Search Results (25,115)

This study investigates the environmental dimension of sustainable development across fifteen post-Soviet republics in 2022. While sustainability is generally understood as a triadic construct—economic, social, and environmental—this paper isolates the ecological pillar to highlight cross-country differences shaped by industrial legacies, institutional capacity, and governance models. A composite Environmental Performance Index (EPI) is developed using the Mazziotta–Pareto Index (MPI), which captures both average performance and internal consistency across three SDG-related domains: SDG 6 (Clean Water and Sanitation), SDG 13 (Climate Action), and SDG 15 (Life on Land). The study adds to existing literature as it includes a non-compensatory composite index and cluster analysis, and in policy terms, it provides a benchmarking system for facilitating ecological transition in the post-Soviet context. The results reveal strong divergence across the region: Baltic countries and Moldova achieve higher scores, reflecting policy convergence with the European Union and stronger environmental institutions, while Central Asian republics lag due to resource dependence, water scarcity, and weaker governance. Geographic cluster analysis corroborates these differences, showing clear spatial patterns of environmental convergence and divergence. Correlation analysis further demonstrates that environmental sustainability is positively associated with GDP per capita, HDI, and life expectancy, while negatively linked with inequality and fertility rates. These findings stress the need for context-sensitive and evidence-based policies, intra-regional cooperation, and integrated governance mechanisms to advance ecological transition in line with the 2030 Agenda for Sustainable Development. Full article

Achieving environmentally sustainable growth is a core challenge for developing economies, yet the welfare consequences of green development policies for vulnerable populations remain understudied. This article investigates the distributional impacts of one of the world’s largest development interventions: China’s energy transition. By integrating provincial-level energy metrics with a decade-long household panel survey (CFPS), we employ a fixed-effects model to provide a holistic assessment of the policy’s effects on household well-being. The analysis reveals a stark trade-off: a 10% increase in clean energy adoption generates significant non-monetary well-being gains, equivalent to a 190,000 CNY annual income rise, primarily through improved environmental quality and cleaner cooking fuel access. However, these benefits are partially offset by rising energy costs. Our heterogeneity analysis reveals a clear regressive burden: the transition significantly increases energy expenditures for rural and low-income households, while having a negligible or even cost-reducing effect on their urban and high-income counterparts. Our findings demonstrate that while the energy transition promotes aggregate welfare, its benefits are unevenly distributed, potentially exacerbating energy poverty and inequality. This underscores a critical development challenge: green growth is not automatically inclusive. We argue that for the energy transition to be truly pro-poor, it must be accompanied by robust social protection mechanisms, such as targeted subsidies, to shield the most vulnerable from the adverse economic shocks of the policy. Full article

Conventional separation methods often prove ineffective for complex, refractory high-phosphorus iron ores. Recent advances propose a coal-based direct reduction dephosphorization-magnetic separation process, achieving significant dephosphorization efficiency. This review systematically analyzes phosphorus occurrence states in high-phosphorus oolitic iron ores across global deposits, particularly within iron minerals. We categorize contemporary research and elucidate dephosphorization mechanisms during coal-based direct reduction. Key factors influencing iron mineral phase transformation, iron enrichment, and phosphorus removal are comprehensively evaluated. Phosphorus primarily exists as apatite and collophane gangue m horization agents function by: (1) inhibiting phosphorus-bearing mineral reactions or binding phosphorus into soluble salts to prevent incorporation into metallic iron; (2) enhancing iron oxide reduction and coal gasification; (3) disrupting oolitic structures, promoting metallic iron particle growth, and improving the intergrowth relationship between metallic iron and gangue. Iron mineral phase transformations follow the sequence: Fe₂O₃ → Fe₃O₄ → FeO (FeAl₂O₄, Fe₂SiO₄) → Fe. Critical parameters for effective dephosphorization under non-reductive phosphorus conditions include reduction temperature, duration, reductant/dephosphorization agent types/dosages. Future research should focus on: (1) investigating phosphorus forms in iron minerals for targeted ore utilization; (2) reducing dephosphorization agent consumption and developing sustainable alternatives; (3) refining models for metallic iron growth and improving energy efficiency; (4) optimizing reduction atmosphere control; (5) implementing low-carbon emission strategies. Full article

In response to escalating global concerns about waste generation throughout the product life cycle, the Circular Economy (CE) has emerged as a central alternative to the dominant linear economic model. The integration of CE principles into supply chain management is manifested in Circular Supply Chain Management (CSCM), offering a novel perspective on supply chain sustainability. Despite the growing research interest in developing CSCM to enhance supply chain sustainability, assessment approaches of this concept are notably absent in the literature. This study addresses this gap by focusing on the assessment and performance measurement of circular practices in the context of supply chains. At first, the research presents a bibliometric analysis to delve into the performance and science mapping of CSCM assessment, providing a comprehensive view of the scientific landscape. Subsequently, a content analysis is then used to identify current assessment approaches, focusing on frameworks, methodologies, barriers, enablers, and CE strategies. The study proposes a conceptual model based on the SCOR framework, including core categories such as enablers (business model, technology, collaboration, design) and results (material, water, energy flows) represented by the Rs strategies. This model contributes to bridging theoretical gaps and guiding practitioners and policymakers in the design, evaluation, and implementation of circular supply chains. Full article

Increasingly, discussions that once took place in social environments are transitioning to digital platforms. The role of news media is significant in shaping and enhancing discussions around many topics. This study argues that health-related topics in public discourse, transitioning from offline to online, necessitate rigorous validation. That is why this study proposed the application of deep learning techniques to the boundaries and deviation of accuracies in health-related topics by analyzing health-related tweets from major news outlets such as BBC, CNN, CBC, and Reuters. The study developed LSTM and CNN classifiers to categorize content pertinent to the discourse following the formal deep learning process and employed a sequence of VAEs to verify the learnability and stability of the classifiers. The LSTM demonstrated superior performance compared to CNN, attaining validation accuracies of 98.4% on BBC and CNN, 97.8% on CBC, and 97.3% on Reuters. The optimal configuration of our LSTM achieved a precision of 98.69%, a recall of 98.20%, and an F1-score of 97.90% and recorded the lowest false positive rate, at 1.30%. This provided us with the optimal overall equilibrium for operational oversight. The VAE runs demonstrated that the model exhibited stability and the ability to generalize across different sources, achieving approximately 99.6% for Reuters and around 98.4% for BBC. The findings confirm that deep learning models are capable of reliably tracking the online migration of health discourse driven by news media. This provides a solid foundation for near-real-time monitoring of public engagement and for informing sustainable healthcare recommendation systems. Full article

Understanding adsorption and interfacial properties of surface-active agents at interfaces is crucial to the formation and stability of colloidal systems such as emulsions and foams. In this work, interfacial tension and viscoelasticity of saponin at the β-pinene/water interface were studied using drop tensiometry and dilational rheology measurement. For comparison, saponin at the air/water interface was also evaluated. Both saponin and β-pinene are bio-based, eco-friendly, and abundant in plants, trees, and agricultural wastes. Results showed that dynamic interfacial tensions σ(t) of saponin adsorbed at β-pinene/water and air/water interfaces could be well described by the Ward and Tordai model, suggesting that the saponin adsorption kinetics at both interfaces are controlled by a kinetically limited mechanism. The equilibrium interfacial pressure π_e data prior to critical micelle concentration (cmc) were adequately fitted by the Gibbs adsorption isotherm. At the β-pinene/water interface, a higher cmc and a larger area per molecule, but a lower π_e, were observed compared to the air/water interface. Interestingly, the dilational moduli of saponin at β-pinene/water increased with increasing oscillating frequency, but with less significant frequency dependence than their counterparts at the air/water interface. The dilational moduli of saponin at β-pinene/water passed through a minimum with increasing saponin bulk concentration, while the air/water interface exhibited a strikingly different trend in terms of concentration dependence and a higher magnitude for the dilational moduli. The correlation between adsorption behaviors and dilational properties of saponin at the two interfaces is discussed. Fundamental knowledge gained from this study will be beneficial for the rational development of new biocompatible emulsions and foam products for more sustainable applications. Full article

Accurate prediction of geopolymer concrete compressive strength is vital for sustainable construction. Traditional experiments are time-consuming and costly; therefore, computer-aided systems enable rapid and accurate estimation. This study evaluates three ensemble learning algorithms (Extreme Gradient Boosting (XGB), Random Forest (RF), and Light Gradient Boosting Machine (LightGBM)), as well as two baseline models (Support Vector Regression (SVR) and Artificial Neural Network (ANN)), for this task. To improve performance, hyperparameter tuning was conducted using Bayesian Optimization (BO). Model accuracy was measured using R², RMSE, MAE, and MAPE. The results demonstrate that the XGB model outperforms others under both default and optimized settings. In particular, the XGB-BO model achieved high accuracy, with RMSE of 0.3100 ± 0.0616 and R² of 0.9997 ± 0.0001. Furthermore, Shapley Additive Explanations (SHAP) analysis was used to interpret the decision-making of the XGB model. SHAP results revealed the most influential features for compressive strength of geopolymer concrete were, in order, coarse aggregate, curing time, and NaOH molar concentration. The graphical user interface (GUI) developed for compressive strength prediction demonstrates the practical potential of this research. It contributes to integrating the approach into construction practices. This study highlights the effectiveness of explainable machine learning in understanding complex material behaviors and emphasizes the importance of model optimization for making sustainable and accurate engineering predictions. Full article

Greater Casablanca, one of Africa’s largest metropolitan regions, is undergoing significant spatial and demographic transformation. Yet, the underlying patterns of these dynamics remain poorly understood. This study investigates population dynamics and spatial inequalities in Greater Casablanca between 2014 and 2024. The analysis combines geospatial data, regression modeling, and clustering techniques to explore the interplay between demographic change, housing affordability, public-transport accessibility, and economic activity, providing a data-driven perspective on how these factors shape spatial inequalities and the region’s urban development trajectory. The results reveal a clear core–periphery divide. The central prefecture has lost population despite continued land consumption, while peripheral communes have experienced rapid demographic and economic expansion. This growth is strongly associated with affordable housing and high rates of new-firm formation, but it occurs where transport access remains weakest. Cluster analysis identifies four socio-spatial types, ranging from a shrinking but well-served core to fast-growing, poorly connected peripheries. The study underscores the need for integrated policy interventions to improve transport connectivity, implement inclusive housing strategies, and manage economic decentralization in ways that foster balanced and sustainable metropolitan development. By situating Greater Casablanca’s trajectory within global urbanization debates, this research extends core–periphery and shrinking-city frameworks to a North African context and provides evidence-based insights to support progress towards Sustainable Development Goal 11. Full article

High energy consumption in Chinese rural residential buildings, caused by rudimentary construction methods and the poor thermal performance of building envelopes, poses a significant challenge to national sustainability and “dual carbon” goals. To address this, this study proposes a comprehensive modeling and analysis framework integrating an improved Bio-inspired Black-winged Kite Optimization Algorithm (IBKA) with Support Vector Regression (SVR). Firstly, to address the limitations of the original B-inspired BKA, such as premature convergence and low efficiency, the proposed IBKA incorporates diversification strategies, global information exchange, stochastic behavior selection, and an NGO-based random operator to enhance exploration and convergence. The improved algorithm is benchmarked against BKA and six other optimization methods. An orthogonal experimental design was employed to generate a dataset by systematically sampling combinations of influencing factors. Subsequently, the IBKA-SVR model was developed for energy consumption prediction and analysis. The model’s predictive accuracy and stability were validated by benchmarking it against six competing models, including GA-SVR, PSO-SVR, and the baseline SVR and so forth. Finally, to elucidate the model’s internal decision-making mechanism, the SHAP (SHapley Additive exPlanations) interpretability framework was employed to quantify the independent and interactive effects of each influencing factor on energy consumption. The results indicate that: (1) The IBKA demonstrates superior convergence accuracy and global search performance compared with BKA and other algorithms. (2) The proposed IBKA-SVR model exhibits exceptional predictive accuracy. Relative to the baseline SVR, the model reduces key error metrics by 37–40% and improves the R² to 0.9792. Furthermore, in a comparative analysis against models tuned by other metaheuristic algorithms such as GA and PSO, the IBKA-SVR consistently maintained optimal performance. (3) The SHAP analysis reveals a clear hierarchy in the impact of the design features. The Insulation Thickness in Outer Wall and Insulation Thickness in Roof Covering are the dominant factors, followed by the Window-wall Ratios of various orientations and the Sun space Depth. Key features predominantly exhibit a negative impact, and a significant non-linear relationship exists between the dominant factors (e.g., insulation layers) and the predicted values. (4) Interaction analysis reveals a distinct hierarchy of interaction strengths among the building design variables. Strong synergistic effects are observed among the Sun space Depth, Insulation Thickness in Roof Covering, and the Window-wall Ratios in the East, West, and North. In contrast, the interaction effects between the Window-wall Ratio in the South and other variables are generally weak, indicating that its influence is approximately independent and linear. Therefore, the proposed bio-inspired framework, integrating the improved IBKA with SVR, effectively predicts and analyzes residential building energy consumption, thereby providing a robust decision-support tool for the data-driven optimization of building design and retrofitting strategies to advance energy efficiency and sustainability in rural housing. Full article

Underwater exploration relies heavily on autonomous underwater vehicles and sensor platforms for sustained monitoring of marine environments, yet their operational duration is limited by energy constraints. To enhance energy efficiency, various control strategies have been proposed, including robust, optimal, and disturbance-aware approaches. Recent work introduced a variable structure controller (VSC) with a constant-amplitude control action for depth control of a platform equipped with a variable buoyancy module, achieving an average 22% reduction in energy use in comparison with conventional PID-based controllers. In a separate paper, the conditions for its closed-loop stability were proven. This study extends these works by proposing a controller with a variable-amplitude control action designed to minimize energy consumption. A formal proof of stability is provided to guarantee safe operation even under conservative assumptions. The controller is applied to a previously developed depth-regulated sensor platform using a validated physical model. Additionally, this study analyzes how the controller parameters and mission requirements affect stability regions, offering practical guidelines for parameter tuning. A method to estimate oscillation amplitude during hovering tasks is also introduced. Simulation trials validate the proposed approach, showing energy savings of up to 16% when compared to the controller using a constant-amplitude control action. Full article

The global shift towards renewable energy sources necessitates efficient methods for assessing solar potential in urban areas. Rooftop photovoltaic (PV) systems present a sustainable solution for decentralized energy production; however, their effectiveness is influenced by structural and environmental factors, including roof slope, azimuth, and shading. This study aims to develop and validate a UAV-based methodology for assessing rooftop solar potential in urban areas. The authors propose a low-cost, innovative tool that utilizes a commercial unmanned aerial vehicle (UAV), specifically the DJI Air 3, combined with advanced photogrammetry and 3D modeling techniques to analyze rooftop characteristics relevant to PV installations. The methodology includes UAV-based data collection, image processing to generate high-resolution 3D models, calibration and validation against reference objects, and the estimation of solar potential based on rooftop characteristics and solar irradiance data using the proposed Model Analysis Tool (MAT). MAT is a novel solution introduced and described for the first time in this study, representing an original computational framework for the geometric and energetic analysis of rooftops. The innovative aspect of this study lies in combining consumer-grade UAVs with automated photogrammetry and the MAT, creating a low-cost yet accurate framework for rooftop solar assessment that reduces reliance on high-end surveying methods. By being presented in this study for the first time, MAT expands the methodological toolkit for solar potential evaluation, offering new opportunities for urban energy research and practice. The comparison of PVGIS and MAT shows that MAT consistently predicts higher daily energy yields, ranging from 9 to 12.5% across three datasets. The outcomes of this study contribute to facilitating the broader adoption of solar energy, thereby supporting sustainable energy transitions and climate neutrality goals in the face of increasing urban energy demands. Full article

Throughout history, cultural heritage has accumulated, and is often embodied in monuments, structures, and notable figures. Cultural heritage preservation and management also include digitalization, allowing tangible monuments to be managed as digital inventory with “digital twins”. This provides innovative ways to experience and interact with the real world, in particular by using modern mobile devices. The digitalization of monuments opens new ways to produce decorative items based on the shape of the monuments. Usually, decorative items are produced by craft businesses, family-run for generations, with specialized skills in metal and stone processing. We developed and tested a methodological proposal for frugal innovation: how to produce decorative items with minimal costs based on digital twins, which are particularly in demand in tourism-driven countries like Croatia. A micro-business with three employees, specializing in “metal art,” aims to innovate and expand by producing small-scale replicas of cultural heritage objects, such as busts, statues, monuments, or profiles. A method has been developed to create replicas in the desired material and at a desired scale, faithfully reproducing the original—whether based on a physical object, 3D model, or photograph. The results demonstrate that this sustainable frugal innovation can be successfully implemented using affordable tools and licenses. Full article

The linear construction model is characterized by resource-intensive processes that generate significant waste, whereas adopting circular economy principles facilitates sustainable, adaptable, and recyclable building practices to mitigate waste and conserve resources. The primary objective of this study is to empirically analyze the impact of barriers and drivers on sustainable construction practices and to evaluate the role of organizational culture in moderating this relationship. This study, grounded in Circular Economy theory, distributed 210 questionnaires using simple random sampling to large contractors (501–3000 employees) in Saudi Arabia’s Eastern Region, yielding 154 acceptable responses and a 73% completion rate. Data analysis was conducted using SmartPLS software, revealing that barriers, drivers and organizational culture positively impact sustainable construction practices, with organizational culture also positively moderating the connection among drivers and sustainable construction practices. However, organizational culture was not observed to substantially influence the connection between barriers and sustainable practices. The results highlight the main contribution of organizational culture in supporting sustainable development, offering significant theoretical contributions and practical implications for industry leaders and policymakers to develop regulatory framework and implement strategies that support sustainability. Full article

Water demand has increased due to population growth and rapid socioeconomic development, creating conflicts between human activities and water resources and having a substantial impact on the balance between blue and green water supplies. Existing study lacks a spatial perspective to examine the inherent relationship between blue and green water supply and demand, particularly in terms of geographical differentiation characteristics and rational allocation of blue and green water supply–demand balance in inland river basins. Using the Taolai River Basin as a case study, this research uses the distributed hydrological model SWAT from a blue–green water resources viewpoint to simulate the spatiotemporal distribution features of blue and green water resources at the sub-basin scale from 2002 to 2021. The supply and demand balance relationship of blue and green water resources within the basin was investigated, an assessment index system for water resource security was developed, and the realizable potential of blue water resources was quantified using various indicators. The findings show that during the study period, the average annual green water resources in the Taolai River Basin were 1.95 times greater than blue water resources, making green water the most abundant component of regional water resources. Spatially, both blue and green water resources showed considerable latitudinal zonality, with a declining tendency from south to north and very consistent distribution patterns. Blue water resources showed high geographic variability, with a safety index more than one, suggesting that supply–demand imbalances were most concentrated in the upper and intermediate ranges of the irrigated region, as well as the desert zone, where safety levels were relatively low. In contrast, green water resources had a safety score ranging from 0.7 to 1.0, indicating great overall safety and negligible regional variability. During the research period, the average annual theoretical transferable blue water resources were 4.06 × 108 m³, based on cross-regional water resource allocation potential analysis. This reveals tremendous potential for enhancing regional water resource allocation, hence providing substantial support for effective water consumption within the Taolai River Basin and regional economic growth. In conclusion, the assessment method developed in this work provides a solid foundation for improving water resource allocation and sustainable management in river basins. It provides technical assistance in the construction of water network systems in inland river basins, which is critical in establishing reasonable water resource distribution across various areas within these basins. Full article

Jiangsu Province is an important economic province on the eastern coast of China, revealing the spatial–temporal characteristics, dynamic degree, and transition direction of land use/cover change, and its main driving factors are significant for the effective use of land resources and the promotion of regional human–land coordinated development. Based on land use data of Jiangsu Province from 2000 to 2020, this study investigates the spatiotemporal evolution characteristics of land use/cover using the dynamics model and the transfer matrix model, and examines the influence and interaction of the driving factors between human activities and the natural environment based on 10-factor data using Geodetector. The results showed that (1) In the past 20 years, the type of land use/cover in Jiangsu Province primarily comprises cropland, water, and impervious, with the land use/cover change mode mainly consisting of a dramatic change in cropland and impervious and relatively little change in forest, grassland, water, and barren. (2) From the perspective of the dynamic rate of land use/cover change, the single land use dynamic degree showed that impervious is the only land type whose dynamics have positively increased from 2000 to 2010 and 2010 to 2020, with values of 3.67% and 3.03%, respectively. According to the classification of comprehensive motivation, the comprehensive land use motivation in Jiangsu Province in each time period from 2000 to 2010 and 2010 to 2020 is 0.46% and 0.43%, respectively, which belongs to the extremely slow change type. (3) From the perspective of land use/cover transfer, Jiangsu Province is mainly characterized by a large area of cropland transfer (−7954.30 km²) and a large area of impervious transfer (8759.58 km²). The increase in impervious is mainly attributed to the transformation of cropland and water, accounting for 4066.07 km² and 513.73 km² from 2010 to 2020, which indicates that the non-agricultural phenomenon of cropland in Jiangsu Province, i.e., the process of transforming cropland into non-agricultural construction land, is significant. (4) From the perspective of driving factors, population density (q = 0.154) and night light brightness (q = 0.156) have always been important drivers of land use/cover change in Jiangsu Province. The interaction detection indicates that the land use/cover change is driven by both socio-economic factors and natural geographic factors. (5) In response to the dual pressures of climate change and rapid urbanization, coordinating the multiple objectives of socio-economic development, food security, and ecological protection is the fundamental path to achieving sustainable land use in Jiangsu Province and similar developed coastal areas. By revealing the characteristics and driving factors of land use/cover change in Jiangsu Province, this study provides qualitative and quantitative theoretical support for the coordinated decision-making of economic development and land use planning in Jiangsu Province, specifically contributing to sustainable land planning, climate adaptation policy-making, and the enhancement of community well-being through optimized land use. Full article