Search Results (12,459)

Groundwater plays a crucial role in sustaining agriculture and livelihoods in the arid Middle Draa Valley (MDV) of southeastern Morocco. However, increasing groundwater extraction, declining rainfall, and the absence of effective floodwater harvesting systems have led to severe aquifer depletion. This study applies and compares six machine learning (ML) algorithms—decision trees (CART), ensemble methods (random forest, LightGBM, XGBoost), distance-based learning (k-nearest neighbors), and support vector machines—integrating GIS, satellite data, and field observations to delineate zones suitable for groundwater recharge. The results indicate that ensemble tree-based methods yielded the highest predictive accuracy, with LightGBM outperforming the others by achieving an overall accuracy of 0.90. Random forest and XGBoost also demonstrated strong performance, effectively identifying priority areas for artificial recharge, particularly near ephemeral streams. A feature importance analysis revealed that soil permeability, elevation, and stream proximity were the most influential variables in recharge zone delineation. The generated maps provide valuable support for irrigation planning, aquifer conservation, and floodwater management. Overall, the proposed machine learning–geospatial framework offers a robust and transferable approach for mapping groundwater recharge zones (GWRZ) in arid and semi-arid regions, contributing to the achievement of Sustainable Development Goals (SDGs))—notably SDG 6 (Clean Water and Sanitation), by enhancing water-use efficiency and groundwater recharge (Target 6.4), and SDG 13 (Climate Action), by supporting climate-resilient aquifer management. Full article

Ecosystem restoration represents a promising solution for enhancing ecosystem services and environmental sustainability. However, border regions—characterized by ecological fragility and geopolitical complexity—remain underrepresented in ecosystem service and restoration research. To fill this gap, we coupled spatially explicit models (e.g., InVEST and RUSLE) with scenario analysis to quantify the ecosystem service potential that could be achieved in China’s Tibetan borderlands under two interacting agendas: ecological restoration and border-strengthening policies. Restoration feasibility was evaluated through combining local biophysical constraints, economic viability (via restoration-induced carbon gains vs. opportunity costs), operational practicality, and simulated infrastructure expansion. The results showed that per-unit-area ecosystem services in border counties (particularly Medog, Cona, and Zayu) exceed that of interior Tibet by a factor of two to four. Combining these various constraints, approximately 4–17% of the border zone remains cost-effective for grassland or forest restoration. Under low carbon pricing (US$10 t⁻¹ CO₂), the carbon revenue generated through restoration is insufficient to offset the opportunity cost of agricultural production, constituting a major constraint. Habitat quality, soil conservation, and carbon sequestration increase modestly when induced by restoration, but a pronounced carbon–water trade-off emerges. Planned infrastructure reduces restoration benefits only slightly, whereas raising the carbon price to about US$50 t⁻¹ CO₂ substantially expands such benefits. These findings highlight both the opportunities and limits of ecosystem restoration in border regions and point to carbon pricing as the key policy lever for unlocking cost-effective restoration. Full article

Urban sustainability challenges demand integrated frameworks capable of addressing the dynamic, non-linear nature of cities. This study explores how the principles of the circular economy and complexity theory intersect to support systemic transformation in sustainable urban planning. Through a systematic literature review of 71 peer-reviewed articles published between 2015 and 2025, we analyze conceptual, methodological, and practical articulations across multiple thematic axes, including circular governance, urban metabolism, regenerative design, adaptive planning, digital integration, and environmental justice. Bibliometric and content analyses were conducted using Scopus metadata, VOSviewer for thematic clustering, and the StArt software (Version 3.4) to structure article selection. The findings reveal that circular economy provides practical tools for resource efficiency and regeneration, while complexity theory offers an adaptive framework to navigate uncertainty, emergent behaviors, and feedback dynamics. The synthesis suggests that their integration enables a more holistic and resilient approach to urban transformation. However, gaps remain in social inclusivity, long-term assessment, and the operationalization of complexity-informed planning. This study contributes to advancing a transdisciplinary agenda for circular and adaptive urban futures, offering insights for scholars, planners, and policymakers aiming to reconfigure cities within planetary boundaries. Full article

This study explores the migration intentions of university students—representing the potential creative class—in Erzurum, a medium-sized city in eastern Turkey experiencing shrinkage. Within the theoretical framework of shrinking cities, it investigates how economic, social, physical, and personal factors influence students’ post-graduation stay or leave decisions. Survey data from 742 Architecture and Fine Arts students at Atatürk University were analyzed using factor analysis, logistic regression, and correlation to identify key migration drivers. Findings reveal that, in addition to economic concerns such as limited job opportunities and low income, personal development opportunities and social engagement also play a decisive role. In particular, the perception of limited chances for skill enhancement and the belief that Erzurum is not a good place to meet people emerged as the strongest predictors of migration intentions. These results suggest that members of the creative class are influenced not only by economic incentives but also by broader urban experiences related to self-growth and social connectivity. This study highlights spatial inequalities in access to cultural, educational, and social infrastructure, raising important questions about spatial justice in shrinking urban contexts. This paper contributes to the literature on shrinking cities by highlighting creative youth in mid-sized Global South cities. It suggests smart shrinkage strategies focused on creative sector development, improved quality of life, and inclusive planning to retain young talent and support sustainable urban revitalization. Full article

This study examines the progress, challenges, and successes in implementing Sustainable Development Goal 12 (SDG12), focusing on responsible consumption and production, using Qatar as a case study. The State has integrated Sustainable Consumption and Production (SCP) into national policies, established coordination mechanisms, and implemented action plans aligned with SDG12 targets. Achievements include renewable energy adoption, waste management reforms, and sustainable public procurement, though challenges persist in rationalizing fossil fuel subsidies, addressing data gaps, and enhancing corporate sustainability reporting. Efforts to reduce food loss and waste through redistribution programs highlight the country’s resilience, despite logistical obstacles. The nation has also advanced hazardous waste management, environmental awareness, and sustainable tourism policies, though gaps in data systems and policy coherence remain. Qatar’s approach provides a valuable local-to-global example of balancing resource-dependent economies with sustainability goals. Its strategies and lessons offer potential adaptability for other nations, especially those facing similar challenges in achieving SDG12. By strengthening data systems, enhancing policy integration, and fostering regional and international cooperation, Qatar’s efforts underscore the importance of aligning economic growth with environmental stewardship, serving as a blueprint for global sustainability initiatives. Full article

Wind power has gained attention as a vital renewable energy source capable of reducing emissions and serving as an effective alternative to fossil fuels. Floating wind farms could significantly enhance the energy capacities of Mediterranean countries. However, location selection for offshore wind farms (OWFs) is a challenge for renewable energy policy and marine spatial planning (MSP). To address these issues, this study considers the marine space of Greece to propose a GIS-based multi-criteria decision-making (MCDM) framework employing the Analytic Hierarchy Process (AHP) to identify suitable sites for OWFs. The approach assesses 19 exclusion criteria encompassing legislative, environmental, safety, and technical constraints to determine the eligible areas. Subsequently, 10 evaluation criteria are weighted to determine the selected areas’ level of suitability. The study considers baseline conditions (1981–2010) and future climate scenarios based on RCP 4.5 and RCP 8.5 for two horizons (2011–2040 and 2041–2070), integrating projected wind velocities and sea level rise to evaluate potential shifts in suitable areas. Results indicate the central and southeastern Aegean Sea as the most suitable areas for OWF deployment. Climate projections indicate a modest increase in suitable areas. The findings serve as input for climate-resilient MSP seeking to promote sustainable energy development. Full article

To solve the present housing crisis, the Support for Access to Housing Program, in the context of PRR, mainly focuses on social housing to be built or on housing of social interest to be regenerated. To approach this problem, a research question was raised: “What is the significance of the existing cooperative housing in solving the current housing crisis?” To analyze this issue, a multiple case study was adopted, comparing a collective dwelling of cooperative promotion at controlled costs in Caselas (1980s–1990s) with Expo Urbe (2000–2007) in Parque das Nações, a symbol of the new sustainable cooperative housing, which targets a population with a higher standard of living and thus is excluded from the PRR plan. These cases revealed the discrepancy created by the Cooperative Code of 1998 and its consequences for the urban regeneration of this heritage. They show that Caselas, built in a residential urban neighborhood, is strongly attached to a community, provides good social inclusion for vulnerable groups at more affordable prices, and it is eligible for urban regeneration and reuse (for renting or buying). However, the reuse of Caselcoop’s edifices cannot compromise their cultural and residential values or threaten the individual integrity. Full article

This research examines the compounding risks of global environmental change, including climate change, environmental law, biodiversity loss, and pollution, which threaten the stability of economic systems worldwide. While digital technology and global governance regulation are increasingly being proposed as solutions, their synergistic potential in advancing economic sustainability has been less explored. How can these technologies mitigate environmental risks while promoting sustainable and equitable development, aligning with the Sustainable Development Goals? We analyze policy global environmental data from the World Bank and the United Nations, as well as literature reviews on digital interventions, artificial intelligence, and smart databases. Global environmental change presents economic stability and rule of law threats, and innovative governance responses are needed. This study evaluates the potential for digital technology to be leveraged to enhance climate resilience and regulatory systems and address key implementation, equity, and policy coherence deficits. Policy recommendations for aligning economic development trajectories with planetary boundaries emphasize that proactive digital governance integration is indispensable for decoupling growth from environmental degradation. However, fragmented governance and unequal access to technologies undermine scalability. Successful experiences demonstrate that integrated policies, combining incentives, data transparency, and multilateral coordination, deliver maximum economic and environmental co-benefits, matching digital innovation with good governance. We provide policymakers with an action plan to leverage technology as a multiplier of sustainability, prioritizing inclusive governance structures to address implementation gaps and inform legislation. Full article

With deepening global higher education reforms, outcome-based education has emerged as the core paradigm for teaching model innovation. This study investigates the structural dependencies and teaching effectiveness of the Environmental Design curriculum at Hubei Engineering University in medium-sized cities, China, addressing challenges of enrollment decline and market contraction critical for urban sustainability. Using network analysis, we construct curriculum support and contribution networks and course temporal networks to assess structural dependencies and teaching effectiveness, revealing structural patterns and optimizing the OBE-based Environmental Design curriculum to enhance educational quality and student competencies. Analysis reveals computer basic courses as knowledge transmission hubs, creating a course network with a distinct core–periphery structure. Technical course reforms significantly outperform theoretical course reforms in improving student performance metrics, such as higher average scores, better grade distributions, and reduced performance gaps, while innovative practice courses show peripheral isolation patterns, indicating limited connectivity with core curriculum modules, which reduces their educational impact. These findings provide empirical insights for curriculum optimization, supporting urban sustainable development through enhanced professional talent cultivation equipped to address environmental challenges like sustainable design practices and resource-efficient urban planning. Network analysis applications introduce innovative frameworks for curriculum reform strategies. Future research expansion through larger sample validation will support urban sustainable development goals and enhance professional talent cultivation outcomes. Full article

In China’s Loess Plateau, sustainable agricultural end products are affected by an insufficiency of water resources. Rising crop water use efficiency (WUE) through field management pattern improvement is a crucial plan of action to address this issue. However, there is no agreement among researchers on the most appropriate field management practices regarding WUE, which requires further integrated quantitative analysis. We conducted a meta-analysis by quantifying the effect of agricultural practices surrounding nitrogen (N) fertilizer management. The two experimental cultivars were Yunhan–20410 and Yunhan–618. The subplots included nitrogen 0 kg·ha⁻¹ (N0), 90 kg·ha⁻¹ (N90), 180 kg·ha⁻¹ (N180), 210 kg·ha⁻¹ (N210), and 240 kg·ha⁻¹ (N240). Our results show that higher N rates (up to N210) enhanced water consumption during the node-flowering and flowering-maturity time periods. YH–618 showed higher water use during the sowing–greening and node-flowering periods but decreased use during the greening-node and flowering-maturity periods compared to YH–20410. The N210 treatment under YH–618 maximized water use efficiency (WUE). Increased N rates (N180–N210) decreased covering temperatures (T_max, T_min, T_aver) during flowering, increasing the level of grain filling. Spike numbers rose with N application, with an off-peak at N210 for strong-gluten wheat. The 1000-grain weight was at first enhanced but decreased at the far end of N180–N210. YH–618 with N210 achieved a harvest index (HI) similar to that of YH–20410 with N180, while excessive N (N240) or water reduced the HI. Dry matter accumulation increased up to N210, resulting in earlier stabilization. Soil water consumption from wintering to jointing was strongly correlated with pre-flowering dry matter biological process and yield, while jointing–flowering water use was linked to post-flowering dry matter and spike numbers. Post-flowering dry matter accumulation was critical for yield, whereas spike numbers positively impacted yield but negatively affected 1000-grain weight. In conclusion, our results provide evidence for determining suitable integrated agricultural establishment strategies to ensure efficient water use and sustainable production in the Loess Plateau region. Full article

The rapid growth of electric vehicles (EVs) and new energy systems has put lithium-ion batteries at the center of the clean energy change. Nevertheless, to achieve the best battery performance, safety, and sustainability in many changing circumstances, major innovations are needed in Battery Management Systems (BMS). This review paper explores how artificial intelligence (AI) and digital twin (DT) technologies can be integrated to enable the intelligent BMS of the future. It investigates how powerful data approaches such as deep learning, ensembles, and models that rely on physics improve the accuracy of predicting state of charge (SOC), state of health (SOH), and remaining useful life (RUL). Additionally, the paper reviews progress in AI features for cooling, fast charging, fault detection, and intelligible AI models. Working together, cloud and edge computing technology with DTs means better diagnostics, predictive support, and improved management for any use of EVs, stored energy, and recycling. The review underlines recent successes in AI-driven material research, renewable battery production, and plans for used systems, along with new problems in cybersecurity, combining data and mass rollout. We spotlight important research themes, existing problems, and future drawbacks following careful analysis of different up-to-date approaches and systems. Uniting physical modeling with AI-based analytics on cloud-edge-DT platforms supports the development of tough, intelligent, and ecologically responsible batteries that line up with future mobility and wider use of renewable energy. Full article

Understanding the utilization patterns of nuclear medicine (NM) services is essential for optimizing resource allocation and service provision. This study aimed to address the regional evidence gap by reporting the demand for NM services by sex and age at a public hospital in Jamaica. This was a non-experimental, retrospective study of NM scans that were completed at the University Hospital of the West Indies from 1 June 2022 to 31 May 2024. While all scans were reported in the descriptive totals, for patients with multiple scans during the study period, only the data from the first visit was used in the inferential statistical analysis. This was performed with the IBM SPSS (version 29.0) software and involved the use of chi-square goodness of fit and multinomial logistic regression. A total of 1135 NM scans for 1098 patients were completed (37 patients had more than one scan); 596 (54.3%) were female and 502 (45.7%) were male, with the ages ranging from 3 days to 94 years old. Among the female patients, there was a greater demand in the ≥60 years age group for cardiac amyloid scans (χ² = 6.40, p < 0.05), while females 18–59 years had a greater demand for thyroid scans (χ² = 7.714, p < 0.05) and bone scans (χ² = 3.904, p < 0.05). On the other hand, significantly more males in the ≥60 age group presented for cardiac amyloid (χ² = 4.167; p < 0.05) and bone scans (χ² = 145.79, p < 0.01). Males were significantly less likely to undergo a thyroid scan than females (p < 0.01, OR = 0.072, 95% CI: 0.021, 0.243) while individuals aged 18–59 years were more likely to undergo this scan than patients aged 60 or older (p = 0.02, OR = 3.565, 95% CI: 1.258, 10.104). Males were more likely to do a cardiac amyloid scan (p < 0.05, OR = 2.237, 95% CI: 1.023, 4.891) but less likely to undergo a cardiac rest/stress test than females (p = 0.02, OR = 0.307, 95% CI: 0.114, 0.828). Prolonged life expectancy and an aging population have the potential to impact NM utilization, thus requiring planning for infrastructure, equipment, work force, and supplies. Cancer-related and cardiovascular indications are a top priority at this facility; hence, age- and sex-specific analysis are useful in establishing models for policy makers with regard to the allocation of economic and human resources for the sustainability of this specialized service. Full article

Blast mitigation of structures is an important research topic due to increasing intentional and accidental human-induced threats and hazards. This research area is essential to building capabilities in sustaining structural protection, site planning, protective design efficiency, occupant safety, and response and recovery plans. This paper investigates experimental tests and finite element analysis (FEM) of thin A36 steel sheets subjected to blast. Six field blast tests were performed at standoff distances of 300 mm and 500 mm. The explosive charges comprised 334 g of bare Composition B, and the steel sheets were 2 mm thick. The experimental results, derived from the analysis of high-speed camera recordings of the blast events, were compared with FEM simulations conducted using Abaqus^®/Explicit version 6.10. Three constitutive material models were considered in these simulations. First, the FEM simulation results were compared with experimental results. It was shown that the FEM analysis provided reliable results and was proven to be robust and cost-effective. Second, an extensive set of 460 additional numerical simulations was carried out as a parametric study involving varying standoff distances and steel sheet thicknesses. The results and methodologies presented in this paper offer valuable and original insights for engineers and researchers aiming to predict damage to steel structures during real detonation events and to design blast-resistant structures. Full article

Background: this research presents an integrated approach to enhancing the sustainability of plastic bag supply chains in Indonesia by addressing critical issues related to ineffective post-consumer waste management and low recycling rates. The objective of this study is to develop a combined framework of material flow analysis (MFA) and sustainable supply chain planning to improve demand forecasting and inflow management across the plastic bag lifecycle. Method: the research adopts a quantitative method using the XGBoost algorithm for forecasting and is supported by a polymer-based MFA framework that maps material flows from production to end-of-life stages. Result: the findings indicate that while production processes achieve high efficiency with a yield of 89%, more than 60% of plastic bag waste remains unmanaged after use. Moreover, scenario analysis demonstrates that single interventions are insufficient to achieve circularity targets, whereas integrated strategies (e.g., reducing export volumes, enhancing waste collection, and improving recycling performance) are more effective in increasing recycling rates beyond 35%. Additionally, the study reveals that increasing domestic recycling capacity and minimizing dependency on exports can significantly reduce environmental leakage and strengthen local waste management systems. Conclusions: the study’s novelty lies in demonstrating how machine learning and material flow data can be synergized to inform circular supply chain decisions and regulatory planning. Full article

Urban green infrastructure plays a vital role in the sustainable development of cities. As urban areas expand, green spaces are increasingly affected. The pressure from new developments leads to a reduction in vegetation and raises new public health risks. Addressing this challenge requires effective planning, maintenance, and continuous monitoring. To enhance traditional approaches, remote sensing is becoming a vital tool for city-wide observations. Publicly available large-scale data, combined with machine learning models, can improve our understanding. We explore the potential of Sentinel-2 to classify and extract meaningful features from urban landscapes. Using advanced machine learning techniques, we aim to develop a robust and scalable framework for classifying urban environments. The proposed models will assist in monitoring changes in green spaces across diverse urban settings, enabling timely and informed decisions to foster sustainable urban growth. Full article