Sustainable Development Goal 13: Climate Action (79550)

Climate change is affecting agricultural stability, making farmers’ adaptive capacity crucial for productivity and viability. Existing indices primarily record actions taken but overlook the intention-to-action stage. This paper proposes the Farmers’ Readiness for Climate Adaptation (FRCA) index, a factor-analytic composite index combining economic, environmental, social, institutional, and psychological factors to capture readiness between intention and implementation. A structured-questionnaire survey of farmers in Kilkis, Central Macedonia (Greece) reveals heterogeneity in perceptions, attitudes, and practices linked to social influences, individual beliefs, resource access, and institutional constraints. Farmers widely perceive climate change as a serious threat and express willingness to adopt measures, yet actual uptake varies. FRCA offers a practical diagnostic to inform policies that strengthen sectoral sustainability and resilience. Full article

The need for a green, clean, and sustainable economy has pushed Western societies to create a new economic system to help local economies achieve their primary targets. Many scholars claim that the uncertainty of climate change and the contemporary necessity for environmental protection have led most advanced countries to reconsider and redesign their economies, adding environmental management into every reference system. The European Union’s response to that need is the European Green Deal (EGD). The EGD represents one of the European Union’s major strategies to achieve climate neutrality by 2050 and resets the Commission’s commitment to tackling climate and environmental-related challenges. Creating a fair, healthy, and environmentally friendly food system is essential for that effort toward sustainability, which is why agriculture and farmers play an important role in this transition. Understanding farmers’ perspectives on the EGD is essential for successfully implementing its policies. The EU has already launched a strategic dialogue on the future of EU agriculture. This research investigates farmers’ views on the EGD through a focus group approach, providing a qualitative understanding of their perceptions, concerns, and suggestions for policy improvements. In addition, this research will try to present recommendations for future research. Full article

Climate action has been deemed as fundamental to counteract the impacts of rising global temperatures on health which will disproportionately affect low-income populations, racial and ethnic minorities, women, and other historically marginalized groups. Along with poverty reduction, inequality mitigation, gender equality promotion, and public health protection, climate action has been recognized as a fundamental goal for achieving Sustainable Development Goals (SDGs). However, despite growing recognition of the need to align climate action with development goals, there is a knowledge gap regarding how the implementation of climate change mitigation and adaptation policies impacts social inequalities. To address this knowledge gap, this document proposes a scoping review protocol aimed at identifying and synthesizing research that examines the impacts of climate policies on inequalities at the subnational scales, within the transport, infrastructure and health. The objective of this review is to map existing evidence, identify conceptual and empirical gaps and inform policy strategies that promote climate action in line with values of social justice and equality. Full article

This paper examines the time-frequency dynamics and the spillover effects among the clean energy, fossil fuel, metal, and electricity markets using a wavelet local multiple correlation analysis and a time-varying-parameter vector autoregression model. The findings suggest that the electricity market is isolated from the other markets in the short to medium term, while long-term interdependence is strong during crises such as the global financial crisis, the COVID-19 pandemic, and the Russia-Ukraine conflict. There exists a long-term integration trend across those four markets, with the fossil fuel and metal markets playing dominant roles. The fossil fuels market remained the primary channel through which systemic shocks were transmitted to all other sectors. The clean energy market has transformed from a market that passively absorbed shocks into a systemic driver during crises. These findings provide insights for investors and policymakers across different time horizons. Full article

This study examines how climate change and vegetation are integrated into teacher education curricula across 26 countries, addressing a critical gap in understanding how future teachers are prepared to respond to the climate and biodiversity crises. To evaluate curricular integration systematically, we developed and validated the Climate and Vegetation Curriculum Integration Index (CCVI), which measures four dimensions: climate change, vegetation, links between the two, and pedagogical strategies. Content analysis of 70 official curriculum documents was conducted, with high inter-rater reliability (κ = 0.72–0.85) and internal consistency (Cronbach’s α = 0.89) confirming the robustness of the instrument. Results show that integration remains partial and uneven: climate change content is more common than biodiversity, while vegetation is often marginalized, perpetuating the phenomenon of “plant blindness.” Exemplary cases in Finland, Germany, Mexico, Norway, and Switzerland demonstrate that high levels of integration are achievable, but intra-country variability often exceeds cross-country differences, highlighting the influence of institutional design. The study concludes that teacher education worldwide is not yet aligned with the urgency of global sustainability challenges. The CCVI provides a practical tool for benchmarking progress and guiding reforms, underscoring the need to embed sustainability as a core element of teacher preparation to foster ecological literacy, resilience, and civic engagement. Full article

Forest cover dynamics strongly influence ecological integrity and resource sustainability, particularly in ecotonal landscapes, where vegetation is highly sensitive to climate variability, long-term climate change, and anthropogenic disturbances. This study examined Forest Land (FL), representing all areas of dense, canopy-forming woody vegetation with forest-like structure, aggregated from SANLC classes, in relation to eight other land cover classes across three periods: 1990–2014, 2014–2022, and 1990–2022. The study used South African National Land Cover datasets and the TerrSet–LiberaGIS Land Change Modeller to quantify changes in magnitude, direction, and source–sink relationships. Analyses included post-classification comparison to determine spatial changes, transition matrices to identify land-cover conversions, and asymmetric gain–loss metrics to reveal sources and sinks of forest change. The result shows that between 1990 and 2014, forests remained marginal and fragmented in the eastern central part of the study area, while shrubland increased from 40.4% to 60.2% at the expense of grasslands, cultivated land, bare land, wetlands, and forest land. From 2014 to 2022, FL regeneration was pronouncedly increased from 2% to 6%, especially along riparian corridors and reservoir margins, coinciding with shrubland decline (99.3%) and grassland recovery (261.2%). Over the entire 1990–2022 period, FL increased from 2.4% to 6% expanding into bare land, cultivated land, grassland, shrubland, and wetlands. Asymmetric analysis indicated that forests acted as a sink during the first period but as a source of ecological resilience in the second and final. These findings demonstrate strong vegetation feedback to hydrological and anthropogenic drivers. Overall, the findings underscore the potential for forest recovery to enhance biodiversity, ecosystem services, carbon storage, and hydrological regulation, while identifying priority areas for riparian conservation and integrated catchment management. Full article

Forests play a central role in climate change mitigation by acting as biogenic carbon reservoirs and providing renewable biomass for energy systems. In Portugal, where fire-prone landscapes and species composition dynamics pose increasing management challenges, understanding the carbon storage potential of forest biomass is crucial for designing effective decarbonization strategies. This study provides a comprehensive characterization of the Portuguese forest and quantifies the biogenic carbon stored in live and dead biomass across the main forest species. Species-specific carbon contents, rather than the conventional 50% assumption widely used in the literature, were applied to National Forest Inventory data, enabling more realistic and representative carbon stock estimates expressed in kilotonnes of CO₂ equivalent. While the approach relies on inventory-based biomass data and literature-derived carbon fractions and is therefore subject to associated uncertainties, it provides an improved representation of species-level carbon storage at the national scale. Results show that Pinus pinaster, Eucalyptus globulus, and Quercus suber together represent the largest share of carbon storage, with approximately 300,000 kilotonnes of CO₂ equivalent retained in living trees. Wood is the dominant carbon pool, but roots and branches also account for a substantial fraction, emphasizing the need to consider both above- and below-ground biomass in carbon accounting. In parallel, a bibliometric analysis based on the systematic evaluation of scientific publications was conducted to characterize the evolution, thematic focus, and geographic distribution of global research on forest-based biogenic carbon. This analysis reveals a rapidly expanding scientific interest in biogenic carbon, particularly since 2020, reflecting its growing relevance in climate change mitigation frameworks. Overall, the results underscore both the strategic importance of Portuguese forests and the alignment of this research with the broader international scientific agenda on forest-based biogenic carbon. Full article

Reliable estimates of regional energy consumption are essential to planning sustainable development and achieving decarbonization; however, this information is still not available for several regions worldwide. In this work, we propose a methodological framework that uses satellite-derived Nighttime Light (NTL) imagery and machine learning to predict regional electricity consumption one year ahead. The methodology follows three stages: First, a Random Forest regression model is used to identify the relationship between NTL data and regional energy consumption. Thereafter, NTL values for the year ahead are forecasted using NTL values from previous years. Lastly, the obtained result is applied to estimate regional energy consumption from predicted NTL values for the year ahead. The country of Mexico is considered a case study to apply and validate this methodology, reproducing spatial consumption patterns with high correlation to official data ($R^2>0.85)$, thus confirming the success of this proposal. The proposed methodology demonstrates how energy demand can be estimated, even in areas of scarce information, providing a transparent and replicable approach for energy monitoring in data-limited regions. Full article

Urban flooding has become a critical environmental challenge under global climate change and rapid urbanization. This study develops a multi-indicator hazard mechanism framework for flood hazard assessment in Rizhao, a coastal city in China, by integrating three fundamental hydrological processes: runoff generation, flow convergence, and drainage. Based on geospatial data—including DEM, road networks, land cover, and soil characteristics—six key indicators were evaluated using the TOPSIS method: runoff curve number, impervious surface percentage, topographic wetness index, time of concentration, pipeline density, and distance to rivers. The results show that extreme-hazard zones, covering 6.41% of the central urban area, are primarily clustered in northern sectors, where flood susceptibility is driven by the synergistic effects of high imperviousness, short concentration time, and inadequate drainage infrastructure. Independent validation using historical flood records confirmed the model’s reliability, with 83.72% of documented waterlogging points located in predicted high-hazard zones and an AUC value of 0.737 indicating good discriminatory performance. Based on spatial hazard patterns and causal mechanisms, an integrated mitigation strategy system of “source reduction, process regulation, and terminal enhancement” is proposed. This strategy provides practical guidance for pipeline rehabilitation and sponge city implementation in Rizhao’s resilience planning, while the developed hazard mechanism framework of “runoff–convergence–drainage” provides a transferable methodology for flood hazard assessment in large-scale urban environments. Full article

The use of wireless sensor networks (WSNs) enables multidimensional and high-precision forest environment monitoring around the clock. However, the limited energy supply of sensor nodes using solely batteries is insufficient to support long-term data collection. Furthermore, since the complex terrain, dense vegetation, and variable weather in forests present unique challenges, relying on a single energy source is insufficient to ensure a stable energy supply for sensor nodes. Combining multiple energy sources is a promising way which has not been well studied. In this paper, to effectively utilize multiple energy sources, we propose a novel dynamic clustering routing protocol which considers the inherent diversity and intermittency of energy sources of the WSN in the forest. First, to address the inconsistency in residual energy caused by uneven energy harvesting among sensor nodes, a cluster head selection weight function is developed, and a dynamic weight-based cluster head election algorithm is proposed. This mechanism effectively prevents low-energy nodes from being selected as cluster heads, thereby maximizing the utilization of harvested energy. Second, a Q-learning-based adaptive hybrid transmission scheme is introduced, integrating both single-hop and multi-hop communication. The scheme dynamically optimizes intra-cluster transmission paths based on the current network state, reducing energy consumption during data transmission. The simulation results show that the proposed routing algorithm significantly outperforms existing methods in total network energy consumption, network lifetime, and energy balance. These advantages make it particularly suitable for forest environments characterized by strong fluctuations in harvested energy. In summary, this work provides an energy-efficient and adaptive routing solution suitable for forest environments with fluctuating energy availability. Full article

Modern data centers are becoming increasingly energy-intensive as AI workloads, hyperscale architectures, and high-power processors push power demand to unprecedented levels. This work examines the sources of rising energy consumption, including evolving IT load dynamics, variability, and the limitations of legacy AC-based power-delivery architectures. These challenges amplify the environmental impact of data centers and highlight their growing influence on global electricity systems. The paper analyzes why conventional grid-tied designs are insufficient for meeting future efficiency, flexibility, and sustainability requirements and surveys emerging solutions centered on DC microgrids, high-voltage DC distribution, and advanced wide-bandgap power electronics. The review further discusses the technical enablers that allow data centers to integrate renewable energy and energy storage more effectively, including simplified conversion chains, coordinated control hierarchies, and demand-aware workload management. Through documented strategies such as on-site renewable deployment, off-site procurement, hybrid energy systems, and flexible demand shaping, the study shows how data centers are increasingly positioned not only as major energy consumers but also as key catalysts for accelerating renewable-energy adoption. Overall, the findings illustrate how the evolving power architectures of large-scale data centers can drive innovation and growth across the renewable energy sector. Full article

Eco-cities have become global initiatives in recent years. This paper aims to discuss the construction, evolution and future of eco-city movements in China, especially in areas with abundant ecological resources. Extant literature emphasizes that sustainable development is the purpose of an eco-city. However, in the spatial practice of ecological modernization, many European and American countries develop ecological construction at a slower pace, resulting in sustainable ecological outcomes. Those countries developed ecological practices at a smaller scale, aiming to achieve green towns with zero carbon emission. In contrast, the construction of China’s eco-cities typically involves building new cities in outer suburbs with a larger scale and faster speed. This has led to the rapid construction of so-called ecological cities without sustainable development. In this context, this paper starts from the perspective of political economy and conducts qualitative research on the Shanghai Dongtan Eco-city as a case study. It analyzes the motivation and practical measures of different actors by examining the planning, design and construction process of Dongtan Eco-city during 1998–2024. The results suggest that gaining national political priority through the intervention of international actors and foreign investment is the key to the local pilot ecological city project. This paper further analyzes the differences between the planning concept and the actual practice of Dongtan Eco-city, critically discussing the “Eco-city as the enclave of ecological technology.” This is driven by the integration of eco-city construction and the local government performance appraisal system. Consequently, the pursuit of economic returns redirected Dongtan’s sustainability experiment into a form of green-branded retirement real-estate development between 1998 and 2012. From 2012 to 2024, Chongming’s development model continued to evolve, as the project was reframed from a real-estate-led eco-city paradigm toward an “ecological island” agenda articulated in the language of sustainable development. Full article

Agro-industrial facilities host processes and products that are highly sensitive to thermal fluctuations. Given the projected increase in air temperatures in tropical regions due to climate change, improving indoor thermal conditions in these facilities has become critically important. Conventional cooling systems are widely used to maintain adequate indoor temperatures; however, they are associated with high energy consumption. In this context, Ground Source Heat Pump (GSHP) technology emerges as a promising alternative to reduce cooling loads by exchanging heat with the ground. This study evaluates the reductions in cooling energy consumption and the return on investment of a GSHP system integrated with conventional cooling system, considering a prototype agro-industrial room located in two ecotones of the Brazilian Midwest: the Amazon Forest (AF) and Brazilian Savanna (BS). Building energy simulations were performed using EnergyPlus software v. 9 under current climate conditions and climate change scenarios for 2050 and 2080. Initially, the prototype room was conditioned using a conventional HVAC system; subsequently, a GSHP system was integrated to enhance energy efficiency and reduce energy demand. Under current conditions, cooling energy demand in the BS and AF ecotones is projected to increase by 16.5% and 18.3% by 2050, and by 24.5% and 23.5% by 2080, respectively. The payback analysis indicates that the average return on investment improves under future climate scenarios, decreasing from 14.5 years under current conditions to 10.13 years in 2050 and 9.86 years in 2080. The findings contribute to understanding the thermal resilience and economic feasibility of ground-coupled heat exchangers as a sustainable strategy for mitigating climate change impacts in the agro-industrial sector. Full article

Biogas and methane generated from the anaerobic digestion (AD) of organic waste present a highly effective alternative to fossil fuels. The study assessed using dragon fruit peel (DFP) as a co-substrate to enhance chicken manure (CM) biodegradability and stabilize the AD process for methane during co-digestion. The biochemical methane potential assays were conducted at mono-controls (CM and DFP) and co-digestion at CM-75:DFP-25, CM-50:DFP-50, and CM-25:DFP-75. Compared to the controls, mono-digestion produced 103.3 mL/g of volatile solids (VSs) of CM and 34.6 mL/g VS of DFP, while all treatment groups of co-digestion exhibited an increase in methane production. The highest yield was 180.3 mL/g VS at CM-25:DFP-75 (74.6% and 421.1% increase relative to mono-digestions of CM and DFP, respectively), followed by 148.3 mL/g VS at CM-50:DFP-50 (43.6% higher than CM) and 116.7 mL/g VS at CM-75:DFP-25 (13% higher than CM). Process stability at the optimal DFP co-substrate ratio (CM-25:DFP-75) was confirmed by total volatile fatty acid (VFA) conversion, as below 0.5 g/L VFAs were observed at the end of incubation, indicating highly acceptable performance. The relative abundance of Bacteroidetes and Bacillota in the treatment groups was higher as compared to the control reactors, correlating with enhanced substrate hydrolysis and VFA production. Moreover, the enrichment of acetoclastic methanogens Methanosarcina and Methanosaeta in co-digesters at CM-25:DFP-75 was associated with the efficient degradation of acetic acid and propionic acid, which aligns with the observed increase in methane yield. The study enhances the understanding of DFP as a co-substrate for optimizing methane recovery from AD of CM. Full article

The reinforcement of high-water-content, low-permeability soft soils presents a critical challenge in marine and coastal engineering. While electroosmotic dewatering is a promising technique, its widespread application is often hindered by issues such as high energy consumption and limited strength gain. However, the specific mechanisms by which marine-derived biopolymers modify soil properties and microstructure to enhance electroosmotic efficiency and significantly improve the post-treatment bearing capacity remain insufficiently understood. To address this gap, this study investigates the use of Sodium Alginate (SA) to enhance the electroosmotic dewatering performance of coastal soft soil. Laboratory experiments were conducted using carbon felt electrodes with varying SA mass fractions (0.0%, 0.2%, 0.5%, and 1.0%). The study integrated macroscopic monitoring with Scanning Electron Microscopy (SEM) to evaluate the electroosmotic efficiency and mechanical property evolution. The results demonstrate that the cross-linked structure of SA gel effectively bridges soil particles and fills inter-granular pores, significantly increasing the liquid limit (from 32.34% to 49.15% at 1.0% SA) and mitigating soil cracking. This microstructural alteration enhanced electrical conductivity and accelerated drainage; the average water content reduction increased from 12.78% (0.0% SA) to 20.86% (1.0% SA). Notably, the 0.5% SA treatment improved the average bearing capacity to approximately 86 kPa (about 7 times that of 0.0% SA) with only a 21% increase in the energy consumption coefficient. This study confirms that utilizing SA for electroosmotic reinforcement effectively modifies soil properties to provide a marine solution for coastal soft soil foundation treatment. Full article

This study presents the first comprehensive techno-economic assessment of wind-based green hydrogen production across Senegal, a country highly dependent on fossil fuel imports. Using a novel integrated approach combining 30 years of ERA5 reanalysis data (1993–2023), turbine performance modeling and electrolyzer comparison, it fills an important gap for renewable hydrogen development in West Africa. Wind resources were analyzed at multiple altitudes, revealing strong potential in both coastal and northeastern regions, particularly during the dry season, with higher wind speeds at higher turbine heights. Four turbines (Vestas_150, Goldwind_155, Vestas_126 and Nordex_N100) and two electrolyzer types (alkaline and PEM) were evaluated. The alkaline system performed best. Vestas_150 and Goldwind_155 achieved the highest hydrogen yields of 241 and 183 tons/year and CO₂ reductions of 2951 and 2241 tons/year, generating carbon credits of 0.118 M$ and 0.089 M$, respectively. Their levelized cost of electricity remained low (0.042 and 0.039 $/kWh), while smaller turbines showed higher costs. Vestas_150 also had the shortest payback period of 2.16 years, making it the most competitive option. Sensitivity analyses showed that longer system lifespans and high-performance turbines significantly reduce the levelized cost of hydrogen. Priority investment zones include Saint-Louis, Matam, Louga and Tambacounda, with levelized cost of hydrogen values as low as 3.4 $/kg. Full article

Trajectory planning for intelligent connected vehicles (ICVs) must simultaneously address safety, efficiency, and environmental impact to align with sustainable development goals. This paper proposes a novel hierarchical trajectory planning framework, designed for intelligent connected vehicles (ICVs) that integrates a semantic corridor with a spatiotemporal potential field. First, a spatiotemporal safety corridor, enhanced with semantic labels (e.g., low-carbon zones and recommended speeds), delineates the feasible driving region. Subsequently, a multi-objective sampling optimization method generates candidate trajectories that balance safety, comfort and energy consumption. The optimal candidate is refined using a spatiotemporal potential field, which dynamically integrates obstacle predictions and sustainability incentives to achieve smooth and eco-friendly navigation. Comprehensive simulations in typical urban scenarios demonstrate that the proposed method reduces energy consumption by up to 8.43% while maintaining safety and a high level of comfort, compared with benchmark methods. Furthermore, the method’s practical efficacy is validated using real-world vehicle data, showing that the planned trajectories closely align with naturalistic driving behavior and demonstrate safe, smooth, and intelligent behaviors in complex lane-changing scenarios. The validation using 113 real-world truck lane-changing cases demonstrates high consistency with naturalistic driving behavior. These results highlight the framework’s potential to advance sustainable intelligent transportation systems by harmonizing safety, comfort, efficiency, and environmental objectives. Full article

As the climate crisis intensifies, the importance of carbon mitigation policies has become increasingly prominent. Rural regions, serving as one of China’s major carbon emission sources, are poised to become key focus regions for emission reduction. However, significant disparities in rural development levels and carbon emissions across China’s regions necessitate tailored energy sustainability and carbon mitigation strategies. Notably, advanced and emerging/developing nations exhibit substantial differences in research priorities and practical pathways, offering multifaceted insights for China’s rural carbon emission research. Adopting a hybrid bibliometric and narrative approach, the study retrieves data from the Web of Science, applies CiteSpace for bibliometric visualization, and synthesizes thematic developments in the international literature through a narrative analysis, with a discussion of the implications for China. The findings reveal distinct trajectories: over the past 25 years, advanced countries have shifted their research focus from air quality improvement to low-carbon mitigation, while emerging and developing countries have transitioned from energy demand toward air quality enhancement, with emerging momentum toward low-carbon strategies. By reviewing 95 relevant articles, this study summarizes the differences between the two in terms of their main lines of research. Building on these differences, this study proposes targeted research priorities for advanced and emerging/developing regions of China. Full article

3D concrete printing (3DCP) is an emerging intelligent construction technology that enables the direct transformation of digital models into physical components, thereby facilitating the precise fabrication of complex geometries. This study investigates the anisotropic mechanical properties and construction applicability of low-carbon 3D printed concrete for reusable formwork systems. Axial compression, flexural, and splitting tensile tests were conducted to examine mechanical anisotropy, and the effects of steel slag and iron tailings replacement levels on mechanical performance were evaluated. Carbon emission analysis was also performed. Using the coefficient-of-variation TOPSIS method, an optimal printable low-carbon mixture was identified, comprising 30% steel slag, 40% iron tailings sand, and 0.3% fibre content, balancing both mechanical performance and environmental benefits. To address the challenges associated with printing large monolithic formwork units, such as excessive weight and demoulding difficulties, three connection strategies for curved wall modular reusable formwork were designed. Finite element analyses were conducted to assess the strength and stiffness of each strategy, and an optimized connection configuration was proposed. The findings demonstrate the feasibility of accurately fabricating complex architectural components using low-carbon 3D printed concrete, providing theoretical and practical support for the industrialized production of large-scale, geometrically complex structures. Full article

The rapid growth of electric vehicles (EVs) has created new challenges for the coordinated low-carbon operation of transportation and power systems. To address this issue, this paper proposes an adaptive rolling-horizon dynamic user equilibrium (DUE) optimization framework for the low-carbon operation of coupled transportation–power systems. The framework integrates transportation, power, and environmental dimensions into a unified objective. On the transportation side, a DUE-based traffic assignment formulation captures both road travel times and station-level queuing dynamics, providing a realistic representation of EV user behavior. This DUE-based traffic assignment model is coupled with an optimal AC power flow formulation to ensure grid feasibility and quantify network losses. To internalize environmental costs, a carbon emission flow module propagates generator-specific carbon intensities to charging stations, aligning charging decisions with their true emission sources. These components are coordinated within a rolling-horizon method in which the prediction window adapts its length to the variability of demand and renewable forecasts. The proposed method allows longer horizons to improve foresight in stable conditions and shorter ones to maintain robustness under volatility. Numerical case studies demonstrate the effectiveness and robustness of the proposed framework and its potential to support low-carbon, high-efficiency operation of coupled transportation–power systems. Full article

In order to study the impacts of the carbon-green certificate trading mechanism and the fluctuation of feed-in tariffs on the low-carbon and economic aspects of the investment and operation of the integrated energy system, and to transform the system carbon emission into a low-carbon economic indicator, a two-layer capacity optimization allocation model is established with the objectives of the investment, operation, and maintenance cost and the operation cost, respectively. For the source-load uncertainty, the scenario reduction theory based on Monte Carlo simulation and Wasserstein distance is used to obtain the per-unit value of wind and photovoltaic output, and the K-means clustering method is used to obtain the typical day of electric-heat-cold multi-energy load. Based on the geometric Brownian motion in finance to simulate the feed-in tariffs under different volatilities, the multidimensional analysis scenarios are constructed according to different combinations of carbon emission reduction policies and tariff volatilities. The model is solved using the non-dominated sorting genetic algorithm (NSGA-II) with mixed integer linear programming (MILP) method. Case study results show that under the optimal scenario considering policy interaction and price volatility (δ = 1.0), the total annual operating cost is reduced by approximately 17.9% (from 2.80 million CNY to 2.30 million CNY) compared to the baseline with no carbon policy. The levelized cost of the energy system reaches 0.2042 CNY/kWh, and carbon-green certificate trading synergies contribute about 70% of the operational cost reduction. The findings demonstrate that carbon reduction policies and electricity price volatility significantly affect system configuration and operational economy, providing a new perspective and decision-making basis for integrated energy system planning. Full article

The construction sector is progressively prioritizing environmental norms owing to its substantial role in carbon emissions from clinker manufacture. Industrial waste materials are increasingly used as alternative constituents in cement-based systems, garnering interest as a sustainable strategy. Ceramic waste powder (CWP), produced in substantial quantities with enduring properties, offers a viable alternative. Nonetheless, its elevated water absorption presents issues, requiring modification procedures such as hydrophobization and the use of nanosilica to enhance performance. This study assessed CWP in both raw and modified forms (ground and hydrophobized) as a partial aggregate replacement in concrete. A silane-derived chemical was employed for hydrophobization, with varying amounts of nanosilica. Recent mortar testing encompassed setting time, flow, and density. Durability was evaluated using capillary water absorption, and flexural and compressive strengths were quantified at 2, 7, and 28 days. Mineralogical and microstructural investigations were conducted utilizing XRD and FTIR to monitor hydration phases and reaction processes. Results indicated that unmodified CWP containing up to 1% (wt) nanosilica enhanced mechanical strength; however, elevated nanosilica concentrations diminished early strength. Hydrophobized CWP samples demonstrated improved early strength with nanosilica levels up to 0.5% (wt), but strength diminished at elevated concentrations. Microstructural analysis confirmed reduced portlandite levels and increased C–S–H production, thereby validating the progress of hydration. The regulated and altered application of CWP with nanosilica can improve mechanical performance and durability while promoting ecological sustainability in cement-based systems. Full article

This study examines the relationship between industrial agglomeration and carbon emissions in China’s forestry industry, using panel data from 30 provincial-level regions between 2009 and 2020. The industrial agglomeration level is measured by the Location Quotient (LQ), which is calculated based on regional employment shares to reflect the concentration of the forest products industry. This study finds that LQ exhibits a multiplicative effect—meaning that its influence on carbon emissions amplifies through interactive mechanisms of scale, technology diffusion, and spatial concentration. Four carbon indicators—carbon emissions from export products, carbon emission intensity, energy intensity, and energy structure cleanliness—are analyzed. Employing a threshold regression model, the study identifies nonlinear effects of agglomeration on carbon outcomes. The estimated threshold value (LQ = 0.7122) divides the process into three stages: (1) an embryonic stage (LQ < 0.7122) with rising emissions and declining efficiency; (2) a growth stage (around LQ ≈ 0.7122) with simultaneous increases in emissions and efficiency; and (3) a mature stage (LQ > 0.7122) where emissions decline as efficiency improves. These results reveal that the environmental effects of forestry industrial agglomeration evolve nonlinearly across development stages. Full article

This literature review examined the relationship between energy drink consumption and cardiovascular health in young people. Following PRISMA 2020, we searched Scopus for articles published from 2020 to 2025 and included 33 original studies after screening 133 records. Evidence from observational, clinical, and experimental research was synthesized into six themes: youth consumption; direct cardiovascular outcomes; composition and toxicity; animal or cellular experiments; perceptions and habits; and occupational or sociodemographic factors. Across studies, habitual intake was linked to acute blood-pressure rises, arrhythmias, endothelial dysfunction, and metabolic disturbances, sometimes within 24 h of a single can. Risks were amplified by high caffeine and taurine doses and by co-use with alcohol or intense exercise. Adolescents and young adults were most vulnerable, due to heightened sympathetic responses, frequent use under academic or work stress, and limited risk perception. Authors highlighted five actions: longitudinal research; tighter ingredient monitoring and transparent labeling; consumer education; protection of vulnerable groups; and clinical guidance for responsible use. These results were observed across regions and study designs. Overall, the findings indicate that unregulated energy-drink consumption is a preventable cardiovascular risk in youth, justifying the use of coordinated public-health measures, including curriculum-based education, marketing restrictions, ingredient oversight, and clinical screening to mitigate harm. Full article

Global CO₂ concentrations are gradually increasing, and forests, as the main terrestrial carbon pool, are attracting growing attention in mitigating climate change. However, the impacts of forest types, species diversity, structural diversity, and environmental factors on the carbon sequestration mechanisms of subtropical forests remain unclear. This study established 45 forest plots (20 m × 20 m) in Lishui City, aiming to investigate the relationships between forest diversity, environmental factors, and carbon storage of subtropical forests among different forest types. Results showed that coniferous forests had the lowest species diversity (0.86), which exhibited extremely significant differences from broad-leaved forests (1.47, p < 0.01) and coniferous broad-leaved mixed forests (1.58, p < 0.01). The carbon storage of broad-leaved forests was 97.50 t·ha⁻¹, which was higher than that of coniferous broad-leaved mixed forests (77.08 t·ha⁻¹) and coniferous forests (75.57 t·ha⁻¹). The carbon storage of coniferous forests was significantly positively affected by species diversity (p < 0.05). Tree height was the most significant structural diversity factor affecting forest carbon storage (p < 0.05). The results of the structural equation model (SEM) showed that the proportion of broad-leaved trees in forests and structural diversity had a significant positive effect on carbon storage (p < 0.01). Species diversity had a non-linear relationship with carbon storage. The ecological niche complementarity effect and selection effect interacted with changes in species diversity. When the species diversity was lower than 1.12 (Shannon–Wiener index), the ecological niche complementarity effect dominated and promoted carbon sequestration; when it was above this threshold, the selection effect dominated and weakened carbon sequestration. This study recommends prioritizing the planting of broad-leaved tree species during afforestation and paying attention to the current status of forest diversity. Full article

Developing efficient and durable oxygen reduction reaction (ORR) catalysts is crucial for advancing fuel cell technology and sustainable energy conversion. In this study, a scalable strategy was employed to synthesize ZIF-derived nitrogen-sulfur co-doped carbon nanosheets embedded with in situ generated ZnS and Co₉S₈ nanoparticles. The synergistic effect of heteroatom doping and metal sulfide modification effectively modulated the electronic structure, optimized charge transfer pathways, and enhanced structural stability. The optimized catalyst exhibited a half-wave potential of 0.83 V vs. RHE, close to that of commercial 20 wt% Pt/C (0.85 V), excellent 4e⁻ ORR selectivity, and exceptional stability, with only a ~15 mV degradation after 10,000 cycles. These results demonstrate that the combination of nitrogen sulfur co-doping and in situ metal sulfide addition pro-vides an effective approach for designing highly active and durable non-precious metal catalysts for the ORR. This synthetic concept provides practical guidance for the scalable preparation of multifunctional nanomaterial-based catalysts for electrochemical energy applications. Full article

Symplocos is an ecologically important genus that plays vital roles in subtropical evergreen broad-leaved mountain forests, including contributing to nutrient cycling, providing shelter and habitats for various organisms, and supporting overall plant diversity across East and Southeast Asia. Many species exhibit high levels of endemism and sensitivity to environmental change. China, with its wide range of ecosystems and climatic zones, is home to 18 endemic Symplocos species. Studies revealed that global warming is driving shifts in species diversity, particularly in mountains. Our study explores the current and projected richness patterns of endemic Symplocos species in China under climate change scenarios, emphasizing the implications for conservation planning. We applied stacked species distribution models (SSDMs), using key bioclimatic and environmental variables to predict current and future habitat suitability for endemic Symplocos species, evaluated model performance through multiple accuracy metrics, and generated ensemble projections to assess richness patterns under climate change scenarios. To assess the spatial configuration and fragmentation patterns of the endemic species richness under current and future climate scenarios, landscape metrics were calculated based on classified richness maps. The produced models demonstrated high accuracy with AUC > 0.9 and TSS > 0.75, highlighting the critical role of bioclimatic variables, particularly precipitation and temperature, in shaping endemic Symplocos distribution. Our analysis identifies the current hotspots of Symplocos endemism along southeastern China, particularly in Zhejiang, Fujian, Jiangxi, Hunan, southern Anhui, and northern Guangdong and Guangxi. These areas are at high risk, with up to 35% of endemic Symplocos species richness predicted to be lost over the next 60 years due to climate change. The study predicts a high decrease in endemic Symplocos species richness, especially in South China (e.g., Fujian, Guangdong, Guizhou, Yunnan, southern Shaanxi), and mid-level decreases in East China (e.g., Heilongjiang, Jilin, eastern Inner Mongolia, Liaoning). Conversely, potential increases in endemic Symplocos species richness are projected in northern and western Xinjiang, western Tibet, and parts of eastern Sichuan, Guangxi, Hunan, Hebei, and Anhui, suggesting these regions may serve as future refugia for endemic Symplocos species. The analysis of the landscape structure and configuration revealed relatively minor but notable variations in the spatial structure of endemic Symplocos richness patterns under current and future climate scenarios. However, under the SSP585 scenario by 2080, the medium richness class showed a more pronounced decrease in aggregation index and increase in number of patches relative to other richness classes, suggesting that higher emissions may drive fragmentation of moderately rich areas, potentially isolating populations of Symplocos. These structural changes suggest a potential reduction in habitat quality and connectivity, posing significant risks to the persistence of endemic Symplocos populations, which underscores the urgent need for targeted smart-climate conservation strategies that prioritize both current hotspots and potential future refugia to enhance the resilience of endemic Symplocos forests and their ecosystems in the face of climate change. Full article

The construction industry significantly contributes to global sustainability challenges, producing 30–40 percent of global carbon dioxide emissions and consuming large amounts of natural resources. Pervious concrete has emerged as a sustainable alternative to conventional pavements due to its ability to promote stormwater infiltration and groundwater recharge. However, the absence of fine aggregates creates a highly porous structure that results in reduced compressive strength, limiting its broader structural use. Determining compressive strength traditionally requires destructive laboratory testing of concrete specimens, which demands considerable material, energy, and curing time, often up to 28 days—before results can be obtained. This makes iterative mix design and optimization both slow and resource intensive. To address this practical limitation, this study applies Machine Learning (ML) as a rapid, preliminary estimation tool capable of providing early predictions of compressive strength based on mix composition and curing parameters. Rather than replacing laboratory testing, the developed ML models serve as supportive decision-making tools, enabling engineers to assess potential strength outcomes before casting and curing physical specimens. This can reduce the number of trial batches produced, lower material consumption, and minimize the environmental footprint associated with repeated destructive testing. Multiple ML algorithms were trained and evaluated using data from existing literature and validated through laboratory testing. The results indicate that ML can provide reliable preliminary strength estimates, offering a faster and more resource-efficient approach to guiding mix design adjustments. By reducing the reliance on repeated 28-day test cycles, the integration of ML into previous concrete research supports more sustainable, cost-effective, and time-efficient material development practices. Full article

With the development of avionics systems towards high integration and high power density, the thermal management of electronic equipment in ATR chassis is facing severe challenges, and the extreme aviation environment further exacerbates the difficulty of heat dissipation. Traditional fixed control strategies suffer from problems such as energy consumption, redundancy, and local overheating, whereas single-model predictive control (MPC) is prone to local optimization. This paper proposes a thermal management optimization scheme based on the ACO-MPC hybrid framework: Firstly, a compact thermal model integrating aviation environmental parameters, such as high-altitude, low-pressure conditions and vibration impacts, is constructed. The balanced truncation method is adopted for model order reduction in this study. By retaining the key thermodynamic characteristics of the system, the original three-dimensional thermal model containing more than 800 nodes is simplified to 25 core nodes, which ensures simulation accuracy while improving computational efficiency; Secondly, the ACO-MPC hybrid framework is designed, which uses Ant Colony Optimization (ACO) for global optimization to provide optimized initial values for Model Predictive Control (MPC), breaking through the local optimization limitation of MPC and realizing the collaboration of “global optimization—dynamic control”; Finally, the effectiveness of the framework is verified in three typical aviation scenarios. The results show that compared with traditional methods, this framework has significantly improved heat dissipation efficiency, energy consumption control, and temperature stability, and has strong adaptability to environmental disturbances, which can be migrated to the ATR chassis of different specifications. Full article

This study examines the impacts of climate change on agriculture in the Prefecture of Imathia and highlights the role of agricultural advisory services. The study evaluates existing adaptation measures and demonstrates the importance of agricultural advisory services. The methodology is based on a combined approach. A literature review was conducted, followed by the primary collection of data through structured questionnaires administered to a sample of 78 farmers in Imathia Prefecture. It was found that producers with access to advisory services more readily adopt innovative services and sustainable practices, thus contributing to reducing the impacts of climate change on their productivity. Full article

Spiking Neural Networks (SNNs) offer promising low-power alternatives to conventional neural models but often incur considerable redundancy in parameters and computations. To address these inefficiencies, we propose SpikingDynamicMaskFormer (SDMFormer), a novel framework that integrates dynamic masking and lightweight position encoding into a spike-based Transformer backbone. Specifically, our Dynamic Mask Encoder Block adaptively suppresses ineffective spike channels by learning mask parameters, reducing parameter count to 37.93–42.69% of the original Spikformer. Simultaneously, a redesigned lightweight position embedding replaces resource-intensive relative position convolutions, further lowering complexity. Experiments on three neuromorphic vision datasets—DVS128, CIFAR10-DVS and N-Caltech101—demonstrate that SDMFormer cuts energy consumption by 42.79–50.13% relative to Spikformer while maintaining or slightly surpassing accuracy. Moreover, compared with recent leading works, SDMFormer achieves competitive accuracy with substantially fewer parameters and delivers higher inference efficiency, reaching up to 196.20 img/s on CIFAR10-DVS. These results highlight the efficacy of combining event-driven attention with structured pruning and parameter-efficient position encoding, indicating the potential of SDMFormer for resource-efficient SNN deployment in low-power applications. Full article

Global warming is modifying precipitation patterns, and hence increasing the hazards of severe and extended rainstorms. Addressing the gap in integrating economic and environmental assessments into urban stormwater management—a key challenge in urban water resource analysis—this study utilizes the analytical hierarchy process (AHP) and SUSTAIN model to identify and evaluate low-impact development (LID) stormwater management strategies, assessing their impacts on runoff volume, peak flow reduction, chemical oxygen demand (COD), and suspended solids (SS) across four planning scenarios under five rainfall recurrence intervals, culminating in a cost–benefit analysis to ascertain the optimal scenario. The reduction rates for COD and SS varied from 41.85% to 87.11% across different scenarios, with Scenario Three (RM03) demonstrating the highest efficacy in pollutant management. (The four labels RM01–RM04 are used throughout the text to represent the four scenarios) Implementing the best plan may result in a reduction of yearly carbon emissions of 189.70 metric tons, with emissions from the operational load of the drainage network and COD pollution treatment potentially decreasing by 2.44% and 2.06%, respectively, indicating an overall annual reduction of 85.46%. This approach not only mitigates urban rainwater and flooding issues but also prevents resource wastage, optimizes resource utilization and benefits, offers a scientific foundation for urban construction and planning, and serves as a reference for sponge city development in other regions. Full article

Freshwater resources are on the verge of depletion due to the rapid increase in population, lifestyle changes, and especially during climate change in Iraq. Therefore, treating domestic wastewater correctly will significantly contribute to keeping the balance of water purity and its usage. To fulfil this, the Sustainable Project Appraisal Routine (SPeAR^TM) program, which leverages Multi-Criteria Decision Analysis with operational sustainability indicators, is used to compare the relative sustainability performance of the novel Modified Sequencing Batch Reactor by visualising the results of the degree of its sustainability compared to the Moving Bed Biofilm Reactor and the conventional Sequencing Batch Reactor system. Although selecting the most sustainable treatment depends on specific treatment goals, available resources, site conditions, and stakeholder preferences, this study considers the equal weighting of sustainability assessment across environmental, social, and economic indices to inform sustainable decision making. The results show that integrating both conventional treatment plants into the novel modified treatment plant demonstrates a comparatively more balanced and stable sustainability performance under the assessed operational conditions. As at a design capacity of 100 m³·day⁻¹, the MSBR achieved a higher organic and nutrient removal efficiencies relative to the conventional SBR and MBBR systems while operating at an intermediate energy demand (187.7 kWh·day⁻¹) compared with the SBR (121.7 kWh·day⁻¹) and the MBBR (211.8 kWh·day⁻¹). Thus, it can compensate for the weaknesses and combines the strengths of the sustainability indices of the two systems, which supports the Modified Sequencing Batch Reactor as a comparatively favourable option for wastewater treatment within the assessed sustainability framework. Full article

Focusing on Hebei Province in China, the work investigated the impact of implicit land use transition (ILUT) on land use carbon emissions (LUCEs) for dual carbon goals. A county-level evaluation system and a measurement model were constructed to explore ILUT and carbon emissions’ spatiotemporal progression, respectively. The optimal spatial econometric model was selected by employing different testing methods to elucidate how ILUT affected carbon emissions. LUCEs increased from 49.7964 million tons (2000) to 107.401 million tons (2015) and dropped to 92.2173 million tons by 2020. The overall exhibited an inverted V-shape. Values were generally higher in the southeast and lower in the northwest. ILUT index across counties increased from 2000 to 2020, with polarization of implicit indices intensified. Spatial distribution showed that the southeastern area exhibited notably higher values compared to the northwestern parts. Significant positive spatial correlation existed between ILUT and carbon emissions within the county, while a significant negative spatial correlation was observed with carbon emissions in neighboring counties. These findings provide scientific support for formulating differentiated land use policies and optimizing carbon emission control strategies in Hebei Province, holding significant practical value for regional dual carbon targets. Full article

Industrial heat exchangers are widely used in industries such as petrochemicals, energy and power, and food processing, making them one of the most important pieces of heat and mass transfer equipment in industry. During operation, a layer of fouling often adheres to the heat transfer surfaces, which reduces the heat transfer coefficient of the equipment and increases the thermal resistance of the surfaces. Additionally, fouling can corrode the material of the heat transfer surfaces, compromise their integrity, and even lead to perforations and leaks, severely impacting equipment operation and safety while increasing energy consumption and costs for enterprises. The application of anti-fouling coatings on surfaces is a key technology to address fouling on heat transfer surfaces. This paper focuses on introducing major types of anti-fouling coatings, including polymer-based coatings, “metal material + X”-type coatings, “inorganic material + X”-type coatings, carbon-based material coatings, and other varieties. It analyzes and discusses the current research status and hotspots for these coatings, elaborates on their future development directions, and proposes ideas for developing new coating systems. On the other hand, this paper summarizes the current research on the main factors—surface roughness, surface free energy, surface wettability, and coating corrosion resistance—that affect the anti-fouling performance of coatings. It outlines the research hotspots and challenges in understanding the influence of these three factors and suggests that future research should consider the synergistic effects of multiple factors, providing valuable insights for further studies in the field of anti-fouling coatings. Full article

The agroecological (AE) transition of agri-food systems can help address climate change impacts in Tunisia, including reduced local food production and high import dependency, but it requires understanding consumer behavior toward eco-friendly food products. Thus, a survey of 521 Tunisian consumers was conducted to assess environmental awareness and willingness to pay (WTP) for food produced under AE practices. Principal Component Analysis (PCA) indicated that sustainable consumption is mainly influenced by knowledge of AE practices, which is stronger among consumers with higher education and income. However, WTP for sustainable products remains low, making it essential to develop marketing strategies that target distinct demographic groups, improve product labeling, and enhance environmental education. Full article

This study investigates the role of biogas and biomethane in accelerating the decarbonization of district heating systems in Europe. A structured literature review combined with two representative case studies evaluate technological, economic, and environmental performance across different system scales. The Meppel optimization model developed for the Netherlands and the large-scale Backbone energy system modelling framework for Finland are compared to identify methodological synergies and operational insights for integrating bioenergy into heating networks. The results show that biogas-based combined heat and power systems can reduce carbon dioxide emissions by more than 70 percent compared with fossil-based alternatives and significantly improve local energy security, especially when coupled with heat pumps and thermal storage. Large-scale modelling further demonstrates that biomethane and bioenergy resources provide valuable system flexibility, facilitating sector coupling and supporting the balancing of variable renewable electricity production. This study’s main contribution is an integrated comparative assessment at two different scales (local and regional), linking operational data, modelling, and performance results to determine how biogas and biomethane can optimize the energy system in the short and long term for centralized heat supply. The findings confirm that biogas and biomethane are essential, dispatchable renewable resources capable of supporting scalable, low-carbon, and resilient district heating systems across Europe. Full article

Although the literature is rich in studies of indoor thermal comfort, there is a lack of research on outdoor thermal comfort, despite its importance in response to global warming and the rise of urban heat islands. Physics models addressing spatial (urban energy form, green areas) and temporal (climate variability) factors are urgently needed. This study proposes a useful method for outdoor comfort evaluation at a district scale, based on the energy form of built-up areas and hyperlocal climatic conditions. It enables the determination of distributed Physiological Environmental Temperature values at a district scale, assessing the greenery effect and mutual radiative exchanges. Applied to a case study in Florence, Italy, it integrates multiple measurement techniques. The main results highlight the model’s ability to evaluate outdoor thermal perception through the new identified indicator of Virtual Physiological Environmental Temperature (PET*) spread, ranging from 23.5 to 101.0 °C, specifically referring to the worst climatic conditions inside an urban canyon in relation to different real scenarios. The results confirm the method’s effectiveness as a tool for thermodynamics and planning for the well-being of an urban built-up environment. It offers useful support for sustainability and human-centric design, oriented to UHI mitigation and climate change adaptation strategies. Full article

The frictional resistance of impeller machinery blades such as aircraft engines, gas turbines, and wind turbines has a decisive impact on their efficiency and energy consumption. Inspired by the micro-tooth structure on the surface of shark skin, microstructural drag reduction technology has become a cutting-edge research direction for improving aerodynamic performance and a continuous focus of researchers over the past 20 years. However, the significant difficulty in fabricating microstructures on three-dimensional curved surfaces has led to the limited widespread application of this technology in engineering. Addressing the issue of drag reduction and efficiency improvement for small axial flow fans (local Reynolds number range: (36,327–40,330), this paper employs Design of Experiments (DOE) combined with high-precision numerical simulation to clarify the drag reduction law of bionic microgroove surfaces and determine the dimensions of bionic microstructures on fan blade surfaces. The steady-state calculation uses the standard k-ω model and simpleFoam solver, while the unsteady Large Eddy Simulation (LES) employs the pimpleFoam solver and WALE subgrid-scale model. The dimensionless height (h⁺) and width (s⁺) of microgrooves are in the range of 8.50–29.75, and the micro-grooved structure achieves effective drag reduction. The microstructured surface is fabricated on the suction surface of the blade via a spray coating process, and the dimensions of the microstructures are determined according to the drag reduction law of grooved flat plates. Aerodynamic performance tests indicate that the shaft power consumed by the bionic fan blades during the tests is significantly reduced. The maximum static pressure efficiency of the bionic fan with micro-dimples is increased by 2.33%, while that of the bionic fan with micro-grooves is increased by 3.46%. The fabrication method of the bionic microstructured surface proposed in this paper is expected to promote the engineering application of bionic drag reduction technology. Full article

In January 2025, multiple wildfires erupted across the Los Angeles region, fueled by prolonged dry conditions and intense Santa Ana winds. Southern California has faced increasingly frequent and severe wildfires in recent years, driven by prolonged drought, high temperatures, and the expanding wildland–urban interface. These fires have caused major loss of life, extensive property damage, mass evacuations, and severe air-quality decline in this densely populated, high-risk region. This study integrates passive and active satellite observations to characterize the spatiotemporal and vertical distribution of wildfire emissions and assesses their impact on air quality. TROPOMI (Sentinel-5P) and the recently launched TEMPO geostationary instrument provide hourly high temporal-resolution mapping of trace gases, including nitrogen dioxide (NO₂), carbon monoxide (CO), formaldehyde (HCHO), and aerosols. Vertical column densities of NO₂ and HCHO reached 40 and 25 Pmolec/cm², respectively, representing more than a 250% increase compared to background climatological levels in fire-affected zones. TEMPO’s unique high-frequency observations captured strong diurnal variability and secondary photochemical production, offering unprecedented insights into plume evolution on sub-daily scales. ATLID (EarthCARE) lidar profiling identified smoke layers concentrated between 1 and 3 km altitude, with optical properties characteristic of fresh biomass burning and depolarization ratios indicating mixed particle morphology. Vertical profiling capability was critical for distinguishing transported smoke from boundary-layer pollution and assessing radiative impacts. These findings highlight the value of combined passive–active satellite measurements in capturing wildfire plumes and the need for integrated monitoring as wildfire risk grows under climate change. Full article

This study presents an environmentally sustainable finishing approach for hemp fabrics by combining soy protein isolate (SPI) pretreatment with an in situ infrared (IR)-assisted synthesis of zinc oxide nanoparticles (ZnO NPs). IR heating was employed to reduce energy consumption while promoting efficient nanoparticle formation compared to conventional thermal processing, while SPI acted as a bio-based stabilizer to enable uniform ZnO NP distribution on the fabric surface. Transmission electron microscopy revealed predominantly spherical to polyhedral ZnO NPs with minimal agglomeration, and X-ray diffraction confirmed their characteristic wurtzite crystalline structure. Scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy mapping further verified the homogeneous deposition of ZnO NPs on hemp fibers. The treated fabrics exhibited multifunctional performance, showing significantly enhanced ultraviolet (UV) protection with a UV protection factor (UPF) of 50+ compared with untreated hemp. Antibacterial activity against Staphylococcus aureus and Escherichia coli was confirmed by the AATCC TM147 test, while a quantitative AATCC TM100 assessment demonstrated an excellent antibacterial efficiency of 99.99% bacterial reduction against S. aureus. Additionally, the incorporation of 2 wt% SPI significantly improved fabric hydrophilicity and wettability. Overall, this work demonstrates a green and effective strategy for producing antibacterial and UV-protective hemp textiles. Full article

To address the issue of climate change caused by greenhouse gases, extensive research has been conducted on technologies for separating and capturing carbon dioxide. This study aimed to investigate the internal flow behavior and relative spatial distribution of CO₂-related features inside a vortex tube using the Schlieren method. Due to the presence of numerous components in a typical counter-flow vortex tube that may cause optical refraction along the measurement path, a simplified tube with a single nozzle was designed and manufactured for the experiments. The experiments consisted of CO₂ single-phase flow and air–CO₂ mixture flow tests. Images captured during the experiments were processed using Gaussian filtering and background correction to enhance the visibility of boundary layers and internal flow structures. Based on the pixel intensity values of the processed Schlieren images, relative intensity distributions associated with CO₂-related flow behavior inside the tube were estimated and visualized. The experimental results revealed that, in both CO₂ single-phase and air–CO₂ mixture flows, regions of relatively high Schlieren intensity consistently appeared at specific locations within the tube. These observations indicate that the internal flow structure and relative distribution patterns are sensitive to the local flow features near the nozzle region under the tested conditions. The temporal evolution of the normalized Schlieren pixel intensity and its standard deviation was quantitatively evaluated, in a relative sense, to characterize the development of vortex flow structures under different operating conditions. The proposed visualization and analysis framework provides a systematic qualitative approach, supported by relative quantitative indicators, for investigating vortex-induced flow behavior. This framework may serve as a foundation for future studies that integrate complementary diagnostics and numerical analyses to further explore the vortex-based gas separation mechanism. Full article

In this study, we compare two contrasting years within the 2020–2025 period—one characterized by extreme heat and drought, and another by unusually high precipitation. We used five years of climatic data provided by the Hungarian Meteorological Service (OMSZ), along with vegetation activity indices (NDVI—Normalized Difference Vegetation Index; NDWI—Normalized Difference Water Index) derived from Sentinel-2A satellite imagery. In parallel, during three years of the study period (2020, 2022, and 2025), we collected five phytosociological relevés in each of the five vegetation types subjected to different management regimes. For data analysis, we applied Principal Component Analysis (PCA), Detrended Correspondence Analysis (DCA), and the Additive Main Effects and Multiplicative Interaction (AMMI) model. Vegetation index patterns were compared with the relative water requirements of the constituent plant species. In the ungrazed dry sandy site, climatic fluctuations did not significantly affect vegetation composition and the habitat remained a stable open sandy grassland. Among the four grazed sites, grazing intensity remained unchanged during the study in three cases (N1, N2, and SZ). Thus, vegetation changes observed in these areas can be attributed to climatic factors. Vegetation composition shifted in N1 and N2, whereas no significant change was detected in the drier SZ site. This indicates higher resistance to grazing in SZ, which can therefore be sustainably used as pasture, while the N1–N2 sites responded sensitively to precipitation variability under identical grazing pressure and are better suited for use as meadows. The most pronounced changes occurred at the P site, which had previously functioned as an animal resting area and began regenerating after abandonment in 2022. Vegetation composition shifted markedly within two years, demonstrating that land-use practices exert a stronger influence on sandy grassland vegetation than climatic fluctuations. Overall, the drier habitats were more resilient to both grazing pressure and climatic variability and are suitable for grazing, whereas the moister vegetation types were more sensitive and should preferably be managed as hay meadows. Full article

Fertilization regimes impact the carbon cycle processes in paddy soils. However, the effects of shifting fertilization regimes on the structure of microbial communities and functional genes involved in soil carbon (C)-cycling remain unclear. A long-term field experiment was established with three paired fertilization shift treatments: chemical fertilizer (CF) and CF to normal-rate organic fertilizer (CF-NOM); normal-rate organic fertilizer (NOM) and NOM to CF (NOM-CF); high-rate organic fertilizer (HOM) and HOM to CF (HOM-CF). Metagenomic sequencing and bioinformatics analysis were employed to investigate the effects of fertilization shifts on soil C-cycling microbial community structure, functional genes, and environmental factors. The results showed that compared to CF treatment, CF-NOM significantly increased soil organic carbon (SOC), mineral-associated organic carbon (MAOC), particulate organic carbon (POC), microbial biomass carbon (MBC), dissolved organic carbon (DOC), and the emissions of CO₂ and CH₄ (p < 0.05). The NOM-CF led to significant reductions in MAOC, MBC, DOC, and CO₂ and CH₄ emissions. The HOM-CF shift caused significant decreases in SOC, MAOC, POC, MBC, DOC, and CO₂ and CH₄ emissions. Fertilization shifts had no significant effect on the α-diversity of C-cycling microbial communities (p > 0.05), but β-diversity showed a significant restructuring of community composition. Network analysis indicated that fertilization shifts increased positive microbial correlations while reducing network modularity. C-cycling functional genes responded sensitively to fertilization disturbances, especially key genes in the carbon fixation pathway (cdhDE, cooS). Redundancy analysis indicated that soil bulk density (BD) and POC are key environmental factors regulating functional differences in carbon metabolism, which collectively influenced microbial community structure and functional gene abundance along with other factors. We concluded that the C-cycling process in paddy soil was greatly altered by shifts in fertilization regimes, influenced by microbial diversity, functional genes, and network structure linked to soil characteristics. Full article

Three-dimensional unmanned aerial vehicle (UAV) path planning presents a challenging optimization problem characterized by high dimensionality, strong nonlinearity, and multiple constraints. To address these complexities, this study proposes an Enhanced Protozoan Optimizer (EAPO) by refining the initialization, behavioral decision-making, environmental perception, and population diversity preservation mechanisms of the original Protozoan Optimizer. Specifically: Latin hypercube sampling enriches initial population diversity; a behavior adaptation mechanism based on historical success dynamically adjusts the exploration-exploitation balance; environmental structure modeling using perception fields enhances local exploitation capabilities; an adaptive hibernation-reconstruction strategy boosts global escape ability. Ablation experiment validates the effectiveness of each enhancement module, while exploration-exploitation analysis demonstrates EAPO maintains an optimal balance throughout the optimization process. Comprehensive evaluations using CEC2022 and CEC2020 benchmark datasets, ten real-world engineering design problems, and four drone path planning scenarios of varying scales and complexities further validate its excellent performance. Experimental results demonstrate that EAPO outperforms the baseline APO and twelve advanced optimizers in convergence accuracy, stability, and robustness. In UAV path planning applications, paths generated by EAPO satisfy all constraints and outperform APO-generated paths across multiple path quality evaluation metrics concerning safety, smoothness, and energy consumption. Compared to APO, EAPO achieved average fitness improvements of 14.0%, 4.5%, 8.7%, and 31.42% across the four maps, respectively, fully demonstrating its practical value and formidable capability in tackling complex engineering optimization problems. Full article

Exemplified by EcoStruxure from Schneider and CASICloud, production-capacity-sharing platforms typically operate as two-sided platforms. These platforms contribute to carbon emission reductions while generating revenue through uniform membership fees, fixed service charges, or commission fees applied to one or both sides of the platform. As B2B platforms, however, they must determine whether to offer online payment options to participants on both sides of the market. We employ the theory of two-sided markets and the method of comparative analysis, developing a two-sided market model to investigate: (1) the platform’s optimal payment method selection strategies and (2) how same-side network externalities and user online search costs affect platform performance. We examine two payment modes: M mode (offline payments only) and F mode (combined offline/online payments). The F mode comprises two sub-modes—FF (two-sided users choose to pay offline) and FN (two-sided users choose to pay online)—determined by users’ payment preferences on each side. We conduct pairwise comparisons of the platform’s membership fee, fixed service fee, and online service level between: (i) FF and M modes and (ii) FN and M modes. Our results indicate that the optimal payment method selection varies across market conditions, with each mode demonstrating superior performance under specific market characteristics. The FF mode consistently yields higher profits compared to the M mode. When suppliers’ expected revenues fall below a certain threshold, the FN mode outperforms the M mode in terms of profit generation. Conversely, the M mode becomes preferable above this threshold. Furthermore, the effects of same-side network externalities and search costs vary significantly across different payment modes. Under the M mode and the FF₁ mode, the effects of the same-side network externality on the platform’s membership fee are associated with two-sided users’ online search costs, which are more monotonous. Under the FN₁ and FN₂ modes, both the same-side network externality and two-sided users’ online search costs impact the platform’s optimal strategies monotonously, but they are not always the same in these two modes. Full article

In the 21st century, Urbanization, population growth, and climate change have created significant problems in water resource management. Recent advancements in technologies such as Internet of Things (IoT), Edge Computing (EC), Artificial Intelligence (AI), and Big Data Analytics (BDA) are changing the operations of the water resource management systems. In this study, we present a systematic review, highlighting the contributions of these technologies in water management systems. More specifically, we highlight the IoT and EC water monitoring systems that enable real-time sensing of water quality and consumption. In addition, AI methods for anomaly detection and predictive maintenance are reviewed, focusing on water demand forecasting. BDA methods are also discussed, highlighting their ability to integrate data from different data sources, such as sensors and historical data. Additionally, a discussion is provided of how Water management systems could enhance sustainability, resilience, and efficiency by combining big data, IoT, EC, and AI. Lastly, future directions are outlined regarding how state-of-the-art technologies may further support efficient water resources management. Full article

The global shift towards sustainable energy and stringent climate policies has underscored the need for decarbonizing energy systems, electrifying transportation, and transforming supply chains. In this context, China’s new energy vehicle (NEV) industry, as the largest global producer and consumer of automobiles, is pivotal in advancing energy substitution and achieving carbon reduction goals. This study investigates the energy efficiency and supply chain transformation within China’s NEV sector, leveraging panel data from 12 representative provinces over the period 2017–2023. Employing a robust analytical framework that integrates the DEA-BCC model, Malmquist index, and Tobit regression, the study provides a dynamic and regionally differentiated assessment of NEV industry efficiency. The results reveal significant improvements in total factor energy efficiency, predominantly driven by technological progress. R&D intensity, infrastructure development, and environmental regulation are identified as key enablers of efficiency, while excessive government intervention tends to hinder performance. The findings offer valuable empirical insights and policy recommendations for optimizing China’s NEV industry in the context of energy system transformation and sustainable industrial development. Full article

This study employs a mixed-method approach, including surveys with 498 smallholder farmers as respondents (186 in Mozambique and 312 in Zambia) and focus group discussions, to compare sustainable soil management and livestock feed management practices. This study shows critical gaps in agricultural extension, significant differences were found, with a higher proportion of Zambian farmers receiving training on soil fertility management (42.2% versus 3.2% in Mozambique, p < 0.001) and using locally produced feeds (78.5% versus 1.6%, p < 0.001). Whereas access to weather information was higher in Mozambique (50.5%) than in Zambia (22.8%). The findings show critical gaps in agricultural extension in Mozambique and Zambia in areas under cowpea, oilseed crops, and vegetables (t = 8.375, p < 0.001; t = 4.138, p < 0.001; and t = 3.104, p < 0.002, respectively). We recommend targeted investment in farmer training programs, including feed formulation and context-specific weather information dissemination to enhance climate resilience and food security. Full article

Ancient walnut trees (Juglans regia L.), revered as “cultural heritage in motion,” have coexisted harmoniously with dense clusters of Tibetan traditional villages for centuries. However, accelerating climate change and expanding human activities along the middle reaches of the Yarlung Tsangpo River have increasingly threatened their survival. To quantitatively evaluate the health of these ancient trees and identify the underlying driving mechanisms, this study developed a remote sensing-enhanced Structural Equation Model (SEM) that integrated satellite-derived ecological indices, land-use intensity, and field-measured morphological and physiological indicators. A total of 135 ancient walnut trees from villages such as Gamai in Jiacha County, Tibet, were examined. Key findings: (1) The SEM demonstrated an excellent model–data fit (Minimum Discrepancy Divided by Degrees of Freedom (CMIN/DF) = 1.372, Root Mean Square Error of Approximation (RMSEA) = 0.053, Tucker–Lewis Index (TLI) = 0.956, and Comparative Fit Index (CFI) = 0.962), confirming its robustness. (2) Among the latent variables, overall condition exerted the strongest influence (weight = 0.360), whereas foliage condition contributed least (0.289). (3) Approximately 35.56% of trees were healthy or sub-healthy, while 61.48% showed varying levels of decline. (4) Tree health was jointly shaped by intrinsic and extrinsic factors, with intrinsic drivers exhibiting stronger explanatory power. Externally, human disturbance negatively affected health, whereas ecological quality was positively associated. These results highlight the effectiveness of integrating remote sensing and SEM for ancient tree assessment and underscore the urgent need for long-term monitoring and adaptive conservation strategies to enhance ecological resilience. Full article