Search Results (707)

The housing sector in major cities is facing escalating challenges due to rapid population growth and land scarcity. Consequently, vertical growth has been adopted as a strategic solution to optimize land use while balancing economic, social, and environmental needs. This study examines the phenomenon of vertical growth of the Dammam Metropolitan Area (DMA) in Saudi Arabia, from an urban sustainability perspective, focusing on evaluating the current state of multi-story buildings, their determinants, and their impact on quality of life and infrastructure efficiency. This study utilizes a systematic review methodology and a conceptual approach to develop an integrated framework for sustainable vertical growth. Furthermore, an empirical validation was conducted by projecting this framework onto vertical housing projects in Dammam, focusing on challenges related to design, construction quality, shared service management, and the suitability of apartments for family needs. The results indicate that the shift toward vertical growth achieves land-use efficiency, limits random horizontal expansion, and provides economic opportunities. However, it faces social and cultural constraints, most notably the resistance of some families to changing traditional ownership patterns, limited privacy and green spaces, and challenges in building maintenance and operations. The study highlights the importance of integrating urban planning, governance, architectural design, and infrastructure to ensure the sustainability of vertical growth and provide suitable housing alternatives. The study recommends further field research to assess social acceptance, improve quality-of-life indicators, and develop policies encouraging sustainable vertical expansion in alignment with Saudi Vision 2030 and the 2030 Sustainable Development Goals (SDGs), ensuring cities are more resilient, efficient, sustainable, and liveable. Full article

Sustainable irrigation planning under increasing water scarcity requires quantitative optimization tools to balance land and water resources. This study develops a linear programming (LP)-based framework to determine optimal cropping patterns under variable seasonal water availability in three irrigation projects in Lao PDR: Nam Tong 2 (1000 ha; ≈48.16 million m³ (MCM)), Nam Hin (80 ha; ≈0.73 MCM), and Xe Salalong (1530 ha; ≈30.80 MCM). Six major crops were analyzed for each project, with crop water requirements ranging from 4000 to 12,000 m³ ha⁻¹ and gross revenues from 1200 to 41,322 US$ ha⁻¹. Eight irrigation scenarios were constructed by combining land suitability (suitable vs. unsuitable), crop water requirement levels, and gross revenue assumptions. The model maximizes total gross revenue subject to seasonal water and land constraints. The results indicate that under limited water availability (e.g., 5.35–6.20 MCM in Nam Tong 2), crops with lower water demand (≤6000 m³ ha⁻¹) and higher economic return per unit of water are prioritized, improving water-use efficiency. As water availability increases, high-value but water-intensive crops expand until land suitability becomes the dominant constraint. Expanding irrigation on unsuitable land produces diminishing economic returns. The framework enhances the realism of irrigation planning and supports economically efficient, water-sustainable crop allocation in water-scarce regions. Full article

Across semi-arid and environmentally vulnerable regions, intensifying climate pressures, land degradation, and resource scarcity are placing growing demands on institutions, communities, and land users. However, the knowledge and technical skills required to respond effectively remain uneven and often poorly aligned with local needs. This study presents a comparative skills audit in Kimberley, Upington, and Rietfontein in the Northern Cape, identifying capacity gaps, stakeholder-specific training priorities, and structural barriers in natural resource and sustainable land management. Using questionnaires, semi-structured interviews, participatory site visits, and multi-stakeholder consultations, competencies were assessed across GIS and remote sensing, climate resilience, soil and land restoration, water conservation, sustainable agriculture, and policy literacy. Results show significant disparities in skills proficiency. GIS and remote sensing (0.8) and climate resilience strategies (1.0) were weakest, while policy literacy (1.5) and soil management (2.0) were also limited. Sustainable agriculture (4.0) and water conservation (2.8) showed relatively stronger capacity. Training needs varied by stakeholder, with government prioritizing geospatial tools and governance, and farmers emphasizing climate adaptation and resource management. Key barriers include limited digital infrastructure (83%), insufficient government support (80%), high training costs (78%), and contextual mismatches (50%). Integrated, place-based capacity development is essential to strengthen adaptive governance and long-term resilience. Full article

Anaerobic digestion is a viable pathway to mitigate environmental impacts from swine manure in tropical regions while contributing to circular economy strategies. However, no standardized or integrated framework currently exists that simultaneously quantifies the closure of energy, material, carbon, nutrient, and water loops at the farm scale. This research presents the techno-economic design and environmental assessment of a covered, mechanically agitated lagoon biodigester for a 10,000-head swine fattening module located in Matanzas, Cuba. The system is sized by integrating hydraulic, thermal, and structural parameters, and its economic viability is assessed through Net Present Value (NPV = $1.09 million), Internal Rate of Return (IRR = 32%), and a payback period of approximately three years. A comparative screening-level life cycle assessment shows that biogas-based electricity generation substantially reduces impacts on climate change, air quality, and fossil fuel scarcity compared with conventional diesel-based generation, with trade-offs in eutrophication and ecotoxicity. As a key methodological contribution, five quantitative circular economy indicators are proposed and calculated: the Energy Self-Sufficiency Ratio (ESSR = 1.71), the Waste Valorization Index (WVI = 0.91), the Decarbonization Index (DCI = 6.7), the Fertilizer Substitution Rate (FSR = 16.3 t N year⁻¹), and the Water Closure Factor (WCF = 1.30). These indicators show that the system achieves a 71% net energy surplus, valorizes over 90% of the input mass, avoids 6.7 times more emissions than it generates, replaces synthetic fertilizers, and returns more water than it consumes. The findings provide quantitative evidence that the convergence of mesophilic operation without auxiliary heating, high carbon intensity of the power grid, and availability of agricultural land enhances circularity performance in tropical covered lagoon bioreactors, and the proposed integrated indicator framework, aligned with ISO 59020:2024, provides a reproducible and transferable methodological basis for the comparative assessment of anaerobic digestion systems for livestock waste. Full article

Rapid urbanization and climate change are exerting unprecedented pressure on regional water resources, particularly in emerging megacities. This study examines the Xiong’an New Area (XNA) in the water-stressed North China Plain, where high-intensity urbanization coincides with rigorous ecological restoration mandates. To overcome the limitations of single-model assessments, a coupled SD–PLUS–InVEST framework was developed, integrating System Dynamics for socio-economic and policy drivers, Patch-Generating Land-Use Simulation for fine-scale urban expansion, and InVEST for hydrological process assessment. Projecting spatiotemporal water dynamics to 2035 under three Shared Socio-Economic Pathways (SSPs), results reveal that urbanization-driven water demand growth consistently outpaces climate-induced supply gains. While precipitation increases are projected to raise water yield by 8.91–19.58% by 2035, demand surges by up to ~26% under the extensive expansion scenario (SSP5–8.5), driven predominantly by impervious surface proliferation. External water transfers are projected to sustain 40–45% of total supply by 2035, yet this dependency introduces systemic vulnerabilities. Quantitative assessment further indicates severe spatiotemporal mismatches, with Seasonal Water Shortage Rates of 26.1–27.3% and a Spatial Mismatch Index rising from 0.44 to 0.98. These findings indicate that climate-driven precipitation increments alone cannot offset water deficits induced by unregulated urban sprawl, and that integrating strategic land-use planning, resilient infrastructure, and adaptive governance is essential for water security in rapidly developing regions. Full article

Under increasing land constraints and food security pressures, understanding the direction of agricultural technological change is essential for improving land use efficiency. This study investigates the regional disparities, distributional dynamics, and spatial convergence of biased technological change in Chinese agriculture. Dagum Gini decomposition is used to identify regional differences and their sources, kernel density estimation examines distributional dynamics, and spatial econometric models test convergence patterns. The results show that agricultural technological progress in China is predominantly biased toward labor and capital, with labor–land bias being the strongest and continuously increasing. This indicates a gradual shift from land-dependent growth toward more intensive use of non-land inputs under farmland constraints. Regional disparities in technological bias have widened over time, mainly driven by interregional differences and distributional overlap. Kernel density analysis reveals dynamic but largely non-polarized evolution, suggesting gradual adjustment rather than structural divergence. Although no σ-convergence is observed, both absolute and conditional β-convergence exist, with faster convergence in the labor–land dimension and under conditional settings. These findings imply that regions tend to adapt to land constraints through differentiated technological pathways, resulting in uneven improvements in land-related productivity. Overall, biased technological change plays an important role in shaping land use efficiency under resource constraints. The study provides evidence for understanding agricultural adaptation to land scarcity and offers implications for sustainable agricultural development, farmland protection, and long-term food security. Full article

Deep learning has exhibited potential in land use and land cover (LULC) classification applications. However, the effectiveness of deep learning remains constrained by the availability and quality of annotated training data. The persistent scarcity of labeled samples and spatial heterogeneity of remote sensing imagery hinder the robustness and generalization of trained models. Meta-learning, commonly referred to as “learning to learn”, is a paradigm that trains models over a distribution of tasks to acquire transferable knowledge, enabling rapid adaptation to new tasks with only a few labeled samples. This cross-task learning capability makes meta-learning a promising solution to data scarcity and spatial heterogeneity in the remote sensing context. This paper provides a systematic review of meta-learning applications in LULC classification, identifying a total of 70 relevant studies between 2018 and 2025. Three mainstream meta-learning paradigms (memory-augmented, optimization-based, and metric-based) are reviewed, and the applications are analyzed across four core challenges in LULC remote sensing: label scarcity, cross-region and cross-domain distribution shifts, temporal dynamics modeling, and multimodal data integration. The review reveals that optimization-based and metric-based methods dominate current research, with MAML and its variants being the most widely adopted due to the model-agnostic property, while memory-augmented methods remain underexplored. A consistent finding is that meta-learning outperforms conventional pre-training followed by fine-tuning under significant domain shifts across multiple data modalities. Current limitations, including computational overhead, episodic training constraints, and the lack of standardized evaluation protocols, are discussed. Future directions in cross-domain generalization, integration with foundation models, novel architectures, and standardized benchmarks are identified. Full article

Today, the reuse of treated wastewater is considered an important and strategic key driver of integrated and sustainable water and soil management in extremely arid desert regions, where significant constraints due to water scarcity, soil salinization, and the fragility of agricultural ecosystems within palm oases place a strain on all sustainable development policies. Through this study, we conducted a comprehensive evaluation of the performance of the treatment, as well as the constraints related to salinity and the implications for land management of the activated sludge wastewater treatment plant located in the Timimoun desert oasis in southern Algeria. Through monthly monitoring over a 12-month period, we conducted an analysis of physicochemical, nutritional, and microbiological parameters, as well as a seasonal analysis, in addition to calculating irrigation suitability indicators using first-order kinetic modeling of COD degradation. The results showed high reduction rates for COD (90%), BOD₅ (90.5%), and TSS (93.8%), confirming the resilience and effectiveness of biological treatment under very difficult and hostile climatic conditions. Furthermore, the ultraviolet disinfection process ensures microbiological quality that enables the reuse of treated water for agriculture. Despite this, the treated wastewater exhibited moderate salinity and sodicity levels, reflected by EC values ranging from 2.4 to 2.8 dS/m and an SAR value of 6.2, which remain important limiting factors for the long-term sustainability of wastewater reuse. Therefore, this study provides valuable scientific data for developing sound and sustainable water and land management policies in the harsh climate of Saharan oases. Full article

Vegetation phenology refers to the cyclical growth patterns of vegetation in nature, which are influenced by climatic conditions, human activities, and genetic factors. It plays an irreplaceable role in regulating carbon cycling and energy flow within natural ecosystems. However, the combination of a cloudy and rainy climate with a landscape characterized by the interplay of land and water and fragmented patches has long posed challenges for remote sensing phenological monitoring data, including a scarcity of valid observations, frequent temporal gaps, and spectral distortion in mixed pixels. These issues make it difficult to reliably support the needs of wetland phenological inversion and mapping. To address this issue, this study uses vegetation inversion in the Poyang Lake wetlands as a case study and reconstructs high-spatiotemporal-resolution time-series kNDVI data based on multi-source remote sensing data. Methodologically, we propose an improved and enhanced spatiotemporal adaptive reflectance fusion model, IESTARFM. This model enhances the homogeneity of similar pixel selection through adaptive matching windows and land cover constraints. Additionally, it explicitly incorporates cloud probability and time-lag factors into the weighting structure to systematically downweight unreliable observations, and further employs quadratic term corrections to account for the nonlinear growth response of kNDVI. Using the reconstructed dataset, key phenological information is extracted by combining third-order harmonic analysis with a dynamic thresholding method, thereby enhancing the robust characterization of seasonal trajectories under conditions of missing data and noise. Accuracy evaluation results show that the 10m/8d high-frequency kNDVI dataset reconstructed by IESTARFM achieves at least a 12.61% improvement in fusion accuracy compared to classical methods such as ESTARFM, STARFM, and FSDAF, with a maximum reduction in RMSE of 0.026, and effectively restores details in areas with thin cloud cover. The reconstructed kNDVI series achieved a coefficient of determination $R^2$ = 0.875 and RMSE = 0.066 relative to Sentinel-2 observations, indicating that the reconstructed series closely reproduces the reference imagery in both amplitude and spatial structure. The phenological parameters derived from kNDVI exhibit an RMSE of 4.81 days compared to field observations, demonstrating that the reconstructed time series reliably captures the timing of key phenological events. It should be noted that the proposed approach is designed for post-event time-series reconstruction and is not intended for real-time forecasting. In summary, this study collaboratively enhanced the reliability of high-resolution index time-series reconstruction and phenological identification in cloudy and rainy wetlands through three key aspects: cloud noise suppression, heterogeneous boundary preservation, and nonlinear growth characterization. It provides a generalizable technical foundation for dynamic monitoring of wetland vegetation, ecological restoration assessment, and refined management in regions with frequent cloud and rainfall. Full article

Communities across the Southwestern United States (US) and Northern Mexico are making critical decisions regarding how they create long-term water resilience, including by reducing water demand and diversifying water supplies in the face of scarcity. There are several emerging frameworks encouraging collaborative governance approaches to water scarcity, such as Collaborative Water Governance and Adaptive Water Governance; however, examples of ongoing implementation of these frameworks by local governments in academic literature are less prevalent. This paper addresses this gap in the literature by sharing case studies and practitioner recommendations resulting from Growing Water Smart (GWS)—a training and assistance program for local communities to conduct collaborative water resilience action planning across jurisdictional borders, as well as between the historically separated disciplines of water resources management and land use planning. This paper presents and assesses the GWS curriculum as a model for local, cooperative responses to water scarcity, grounded in Collaborative Water Governance, Adaptive Governance, and related frameworks. This paper utilizes primary GWS program documents, firsthand participant perspectives, and direct practitioner experiences to present three case studies of GWS communities working across disciplinary and jurisdictional borders: a regionally collaborative facilitation process and intergovernmental agreement regarding water exports in the San Luis Valley of Colorado; a regional GWS workshop and emerging county-wide convening of jurisdictions within the Verde Watershed of central Arizona; and binational collaboration across the US-Mexico border through a workshop between the cities of Douglas, Arizona and Agua Prieta, Sonora, resulting in a deepened understanding of shared effluent flows. Finally, this paper posits that the GWS model initiates more collaborative and informed decision-making, builds capacity for localities through the support of third-party conveners and facilitators, and maximizes the limited financial and human resources available to local jurisdictions—resulting in a valuable and replicable process to advance water resilience across disciplinary and jurisdictional borders. Full article

Cities worldwide face persistent inequalities in access to urban green spaces, a condition associated with reduced physical activity and poorer mental wellbeing. In high-density metropolises, land scarcity further intensifies these disparities. Although recent studies have highlighted the potential of small-scale green spaces, limited attention has been paid to their system-level and spatially differentiated roles within urban green infrastructure. Consequently, the equality implications of micro-scale interventions such as pocket parks across urban–rural gradients remain insufficiently understood. This study addresses this gap by examining the accessibility impacts of 475 pocket parks in conjunction with 433 large parks in Shanghai, using a multidimensional, citywide analytical framework. The Gaussian two-step floating catchment area (G2SFCA) method was applied within the 15-min community life circle framework to assess service coverage, population served, and per capita accessible green space, as well as their urban–rural differentiation patterns. Results indicate that the inclusion of pocket parks modestly increases overall service coverage (+3.41%) but substantially improves population access (+7.83%), converting 143.79 km² of previously unserved areas into areas with basic green space provision. Spatial effects vary along the urban–rural gradient: pocket parks generate high marginal population-service benefits and improve spatial equality in urban cores, strengthen green space service networks in peri-urban areas, and produce incremental accessibility gains in outer suburbs. Taken together, these findings provide a novel system-level understanding of how pocket parks function within urban green infrastructure networks, offering policy-relevant evidence to guide equality-oriented planning in high-density cities. Full article

The Jing-Jin-Ji region in China has highly intensive agriculture but faces challenges of feed protein shortage and manure surplus, threatening environmental sustainability and industrial development. The black soldier fly (Hermetia illucens) is a promising sustainable feed protein source, capable of efficiently converting organic waste into high-quality insect protein to alleviate feed scarcity while mitigating waste pollution. Existing research on black soldier fly (BSF) treatment in this area is limited, lacking comprehensive benefit evaluations. This study, conducted in Hebei Province, integrated sub-county-scale manure resource mapping, life cycle assessment, and economic assessment to compare traditional composting with BSF-based manure management. Results show BSF treatment can produce 0.36 to 0.39 teragrams (Tg) of feed protein per year. Compared with conventional composting, BSF treatment reduced direct manure-treatment emissions, while additional mitigation benefits were obtained through feed-protein substitution, fertilizer substitution, and BECCS-related land-use savings. Overall, the BSF scenarios achieved a total GHG mitigation potential of 2.85–6.19 Tg CO₂eq yr⁻¹. Economically, low-technology BSF production is cost-competitive, with a total cost of $121 for treating 1 ton of fresh chicken manure. With its protein output roughly doubling the local soybean production capacity, BSF technology provides a viable, low-carbon solution to the dual challenges of feed security and waste management in intensive agricultural systems. Full article

Arid inland river basins are constrained by severe water scarcity and fragile ecosystems. Although large-scale water transfer projects are critical interventions, studies of their comprehensive impacts on eco-socioeconomic systems remain limited. To address this gap, this study proposes an integrated assessment framework. A global Remote Sensing Ecological Index (gRSEI) was developed by incorporating a salinity indicator, employing optimal indicator selection, and utilizing a full-period global normalization strategy. A Gridded Socioeconomic Index (GSEI) was constructed by integrating nighttime light (NTL), population (POP), and gross domestic product (GDP) data. The coupling coordination degree (CCD) model, spatial autocorrelation analysis, and the optimal parameters-based geographical detector (OPGD) were applied to analyze spatial patterns across subregions. Focusing on the Shiyang River Basin (SYRB), this study analyzed the spatiotemporal responses and coupling coordination of the eco-socioeconomic system to the 2001 Jingdian Phase II Water Transfer Project. Results indicate that ecological quality improved significantly after the water transfer, with gRSEI increasing from 0.225 to 0.334. Socioeconomic development also improved overall. The eco-socioeconomic system exhibited high coupling but moderate coordination. The coupling degree (C) and coordination degree (D) increased from 0.824 and 0.370 to 0.852 and 0.442, respectively, with clear regional heterogeneity. The water transfer project shifted the dominant driver of coordinated development from water-related factors to land cover. This study provides a practical framework for assessing ecological and socioeconomic dynamics and their interactions in arid basins under major water transfer project interventions. Full article

Global warming and associated environmental changes are reducing arable land and intensifying salinization risks, posing growing threats to food security. Soil salinity is an increasing threat to agricultural productivity worldwide, particularly in arid and semi-arid areas. Wheat (Triticum aestivum L.) is one of the most important and widely cultivated cereal crops for human consumption and livestock feed. However, with increasing water scarcity and the incidence of salt-affected lands, wheat productivity is increasingly affected by salinity. Previous studies have investigated salinity tolerance mechanisms mainly at the seedling and reproductive stages of wheat; however, comparatively fewer studies integrate rapid biochemical and physiological responses during the first hours of germination stress exposure together with transcriptional analyses during early seedling establishment, even though this stage is critical for stand establishment. Here, we evaluated early physiological and transcriptional responses of salt-tolerant, moderate, and sensitive wheat cultivars exposed to 0 or 150 mM NaCl during germination and the early seedling stage. Tolerant and sensitive cultivars showed contrasting germination performance under salinity. Physiological analysis showed that salt-tolerant cultivars exhibited higher proline accumulation and higher antioxidant enzyme activities (CAT, SOD, and GR), while maintaining lower MDA levels under salinity compared with sensitive cultivars. Notably, tolerant cultivars showed marked upregulation of TaHKT1;4, TaP5CS, TaMYB, and TaDHN genes associated with ion homeostasis, osmoprotectant metabolism, and stress-responsive regulation. These responses represent integrated early-stage biochemical, physiological, and transcriptional indicators of salinity responsiveness rather than direct predictors of final yield performance. Full article

Evapotranspiration (ET) is a fundamental process within the water cycle and the agricultural water balance, optimizing resource allocation, maintaining soil health, and enhancing ecosystem resilience to climate change. Because ET represents a primary consumptive use of irrigation on agricultural lands, enhancing water-use efficiency and sustainable water management requires accurate estimation of evapotranspiration to support long-term sustainability and productivity. This study offers an effective means to visualize spatial and temporal patterns of reference evapotranspiration (ETo) across various vegetation management practices. This study examined the impacts of agroforestry buffers (ABs), grass buffers (GBs), biofuel crops in an agroforestry watershed (BCa), and biofuel crops in a grass buffer watershed (BCg) on ETo, compared to a corn (Zea mays L.)–soybean (Glycine max L.) rotation (RC) for claypan soil in Northern Missouri, USA. The experimental watersheds were located at the Greenley Memorial Research Center, Missouri, USA. Campbell Scientific sensors and Photosynthetically Active Radiation (PAR) smart sensors were installed to measure net radiation, anemometers, humidity, and air temperature. All instruments were mounted on masts at a height of 2 m above ground level in crop, tree, grass, and biofuel areas. Measured meteorological data were recorded hourly from April to October during 2017 and 2018. Daily ETo predictions were calculated using the Penman–Monteith model. These ETo predictions were displayed across the landscape using Python-based GIS for selected dates (each Saturday) for the watersheds. The methodology was implemented using the software programs of Python 2.7.10 and ArcGIS 10.3.1. The results indicated that ETo increased by 11%, 17%, 18%, and 25% in 2017, and by 7%, 9%, 14%, and 20% in 2018 for AB, BCa, BCg, and GB, respectively, compared to RC management. This process may improve soil water recharge in perennial management systems. Accurate estimation of ET in agricultural regions is critical for understanding water balance, hydrological and ecosystem processes, and climate variability. Given that agriculture constitutes the majority of global water consumption, precise ET estimation is particularly significant for sustainable water management, especially in regions experiencing water scarcity. These outcomes may support effective planning and management of agricultural water resources by enabling optimized irrigation and agricultural production. Full article