Search Results (3,496)

Accidents between vehicles and wildlife (WVCs) represent a complex management challenge, requiring integrated strategies that balance biodiversity conservation with public security and socio-ecological resilience. However, existing GIS hotspot analyses often identify spatial patterns without quantifying the structural landscape drivers that compel animal–road interactions. This study aims to identify “ecological traps” through an integrated landscape diagnostic framework combining Kernel Density Estimation (KDE) for statistical hotspot identification and landscape metrics (FRAGSTATS) for structural diagnosis, using the wild boar (Sus scrofa) as a focal species. An exploratory case analysis of high-collision locations was conducted, utilizing a high-quality dataset of 161 precisely georeferenced incidents recorded between 2015 and 2020 within the most critical municipalities of the Province of Avellino (Southern Italy). Results highlight two primary hotspots: the Guardia Lombardi-Conza corridor and the Avellino Nord-Pratola Serra axis. Quantitative analysis reveals that 39.1% of incidents occurred in non-irrigated arable lands and 19.9% in broad-leaved forests, with 52.8% of events situated within 500 m of river systems, which function as primary ecological movement corridors. Furthermore, fragmentation indices (Patch Density, Edge Density) were significantly higher in these focus areas, confirming that habitat isolation and the loss of core patches force animals to traverse infrastructure. These findings underscore the urgency of evidence-based spatial planning, offering a methodological framework with potential applicability for prioritizing mitigation actions, such as ecological corridors and intelligent signaling, to enhance the resilience of socio-ecological systems. This framework provides a scalable model for sustainable land management, ensuring that biodiversity conservation is integrated into long-term infrastructure governance. Full article

Road tunnels are highly carbon-intensive due to material use, energy-intensive construction, and long service lives, yet major gaps remain regarding emission hotspots, construction method comparisons, and regional differences, particularly in Central and Eastern Europe. This article combines a PRISMA 2020-guided systematic literature synthesis with a Polish case-study life-cycle assessment (ISO 14040/14044, cradle to grave, functional unit of 1 m of tunnel, 100-year horizon) using Ecoinvent factors and the Polish energy mix, covering material production, construction, operation, maintenance, and end of life. The literature synthesis found substantial variability in tunnel carbon emissions, ranging from 1500 to 22,062 t CO₂-eq per lane-kilometer depending on the construction method, tunnel type, and region. Material production was the largest contributor to construction-phase emissions (70–95%), with concrete and steel responsible for over 90% of material-phase impacts and 75–80% of construction-phase emissions, while operational energy use dominates over the full life cycle. Concrete and steel substitution (e.g., GFRP bars and calcium sulfoaluminate cement) offers the greatest construction-phase reduction potential, while operational measures, such as LED lighting, demand-controlled ventilation, and renewable energy, can cut long-term energy use by 30–50%. For Poland, low-carbon concrete, prefabrication, and renewable electricity could reduce tunnel emissions by 40–60%. These findings highlight pathways for decarbonizing tunnel infrastructure through material innovation, energy-efficient operation, and circular economy principles. Full article

Ecological corridors can promote regional integration, socio-economic development, and biodiversity conservation by enhancing cross-border connectivity. The Nepal–China Himalayan Corridor (NCHC), a strategic component of the Belt and Road Initiative, traverses an ecologically fragile and disaster-prone mountain region where infrastructure development, local livelihoods, and ecological sustainability need to be balanced. This study combines Q-methodology with the lens of imaginaries to examine stakeholder perspectives on SDG-oriented development pathways for the NCHC. Five major factors were identified: sustainable green infrastructure development, Himalayan climate adaptive capacity, inclusive growth through bilateral collaboration, trans-Himalayan multi-dimensional connectivity through viable projects, and local capacity building through transnational policy transformation. These factors reveal different stakeholder priorities and the synergies and trade-offs among SDGs in corridor development. The findings highlight the importance of multi-stakeholder cooperation and differentiated development pathways for advancing sustainable corridor development in fragile transboundary mountain regions. Full article

Flood disaster risk assessment serves as an important foundation for formulating regional sustainable development strategies. This study establishes a risk assessment model for flood disasters in small and medium-sized rivers based on a theoretical framework integrating Geographic Information Systems (GIS), the Delphi method, and Bayesian networks (Delphi–BNs). An indicator system for the assessment was developed from three dimensions: hazard, vulnerability, and exposure. Hazard is represented by flood inundation area and depth; vulnerability is indicated by population distribution and economic layout; and exposure is reflected by road accessibility. By constructing a GIS-based Bayesian network and employing the Delphi method to create a probabilistic and spatially explicit model, this approach quantifies various sources of uncertainty in the assessment process, enabling a probabilistic expression of risk. Based on the risk assessment results, a stratified, phased flood emergency rescue and personnel transfer plan was established, designating extremely high-risk areas as the core zones for the first phase of personnel transfer, high-risk areas as the second-phase rescue zones, and medium-risk areas as the third-phase rescue zones, thereby providing clear operational guidance for flood emergency response in the basin. The Delphi–BN assessment framework developed in this study focuses on the core elements of flood disaster risk formation, organically integrates expert experience with spatial big data, and effectively overcomes the limitations of traditional assessment methods, such as strong subjectivity, insufficient accuracy, and poor quantification. It achieves a refined and quantitative assessment of flood risk in small and medium-sized river basins in semi-arid regions. The outcomes of this research contribute to a clearer understanding of both the driving mechanisms and the spatial patterns of regional flood risk. Furthermore, they establish a scientifically credible and operationally relevant foundation for key disaster-response decisions, encompassing timely emergency actions, phased population transfers, and the optimized deployment of limited emergency resources. Full article

Road infrastructure risk management often relies on aggregate rankings that assume stable priorities across projects. This retrospective, document-based study examines between-project differences in risk structure and directed associations between primary and secondary risks within adverse events. The analysis uses 1177 consolidated analytical records of documented cost- and/or time-related adverse events from 28 completed road infrastructure projects implemented during the construction of Pan-European Corridor X and adjacent road infrastructure in Serbia. Primary-risk, secondary-risk, and joint primary–secondary edge profiles were compared using Jensen–Shannon divergence, permutation testing, empirical-Bayes adjustment, and a beta-binomial model. Directed network analysis identified source, receiver, and bridge roles among seven risk groups. Significant heterogeneity was found in all three profile types. At the record level, 69.8% of the 1053 records containing at least one secondary risk included a secondary risk from a different group, whereas at the link level, 53.8% of the 1717 directed group-level link occurrences were cross-group. Project documentation was the dominant source and bridge, generating 59.6% of cross-group links, whereas external factors were the largest receiver and had the highest authority score. The most frequent link, from project documentation to external factors, was not overrepresented after project-specific composition was preserved. Several less frequent links were significantly overrepresented, especially those connecting project-wide factors with employer-related risks. The findings support coordinated preventive planning. The proposed data-driven framework can support the development of digital decision-support systems for project-specific risk monitoring, coordinated preventive planning, and sustainable management of road infrastructure delivery. Full article

Industry 4.0 has produced one of the fastest-growing bodies of engineering and management research; much of this output remains siloed by technology domain. This study addresses that fragmentation through a structured critical review (a review-of-reviews), synthesising 70 peer-reviewed review articles and foundational sources (2003–2026) spanning 14 technology domains. The review introduces the I4.0-STS framework, an original four-layer structure organising evidence across physical, cyber, cognitive, and socio-organisational dimensions. Five principal findings emerge. The physical and cyber layers show consistent evidence of maturity. Industry-reported lighthouse IIoT deployments show 20–30% energy and up to 39% lead-time reductions. AI-driven predictive maintenance shows 30–50% unplanned-downtime reductions. The cognitive layer (LLM-augmented digital twins and generative AI interfaces) is technically feasible but outpaces its governance frameworks. Cybersecurity remains insufficiently governed, with documented ransomware incidents in manufacturing OT environments underscoring the risks of OT–IT convergence. SME adoption and developing-economy manufacturing transformation remain comparatively under-addressed. Finally, 12 research gaps are assessed as of June 2026, five rated Open, with future research directions proposed for each, framed against the emerging Industry 5.0 agenda. All findings are second-order interpretations from the source reviews, and their limitations are stated explicitly. Full article

Urban transport emissions are a major contributor to climate change and urban air pollution. Although previous studies have demonstrated that traffic demand, fleet electrification, and driving behavior individually influence vehicular emissions, their combined effects under different congestion conditions remain insufficiently understood. This study investigates the interactions among these factors using the microscopic traffic simulator SUMO (Simulation of Urban MObility). A synthetic urban corridor consisting of five signalized intersections was developed to represent arterial roads in medium-sized cities. A full factorial experimental design was implemented by considering three traffic demand levels, three electric vehicle adoption percentage levels, and three driving behavior profiles, resulting in 27 experimental scenarios with 10 stochastic replications per scenario. Traffic performance and pollutant emissions were evaluated to quantify both the individual and interaction effects of the experimental factors. The results indicate that traffic demand is the primary determinant of CO₂ and NO_x emissions, while fleet electrification substantially reduces emissions, particularly under congested conditions. Driving behavior also plays a role by influencing acceleration and deceleration patterns. Furthermore, statistically significant interaction effects among the experimental factors ($p<0.05$) reveal the benefits of fleet electrification considering the traffic demand and the driving behavior. These findings contribute to the understanding of sustainable urban mobility by providing a comprehensive assessment of how traffic demand, fleet electrification, and driving behavior jointly influence urban traffic performance and vehicle emissions, offering valuable insights for the design of integrated transportation and environmental policies. Full article

Amid the well-researched negative effects of road congestion and increased private car use, there is a need for more sustainable modes of transport. The literature points towards trains as being a vital part of the solution to the current problems, but their success depends on a number of variables, especially when it comes to the main railway stations in the largest cities. The first goal of this study was to identify the relationship between passenger numbers at major railway stations in Poland and selected parameters characterizing public transport services; the second was to assess the usefulness of the NOAH (Nest of Apes Heuristic) method for data analysis. In Poland, the number of major transfer hubs is limited, and there is a lack of an existing method allowing comparison of variables in such small datasets in a way that infers statistical significance. This is a research gap that the authors aimed to address using the NOAH algorithm combined with an analysis of regression. The initial dataset had been successfully expanded in a way that dependencies could be observed, with both goals being met. Passenger numbers relied most on the number of trains departing at each station daily, while walking distance during transfers impacted that number most negatively. The results point towards other variables influencing the passenger numbers, which were not considered in this study but could form the basis of further research. The utilized method could also be applied to a different group of cities, and in other countries. Additionally, the study added to the development of the NOAH algorithm itself, improving the method. Full article

Land transport infrastructure (LTI) is a core component of sustainable transport systems, shaping mobility, efficiency, and the spatial distribution of development gains. Existing studies of urban–rural income inequality mainly focus on individual transport modes or aggregate infrastructure scale, with limited attention to transport-system composition and its contextual dependence. This study addresses this gap by conceptualizing LTI as a layered system and examining how its internal composition is associated with urban–rural income inequality across different levels of urbanization and economic development. Using a balanced panel of 286 prefecture-level cities from 2013 to 2023, the study constructs ratio-based indicators of compositional shifts within road systems, within rail systems, and between rail and road infrastructure. Two-way fixed-effects models incorporate interactions with urbanization and economic development. Conditional marginal-effect maps are then used to identify how these associations change across development contexts. The results reveal a clear stage-dependent pattern. Urbanization generally attenuates the inequality-widening association of mobility-oriented upgrading, whereas economic development influences whether such upgrading reinforces spatial polarization or supports wider diffusion. When urbanization and development are both sufficiently advanced, the marginal association may shift toward inequality reduction. At earlier stages, accessibility-oriented roads and conventional rail tend to show stronger equalizing associations. Mobility-oriented roads and high-speed rail are more likely to be associated with narrower inequality in more advanced settings. Mechanism-oriented analyses yield evidence consistent with two potential channels: the agricultural–non-agricultural labor-productivity gap and the non-agricultural employment share. The extended analyses and robustness checks broadly support the main findings. These findings indicate that transport infrastructure upgrading should be evaluated not only in terms of efficiency, but also according to whether the resulting infrastructure mix broadens access to opportunities, improves resource allocation, and supports inclusive regional development. Full article

Urban transportation systems face increasing sustainability and resilience challenges due to the growing frequency and intensity of weather extremes. Weather-related congestion may increase travel delays, fuel consumption, and unequal economic costs, thereby undermining urban sustainability. Although previous studies have examined the relationship between weather conditions and traffic congestion, limited attention has been paid to whether street-network design can enhance transportation resilience under extreme weather conditions. This study investigates the relationships among extreme weather, traffic congestion, and street density using daily congestion and meteorological data from 35 major Chinese cities between 2018 and 2024. Fixed-effects regressions estimate the associations between multiple weather extremes and congestion and examine the moderating role of street density. Heavy rainfall, extreme cold, and low visibility are associated with increased congestion, whereas extreme heat is associated with reduced congestion. Street density could buffer congestion under extreme cold and heavy snow cover, suggesting that denser networks may improve resilience to localized road-surface disruptions. Heterogeneity analyses reveal weaker weather-related congestion responses in megacities and clustered cities, and during the COVID-19 period. These findings highlight the potential role of street-network design in supporting sustainable and climate-resilient transportation by reducing vulnerability to weather-related congestion. Full article

China–Iraq international energy cooperation on natural gas constitutes a critical component of energy diplomacy under the Belt and Road Initiative. This study develops a multidimensional analytical framework encompassing geopolitical, economic–market, legal–policy, resource–technology, social–environmental, and bilateral–institutional dimensions. Subsequently, an integrated fuzzy DEMATEL-ISM model is employed to investigate the hierarchical structure and transmission pathways of influence among these factors. The findings reveal that the multiple factors can be classified into four clusters (driving, linkage, independent, and dependent), each exhibiting distinct roles in system evolution. Meanwhile, the model identifies a six-tier hierarchical structure, with directed pathways transmitting from deep-rooted factors through intermediate nodes to surface-level outcomes. Surface-level factors occupy the upper tier and directly shape cooperative performance, while intermediate-level factors act as transmission conduits that relay and transform deeper influences. Deep-level factors, including great-power rivalry, resource endowment, and market demand, form the system’s foundational layer. They remain immune to influence from upper tiers and thus require strategic governance to fundamentally ensure enduring cooperation sustainability. Consequently, policy priorities should center on deep-level drivers, complemented by targeted adjustments to intermediate and surface factors. This study offers a novel multi-level structural analytical lens and provides actionable policy recommendations to enhance cooperative resilience under the Belt and Road framework. Full article

As vehicle intelligence and automotive electrification advance, the extensive deployment of distributed sensing nodes for comprehensive monitoring has grown rapidly. This poses severe challenges, such as rising onboard power consumption and the inability of conventional centralized power supply systems to sustain these sensors. Triboelectric nanogenerators (TENGs), an emerging technology for energy harvesting and self-powered sensing, exhibit great potential to address the above challenges. This review systematically summarizes research on TENGs for vehicle energy harvesting and intelligent sensing, covering their fundamental working principles and applications in diverse vehicle scenarios. First, the basic principle and working modes of TENGs are described, and their suitability for complex and variable vehicle environments is evaluated. Subsequently, existing applications are categorized into three domains: vehicle vibration systems, wheel–road systems, and intelligent vehicle systems. Studies on various topics are reviewed, including vibration energy harvesting and sensing, vehicle collision monitoring, tire energy harvesting, road sensing, smart cockpits, human–machine interaction, and vehicle fluid monitoring. Emphasis is placed on their technical approaches and application prospects. Finally, the state-of-the-art research and prevailing technical bottlenecks are summarized, and potential solutions and future research perspectives are discussed. This review aims to support the reliable practical deployment of TENG technology in vehicle engineering and to provide a technical basis for energy-saving strategies and in situ sensing technologies for future intelligent vehicles. Full article

Climate change education requires learners to understand complex systems and dynamic interdependencies, making systems modeling an important competence within education for sustainable development. This study investigated how instructional guidance influences students’ modeling of causal loop diagrams when working with the climate simulation En-ROADS. In a two-phase design, 242 secondary school students first explored En-ROADS and constructed an initial diagram representing their intermediate knowledge, then refined it while receiving conceptual guidance, procedural guidance, both, or none. Structural complexity of the causal loop diagrams and cognitive load were assessed after each phase. Structural complexity increased significantly in all four conditions, η²_p = 0.22. Procedural guidance was associated with a smaller increase, η²_p = 0.02, located primarily in the branching structures of the diagrams, and with higher germane cognitive load, η²_p = 0.02. Conceptual guidance showed no overall effect but interacted with intermediate knowledge, β = 0.23; as the conditional effects were not significant, this indicates that its influence differed across levels of intermediate knowledge rather than that it benefited either group. The structural complexity of the initial diagram was by far the strongest predictor of that of the refined one, underlining the importance of aligning instructional guidance with learners’ intermediate knowledge in complex digital modeling environments. Full article

Provincial cold-chain investment decisions must reconcile food-loss prevention, digital visibility, logistics capability and the environmental burden of freight-intensive growth. This study develops a sustainability-oriented readiness–intensity framework for 31 provincial-level regions in mainland China. The baseline model uses 14 auditable public-data criteria and combines Criteria Importance Through Intercriteria Correlation (CRITIC) with the standard Combined Compromise Solution (CoCoSo) algorithm. Because the observations combine 2024 statistics, a 2023 digital-finance index and the cumulative 2020–2025 cold-chain-base list, the design is described as an asynchronous cross-sectional snapshot rather than a single-year panel. Municipal sewage and green-space variables are interpreted as regional enabling capacity, not direct cold-chain environmental performance; road freight turnover relative to gross domestic product is treated as a cost-type freight-intensity transition-pressure proxy. A separate diagnostic replaces the earlier inverse-size term with logistics residuals conditional on agri-food output. Shandong, Guangdong, Jiangsu, Henan and Zhejiang form the leading demonstration-readiness group. Equal-weight CoCoSo closely matches the CRITIC result (Spearman ρ = 0.996), while TOPSIS and VIKOR retain the broad ordering but expose local method sensitivity. Dropping either digital criterion, removing the three indirect green proxies, winsorizing the normalization range, varying the CoCoSo compromise parameter and substituting 2022 digital data do not alter the leading pattern. Under an assumed 5% indicator-error perturbation, Shandong and Guangdong remain within ranks 1–2, whereas the ordering of several adjacent provinces is less secure. The framework supports sequenced investment packages rather than a deterministic league table and distinguishes demonstration-ready, scale-led, intensity-led and coverage-building contexts. Full article

The spatial allocation of urban road infrastructure directly affects urban operational efficiency, social equity, and ecological environmental quality. Taking 36 major Chinese cities as the research subjects and drawing on data from municipal statistical yearbooks, this study employs a composite index method, Theil index decomposition, and correlation analysis to reveal spatial differentiation patterns, imbalances, and driving factors of urban road infrastructure levels, based on both aggregate and average indicators. The results indicate that the aggregate road infrastructure level exhibits a “high in the southeast, low in the northwest” pattern along the Hu Huanyong Line, while the average road infrastructure level reveals relatively lower performance in some first-tier cities. Imbalances exist in both aggregate and average dimensions, with Theil indices of 0.231 and 0.059, respectively; intra-regional disparities contribute more to total inequality than inter-regional disparities. Urban permanent population (ridge regression coefficient: 0.1991) and fiscal revenue (ridge regression coefficient: −0.1087) are the core driving factors among the four influencing factors of aggregate road infrastructure level spatial differentiation, whereas GDP (−0.0318) and built-up area (0.0703) exert only marginal effects. This suggests that current aggregate urban road infrastructure levels are shaped by the interplay of urbanization stage, economic development level, fiscal system, and spatial planning policies, all operating within the constraints imposed by the city’s natural geographical conditions, and have not yet adequately addressed residents’ demand for spatial equity. The study recommends establishing differentiated investment mechanisms, optimizing road network density in developed cities, and constructing a spatial matching early-warning system to promote people-oriented new urbanization and coordinated regional sustainable development. Full article