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Fast urbanization brings great challenges to sustainable development goals, such as excessive exploitation and population explosion. Classical cellular automata (CA) have been widely used to independently simulate the change of spatial features, i.e. land use, population, economic production, etc. However, most CA models rely on historical data as static driving factors to simulate future scenarios while ignoring the inter-wined influences among multiple features in the development process. To address this issue, this study proposes a spatial cooperative simulation (SCS) approach to simulate the land use, population, and economy changes. The SCS approach starts with a separate CA model to obtain the initial scenes of each feature. Then, the simulation results of each other two features are used as dynamically updated driving factors, rather than the static historical data, to capture the inter-wined influence of multiple features during the development process. This step is iteratively performed until the changes of each feature converge and the final simulation results will be reported. The simulation experiment in Greater Bay Area demonstrates that the SCS approach can well capture the simultaneous development process and outperforms baseline approaches. The SCS approach is capable of forecasting future development scenarios and facilitates spatial planning and infrastructure synergies. [ABSTRACT FROM AUTHOR]

The conservation of historic buildings and urban environments can have the potential to improve the quality of life, stimulate economic development and reduce carbon emissions from energy consumption. However, given increasing expectations of indoor thermal comfort and an increasing dependency on modern living technologies, energy consumption in the urban historic environment is expected to rise. This paper examines the retrofitting of windows to historic buildings and issues of energy consumption and carbon emission in Lhasa's historic city centre, focusing on the relationship between energy use and window retrofitting. First, the thermal performance of a traditional Tibetan building in Lhasa is simulated and examined. Second, four window types for retrofitting are evaluated through computer modelling and their impacts on energy consumption are compared. Finally, the energy demand and carbon emission of the whole historic city centre with various window types are estimated and compared. In terms of energy efficiency and historic preservation, current single-layered window retrofitting practices perform badly, while windows with wooden frames and secondary glazing may achieve better performance. The approach offers the opportunity to halve the energy consumption and carbon emissions in the Lhasa historic city centre. [ABSTRACT FROM PUBLISHER]

Exposure to ambient fine particulate matter (PM) in transit microenvironments (TMEs), which mainly comes from traffic emissions, is associated with various adverse health effects. Many studies have investigated the exposure levels and health risk to commuters in TMEs; however, the application of spatial techniques has been limited to a handful of in-transit studies, and information on spatiotemporal variations of in-transit particle concentrations remains sparse. This study aims to examine spatiotemporal variations of in-cabin particle concentrations in transit and the correlations between en-route environmental factors and the variations of particle concentrations in transit. To this end, we propose a portable monitoring system for measuring the particle concentrations and corresponding travel conditions on public transit trips. The measurements were performed during peak hours of both bus and metro services in Shenzhen China. Our results highlight that temporal variations of particle concentrations exist between different trips during the same peak hours. The particle concentrations are higher on the start/end segments of the routes than on any other segment. The variations in particle concentrations on different route segments are significantly correlated with travel conditions (p < 0.01 and p < 0.05) and urban ambient conditions (p < 0.01). Moreover, opposite effects of ventilation, wind speed, and the posture of the investigators on the variations of particle concentrations on different segments are identified between bus and metro. In addition, passengers are exposed to higher particle concentrations on longer trips. Utilizing our methodology and results, policy makers and other researchers may further explore the exposure to particle pollution on public transit. • This study proposes a portable monitoring system to monitor in-transit microenvironment. • Spatiotemporal variations in particle concentrations in transit are highlighted. • The particle concentrations are higher on the start/end points of the routes than on any other segment. • Passengers are exposed to higher particle concentrations on longer trips. [ABSTRACT FROM AUTHOR]

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., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Ma et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Vegetation is essential for ecosystem function and sustainable urban development. In the context of urbanization, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), as the typical urban-dominated region, has experienced a remarkable increase in social and economic activities. Their impact on vegetation is of great significance but unclear, as interannual flow data and linear methods have limitations. Therefore, in this study, we used human and vehicle flow data to build and simulate the indices of mobile human activity. In addition, we used partial least squares regression (PLSR), random forest (RF), and geographical detector (GD) models to analyze the impact of mobile human activities on vegetation change. The results showed that indices of mobile human and vehicle flow increased by 1.43 and 7.68 times from 2000 to 2019 in the GBA, respectively. Simultaneously, vegetation increased by approximately 64%, whereas vegetation decreased mainly in the urban areas of the GBA. Vegetation change had no significant linear correlation with mobile human activities, exhibiting a regression coefficient below 0.1 and a weight of coefficients of PLSR less than 40 between vegetation change and all the factors of human activities. However, a more significant nonlinear relationship between vegetation change and driving factors were obtained. In the RF regression model, vegetation decrease was significantly affected by mobile human activity of vehicle flow, with an importance score of 108.11. From the GD method, vegetation decrease was found to mainly interact with indices of mobile human and vehicle inflow, and the highest interaction force was 0.82. These results may support the attainment of sustainable social-ecological systems and global environmental change.