Search Results (1,703)

Pan evaporation (PE) is widely used as an indicator of atmospheric evaporative demand and is relevant to irrigation demand and climate-related hydrological changes. Using daily records from 759 meteorological stations across China during 2002–2018, this study investigated the temporal trends, spatial patterns, and climatic controls of PE across seven major climate zones. Multiple decomposition techniques revealed a dominant annual cycle and a pronounced peak in 2018, while a decreasing interannual trend was observed nationwide. Spatial analyses showed a clear north–south contrast, with the strongest declines occurring in northern China. A random forest (RF) model was employed to quantify the contributions of climatic variables, achieving high predictive performance. RF results indicated that the dominant drivers of PE varied substantially across climate zones: sunshine duration (as a proxy for solar radiation) and air temperature mainly controlled PE in humid regions, while wind speed and relative humidity (RH) exerted stronger influences in arid and semi-arid regions. The widespread decline in northern China is consistent with concurrent changes in wind speed and sunshine duration, together with humidity conditions, which modulate evaporative demand at monthly scales. These findings highlight substantial spatial heterogeneity in PE responses to climate forcing and provide insights for drought assessment and water resource management in a warming climate. Full article

This study examines the potential implications of the EU–Mercosur free trade agreement for the Polish horticultural sector, with particular emphasis on sustainability, trade competitiveness, and structural complementarities between the regions. Drawing on production, trade, and demographic data for the EU, Poland, and Mercosur countries, the analysis evaluates the alignment of horticultural supply and demand structures, the degree of intra-industry exchange, and the economic conditions shaping bilateral trade. The research applies the Grubel–Lloyd index and a Poisson Pseudo-Maximum Likelihood (PPML) gravity model to assess the determinants of Poland’s horticultural exports to Mercosur. The results indicate that trade remains predominantly inter-industry, reflecting substantial differences in agricultural specialisation and regulatory frameworks. At the same time, rising income levels in Mercosur, together with selected product-level complementarities, indicate emerging export opportunities for Poland. Poland’s trade with the Southern Common Market remains mainly as inter-industry, with the greatest export potential concentrated in high-value-added processed goods. Divergent sustainability standards, particularly in pesticide use, environmental regulation, and carbon-intensive transport, pose structural challenges that may affect the competitiveness and environmental footprint of expanded trade. Overall, the findings provide evidence that closer integration with Mercosur may support export diversification, but requires careful alignment with the EU’s sustainability objectives to ensure resilient and environmentally responsible development of the horticultural sector. Full article

This study presents an experimental 3E (energy–exergy–environmental) assessment of a PLC-controlled solar air heater (SAH) equipped with adjustable internal baffles. Unlike conventional passive systems, the proposed design enables active airflow regulation to maintain stable outlet temperatures of 54 °C and 60 °C, achieving rapid stabilization within 3–10 s under outdoor conditions. Experimental results show that increasing the baffle inclination significantly enhances convective heat transfer and thermal efficiency, while the friction factor remains primarily governed by the Reynolds number and exhibits minimal sensitivity to baffle angle. Exergy efficiency values remain relatively low (1.24–2.69%), and the sustainability index stays close to unity, reflecting the inherent thermodynamic limitations of low-temperature solar air heaters rather than deficiencies in system design. A regression-based airflow velocity model is developed to support fan-speed optimization and to clarify the trade-off between thermal enhancement and auxiliary power demand. Long-term projections based on regional solar data indicate that the proposed SAH can deliver approximately 20–22 MWh of useful heat and mitigate nearly 9 tons of CO₂ emissions over a 20-year operational lifetime. Overall, the results demonstrate that PLC-assisted dynamic baffle control provides a flexible and effective approach for improving the performance and operational stability of solar air heaters for low-temperature drying applications. Full article

The combined effects of climate change and socio-economic development have intensified the risk of water supply–demand imbalance in China. To project future trends, this study develops a multi-scenario coupled prediction framework integrating climate, socio-economic, and human activity drivers, combining data-driven and physically based modeling approaches to assess terrestrial water storage imbalance in nine major river basins under six representative SSP–RCP scenarios through the end of the 21st century. Using ISIMIP multi-model runoff outputs along with GDP and population projections, agricultural, industrial, and domestic water demands were estimated. A Water Conflict Index was proposed by integrating the Water Supply–Demand Stress Index and the Standardized Hydrological Runoff Index to identify high-risk basins. Results show that under high-emission scenarios, the WCI in the Yellow River, Hai River, and Northwest Rivers remains high, peaking during 2040–2069, while low-emission scenarios significantly alleviate stress in most basins. Water allocation inequity is mainly driven by insufficient supply in arid northern regions and limited redistribution capacity in resource-rich southern basins. Targeted strategies are recommended for different risk types, including inter-basin water transfer, optimization of water use structure and pricing policies, and the development of resilient management systems, providing scenario-based quantitative support for future water security and policy-making in China. Full article

Cultural ecosystem services are intangible benefits people gain from ecosystems that enhance well-being. However, the Millennium Ecosystem Assessment indicates that about 70% of cultural ecosystem services are degraded or unsustainably used. To mitigate this decline, many regions and policies promote the assessment and mapping of cultural ecosystem services. Since 2005, related research and publications have increased, yet place-based cultural ecosystem services assessments remain limited. This study aims to clarify key aspects of cultural ecosystem services assessment, including categories, methods, and case study area types. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses method, this study systematically reviewed 163 articles on place-based cultural ecosystem services assessment from Web of Science and Scopus from 2010 to September 2024. The results show diverse ecosystem types, assessment categories, and methods, with urban ecosystems most frequently studied. Fourteen cultural ecosystem service categories were identified based on term definitions and relevance. Non-monetary methods, such as questionnaires and social media data, were most commonly applied. Future research trends will focus on spatial visualization and mapping of supply and demand of cultural ecosystem services, emphasizing public perception. These findings provide planners and decision-makers with more detailed and specific information to better manage, design, and develop regions in a sustainable and culturally sensitive way. Full article

Background/Objectives: Family members are the principal providers of home-based care for migrant older adults. Linguistic, cultural, and structural barriers within health systems exacerbate the caregiver burden across emotional, physical and financial domains. Although home healthcare services may alleviate this burden, variability in access, cultural safety, and care coordination can also intensify it. This scoping review maps the evidence on the burden experienced by family caregivers who deliver home-based healthcare to migrant older adults and examines how these arrangements affect caregivers’ health and well-being. It synthesizes the literature on facilitators and barriers—including access, cultural-linguistic fit, coordination with formal services, and legal/immigration constraints—and distills implications for policy and practice to strengthen equitable, culturally responsive home care. Method: The Joanna Briggs Institute (JBI) scoping review framework was used to conduct the review. A comprehensive search was performed across six databases (CINAHL, Scopus, Web of Science, PsycINFO, MEDLINE and Sociological Abstracts) for articles published between 2000 and 2025. Studies were selected based on predefined inclusion criteria focusing on the family caregiver burden in providing home healthcare for migrant older adults. Data extraction and thematic analysis were conducted to identify key themes. Results: The review identified 20 studies across various geographical regions, highlighting four key themes: (1) Multidimensional Caregiver Burden, (2) The Influence of Gender, Family Hierarchy, and Migratory Trajectories on Caregiving, (3) Limited Access to Formal and Culturally Appropriate Support, and (4) Health Outcomes, Coping, and the Need for Community-Based Solutions. Conclusions: System-level reforms are required to advance equity in home healthcare for aging migrants. Priorities include establishing accountable cultural-safety training for providers; expanding multilingual access across intake, assessment, and follow-up; and formally recognizing and resourcing family caregivers (e.g., navigation support, respite, training, and financial relief). Investment in community-driven programs, frameworks and targeted outreach—co-designed with migrant communities—can mitigate isolation and improve uptake. While home healthcare is pivotal, structural inequities and cultural barriers continue to constrain equitable access. Addressing these gaps demands coordinated policy action, enhanced provider preparation, and culturally responsive care models. Future research should evaluate innovative frameworks that integrate community partnerships and culturally responsive practices to reduce the caregiver burden and improve outcomes for migrant families. Full article

Amid fossil-fuel depletion and worsening environmental impacts, battery electric vehicles (BEVs) are pivotal to the energy transition. Energy management in BEVs relies on accurate motor efficiency maps, yet real-time onboard control demands models that balance fidelity with computational cost. To address map inaccuracy under real driving and the high runtime cost of 2-D interpolation, we propose a driving-cycle-aware, physically interpretable quadratic polynomial-surface framework. We extract priority operating regions on the speed–torque plane from typical driving cycles and model electrical power $P_e$ as a function of motor speed $n$ and mechanical power $P_m$. A nested model family (M3–M6) and three fitting strategies—global, local, and region-weighted—are assessed using $R^2$, RMSE, a computational complexity index (CCI), and an Integrated Criterion for accuracy–complexity and stability (ICS). Simulations on the Worldwide Harmonized Light Vehicles Test Cycle, the China Light-Duty Vehicle Test Cycle, and the Urban Dynamometer Driving Schedule show that region-weighted fitting consistently achieves the best or near-best ICS; relative to Global fitting, mean ICS decreases by 49.0%, 46.4%, and 90.6%, with the smallest variance. Regarding model order, the four-term M4 $\left(+P_m^2\right)$ offers the best accuracy–complexity trade-off. Finally, the region-weighted fitting M4 $\left(+P_m^2\right)$ polynomial model was integrated into the vehicle-level economic speed planning model based on the dynamic programming algorithm. In simulations covering a 27 km driving distance, this model reduced computational time by approximately 87% compared to a linear interpolation method based on a two-dimensional lookup table, while achieving an energy consumption deviation of about 0.01% relative to the lookup table approach. Results demonstrate that the proposed model significantly alleviates computational burden while maintaining high energy consumption prediction accuracy, thereby providing robust support for real-time in-vehicle applications in whole-vehicle energy management. Full article

Artificial intelligence (AI) is increasingly regarded as a transformative enabler of sustainable logistics and supply chain management, particularly in the context of global energy transition and decarbonization efforts. This study provides a comprehensive conceptual and exploratory assessment of the multidimensional role of AI, highlighting both its potential to enhance energy efficiency and reduce greenhouse gas emissions, as well as its inherent environmental costs associated with digital infrastructures such as data centers. The findings reveal the dual character of digitalization: while predictive algorithms and digital twin applications facilitate demand forecasting, process optimization, and real-time adaptation to market fluctuations, they simultaneously generate additional energy demand that must be offset through renewable energy integration and intelligent energy balancing. The analysis underscores that the effectiveness of AI deployment cannot be captured solely through economic metrics but requires a holistic evaluation framework that incorporates environmental and social dimensions. Moreover, regional disparities are identified, with advanced economies accelerating AI-driven green transformations under regulatory and societal pressures, while developing economies face constraints linked to infrastructure gaps and investment limitations. The analysis emphasizes that AI-driven predictive models and digital twin applications are not only tools for energy optimization but also mechanisms that enhance systemic resilience by enabling risk anticipation, adaptive resource allocation, and continuity of operations in volatile environment. The contribution of this study lies in situating AI within the digital–green synergy discourse, demonstrating that its role in logistics decarbonization is conditional upon integrated energy–climate strategies, organizational change, and workforce reskilling. By synthesizing emerging evidence, this article provides actionable insights for policymakers, managers, and scholars, and calls for more rigorous empirical research across sectors, regions, and time horizons to verify the long-term sustainability impacts of AI-enabled solutions in supply chains. Full article

This paper presents an intelligent operational strategy that performs the coordinated dispatch of active and reactive power from PV distributed generators (PV DGs) and Distributed Static Compensators (D-STATCOMs) to support secure and economical operation of active distribution networks. The problem is formulated as a nonlinear optimization problem that explicitly represents the P and Q control capabilities of Distributed Energy Resources (DER), encompassing small-scale generation and compensation units connected at the distribution level, such as PV generators and D-STATCOM devices, adjusting their reference power setpoints to minimize daily operating costs, including energy purchasing and DER maintenance, while satisfying device power limits and the voltage and current constraints of the grid. To solve this problem efficiently, a parallel version of the Population Continuous Genetic Algorithm (CGA) is implemented, enabling simultaneous evaluation of candidate solutions and significantly reducing computational time. The strategy is assessed on the 33- and 69-node benchmark systems under deterministic and uncertainty scenarios derived from real demand and solar-generation profiles from a Colombian region. In all cases, the proposed approach achieved the lowest operating cost, outperforming state-of-the-art metaheuristics such as Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), and Crow Search Algorithm (CSA), while maintaining power limits, voltages and line currents within secure ranges, exhibiting excellent repeatability with standard deviations close to 0.0090%, and reducing execution time by more than 68% compared with its sequential counterpart. The main contributions of this work are: a unified optimization model for joint P–Q control in PV and D–STATCOM units, a robust codification mechanism that ensures stable convergence under variability, and a parallel evolutionary framework that delivers optimal, repeatable, and computationally efficient energy management in distribution networks subject to realistic operating uncertainty. Full article

Agricultural non-point source pollution (ANPSP) represents a major threat to water quality, yet its spatiotemporal dynamics in arid and semi-arid regions remain poorly quantified. This study establishes an integrated assessment framework to analyze the spatiotemporal patterns and driving mechanisms of ANPSP in Inner Mongolia, China, from 2002 to 2023. Using a combination of inventory analysis, pollution load equivalence assessment, and the Tapio decoupling model, we systematically examined the evolution of four pollution sources—chemical fertilizers, livestock breeding, agricultural solid waste, and rural domestic discharge—across 12 administrative regions. These methods were sequentially applied to quantify loads, standardize impacts, and evaluate the economy–environment relationship, forming a coherent analytical chain. Key results indicate the following: (1) Pollutant loads increased consistently over the study period, with chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) rising by 24.21%, 31.67%, and 31.14%, respectively, largely driven by livestock sector expansion. (2) Spatial distribution was highly heterogeneous, with Tongliao, Chifeng, and Hulunbuir contributing 50.58–58.31% of total emissions, in contrast to minimal impacts in western regions. (3) Decoupling analysis indicated variable environment–economy relations, where fertilizer use and grain output reached strong decoupling in 2010–2011 and 2018–2019, whereas livestock pollution exhibited more unstable decoupling trajectories. A cluster-derived risk zoning scheme identified Bayannur as the only high-risk area and highlighted the need for tailored management approaches in medium- and low-risk zones. This study offers a scientific foundation for targeted ANPSP mitigation and sustainable agricultural strategy formulation in ecologically vulnerable areas. Full article

Confronting severe water scarcity challenges, China’s industrial water circularity demands robust efficiency evaluation frameworks. This research pioneers a two-stage network model integrating undesirable outputs and feedback mechanisms to assess 30 provincial systems. The methodology captures interconnected processes where production generates wastewater, which is then treated to yield reusable water fed back into production. Comprehensive efficiency gaps were quantified using weighted optimization, enabling tailored provincial enhancement paths: wastewater volume reduction and reclaimed water augmentation strategies. Results reveal striking regional disparities, with only two regions initially achieving full efficiency while coastal manufacturing hubs exhibited paradoxical inefficiency despite high output. Implementation demonstrated reclaimed water enhancement’s superior efficacy—enabling over half of regions to reach full efficiency—while wastewater reduction alone proved insufficient for most provinces. Crucially, ecologically fragile regions achieved optimal performance through minimal precision interventions. The study establishes that effective water circularity requires coordinated optimization of both production and treatment stages, with region-specific sequencing strategies. This approach delivers policymakers a diagnostic toolkit for spatially differentiated resource transition planning, balancing economic output with environmental sustainability. Full article

One of the key functions of Air Traffic Management (ATM) is to balance airspace capacity and demand. Despite measures that are taken during the strategic and pre-tactical phases of flight, demand–capacity imbalances still occur in flight, often manifesting as localised regions of high traffic complexity, known as hotspots. These hotspots emerge dynamically, leaving air traffic controllers with limited anticipation time and increased workload. This paper proposes a Machine Learning (ML) framework for the prediction and resolution of hotspots in congested en-route airspace up to an hour in advance. For hotspot prediction, the proposed framework integrates trajectory prediction, spatial clustering, and complexity assessment. The novelty lies in shifting complexity assessment from a sector-level perspective to the level of individual hotspots, whose complexity is quantified using a set of normalised, sector-relative metrics derived from historical data. For hotspot resolution, a Reinforcement Learning (RL) approach, based on Proximal Policy Optimisation (PPO) and a novel neural network architecture, is employed to act on airborne flights. Three single-clearance type agents—a speed agent, a flight-level agent, and a direct routing agent—and a multi-clearance type agent are trained and evaluated on thousands of historical hotspot scenarios. Results demonstrate the suitability of the proposed framework and show that hotspots are strongly seasonal and mainly occur along traffic routes. Furthermore, it is shown that RL agent performance tends to degrade with hotspot complexity in terms of certain performance metrics but remains the same, or even improves, in terms of others. The multi-clearance type agent solves the highest percentage of hotspots; however, the FL agent achieves the best overall performance. Full article

With the intensification of global environmental challenges and the growing demand for sustainable agricultural transformation, understanding how environmental regulation shapes enterprise innovation has become increasingly important. This study examines the impact of command-and-control environmental regulation on green innovation in agricultural enterprises using panel data from agriculture-related enterprises listed on the Shanghai and Shenzhen A-share exchanges. The analysis focuses on the period 2012–2021, which is characterised by relatively stable environmental regulation and reliable data, providing a consistent empirical context for assessing the effects of command-and-control environmental regulation. By analyzing the characteristics of command-and-control environmental regulation and green innovation in agricultural enterprises, this research constructs and estimates a two-way fixed effects model, a moderating effects model, a mediating effects model, and a spatial Durbin model to explore both direct and spillover effects. The empirical results show that the following findings: (1) Command-and-control environmental regulation significantly promotes green innovation in agricultural enterprises, and this effect remains robust across alternative measurements and model specifications. (2) Heterogeneity analysis indicates that the direct effect of command-and-control environmental regulation is most pronounced in eastern regions, non-state-owned enterprises, and enterprises with weaker environmental, social, and governance performance. (3) Moderation analysis shows that agricultural industrial coordination and executive green cognition significantly strengthen the positive relationship between command-and-control environmental regulation and green innovation in agricultural enterprises. (4) Mediation analysis demonstrates that green management costs serve as a partial mediator in this relationship. (5) Spatial analysis reveals that spatial correlation patterns are evolving over time, with significant positive spillover effects observed among geographically and economically adjacent regions. The findings provide theoretical and empirical evidence to inform the design of coordinated environmental regulation frameworks that effectively stimulate green innovation and foster sustainable agricultural development. Full article

Microalgae display remarkable resilience to harsh environments, partly through the biosynthesis of diverse secondary metabolites. Cyanobacteria and red algae are well known to produce mycosporine-like amino acids (MAAs)—low-molecular-weight, water-soluble UV-absorbing compounds with anti-inflammatory, anticancer, and antimicrobial activities. By contrast, green microalgae typically lack detectable MAAs under standard conditions, and their responses under abiotic stress remain poorly characterized. Here, we investigated the freshwater green microalga Jaagichlorella luteoviridis grown under three stressors (salinity, heat, and UV) and assessed MAA induction. High-performance liquid chromatography (HPLC) revealed that stressed cultures accumulated multiple MAAs, whereas untreated controls showed no such accumulation. All stress treatments (UV, salinity, and heat) produced a substantial increase in peak intensity at 323–350 nm, whereas the control samples showed significantly lower absorption in this region. We also optimized an MAA extraction protocol suitable for “green” downstream applications in the pharmaceutical, nutraceutical, and cosmeceutical sectors and formulated an emulsion showing preliminary positive results and exhibiting an increased SPF index from 3.60 (control) to 3.78 when 0.2% MAA extract was added. Transcriptomic profiling against a reference genome revealed stress-specific differential gene expression and overexpression of specific genes of the MAA pathway, like ArioC and AroM/Aro1 SAM methyltransferases, thus identifying candidate targets for engineering enhanced MAA production. Given market demand for environmentally friendly and safe bioactives, microalgae represent a promising source of these valuable molecules. Full article

The textile sector provides essential goods, yet it remains environmentally and socially intensive, driven by high water use, pesticide dependent monocropping, chemical pollution during processing, and growing waste streams. This review examines credible pathways to sustainability by integrating emerging plant-based fibres from hemp, abaca, stinging nettle, and pineapple leaf fibre. These underutilised crops combine favourable agronomic profiles with competitive mechanical performance and are gaining momentum as the demand for demonstrably sustainable textiles increases. However, conventional fibre identification methods, including microscopy and spectroscopy, often lose reliability after wet processing and in blended fabrics, creating opportunities for mislabelling, greenwashing, and weak certification. We synthesise how advanced molecular approaches, including DNA fingerprinting, species-specific assays, and metagenomic tools, can support the authentication of fibre identity and provenance and enable linkage to Digital Product Passports. We also critically assess environmental Life Cycle Assessment (LCA) and social assessment frameworks, including S-LCA and SO-LCA, as complementary methodologies to quantify climate burden, water use, labour conditions, and supply chain risks. We argue that aligning fibre innovation with molecular traceability and harmonised life cycle evidence is essential to replace generic sustainability claims with verifiable metrics, strengthen policy and certification, and accelerate transparent, circular, and socially responsible textile value chains. Key research priorities include validated marker panels and reference libraries for non-cotton fibres, expanded region-specific LCA inventories and end-of-life scenarios, scalable fibre-to-fibre recycling routes, and practical operationalisation of SO-LCA across diverse enterprises. Full article