Search Results (87)

The intensification of anthropogenic pressures, particularly those related to agriculture driven by increasing demands for food and cash crops, generates negative environmental externalities. Assessing these externalities is essential to better identify and implement measures that promote the environmental sustainability of rural landscapes. This study aims to develop a multi-criteria assessment method of the negative environmental externalities of rural landscapes in the northern Benin agricultural basin, based on satellite-derived data. Starting from a 12-class land cover map produced through satellite image classification, the evaluation was conducted in three steps. First, the 12 land cover classes were reclassified into Human Disturbance Coefficients (HDCs) via a weighted sum model multi-criteria analysis based on nine criteria related to the negative environmental externalities of anthropogenic activities. Second, the HDC classes were spatially aggregated using a regular grid of 1 km² landscape cells to produce the Landscape Environmental Sustainability Index (LESI). Finally, various discretization methods were applied to the LESI for cartographic representation, enhancing spatial interpretation. Results indicate that most areas exhibit moderate environmental externalities (HDC and LESI values between 2.5 and 3.5), covering 63–75% (HDC) and 83–94% (LESI) of the respective sites. Areas of low environmental externalities (values between 1.5 and 2.5) account for 20–24% (HDC) and 5–13% (LESI). The LESI, derived from accessible and cost-effective satellite data, offers a scalable, reproducible, and spatially explicit tool for monitoring landscape sustainability. It holds potential for guiding territorial governance and supporting transitions towards more sustainable land management practices. Future improvements may include, among others, refining the evaluation criteria and introducing variable criteria weighting schemes depending on land cover or region. Full article

Rapid identification of vessel height within the navigable space beneath bridges is crucial for ensuring bridge safety. To prevent bridge collisions caused by vessels exceeding their height limits, this article introduces a real-time warning framework for excessive vessel height based on video spatial transformation. The specific contributions include the following: (1) A spatial transformation-based method for locating vessel coordinates in the channel using buoys as control points, employing laser scanning to obtain their world coordinates from a broad channel range, and mapping the pixel coordinates of the buoys from side channel images to the world coordinates of the channel space, thus achieving pixel-level positioning of the vessel’s waterline intersection in the channel. (2) For video images, a key point recognition network for vessels based on attention mechanisms is developed to obtain pixel coordinates of the vessel’s waterline and top, and to capture the posture and position of multiple vessels in real time. (3) Analyzing the posture of vessels traveling in various directions within the channel, the method accounts for the pixel distance of spatial transformation control points and vessel height to determine vessel positioning coordinates, solve for the vessel’s height above water, and combine with real-time waterline height to enable over-height vessel collision warnings for downstream channel bridges. The method has been deployed in actual navigational scenarios beneath bridges, with the average error in vessel height estimation controlled within 10 cm and an error rate below 0.8%. The proposed approach enables real-time automatic estimation of vessel height in terms of computational speed, making it more suitable for practical engineering applications that demand both real-time performance and system stability. The system exhibits outstanding performance in terms of accuracy, stability, and engineering applicability, providing essential technical support for intelligent bridge safety management. Full article

Peatland research has expanded rapidly in the last two decades encompassing a diverse, multi-disciplinary evidence base, as countries seek to manage this resource sustainably along with meeting climate and biodiversity targets. There is growing global interest in the role of peatlands in carbon and water cycles, leading to more interdisciplinary research that applies ecosystem services and other integrative frameworks to generate knowledge and provide guidance for action. These trends have been replicated in Ireland with increasing research in peatland science, applied work on these degraded ecosystems, and a growing interest from civil society, landowners, and communities in the stewardship of this resource. This paper presents evidence-based insights from over two decades of Irish peatland research, with practical lessons for peatland policy and management in other national contexts. Analyses of the evidence from the literature, specialist expertise, and stakeholder knowledge were carried out under ten themes: biodiversity, soil, climate change, water, archaeology and palaeoenvironment, technology and mapping, society and culture, management, growing media and policy and law. The research identified four foundational pillars (accountability, longevity, equity and holistic knowledge) as critical to achieving sustainable peatland management in Ireland, with broader application to other regions. Peatland restoration is widely recognised across research disciplines as a key tool to meet regulatory targets related to climate, biodiversity, and water quality, while also delivering societal benefits. The findings of this research provide accessible, reliable and up-to-date evidence for sustainable peatland management. This study addresses a critical global knowledge gap by developing a novel, interdisciplinary evidence synthesis framework—applied here to Ireland but replicable worldwide—that systematically integrates 20 years of multi-disciplinary peatland research, expert insights, and stakeholder perspectives across ten thematic pillars. Full article

The convergence of blockchain and metaverse technologies is poised to redefine how Global Value Chains (GVCs) create, capture, and distribute value, yet scholarly insight into their joint impact remains scattered. Addressing this gap, the present study aims to clarify where, how, and under what conditions blockchain-enabled transparency and metaverse-enabled immersion enhance GVC performance. A systematic literature review (SLR), conducted according to PRISMA 2020 guidelines, screened 300 articles from ABI Global, Business Source Premier, and Web of Science records, yielding 65 peer-reviewed articles for in-depth analysis. The corpus was coded thematically and mapped against three theoretical lenses: transaction cost theory, resource-based view, and network/ecosystem perspectives. Key findings reveal the following: 1. digital twins anchored in immersive platforms reduce planning cycles by up to 30% and enable real-time, cross-border supply chain reconfiguration; 2. tokenized assets, micro-transactions, and decentralized finance (DeFi) are spawning new revenue models but simultaneously shift tax triggers and compliance burdens; 3. cross-chain protocols are critical for scalable trust, yet regulatory fragmentation—exemplified by divergent EU, U.S., and APAC rules—creates non-trivial coordination costs; and 4. traditional IB theories require extension to account for digital-capability orchestration, emerging cost centers (licensing, reserve backing, data audits), and metaverse-driven network effects. Based on these insights, this study recommends that managers adopt phased licensing and geo-aware tax engines, embed region-specific compliance flags in smart-contract metadata, and pilot digital-twin initiatives in sandbox-friendly jurisdictions. Policymakers are urged to accelerate work on interoperability and reporting standards to prevent systemic bottlenecks. Finally, researchers should pursue multi-case and longitudinal studies measuring the financial and ESG outcomes of integrated blockchain–metaverse deployments. By synthesizing disparate streams and articulating a forward agenda, this review provides a conceptual bridge for international business scholarship and a practical roadmap for firms navigating the next wave of digital GVC transformation. Full article

Forest age is a key factor in determining the carbon sequestration capacity and trends of forests. Based on the Google Earth Engine platform and using the topographically complex and climatically diverse Southeastern Tibet as the study area, we propose a new method for forest age estimation that integrates multi-source remote-sensing data with machine learning. The study employs the Continuous Degradation Detection (CODED) algorithm combined with spectral unmixing models and Normalized Difference Fraction Index (NDFI) time series analysis to update forest disturbance information and provide annual forest distribution, mapping young forest distribution. For undisturbed forests, we compared 12 machine-learning models and selected the Random Forest model for age prediction. The input variables include multiscale satellite spectral bands (Sentinel-2 MSI, Landsat series, PROBA-V, MOD09A1), vegetation parameter products (canopy height, productivity), data from the Global Ecosystem Dynamics Investigation (GEDI), multi-band SAR data (C/L), vegetation indices (e.g., NDVI, LAI, FPAR), and environmental factors (climate seasonality, topography). The results indicate that the forests in Southeastern Tibet are predominantly overmature (>120 years), accounting for 87% of the total forest cover, while mature (80–120 years), sub-mature (60–80 years), intermediate-aged (40–60 years), and young forests (< 40 years) represent relatively lower proportions at 9%, 1%, 2%, and 1%, respectively. Forest age exhibits a moderate positive correlation with stem biomass (r = 0.54) and leaf-area index (r = 0.53), but weakly negatively correlated with L-band radar backscatter (HV polarization, r = −0.18). Significant differences in reflectance among different age groups are observed in the 500–1000 nm spectral band, with 100 m resolution PROBA-V data being the most suitable for age prediction. The Random Forest model achieved an overall accuracy of 62% on the independent validation set, with canopy height, L-band radar data, and temperature seasonality being the most important predictors. Compared with 11 other machine-learning models, the Random Forest model demonstrated higher accuracy and stability in estimating forest age under complex terrain and cloudy conditions. This study provides an expandable technical framework for forest age estimation in complex terrain areas, which is of significant scientific and practical value for sustainable forest resource management and global forest resource monitoring. Full article

The aim of the present research is to analyze community participation in disaster risk management due to tailings dam failures (DRM-TDF). Conceição do Mato Dentro, Minas Gerais State (Brazil) was used as case study. The aims of the study are to help developing more effective DRM-TDF strategies and to strengthen community participation in decision making, and in mapping and categorizing vulnerabilities (criticality and support capacity) by assessing current practices and prioritizing future strategies. Semi-structured questionnaires were applied to community leaders and open interviews were carried out with DRM experts for information collection purpose. The collected responses were categorized based on vulnerabilities by taking into account criticality (communities) and support capacity (public management and mining entrepreneurs). SWOT analysis identified “Weaknesses” (criticality) and “Threats” (support capacity), whereas Pareto analysis highlighted the most critical aspects. The results indicate that public policies and the Brazilian legal framework have made limited contributions toward achieving the Sendai Framework guidelines and the Sustainable Development Goals. A review of current practices is necessary to safeguard the rights of affected communities through their meaningful participation in decision-making processes. Full article

Diffuse agricultural pollution is a leading contributor to surface water degradation, particularly in regions undergoing rapid land use change and agricultural intensification. In many developing countries, conventional assessment approaches fall short of capturing the spatial complexity and cumulative nature of multiple environmental drivers that influence surface water vulnerability. This study addresses this gap by introducing the Integral Index of Vulnerability to Diffuse Contamination (IIVDC), a spatially explicit, multi-criteria framework that combines the Analytical Hierarchy Process (AHP) with Geographic Information Systems (GIS). The IIVDC integrates six key indicators—slope, soil erodibility, land use, runoff potential, hydrological connectivity, and observed water quality—weighted through expert elicitation and mapped at high spatial resolution. The methodology was applied to the Guachal River watershed in Valle del Cauca, Colombia, where agricultural pressures are pronounced. Results indicate that 33.0% of the watershed exhibits high vulnerability and 4.3% very high vulnerability, with critical zones aligned with steep slopes, limited vegetation cover, and strong hydrological connectivity to cultivated areas. By accounting for both biophysical attributes and pollutant transport pathways, the IIVDC offers a replicable tool for prioritizing land management interventions. Beyond its technical application, the IIVDC contributes to sustainability by enabling evidence-based decision-making for water resource protection and land use planning. It supports integrated, spatially targeted actions that can reduce long-term contamination risks, guide sustainable agricultural practices, and improve institutional capacity for watershed governance. The approach is particularly suited for contexts where data are limited but spatial planning is essential. Future refinement should consider dynamic water quality monitoring and validation across contrasting hydro-climatic regions to enhance transferability. Full article

Terraces have long transformed steep slopes into gradual steps, reducing erosion and enabling agriculture on marginal land. In France’s Roya Valley, these dry stone structures, neglected for decades, demonstrated remarkable resilience during storm Alex in October 2020. This prompted civil society and researchers to identify terraces that could support food security and agri-tourism initiatives. This study aimed to develop a semi-automatic method for detecting and mapping terraced areas using LiDAR and orthophoto data from French repositories, processed with GIS and analyzed through a Support Vector Machine (SVM) classification algorithm. The model identified 18 terraces larger than 1 hectare in Saorge and 35 in La Brigue. Field visits confirmed evidence of abandonment in several areas. Accuracy tests showed a user accuracy (UA) of 97% in Saorge and 72% in La Brigue. This disparity reflects site-specific differences, including terrain steepness, vegetation density, and data resolution. These results highlight the value of machine learning for terrace mapping while emphasizing the need to account for local geomorphological and data-quality factors to improve model performance. Enhanced terrace detection supports sustainable land management, agricultural revitalization, and risk mitigation in mountainous regions, offering practical tools for future landscape restoration and food resilience planning. Full article

Accurate mapping of soil organic carbon (SOC) supports sustainable land management practices and carbon accounting initiatives for mitigating climate change impacts. This study presents a novel meta-learner framework that combines multiple machine learning algorithms and spectra processing algorithms to optimize SOC prediction using the PRISMA hyperspectral satellite imagery in the Doukkala plain of Morocco. The framework employs a two-layer structure of prediction models. The first layer consists of Random Forest (RF), Support Vector Regression (SVR), and Partial Least Squares Regression (PLSR). These base models were configured using data smoothing, transformation, and spectral feature selection techniques, based on a 70/30% data split. The second layer utilizes a ridge regression model as a meta-learner to integrate predictions from the base models. Results indicated that RF and SVR performance improved primarily with feature selection, while PLSR was most influenced by data smoothing. The meta-learner approach outperformed individual base models, achieving an average relative improvement of 48.8% over single models, with an R² of 0.65, an RMSE of 0.194%, and an RPIQ of 2.247. This study contributes to the development of methodologies for predicting and mapping soil properties using PRISMA hyperspectral data. Full article

This study presents a practical implementation of Business Process Reengineering (BPR) to streamline teaching assignment workflows at the University of Basilicata, a higher education institution (HEI) facing administrative inefficiencies exacerbated by rigid regulatory frameworks. By integrating process modeling, simulation, and digital tools, the research addresses systemic bottlenecks in resource allocation, transparency, and procedural delays inherent in traditional academic workflows. The methodology employs a dual-phase approach: (1) a detailed “AS-IS” analysis using BPMN 2.0 to map existing processes and (2) a data-driven “TO-BE” redesign validated through discrete event simulation (Simul8^®, Version 31). Key innovations include the automation of approval workflows, dynamic resource prioritization, and stakeholder communication protocols. Simulation results demonstrate a 35% reduction in end-to-end processing time and a 22% improvement in administrative staff utilization while maintaining compliance with national accreditation standards (the AVA framework) and legislative mandates (Law 240/2010). The case study underscores BPR’s role in balancing bureaucratic constraints with operational agility, offering actionable insights for HEIs navigating digital transformation. By prioritizing transparency and stakeholder alignment, the redesigned process not only enhances efficiency but also strengthens accountability in resource management—a critical factor for public institutions under increasing scrutiny for fiscal and educational quality outcomes. This work contributes to the growing discourse on BPR in academia, advocating for simulation-driven methodologies as catalysts for sustainable, stakeholder-centric process innovation in bureaucratic environments. Full article

High-standing islands, such as Taiwan, offer unique opportunities to study soil organic carbon (SOC) dynamics due to their steep terrains, rapid erosion, and strong climatic gradients. In this study, we investigated 54 forest soil profiles across northern, central, and southern Taiwan to assess SOC inventories and turnover using stable carbon isotope (δ¹³C) analyses. We applied Rayleigh fractionation modeling to vertical δ¹³C enrichment patterns and derived the parameter β, which serves as a proxy for SOC turnover rates. Our findings reveal that SOC stocks increase notably with elevation, aligning with lower temperatures and reduced decomposition rates at higher altitudes. Conversely, mean annual precipitation (MAP) did not show a straightforward relationship with SOC stocks or β, highlighting the moderating effects of soil drainage, topography, and local hydrological conditions. Intriguingly, higher soil nitrogen levels were associated with a negative correlation to ln(β), underscoring the complex interplay between nutrient availability and SOC decomposition. Overall, temperature emerges as the dominant factor governing SOC turnover, indicating that ongoing and future warming could accelerate SOC losses, especially in cooler, high-elevation zones currently acting as stable carbon reservoirs. These insights underscore the need for models and management practices that account for intricate temperature, moisture, and nutrient controls on SOC stability, as well as the value of stable isotopic tools for evaluating soil carbon dynamics in mountainous environments. Full article

The construction industry is critical to economic growth and environmental sustainability. However, its substantial resource consumption and waste generation highlight the urgent need for a transition toward circular construction (CC) practices. This study uses scientometric and content analyses of 199 articles published between 2017 and 2024 to uncover the barriers to adopting CC principles. This study aims to identify these barriers, map key research trends, and propose future directions for addressing obstacles to CC adoption. This research focuses on global contributions to CC, highlighting influential nations, journals, and scholars and analyzing keyword trends over time. Additionally, it examines the recurring themes and patterns to provide a holistic understanding of the systemic challenges faced by the construction industry in embracing CC principles. By presenting the first comprehensive overview of barriers to CC, this study fills a critical research gap and offers insights for researchers and policymakers. The findings reveal that 12% of the total publications in the field originate from Australia and China, leading in contributions, while journals such as Sustainability and the Journal of Cleaner Production account for 31.5% of the articles. Keyword co-occurrence analysis identifies “management”, “barriers”, and “waste management” as prevailing themes. The annual growth rate of CC-related publications is 44.78%, underscoring its rising importance. Furthermore, 41 barriers to CC were revealed with content analysis. These insights offer a foundational understanding for policymakers and researchers, emphasizing collaboration, government intervention, and innovation in materials and technology to overcome barriers and transition to a circular, resource-efficient construction model. Full article

Among the land use–land cover products, tree cover maps are essential tools for assessing forest functionality and ecosystem services, and implementing sustainable forest management. By combining open-source and ancillary high-resolution cartographic datasets, this study aims to map trees and forests in the Latium region in central Italy, highlighting their spatial configuration, function, and forest typology. The main findings show that trees cover 44.2% of the regional land area. Forests cover 508,056 ha, forming the core matrix of the Latium mountain landscape, providing significant ecological and socio-economic value for forest management and the regional wood supply chain. Although trees outside the forest represent only 3.1% of regional tree cover, they play a crucial role in enhancing ecological connectivity and landscape resilience. Approximately 2% of the tree and forest cover occurs in urban areas, contributing significantly to climate regulation and air quality in densely populated environments. The dominant forest types in Lazio include Turkey oak, temperate broadleaf, beech, downy oak, and Holm oak, which together account for 58.6% of the total tree cover. The accuracy tests confirm the feasibility of using open-source data for reliable, cost-effective forest mapping. Regular updates of these maps can support continuous monitoring and promote sustainable forest management practices. Full article

The grand challenge of sustainable development, increased demands for resilient critical infrastructure systems, and cost efficiency calls for thinking and acting “out of the box”. We must strive to search for, identify, and utilize new and emerging technologies and new combinations of existing technologies that have the potential to improve present best practices. In integrity management of, e.g., bridge, offshore, and marine structures, relatively new technologies have shown substantial potentials for improvements that not least concern structural health monitoring (SHM), digital twin (DT)-based structural and mechanical modeling, and risk-based inspection (RBI) and maintenance planning (RBI). The motivation for the present paper is to investigate and document to what extent such technologies in isolation or jointly might have the potential to improve best practices for integrity management of offshore wind turbine structures. In this pursuit, the present paper conducts a comprehensive bibliometric analysis to explore the current landscape of advanced technologies within the offshore wind turbine industry suitable for integrity management. It examines the integration of these technologies into future best practices, taking into account normative factors like risk, resilience, and sustainability. Through this analysis, the study sheds light on current research trends and the degree to which normative considerations influence the application of RBI, SHM, and DT, either individually or in combination. This paper outlines the methodology used in the bibliometric study, including database selection and search term criteria. The results are presented through graphical representations and summarized key findings, offering valuable insights to inform and enhance industry practices. These key findings are condensed into a road map for future research and development, aimed at improving current best practices by defining a series of projects to be undertaken. Full article

Traffic crash prediction (TCP) is a fundamental problem for intelligent transportation systems in smart cities. Improving the accuracy of traffic crash prediction is important for road safety and effective traffic management. Owing to recent advances in artificial neural networks, several new deep-learning models have been proposed for TCP. However, these works mainly focus on accidents in regions, which are typically pre-determined using a grid map. We argue that TCP for roads, especially for crashes at or near road intersections which account for more than 50% of the fatal or injury crashes based on the Federal Highway Administration, has a significant practical and research value and thus deserves more research. In this paper, we formulate TCP at Road Intersections as a classification problem and propose a three-phase data-driven deep learning model, called Road Intersection Traffic Crash Prediction (RoadInTCP), to predict traffic crashes at intersections by exploiting publicly available heterogeneous big data. In Phase I we extract discriminative latent features called topological-relational features (tr-features), of intersections using a neural network model by exploiting topological information of the road network and various relationships amongst nearby intersections. In Phase II, in addition to tr-features which capture some inherent properties of the road network, we also explore additional thematic information in terms of environmental, traffic, weather, risk, and calendar features associated with intersections. In order to incorporate the potential correlation in nearby intersections, we utilize a Graph Convolution Network (GCN) to aggregate features from neighboring intersections based on a message-passing paradigm for TCP. While Phase II serves well as a TCP model, we further explore the signals embedded in the sequential feature changes over time for TCP in Phase III, by exploring RNN or 1DCNN which have known success on sequential data. Additionally, to address the serious issues of imbalanced classes in TCP and large-scale heterogeneous big data, we propose an effective data sampling approach in data preparation to facilitate model training. We evaluate the proposed RoadInTCP model via extensive experiments on a real-world New York City traffic dataset. The experimental results show that the proposed RoadInTCP robustly outperforms existing methods. Full article