Search Results (10,742)

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

Despite growing recognition of blockchain technology’s potential to enhance traceability, verifiability, and integrity in financial reporting, empirical evidence from regulated banking environments in developing economies remains scarce. This study investigates whether blockchain adoption is positively associated with transparency and accounting disclosure in digital financial reports among Jordanian listed banks. A structured questionnaire was distributed to managers, financial managers, and accountants across 15 banks listed on the Amman Stock Exchange, yielding 312 valid responses. Partial Least Squares Structural Equation Modeling (PLS-SEM) with 5000 bootstrap subsamples was employed for data analysis. The results show that blockchain adoption is positively and significantly associated with transparency (β = 0.361, p < 0.001) and accounting disclosure (β = 0.437, p < 0.001), explaining 13.0% and 19.1% of the variance, respectively. These findings suggest that blockchain-enabled systems are perceived by banking professionals as contributing to greater reporting credibility. By providing empirical evidence from a developing economy banking sector, this study indicates that blockchain adoption may serve as a governance-supporting mechanism associated with improved perceived transparency and disclosure quality. Full article

Under the “dual carbon” strategic framework, the low-carbon transition of the logistics sector—a major source of carbon emissions in the national economy—has become imperative for achieving green development. The adoption of new-energy vehicles (NEVs) represents a critical pathway for decarbonizing logistics operations. Initiated in 2009, China’s “Ten Cities, Thousand Vehicles” Demonstration Project served as a pioneering policy to accelerate NEV deployment, offering a valuable use case for reducing emissions in urban logistics. Using this initiative as a quasi-natural experiment, we employ a multi-period difference-in-differences (DID) approach and panel data from 275 Chinese prefecture-level cities (2000–2021) to evaluate the causal effect of the policy on urban logistics CO₂ emissions. The robustness of the findings is confirmed through parallel trend tests, placebo tests with reassigned treatment timing, alternative dependent variable construction, and instrumental variable estimation. Mechanism and heterogeneity analyses are further conducted to uncover underlying channels and contextual variations. The results indicate a statistically significant reduction in logistics carbon emissions in pilot cities, which remains consistent across multiple robustness checks. Mediation analysis reveals that the policy effect is partially transmitted through increased NEV stock. Moreover, the emission reduction effect is more pronounced in cities with lower logistics dependency and non-consumer-oriented economic structures, while it is weaker in consumer and highly logistics-dependent cities. These findings confirm the sustainable contribution of early NEV policies through advancing the transition to low-carbon logistics and supporting dual carbon goals, fill the empirical gap in developing countries’ freight decarbonization, and offer actionable insights for targeted regional sustainable logistics strategies. Full article

This study investigates the integration of ABC/XYZ (value-based classification/ demand variability classification) inventory classification with demand forecasting models (ETS - Error, Trend, Seasonality, ARIMA - AutoRegressive Integrated Moving Average, Prophet - type of additive model) in a manufacturing enterprise to support sustainable resource management. The research aims to evaluate the inventory structure, demand variability, and forecasting accuracy across different material categories. The results confirm a strong concentration of inventory value in A-class items and significant differences in forecast accuracy across ABC/XYZ segments. While AX items generally exhibit lower forecast errors, notable exceptions highlight the need for additional diagnostic analysis. The findings demonstrate that integrating classification and forecasting improves inventory decision-making, reduces excess stock, and supports sustainable resource utilization. The proposed approach provides practical guidance for optimizing inventory management in industrial environments. Full article

Tropical forests differ widely in floristic composition, stand structure, standing volume, and carbon storage, yet comparative evidence across contrasting tropical forest types remains limited. This study examined whether variation in standing volume and carbon stocks among contrasting tropical forests was more closely associated with structural attributes or with diversity-related patterns. Two tropical wet forests in Colombia and one tropical semi-deciduous forest in Mexico were evaluated using 40 circular plots of 500 m² established within a 100 ha reference area in each forest, where all trees with DBH > 10 cm were measured. Floristic composition, ecological dominance, diversity, dendrometric attributes, standing volume, biomass, and carbon stocks were estimated using a common analytical framework. The two wet forests showed higher effective diversity, broader taxonomic dominance, greater basal area, mean height, standing volume, biomass, and carbon stocks than the tropical semi-deciduous forest. In contrast, the semi-deciduous forest showed stronger dominance concentrated in fewer taxa, especially Euphorbiaceae, a pattern that may reflect the ecological suitability of this family under more seasonal and water-limited conditions. At the family level, standing volume, biomass, and carbon were distributed more evenly among dominant families in the wet forests, whereas they were more concentrated in fewer lineages in the semi-deciduous forest. Basal area showed the strongest association with standing volume, total biomass, and total carbon, followed by mean height and mean DBH. Overall, the results indicate that, under the conditions evaluated, structural organization was more closely associated with standing volume and carbon storage than diversity alone, while diversity acted as a complementary correlate. Full article

Vegetation carbon stock is a key component of the terrestrial carbon cycle and supports climate-change mitigation and carbon-neutrality strategies. While field inventories provide accurate references, they are constrained by cost and limited scalability, motivating the rapid adoption of remote sensing for large-scale spatial estimation and mapping. However, the literature lacks a consolidated bibliometric and critical synthesis focused on above-ground vegetation carbon stock estimation. Therefore, this review aims to provide a quantitative overview of publication trends, synthesise methodological developments, and identify key research gaps in remote-sensing-based above-ground vegetation carbon stock estimation. A total of 1825 Web of Science records (2015–2024) were retrieved, of which 763 were included for bibliometric mapping using VOSviewer version 1.6.20 and CiteSpace version 6.3.R2, complemented by a critical review of 32 high-quality studies. Results indicate a shift from passive optical and single-index approaches toward active sensing and multi-sensor, multi-platform integration, alongside broad uptake of machine learning and an emerging dominance of deep learning for nonlinear modelling and feature learning. Research attention is expanding beyond forests to non-forest ecosystems, yet challenges persist in spatial resolution, validation data availability, and cross-biome generalizability. This review summarizes methodological trajectories and identifies priorities for robust, transferable above-ground carbon estimation. 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

Urban sprawl constantly reconfigures the land use pattern, and such transformations may significantly modify regional carbon stocks. Utilizing Tai’an City as the study site, this research established a comprehensive integrated Patch-generating Land Use Simulation (PLUS), Integrated Valuation of Ecosystem Services and Trade-offs (InVEST), and Optimal Parameters-based Geographical Detector (OPGD) system to reconstruct carbon storage shifts from 2000 to 2020, project its reaction to four diverse development trajectories in 2030, and investigate the drivers underlying spatial disparities. The results indicate a persistent decline in carbon storage throughout the past two decades, with peak concentrations primarily gathered in mountain regions dominated by forest and grassland, whereas lesser amounts were grouped in urban and suburban areas defined by built-up land. Compared to 2020, the projected carbon stock in 2030 drops by 1,803,966 t under the natural growth trajectory and by 2,417,778 t under the high-quality economic growth pathway, whereas it rises by 47,326 t under cultivated land conservation and by 7679 t under ecological conservation. Elevation represents the most crucial driver among the selected variables in clarifying the spatial fluctuation of carbon storage (q = 0.3985), followed by slope (0.3323), mean annual temperature (0.2382), and the Normalized Difference Vegetation Index (NDVI) (0.1219). The synergy between elevation and NDVI produces the highest integrated explanatory power (q = 0.4906). These outcomes imply that constraining construction land growth while protecting agricultural and ecological land is vital for preserving and enhancing regional carbon sink potential. Full article

This study aims to predict stock market risk and improve preparedness for potential economic crises by identifying sharp declines in stock returns using classification-based machine learning models. Using ten years of KOSPI 200 index data (2015 to 2024), a daily return series was constructed. A day was labeled a risk event (1) if its return fell below the 5th percentile of the returns observed over the preceding 100 trading days, indicating a sharp decline. Nine classification models—Logistic Regression, k-nearest Neighbor, Decision Tree, Random Forest, Linear Discriminant Analysis, Naive Bayes, Quadratic Discriminant Analysis, AdaBoost, and Gradient Boosting—were trained and validated. Among these, Logistic Regression demonstrated the strongest overall performance across multiple evaluation metrics, including accuracy, non-risk F1 score, risk F1 score, and AUC. 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

In the context of a rapidly evolving data-driven economy and increasingly stringent carbon reduction policies, the impact of supply chain digitalization (SCD) on corporate carbon emission intensity (CEI) has become an important research topic. Using panel data on Chinese A-share listed firms from the Shanghai and Shenzhen stock exchanges over the period 2013–2023, this study employs Python-based text analysis of corporate annual reports to explore the effect of SCD on corporate CEI. The results show that SCD significantly reduces corporate CEI. Mechanism analysis further indicates that this effect operates through three channels: alleviating financing constraints, promoting green innovation, and reducing supply chain disruption risk. Heterogeneity analysis reveals that the mitigating effect of SCD on corporate CEI is more pronounced among non-state-owned firms, large-scale firms, firms in non-high-tech industries, firms in highly environmentally sensitive industries, and firms located in regions with more developed digital infrastructure. Further analysis shows that SCD contributes to improvements in both firms’ sustainability and financial performance. Overall, this study provides important policy implications for both governments and firms. Full article

This study examines the economic consequences associated with the mandatory adoption of International Financial Reporting Standards (IFRS) in the Iraqi banking sector. Motivated by growing evidence that the outcomes of IFRS adoption depend on institutional and market conditions, the study focuses on a bank-based emerging economy characterized by relatively underdeveloped capital markets and evolving enforcement mechanisms. Using a balanced panel of 24 banks listed on the Iraq Stock Exchange over the period 2014–2018, the analysis exploits the mandatory IFRS adoption in 2016 within a before–after regulatory framework. Panel regression techniques are employed to examine the associations between IFRS adoption and stock market liquidity, firm value, information asymmetry, and the cost of debt, while controlling for bank-specific characteristics and macroeconomic conditions. The results indicate that IFRS adoption is positively significantly associated with stock market liquidity, and negatively significantly associated with information asymmetry, consistent with improvements in the informational environment of Iraqi banks following enhanced disclosure and comparability. The findings also reveal a positive and significant relationship between IFRS adoption and the cost of debt, suggesting higher perceived financial risk by creditors. In contrast, no statistically significant association is observed between IFRS adoption and bank market valuation, highlighting the limited sensitivity of equity prices to accounting reforms in thin and institutionally constrained markets. Overall, the study contributes to the literature on the economic consequences of IFRS adoption by providing evidence from an underexplored emerging market and a highly regulated banking sector. The findings underscore the role of institutional context in shaping the outcomes of accounting standard convergence and offer policy-relevant insights for regulators and standard-setters in bank-oriented financial systems. Full article

The decline of salmonid stocks in the Baltic Sea region is a matter of serious concern, prompting many countries to implement widespread stocking of artificially reared individuals to restore or enhance populations. While such interventions are intended to be beneficial, their efficacy remains a subject of ongoing debate. Artificially reared fish often face challenges in adapting to natural environments and may struggle to compete with wild counterparts, potentially leading to reduced growth rates and diminished overall fitness. This study evaluated the growth and physiological condition of naturally hatched versus artificially reared Salmo trutta juveniles during their first two years of life, prior to smoltification and seaward migration. The results demonstrated that stocked juveniles exhibited significantly slower growth, a higher incidence of fin damage, and a greater abundance of cultivable gut bacteria compared to wild individuals. Conversely, no significant differences were observed in blood parameters. Such growth retardation suggests potential difficulties in adaptation and recruitment. Consequently, while the release of artificially reared S. trutta fry facilitates the restoration of extinct populations, its capacity to enhance existing stocks within Baltic Sea riverine ecosystems may be limited. Full article