Search Results (5,599)

While objective street metrics derived from imagery or GIS have become standard in urban analytics, they remain insufficient to capture subjective perceptions essential to inclusive urban design. This study introduces a novel Multimodal Street Evaluation Framework (MSEF) that fuses a vision transformer (VisualGLM-6B) with a large language model (GPT-4), enabling interpretable dual-output assessment of streetscapes. Leveraging over 15,000 annotated street-view images from Harbin, China, we fine-tune the framework using Low-Rank Adaptation(LoRA) and P-Tuning v2 for parameter-efficient adaptation. The model achieves an F1 score of 0.863 on objective features and 89.3% agreement with aggregated resident perceptions, validated across stratified socioeconomic geographies. Beyond classification accuracy, MSEF captures context-dependent contradictions: for instance, informal commerce boosts perceived vibrancy while simultaneously reducing pedestrian comfort. It also identifies nonlinear and semantically contingent patterns—such as the divergent perceptual effects of architectural transparency across residential and commercial zones—revealing the limits of universal spatial heuristics. By generating natural-language rationales grounded in attention mechanisms, the framework bridges sensory data with socio-affective inference, enabling transparent diagnostics aligned with Sustainable Development Goal 11(SDG 11). This work offers both methodological innovation in urban perception modeling and practical utility for planning systems seeking to reconcile infrastructural precision with lived experience. Full article

The South African Government prioritises Small, Micro-, and Medium Enterprises (SMMEs) as catalysts for employment creation, in alignment with Sustainable Development Goal 8 (SDG 8), Decent Work and Economic Growth, which advocates for sustained, inclusive, and sustainable economic growth. However, the extent to which agro-processing SMMEs translate this policy ambition into measurable socio-economic gains remains contested due to persistent structural, financial, and operational constraints. This study develops a comprehensive, data-driven business support framework tailored to agro-processing SMMEs in the Free State province of South Africa. Employing a mixed-methods approach, survey data from 88 agro-processing SMMEs were analysed across 18 business performance dimensions. Average agreement scores and performance gaps were utilised to diagnose strengths and vulnerabilities within the sector. While overall performance was relatively strong (average agreement score: 86.7%), a critical weakness emerged in operational cost management (76.1%), revealing a 14.2% gap relative to the highest-performing dimension, equipment selection (90.3%). Based on these empirical insights, the study proposes a three-tiered business support architecture: (i) maintaining and leveraging high-performing dimensions (≥85% agreement), (ii) targeted enhancement for moderate-performing areas (80–84.9%), and (iii) crisis intervention for critical weaknesses (<80%). The framework integrates cross-cutting support services, including financing, regulatory guidance, and technology access, delivered through a phased implementation strategy comprising crisis intervention, system establishment, and optimisation and scaling. A multi-channel delivery mechanism, combining a hub-and-spoke model, mobile support units, and a digital platform, ensures provincial accessibility. By translating performance diagnostics into differentiated policy action, the framework promotes efficient resource allocation, supports both high-potential and vulnerable agro-processing SMMEs, and embeds a robust monitoring and evaluation system to track key performance indicators. The study contributes to the SMME development literature by demonstrating how structured, tiered, and context-specific support models can strengthen resilience, competitiveness, and sustainable agro-industrial growth in developing-country settings. Full article

This study explores how the multifaceted servicescape of wellness hotels influences customers’ intentions to revisit and recommend, with customer satisfaction acting as a mediating mechanism. Drawing on the Stimulus–Organism–Response (S-O-R) model and experiential marketing theory, this study conceptualizes servicescape across four dimensions: sensory, social, wellness-related activities, and cultural experiences. Survey responses were gathered from 483 Korean adults who had visited a wellness hotel within the last six months. The data were processed using SPSS (version 27.0) and AMOS (version 23.0). Findings suggest that while sensory, social, and wellness activity experiences have a significant positive impact on satisfaction, cultural experience does not yield the same effect. Satisfaction mediates both revisit and recommendation intentions. Moreover, multi-group analysis confirmed that wellness interest moderates the influence of sensory and wellness activity experiences on satisfaction. Notably, individuals with a high interest in wellness report increased satisfaction through active participation in wellness programs, whereas those with low wellness interest show greater responsiveness to sensory aspects. Theoretically, this study contributes to the existing body of literature by embedding wellness psychology and sustainable development goals (SDGs 3 and 12) into servicescape research. In terms of managerial implications, this study emphasizes the need for wellness hotels to improve sensory-based designs and experiential service quality, while also segmenting their strategies based on customers’ wellness profiles. By presenting an integrated model that connects experiential value, satisfaction, and behavioral intention, this study provides deeper insights into sustainable wellness tourism from both academic and practical perspectives. Full article

Enhancing the carrying capacity of the ecological environment serves as a pivotal pathway to achieving sustainable development and also constitutes a concrete response to the UN SDGs. Based on a provincial panel dataset covering 30 Chinese provinces spanning 2011–2023, the present work examines how digital finance shapes EECC and explores the corresponding transmission mechanisms. Findings from the empirical analysis confirm that digital finance exerts a significant positive effect in boosting ecological environmental carrying capacity. Heterogeneity tests further show that this catalytic influence is most salient in eastern China, while it lacks statistical significance or even turns negative in the central and western areas. Meanwhile, the catalytic function of digital finance becomes more distinct in highly urbanized areas. Mechanism analysis verifies that digital finance assumes a partial mediating function by cutting down energy consumption intensity and boosting human capital accumulation. Further analysis reveals that as digital finance matures, the above impact exhibits increasing marginal returns. Our spatial spillover assessment further indicates that digital finance contributes to stronger EECC within host provinces, while also facilitating coordinated improvements in this key indicator across neighboring jurisdictions. Accordingly, we propose that economies speed up the building of digital-related infrastructure, expand the outreach of digital finance, and properly steer the orderly movement of population, thus facilitating the eco-friendly sustainable advancement of the natural environment. Full article

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