Search Results (150)

Moringa oleifera (MO), a nutritionally and pharmacologically potent species, is emerging as a sustainable candidate for applications across bioenergy, agriculture, textiles, pharmaceuticals, and biomedicine. This review explores recent advances in MO-based biotechnologies, highlighting novel extraction methods, green nanotechnology, and clinical trial findings. Although MO’s resilience offers promise for climate-smart agriculture and public health, challenges remain in standardizing cultivation and verifying therapeutic claims. This work underscores MO’s translational potential and the need for integrative, interdisciplinary research. MO is used in advanced materials, like electrospun fibers and biopolymers, showing filtration, antibacterial, anti-inflammatory, and antioxidant properties—important for the biomedical industry and environmental remediation. In textiles, it serves as an eco-friendly alternative for wastewater treatment and yarn sizing. Biotechnological advancements, such as genome sequencing and in vitro culture, enhance traits and metabolite production. MO supports green biotechnology through sustainable agriculture, nanomaterials, and biocomposites. MO shows potential for disease management, immune support, metabolic health, and dental care, but requires further clinical trials for validation. Its resilience is suitable for land restoration and food security in arid areas. AI and deep learning enhance Moringa breeding, allowing for faster, cost-effective development of improved varieties. MO’s diverse applications establish it as a key element for sustainable development in arid regions. Full article

Coastal blue carbon ecosystems (traditional types such as mangroves, salt marshes, and seagrass meadows; emerging types such as tidal flats and mariculture) play pivotal roles in capturing and storing atmospheric carbon dioxide. Reliable assessment of the spatial and temporal variation and the carbon storage potential holds immense promise for mitigating climate change. Although previous field surveys and regional assessments have improved the understanding of individual habitats, most studies remain site-specific and short-term; comprehensive, multi-decadal assessments that integrate all major coastal blue carbon systems at the national scale are still scarce for China. In this study, we integrated 30 m Landsat imagery (1992–2022), processed on Google Earth Engine with a random forest classifier; province-specific, literature-derived carbon density data with quantified uncertainty (mean ± standard deviation); and the InVEST model to track coastal China’s mangroves, salt marshes, tidal flats, and mariculture to quantify their associated carbon stocks. Then the GeoDetector was applied to distinguish the natural and anthropogenic drivers of carbon stock change. Results showed rapid and divergent land use change over the past three decades, with mariculture expanded by 44%, becoming the dominant blue carbon land use; whereas tidal flats declined by 39%, mangroves and salt marshes exhibited fluctuating upward trends. National blue carbon stock rose markedly from 74 Mt C in 1992 to 194 Mt C in 2022, with Liaoning, Shandong, and Fujian holding the largest provincial stock; Jiangsu and Guangdong showed higher increasing trends. The Normalized Difference Vegetation Index (NDVI) was the primary driver of spatial variability in carbon stock change (q = 0.63), followed by precipitation and temperature. Synergistic interactions were also detected, e.g., NDVI and precipitation, enhancing the effects beyond those of single factors, which indicates that a wetter climate may boost NDVI’s carbon sequestration. These findings highlight the urgency of strengthening ecological red lines, scaling climate-smart restoration of mangroves and salt marshes, and promoting low-impact mariculture. Our workflow and driver diagnostics provide a transferable template for blue carbon monitoring and evidence-based coastal management frameworks. Full article

The efficient management of urban parking systems has emerged as a pivotal issue in today’s smart cities, where increasing vehicle populations strain limited parking infrastructure and challenge sustainable urban mobility. Aligned with the United Nations 2030 Agenda for Sustainable Development and the strategic goals of smart city planning, this study presents a sustainability-driven, multiagent simulation-based framework to model, analyze, and optimize smart parking dynamics in congested urban settings. The system architecture integrates ground-level IoT sensors installed in parking spaces, enabling real-time occupancy detection and communication with a centralized system using low-power wide-area communication protocols (LPWAN). This study introduces an intelligent parking guidance mechanism that dynamically directs drivers to the nearest available slots based on location, historical traffic flow, and predicted availability. To manage real-time data flow, the framework incorporates message queuing telemetry transport (MQTT) protocols and edge processing units for low-latency updates. A predictive algorithm, combining spatial data, usage patterns, and time-series forecasting, supports decision-making for future slot allocation and dynamic pricing policies. Field simulations, calibrated with sensor data in a representative high-density urban district, assess system performance under peak and off-peak conditions. A comparative evaluation against traditional first-come-first-served and static parking systems highlights significant gains: average parking search time is reduced by 42%, vehicular congestion near parking zones declines by 35%, and emissions from circling vehicles drop by 27%. The system also improves user satisfaction by enabling mobile app-based reservation and payment options. These findings contribute to broader sustainability goals by supporting efficient land use, reducing environmental impacts, and enhancing urban livability—key dimensions emphasized in sustainable smart city strategies. The proposed framework offers a scalable, interdisciplinary solution for urban planners and policymakers striving to design inclusive, resilient, and environmentally responsible urban mobility systems. Full article

The change in the socio-political formation of Russia from a socialist planned system to a capitalist market system significantly influenced agriculture and one of its components—arable land. The loss of the sustainability of land management for arable land led to a reduction in sown areas by 38% (from 119.7 to 74.7 million ha) and a synchronous drop in gross harvests of grain and leguminous crops by 48% (from 117 to 61 million tons). The situation stabilized in 2020, with a sowing area of 80.2 million ha and gross harvests of grain and leguminous crops of 120–150 million tons. This process was not formalized legally, and the official (legal) area of arable land decreased by only 8% from 132.8 to 122.3 million ha. Legal conflict arose for 35 million ha for unused arable land, for which there was no classification of its condition categories and no monitoring of the withdrawal time of the arable land from actual agricultural use. The aim of this study was to resolve the challenges in the method of retrospective monitoring of soil–land cover, which allowed for the achievement of the aims of the investigation—to elucidate the history of land use on arable lands from 1985 to 2025 with a time step of 5 years and to obtain a detailed classification of the arable lands’ abandonment degrees. It was also established that on most of the abandoned arable land, carbon sequestration occurs in the form of secondary forests. In the course of this work, it was shown that the reasons for the formation of an array of abandoned arable land and the stabilization of agricultural production turned out to be interrelated. The abandonment of arable land occurred proportionally to changes in the soil’s natural fertility and the degree of land degradation. Economically unprofitable lands spontaneously (without centralized planning) left the sowing zone. The efficiency of land use on the remaining lands has increased and has allowed for the mass application of modern farming systems (smart, precise, landscape-adaptive, differentiated, no-till, strip-till, etc.), which has further increased the profitability of crop production. The prospect of using abandoned lands as a carbon sequestration zone in areas of forest overgrowth has arisen. Full article

Traditional farming practices are becoming increasingly inadequate to meet global food demand due to water scarcity, prolonged production cycles, climate variability, and declining arable land. In contrast, aeroponic, smart, soil-free farming technologies offer a more sustainable alternative by reducing land use and providing efficient water use, given that aeroponics intermittently delivers water in mist form rather than maintaining continuous root zone moisture. However, aeroponics faces critical challenges in irrigation management due to non-standardized structures and limited real-time control. A key limitation is the inability to dynamically respond to temperature (T), relative humidity (RH), light intensity (L_i), electrical conductivity (EC), pH, and photosynthesis rate (P_n), resulting in suboptimal crop yields and resource wastage. Despite growing interest, there remains a research gap in integrating internet of things (IoT) and machine learning technologies into aeroponic systems for adaptive control. IoT-enabled sensors provide real-time data on ambient conditions and plant health, while ML models can adaptively optimize misting intervals based on the fluctuations in P_n and environmental inputs. These technologies are particularly well suited to address the dynamic, data-intensive nature of aeroponic environments. This review purposes a novel, standardized IoT–ML framework to control irrigation by emphasizing IoT sensing and ML-based decision making in aeroponics. This integrated approach is essential for minimizing water loss, enhancing resource efficiency, and advancing the sustainability of controlled-environment agriculture. Full article

In recent years, the intense urbanisation processes in Latin American cities have generated fragmented landscapes in the transition zones between urban and rural areas. There is growing interest among urban planners in the delimitation and management of the urban fringe due to the challenges these areas face, including the accelerated conversion of agricultural land, the loss of biodiversity, ecosystem fragmentation, and increasing socioeconomic inequalities resulting from limited regulation in Latin American cities. This study aims to identify criteria for the delimitation of the urban fringe in Latin American cities, oriented toward policy development and the creation of new management tools. A three-stage mixed-method approach was used, i.e., (1) a structured literature review following the PRISMA protocol, identifying 58 criteria from Global South studies, grouped into seven thematic categories; (2) a qualitative analysis using the SMART methodology to identify feasible criteria; and (3) a survey of 19 Latin American experts to prioritise criteria based on data availability and spatial comparability in the region’s cities. The results reveal a growing concern regarding the conceptual development of the urban fringe in Latin America. Methodological gaps persist in the delimitation of these areas. The final catalogue of criteria shows a trend toward measurable and comparable criteria and highlights the need to approach the urban fringe from a multidimensional perspective, which could facilitate its integration into comparative studies at the regional scale. Full article

Smart Land Use Planning (SLUP) has gained increasing attention in urban development, yet few studies examine its implementation from an urban governance perspective. This study investigates municipal SLUP project characteristics, their spatial distribution, and intercity cooperation networks by analyzing 3689 SLUP government procurement contracts in China’s Yangtze River Delta urban agglomeration. Using the Latent Dirichlet Allocation model, this study identified four main SLUP project types: real estate management, land resource protection, land use planning, and geographic information services. Spatial analysis revealed significant imbalances across cities, with SLUP projects concentrated in central cities while other cities heavily depend on intercity cooperation for technical support and services. Network analysis showed a core–periphery structure, with industrial structure and institution similarities significantly facilitating cooperation, while geographic distance and cultural similarity had limited impact. Future research should expand data sources to enable cross-regional comparative analysis. This study offers empirical evidence for policymaking in the implementation of SLUP and regional coordinated development. Full article

Rapid urbanization, climate variability, and land degradation are increasingly challenging traditional open-field farming systems. Soilless farming (SLF) has emerged as a complementary approach to enhance horticultural resilience in space-constrained and climate-stressed environments. This review critically evaluates the role of SLF within the broader framework of climate-smart agriculture (C-SA), with a particular focus on its applications in urban and peri-urban settings. Drawing on a systematic review of the existing literature, the study explores how SLF technologies contribute to efficient resource use, localized food production, and environmental sustainability. By decoupling crop cultivation from soil, SLF enables precise control over nutrient delivery and water use in enclosed environments, such as vertical farms, greenhouses, and container-based units. These systems offer notable advantages regarding water conservation, increased yield per unit area, and adaptability to non-arable or degraded land, making them particularly relevant for high-density cities, arid zones, and climate-sensitive regions. SLF systems are categorized into substrate-based (e.g., coco peat and rock wool) and water-based systems (e.g., hydroponics, aquaponics, and aeroponics), each with distinct design requirements, nutrient management strategies, and crop compatibility. Emerging technologies—including artificial intelligence, the Internet of Things, and automation—further enhance SLF system efficiency through real-time data monitoring and precision control. Despite these advancements, challenges remain. High setup costs, energy demands, and the need for technical expertise continue to limit large-scale adoption. While SLF is not a replacement for traditional agriculture, it offers a strategic supplement to bolster localized food systems and address climate-related risks in horticultural production. Urban horticulture is no longer a peripheral activity; it is becoming an integral element of sustainable urban development. SLF should be embedded within broader resilience strategies, tailored to specific socioeconomic and environmental contexts. Full article

Climate variability and land-use changes are critical challenges impacting agriculture globally, with Zimbabwe’s Gwayi catchment area experiencing noticeable effects. This study investigated how smallholder farmers in the region perceive these changes and their influence on agricultural productivity and livelihoods. The research addresses the gap in understanding local farmer experiences with climate change and land-use modifications in the context of food security. A cross-sectional survey (n = 483) was conducted using self-administered questionnaires to capture demographic information, perceptions of climate variability, land-use changes, and their impacts on agriculture. The results indicate a trend of increasing droughts, dry spells, and heatwaves, alongside altered rainfall patterns and rising temperatures, corroborating the observed climate data. Environmental degradation, including deforestation, gully formation, and land expansion, exacerbates these changes. Consequently, farmers reported substantial reductions in crop yields, with 84.05% experiencing significant-to-very negative impacts, alongside declining livestock health (32.51% reporting very negative impacts), increased water scarcity (43.3% reporting drying water sources), and more frequent disease outbreaks. These challenges collectively contributed to heightened food insecurity, with 74.12% of households reporting negative impacts on their food supply. The study underscores the synergistic impacts of climate variability and land-use changes, highlighting the urgent need for climate-smart agricultural practices and sustainable land management to enhance resilience and ensure long-term food security for smallholder farmers in the Gwayi catchment. Full article

Urban agriculture is key to sustainable city development, particularly through public engagement with the Water–Energy–Land–Food–People (WELFP) Nexus. This study examines the effectiveness of serious games in enhancing WELFP understanding and promoting pro-environmental and pro-social behaviors. A game-based learning model was developed using the Stimulus–Organism–Response (SOR) and Easy–Attractive–Social–Timely (EAST) frameworks, along with the Revised New Ecological Paradigm (NEP) Scale. The model simulates real-world urban agriculture challenges to foster participatory decision-making. A survey of 200 urban agriculture practitioners, analyzed via structural equation modeling (SmartPLS 4.0), found that perceived timeliness (PT) and perceived usefulness (PU) significantly influenced both the perceived sustainable livelihood value (PT: p = 0.000; PU: p = 0.006) and users’ attitudes toward the game (PT: p = 0.000; PU: p = 0.038). While enjoyment positively affected attitude (p = 0.002), it negatively impacted perceived value (p = 0.002), revealing a trade-off between fun and practical relevance. Perceived ease of use improved perceived value (p = 0.000) but did not affect attitude, suggesting emotional engagement matters more. Both attitude and perceived value strongly predicted users’ intention to engage with the game. Post-game reflections highlighted the need for cross-sector collaboration, strategic resource use, access to real-time data, and responsive crisis management. Participants also stressed the importance of public awareness, civic responsibility, and volunteerism in advancing community-driven sustainable agriculture. These findings highlight the need to balance engagement and educational depth in game-based learning for sustainability. Full article

Deep-learning-based methods are crucial for building extraction from high-resolution remote sensing images, playing a key role in applications like natural disaster response, land resource management, and smart city development. However, extracting precise building from complex urban and rural environments remains challenging due to spectral variability and intricate background interference, particularly in densely packed and small buildings. To address these issues, we propose an enhanced U²-Net architecture, MFA-Net, which incorporates two key innovations: a Multidimensional Feature Adjustment (MFA) module that refines feature representations through Cascaded Channel, Spatial, and Multiscale Weighting Mechanisms and a Dynamic Fusion Loss function that enhances edge geometric fidelity. Evaluation on three datasets (Urban, Rural, and WHU) reveals that MFA-Net outperforms existing methods, with average improvements of 6% in F1-score and 7.3% in IoU and an average increase of 9.9% in training time. These advancements significantly improve edge delineation and the segmentation of dense building clusters, making MFA-Net especially beneficial for urban planning and land resource management. Full article

Drought affects the agricultural sector, posing challenges for farm management, particularly among medium- and small-scale producers. This study uses climate data from remote sensing products to evaluate drought trends in the Semear Digital Center’s Agrotechnological Districts (DATs), which are characterized by a high concentration of small- and medium-sized farms in Brazil. Precipitation data from Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) and land surface temperature data from Moderate Resolution Imaging Spectroradiometer (MODIS) were applied to calculate the Standardized Precipitation–Evapotranspiration Index (SPEI) for a 6-month timescale from 2000 to 2024, with analysis divided into 2000–2012 and 2013–2024. Some limitations were noted: MODIS systematically underestimated temperatures, while CHIRPS tended to underestimate precipitation for most of the DATs. Despite discrepancies, these datasets remain valuable for drought monitoring in areas where long-term ground weather station data are lacking for SPEI assessments. Agricultural drought frequency and severity increased in the 2013–2024 period. Exceptional, extreme, severe, and moderate drought events rose by 7.3, 5.4, 2.2 and 1.0 times, respectively. These trends highlight the importance of adopting smart farming technologies to enhance the resilience of the DATs to climate change. Full article

Leaky dams have become essential nature-based solutions for flood management, providing sustainable alternatives to traditional engineered flood control methods. This review delves into the mechanisms by which leaky dams operate, including the regulation of water flow through velocity reduction and distribution across floodplains, effective sediment trapping and soil quality enhancement, and the facilitation of groundwater recharge and water table stabilization. These structures not only mitigate peak flood flows and reduce erosion but also contribute to enhanced biodiversity by creating diverse aquatic habitats and maintaining ecological connectivity. The effectiveness of leaky dams is assessed through various performance metrics, demonstrating significant reductions in peak flows, improved sediment management, and increased groundwater levels, which collectively enhance ecosystem resilience and water quality. However, the implementation of leaky dams presents several technical challenges, such as design complexity, hydrological variability, maintenance requirements, and socio-economic factors like land use conflicts and economic viability. Additionally, while leaky dams offer numerous environmental benefits, potential negative impacts include habitat disruption, sediment accumulation, and alterations in water quality, which necessitate careful planning and adaptive management strategies. Emerging trends in leaky dam development focus on the integration of smart technologies, such as real-time monitoring systems and artificial intelligence, to optimize performance and resilience against climate-induced extreme weather events. Advances in modeling and monitoring technologies are facilitating the effective design and implementation of leaky dam networks, promoting their incorporation into comprehensive watershed management frameworks. This review highlights the significant potential of leaky dams as integral components of sustainable flood management systems, advocating for their broader adoption alongside conventional engineering solutions to achieve resilient and ecologically balanced water management. Full article

With the acceleration in urbanization, surface hardening has increased, urban flooding and soil erosion problems are frequent, and urban water resource management faces great challenges. Sponge city construction can effectively alleviate these problems by simulating the natural water cycle and constructing blue–green infrastructure. In this study, the analytic hierarchy process (AHP) and the ArcGIS weighted overlay tool were used to construct a framework for assessing the suitability of sponge city construction in Suzhou from the three dimensions of Geo-Smart spatial productive forces, Eco-Dynamic green productive forces, and Resilio-Tech responsive productive forces. A zoning strategy based on new quality productive forces is also proposed. The results show that Suzhou can be divided into three types of construction zones according to the suitability level: key construction zones, secondary key construction zones, and general construction zones. The key construction zones account for about 28.01% of the total land area, mainly covering the built-up areas of Suzhou, covering the developed urban areas such as Gusu District, Xiangcheng, Suzhou Industrial Park, and other key zones such as Northern Kunshan. The secondary key construction area and general construction area, on the other hand, account for 61.94% and 10.05% of the total area, respectively. From the new quality productive forces, this study proposes the following construction guidelines for sponge city zones: (1) enhance the coordinated development of urban planning and sponge city construction; (2) promote blue–green infrastructure development, strengthen inter-departmental cooperation, and ensure ecological and economic co-development; and (3) encourage public participation in governance. This research offers theoretical and practical guidance for sponge city construction in Suzhou and other cities from the perspective of new quality productive forces. Full article

Land consolidation (LC) plays an important role in disturbing carbon storage (CS) change. Evaluating how LC affects CS is crucial for mitigating global climate change. However, existing research often overlooks differences in various aspects of land remediation, making it challenging to propose targeted policy adjustments to enhance CS effectiveness. This study presents a framework to assess the effectiveness of CS changes throughout the LC process, encompassing policy formulation stages (PF), construction stages (CO), and post-management stages (PM). Carbon density, a key factor in measuring CS changes, is adjusted using biomass model-integrated empirical measurements with dynamic growth coefficients calibrated through phenological monitoring. The Guangdong Demolition and Reclamation (D&R) project, a specific type of LC, serves as a case study. The findings are as follows: (1) D&R increased forest and garden land by 1420 hm² and 1674 hm², respectively, leading to a regional CS increase of 359,000 t, a five-fold rise per hectare. (2) The effectiveness of PF is 5.81%, with a discrepancy of over 36 million tons. The policy content’s adaptability is low, indicating significant room for improvement in CS outcomes at this stage. (3) The effectiveness of CO is 24.71%, with considerable variation between counties, ranging from 1.26% to 97.55%, due to the varying capabilities of executors and the diverse regional topographical features. Refining implementation content and encouraging collaborative efforts are effective strategies to enhance CS. (4) The effectiveness of PM is 65.03%, and the counties in the east are lower than the west. Scientific post-care is essential for improving CS. This framework provides theoretical support for optimizing LC to enhance regional CS and lays the groundwork for future investigations into the long-term impacts of LC on CS, as well as the potential for applying the methods used in this study to other regions and types of land consolidation projects. Full article