Search Results (2,349)

Sustainable forest management is increasingly recognized as an important climate change mitigation strategy because forests capture and store large amounts of carbon. This study presents a regional framework that integrates LiDAR data, forest cartography, and Life Cycle Assessment (LCA) to quantify biomass-related carbon dynamics and greenhouse gas emissions associated with forest management operations. The methodology was applied to the Autonomous Community of La Rioja (Spain) for the period 2010–2016 using public LiDAR campaigns, the Forest Map of Spain, and inventory data for reforestation and logging operations. Results show that above-ground biomass increased from 4,537,956 t in 2010 to 7,092,890 t in 2016, which corresponds to an increase of 1,200,819 t C in above-ground carbon stock. A complementary first-order estimate based on IPCC default root/shoot ratios suggests that total living biomass carbon (above- plus below-ground) increased by approximately 1,495,269 t C during the same period. In parallel, LCA results indicate that logging has substantially higher operational impacts than reforestation, particularly in terms of global warming potential. Even under a conservative scenario in which part of the carbon removed through logging is returned to the atmosphere, the regional balance remains net negative in CO₂-equivalent terms, indicating a net sink over the analyzed period. However, the approach has important limitations, including the absence of independent field validation, stand-age stratification, and explicit soil-carbon accounting. Full article

The structural integrity of bogie frames is a critical factor in the safety and reliability of railway rolling stock, requiring advanced assessment methods to handle complex, multi-axial stress states. This research presents a robust numerical framework for the preliminary fatigue evaluation of a metro bogie frame, integrating high-fidelity Finite Element Analysis (FEA) with the Findley multi-axial fatigue criterion. The methodology overcomes the limitations of traditional uniaxial verification methods by employing a localized critical plane approach, implemented through a proprietary computational code. The investigation simulates a realistic operational scenario by superimposing a static vertical load of 15 tons per side with dynamic components derived from on-track accelerometric data. This integrated loading condition enables a precise reproduction of the “rotating” stress states typically encountered in service. Global structural analysis identified critical transverse welded joints as high-stress concentration zones, which were then subjected to a detailed multi-axial investigation. By correlating the extracted stress tensors with the resistance category included in the reference standard, over a regulatory life of 10 million cycles, a maximum cumulative damage index of 0.4602 was recorded. The results demonstrate that while the frame possesses adequate structural reserves, nearly half of its fatigue life is consumed in localized nodes. This methodology provides a reliable and computationally efficient tool for the structural health monitoring and development of innovative railway geometries, offering a superior predictive capability that remains scarcely utilized by major rolling stock manufacturers. Full article

Forests worldwide are increasingly affected by climate-driven stressors and large-scale disturbances that impair tree physiology, disrupt water and carbon balance, and increase mortality risk. In this context, successful natural and artificial regeneration is essential for maintaining forest continuity, carbon storage, and biodiversity. However, regeneration outcomes depend not only on site conditions but also on biotic pressures, especially browsing by cervids in temperate and boreal forests. The aim of this review was to identify and synthesize evidence on how silvicultural methods can reduce browsing damage in forest regeneration and to assess how these methods influence the underlying drivers of cervid pressure through stand structure and forage availability. We examine mechanisms operating at two spatial scales: at the microscale, regeneration type, planting density, structural heterogeneity, planting stock, and how species mixture influences browsing probability and intensity; at the macroscale, how cutting systems and the spatial and temporal arrangement of harvests shape foraging landscapes by concentrating or dispersing browse resources and edge habitats. The reviewed evidence shows that dense, structurally diverse natural regeneration can dilute browsing pressure, whereas uniform artificial regeneration may increase repeated damage, and that species composition and mixture patterns can either protect or expose palatable species. We conclude that integrating microscale regeneration design with landscape-level harvest planning can strengthen stand resilience, reduce dependence on fencing, and support climate-adaptive forest development. To the best of our knowledge, no previous review has synthesized this evidence across both micro- and macroscale silvicultural contexts. Although most of the studies included in this review originate from Europe, we believe that the knowledge presented here is relevant to the majority of boreal and temperate forests worldwide. Full article

African swine fever (ASF), caused by African swine fever virus (ASFV), is a highly destructive transboundary disease in domestic pigs. The circulating virus in this study belonged to ASFV genotype II, commonly associated with high virulence. In endemic regions such as Thailand, limited vaccine availability and shortages of naïve breeding stock necessitate reliance on early detection, surveillance, and the retention of convalescent sows, thereby raising concerns regarding viral persistence and reproductive performance. This study evaluated the long-term reproductive performance of convalescent sows compared with naïve cohorts under co-habitation conditions, while assessing the efficacy of passive surveillance and strict biosecurity in preventing viral transmission from both internal and external sources. Convalescent sows showed reproductive performance comparable to naïve cohorts across two parities. Long-term co-habitation with naïve sentinel pigs was not associated with detectable viral transmission, although low-level viral persistence or intermittent shedding cannot be excluded. From a disease control perspective, the transition from delayed detection to enhanced passive surveillance facilitated early clinical recognition and targeted removal (“tooth extraction”) of infected animals, effectively limiting intra-herd transmission without full depopulation. Importantly, irrespective of the uncertain carrier status, strict biosecurity and rapid response protocols appeared effective in mitigating both external introduction and within-farm transmission of ASFV. These findings suggest that, under appropriate management and biosecurity conditions, convalescent sows may be reintegrated into production systems with caution. Full article

This study provides a comparative evaluation of three state-of-the-art large language models (LLMs), namely OpenAI’s (San Francisco, CA, USA) GPT-4.0, Google’s Google LLC, Mountain View, CA, USA) Gemini 2.0 Flash, and Meta’s (Meta Platforms, Menlo Park, CA, USA) LLaMA-4-Scout-17B-16E, in a decision-oriented framework in which the models generate structured outputs based only on historical closing-price data. The evaluation covers 150 stocks sampled from three countries (India, the United States, and South Africa) across ten economic sectors, including Information Technology, Banking, and Pharmaceuticals. Unlike many prior studies that combine numerical and textual inputs, this study relies solely on three years of numerical time series data and examines model responses in terms of decision labels such as buy, sell, or hold. The LLMs were provided with historical closing-price sequences and prompted with three types of finance-related questions: (a) whether to buy a stock, (b) whether to sell or hold a stock, and (c) in a pairwise comparison, which stock to buy or hold. These prompts were evaluated across two investment horizons: 1 month and 3 months. Model outputs were compared against realized market outcomes during the corresponding test periods. Performance was assessed across four key dimensions: country, sector, annualized volatility, and question type. The models were not given any supplementary financial information or instructions on specific analytical methods. The results indicate that GPT-4.0 achieves the highest average accuracy (56%), followed by LLaMA-4-Scout-17B-16E (48%) and Gemini 2.0 Flash (39%). Overall performance remains moderate and varies across market conditions, with relatively higher accuracy observed in high-volatility regimes (51%). This work evaluates how LLMs behave when presented with structured numerical price sequences in a controlled decision-labeling setting and contributes to the broader discussion on the potential and limitations of LLMs for numerical decision tasks in finance. Full article

Extensive animal production systems based on dryland pastures in Mediterranean regions have low profit margins. Improvements in soil fertility or grazing management and stocking rates are recognized strategies for reversing this situation and to ensure long-term agricultural sustainability. This article aims to assess whether this strategy of possible intensification of sheep production has a significant impact on soil compaction, which is a manifestation of soil functionality degradation resulting from trampling. An experimental design with four treatments was implemented (with and without dolomitic limestone application; continuous grazing with low stocking rates, CG-LSR, and deferred grazing with high stocking rates, DG-HSR). The study involved cone index (CI, in kPa) measurements (48 sampling areas, 12 in each treatment) on eight dates during two annual pasture/grazing cycles (2023/2024 and 2024/2025). Other soil parameters, the presence of trees and grazing preferences were also monitored and correlated with CI. The main results showed: (i) significantly higher soil compaction under CG-LSR than under DG-HSR; (ii) a negative and significant effect of soil moisture content (SMC) on CI (r = −0.381; p < 0.05); (iii) a significant CI increase in preferential grazing areas, but only in the topsoil layer (0–10 cm) and with a very weak correlation coefficient (r = 0.172; p < 0.05); and (iv) no significant differences in CI under and outside tree canopy areas (UTC and OTC, respectively) for the depth range of 0–30 cm. These results are good indicators of the desired and sustainable intensification of extensive livestock grazing systems. Full article

Purchasing portfolio models, particularly the Kraljic matrix, are widely used to support sourcing decisions under supply risk. Yet, they are often criticized for relying on subjective assessments and focusing mainly on upstream uncertainty while neglecting downstream demand volatility. This study develops a quantitatively grounded purchasing portfolio framework that integrates demand volatility and lead-time volatility into a unified measure of supply risk to support more sustainable sourcing decisions. Using transactional data for 876 stock-keeping units (SKUs) from a pharmaceutical distribution company, demand and lead-time volatility are measured through coefficients of variation and combined using an adjusted multifactor model that accounts for their interdependence. Financial importance is measured objectively through gross profit and classified according to the 80–20 Pareto principle. These metrics are incorporated into a revised purchasing portfolio matrix that classifies items into strategic, leverage, bottleneck, and routine categories. The findings reveal substantial variation in combined volatility across SKUs and show that incorporating demand uncertainty significantly changes portfolio positioning compared with traditional approaches. By linking purchasing and marketing perspectives, the proposed model reduces subjectivity, improves risk visibility, and supports sustainable sourcing and inventory decisions in volatile environments. Full article

Maintenance is crucial for the durability of the existing building stock and should be perceived as an opportunity to improve the built environment. The implementation of thermal retrofitting measures to the building’s envelope enhances global energy performance, which is economically and environmentally beneficial. Building-related energy consumption during the operation phase is key to tackling carbon neutrality and climate change. Introducing thermal retrofitting within the context of maintenance planning can be cost-optimizing, as it reveals the technical–economic synergy between building pathology and energy efficiency. Maintenance activities and energy demand throughout the building’s service life influence life-cycle costs (LCCs). Decision-making based on LCC awareness is an advantage for owners. This study discusses the impact of implementing an optimal retrofitting solution (ORS), according to different maintenance strategies, on the LCC of an existing single-family home. The ORS comprises the following measures: adding an external thermal insulation composite system (ETICS) to external walls, extruded polystyrene (XPS) panels to the roof, and replacing the existing windows with others with improved thermal performance. The three maintenance strategies involve different complexity levels, concerning the type, number and timing of activities. Moving beyond isolated assessments, this study develops an integrated framework that bridges based on two existing background methodologies, involving optimal thermal retrofitting and condition-based maintenance planning, which, combined with new research, enable the assessment of maintenance, energy and global LCC for a time horizon of 100 years. The evaluation of energy-related LCC is based on simulations. The results indicate that these costs represent the majority of the global LCC. The ORS has a considerable positive impact on energy and global LCC. Adopting a maintenance strategy characterized by fewer planned activities and an earlier schedule of replacement interventions, which determines the implementation of the retrofitting measures, is better in terms of LCC savings. Full article

The ongoing digitalization of building operation processes creates new opportunities to improve maintenance and renovation decision-making. Despite the increasing use of BIM, renovation decisions in residential buildings are still often based on fragmented data, heterogeneous documentation, and subjective expert assessments. This challenge is particularly relevant for large-panel housing in Central and Eastern Europe, where aging building stock requires systematic long-term modernization strategies. This paper presents a Renovation Decision Support System (RDSS) integrating a simplified BIM model, technical documentation, diagnostic data, and machine learning methods to support renovation planning. The system consists of five modules: the Building Information Model Module (BIMM), Geometric and Technical Documentation Module (GTDM), Building Condition Assessment Module (BCAM), Building Performance and Condition Prediction Module (BPCM), and Renovation Decision Optimization Module (RDOM). Data exchange is managed through a Common Data Environment (CDE). The system combines multi-criteria building condition assessment with fuzzy inference to determine renovation urgency and long-term optimization using Mixed-Integer Linear Programming (MILP). Budget constraints, activity sequences, time horizons, and user preferences are considered to generate alternative renovation scenarios. The proposed approach supports sustainable management of existing buildings, improves decision transparency, and enables data-driven renovation planning consistent with life-cycle management principles. Full article

Masonry minarets constitute an important component of Islamic architectural heritage. Beyond their religious function, they stand as social and cultural landmarks reflecting the diversity of architectural styles and building techniques of the regions in which they are located. Historical minarets have demonstrated remarkable resilience against environmental degradation and aging; however, in seismically active regions, earthquakes pose a major threat to their integrity. Due to their slender geometry and material characteristics, these structures are particularly vulnerable to seismic effects. Many historical records document that minarets have suffered severe damage and collapse during earthquakes. This study presents a state-of-the-art review of seismic vulnerability assessments of masonry minarets. It concentrates on Southwest Asia and the Mediterranean, regions that are characterized by high seismic risk and a rich inventory of this structural typology. Currently employed approaches to the seismic analysis of minarets typically require substantial computational resources and expertise. Recognizing the need for rapid and accessible methodologies in place of them, this study proposes a Kinematic Limit Analysis framework that is suitable for fast vulnerability assessment of large-scale building stocks. This allows for the most critical structures to be identified for further scrutiny using more sophisticated approaches. Full article

Understanding the reproductive physiology of marine fish is critical for sustainable fisheries management, particularly under environmental variability. This study evaluated seasonal changes in body parameters (condition factor, Kn, and gonadosomatic index, GSI, as proxies for body condition and reproductive status, respectively) and biochemical composition (P, proteins; G, glucose; L, lipids; fatty acids; and bioenergetic ratios L/P, LG, all as proxy of integrated biochemical condition) of female gonads in Strangomera bentincki, a key pelagic species in the Humboldt Current System (HCS) off south-central Chile. Moreover, environmental factors (sea surface temperature and chlorophyll-a) were also analyzed to explore their influence on the FA profile of gonads. Female body parameters showed significant seasonal variations, with high values of Kn and GSI in autumn and spring, respectively. The biochemical composition also revealed significant seasonal variation in protein and glucose content, with the highest protein levels in winter and elevated glucose in autumn. While total lipid and energy content remained relatively stable across seasons, the L/P and L/G ratios presented seasonal variations. Similarly, the fatty acid composition showed pronounced seasonal differences, particularly with increased polyunsaturated fatty acids (e.g., DHA) in winter. The SST was the environmental factor with the greatest influence on the seasonal variations in the gonadal FA profile. Altogether, these findings suggest a partial capital breeding strategy in S. bentincki, where reproductive investment depends on both accumulated reserves and environmental conditions during reproduction. This study underscores the importance of incorporating reproductive biochemical indicators into ecosystem-based fisheries management models to improve assessments of stock health and reproductive potential. Full article

Interpretability in corporate financial risk profiling must support not only predictive performance but also governance-oriented decision-making. This study proposes a three-class financial risk assessment workflow for B2B settings and introduces Risk-Weighted Uncertainty-Conditioned Feature Importance (RW-UCFI) as a post-explanation prioritization framework. RW-UCFI is not a new attribution method; rather, it reorganizes existing explanation outputs according to class sensitivity, predictive uncertainty, and asymmetric risk relevance. The empirical analysis uses a single cross-sectional dataset of 954 Indonesia Stock Exchange-listed firms with organizationally provided Low Risk, Medium Risk, and High Risk labels. A stacked ensemble model is used as the explanatory substrate, followed by calibration analysis, uncertainty analysis, and governance-oriented explainability aggregation. On the held-out validation set, the model achieved an accuracy of 0.7487 and a macro ROC-AUC of 0.8630. Repeated stratified validation indicated moderately stable aggregate performance, although class-level reliability remained uneven, with High Risk recall emerging as the weakest and most variable component. The original model showed the most favorable probability reliability among the evaluated variants, whereas temperature scaling and one-vs-rest isotonic regression did not improve calibration. Uncertainty analysis further showed that the most uncertain cases concentrated substantially more misclassifications and High Risk misses; the top 30% most uncertain cases captured 52.1% of all errors and 43.8% of High Risk misses. RW-UCFI produced a materially different feature-priority structure from standard global SHAP ranking, suggesting that explanation outputs may become more decision-relevant for governance-oriented review when contextualized by uncertainty and asymmetric risk conditions in the present setting. Full article

This study evaluates the extent and quality of tax transparency reporting among the Top 40 firms listed on the Johannesburg Stock Exchange (JSE), distinguishing between mandatory tax disclosures and voluntary transparency practices. A qualitative, disclosure-based research design was employed, involving content analysis of publicly available annual reports, integrated reports, and sustainability reports. A structured tax transparency framework grounded in stakeholder theory and legitimacy theory, and adapted from prior empirical studies was applied to systematically assess tax-related disclosures. Findings indicate high compliance with mandatory tax disclosure requirements, reflecting strong adherence to accounting standards and regulatory obligations. In contrast, voluntary tax transparency shows considerable variation: firms predominantly provide narrative, policy-oriented, and governance-related information, while detailed, forward-looking, and jurisdiction-specific disclosures remain limited. The discussion highlights that voluntary transparency is shaped by stakeholder expectations, legitimacy concerns, and perceived reputational and commercial risks, leading to selective disclosure. Regulatory compliance emerges as the primary driver of tax reporting, whereas voluntary practices are influenced by firm-specific and contextual factors. The results hold relevance for investors, regulators, and policymakers seeking greater corporate accountability, and for standard-setters aiming to enhance the consistency and depth of tax transparency reporting. Overall, the study enriches the limited literature on corporate tax transparency in emerging markets by offering contemporary empirical evidence from South Africa and identifying key areas requiring improvement in voluntary tax disclosures. Full article

For increasing the sustainability of existing building stock, energy renovation programs for existing buildings are being implemented worldwide with the aim of reducing the CO₂e footprint associated with building operation. In countries with high seismicity, the long-term effectiveness of energy renovation programs is called into question, since a strong earthquake can severely affect existing buildings and compromise the sustainability of the implemented works. As a result, the design of energy renovation programs in seismically active countries must explicitly account for seismic risk. Integrated intervention programs were developed, in which energy renovation measures are implemented simultaneously with seismic strengthening interventions. Romania represents a particular case due to the specificity of the intermediate-depth Vrancea seismic source, which strongly affects more than 60% of the national territory, covering over 120,000 km². Consequently, a large existing building stock is susceptible to seismic damage in the event of a major earthquake. This paper proposes the assessment of the specific CO₂e footprint of the Romanian residential building stock for the two types of interventions. The results show that preventive seismic strengthening has the lowest CO₂e footprint when compared to reactive seismic strengthening, the computed values for different scenarios ranging between 6 kg/m² and 45 kg/m² in case of preventive retrofitting and 23 kg/m² to 121 kg/m² in case of reactive retrofitting. Energy renovation leads to midrange values of 27 kg/m² to 58 kg/m². Nevertheless, all calculated values are significantly lower than the specific CO₂e footprint associated with new construction, proving the sustainability of existing building stock rehabilitation techniques. The research presented in this paper can be further extended through the implementation of scenario-based analyses concerning the improvement of the existing building stock through seismic strengthening and energy renovation, considering the occurrence of a major earthquake, in order to determine the optimal solution for the implementation of national programs in relation to the assumed objective of reducing CO₂e emissions at the building stock level. Full article

This study proposes an integrated analytical framework (IAF) as a tool to simultaneously assess vulnerable social groups within their administrative context. This study hypothesizes that analyzing vulnerable groups through socio-spatial delineation reveals subnational disparities and sub-regional heterogeneity in energy poverty (EP) indicators, associated with additional context-sensitive environmental consequences of energy use. Using Hungarian deprived rural settlements (DRSs) (n = 300) as an example, mixed methods were applied to examine national–regional disparities, intra-regional variations, and the environmental implications of extreme household energy use practices. Results show that both socio-economic indicators and building energy efficiency, and energy-use profiles, fall short of national indicator performance. The sample outlined by the IAF performed homogeneously regarding socio-economic circumstances and showed mild differences in housing quality and energy access. These results indicate not structural differences but variation in underlying regional drivers, highlighting the region-specific manifestation of EP. The energy-use-related environmental assessment was performed using a parametrized building-stock model and the two most extreme energy-use scenarios for households relying on solid fuels. The results suggest that the use of substitute fuels substantially increases the combined emissions of CO₂, CO, PM, NOx, and SOx by up to 32 percentage points. Although limitations constrain the reporting of empirically representative results, findings underscore the potential policy relevance of DRSs in national climate objectives. Full article