Search Results (11,522)

Broad-scale assessments often track forest productivity, yet they rarely quantify how soil conditions determine whether these gains persist as long-lived carbon and generate measurable economic value. This study focused on Northeast China, where forests include boreal coniferous stands dominated by Dahurian larch, temperate conifer–broadleaf mixed forests with Korean pine, and temperate deciduous broadleaf forests dominated by Mongolian oak. We combined GLASS net primary productivity and ESA CCI Land Cover to delineate forest pixels, used 2000 to 2005 as the baseline, and converted productivity anomalies into pixel level carbon economic value using a consistent pricing rule. Forest NPP increased significantly during 2000 to 2018 (slope = 1.57, p = 0.019), and carbon economic value also increased over time during 2006 to 2018 (slope = 2.24, p = 0.002), with the highest values in core mountain forests and lower values in the western forest–grassland transition zone. Correlation analysis, explainable random forests, and variance partitioning characterized spatial and temporal dynamics from 2000 to 2018 and identified environmental controls. Carbon value increased over time and showed marked spatial heterogeneity that mirrored productivity patterns in core mountain forests. Climate was the dominant predictor of value, while higher soil pH and clay content were negatively associated with value. The random forest model explained about 70% of the variance in carbon value (R² = 0.695), and variance partitioning indicated substantial unique and joint contributions from climate and soil alongside secondary topographic effects. The automatable framework enables periodic updates with new satellite composites, supports ecological compensation zoning, and informs soil-oriented interventions that enhance the monetized value of forest carbon sinks in data-limited regions. Full article

Strategic reverse logistics management is a key driver of sustainability in supply chains, where challenges in recyclable waste must be aligned with transportation systems to achieve optimal outcomes. A systematic review using the PRISMA methodology was conducted in December 2024 by searching Scopus, Google Scholar, and Thai Journals Online to examine the global research landscape and the strategic approaches applied in reverse logistics for recyclable waste and transportation. Analysis of 32 publications shows a steady rise in research, with most studies in Asia and dispersed across multiple journals, reflecting the field’s multidisciplinary nature. Four strategic approaches were identified. Model-driven approaches demonstrate strong capability through mathematical, computational, conceptual, and hybrid models, achieving reductions of 44% in climate impacts and 34% in costs. Technology-driven approaches contribute innovations to enhance battery transport safety. Exploratory approaches reveal contextual policy gaps and financial limitations. Hybrid approaches can improve efficiency and reduce CO₂ emissions. The future development of hybrid approaches still offers substantial room for broader application and deeper integration. This review supports the development of more effective systems, policies, and future research. Full article

Forest landscape restoration (FLR) in tropical Africa seeks to improve the ability of degraded forest to provide ecosystem services (ESs) to local communities. The purpose of this study is to present ESs that are mentioned in studies on FLR and methods that best integrate the different categories of ESs that have been identified in tropical Africa. The study followed the PRISMA 2020 statement for reporting systematic reviews. Qualitative and quantitative data were analyzed using agglomerative clustering and multiple correspondence analysis (MCA). The systematic literature review analyzes modalities of ES integration through various studies on FLR in tropical Africa. In most cases, only three of the four ES categories are mentioned, namely provisioning, regulating and supporting services. Primary production is the ES category most frequently mentioned in tropical Africa. In this region, various methods are used to restore forest landscapes (reforestation, savannah protection, agroforestry). This review shows a strong link between ESs, the ES categories, use values and methods of FLR. Therefore, integration of ESs in FLR can contribute to the understanding of how FLR impacts biodiversity, climate change mitigation. improvement of human well-being, etc. Full article

Green facades, commonly referred to as vertical plant systems, offer sustainable solutions. They improve the energy efficiency of buildings, reduce energy consumption, and positively impact the microclimate both at the microscale and at the urban level. Their ability to regulate temperature and improve thermal comfort, including mitigating the heat island effect, makes them a valuable element of sustainable architectural design. They also contribute to reduced energy consumption, reduced noise, mitigation of air pollution, and aesthetic and wind protection. The main goal of the study was to analyse the cooling effectiveness of green walls in a transitional temperate climate zone. The study was conducted on two experimental models located on the campus of the Wrocław University of Environmental and Life Sciences and at the Research and Educational Station in a suburban area. Both locations had different characteristics: the former contained urban development, while the latter contained open and sparsely developed areas. On warm and sunny days, the cooling effects of the systems were observed independently for both locations and their exposures. For data acquisition at a distance of 5 cm from the plants, a higher data concentration and a lower variability in the mean temperature drop were observed. In the same group, on sunny days, the cooling effect averaged 4–7 °C and depended on the location. On cloudy days, the mean maximum cooling in this group did not exceed 4 °C. Full article

Unique skin microbial communities have been shaped by the harsh climatic conditions in high-altitude areas, such as intense ultraviolet radiation and low oxygen concentration. However, it is unknown whether high altitude contributes to shaping common microbiota inhabiting the skin across different mammals. The skin microbial communities of humans and horses living in high-altitude (Tibetan) and low-altitude areas were analyzed using full-length 16S rRNA sequencing technology. Alpha diversity differed between high- and low-altitude groups (p < 0.01). Skin microbial community composition also differed between high- and low-altitude areas (p < 0.05). Some of the common taxa present in the skin of humans and horses in high-altitude areas were identified as extreme microorganisms capable of adapting to the harsh high-altitude environment. Five bacterial taxa, including the genera Sphingomonas, Brevundimonas, and Kocuria, as well as the species Acinetobacter guillouiae and Arboricoccus pini, were significantly enriched (p < 0.01) on the skin of both humans and horses in high-altitude areas. Meanwhile, some taxa enriched on the skin surface at the same altitude showed preferences for mammalian species. Acinetobacter johnsonii, Anaerococcus nagyae, and Anaerococcus octavius were significantly enriched (p < 0.05) in the skin of humans at both high and low altitudes, whereas Acinetobacter pseudolwoffii and Armatimonas rosea, Archangium gephyra and Acinetobacter lwoffii were significantly enriched (p < 0.05) in the skin of horses at both high and low altitudes. In the network analyses, a positive correlation (p < 0.01) was shown between the skin taxa enriched in high-altitude areas and each other, while a negative correlation (p < 0.01) was found between the skin microorganisms enriched in high-altitude areas and those enriched in low-altitude areas. Overall, our findings indicate that high-altitude extreme environments drive convergent evolution of skin microbiota across mammals, reflecting the joint effects of environmental selection and host-related filtering on community assembly. This cross-species comparison provides a framework for understanding skin microbiome responses to extreme environments in plateau mammals. Full article

Coastal wetlands, situated at the critical land–sea ecotone, play a vital role in sustaining ecological balance and supporting human activities. Currently, these ecosystems face dual stresses from climate change and intensified anthropogenic activities, making the quantitative assessment of ecosystem functions—represented by Gross Primary Productivity (GPP)—essential for their protection and management. However, a knowledge gap remains regarding coastal–urban complex ecosystems, and existing studies on coastal wetlands often overlook macro-environmental drivers beyond sea-level rise. This study leveraged the MOD17A2H V006 dataset to generate a 500 m GPP product for Zhanjiang City. We analyzed the spatiotemporal dynamics of GPP, utilized land use data to examine the evolution of coastal wetlands, and employed the Geodetector model to quantify the contributions of various factors to GPP in Zhanjiang and its coastal wetlands. The results indicate that: (1) GPP in Zhanjiang exhibited an overall steady upward trend, increasing at an average rate of $13.8\mathrm{g}\mathrm{C}·{\mathrm{m}}^{-2}·{\mathrm{yr}}^{-1}$. However, it displayed strong spatial heterogeneity, characterized by higher values in the southwest and lower values in the northern and coastal regions. (2) The land use pattern in Zhanjiang underwent significant transformations over the past two decades. Cropland and impervious surfaces expanded markedly, increasing by 194.6 km² and 290.42 km², respectively, while coastal wetland areas showed a continuous decline, with degraded and newly formed areas of 101.5 km² and 42 km², respectively. (3) The Geodetector results revealed that the q-value of Nighttime Light (NTL) increased from negligible values to over 0.1, emerging as a dominant driving factor. Although the driving force of anthropogenic activity factors on Zhanjiang and its coastal wetlands has steadily increased, natural factors currently remain the dominant forces. These findings unravel the driving mechanisms of natural and anthropogenic factors on GPP in Zhanjiang, providing valuable scientific evidence for the sustainable development of coastal ecosystems. Full article

Projected climate changes in the Mediterranean exceed those in most European regions, yet their effects on vegetation remain uncertain. We investigated vegetation changes in the Agri Valley (Basilicata, Italy) using 318 plots, including 40 resurveys. Community-weighted Ellenberg indicator values (EIVs) and plant ecological groups were combined with long-term hydroclimatic anomalies reconstructed via the BIGBANG model (1951–2024), providing a long-term climatic baseline for interpretation. Significant shifts emerged in several EIVs, with clear habitat-specific patterns. Forests showed decreasing light and increasing moisture values, reflecting a higher presence of forest-associated species, though some diagnostic taxa declined. Grasslands exhibited increasing aridity, with a growing contribution of dry-grassland species and a decline in winter therophytes. Climatic analyses revealed pronounced long-term warming, accelerating after the 1980s, while annual precipitation remained highly variable without a monotonic trend. Recent years were marked by intensified drought, evidenced by declining SPEI values (2013–2022) and a higher frequency of dry months (SPEI ≤ −1). The convergence of vegetation responses, species turnover, and climatic anomalies supports climate-driven community trajectories. Despite limited land-use data, this multi-indicator framework effectively detects early ecological responses and identifies vulnerable habitats, providing valuable insights for the conservation and management of Mediterranean mountain ecosystems under ongoing climate change. Full article

Background: The extraordinary disturbances faced by the hotel industry, ranging from worldwide health problems to political instability and climate change, have highlighted the insistent need for more resilient and agile supply chain (SC) systems. This study explored how artificial intelligence (AI) capabilities can generate competitive advantage (CA) through supply chain agility (SCA) and supply chain resilience (SCR) as mediators and competitive pressure (CP) as a moderator. Methods: Drawing on the resource-based view (RBV) framework, we suggested and empirically tested the study model. Using data collected from 432 hotel managers and analyzed using Partial Least Squares Structural Equation Modelling (SEM-PLS). Results: the results reveal that AI-driven SC can significantly strengthen SCA and SCR. Furthermore, SCA and SCR can act as powerful mediators, and CP can strengthen the tested relationships (the links from AI adoption and CA) as a moderator. Conclusions: The study made several theoretical and practical contributions by integrating AI capabilities into SCR and SCA frameworks in the hotel and tourism context, and by providing practical evidence for professionals aiming to leverage AI-driven SC tools to navigate uncertainty and create sustainable CA. Full article

While domestic green finance is widely recognized for its role in fostering green innovation and supporting climate change mitigation, the impact of international green finance (IGF) remains critical, particularly for developing economies where external finance inflows can catalyse transitions toward low-carbon development. This study investigates the long-run and short-run effects of IGF on green innovation and further examines the influence of green innovation on carbon dioxide (CO₂) emissions across a panel of 76 developing countries from 2000 to 2019. Using second-generation panel cointegration and the vector error correction mechanism, our findings reveal a nonlinear long-run relationship between IGF and total innovation, indicating that IGF must exceed a threshold before significantly boosting total innovation in developing economies. We also identify an inverted U-shaped relationship between IGF and green innovation, in which the positive effects of IGF diminish beyond a certain point. Crucially, IGF emerges as a significant driver of CO₂ emissions reduction in both the short- and long-run. While total innovation is associated with increased emissions over the long term, green innovation contributes to a substantial and sustained decrease in CO₂ emissions. These results emphasize the need to design targeted policies that prioritize green innovation and scale up IGF to support sustainable growth in developing countries. Full article

Glacier surface albedo controls solar energy absorption and Arctic mass balance, yet comprehensive modelling approaches remain limited. This study develops and validates multiple modelling frameworks for glacier albedo prediction using automatic weather station (AWS) data from Hansbreen and Werenskioldbreen in southern Svalbard during the 2011 ablation season. We compared three point-based approaches across elevation zones. At lower elevations (190 m), linear regression models emphasising snowfall probability or temperature controls achieved excellent performance (R² = 0.84–0.86), with snowfall probability contributing 65% and daily positive temperature contributing 86.3% feature importance. At higher elevations (420 m) where snow persists, neural networks proved superior (R² = 0.65), with positive degree days (72.5% importance) driving albedo evolution in snow-dominated environments. Spatial modelling extended point predictions across glacier surfaces using elevation-dependent probability calculations. Validation with Landsat 7 imagery and multi-algorithm comparison (n = 5) revealed that while absolute albedo values varied by 12% (0.54–0.60), temporal dynamics showed remarkable consistency (27.8–35.2% seasonal decline). Point-to-pixel validation achieved excellent agreement (mean absolute difference = 0.03 ± 0.02, 5.3% relative error). Spatial validation across 173,133 pixel comparisons demonstrated good agreement (r = 0.62, R² = 0.40, RMSE = 0.15), with an accuracy of reproducing temporal evolution within 0.001–0.021 error. These findings demonstrate that optimal glacier albedo modelling requires elevation-dependent approaches combining physically based interpolation with machine learning, and that temporal pattern reproduction is more reliably validated than absolute values. The frameworks provide tools for understanding albedo-climate feedback and improving mass balance projections in response to Arctic warming. Full article

The urban heat island (UHI) effect, intensified by rapid urbanization, necessitates the precise identification and mitigation of thermal sources and sinks. However, existing studies often overlook landscape connectivity and rarely analyze integrated source–sink networks within a unified framework. To address this gap, this research combines source–sink theory with the local climate zone classification to examine the spatiotemporal patterns of thermal characteristics in Fuzhou, China, from 2016 to 2023. Using morphological spatial pattern analysis, the minimum cumulative resistance model, and a gravity model, we identified key thermal source and sink landscapes, their connecting corridors, and barrier points. Results indicate that among built-type local climate zones, low-rise buildings exhibited the highest land surface temperature, while LCZ E and LCZ F were the warmest among natural types. Core heat sources were primarily LCZ 4, LCZ 7, and LCZ D, accounting for 19.71%, 13.66%, and 21.72% respectively, whereas LCZ A dominated the heat sinks, contributing to over 86%. We identified 75 heat source corridors, mainly composed of LCZ 7 and LCZ 4, along with 40 barrier points, largely located in LCZ G and LCZ D. Additionally, 70 heat sink corridors were identified, with LCZ A constituting 96.39% of them, alongside 84 barrier points. The location of these key structures implies that intervention efforts—such as implementing green roofs on high-intensity source buildings, enhancing the connectivity of cooling corridors, and performing ecological restoration at pinpointed barrier locations—can be deployed with maximum efficiency to foster sustainable urban thermal environments and support climate-resilient city planning. Full article

The forestry industry has evolved during its history by continuously adapting to the natural environment and new technological solutions, but its progression into the future has taken some different paths depending on the level of understanding for a sustainable greener future. Countries with a long wood culture have learned from the past and brought their knowledge into a sustainable forestry and building industry. This paper presents possible solutions for reusing timber, using logs as building materials, contributing to the regeneration of traditional heritage, and exploring the possibility of recycling and reusing materials after the building’s life cycle. The study includes solutions regarding the reuse and recirculation of old weathered wooden constructions, inspired by the Nordic countries. Climate adaptation has been a challenge since ancient times, and the Nordic climate offers more than difficult conditions. The buildings are adapted to the harsh climate and local resources, and the mountainous landscape offers building materials mainly in the form of wood and stone. Reuse and recirculation have always been practiced in the traditional construction of wood culture. Full article

Understanding the elevation-dependent response of mountain snow cover to climate change requires transcending statistical correlations to reveal the underlying physical mechanisms. This study investigates these mechanisms in the Upper Yarkand River Basin (U-YRB, located on the northwestern edge of the Qinghai–Tibet Plateau) from 2002 to 2020 by integrating a Gradient-Boosted Decision Tree (GBDT) model, a process-based degree-day model, and Structural Equation Modeling (SEM). Our analysis reveals a significant overall decline in Snow Cover Area (SCA) at a rate of −0.25%·a⁻¹, with the rate of decrease accelerating below 4000 m but slowing above this threshold. Snow Depth (SD) exhibited a distinct elevation-dependent trend, decreasing at elevations below 3500 m while increasing above 4000 m. GBDT analysis quantified the shifting dominance of climatic drivers: temperature was the primary factor reducing SCA across all elevations, though its contribution diminished with increasing elevation. Precipitation played a critical yet contrasting role, emerging as the key positive driver for SD accumulation at high elevations (>4500 m). A comparative analysis of snowfall and snowmelt processes identified snowmelt as the key process governing elevation-dependent patterns, peaking around 4000 m. Crucially, SEM elucidated a mechanistic shift across the 4000 m threshold: below 4000 m, snow cover loss was primarily driven by temperature via its strong positive effect on snowmelt. Above 4000 m, while the influence of temperature persisted, the dominant positive effect of precipitation on snowfall became the key driver of the observed SD increase. This shift signals a fundamental transition from melt-dominated dynamics at lower elevations to accumulation-influenced dynamics at higher elevations. Our findings clarify the physical processes behind elevation-dependent snow cover changes and underscore the necessity of elevation-stratified frameworks for hydrological prediction and water resource management in alpine basins. Full article

Cheesemaking is an energy-intensive process that relies heavily on heating and cooling operations traditionally powered by fossil fuels and electricity from the national grid. Reducing this dependence and integrating renewable energy sources are essential to align the sector with European decarbonization targets. This study presents the development of a simulation tool for optimizing the energy management of a cheese production facility by integrating solar, wind, and biomass systems. The model evaluates techno-economic and environmental performance under different climatic conditions and operational scenarios. Experimental validation was carried out using a prototype installed at the Polytechnic Institute of Beja (Portugal), achieving a deviation of only 2.3% in renewable energy contribution between simulated and measured data. Results demonstrate that renewable integration can reduce non-renewable energy consumption, achieving weekly profits up to 0.019 €/kg of cheese and carbon emissions as low as 0.0109 kg CO₂e/kg. The proposed approach provides a reliable decision-support tool for small- and medium-scale cheese producers, promoting both environmental sustainability and economic competitiveness in rural regions. Full article

Iron oxides are crucial for the long-term storage of soil organic carbon (SOC) in paddy soils, making them a key factor in global carbon cycles and important for strategies aimed at combating climate change. This review examines the role of iron oxides in paddy soils, particularly their interaction with SOC, which helps stabilize carbon and contributes to mitigating climate change. These processes of iron oxide phase transformations, wet–dry cycles, and microbial activity help trap carbon in the soil, supporting climate change mitigation efforts. Wet–dry cycles promote mineral dissolution and re-precipitation, forming new reactive surfaces and OC-Fe complexes. Future research should adopt a multi-scale approach to better connect molecular mechanisms with ecosystem-level carbon processes. A deeper understanding of iron oxide behavior in paddy soils will support the development of sustainable soil management practices and improve models for predicting soil carbon sequestration under climate change. Full article