy intensity being the most critical. (c) In the baseline scenario, regional CO2 emissions in 2030 are 1.25 times higher than those in 2019 and continue to grow. (d) Technological innovation measures are the most effective among individual emission reduction policies, followed by optimization of industrial structure. Furthermore, energy structure adjustment, vehicle licensing restrictions, and residents\' green living are less effective. (e) Under comprehensive emission reduction measures, the region can achieve carbon emissions peaking in 2026 and reduce the regional carbon intensity by 66.24 % in 2030 compared with 2005. This study provides effective data support for the GBA and surrounding cities to formulate low carbon policies, promote carbon emission reduction and achieve carbon emissions peaking early.

The urban heat island (UHI) effect caused by urbanization negatively impacts the ecological environment and human health. It is crucial for urban planning and social development to monitor the urban heat island effect and study its mechanism. Due to spatial and temporal resolution limitations, existing land surface temperature (LST) data obtained from remote sensing data is challenging to meet the long-term fine-scale surface temperature mapping requirement. Given the above situation, this paper introduced the ResNet-based surface temperature downscaling method to make up for the data deficiency and applied it to the study of thermal environment change in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from 2000 to 2020. The results showed (1) the ResNet-based surface temperature downscaling method achieves high accuracy (R2 above 0.85) and is suitable for generating 30 m-resolution surface temperature data from 1 km data; (2) the area of severe heat islands in the GBA continued to increase, increasing by 7.13 times within 20 years; and (3) except for Hong Kong and Macau, the heat island intensity of most cities showed an apparent upward trend, especially the cities with rapid urban expansion such as Guangzhou, Zhongshan, and Foshan. In general, the evolution of the heat island in the GBA diverges from the central urban area to the surrounding areas, with a phenomenon of local aggregation and the area of the intense heat island in the Guangzhou-Foshan metropolitan area is the largest. This study can enrich the downscaling research methods of surface temperature products in complex areas with surface heterogeneity and provide a reference for urban spatial planning in the GBA.