Search Results (9,054)

The COVID-19 pandemic led to an unprecedented slowdown in global economic and transportation activities, offering a unique opportunity to assess the relationship between human activity and atmospheric pollution. This study analyzes global variations in major air pollutants and meteorological conditions during the pandemic period using multi-satellite and reanalysis datasets. Nitrogen dioxide (NO₂) data were obtained from the OMI sensor aboard NASA’s Aura satellite, while carbon monoxide (CO) observations were taken from the MOPITT instrument on Terra. Reanalysis products from MERRA-2 were used to assess CO, sulfur dioxide (SO₂), black carbon (BC), organic carbon (OC), and key meteorological variables, including temperature, precipitation, evaporation, wind speed, and direction. Average concentrations of pollutants for April, May, and June 2020, representing the lockdown phase, were compared with the average values of the same months during 2017–2019, representing pre-pandemic conditions. The difference between these multi-year means was used to quantify spatial changes in pollutant levels. Results reveal widespread reductions in NO₂, CO, SO₂, and BC concentrations across major industrial and urban regions worldwide, consistent with decreased anthropogenic activity during lockdowns. Meteorological analysis indicates that the observed reductions were not primarily driven by short-term weather variability, confirming that the declines are largely attributable to reduced emissions. Unlike most previous studies, which examined local or regional air-quality changes, this work provides a consistent global-scale assessment using harmonized multi-sensor datasets and uniform temporal baselines. These findings highlight the strong influence of human activities on atmospheric composition and demonstrate how large-scale behavioral and economic shifts can rapidly alter air quality on a global scale. The results also provide valuable baseline information for understanding emission–climate interactions and for guiding post-pandemic strategies aimed at sustainable air-quality management. Full article

Chennai’s transport sector is undergoing a structural transition as the city seeks to accommodate rapidly growing travel demand while reducing energy consumption and emissions. This study develops a city-scale system dynamics model using STELLA to simulate long-term transitions in bus and Intermediate Public Transport (IPT) systems over the period 2011–2038. Four policy scenarios—Do Minimum, Partial, Desirable, and Ideal—are evaluated to examine how fleet expansion, propulsion technology substitution, and service restructuring influence urban transport energy sustainability. The model integrates demographic growth, service-level fleet benchmarks, and multiple propulsion pathways, including diesel, CNG, LPG, bio-CNG, hydrogen, and battery- and solar-electric technologies. Full article

The construction sector generates a substantial proportion of Australia’s total solid waste, underscoring the urgent need for sustainable and circular resource management approaches to mitigate environmental impacts. This study evaluates the environmental performance and circularity potential of construction and demolition waste (C&DW) management across five Australian states. Three representative building cases were modelled using both national-average and state-specific recycling rates and electricity generation mixes. A Life Cycle Assessment (LCA) was conducted to compare two end-of-life pathways: landfill and recycling. Key parameters, including transport distance and substitution ratio, were also examined to assess their influence on carbon outcomes. The results show that regional variations in electricity generation mix and recycling rate have a strong influence on the total Global Warming Potential of C&DW management. States with cleaner electricity grids and higher recycling rates, such as South Australia, exhibited notably lower recycling-related emissions than those relying on fossil-fuel-based power. The findings highlight the importance of incorporating regional characteristics into sustainability assessments of C&DW management and provide practical insights to support Australia’s transition toward a circular and low-carbon construction industry. Full article

Universal accessibility remains a critical challenge for effective public transport and urban equity. This study addresses the need for operational prioritisation tools by proposing a robust hybrid methodology to rank interventions at urban mobility nodes. The approach combines the Analytic Hierarchy Process (AHP) for integrating expert and participatory criteria weighting with four Multi-Criteria Decision-Making (MCDM) techniques (TOPSIS, VIKOR, COPRAS, and ARAS) to ensure solution reliability. Empirical validation, conducted on 30 bus stops in Segovia, Spain, confirmed the methodological soundness, evidenced by near-perfect correlations (ρ = 0.99) among the compromise and additive ratio models (TOPSIS–VIKOR and COPRAS–ARAS) and stability across over 85% of sensitivity simulations. The findings validate that the methodology effectively guides resource allocation towards interventions yielding maximum social impact and demonstrate its transferability to complex urban supply chain contexts, such as logistics microhubs. Ultimately, this replicable and adaptable model supports the transition towards more equitable, resilient urban systems, aligning directly with Sustainable Development Goal 11 (Sustainable Cities and Communities). Full article

This case study in Foz do Iguaçu, Brazil, addresses the urban problem of the degradation of road corridors used by public transport, affecting the accessibility, safety, and efficiency of urban mobility. To address this issue, a multi-criteria methodology based on MIVES (Integrated Value Model for Sustainable Assessments) was applied, combined with the AHP (Analytic Hierarchy Process) method, allowing the evaluation of 20 key urban roads using a hierarchical set of indicators linked to infrastructure, accessibility, and mobility. The assessment was operationalized through the Transport Infrastructure Condition Index (TICI), which yielded results ranging from 0.32 to 0.88, reflecting significant contrasts in the road’s upkeep and maintenance conditions. The lowest scores were associated with deficiencies in universal accessibility, cycling infrastructure, signage, and adaptations for people with reduced mobility, highlighting structural limitations in sustainability and urban inclusion. The model facilitates the prioritization of road interventions based on urgency and criticality, becoming a useful tool for guiding public investment decisions. Its comprehensive approach and replicability make it a valuable methodological alternative for other Latin American contexts, where pressure to improve urban services coexists with budgetary constraints, contributing to more efficient and sustainable strategic planning of public transportation. Full article

Higher education institutions face a critical methodological challenge in pursuing net-zero commitments: Within the amount ofhe emissions related to Scope 3, including indirect emissions from water consumption, waste disposal, business travel, and mobility, employees commuting represents 50–92% of campus carbon footprints, yet reliable quantification remains elusive due to fragmented data collection and governance silos. The present research investigates how purposeful integration of the Home-to-Work Commuting Plan (HtWCP)—mandatory under Italian Decree 179/2021—into the Climate Neutrality Plan (CNP) could constitute an innovative strategy to enhance emissions accounting rigor while strengthening institutional governance. Stemming from the University of Genoa case study, we show how leveraging mandatory HtWCP survey infrastructure to collect granular mobility behavioral data (transportation mode, commuting distance, and travel frequency) directly addresses the GHG Protocol-specified distance-based methodology for Scope 3 accounting. In turn, the CNP could support the HtWCP in framing mobility actions into a wider long-term perspective, as well as suggesting a compensation mechanism and paradigm for mobility actions that are currently not included. We therefore establish a replicable model that simultaneously advances three institutional dimensions, through the operationalization of the Avoid–Shift–Improve framework within an integrated workflow: (1) methodological rigor—replacing proxy methodologies with actual behavioral data to eliminate the notorious Scope 3 data gap; (2) governance coherence—aligning voluntary and regulatory instruments to reduce fragmentation and enhance cross-functional collaboration; and (3) adaptive management—embedding biennial feedback cycles that enable continuous validation and iterative refinement of emissions reduction strategies. This framework positions universities as institutional innovators capable of modeling integrated governance approaches with potential transferability to municipal, corporate, and public administration contexts. The findings contribute novel evidence to scholarly literature on institutional sustainability, policy integration, and climate governance, whilst establishing methodological standards relevant to international harmonization efforts in carbon accounting. Full article

A comfortable, reliable, safe and environmentally friendly high-speed train journey that saves time and offers an unforgettable experience for passengers is not a dream. Passengers can enjoy panoramic views, delicious cuisine and use their mobile devices without restrictions. High-speed trains, powered by environmentally friendly methods, are a sustainable form of transport, reducing harmful emissions. Integrating intelligent control and management into railway networks has the capacity to increase efficiency and improve reliability and safety, as well as reduce development and maintenance costs. Future intelligent railway network architectures are expected to focus on integrated, multi-layered systems that deeply embed artificial intelligence (AI), the Internet of Things (IoT) and advanced communication technologies (5G/6G) to ensure intelligent operation, improved reliability and increased safety. Distributed intelligent control in railways refers to an advanced approach in which decision-making capabilities are distributed across network components (trains, stations, track sections, control centers) rather than being concentrated in a single central location. The recent advances in AI in railways are associated with numerous scientific papers that enable intelligent traffic management, automatic train control, and predictive maintenance, with each of the proposed intelligent solutions being evaluated in terms of key performance indicators such as latency, reliability, and accuracy. This study focuses on how different intelligent solutions in railways can be implemented in network components based on the requirements for real-time control, near-real-time control, and non-real-time operation. The analysis of related works is focused on the proposed intelligent railway frameworks and architectures. The description of typical use cases for implementing intelligent control aims to summarize latency requirements and the possible distribution of control logic between network components, taking into account time constraints. The considered use case of automatic train protection aims to evaluate the added latency of communication. The requirements for the nodes that host and execute the control logic are identified. Full article

In the field of the road transportation industry, quantitative research on the relationship between artificial intelligence (AI) technology and corporate sustainable development is relatively scarce. This disparity has led to discussions about whether artificial intelligence technology can truly promote the sustainable development level of the highway maintenance industry. Therefore, this study aims to quantify the relationship between artificial intelligence technology and the sustainable development of the highway maintenance industry, and to analyze the reasons behind the current controversies. The research results show: (1) Each exogenous variable has an impact on sustainable development, although the degree of influence varies, especially the economic development level (ED) has the strongest direct effect on sustainable development, followed by the level of market demand (MD), the level of policy support (PS), and the level of enterprise capital (EC); (2) Moderating variables can enhance this direct impact, among which the moderating effect of ED on the relationship between ED and sustainable development is the strongest; (3) Artificial intelligence technology has different impacts on enterprises at different positions in the industrial chain, thereby explaining the controversy over whether to adopt it or not. These conclusions highlight the value of artificial intelligence technology and provide a reasonable explanation for the existing controversies in the industry and research field. Full article

Blueberries (Vaccinium spp.) are highly sensitive to winter chilling fulfillment, growing degree days above 7 °C (GDD7), and water balance (WB). By integrating a climate-based natural suitability index (CNSI), three-dimensional kernel density estimation, traditional and spatial Markov chains, and optimal geographic detector analysis, this study examines the spatiotemporal evolution and driving mechanisms of blueberry climatic suitability realization in 19 major producing provinces in China during 2008–2023. Results show that CNSI exhibits a stable and moderately right-skewed distribution, with partial convergence and a narrowing interprovincial gap. Suitability realization is highest in the middle and lower Yangtze River rice-growing belt, whereas the northern dryland belt and the southern subtropical mountainous belt show persistent mismatches between climatic potential and production advantages. Markov results reveal path dependence and moderate mobility, with “low–low lock-in” and “high–high club” phenomena reinforced under neighborhood effects. GeoDetector results indicate that effective facility irrigation and fertilizer input are dominant factors explaining spatial variation in CNSI, while comprehensive transportation accessibility and agricultural labor act as stable complements. Interaction analysis suggests that multi-factor synergies, particularly irrigation-centered combinations, yield strong dual-factor enhancement and near-nonlinear enhancement. These findings highlight the importance of aligning climatic suitability with adaptive infrastructure investment and region-specific management to promote sustainable production-share advantages in China’s blueberry industry. Full article

The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT), known as Artificial Intelligence of Things (AIoT), has emerged as a transformative paradigm for enabling intelligent, data-driven, and context-aware decision-making in urban environments to reduce the carbon footprint of mobility and industry. This review examines the conceptual foundations, and state-of-the-art developments of AIoT, with a particular emphasis on its applications in smart cities and its relevance to climate change management. AIoT integrates sensing, connectivity, and intelligent analytics to provide optimized solutions in transportation systems, energy management, waste collection, and environmental monitoring, directly influencing urban sustainability. Beyond urban efficiency, AIoT can play a critical role in addressing the global challenges and management of climate change by (a) precise measurements and autonomously remote monitoring; (b) real-time optimization in renewable energy distribution; and (c) developing prediction models for early warning of climate disasters. This paper performs a literature review and bibliometric analysis to identify the current landscape of AIoT research in smart city contexts. Over 1885 articles from Web of Sciences and over 1854 from Scopus databases, published between 1993 and January 2026, were analyzed. The results reveal a strong and accelerating growth in research activity, with publication output doubling in the most recent two years compared to 2023. Waste management and air quality monitoring have emerged as leading application domains, where AIoT-based optimization and predictive models demonstrate measurable improvements in operational efficiency and environmental impact. Altogether, these support faster and more effective decisions for reducing greenhouse gas emissions and ensuring the sustainable use of resources. The reviewed studies reveal rapid advancements in edge intelligence, federated learning, and secure data sharing through the integration of AIoT with blockchain technologies. However, significant challenges remain regarding scalability, interoperability, privacy, ethical governance, and the effective translation of research outcomes into policy and citizen-oriented tools such as climate applications, insurance models, and disaster alert systems. By synthesizing current research trends, this article highlights the potential of AIoT to support sustainable, resilient, and citizen-centric smart city ecosystems while identifying both critical gaps and promising directions for future investigations. Full article

Against the backdrop of the suddenness and inherent uncertainty of emergencies, pre-disaster emergency facility location and emergency relief stockpiling are critical for improving the efficiency and sustainability of emergency response. This paper focuses on the emergency response network design problem considering uncertain transportation time and emergency demands. We cluster historical disaster events and extract cluster-specific statistical features, such as the average value, mean absolute deviation, and probabilistic statistical distance of uncertain parameters, constructing an ambiguity set based on the disaster feature and multivariate probability distribution information. Then, to minimize the total rescue cost, a feature-driven two-stage distributionally robust optimization model is formulated to determine reliable pre-disaster emergency facility locations, inventory decisions, and post-disaster resource allocation strategies. Finally, through an earthquake case in Sichuan Province of China, this work verifies that incorporating disaster clustering information enables a superior trade-off between the robustness and conservatism of emergency rescue decisions. Compared with the benchmark model, the proposed method displays better out-of-sample performance and can effectively enhance the sustainability of emergency response in uncertain environments. Full article

Addressing the growing threat of climate change requires urgent and sustainable solutions for managing carbon dioxide (CO₂) emissions. This review investigates the latest advancements in technologies for capturing and converting CO₂, with a focus on approaches that prioritize energy efficiency, environmental compatibility, and economic viability. Emerging strategies in CO₂ capture are discussed, with attention to low-carbon-intensity materials and scalable designs. In parallel, innovative CO₂ conversion pathways, such as thermocatalytic, electrocatalytic, and photochemical processes, are evaluated for their potential to transform CO₂ into valuable chemicals and fuels. A growing body of research now focuses on integrating capture and conversion into unified systems, eliminating energy-intensive intermediate steps like compression and transportation. These integrated carbon capture and conversion/utilization (ICCC/ICCU) technologies have gained significant attention as promising strategies for sustainable carbon management. By bridging the gap between CO₂ separation and reuse, these sustainable technologies are poised to play a transformative role in the transition to a low-carbon future. Full article

Heat and high-irradiance stress increasingly threaten almond production in Mediterranean environments, where rising temperatures and prolonged summer droughts impair photosynthetic performance and yield. This study evaluated the effectiveness of three mineral-based shielding materials: kaolin, basalt powder, and zeolite. We hypothesized that the foliar application of reflective mineral materials would reduce leaf temperature, enhance photosynthetic efficiency, and improve yield without altering nut nutraceutical quality. A two-year field experiment (2024–2025) was conducted using a randomized block design with four materials (untreated control, kaolin, basalt powder, and zeolite). Physiological traits (gas exchange, chlorophyll fluorescence, leaf temperature, and SPAD index), morpho-biometric and biochemical parameters, and yield components were assessed. Kaolin and basalt powder significantly lowered leaf temperature (−1.6 to −1.8 °C), increased stomatal conductance and net photosynthesis, and improved photochemical efficiency (Fv′/Fm′) and electron transport rates. These treatments also enhanced drupe weight, kernel dry matter, and productive yield (up to +32% compared with the control). Zeolite produced positive but less prominent effects. No significant differences were detected in fatty acid profile, total polyphenols, or antioxidant capacity, indicating that the materials did not affect almond nutraceutical quality. Principal component analysis confirmed the strong association between kaolin and basalt powder and improved eco-physiological performance. Overall, mineral shielding materials, particularly kaolin and basalt powder, represent a promising, sustainable strategy for enhancing almond orchard resilience under Mediterranean climate change scenarios. Full article

Root exudates are key mediators of plant–soil–microbe interactions, shaping rhizosphere dynamics and influencing agroecosystem resilience. Comprising diverse primary and secondary metabolites, these compounds are actively secreted through specific transport pathways and are modulated by intrinsic plant traits and environmental conditions. Root exudates serve as chemical signals that recruit and structure microbial communities, facilitating nutrient mobilization, microbial feedbacks, and the regulation of plant growth and stress responses. By modulating soil chemical, physical, and biological properties, exudates contribute to carbon cycling, soil health, and the maintenance of ecosystem services. Moreover, they play multifunctional roles in enhancing plant tolerance to abiotic and biotic stresses, while also mediating interactions with neighboring plants. This review provides a holistic perspective on root exudation, encompassing their mechanisms and drivers, roles in rhizosphere ecology and plant stress adaptation, and methodological advances, while highlighting opportunities to harness these processes for resilient, productive, and sustainable agroecosystems. Full article

Urban sustainability depends on balancing transportation accessibility, housing affordability, and social equity. Displacement—defined in this study as the population-level loss of low-income households from a census block over time—poses a growing challenge to inclusive urban development. This study examines statistical relationships and spatial patterns linking transit accessibility, housing affordability, and low-income household displacement across the four largest counties in Tennessee. Negative binomial regression models are used to quantify relationships between transit accessibility, housing affordability, and displacement, revealing that housing affordability is consistently linked to displacement, while the effects of transit accessibility vary substantially across counties. Bivariate Local Indicators of Spatial Association (LISA) identify localized clusters where displacement coincides with transit or housing constraints, and Multivariate Cluster Typology Analysis classifies census blocks into distinct typologies, highlighting region-specific trade-offs between accessibility and affordability. Together, the results demonstrate that displacement dynamics are highly context dependent, underscoring the need for place-based and sustainability-oriented policy responses. The findings provide an empirical basis for integrating transportation and housing strategies to reduce displacement risks and support equitable and sustainable urban development in diverse metropolitan contexts. Full article