Bay Area Research Articles

Exploring tsunami generation and propagation: A case study of the 2018 Palu earthquake and tsunami

The tsunami disaster in Palu, Indonesia, on September 28, 2018, caused many casualties and damage along Palu Bay, Indonesia. Prior to the tsunami, an earthquake was recorded 25 km northeast of Donggala. The analysis suggests that the tsunami was triggered by a landslide beneath the waters of Palu Bay. The investigation then focused on the earthquake's impact on the tsunami. This study conducts simulations to discern the dominant causes of tsunamis, comparing the effects of landslides with earthquakes. Three distinct tsunami sources were identified: earthquake-only (T-E), earthquake and submarine landslide (T-EL), and submarine landslide-only (T-L). Interpretation of tsunami source parameters for underwater landslides is carried out accurately. A nesting grid numerical method was employed to increase resolution and accuracy in the simulations. This study accurately interprets tsunami source parameters using data from the Bathymetry National Data-Geospatial Information Agency (185 m resolution) and the field survey data from Baruna Jaya IV-BPPT Research Vessel Indonesia (25 m resolution), thereby contributing to the creation of a tsunami inundation model map. This map serves to inform preparedness efforts and guide recommendations for infrastructure development in the Palu Bay area. The tsunami inundation model map is verified with field data and estimated using tide gauge monitoring data. The characteristics of the tsunami waves on September 28, 2018, reflect a T-EL-type tsunami source, while the T-L type source produces tsunami waves with longer periods compared to earthquake-generated tsunamis (T-EL). The T-E type, located on land, had minimal influence in causing the tsunami, despite the destructive potential of the shallow-depth M7.5 earthquake.

Analysis of the Prospects and Development of Insurance Premium Trust Products in Hong Kong

Insurance premium trust is a hybrid model that combines insurance and trust services. In recent years, with the gradual improvement of China’s economy, there has been a heightened awareness of wealth management among people. To meet this growing demand, the insurance market has expanded greatly, resulting in the emergence of various new insurance products, including insurance premium trust products. Based on the status of the domestic insurance market and Hong Kong insurance market in recent years, this paper summarizes the advantages and disadvantages of insurance premium trust products launched by the Hong Kong insurance industry. Furthermore, it discusses the prospects and development of the Hong Kong insurance premium trust model against the background of the establishment of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA).

Caregiver Experiences of Racialization While Accessing Early Intervention (EI) Services for their Children

Early intervention (EI) services—including but not limited to speech, physical, occupational, and mental health therapies—have been proven to significantly benefit young children’s development, shaping their readiness for school and offering pivotal support for caregivers. However, racial disparities persist in identification for and access to EI services. Little is known about the ways in which racism affects how caregivers experience and navigate the screening and EI referral process. Through in-depth qualitative interviews, we explored the experiences of nine minoritized caregivers with experience seeking EI services in the San Francisco Bay Area. Using Critical Race Theory, we investigated how caregivers are racialized in the process. Our findings elucidate mechanisms of racialization, including but not limited to provider bias, managing stereotype threat, and dismissal of caregiver concerns. We also highlight ways in which such racialization contributes to disparities in who receives EI services. Understanding how this process racializes its users will elucidate pathways of change toward a more equitable system, in which minoritized children receive screening and early intervention services in a timely and appropriate manner.

Regional-scale fault-to-structure earthquake simulations with the EQSIM framework: Workflow maturation and computational performance on GPU-accelerated exascale platforms

Continuous advancements in scientific and engineering understanding of earthquake phenomena, combined with the associated development of representative physics-based models, is providing a foundation for high-performance, fault-to-structure earthquake simulations. However, regional-scale applications of high-performance models have been challenged by the computational requirements at the resolutions required for engineering risk assessments. The EarthQuake SIMulation (EQSIM) framework, a software application development under the US Department of Energy (DOE) Exascale Computing Project, is focused on overcoming the existing computational barriers and enabling routine regional-scale simulations at resolutions relevant to a breadth of engineered systems. This multidisciplinary software development—drawing upon expertise in geophysics, engineering, applied math and computer science—is preparing the advanced computational workflow necessary to fully exploit the DOE’s exaflop computer platforms coming online in the 2023 to 2024 timeframe. Achievement of the computational performance required for high-resolution regional models containing upward of hundreds of billions to trillions of model grid points requires numerical efficiency in every phase of a regional simulation. This includes run time start-up and regional model generation, effective distribution of the computational workload across thousands of computer nodes, efficient coupling of regional geophysics and local engineering models, and application-tailored highly efficient transfer, storage, and interrogation of very large volumes of simulation data. This article summarizes the most recent advancements and refinements incorporated in the workflow design for the EQSIM integrated fault-to-structure framework, which are based on extensive numerical testing across multiple graphics processing unit (GPU)-accelerated platforms, and demonstrates the computational performance achieved on the world’s first exaflop computer platform through representative regional-scale earthquake simulations for the San Francisco Bay Area in California, USA.

Environmental Justice and Systems Analysis for Air Quality Planning in the Port of Oakland in California.

Many frontline communities experience adverse health impacts from living in proximity to high-polluting industrial sources. Securing environmental justice requires, in part, a comprehensive set of quantitative indicators. We incorporate environmental justice and life-cycle thinking into air quality planning to assess fine particulate matter (PM2.5) exposure and monetized damages from operating and maintaining the Port of Oakland, a major multimodal marine port located in the historically marginalized West Oakland community in the San Francisco Bay Area. The exposure domain for the assessment is the entire San Francisco Bay Area, a home to more than 7.5 million people. Of the more than 14 sources included in the emissions inventory, emissions from large container ships, or ocean-going vessels (OGVs), dominate the PM2.5 intake, and supply chain sources (material production and delivery, fuel production) represent between 3.5% and 7.5% of annual intake. Exposure damages, which model the costs from excess mortalities resulting from exposure from the study's emission sources, range from USD 100 to 270 million per annum. Variations in damages are due to the use of different concentration-response relationships, hazard ratios, and Port resurfacing area assumptions. Racial and income-based exposure disparities are stark. The Black population and people within the lowest income quintile are 2.2 and 1.9 times more disproportionately exposed, respectively, to the Port's pollution sources relative to the general population. Mitigation efforts focused on electrifying in-port trucking operations yield modest reductions (3.5%) compared to strategies that prioritize emission reductions from OGVs and commercial harbor craft operations (8.7-55%). Our recommendations emphasize that a systems-based approach is critical for identifying all relevant emission sources and mitigation strategies for improving equity in civil infrastructure systems.

Potential ecological risk by metals in Sepetiba Bay (SE Brazil): exporting metals to the oceanic region

Sepetiba Bay (SB) is one of the Brazilian coastal regions most impacted by anthropogenic activities due to increased population density, the installation of tourist and industrial mega-developments, and essential port, agricultural and fishing activities. Given the great concern about its environmental quality, several studies have focused on it, especially since the 1980s. This study aims to analyze the current ecological risk by potentially toxic elements (PTEs) in the bottom sediments of Sepetiba Bay (SB). The work integrates numerous variables (physicochemical, textural, and geochemical) analyzed at 73 stations in SB and the transition area to Ilha Grande Bay. The results show that the sediments in the eastern-inner region of Sepetiba Bay, along the Itacuruçá coast, and in some areas near the Marambaia Barrier Island (MBI) are severely polluted by Cd (4.47 mg kg-1)>Sn (7.31 mg kg-1)>Zn (824 mg kg-1)>Co (12.50 mg kg-1)>Pb (36.6 mg kg-1)>Cr (65.0 mg kg-1)>Fe (4.98 %)>Ni (21.4 mg kg-1)>V (66.0 mg kg-1), (maximum values), and highly polluted by U (2.8 mg kg-1)>Mn (857 mg kg-1)>Sb (0.32 mg kg-1)>Tl (0.63 ppm)>Hg (170 μg kg-1)>As (11.2 mg kg-1), maximum values. The pollution load index (PLI) and contamination degree (Cdeg) indexes also indicate considerable to very high contamination, indicating severe pollution in that zone. The Potential Ecological Risk Index (PERI) suggests high (160≤RI<320) and very high (RI ≥320) ecological risk in the inner and middle areas of Sepetiba Bay by Cd>Hg>Cu>Co>Pb>Sn>Tl>Ni. Relatively low pH and Eh values were identified in the inner region of Sepetiba Bay, indicating eutrophication processes. A comparison of the concentration factors in the surface sediments of SB in 2009-2011 and 2022 suggests that there has been a general increase in contamination by metals, namely Fe, Mn, Pb, Co, Cr, Ni, U, V, and Hg. Although metals accumulate more pronounced in the inner and the most impacted marginal areas of Sepetiba Bay, their enrichment factors (EF) indicate an increase in contamination in the outer sector. Metals are being imported from the nearby oceanic region, where there is a dredging site, to the outer sector of Sepetiba Bay. Thus, the choice of dredged spoil disposal sites and how contaminated dredged material is deposited needs to be better addressed and is still a challenging task. This aspect often results in problems being transferred from one region to another. The results suggest that mitigation measures must be adopted to prevent the situation from worsening.

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 evolution of ozone pollution in China: A spatiotemporal characteristics reconstruction from 1980 to 2021

Ozone pollution emerged as a significant environmental concern in China in recent years. Given the enduring impact of anthropogenic activities on the atmosphere, understanding the long-term evolutionary patterns of ozone pollution is crucial for effective pollution mitigation strategies. However, there have been limited studies to reconstruct spatiotemporal continuity ozone concentrations in China since 1980. In this research, monthly and daily MDA8 ozone concentrations from 1980 to 2021 were first estimated by reproducing the numerical relations between ozone pollution and the atmospheric environment based on a machine learning approach. The verification results from temporal, spatial, and sample-based cross-validation methods demonstrated the model performance in simulating MDA8 ozone concentration spatiotemporally, with correlation coefficients (R) ranging from 0.76 to 0.79, mean square error (RMSE) ranging from 22.72 μg·m−3 to 24.28 μg·m−3, and mean absolute error (MAE) ranging from 17.06 μg·m−3 to 18.25 μg·m−3. The evolution of ozone pollution in China can be delineated into two phases, characterized by an increasing trend since 2001, coinciding with significant rises in average annual temperature and hot weather frequency over the past four decades. The 90th percentile of ozone concentration from 2001 to 2021 increased by 1.79% compared to the period from 1980 to 2001. Notably, ozone episodes have expanded beyond summer months, partly due to global warming and frequent extreme climate events, contributing to an annual temperature increase of 0.03 °C. Despite several pollutant emission restrictions since 2013, the ozone pollution in China has persisted due to the increased emissions of VOCs. Spatially, severe ozone pollution hotspots are evident in Xinjiang province, the Jing-Jin-Ji urban agglomeration, and Northeast China, with notable increases observed in Tibet and the Guangdong-Hong Kong-Macao Greater Bay Area over the past six years. Moreover, the influence factors were identified by the average reduction of node impurity calculation. The evolution of ozone pollution was inextricably linked to solar radiation and atmospheric temperature, which played pivotal positive roles in ozone formation. Whilst, there is significant heterogeneity in the effect of meteorological conditions on ozone concentrations due to the disparity between land and oceanic climates.

Impacts of extreme precipitation on water conservation in Beijiang River Basin, China

Water conservation, which has been regarded as one of the most crucial ecosystem service functions of river basins, is a key factor for sustainable development, that has garnered widespread attention in the context of climate change. However, little attention has been paid to daily water conservation and its response to extreme precipitation events. In this study, we selected the Beijiang River Basin (BJRB) as the study area, which is an important ecological barrier for the Guangdong-Hong Kong-Macau Greater Bay Area (GBA), based on the distributed hydrological model WEP-L (Water and Energy transfer Processes in Large River basin) and water balance equation, the spatiotemporal characteristics of extreme precipitation and four daily water conservation indicators (maximum 1 day, maximum 3 day, maximum 5 day, maximum 7 day water conservation) in the BJRB from 1965 to 2009 were analysed, and the responses of these indicators to extreme precipitation were investigated. The results reveal that (1) the amount (RX1day, RX3day, RX5day, RX7day, R95p, and R99p), intensity (SDII), and frequency (R50mm) of extreme precipitation increased in the BJRB during 1965–2009, with SDII undergoing a significant increase. (2) The four water conservation indicators were positive, indicating that the watershed reduced peak flow during the extreme precipitation period by accumulating precipitation. The rate of increase of the four water conservation indicators were lower than that of extreme precipitation indices, there will be more extreme precipitation converted into surface runoff, thereby increasing the risk of flooding. The four indicators increased in the southern and northern regions of the BJRB, while decreased in the central region during 1965–2009. (3) The four indicators were generally positively correlated with most of the extreme precipitation indices in the northern BJRB, especially RX1day, RX3day, RX5day, RX7day, R99p, and R50mm. The results provide a scientific reference for water conservation adaptation to extreme precipitation variations, protection of the ecological environment, and the prevention and reduction of disasters in the region.

Spatio-temporal evolution and influencing factors of high quality economic development: Case study of Guangdong-Hong Kong-Macao Greater Bay Area

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.

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.

Characteristics of mesoscale eddies in the Mozambique Channel.

The mesoscale eddy characteristics of the Mozambique Warm Current were investigated by detecting and tracking satellite altimetry data from 2010 to 2019. A total of 1,086 eddies were identified in the Mozambique Channel, comprising 509 cyclonic eddies and 577 anticyclonic eddies. The results revealed that the bay area on the northwest coast of Madagascar was the main hotspot of eddy generation, and the mean amplitude and radius of the anticyclonic eddies in the Mozambique Channel were 24.23 cm and 82.7 km, respectively, which are larger than those of the cyclonic eddies. Local wind forcing had a significant impact on the formation of mesoscale eddies in the Mozambique Channel. In winter, the wind stress in the northern and southern areas of the Mozambique Channel exhibited a strong correlation with the distribution of eddy kinetic energy (EKE), where both monsoonal winds in the north and trade winds in the south could facilitate mesoscale anticyclonic eddy formation. In addition, the variability in the number of anticyclonic and cyclonic eddies in the Mozambique Channel may have exerted a significant influence on the seasonal anomalous fluctuations in local sea surface temperatures (SSTs). This study presented a novel analysis of the mesoscale eddy characteristics in the Mozambique Channel.

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.

Prediction of storm surge in the Pearl River Estuary based on data-driven model

Storm surges, a significant coastal hazard, cause substantial damage to both property and lives. Precise and efficient storm surge models are crucial for long-term risk assessment and guiding emergency management decisions. While high-fidelity dynamic models offer accurate predictions, their computational costs are substantial. Hence, recent efforts focus on developing data-driven storm surge surrogate models. This study focuses on the Pearl River Estuary in Guangdong Province. Initially, the dynamic ADvanced CIRCulation (ADCIRC) model was utilized to construct storm surge data for 16 historical typhoons, serving as training, validation, and testing data for data-driven models. Subsequently, Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Informer deep learning (DL) models were employed for forecasting of storm surge over the next 1h, 3h, 6h, 12h, and 18h. Finally, Shapley Additive exPlanations (SHAP) values were used for interpretability analysis of the input factors across different models. Results indicated that the proposed DL storm surge prediction model can effectively replicate the dynamic model’s simulation results in short-term forecasts, significantly reducing computational costs. This model offers valuable scientific assistance for future coastal storm surge forecasts in the Greater Bay Area.

Multi-Scenario Simulation of Land System Change in the Guangdong–Hong Kong–Macao Greater Bay Area Based on a Cellular Automata–Markov Model

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

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.

Hotel Performance in the Guangdong-Hong Kong-Macau Greater Bay Area: A Non-Homogeneous Perspective

Improving upon previous studies on hotel performance evaluation in terms of efficiency, which usually treat different hotels as homogeneous in their product provision, this study examines the performance of 53 hotels in the Guangdong-Hong Kong-Macau Greater Bay Area (GBA) during 2015 to 2019, by employing data envelopment analysis with a non-homogeneous decision making units (DMUs) model. The results show that the hotels in GBA were inefficient, and those with high product diversification are generally closer to achieving optimal efficiency levels, particularly those providing rooms, food and beverage, meeting services, and spa services, while those providing only rooms had the worst performance. Among the GBA cities included in this study, the non-homogeneous efficiency scores of the hotels in Guangzhou outperformed those of hotels in the other cities, and the overall hotel efficiency score of the core cities was better than that of the key node cities.

Spatial system perspective of understanding “fuel-sensitive routes” using regional-scale case studies

Eco-routing is widely researched as a promising way to reduce traffic-related environmental externalities. Past studies indicate that fuel-sensitive eco-routing trades the fastest routes with shorter, slower urban roads to take advantage of the “optimal fuel economy”. However, past studies focusing on individual routes often fail to depict the spatial redistribution of traffic being closer to the residential areas when eco-routing is adopted at a large scale. This research conducts city-scale fuel-sensitive routing simulations for two large metropolitan areas: the San Francisco Bay Area case and the Tokyo Area case. Combining agent-based simulations with data-driven fuel model, it is revealed that fuel-sensitive routing exerts heavier traffic on the local streets and increases residents’ direct exposure to traffic. Furthermore, by comparing different sub-areas, the varied degrees of traffic redistribution are found to be mainly dependent on the availability of major roads as alternative routes, and existing traffic congestion levels in an area.

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.