Search Results (76,739)

While most existing studies addressing greenwashing emphasize governmental regulation, they often overlook the role of public participation. This study examines how managers’ risk preferences affect enterprise decision-making under public scrutiny. In this study, an agent-based simulation model incorporating enterprises, media, and consumers is established on the basis of signaling theory and the wolf-sheep model. The conclusions are as follows: (1) Both positive and negative public opinion can curb greenwashing behavior in building material enterprises, but negative public opinion intensity is more effective in this context. (2) Regardless of whether building material enterprises operate in positive or negative public opinion scenarios, constraining managers’ risk preferences consistently serves as an effective internal mechanism for curbing greenwashing behavior. (3) The effectiveness of constraint managers’ risk preferences in suppressing greenwashing behavior is influenced by the external public opinion scenario, with its inhibitory effect being more pronounced under negative public opinion scenarios. This study incorporates public opinion as a key external factor and integrates dynamic managers’ risk preferences into a behavioral simulation framework. It expands the theoretical understanding of greenwashing decision-making mechanisms and offers practical implications for regulatory enforcement and enterprise governance. Full article

EigenPlaces is a state-of-the-art visual place recognition (VPR) method that constructs training classes via SVD-based focal points, where a fixed focal distance D controls how far the focal point is placed from each cell center. However, this globally fixed D cannot adapt to the diverse scene geometries encountered across different urban environments. In this work, we systematically analyze the sensitivity of D across multiple benchmark datasets and reveal that the optimal D value is highly dataset-dependent, with performance gaps of up to 4.4 percentage points between the best and worst D choices. We then propose a depth-aware adaptive D strategy that leverages monocular depth estimation to compute per-cell focal distances, combined with quantile mapping to ensure sufficient variance in the assigned D values. By establishing a principled connection between visual sensor data and geometric training supervision, our method enhances the environmental perception reliability of intelligent sensing platforms. Experiments on three benchmarks (Pitts30k, AmsterTime, SF-XL) validate the dataset-dependent nature of D and confirm that our depth-aware approach achieves the best same-distribution performance among all tested configurations. We further conduct a multi-strategy ablation comparing depth raw, depth quantile, and SVD eigenvalue ratio approaches, providing practical guidance for adaptive focal distance selection in VPR training pipelines. Full article

This study analyzes the closure, decommissioning, and abandonment (CDA) of a fuel storage and distribution facility in southern Quito, Ecuador, conceptualizing the process as a socio-technical urban transition embedded within territorial governance dynamics. While infrastructure decommissioning is commonly addressed from a predominantly technical perspective, limited research integrates reverse logistics design, stakeholder influence structures, and territorial development into a unified analytical framework, particularly in Latin American metropolitan contexts. Using a mixed-methods case study approach, the research combines documentary analysis, operational data, and 34 semi-structured interviews with public authorities, engineers, fuel marketers, business owners, and community representatives. A thematic analysis was applied to reconstruct the decommissioning logistics chain and to develop a stakeholder mapping and influence matrix assessing actor positions, economic interdependencies, and legitimacy claims. The findings show that decommissioning operates as a structured reverse logistics system embedded within asymmetric governance configurations, where economic dependency, risk perception, and urban redevelopment expectations generate competing territorial imaginaries. Technical feasibility alone proves insufficient to guide decision-making; instead, legitimacy emerges through the alignment of engineering planning, institutional coordination, and community-level expectations. The study advances an integrated socio-technical framework that articulates Engineering Management, Social Management, and Territorial Development, positioning decommissioning as a governance-driven transition rather than a purely technical operation. The results contribute to sustainability and infrastructure transition scholarship while offering practical guidance for managing urban hydrocarbon infrastructure closure in socially vulnerable territories. Full article

The low-carbon transition of the power sector is fundamental to achieving “Dual Carbon” goals, where demand-side management plays an increasingly vital role in transforming flexible loads into renewable energy accommodation and active emission-reduction resources. However, existing low-carbon demand response mechanisms based on dynamic carbon emission factors only reflect average system states and fail to quantify the incremental carbon impact of marginal load changes. To address this limitation, this paper proposes a novel marginal carbon emission factor-driven low carbon demand response mechanism. Unlike traditional methods, the proposed mechanism utilizes marginal carbon emission factors as a high-sensitivity guiding signal to inform users of the real-time emission and renewable energy consumption variations caused by their consumption adjustments. Furthermore, considering the forecasting errors of high-penetration renewable energy, the uncertainty of marginal carbon emission factors is explicitly considered. Case studies are conducted to compare the proposed method with the conventional method through comparative analyses based on the modified PJM-5 system. Results demonstrate that the MCEF-driven approach provides more precise carbon-reduction and renewable energy utilization signals to achieve superior system-wide decarbonization performance and sustainable development. Full article

Inadequate management of urban organic waste generates significant environmental impacts, including the accumulation of biodegradable residues and greenhouse gas emissions. Composting represents a key biotechnological strategy for the valorization of organic waste; however, its efficiency may be limited by long stabilization periods. This study evaluated the effect of a microbial consortium composed of Klebsiella oxytoca, Sphingomonas paucimobilis, and Pantoea spp. on the composting of organic waste generated at a higher education institution in Colombia using the Earthgreen SAC-2250 Autonomous Composting System. Four treatments based on different proportions of organic waste (OW) and sawdust (DM) were evaluated, and the best-performing formulation was subsequently inoculated with the microbial consortium and compared with a non-inoculated control pile. The 3:1 ratio (OW:DM) showed the best performance, reaching compost stabilization within 45 days. Inoculation with the microbial consortium reduced the stabilization time by approximately 20 days compared with the control treatment. These results demonstrate that microbial bioaugmentation using selected environmental isolates can significantly accelerate organic matter degradation and improve the efficiency of composting systems, providing a promising strategy for sustainable organic waste management in institutional environments. Full article

Rapid urbanization in plateau mountain regions exacerbates the tension between rigid resource demands and fragile ecological carrying capacities. Enhancing the resilience of the Water–Energy–Food (W–E–F) nexus is therefore essential for coping with external shocks. This study constructs a multidimensional resilience evaluation framework based on the Pressure-State-Response (PSR) model, taking the Kunming Metropolitan Area—a typical plateau mountain region—as a case study. Integrating the TOPSIS model, Coupling Coordination Degree (CCD) model, and spatial autocorrelation analysis, we systematically assessed both individual subsystem and comprehensive W–E–F nexus resilience from 2005 to 2020. Results show that W–E–F nexus resilience generally improved from 2005 to 2020, but subsystem development remained uneven across space, with water resilience characterized by a peripheral-high/central-low pattern, energy resilience by a core-high/periphery-low structure, and food resilience by the strongest spatial heterogeneity and volatility. By 2020, the mean comprehensive resilience reached 0.67, with 58.3% of counties above the average, exhibiting significant spatial clustering. Based on resilience levels and limiting subsystems of 2020, the metropolitan area was classified into Enhancement, Adjustment, and Maintenance zones, comprising 6, 16, and 2 counties respectively, to support differentiated regional governance. This study provides a spatially explicit regulation paradigm to bolster urban resilience against resource constraints and climate uncertainty. Full article

Urban low-altitude logistics is increasingly constrained by obstacle-rich city morphology and wind-induced flight disturbances, which makes conventional path-planning methods insufficient for simultaneously ensuring efficiency, feasibility, and robustness. To address this issue, this study proposes an improved whale optimization algorithm (IWOA) for wind-field-coupled three-dimensional UAV path planning in urban environments. A voxel-based urban model is established, and the planning objective integrates flight time, energy consumption, wind-field penalty, and path smoothness. On the basis of the original whale optimization algorithm, the proposed method introduces a wind-field-guided local adjustment operator, adaptive convergence control, elite preservation, large-scale mutation, and feasibility repair. The proposed method is evaluated through a structured simulation framework comprising four scenarios: a baseline case, urban density variation, complex wind-field variation, and multi-destination delivery. The results show that IWOA consistently yields the lowest composite cost among the compared algorithms and exhibits better path smoothness, stronger wind adaptation, and earlier convergence stability. In the baseline case, the total cost of IWOA is reduced by 17.3%, 13.1%, and 6.7% relative to A*, GA, and WOA, respectively. Under the high-density urban environment and the complex wind field, IWOA also maintains the best performance, indicating stronger robustness under increased environmental difficulty. Sensitivity analyses further show that wind speed and wind direction have pronounced effects on the total cost, while the energy coefficient mainly affects the energy-related component. These results demonstrate that the proposed framework provides an effective and practically relevant solution for urban low-altitude UAV logistics path planning. Full article

In deep-buried tunnels, the loads acting on supporting structures continuously increase due to the rheological behavior of surrounding rock, while the performance of the secondary lining gradually degrades under environmental effects. These delayed features have significant implications for tunnel safety but are rarely incorporated into the reliability evaluation of tunnels. In this study, the surrounding rock is modeled using the Burgers model, and an analytical solution is developed by incorporating the degradation and damage of the secondary lining. Parametric analysis is conducted to identify the key factors governing tunnel response. Subsequently, limit state functions are established, and a time-dependent system reliability analysis is performed. Results indicate that tunnel response and reliability are highly sensitive to rheological parameters. Among the rheological parameters, the elastic shear modulus of the Maxwell elements G_e has the most pronounced influence on deformation, whereas the elastic shear modulus of the Kelvin elements G_k governs the stress response of the secondary lining. The time-dependent failure probability increases rapidly in the early stage and gradually stabilizes thereafter. Insufficient initial support strength is identified as the dominant failure mode of system failure. Furthermore, G_e and G_k are the key parameters affecting tunnel reliability, and increasing G_k improves the reliability index by more than 1500%. Meanwhile, the variation in system reliability is mainly affected by the failure mode of insufficient initial support strength. These findings provide quantitative guidance for the design, construction, and long-term maintenance of deep-buried tunnels in rheological rock. Full article

Artificial Intelligence systems increasingly shape environmental decision making, infrastructure planning, and resource use across public and urban domains. However, prevailing AI trust and governance mechanisms, including labels, certifications, and assurance schemes, remain primarily focused on ethical and legal accountability, with limited operational attention to environmental sustainability. This paper reconceptualises AI trust mechanisms as socio-technical governance infrastructures that can support both ethical assurance and environmental accountability. Drawing on a comparative qualitative analysis of nine AI trust initiatives, the study develops a three-dimensional analytical framework embedding Environmental Performance Indicators across three governance dimensions: trust-building effectiveness, governance readiness, and sustainable adoption. Applying a systems governance lens, the framework examines how governance instruments structure information flows, institutional practices, and lifecycle feedback relevant to environmental performance. It is analytically illustrated through two urban mobility cases, Helsinki’s Whim application and Barcelona’s smart mobility system, to examine how governance conditions enable or constrain the integration of Environmental Performance Indicators in practice. Findings show that current trust mechanisms lack measurable and publicly visible environmental criteria, indicating a gap between AI assurance and environmental governance. The study contributes a systems-oriented framework for evaluating AI trust mechanisms as governance instruments capable of supporting environmental accountability. While exploratory and based on secondary data, the results indicate that future AI trust mechanisms must incorporate measurable sustainability indicators to support eco-efficient and accountable digital transformation. Full article

Aramid fiber is a high-performance fiber with excellent mechanical properties, heat resistance, and corrosion resistance. Its exceptional shear and fatigue properties make it a promising material for civil engineering applications. This study summarizes the basic properties and current development of aramid fiber, as well as the applications of aramid fiber and its composites in civil engineering, including aramid fiber-reinforced composite (AFRP)-concrete/steel composite structures, AFRP rebars, and AFRP rock anchors. The results indicate that the poor interfacial bonding performance between aramid fibers and the resin matrix is the primary bottleneck restricting the application of AFRP composites in civil engineering. Consequently, developing a continuous surface treatment method suitable for industrial-scale production remains a key challenge for the widespread adoption of these composites. Furthermore, in certain specific working conditions and environments—such as seismic retrofitting of rectangular concrete columns, impact/explosion resistance reinforcement, and rock anchoring—AFRPs show the potential to replace traditional inorganic fiber-reinforced polymer composites. However, systematic investigation into the fundamental mechanical properties and long-term service performance of AFRP is still required prior to their practical application. Full article

China’s rural public open spaces (POS) are largely governed as common-pool resources through self-organized collective arrangements, often regarded as a viable pathway to sustainable commons management. Yet, in practice, these systems remain prone to overuse and under-maintenance, reflecting collective action failures associated with the tragedy of the commons. The governance of rural POS therefore constitutes a complex social–ecological problem shaped by the interplay of institutional rules, biophysical conditions, and user–stakeholder interactions. Taking Taigu District in Shanxi Province—characterized by heterogeneous social–ecological contexts and collective action dilemmas—as the empirical case, this study develops a meso-level baseline model to identify the key conditions (design principles) for sustainable rural POS governance. Adopting an expert-based epistemological approach, 24 specialists in rural governance (scholars, planners, and local administrators) were engaged. Grounded in commons and collective action theories within the Social–Ecological Systems (SES) framework and informed by Transaction Cost Economics (TCE), the study operationalizes a Delphic Hierarchy Process (DHP), combining three rounds of Delphi to establish consensus on governance conditions with the Analytic Hierarchy Process (AHP) to derive their relative weights. The model specifies 14 governance conditions across four interrelated dimensions: ecological (e.g., clearly defined resource boundaries and congruence between resource characteristics and user needs), institutional (e.g., simple and enforceable rules, accessible conflict-resolution mechanisms, accountable monitoring, and calibrated external support), social (e.g., social capital, leadership capacity, clearly defined user boundaries, and group interdependence), and interactional (e.g., resource dependence, equity in benefit distribution, and supply–demand alignment). It further clarifies their relative importance and systemic interdependencies. By operationalizing commons design principles within a meso-level analytical framework, the study advances their empirical application in rural planning and offers five targeted managerial implications to strengthen institutional robustness and the long-term sustainability of self-governed rural POS. Full article

Background/Objectives: Neglect of children’s oral health is a major concern at international, national, and regional levels. Of all the health problems that can occur in childhood, dental ones are among the most common. Tooth decay, for example, is a chronic condition in children and can have long-term consequences, especially in otorhinolaryngology and pediatric diseases if not treated properly. Methods: The data collection method was questionnaire. Questionnaires were administered to parents regarding oral hygiene habits and access to dental services; data were collected in dental offices across Timiș County, encompassing urban, peri-urban, and rural settings. Children enrolled in the study underwent clinical dental examinations to assess their oral health status (dental caries, gingival diseases, developmental anomalies). Results: Parental education level was not significantly associated with the habit of annual dental check-ups (χ², p = 0.092); however, a directional trend was observed. Total monthly family income was significantly associated with the stated reason for not attending dental check-ups (one-way ANOVA, p = 0.043): families with lower incomes more frequently cited financial and logistical barriers, whereas higher-income families cited lack of time or perceived lack of necessity. Parental education level (p < 0.001) and family income (p < 0.001) were both significantly associated with daily tooth-brushing frequency. Conclusions: The efforts of specialists must be increased through coherent policies, adapted education, and real support for vulnerable groups. An informed child, with supported parents, is a child with a real chance at a healthy life. This is not just a professional opinion, but a collective responsibility. Full article

Small- and mid-sized municipalities increasingly need to assess the long-term effectiveness of urban greening projects under climate change, but typically lack the resources for extensive measurement campaigns or complex simulation infrastructure. We propose and demonstrate a practical three-component assessment framework that can be deployed with limited resources: one or two UAV thermal surveys to capture the spatial baseline, a small network of fixed monitoring stations for temporal context, and a single microscale model to project future scenarios. We apply this framework to a 0.5 ha parking-to-park conversion in Aalen, Germany (pop. 68,000). A diurnal drone survey (four flights over 14 h) established surface temperature contrasts of up to 34 K between sealed and tree-shaded areas. Six fixed stations operating over six weeks confirmed that an existing mature linden reduced hot days by nearly 50%. The ASMUS_green microscale model, evaluated against the drone observations (mean absolute difference 3.4 K across surface types), was then used to project Physiologically Equivalent Temperature (PET) under EURO-CORDEX scenarios for 2035 (+1.3 K) and 2055 (+2.5 K) combined with tree growth. The results show that young trees provide limited near-term relief, but mature canopy reduces PET by 6–8 K, offsetting the projected warming. We discuss how each component compensates for the limitations of the others, making the combined framework more robust than any single method. The approach is designed to be transferable to other municipalities facing similar planning decisions with comparable resource constraints. Full article

Rapid urbanization has profoundly reshaped land use patterns and intensified pressures on ecosystem structures, thereby exacerbating trade-offs and synergies among ecosystem services (ESs). Understanding ecosystem service trade-offs, synergies, and their attribution mechanisms is critical for balancing ecological conservation and regional sustainable development in rapidly developing regions. This study quantified provisioning, regulating, supporting, and cultural ecosystem services in Shandong Province from 2000 to 2020 using the InVEST model and spatial analysis. An integrated framework combining Pearson correlation and bagplot analysis was developed to identify linear and nonlinear ES trade-offs and synergies, while the XGBoost–SHAP model was applied to quantify the relative contributions of natural and socioeconomic drivers. Ecosystem service bundles were further identified using a self-organizing map to delineate spatially functional zones. The results showed that: (1) Provisioning and cultural services increased markedly, whereas regulating and supporting services generally declined. Spatially, provisioning services were concentrated in the western plains, regulating and supporting services in the central mountains and eastern hills, and cultural services in urban areas. (2) Strong trade-offs emerged between provisioning services and most regulating/supporting services, while regulating and supporting services exhibited pronounced synergies. Cultural services reflected a generally compatible relationship with other ESs. (3) Regulating and supporting services were primarily shaped by natural conditions and land use patterns, whereas provisioning and cultural services were more strongly driven by socioeconomic factors. (4) SOM clustering identified four major functional zones, the ecological core zone, the ecological degraded zone, the food production zone, and the urban composite zone, each corresponding to differentiated ecosystem functions and development trajectories. The integrated framework provides a scientific basis for ecosystem-service-oriented spatial zoning and targeted management strategies to reconcile ecological protection and urbanization in rapidly developing regions. Full article

Urban housing rents are central to socioeconomic dynamics and urban sustainability, shaping affordability and quality of life. Existing research largely relies on linear models and focuses on economic, demographic, and locational factors, often neglecting complex nonlinear interactions and the impact of human perceptions. This study introduces a comprehensive, multi-perspective framework that integrates housing attributes, living convenience, competition, location, accessibility, and quantified perceptual metrics using multimodal machine learning. Advanced techniques, including XGBoost, SHAP, Partial Dependence Plots (PDPs), Interpretative Structural Modeling (ISM), and Bayesian Network (BN), capture nonlinearities, interactions, and hierarchical dependencies among rent determinants. Housing attributes and living convenience indicators exert the strongest cumulative influence on rents, while perceptual variables rank third, providing significant, threshold-dependent contributions and explaining up to 21.66% of rent variation. Notable interactions are identified between accessibility, facility density, and perceptual quality. The ISM–BN analysis uncovers multi-level pathways, demonstrating how both environmental features and human perceptions jointly influence rents. This framework offers actionable insights for equitable housing and urban planning policies, supporting data-driven decisions in complex urban rental markets. Full article