Search Results (43,035)

Background/Objectives: Early childhood education and care is an ideal setting to promote healthy eating behaviours in young children. However, successful implementation and sustainment of healthy eating policies and practices remains a key challenge in the Australian early childhood education and care (ECEC) context. This study aimed to understand the contextual factors influencing early childhood education directors’ decisions to implement healthy eating policies and practices. Methods: Twelve directors from centre-based long day care centres in New South Wales, Australia, participated in semi-structured interviews. Interview data were analysed using reflexive thematic analysis and the Consolidated Framework of Implementation Research. Results: Directors (n = 12) described alignment with centre values and goals, compatibility with work infrastructure, local champions to lead implementation, and external partnerships with government support services as key facilitators. Directors identified a need for further support to address factors within the broader ECEC sector. Directors described a lack of external partnerships with the community, competing demands for available resources, unrealistic expectations from guidelines and parents, and inconsistent practices across settings as factors inhibiting implementation success. Conclusions: Implementation and sustainment of healthy eating policies and practices can be improved by strengthening parent and community partnerships, investment in the workforce, and a coordinated approach to the provision of support. Full article

The digital creative industries have emerged as a critical driver of regional economic transformation, upgrading, and sustainable development. While previous research has primarily focused on creative industry layout and agglomeration in urban areas, with the integration of digital technology and the creative industry, existing research has an insufficient explanation for the digital creative industry. Specifically, few people have studied the spatial distribution and diffusion of digital creative industries in emerging economies from the macro-regional level. To address this gap, this study analyzes the spatial diffusion mode and regional spatial response law of digital creative industries in the Yangtze River Delta during three critical time windows (2016, 2019, and 2022) in the context of national strategy implementation. A range of spatial analysis technologies is utilized to process the full sample of big data from digital creative industries. This study utilizes OLS and a quantile regression model to determine the dominant factors that affect spatial diffusion and response in the digital creative industries. The results demonstrate that, against the backdrop of regional development strategies, digital creative industries exhibit a variety of diffusion modes, including contagious, hierarchical, corridor, and jump diffusion. The response of industries to regional strategies has different rules in terms of regional space, urban development, and sub-industries. Furthermore, the comprehensive influence of institutional environment, urban economy, development and innovation significantly impacts industrial spatial diffusion and regional response. Among them, government investment in science and technology and the number of universities have consistently been important influencing factors, and policy exhibits nonlinear effects and asymmetric characteristics on industry agglomeration and diffusion. This study enhances the understanding of digital creative industry development in the YRD and offers a theoretical basis for optimizing regional industrial spatial structure and promoting the sustainable development of digital industries. Full article

Pre-disaster and post-disaster allocation strategies are widely investigated as the single optimization problem in humanitarian supply chain management, while integrated decisions including the above two problems are seldom discussed in the existing literature. Here, this paper proposes a mixed-integer programming model to determine these decisions, including the location of central warehouses and emergency storage points and the quantities of relief items pre-deployed and distributed. Specially, two preferences regarding costs and cost-resilience are considered, and a comparison of two models concerning the above preferences is performed. The results are as follows: (i) When the impact of disasters is at a relatively low or moderate level, the cost-oriented model can reduce the government’s financial burden and increase the coverage of relief items. However, when the severity of the disaster is high, the cost resilience-oriented model can respond to the needs of victims within the shortest time, although these needs cannot be completely met. (ii) Increasing the initial inventory level of emergency storage points and enhancing the victims’ tolerance time through social support can effectively reduce the total costs, while increasing the transportation speed can effectively reduce the response delay time. (iii) Adjusting the unit penalty cost can make the total penalty costs and transportation costs decline within a certain range, but such an adjustment has no influence on the response delay time. This paper not only proposes an integrated framework for pre- and post-disaster allocation decisions but also highlights the importance of incorporating resilience into relief item allocation in disaster contexts. Full article

Urban sustainability has become the most important urban development issue globally. Facing the problem of spatial structure optimization during urbanization, how to effectively use public data access to promote urban polycentric development has become a new area of concern for urban planners and policy makers. To quantify how government open-data platforms shape polycentric urban spatial structure across Chinese cities, this study takes the launch of government data platforms as a quasi-natural experiment, constructs the multi-period differences-in-differences model, uses data of 271 Chinese prefectural-level cities from 2010 to 2021, and examines the impact and mechanism of public data access on urban spatial structure. We find that public data access promotes urban polycentric development, especially in large cities, those in urban agglomerations, and resource-abundant cities. The effect follows an inverted ‘N’ trend, which reflects the evolving role of PDA across different urban development stages, highlighting the need for adaptive policies to optimize its benefits. Mechanisms include information process radicalization and industrial structure upgrading, moderated positively by government intervention and regional competition. These insights can inform policies for optimizing urban spatial patterns and advancing sustainable urban development. Full article

This study investigates how local governance actors in northern Taiwan navigate agricultural irrigation coordination under intensifying climate-induced water stress. Although conventional water governance models prioritize structural alignment and centralized integration, they frequently prove to be inadequate under conditions marked by institutional ambiguity and semantic volatility. Focusing on the transitional phase between early drought signaling and the formal implementation of water rationing, this research adopts Situational Grounded Theory (SGT) to examine how actors discursively interpret, negotiate, and adapt to evolving hydrological and institutional constraints. Based on unstructured interviews with irrigation officials, farmers, and public administrators, this study traces how expressions such as “under review” and “adjusting regionally” function as semantic instruments for deferral, alignment, and legitimacy building. These phrases are not merely rhetorical fillers; rather, they operate as situated mechanisms through which actors reposition their roles and recalibrate the meanings of governance. Through iterative coding, semantic clustering, and reflexive mapping grounded in SGT, this study develops the LAWFGS (Local Adaptive Water Governance under Flexible Governance Settings) framework. This tri-axial interpretive framework comprises three interrelated dimensions: (1) governance contexts, which captures the hydrological and institutional phase; (2) actor strategy roles, which reflect how actors adopt and shift their discursive positions; and (3) interpretive flexibility, which denotes the degree of semantic maneuvering exercised in response to governance tensions. The LAWFGS framework offers a situated analytical perspective for understanding how coordination is maintained through meaning-making practices under environmental pressure. The framework emphasizes the relational dynamics through which governance unfolds across shifting and often uncertain contexts. Full article

The existence of dissolved organic matter (DOM) in aquatic environments presents significant challenges to both the environment and public health. This study examines the adsorption efficacy of six organic adsorbents, such as three commercial (coconut shells [CS], palm kernel shells [PKS], and graphite [GR]) and three waste-based materials (plantain peels [PP], water hyacinth leaves [WHL], and corn cobs [CC]) for DOM removal. The waste-derived adsorbents were prepared using thermal and chemical activation techniques, while the commercial adsorbents were used in their standard forms. Adsorption experiments were conducted and analyzed using both kinetic and isotherm models to evaluate removal efficiency and underlying mechanisms. Kinetic modeling revealed that CS, PP, CC, and GR followed pseudo-second-order kinetics, PKS conformed to pseudo-first-order kinetics, and WHL exhibited intra-particle diffusion dominance. The Freundlich isotherm model effectively characterizes the adsorption equilibrium for every material, indicating the multilayer adsorption and heterogeneity of the adsorbent surfaces. Among all tested materials, GR showed the highest DOM removal efficiency (up to 96%) and excellent thermal stability, making it the most effective adsorbent overall. WHL also showed competitive performance, while CS emerged as the most economically viable option despite having slightly lower removal efficiency. Surface area alone does not guarantee adsorption efficiency. Pore accessibility (governed by size/distribution) and surface chemistry (functional group diversity) are equally critical. The findings suggest that both commercial and waste-derived adsorbents hold promise for sustainable and cost-effective water treatment applications. Integrating such materials could enhance the circular economy and offer scalable solutions for addressing water quality issues in developing regions. Full article

This paper presents a finite element implementation of a fractional rheological consolidation model in ABQUS, in which the fractional Merchant model governs the mechanical behavior of the soil skeleton, and the water flow is controlled by the fractional Darcy’s law. The implementation generally involves two main parts: subroutine-based fractional constitutive models’ development and their coupling. Considering the formal similarity between the energy equation and the mass equation, the fractional Darcy’s law was implemented using the UMATHT subroutine. The fractional Merchant model was then realized through the UMAT subroutine. Both subroutines were individually verified and then successfully coupled. The coupling was achieved by modifying the stress update scheme based on Biot’s poroelastic theory and the effective stress principle in UMAT, enabling a finite element analysis of the fractional consolidation model. Finally, the model was applied to simulate the consolidation behavior of a multi-layered foundation. The proposed approach may serve as a reference for the finite element implementation of consolidation models incorporating a fractional seepage model in ABAQUS. Full article

Carbon emission reduction serves as a pivotal strategy for advancing global environmental quality and sustainable socioeconomic development. Private enterprises serve as the primary contributors to industrial carbon emissions. Their low-carbon transition is directly tied to the achievement of China’s Dual Carbon Goals. However, constrained by market failures and the profit-driven nature of capital, these enterprises face significant challenges in both motivation and capacity for carbon emission reduction. As a critical link connecting government and market forces, whether state-owned capital can effectively drive private enterprises to reduce emissions and conserve energy still lacks systematic empirical evidence. Leveraging a panel dataset of private industrial listed companies on China’s Shanghai and Shenzhen A-share markets spanning 2008–2022, we examine the impact of state-owned capital participation on carbon emission reduction and the underlying mechanisms. The empirical results demonstrate that state-owned capital participation can significantly drive carbon emission reduction and propel the low-carbon transformation of private enterprises. Mechanism analysis reveals that state-owned capital participation promotes carbon emission reduction through multiple avenues, including enriching the green resource base, strengthening the value recognition of environmental social responsibility, and improving energy efficiency. Further analysis indicates that the emission reduction effect of state-owned capital participation is more pronounced under conditions of weaker government environmental regulation, lower regional marketization, greater industry competition, and tighter green financing constraints. This study enriches the research on mixed-ownership reform and low-carbon transition of enterprises, deepens the theoretical understanding of the internal mechanism of state-owned capital participation affecting carbon emission reduction, and offers empirical evidence for emerging economies to address the dilemma of emission reduction through property rights integration. Full article

Both behavioral intentions and structural constraints shape household waste disposal in low-resource settings. This study integrates the Theory of Planned Behavior (TPB) with Environmental Justice (EJ) to examine informal waste disposal in Vhembe District, South Africa, a region marked by infrastructural deficits and uneven municipal services. A cross-sectional survey of 399 households across four municipalities assessed five disposal behaviors, including river dumping and domestic burial. Only 8% of households used formal bins, while over 50% engaged in open or roadside dumping. Although education and income were inversely associated with harmful practices, inadequate service access was the most significant constraint on formal disposal. Logistic regression revealed that rural residents and households in underserved municipalities were significantly more likely to engage in hazardous methods, regardless of socioeconomic status. These findings extend TPB by showing that perceived behavioral control reflects not only psychological agency but also material and institutional limitations. By reframing informal disposal as a structurally conditioned response rather than a behavioral deficit, the study advances EJ theory and provides a transferable TPB–EJ framework for decentralized, justice-oriented waste governance. The results underscore the need for Sustainable Development Goal (SDG)-aligned interventions that integrate equitable infrastructure with context-sensitive behavioral strategies. Full article

This study proposes a novel vehicle speed prediction model for plateau transportation—CLS-DW Stacking (Constrained Least Squares Dynamic Weighting Model Stacking)—which holds significant implications for the sustainable development of transportation systems in high-altitude regions. Research on sharp-curved roads on mountainous plateaus remains scarce. Compared with plain areas, data acquisition in such regions is constrained by government confidentiality policies, while complex environmental and topographical conditions lead to substantial variations in road alignment and elevation. To address these challenges, this study presents a sustainable data acquisition and construction method: unmanned aerial vehicle (UAV) video data are processed through road image segmentation, trajectory tracking, and three-dimensional modeling to generate multi-source heterogeneous datasets for both single-curve and continuous-curve scenarios. Building upon these datasets, the proposed framework integrates constrained least squares with multiple deep learning methods to achieve accurate traffic flow prediction. Bi-LSTM (Bidirectional Long Short-Term Memory), Informer, and GRU (Gated Recurrent Unit) are employed as base learners, and the loss function is redefined with non-negativity and normalization constraints on the weights. This ensures optimal weight coefficients for each base learner, with the final prediction obtained via weighted summation. The experimental results show that, compared with single deep learning models such as Informer, the proposed model reduces the mean squared error (MSE) by 1.9% on the single curve dataset and by 7.7% on the continuous curve dataset. Furthermore, by combining vehicle speed predictions across different altitude gradients with decision tree-based interpretable analysis, this research provides scientific support for developing altitude-specific and precision-oriented speed limit policies. The outcomes contribute to accident risk reduction, traffic congestion mitigation, and carbon emission reduction, thereby improving road resource utilization efficiency. This work not only fills the research gap in traffic prediction for sharp-curved plateau roads but also supports the construction of green transportation systems and the broader objectives of sustainable development in high-altitude regions. Full article

Global warming, anthropogenic pressure, and urban expansion at the expense of green spaces are leading to an increase in the incidence of urban heat islands, creating discomfort and health issue for citizens. This present research aimed at quantifying the impact of nature-based solutions to support decision-making processes in sustainable energy action plans. A simple method is provided, linking applied thermodynamics to physics-informed modeling of urban built-up and green areas, high-resolution climate models at urban scale, greenery modeling, spatial georeferencing techniques for energy, and entropy exchanges evaluation in urban built-up areas, with and without vegetation. This allows the outdoor climate conditions and thermo-hygrometric well-being to improve, reducing the workload of cooling plant-systems in buildings and entropy flux to the environment. The finalization and post-processing of obtained results allows the definition of entropy footprints. The main findings show a decrease in greenery’s contribution for different scenarios, referring to a different climatological dataset, but an increase in entropy that becomes higher for the scenario with higher emissions. The comparison between the entropy footprint values for different urban zones can be a useful support to public administrations, stakeholders, and local governments for planning proactive resilient cities and anthropogenic impact reduction and climate change mitigation. Full article

In recent years, artificial intelligence (AI) has emerged as a transformative force across various sectors of modern society, reshaping economic landscapes, social interactions, and ethical considerations. This paper explores the multifaceted impact of AI, analyzing its implications for employment, privacy, and decision-making processes. By synthesizing recent research and case studies, we investigate the dual nature of AI as both a catalyst for innovation and a source of potential disruption. The findings highlight the necessity for proactive governance and ethical frameworks to mitigate risks associated with AI deployment while maximizing its benefits. Ultimately, this paper aims to provide a comprehensive understanding of how AI is redefining human experiences and societal norms, encouraging further discourse on the sustainable integration of these technologies in everyday life. Full article

With the increasing frequency of digital economic activities on a global scale, internet governance has become a pressing issue. Traditional multilateral approaches to formulating internet governance rules have struggled to address critical challenges such as privacy leakage and low global internet defense capabilities. To tackle these issues, this study integrates SAR data processing and interpretation using AI techniques with the development of governance rules through international agreements and multi-stakeholder mechanisms. This approach aims to strengthen privacy protection and enhance the overall effectiveness of internet governance. This study incorporates differential privacy protection laws and cert-free cryptography algorithms, combined with SAR data analysis powered by AI techniques, to address privacy protection and security challenges in internet governance. SAR data provides a unique layer of spatial and environmental context, which, when analyzed using advanced AI models, offers valuable insights into network patterns and potential vulnerabilities. By applying these techniques, internet governance can more effectively monitor and secure global data flows, ensuring a more robust defense against cyber threats. Experimental results demonstrate that the proposed approach significantly outperforms traditional methods. When processing 20 GB of data, the encryption time was reduced by approximately 1.2 times compared to other methods. Furthermore, satisfaction with the newly developed internet governance rules increased by 13.3%. By integrating SAR data processing and AI, the model enhances the precision and scalability of governance mechanisms, enabling real-time responses to privacy and security concerns. In the context of the Global Digital Compact, this research effectively improves the standards, rules, and practical pathways for internet governance. It not only enhances the security and privacy of global data networks but also promotes economic development, social progress, and national security. The integration of SAR data analysis and AI techniques provides a powerful toolset for addressing the complexities of internet governance in a digitally connected world. Full article

The integration of artificial intelligence (AI) into environmental, social, and governance (ESG) disclosure represents a critical frontier for corporate transparency in emerging markets. This study investigates the relationship between AI adoption in ESG reporting, disclosure quality, and financial performance among 180 Saudi-listed companies (2021–2024) within Vision 2030’s transformative context. Using the System Generalized Method of Moments (GMM) estimation with panel unit root and cointegration testing to ensure stationarity assumptions and addressing endogeneity through bounding analysis, the study finds that AI adoption intensity significantly enhances ESG disclosure quality (β = 0.289, p < 0.001), with coefficient significance assessed through t-tests using firm-clustered robust standard errors. Enhanced disclosure quality translates into meaningful financial performance improvements: 0.094 percentage points in return on assets (ROA), 0.156 in return on equity (ROE), and 0.0073 units in Tobin’s Q. Mediation analysis reveals that 73% of AI’s total effect operates through improved ESG quality rather than direct operational benefits. The findings demonstrate parametric bounds robust to macroeconomic confounders, suggesting AI-enhanced transparency creates substantial shareholder value through strengthened stakeholder relationships and reduced information asymmetries. Full article

As governments pursue digital transformation to improve service delivery and administrative efficiency, AI chatbots have emerged as a promising innovation in smart public administration. However, their adoption remains limited, particularly in transitioning countries where institutional, organizational, and technological conditions are complex and evolving. This study aims to empirically examine the key aspects, challenges, and strategic implications of AI chatbots’ adoption in public administration of Uzbekistan, a transitioning government in Central Asia. The study offers a novel contribution by employing an extended technology–organization–environment (TOE) framework. Data were collected through a survey among 501 public employees and partial least squares structural equation modeling was used to analyze data. The results reveal that perceived usefulness, compatibility, organizational readiness, effective accountability, and ethical AI regulation are key enablers, while system complexity, traditional leadership, resistance to change, and concerns over data management and security pose major barriers. The findings contribute to the literature on effective innovation in public administration and provide practical insights for policymakers and public managers aiming to effectively implement AI solutions in complex governance settings. Full article