Search Results (12,784)

River water quality prediction is a crucial aspect of water environment management and ecological conservation, holding significant importance for ensuring the sustainable utilization of water resources. As a key indicator for assessing river self-purification capacity and aquatic ecosystem health, the accurate prediction of dissolved oxygen (DO) is particularly vital for water quality early warning. To address the challenges that single deep learning models face in collaboratively modeling long- and short-term dependencies, and that most hybrid methods fail to adequately consider the characteristic differences in various components within a time series, this paper proposes an STL-TCN-LSTM model for predicting DO concentration in river water. The proposed model first employs seasonal-trend decomposition using Loess (STL) to decompose the original time series into three components: trend, seasonality, and residual, aiming to separate features at different time scales. Then, three parallel Temporal Convolutional Networks (TCNs) are utilized to extract temporal features from each component and reconstruct the sequence. Finally, the reconstructed results are fed into a Long Short-Term Memory (LSTM) network to further model their dynamic temporal dependencies, thereby enhancing prediction accuracy. The performance of the proposed model is validated on three river water quality datasets from different river basins with varying sampling frequencies. The experimental results on the three river datasets show that the STL-TCN-LSTM model consistently outperforms all baseline models, including LSTM, TCN, BiLSTM, GRU, CNN-LSTM, and XGBoost. Specifically, the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) are reduced by an average of 14.47%, 14.51%, and 14.27%, respectively, while the coefficient of determination (R²) improves by an average of 0.79%. The Wilcoxon signed-rank test confirms that all performance improvements are statistically significant (p < 0.05). These results demonstrate that the proposed model achieves higher prediction accuracy and exhibits stronger generalization capability in DO forecasting, thereby offering a reliable tool for water quality early warning and aquatic environmental management. Full article

In the context of the energy transition and increasing environmental pressures, firms’ competitiveness increasingly depends on effective energy–cost management and environmental performance. However, integrating energy, financial, and environmental dimensions into performance evaluation remains methodologically challenging. This study develops an exergy-based two-stage Network Data Envelopment Analysis (NDEA) framework linking energy and cost management in Stage 1 to financial performance under environmental constraints in Stage 2. Using Refinitiv/London Stock Exchange Group (LSEG) data for 45 firms across 18 industries in Portugal and Spain in 2023, the model integrates thermodynamic, financial, and environmental indicators within a unified efficiency framework. The Exergy-to-Sales ratio serves as a fixed intermediate link between thermodynamic and financial efficiency. Results show that incorporating environmental performance increases the number of fully efficient firms in overall efficiency from 3 to 5, while 27 firms move closer to the efficiency frontier. The environmental specification reduces the average improvement required for Return on Sales (ROS) and Return on Equity (ROE) but increases the adjustment needed for Return on Assets (ROA), indicating heterogeneous profitability responses. The study contributes to sustainable performance assessment literature by integrating exergy analysis and NDEA within a unified decision-support framework for managers and policymakers pursuing competitiveness and decarbonization objectives. Full article

The integration of the Sustainable Development Goals (SDGs) into architectural education has received increasing scholarly attention; however, this discussion has largely focused on design studios, environmental design, sustainable design pedagogies, or program-level curriculum mapping. By contrast, project/construction management, construction economics, design economics, time management, and construction project management courses have received comparatively less attention as specific curricular sites for SDG-oriented integration. This study analyzes course information packages related to project/construction management in selected undergraduate architecture programs in Türkiye through an SDG-oriented curriculum mapping approach. Methodologically, the study combines document-based comparative curriculum mapping, directed qualitative content analysis, six thematic coding areas, 0–3 thematic intensity coding, and SDG alignment. The comparative analysis group consists of eight courses related to project/construction management, construction economics, design economics, time management, and construction project management. The findings show that the project/construction management core is strongly represented across all analyzed courses. Sustainability and SDG orientation are most visible in the Design Economics and Special Topics of Construction Project Management courses, while the Time Management in Building Production course stands out in relation to digital/software-supported management. The SDG alignment profile indicates strong visibility for SDG 11, SDG 4, SDG 16, and SDG 12; moderate-to-strong visibility for SDG 9; and a more indirect visibility for SDG 13 through life-cycle thinking, resource efficiency, and sustainable construction management. The original contribution of the study lies in positioning project/construction management courses as a specialized curricular field within SDG-oriented architectural education and in proposing the SDG-Oriented Project/Construction Management Education Matrix. The results suggest that project/construction management courses are not merely technical professional courses; rather, through time, cost, quality, risk, contracts, life-cycle costing, resource management, BIM, supply chains, facility operation, ethics, and applied learning, they provide a strategic curricular foundation for sustainable built environment education. Full article

Life expectancy is a key indicator of public health and sustainable development in Gulf Cooperation Council (GCC) countries, where rapid economic growth, urbanization, and fossil-fuel dependence create environmental and health challenges. This study examines the determinants of life expectancy in six Gulf Cooperation Council countries from 2000 to 2023, focusing on death rates, renewable energy consumption, gross domestic product (GDP) per capita growth, government health expenditure, and carbon dioxide (CO₂) emissions. The empirical strategy combines cross-sectional dependence and slope heterogeneity tests, second-generation panel unit root tests, panel cointegration analysis, and a dynamic System Generalized Method of Moments (System GMM) estimator, with Driscoll–Kraay fixed-effects estimates used for robustness. The results show that higher death rates significantly reduce life expectancy, whereas renewable energy consumption and government health expenditure improve longevity. GDP per capita growth has a modest positive effect, while CO₂ emissions negatively affect life expectancy, confirming the adverse public health consequences of environmental degradation. Robustness checks support the reliability of the main findings. Overall, the evidence highlights the need for integrated policies that combine clean energy transition, stronger environmental regulation, preventive healthcare investment, and sustainable urban development to improve long-term health outcomes in resource-dependent economies in the region. Full article

As climate change challenges intensify, the low-carbon transition has emerged as a fundamental structural transformation reshaping the global economic system and promoting sustainable development. In China, the “Dual Carbon” goals announced in September 2020 represent a landmark policy shift that imposes substantial environmental and regulatory pressure on high-carbon-emission enterprises. Against this backdrop, understanding how firms are adjusting their financial reporting practices to align with the low-carbon transition holds considerable significance for fostering their long-term sustainable development. Unlike previous studies that primarily attributed accounting conservatism to firm-specific risks or general economic uncertainty, this paper views the low-carbon transition as a structural institutional shock that reshapes firms’ external governance environment and information conditions, thereby offering a policy-driven explanation for accounting conservatism. Analysis using the Difference-in-differences method demonstrates that the low-carbon transition significantly enhances accounting conservatism among these enterprises (coefficient = 0.008, t = 4.13). Furthermore, mechanism analysis reveals that the low-carbon transition increases accounting conservatism through financing constraints and media attention. Heterogeneity analysis further indicates that the relationship between the low-carbon transition and accounting conservatism is more pronounced in non-state-owned enterprises, firms located in the eastern region, those facing intense industry competition, and companies with low levels of green innovation. Overall, the findings suggest that accounting conservatism is shaped not only by firm-level factors but also by large-scale institutional and policy transitions. By emphasizing that environmental regulation is a structural determinant of financial reporting behavior, this study extends the accounting conservatism literature. Furthermore, it demonstrates that improving financial reporting quality and risk identification capabilities enhances firms’ ability to address the challenges of the low-carbon transition, thereby fostering their long-term sustainable development. Full article

Driven by China’s “dual carbon” (carbon peak and carbon neutrality) goals and the national strategy of new-type urbanization, prefabricated construction has emerged as a pivotal pathway toward industrialized and sustainable development in the construction sector—leveraging its distinctive advantages in construction efficiency, cost optimization, environmental performance, and design adaptability. Nevertheless, the inherently sequential and interdependent nature of the full construction process—encompassing off-site component manufacturing, logistics transportation, and on-site assembly—introduces pronounced cross-stage risk transmission mechanisms, with prefabricated components serving as critical risk carriers. Such transmission dynamics significantly impede the scalable and safe deployment of prefabricated construction. To date, scholarly efforts on construction safety in prefabricated buildings have predominantly addressed isolated, stage-specific risks, falling short in quantitatively modeling the coupled propagation of risks across stages, accommodating epistemic uncertainties and latent (i.e., unknown or unobserved) risks, and informing targeted, evidence-based mitigation strategies. To bridge this gap, this study develops a rigorous quantitative framework for assessing cross-stage risk transmission in prefabricated construction safety. Specifically, it aims to (i) uncover the structural patterns and driving mechanisms underlying inter-stage risk propagation; (ii) reduce the likelihood of safety incidents throughout the construction life cycle; and (iii) deliver actionable theoretical insights and methodological guidance for practitioners and policymakers. Methodologically, we first conduct a systematic identification of safety-critical risk factors and establish a hierarchical risk indicator system comprising three first-level dimensions and twenty second-level indicators. Second, using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method, causal relationships among risk factors are clarified, while incorporating the Leaky Noisy-or Gate (LNOG) extended model to account for unknown risks. Risk data are processed using triangular fuzzy functions, and a Bayesian network (BN) topology diagram is constructed via the GeNIe 5.0 platform, forming a DEMATEL-LNOG-BN-based model for assessing cross-phase risk transmission. Finally, applying the model to an actual project—”a prefabricated construction project in Shanghai”—the study conducts a cross-phase risk transmission analysis. Through forward probability inference, backward causality tracing, sensitivity analysis, and pathway decomposition, sensitivity comparisons are performed under different LNOG unknown risk parameters. Results are compared with those from the traditional DEMATEL-BN model to validate the stability and consistency of high-sensitivity risk factor identification, comprehensively verifying the applicability and predictive reliability of the proposed DEMATEL-LNOG-BN model. The study quantitatively reveals the progressive diffusion and amplification mechanisms of risks across the production–transportation–assembly process, providing scientific support and practical reference for precise safety risk prevention, critical node control, and the optimization of management systems in prefabricated construction sites. Full article

In the context of global market volatility and the pursuit of sustainable development, improving the financial resilience of manufacturing firms lays a critical foundation for high-quality development of the real economy. To explore the key channels through which ESG practices sustain financial stability amid external shocks, this study selects listed manufacturing enterprises in the Shanghai and Shenzhen A-share markets from 2015 to 2024 as the research sample based on the CSMAR database. It employs the entropy weight method to measure corporate financial resilience, uses a two-way fixed-effects model for benchmark regression, and conducts mechanism tests through mediation and moderation analyses to explore the underlying channels between ESG performance and financial resilience in manufacturing enterprises. The results indicate that improved ESG performance significantly enhances corporate financial resilience, and these findings remain robust after robustness tests and endogeneity treatments. ESG performance primarily enhances the financial resilience of manufacturing enterprises by alleviating financing constraints, increasing R&D investment intensity, and strengthening corporate environmental governance. Heterogeneity tests show that the positive impact of ESG performance on financial resilience is more pronounced in state-owned enterprises, manufacturing enterprises located in Central China, and those in the recession phase. Based on the above conclusions, this paper puts forward targeted suggestions for the government, manufacturing firms, and investors to promote ESG practices and boost financial resilience. Full article

Neodymium-based permanent magnets are strategic materials for the global energy transition, supporting technologies such as wind turbines and electric motors. However, the concentration of supply and the environmental impacts of rare-earth mining raise concerns about supply-chain security and technological dependence. This study analyzed the international scientific output on neodymium within the energy transition framework, identifying temporal trends, research areas, influential authors, and the geographic distribution of knowledge. The PROKNOW-C method was applied to the Scopus database, covering the period from 2004 to 2024. After filtering and standardization steps, 1384 documents were analyzed using VOSviewer to map networks of co-authorship, co-citation, and keyword co-occurrence. The results indicated a growth in publications after 2015, coinciding with intensifying debates on sustainability and technological innovation. These publications are concentrated around three main themes: energy efficiency in motors and technological advancements in wind turbine generators, circular economy strategies, and the development of alternative materials. Scientific output is led by China, the United States, and Japan. Ultimately, this mapping reveals that while the electromechanical application of neodymium is a consolidated field, there is an urgent need to foster research and public policies focused on recycling technologies to mitigate supply-chain vulnerabilities and ensure the material security of the global energy transition. Full article

Within a global landscape characterised by increasing fragmentation, community empowerment requires interdisciplinary, evidence-based and validated methodology for assuring collaborative and transformative action. This research addresses the need for equity and inclusion in underserved rural areas by investigating the CleanAir@Schools initiative in Romania. The study employed a human-centred, multi-stakeholder methodology, utilising exploratory workshops with educators and pilot implementations to develop a learning framework on Sustainability Education, in which students used passive sensors to measure local air quality. Results indicate that the project successfully mobilised entire school communities, catalysing a pedagogical shift from passive reception to active, inquiry-based environmental education. Furthermore, the strategic use of both digital and analogue technologies ensured accessibility for communities facing digital divides. The research concludes that participatory design acts as a catalyst for long-term community empowerment and social transformation by addressing localised challenges through inclusive, restorative practices. By intentionally centring society’s margins, design research fosters regeneration and care, serving as an essential resource for social innovators and policymakers. Full article

The core objective of the concentrated and contiguous protection of traditional villages is to achieve the large-scale preservation and sustainable development of cultural heritage. Elucidating their spatial distribution characteristics and the underlying driving mechanisms serves as a fundamental prerequisite for the effective implementation of conservation practices. Using Geographic Information Systems (GIS) and the optimal parameter-based geographical detector (OPGD) model, this study quantitatively analyzes the spatial distribution and formation mechanisms of traditional villages in southern Shanxi. The results indicate that traditional villages in southern Shanxi exhibit a “one belt, three cores” spatial agglomeration pattern. This pattern emerges from the nonlinear coupling of multiple factors, including natural environment, socio-economic conditions, and historical and cultural elements, among which historical and cultural factors serve as the most prominent driver. The factor detection q-value for cultural heritage density (X18) reached 0.45, and it exhibited a significant synergistic enhancement effect with natural environmental and socio-economic factors. Interaction detection reveals that the explanatory powers of bivariate interactions are generally stronger than that of individual factors, with the synergistic effect between slope (X4) and annual mean temperature (X9) being the most pronounced (q = 0.56). Based on these findings and emphasizing the pivotal role of historical and cultural factors, this study proposes a four-dimensional collaborative governance framework—“cultural leadership, spatial support, institutional safeguards, and social synergy”. This framework aims to provide theoretical foundations and practical pathways for the concentrated and contiguous protection of traditional villages in intra-provincial cultural regions. Full article

Coastal land reclamation is widely implemented to support coastal development, yet its effects on microbial indicators in coupled surface water–groundwater systems remain poorly understood. This study examined the spatiotemporal variability of four microbial indicators and their environmental associations using 46 months of monthly monitoring (April 2016–January 2020) in eastern Guanghai Bay, China. Total bacterial counts, fecal coliforms, Escherichia coli, and total coliforms were analyzed using multivariate statistical methods. Surface water exhibited elevated levels of fecal indicators, with consistently higher pollution levels in the Xiaoma River than in the Dama River and clear seasonal variation associated with climatic and hydrological conditions. Groundwater showed pronounced spatial heterogeneity: Wells 1 and 2 exhibited relatively elevated microbial contamination, whereas Well 3 maintained persistently low microbial levels under high-salinity and high-alkalinity conditions. These patterns suggest that reclamation may be associated with groundwater microbial distribution through changes in groundwater transport pathways and hydrochemical conditions, while anthropogenic pressures also played an important role in shaping contamination patterns. These findings offer practical insights for groundwater protection and sustainable management in reclaimed coastal environments. Full article

The rapid expansion of space activities requires manufacturing strategies that align environmental performance with engineering functionality, yet sustainability assessments of additive manufacturing (AM) remain affected by significant data uncertainty. This study presents an uncertainty-informed Life-Cycle Assessment (LCA) framework to evaluate the environmental and performance trade-offs between Laser Powder Bed Fusion (LPBF) and conventional CNC machining for a satellite mounting bracket. The assessment adopts a process-based cradle-to-gate approach and integrates a hybrid uncertainty propagation methodology combining Dempster–Shafer theory for epistemic uncertainty with Monte Carlo simulation for aleatory variability. Environmental impacts are represented as interval-valued outcomes with associated belief–plausibility measures, enabling explicit quantification of epistemic uncertainty. In parallel, a performance-based benefit metric based on stiffness-to-mass ratio is introduced and propagated under uncertainty using a consistent framework. Environmental and performance indicators are normalised and combined into a composite trade-off metric, allowing the evaluation of manufacturing alternatives across a range of environmental weighting scenarios. Decision outcomes are expressed in terms of belief and plausibility, capturing both support and indeterminacy under uncertainty. Results indicate that CNC machining exhibits lower midpoint environmental impacts and narrower uncertainty intervals across key categories, while LPBF shows higher potential impacts and substantially wider epistemic uncertainty, primarily driven by powder production and limited inventory data. However, when performance benefits are considered, LPBF may become preferable under specific trade-off conditions. These findings highlight the importance of explicitly accounting for epistemic uncertainty and performance considerations when evaluating sustainability trade-offs in aerospace manufacturing. The proposed framework supports early-stage eco-design by enabling robust decision-making under incomplete knowledge. Full article

Against the backdrop of the automotive industry’s transition to low-carbon operations, assessing the sustainability of pure electric vehicle products remains crucial. Existing multi-criteria evaluation methods often follow a compensatory logic, allowing high carbon emissions to be offset by other advantages. This contradicts the core principle that sustainability must be non-negotiable. To address this issue, we propose a two-stage non-compensatory evaluation framework. First, we apply a carbon footprint threshold based on life cycle assessment: any candidate vehicle exceeding this threshold is eliminated. Second, the remaining models are evaluated across ten indicators (economic, social, and technical), and a comprehensive ranking is generated using entropy weighting, fuzzy analytic hierarchy process (FAHP), and the TOPSIS method. This framework has been validated on seven mainstream BEV midsize sedans. The results show that the non-compensatory screening mechanism eliminated two high-carbon-emission models, confirming that environmental criteria must be considered independently. The top-ranked model was not the one with the lowest carbon emissions but rather the one demonstrating balanced performance, indicating that environmental performance and overall competitiveness can be enhanced synergistically. The ranking results remained relatively robust even under a combination of objective and subjective weightings. This study provides a more logically consistent tool for evaluating pure electric vehicles at the product level. Full article

Over the past two decades, dispersive liquid–liquid microextraction (DLLME) has evolved from an emerging concept into a widely adopted approach within sustainable sample preparation. In parallel, a substantial body of review literature has accumulated, highlighting diverse applications and methodological developments. This umbrella review provides a structured synthesis of 59 review and systematic review articles published between 2006 and 2025, with the aim of examining how the review literature itself has shaped current understanding of DLLME. Methodological quality was appraised using the AMSTAR 2 framework, revealing considerable variability in review design and reporting practices. Key elements such as the transparent reporting of pre-defined review methods, fully reproducible search strategies, and structured assessments of bias were not routinely reported, and the majority of reviews were classified as critically low according to AMSTAR 2 criteria. To contextualize these findings, evidence redundancy was examined through structured overlap analysis, yielding a very low Corrected Covered Area (CCA = 0.0188), which indicates that existing reviews largely address complementary rather than repetitive aspects of DLLME. Thematic synthesis identified three dominant domains: methodological and mechanistic developments, green and sustainable extraction strategies, and application-driven advances in environmental and pharmaceutical analysis. Together, these findings provide a structured basis for improving future review design, evaluation, and editorial assessment in analytical chemistry, supporting more transparent, reproducible, and methodologically aligned evidence synthesis. Full article

This study examines the relationship between environmental, social, and governance (ESG) criteria and profitability in the global insurance sector from two distinct perspectives. The System GMM analysis measures the associations between ESG criteria and asset profitability. The analysis, conducted using the CRADIS method and weighted by the CRISUS, MAXC, and NMV methods, determines the companies’ multidimensional performance rankings. Thus, the financial outcomes of companies’ sustainability investments are comprehensively revealed. According to the System GMM estimation results, environmental and social variables are negatively associated with asset profitability, whereas the governance variable and return on equity are positively associated with asset profitability. The leverage ratio and firm size are negatively associated with profitability. While asset profitability and return on equity stand out as the most significant factors compared with environmental, social, and governance variables, environmental and social variables have become increasingly prominent in decision-making processes since 2020. According to the NMV method, return on equity is the decisive criterion, whereas the CRISUS-MAXC integrated method identifies return on assets as the decisive criterion; in both methods, the leverage ratio remains variable and has the lowest impact. According to the CRADIS method rankings, Admiral Group and Zurich Insurance were identified as having the highest performance and the lowest volatility. CNA Financial, Great Eastern, and Hanwha Corp were identified as the lowest-performing companies. Sensitivity analysis results indicate that the NMV-CRADIS method is more resilient to changes in weights. Full article