Sustainable Cities and Society最新文献

Freight mobility segregation, a phenomenon like residential social segregation, is a crucial aspect of city landscapes that influences the livability and sustainability of cities. However, there is a deficiency in understanding the intrinsic complexity of freight mobility segregation, particularly regarding the micro-level truck behaviors. In this study, we develop a new approach to assess the degree of freight mobility segregation within cities by leveraging large-scale truck GPS data in Chinese cities. The analysis indicates the existence of freight mobility segregation in cities, where certain groups of trucks serve high-demand areas, while another group of trucks serves low-demand areas. The activity spaces of distinct truck groups are largely non-overlapping or segregated. To uncover the correlations between mobility segregation and truck operational patterns, we introduce two metrics to characterize truck multi-tours behavior, focusing on tour pattern predictability and activity explorability. By employing freight point-of-interest (POI) data, we further reveal the influence of local economic structures and industrial compositions on mobility segregation in cities. These findings enrich our understanding of the dynamics of city freight systems, offering implications for improving logistics efficiency and fostering sustainable city development.

Smart cities represent a promising paradigm aimed at enhancing citizens’ quality of life through cutting-edge infrastructure and technological advancements. Collaborative services serve as a cornerstone for any smart city, fostering seamless cooperation among diverse entities, including government agencies, businesses, and individuals, thereby enhancing community outcomes. These services are pivotal, promoting seamless communication and collaboration among various smart applications, and facilitating data exchange, resource sharing, and functional interactions within smart city environments to optimize efficiency, effectiveness, and user experiences. However, the development and deployment of secure, interoperable services in smart cities present significant challenges. These issues encompass, ensuring data security compliance during interoperation, effective management of interconnected services, securely handling sensitive data across services, and addressing issues related to confidentiality, integrity, and availability (CIA) traits. To tackle these challenges, this research proposes an innovative adaptive security governance framework tailored for smart cities. This framework relies on dynamic security policies implemented through smart contracts to guarantee data security and privacy during smart service interoperation. Real-world use cases in collaborative smart city environments validate the framework, integrating multi-chain blockchain technology, smart services APIs, and Software-Defined Networking (SDN), showcasing its ability to enhance security and efficiency in collaborative services. This study contributes to the development of safe and efficient collaborative services inside smart cities, tackling administrative issues while emphasizing data security and privacy. Smart cities may improve citizens’ living conditions while successfully addressing crucial security problems in an ever-changing environment by using this architecture.

While urbanization has increased people's well-being, it has also caused ecological problems, such as resource shortage, land degradation, and environmental pollution. As the highest form of spatial organization at the mature stage of urbanization, an urban agglomeration (UA) can be a key area for studying this urban-ecology interaction. This study integrated the urbanization evaluation, ecosystem services value (ESV) assessment, and optimized coupling coordination degree (CCD) models to explore the coupling coordination of urbanization and ESV at the UA scale. Analyzing data from the Beijing-Tianjin-Hebei UA (BTHUA) from 1985 to 2020 revealed that: (1) urbanization continues to increase and ESV fluctuates; however, both exhibit significant spatial heterogeneity. (2) The CCD is increasing annually but remains relatively low overall; spatially, it is high in the northeast and low in the southwest. (3) The main driving factors of the CCD are urbanization rate and slope, while all five selected factors have a positive effect. (4) Government decision-making can guide the CCD. For which, the development pattern of “One Belt-Four Zones-Multi Cores” and zoning strategies for improving the CCD are proposed. Overall, these findings suggest balancing development and protection to promote the coordinated development of BTHUA and provide valuable insights for enhancing the sustainability of UA.

This paper explores the utilisation of knowledge graphs and an agent-based implementation to enhance urban resilience and accessibility in city planning. We expand The World Avatar (TWA) dynamic knowledge graph to support decision-making in disaster response and urban planning. By employing a set of connected agents and integrating diverse data sources — including flood data, geospatial building information, land plots, and open-source data — through sets of ontologies, we demonstrate disaster response in a coastal town in the UK and various aspects relevant to city planning for a mid-sized town in Germany using TWA. In King’s Lynn, our agent-based approach facilitates holistic disaster response by calculating optimal routes, avoiding flooded segments dynamically, assessing infrastructure accessibility before and during a flood using isochrones, identifying inaccessible population areas, guiding infrastructure restoration, and conducting critical path analysis. In Pirmasens, for city planning purposes, the knowledge graph-driven isochrone generation provides evidence-based insights into current amenity coverage and enables scenario planning for future amenities while adhering to land regulations. The implementation of agents and knowledge graphs achieves interoperability and enhances urban resilience and accessibility by enabling cross-domain correlation analysis that extends various areas including geospatial buildings, population demographics, accessibility coverage, and land use regulations.

Changes in land surface temperature (LST) caused by the diversification of urban structures have garnered widespread attention. Elementary school students, the largest group receiving education, are particularly vulnerable. However, few studies have investigated how such changes manifest around elementary schools where students are concentrated. Herein, the urban structure of Hangzhou was identified with the help of local climate zones (LCZs) based on remote sensing to analyze the land use/land cover characteristics around 458 elementary schools. Moreover, a random forest regression model was employed to explore the relation between LCZs and seasonal LST around elementary schools and the relative importance of LCZs. Results indicate the following: (1) The land use intensity and LST around elementary schools were higher. (2) The LST of different LCZs exhibited significant seasonal differences. (3) A nonlinear relationship existed between LCZs and LST around elementary schools, of which compact midrise, open high-rise, dense trees, scattered trees and water were the five most relevant types. (4) The LST around elementary schools was closely related to the urban morphology of artificial surfaces, in which building density was the dominant factor. These findings provide valuable references for land use planning in the surrounding areas of elementary schools.

Urbanization may result in an accumulation of heavy metals (HMs) in urban air ecosystems, negatively impacting air quality and potentially endangering the health of urban residents. This study utilized Jilin Province, a cold region of China, as a case study. It employed kriging interpolation to unveil heavy metal distribution, Principal Component Analysis (PCA) for source identification, and ecological and health risk assessment methods to evaluate potential health threats. The mean concentrations of HMs were greater than the local background values, indicating that heavy metal pollution in the air in Jilin Province is more serious and may be harmful to the environment and human beings. HMs tend to accumulate in urban areas with high GDP and population density and disperse to neighboring cities with levels of GDP and population density. The study identifies biomass combustion, coal combustion for heating and vehicle emissions as the primary sources of HMs. HMs in the air in Jilin Province pose a risk to both the environment and the human body, with children being particularly vulnerable. These findings emphasize the necessity of implementing comprehensive evaluation and monitoring strategies to mitigate urban air pollution and ensure environmental sustainability.

The concern over the planet environmental crisis is propelling administrations to promote anti-pollution regulations progressively restricting the use of fossil fuels. In this context, the integration of electric vehicles is increasingly being explored to reduce traffic emissions. To facilitate the transition from conventional vehicles to electrical ones, establishment of a robust charging infrastructure is essential. This article presents a numerical proposal for identifying potential optimal locations of additional electrical charging stations building upon the current infrastructure. For this purpose, Voronoi diagrams, constructed using the existing charging stations as point generators of the structure, will be stated as the primary framework. The extracted proximity data, delimited by geographical, legislative or user behavior-related factors, facilitates the identification of locations where companies in the sector are inclined to invest. This procedure holds significant relevance for industry in the sector seeking strategic investment locations that minimize competition while ensuring a high level of convenience for long-distance electrical vehicles users. The significance of this modeling lies in its simplicity and applicability to any region worldwide, making it a versatile tool for similar studies.

Understanding the relationship between urban morphology and land surface temperature (LST) is essential for mitigating urban heat island (UHI). This study investigates the spatiotemporal heterogeneity of the relationship between urban morphology and LST across 5 experiment sites located in different temperature zones of China at a block scale. Normalized difference vegetation index (NDVI), building density (BD), floor area ratio (FAR), average building height (ABH) and open space ratio (OSR) are adopted to indicate the physical urban morphology. The results show positive relationship between BD and LST, and negative relations between LST and NDVI, ABH and OSR from a global perspective. The relations of FAR to LST are mixed. However, variable relationship between urban morphological indicators (UMIs) and LST are significantly observed at a block scale. 12 scenes that generate local relations of UMIs and LST differing from that of the surroundings are identified. Typical strategies for land development, vegetation phenology, large natural elements and human activities may be the most important causative factors to the heterogenous relationship between urban morphology and LST. Findings derived in the study would promote studies on mechanisms of the effects of urban morphology on LST and contribute to practices of UHI mitigation.

Environmental, social, and governance (ESG) considerations are increasingly becoming imperative and obligatory across various industries. The ESG performance within the architecture, engineering, and construction (AEC) industry is under heightened market scrutiny. However, current ESG management in construction is still in its infancy due to two limitations: (1) a deficiency in ESG knowledge, such as indicators pertinent to construction activities, and (2) a lack of data security in ESG management, culminating in inefficient and unreliable environmental management practices. Therefore, this paper employs the Design Science Research Method (DSRM) to introduce a Blockchain-ESG Integrated (BESGI) framework, facilitating traceable ESG data management within construction projects. This framework presents three significant contributions. First, it identifies ten AEC-ESG indicators by analyzing ESG methods. Second, it proposes a mapping approach for AEC-ESG indicators to construction projects for key ESG information access and data source identification. Third, it develops a blockchain-based data management mechanism for traceable ESG data management in the BESGI framework. It validates and evaluates the framework in a construction project in Hong Kong. The results show that the framework is usable and can save labor costs by 20.15 % compared to traditional ESG management. This study offers a secure data management solution for ESG analysis of construction projects.