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Urban tourism has evolved into a complex adaptive system, where unregulated expansion disrupts the ecological balance and intensifies resource stress. Understanding the dynamic evolution and resilience mechanisms of the tourism ecological health network (TEHN) is essential for supporting sustainable urban tourism as a coupled human–natural system. Using Shanghai as a case study, we applied the "vigor–organization–resilience–services" (VORS) framework to evaluate ecosystem health, which served as a constraint for constructing the TEHN, using the minimum cumulative resistance (MCR) model for the period from 2001 to 2023. A resilience framework integrating structural and functional dimensions was further developed to assess spatiotemporal evolution and guide targeted enhancement strategies. The results indicated that as ecosystem health degraded, particularly in peripheral areas, the urban TEHN in Shanghai shifted from a dispersed to a centralized structure, with limited connectivity in the periphery. The resilience of the TEHN continued to grow, with structural resilience remaining at a high level, while functional resilience still required enhancement. Specifically, the low integration and limited choice between the tourism network and the transportation system hindered tourists from selecting routes with higher ecosystem health indices. Enhancing functional resilience, while sustaining structural resilience, is essential for transforming the TEHN into a multi-centered, multi-level system that promotes efficient connectivity, ecological sustainability, and long-term adaptability. The results contribute to a systems-level understanding of tourism–ecology interactions and support the development of adaptive strategies for balancing network efficiency and environmental integrity. Full article

Understanding groundwater and surface water interaction is critical for managing water resources, particularly in water-stressed and rapidly urbanizing areas, such as many parts of Africa. A survey was conducted of borehole, spring, seep and river water radon, δ²H, δ¹⁸O and field parameters in the Jukskei River catchment, Johannesburg. Average values of electrical conductivity (EC) were 274 and 411 μS·cm⁻¹ for groundwater and surface water, and similarly for radon, 37,000 and 1100 Bq·m⁻³, with a groundwater high of 196,000 Bq·m⁻³ associated with a structural lineament. High radon was a good indicator of baseflow, highest at the end of the rainy season (March) and lowest at the end of the dry season (September), with the FINIFLUX model computing groundwater inflow as 2.5–4.7 L·m⁻¹s⁻¹. High EC was a poorer indicator of baseflow, also considering the possibility of wastewater with high EC, typical in urban areas. Groundwater δ²H and δ¹⁸O values are spread widely, suggesting recharge from both normal and unusual rainfall periods. A slight shift from the local meteoric water line indicates light evaporation during recharge. Surface water δ²H and δ¹⁸O is clustered, pointing to regular groundwater input along the stream, supporting the findings from radon. Given the importance of groundwater, further study using the same parameters or additional analytes is advisable in the urban area of Johannesburg or other cities. Full article

Smart cities are technologically advanced urban environments where interconnected systems and data-driven technologies enhance public service delivery and quality of life. These cities rely on information and communication technologies, the Internet of Things, big data, cloud computing, and other Industry 4.0 tools to support efficient city management and foster citizen engagement. Often referred to as digital cities, they integrate intelligent infrastructures and real-time data analytics to improve mobility, security, and sustainability. Ubiquitous sensors, paired with Artificial Intelligence, enable cities to monitor infrastructure, respond to residents’ needs, and optimize urban conditions dynamically. Given the increasing significance of Industry 4.0 in urban development, this study adopts a bibliometric approach to systematically review the application of these technologies within smart cities. Utilizing major academic databases such as Scopus and Web of Science the research aims to identify the primary Industry 4.0 technologies implemented in smart cities, assess their impact on infrastructure, economic systems, and urban communities, and explore the challenges and benefits associated with their integration. The bibliometric analysis included publications from 2016 to 2023, since the emergence of urban researchers’ interest in the technologies of the new industrial revolution. The task is to contribute to a deeper understanding of how smart cities evolve through the adoption of advanced technological frameworks. Research indicates that IoT and AI are the most commonly used tools in urban spaces, particularly in smart mobility and smart environments. Full article

Microbial carbon use efficiency (CUE) is an important indicator of soil organic carbon accumulation and loss and a key parameter in biogeochemical cycling models. Its regulatory mechanism is highly dependent on microbial communities and their dynamic mediation of abiotic factors. Land-use change (e.g., agricultural expansion, deforestation, urbanization) profoundly alter carbon input patterns and soil physicochemical properties, further exacerbating the complexity and uncertainty of CUE. Existing carbon cycle models often neglect microbial ecological processes, resulting in an incomplete understanding of how microbial traits interact with environmental factors to regulate CUE. This paper provides a comprehensive review of the microbial regulation mechanisms of CUE under land-use change and systematically explores how microorganisms drive organic carbon allocation through community compositions, interspecies interactions, and environmental adaptability, with particular emphasis on the synergistic response between microbial communities and abiotic factors. We found that the buffering effect of microbial communities on abiotic factors during land-use change is a key factor determining CUE change patterns. This review not only provides a theoretical framework for clarifying the microbial-dominated carbon turnover mechanism but also lays a scientific foundation for the precise implementation of sustainable land management and carbon neutrality goals. Full article

Smart living is strongly promoted to enhance the quality of life via the application of innovative solutions, and this is driven by domain specialists and policymakers, including designers, urban planners, computer engineers, and property developers. Nonetheless, the actual user, whose views ought to be considered during the design and development of smart living systems, has received little attention. Thus, this study aims to identify and examine the critical smart functions to achieve smart living in smart buildings based on occupants’ perceptions. The aim is achieved using a sequential quantitative research method involving a literature review and 221 valid survey data gathered from a case of a smart student residence in Hong Kong. The method is further integrated with descriptive statistics, the Kruskal–Walli’s test, and the criticality test. The results were validated via a post-survey with related experts. Twenty-six critical smart functions for smart living were revealed, with the top three including the ability to protect personal data and information privacy, provide real-time safety and security, and the ability to be responsive to users’ needs. A need was discovered to consider the context of buildings during the design of smart living systems, and the recommendation is for professionals to understand the kind of digital technology to be integrated into a building by strongly considering the context of the building and how smart living will be achieved within it based on users’ perceptions. The study provides valuable insights into the occupants’ perceptions of critical smart features/functions for policymakers and practitioners to consider in the construction of smart living systems, specifically students’ smart buildings. This study contributes to knowledge by identifying the critical smart functions to achieve smart living based on occupants’ perceptions of smart living by considering the specific context of a smart student building facility constructed in Hong Kong. Full article

Urban parks play an essential role in mitigating the urban heat island (UHI) effect driven by urbanization. A rigorous understanding of the cooling effects of urban parks can support urban planning efforts aimed at mitigating the UHI effect and enhancing urban sustainability. However, previous research has primarily focused on the maximum cooling impact, often overlooking the accumulative effects arising from spatial continuity. The present study fills this gap by investigating 74 urban parks located in the central area of Jinan and constructing a comprehensive cooling evaluation framework through two dimensions: maximum impact (Park Cooling Area, PCA; Park Cooling Efficiency, PCE) and cumulative impact (Park Cooling Intensity, PCI; Park Cooling Gradient, PCG). We further systematically examined the influence of park attributes and the surrounding urban structures on these metrics. The findings indicate that urban parks, as a whole, significantly contribute to lowering the ambient temperatures in their vicinity: 62.3% are located in surface temperature cold spots, reducing ambient temperatures by up to 7.77 °C. However, cooling intensity, range, and efficiency vary significantly across parks, with an average PCI of 0.0280, PCG of 0.99 °C, PCA of 46.00 ha, and PCE of 5.34. For maximum impact, PCA is jointly determined by park area, boundary length, and shape complexity, while smaller parks generally exhibit higher PCE—reflecting diminished cooling efficiency at excessive scales. For cumulative impact, building density and spatial enclosure degree surrounding parks critically regulate PCI and PCG by influencing cool-air aggregation and diffusion. Based on these findings, this study classified urban parks according to their cooling characteristics, clarified the functional differences among different park types, and proposed targeted recommendations. Full article

Plastic pollution is a growing environmental and social concern, particularly in Southeast Asia, where urban rivers serve as key pathways for transporting waste to marine environments. This scoping review examines 110 peer-reviewed studies to understand how plastic pollution in waterways is being researched, addressed, and reconceptualized. Drawing from the literature across environmental science, technology, and social studies, we identify four interconnected areas of focus: urban pollution pathways, innovations in monitoring and methods, community-based interventions, and interdisciplinary perspectives. Our analysis combines qualitative synthesis with visual mapping techniques, including keyword co-occurrence networks, to explore how real-time tools, such as IoT sensors, multi-sensor systems, and geospatial technologies, are transforming the ways plastic waste is tracked and analyzed. The review also considers the growing use of novel theoretical frameworks, such as post-phenomenology and ecological materialism, to better understand the role of plastics as both pollutants and ecological agents. Despite progress, the literature reveals persistent gaps in longitudinal studies, regional representation, and policy translation, particularly across the Global South. We emphasize the value of participatory models and community-led research in bridging these gaps and advancing more inclusive and responsive solutions. These insights inform the development of plastic tracker technologies currently being piloted in Vietnam and contribute to broader sustainability goals, including SDG 6 (Clean Water and Sanitation), SDG 12 (Responsible Consumption and Production), and SDG 14 (Life Below Water). Full article

Promoting sustainable development requires a clear understanding of how short-term fluctuations in anthropogenic emissions affect urban environmental quality. This is especially relevant for cities experiencing rapid industrial changes or emergency policy interventions. Among key environmental concerns, variations in ambient pollutants like ozone (O₃) are closely tied to both public health and long-term sustainability goals. However, traditional chemical transport models often face challenges in accurately estimating emission changes and providing timely assessments. In contrast, statistical approaches such as the difference-in-differences (DID) model utilize observational data to improve evaluation accuracy and efficiency. This study leverages the synthetic difference-in-differences (SDID) approach, which integrates the strengths of both DID and the synthetic control method (SCM), to provide a more reliable and accurate analysis of the impacts of interventions on city-level air quality. Using Shanghai’s 2022 lockdown as a case study, we compare the deweathered ozone (O₃) concentration in Shanghai to a counterfactual constructed from a weighted average of cities in the Yangtze River Delta (YRD) that did not undergo lockdown. The quasi-natural experiment reveals an average increase of 4.4 μg/m³ (95% CI: 0.24–8.56) in Shanghai’s maximum daily 8 h O₃ concentration attributable to the lockdown. The SDID method reduces reliance on the parallel trends assumption and improves the estimate stability through unit- and time-specific weights. Multiple robustness checks confirm the reliability of these findings, underscoring the efficacy of the SDID approach in quantitatively evaluating the causal impact of emission perturbations on air quality. This study provides credible causal evidence of the environmental impact of short-term policy interventions, highlighting the utility of SDID in informing adaptive air quality management. The findings support the development of timely, evidence-based strategies for sustainable urban governance and environmental policy design. Full article

In light of the increasing frequency and intensity of natural phenomena, whether climatic or telluric, the relevance of multi-risk assessment approaches has become an important issue for understanding and estimating the impacts of disasters on complex socioeconomic systems. Two aspects contribute to the worsening of this situation. First, climate change has heightened the incidence and, in conjunction, the seriousness of geohazards that often occur with each other. Second, the complexity of these impacts on societies is drastically exacerbated by the interconnections between urban areas, industrial sites, power or water networks, and vulnerable ecosystems. In front of the recent research on this problem, and the necessity to figure out the best scientific positioning to address it, we propose, through this review analysis, to revisit existing literature on multi-risk assessment methodologies. By this means, we emphasize the new recent research frameworks able to produce determinant advances. Our selection corpus identifies pertinent scientific publications from various sources, including personal bibliographic databases, but also OpenAlex outputs and Web of Science contents. We evaluated these works from different criteria and key findings, using indicators inspired by the PRISMA bibliometric method. Through this comprehensive analysis of recent advances in multi-risk assessment approaches, we highlight main issues that the scientific community should address in the coming years, we identify the different kinds of geohazards concerned, the way to integrate them in a multi-risk approach, and the characteristics of the presented case studies. The results underscore the urgency of developing robust, adaptable methodologies, effectively able to capture the complexities of multi-risk scenarios. This challenge should be at the basis of the keys and solutions contributing to more resilient socioeconomic systems. Full article

The ongoing advancement of urbanization has significantly amplified its impacts on the water environment. Understanding the coupling relationships between urbanization and the water environment (UAWE) is crucial for Chinese policymakers aiming to promote sustainable urban development. In this study, a comprehensive UAWE evaluation model was developed to examine the development trajectories in Neijiang City from 2012 to 2022. Methodologically, a comprehensive evaluation approach was applied to assess urbanization and water resource trends over this period, followed by the development of a Coupling Coordination Degree Model (CCDM) to quantify their synergistic relationship. The results showed that the coupling between the comprehensive urbanization index and the water environment system evolved over time, as reflected in the following key findings: (1) Neijiang underwent three distinct stages from 2012 to 2022 in terms of coupling and coordination between urbanization and the water environment: Basic Coordination (2012–2015), Good Coordination (2016–2020), and Excellent Coordination (2020–2022). (2) Urbanization exerted varying impacts on subsystems of the water environment, with the pressure-response subsystems exhibiting marked volatility from 2012 to 2022. The impact intensity followed the order spatial urbanization > economic urbanization > social urbanization > population urbanization. These findings offer valuable theoretical and practical insights for aligning urban sustainability goals with effective water environment protection measures. This study provides essential guidance for policymakers in Neijiang and similar regions, enabling the development of tailored strategies for sustainable urbanization and enhanced water management. Full article

The integration of artificial intelligence (AI), big data analytics, and blockchain technologies within the digital economy presents transformative opportunities for promoting low-carbon urban development. However, a systematic understanding of how these digital innovations influence urban carbon mitigation remains limited. This study addresses this gap by proposing two research questions (RQs): (1) What are the key success factors for artificial intelligence, big data, and blockchain in urban carbon emission reduction? (2) How do these technologies interact and support the transition to low-carbon cities? To answer these questions, the study employs a hybrid methodological framework combining the decision-making trial and evaluation laboratory (DEMATEL) and interpretive structural modeling (ISM) techniques. The data were collected through structured expert questionnaires, enabling the identification and hierarchical analysis of twelve critical success factors (CSFs). Grounded in sustainability transitions theory and institutional theory, the CSFs are categorized into three dimensions: (1) digital infrastructure and technological applications; (2) digital transformation of industry and economy; (3) sustainable urban governance. The results reveal that e-commerce and sustainable logistics, the adoption of the circular economy, and cross-sector collaboration are the most influential drivers of digital-enabled decarbonization, while foundational elements such as smart energy systems and digital infrastructure act as key enablers. The DEMATEL-ISM approach facilitates a system-level understanding of the causal relationships and strategic priorities among the CSFs, offering actionable insights for urban planners, policymakers, and stakeholders committed to sustainable digital transformation and carbon neutrality. Full article

Coastal ecosystems are increasingly threatened by climate change and anthropogenic pressures, which can disrupt microbial communities and favor the emergence of pathogenic organisms. In this study, we applied metagenomic analysis to characterize fungal communities in sediment samples from an urban mangrove subjected to environmental stress. The results revealed a fungal community with reduced richness—28% lower than expected for similar ecosystems—likely linked to physicochemical changes such as heavy metal accumulation, acidic pH, and eutrophication, all typical of urbanized coastal areas. Notably, we detected an increase in potentially pathogenic genera, including Candida, Aspergillus, and Pseudoascochyta, alongside a decrease in key saprotrophic genera such as Fusarium and Thelebolus, indicating a shift in ecological function. The fungal assemblage was dominated by the phyla Ascomycota and Basidiomycota, and despite adverse conditions, symbiotic mycorrhizal fungi remained present, suggesting partial resilience. A considerable fraction of unclassified fungal taxa also points to underexplored microbial diversity with potential ecological or health significance. Importantly, this study does not aim to compare pristine and contaminated environments, but rather to provide a sanitary alert by identifying the presence and potential proliferation of pathogenic fungi in a degraded mangrove system. These findings highlight the sensitivity of mangrove fungal communities to environmental disturbance and reinforce the value of metagenomic approaches for monitoring ecosystem health. Incorporating fungal metagenomic surveillance into environmental management strategies is essential to better understand biodiversity loss, ecological resilience, and potential public health risks in degraded coastal environments. Full article

Urban health planning requires a multi-scalar understanding of the territory, capable of capturing socio-economic inequalities and health needs at the local level. In the case of Rome, current administrative subdivisions—Urban Zones (Zone Urbanistiche)—are too large and internally heterogeneous to serve as effective units for equitable health planning. This study presents a methodology for the territorial redefinition of Rome’s Municipality III, aimed at supporting healthcare planning through an integrated analysis of census sections. These were grouped using a combination of census-based socio-demographic indicators (educational attainment, employment status, single-person households) and real estate values (OMI data), alongside administrative and road network data. The resulting territorial units—21 newly defined Mesoareas—are smaller than Urban Zones but larger than individual census sections and correspond to socio-territorially homogeneous neighborhoods; this structure enables a more nuanced spatial understanding of health-related inequalities. The proposed model is replicable, adaptable to other urban contexts, and offers a solid analytical basis for more equitable and targeted health planning, as well as for broader urban policy interventions aimed at promoting spatial justice. Full article

Understanding the carbon emissions of multimodal travel—comprising walking, metro, bus, cycling, and ride-hailing—is essential for promoting sustainable urban mobility. However, most existing studies focus on single-mode travel, while underlying spatiotemporal and behavioral determinants remain insufficiently explored due to the lack of fine-grained data and interpretable analytical frameworks. This study proposes a novel integration of high-frequency, real-world mobility trajectory data with interpretable machine learning to systematically identify the key drivers of carbon emissions at the individual trip level. Firstly, multimodal travel chains are reconstructed using continuous GPS trajectory data collected in Beijing. Secondly, a model based on Calculate Emissions from Road Transport (COPERT) is developed to quantify trip-level CO₂ emissions. Thirdly, four interpretable machine learning models based on gradient boosting—XGBoost, GBDT, LightGBM, and CatBoost—are trained using transportation and built environment features to model the relationship between CO₂ emissions and a set of explanatory variables; finally, Shapley Additive exPlanations (SHAP) and partial dependence plots (PDPs) are used to interpret the model outputs, revealing key determinants and their non-linear interaction effects. The results show that transportation-related features account for 75.1% of the explained variance in emissions, with bus usage being the most influential single factor (contributing 22.6%). Built environment features explain the remaining 24.9%. The PDP analysis reveals that substantial emission reductions occur only when the shares of bus, metro, and cycling surpass threshold levels of approximately 40%, 40%, and 30%, respectively. Additionally, travel carbon emissions are minimized when trip origins and destinations are located within a 10 to 11 km radius of the central business district (CBD). This study advances the field by establishing a scalable, interpretable, and behaviorally grounded framework to assess carbon emissions from multimodal travel, providing actionable insights for low-carbon transport planning and policy design. Full article

To date, few studies have been published for cities in Germany that take into account climate change and changing hydrologic patterns due to increases in building density. This study investigates the efficiency of LID for past and future climate in the polycentric agglomeration area Frankfurt, Main (Central Germany) using observed and projected climate (model) data for a standard reference period (1961–1990) and a high emission scenario (RCP 8.5) as well as a climate protection scenario (RCP 2.6), under 40 to 75 percent building density. LID elements included green roofs, permeable pavement and bioretention cells. SWMM₅ was used as model for simulation purposes. A holistic evaluation of simulation results showed that effectiveness increases incrementally with LID implementation percentage and inverse to building density if implemented onto at least 50 percent of available impervious area. Building density had a higher adverse effect on LID efficiency than climate change. The results contribute to the understanding of localized effects of climate change and the implementation of adaption strategies to that end. The results of this study can be helpful for the scientific community regarding future investigations of LID implementation efficiency in dense residential areas and used by local governments to provide suggestions for urban water balance revaluation. Full article