Search Results (5,612)

Urban expansion presents significant challenges and opportunities for ecological conservation in developing countries, particularly in regions such as the Table Bay Nature Reserve in Cape Town, South Africa, where urban development interfaces with sensitive ecosystems. This article examines the complex dynamics between urban growth and ecological implications in this unique landscape, employing multi-temporal remote sensing techniques to analyze changes over time. By investigating the historical trajectory of urbanization in Table Bay, alongside its impacts on biodiversity and ecosystem services, we aim to underscore the urgent need for sustainable urban planning and conservation strategies. To analyze land use/land cover (LULC) dynamics over a 24-year period, this study leveraged a time series of satellite imagery processed within the Google Earth Engine (GEE) platform. Data can be accessed using their respective collection IDs within the GEE platform. The use of remote sensing tools aligns with Sustainable Development Goal (SDG) 15, which focuses on the protection, restoration, and sustainable use of terrestrial ecosystems. Urban encroachment analysis indicates that approximately 0.324 km² of built-up area expanded directly within the reserve boundary, highlighting a measurable degree of infringement into protected zones. The dominance of built-up and bare land classes highlights the early encroachment of urban infrastructure and anthropogenic disturbance, setting the stage for subsequent land cover transformations observed in later years (2012 and 2024). These findings demonstrate a persistent trend of urban encroachment and ecological alteration within the Table Bay Nature Reserve. With the increase in global population levels, urban expansion into protected conservation areas has become a critical environmental concern, threatening biodiversity globally. This challenge is particularly acute in developing countries as seen in regions like the Table Bay Nature Reserve in Cape Town, South Africa, where urban development is interfaced with sensitive ecosystems. Full article

This study investigated the relationship between digital governance and sustainable development across the European Union (EU-27) during the period 2015–2023. Although digital transformation has become a central policy priority, empirical evidence on how e-government adoption contributes to sustainability performance remains limited. Using panel data from Eurostat and the UN Sustainable Development Solutions Network, the analysis employed advanced econometric techniques, including Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), and Method of Moments Quantile Regression (MMQR), to explore both long-run relationships and heterogeneous effects across countries. The model incorporates key indicators such as the percentage of individuals using e-government services, Gross Domestic Product (GDP) per capita growth, and Research and Development (R&D) expenditure, capturing, respectively, digital governance adoption, innovation potential, and economic capacity, as essential drivers of sustainable development. Results indicate a strong and statistically significant positive association between digital governance adoption and sustainable development outcomes. The quantile regression analysis reveals that this effect is more pronounced in countries with higher innovation intensity and stronger economic capacity, suggesting that digital governance amplifies sustainability benefits in countries with more advanced institutional and technological infrastructures. Robustness checks confirm the stability of the findings across multiple estimation techniques. The results underscore the need for inclusive and innovation-driven digital strategies to ensure that the benefits of digital governance are equitably distributed, ultimately enhancing the EU’s progress towards the Sustainable Development Goals. Full article

This study aimed to analyze changes in vegetation, built-up areas, and population growth in Lahore city from 1990 to 2023. The data was acquired from Google Earth Engine, and the spectral bands were retrieved from Landsat 5 and Landsat 8. The decadal analysis of the landscape was conducted from 1993 to 2001, 2001 to 2012, and from 2013 to 2023. Further analysis was conducted in ArcGIS version 10.3 to evaluate the Normalized Difference Vegetation Index and the Normalized Difference Built-up Index to assess vegetation and built-up areas, respectively. To analyze the urban population of Lahore, data were obtained from the Global Human Settlement Layer for 1990, 2000, 2010, and 2020. Results revealed that the total vegetated area of Lahore city decreased from 1453.0 km² in 1993–2001 to 788.2 km² in 2013–2023. Moreover, the urban built-up area expanded from 319.6 km² in 1993–2001 to 966.8 km² in 2013–2023. Sub-district-level analysis indicated that Model Town and Raiwind areas of Lahore depicted better vegetation recovery in this decade. The population of Lahore has been increasing steadily, with the 2010s being a particularly rapid period of growth. The projections for 2030 also depict a continuous growth pattern. This study was further developed by integrating multi-decadal averaging coupled with selected-year analysis to distinguish gradual land transformation from relatively accelerated phases of urban expansion of Lahore. Also, by combining NDVI and NDBI values on both Lahore and its tehsil level, the research provides a collective sub-district- and district-level perspective into the spatial heterogeneity of peri-urban transformations. The findings of the study explain how major infrastructural projects shape the urban growth patterns of cities like Lahore and cause a decline in the green areas of fast-growing cities in South Asia. This study further highlights the consequences of unplanned urban expansion in regions where high population growth has compromised green infrastructure and threatened ecological balance. In addition, it supports several Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), and SDG 15 (Life on Land) by providing spatial evidence of urban expansion of the city and losses of its green spaces. The findings offer empirical insights to support climate-resilient developments. The study also demonstrates the necessity of integrating green infrastructure and providing robust strategies for forthcoming urban planning projects and policy development regarding urban expansion. Full article

Accurate short-term traffic flow prediction is vital for effective signal control and sustainable urban mobility. Deep learning models, such as the Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) networks, have demonstrated strong capability in modelling temporal traffic dynamics. However, the influence of their architectural and hyperparameter configurations remains underexplored. This study proposes a systematic methodology to assess the impact of hyperparameter optimization on GRU and LSTM models for predicting traffic flow at a signalized intersection. The methodology is evaluated using minute-level traffic data from the Globe Roundabout in Jeddah, Saudi Arabia. Bayesian optimization is applied to identify the best-performing hyperparameters. The results show that the optimized GRU model achieves a Root Mean Square Error (RMSE) of 0.0953, representing a 90.2% improvement compared to the baseline GRU (RMSE ≈ 0.969). Likewise, the optimized LSTM model attains an RMSE of 0.0960, corresponding to an 85.2% improvement relative to its baseline (RMSE ≈ 0.648). Similar gains are observed for the Mean Absolute Error. Visual analysis further shows that optimized models reduce smoothing bias, enhance the tracking of transient fluctuations, and produce stable, low-variance residuals. The findings demonstrate that hyperparameter optimization substantially improves predictive accuracy while preserving computational efficiency, enabling lightweight recurrent architectures to perform at a level comparable to more complex models. Full article

Smart city decision systems must balance conflicting objectives including efficiency, sustainability, equity, safety, and public accountability. Existing AI and reinforcement learning approaches often optimize isolated objectives and rarely provide integrated mechanisms for sustainability alignment, transparency, and pre-deployment validation. This paper introduces MORL-SGF, a governance-aware framework that integrates ESG/SDG-aligned multi-objective reinforcement learning, Digital Twin (DT)-based policy validation, and Pareto-based policy auditing within a single learning pipeline. The framework preserves vector-valued rewards to avoid hidden scalarization bias and supports auditable policy selection from a portfolio of Pareto-optimal candidates. MORL-SGF is validated analytically and conceptually through formal modeling and structured evidence synthesis rather than empirical deployment, providing a blueprint for subsequent simulation-based and real-world implementation studies. Future work will focus on large-scale Digital Twin benchmarking, stakeholder preference modeling, and deployment-oriented evaluation. Full article

Attaining education sustainability is indeed important as it ensures the overall economic sustainability of countries and it is directly connected with the United Nations Sustainable Development Goals (SDG-4). However, the literature evidence on the determinants of education sustainability is indeed very scarce and largely inconclusive, particularly in the case of the Kingdom of Saudi Arabia (KSA). Accordingly, this research paper focuses on exploring the determinants of education sustainability by focusing on the role of government education spending. The paper utilized annual time series data for the period 1991–2023 and applied the time series cointegration technique of “Autoregressive Distributed Lag (ARDL)” to assess the long-run and short-run impact of government education expenditures on education sustainability in KSA Our results based on the ARDL approach demonstrated that government expenditures have casted a positive influence on education sustainability both in the long run and short run in the case of KSA. Similarly, we found that trade openness, which is the main determinant of economic performance, has positively contributed to education sustainability in the long run and short run in KSA. On the other hand, the unemployment rate has worsened education sustainability both in the long and short run. The results further demonstrated a negative short-run impact that FDI has on education sustainability, suggesting structural or sectoral dynamics that need further empirical investigation. Moreover, GDP per capita has improved education sustainably only in the long run while its short-run impact is insignificant. Our results offer important policy implications for the policymakers of KSA to attain education sustainability and contribute to the overall economic sustainability, which is aligned with the Vision 2030 of KSA. Full article

Artificial intelligence (AI) marketing technologies are reshaping customer engagement in service sectors, yet their performance within integrated digital ecosystems remains poorly understood. Existing research often examines AI tools in isolation, overlooking how the holistic quality of the virtual customer experience (VCE) shapes their impact on consumer decisions, particularly in intangible service contexts such as telecommunications. This study addresses this gap by investigating the influence of four AI technologies—chatbots, dynamic pricing, voice search, and visual search—on purchasing decisions, with VCE tested as a critical moderating mechanism. Using Partial Least Squares Structural Equation Modeling (PLS-SEM) and survey data from 487 telecommunications customers in Saudi Arabia, the findings confirm significant positive direct effects for all four AI tools. Moreover, the VCE significantly amplifies these individual relationships and further strengthens their combined contribution to decision quality, enabling the model to explain 71.2% of the variance in purchasing decisions. The results indicate that competitive advantage in AI-enabled service markets depends not on deploying isolated technologies, but on orchestrating a coherent, high-quality virtual experience ecosystem. By integrating the Technology Acceptance Model (TAM) and Stimulus–Organism–Response (SOR) framework, this study advances the theoretical understanding of how AI and experience design jointly enhance digital decision-making. Practically, it underscores the need for managers to prioritize integrated VCE design to drive sustainable consumption and strengthen customer loyalty in increasingly digital service environments. Full article

Background/Objectives: Well-child care plays a critical role in promoting child health and monitoring growth and development within Primary Health Care (PHC), in line with international frameworks such as the WHO Global Strategy and the UN Sustainable Development Goals (SDGs). However, the absence of standardized quality indicators limits comparability across studies and hinders continuous improvement worldwide. This study aimed to map and analyze the indicators used to assess the quality of well-child care in global PHC settings. Methods: A scoping review was conducted following PRISMA-ScR and Joanna Briggs Institute methodological guidance, with a pre-registered protocol. Comprehensive searches were performed in May 2025 across fourteen databases and two gray literature sources, without language or time restrictions. Eligible studies assessed quality indicators for well-child care among children up to 5 years, 11 months, and 29 days. Two independent reviewers performed study selection and data extraction. Results: From 6052 records, 62 studies met inclusion criteria. Out of them, most (68%) used composite indicators, primarily from pre-existing tools (67%). While structural and clinical indicators—such as immunization and service accessibility—were predominant, there was a critical absence of relational indicators focusing on patient–provider interaction. This lack of standardization and neglect of the relational dimension significantly hinders international comparability and the assessment of family-centered care quality. Conclusions: Developing and validating a core set of standardized, comprehensive, and context-sensitive indicators integrating structural, clinical, and relational dimensions is essential. These should be linked to information systems to enable robust national and international comparison, strengthen evidence-based management, and drive continuous quality improvement to achieve the 2030 Agenda goals. These findings provide a foundation for policymakers to develop standardized monitoring tools that prioritize neglected relational aspects of care. Full article

This study explores the role of an agricultural education programme in achieving China’s Sustainable Development Goals (SDGs) and promoting rural development. Sustainability development education, a key factor in achieving the SDGs, can be implemented through formal, non-formal, and informal education by promoting sustainable development skills to understand and solve social, economic, and environmental problems. Semi-structured interviews and visits to agricultural education sites in a rural district of Shanghai revealed that stakeholders viewed the agricultural education programme as a rural development strategy and a means of achieving SDGs. While teachers highlighted the programme’s social transformation function, stakeholders participated for varied reasons and expanded their roles in the agricultural education network. Full article

Smart home technologies are increasingly integrated into residential environments jointly inhabited by older adults and young children. However, existing research remains largely ageing-centered and insufficiently addresses the governance challenges arising from generational asymmetries in vulnerability, spatial agency, and authority within shared domestic space. Rather than merely complicating design, these asymmetries fundamentally reshape how safety, autonomy, access, and surveillance are structured in everyday residential practice. This study reconceptualizes senior–child intergenerational co-living as a governance-oriented socio-technical system in which generational asymmetry functions as a structuring principle of design prioritization. An expert-based decision framework integrating interdisciplinary focus groups and the Analytic Hierarchy Process was developed to evaluate five design dimensions and thirty indicators. The findings reveal a differentiated priority structure in which intelligent safety, accessibility, and risk governance together with spatial integration and technological accessibility constitute the foundational architecture of inclusive intergenerational housing, while interaction-oriented functions receive comparatively lower weights. By embedding generational asymmetry within a formal hierarchical evaluation model, this study extends smart housing scholarship beyond ageing-centered optimization and provides a structured decision-support logic for inclusive multi-generational residential design aligned with the objectives of the United Nations Sustainable Development Goals (SDGs), particularly those promoting inclusive communities and health equity. Full article

The intensive management of double-cropping rice systems relies on high inputs of fertilizer and labor to sustain high yields. However, this leads to substantial reactive nitrogen (Nr) losses and severe environmental degradation. Although both enhanced-efficiency nitrogen fertilizers (EENFs) and deep placement are recognized for mitigating specific Nr loss pathways within individual seasons, robust field evidence for their combined, cross-seasonal efficacy across multiple loss pathways remains scarce. This study assessed the integrated agronomic, environmental, and economic performance of deep-placed EENFs in a double-rice cropping system. The EENFs included stabilized urea (SU) and controlled-release urea (CRU). Nitrogen release patterns differed significantly between fertilizers: SU showed strong season-dependent dynamics, while CRU provided a stable, consistent supply across both early and late rice seasons, achieving superior synchronization with crop nitrogen demand. Crucially, deep placement was indispensable for reducing environmental risks. The integrated strategy of deep-placing CRU (CRUD) facilitated a “spatiotemporal dual regulation” of nitrogen, spatially mitigating surface losses via deep placement and temporally synchronizing nutrient release with crop demand via the controlled-release mechanism. Compared with conventional surface-applied urea, CRUD significantly enhanced grain yield (16.1% and 17.5%), increased nitrogen recovery efficiency (41.5% and 67.4%), reduced total N losses (42.3% and 31.3%), and improved net economic benefits (35.0% and 30.9%) in early and late rice, respectively. It provides a concrete, actionable solution for advancing sustainable intensification in double-cropping rice systems, contributing directly to Sustainable Development Goals (SDGs). Full article

The translation of knowledge and methodologies across disciplines represents a valuable source of innovation, particularly in user-centered design approaches that have become essential in medical device development. This study explores the use of the PICO (Population, Intervention, Comparison, and Outcome) framework, a cornerstone of evidence-based medicine for formulating clinical questions, as a conceptual structure to support the alignment between clinical needs and engineering design consideration in robotic rehabilitation devices, with a focus on hand exoskeletons. Through a conceptual reinterpretation and application-oriented exploration supported by illustrative case studies involving both rigid and soft robotic glove prototypes, this study shows how each PICO component can inform engineering parameters, from defining user impairments and intervention strategies to benchmarking and outcome measurements. The analysis highlights the potential of PICO in fostering a user-centered design perspective and bridging clinical and engineering domains while also identifying its structural limitations when applied to device design contexts. This study concludes that while the PICO framework offers a valuable foundational structure, it requires customization to fully address the multifactorial requirements of effective, patient-specific robotic rehabilitation device design. Full article

The topic of sustainability reporting by SMEs is gaining significant importance in European contexts such as Italy. However, recent regulations, constantly evolving in terms of legal requirements and practical standards, do not yet provide solid foundations to guide small and medium-sized enterprises. This study aims to examine how Italian listed SMEs address sustainability issues in terms of Sustainable Development Goals (SDGs) in their sustainability reports, in light of the recent requirements set out in European directives (i.e., Directive 2022/2464/EU—Corporate Sustainability Reporting Directive (CSRD) and Directive 2025/794/EU—Stop the Clock). The analysis is based on a content review of 17 sustainability reports published in 2023 by Italian SMEs listed on Euronext Growth Milan of Borsa Italiana. The research protocol was structured around the key SDG themes found in the reports, using Python 3.14.2 libraries including Pandas, NumPy, NLTK, and Matplotlib. The findings highlight heterogeneous approaches to sustainability. Most firms adopt symbolic approaches based on formal narrative disclosures without addressing sustainability reporting’s substantive dimensions. They overlook both the principle of double materiality, actually recommended by the CSR Directive, and the provision of assurance statements on reports. Although mandatory sustainability reporting is not imminent, particularly in light of the “Stop the Clock” measure, this research offers significant insights into both theoretical and practical implications. From a theoretical standpoint, it contributes to the growing body of literature on sustainability practices among SMEs. From a managerial standpoint, it underscores the importance of designing tailored reporting practices for SMEs that avoid administrative costs and overload issues, at the same time fostering a substantive approach to disclosure able to convey meaningful information to stakeholders. Full article

In the context of rapid urbanization and the pursuit of the United Nations Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities and Communities), cities face multifaceted challenges such as high population density, limited green space, ecosystem degradation, and an insufficient supply of ecological products, all of which undermine urban sustainability. As crucial ecological units, urban park green spaces (UPGS) play a vital role in alleviating environmental pressures and providing cultural ecosystem services (CES) that are essential for human well-being and social sustainability. However, systematic insight into how residents perceive and value CES, along with the underlying drivers, remains underdeveloped, impeding the advancement of refined park management practices. Based on 12,083 social media texts, this study employed BERTopic topic modeling to identify five core dimensions of CES perception: recreational services (RS), aesthetic experiences (AE), health-promoting activities (HA), social interactions (SI), and educational services (ES). Additionally, four underlying drivers with corresponding measurable indicators were also identified: residents’ socioeconomic backgrounds (RSB), external built environment of parks (EBE), internal landscape composition (ILC), and quality of services management (QSM). Subsequently, using 313 valid questionnaires and geographic park data, a Partial Least Squares Structural Equation Modeling (PLS-SEM) framework was constructed to analyze the influence mechanisms of EBE, ILC, and QSM on CES perception differences, with residents’ satisfaction with CES serving as the measure of their perceived CES levels. Hierarchical regression analysis was further employed to examine the moderating effects of RSB on these driving pathways. The findings reveal the following: (1) Significant synergies and heterogeneities existed among CES dimensions, with notable synergistic effects observed between AE and SI, as well as between HA and RS. (2) EBE, ILC, and QSM significantly influenced CES perception differences (p < 0.05). EBE affected these differences through pathways such as EBE → ILC → QSM → CES and EBE → QSM → CES. Notably, QSM was identified as the most critical mediating factor affecting CES perception differences. (3) Age exerted a significant positive moderating effect on the QSM → CES pathway, while monthly income showed a marginally significant negative moderating trend on the ILC → QSM pathway. This study elucidates the multi-level driving mechanisms underlying differences in residents’ perceptions of CES in UPGS. A key innovation lies in the integration of large-scale social media text data with questionnaire surveys, combined with the application of the BERTopic model and PLS-SEM to analyze these perceptual differences. The findings offer both theoretical foundations and practical insights for landscape optimization and service enhancement in park planning and management, contributing to the development of more equitable, resilient, and sustainable urban environments. Full article

Although higher technical efficiency is theoretically expected to enhance farm sustainability, empirical evidence in livestock systems remains ambiguous. This study investigates the interplay between technical efficiency and sustainability using data from 72 farms in Tokat, Türkiye, selected via stratified random sampling. Technical efficiency was calculated using Data Envelopment Analysis (DEA), while a multidimensional Sustainability Index was constructed using the analytic hierarchy process (AHP) for weighting dimensions. Determinants of inefficiency were estimated using a Tobit model. Results revealed an average technical efficiency of 0.717 and a Composite Sustainability Index of 0.41, classifying the sector as “moderate” but fragile. Crucially, the Kruskal–Wallis test showed no statistically significant difference in sustainability scores across efficiency groups (p > 0.05). This finding confirms a “structural disconnect,” demonstrating that high technical efficiency does not guarantee sustainability because of systemic bottlenecks such as dysfunctional organizations and infrastructure deficits. Furthermore, Tobit results showed that non-farm income and internet access were positively associated with technical efficiency, whereas indebtedness was negatively associated. Consequently, achieving lasting sustainability requires a shift from simple productivity support to structural modernization policies, including the integration of sustainability-oriented criteria such as institutional strengthening, environmental management, and financial capacity into existing support schemes. Full article