Search Results (1,204)

This article examines the role of coworking and flexible workspaces in promoting sustainable spatial development in the non-metropolitan areas of Bulgaria. A mixed-method approach was applied, combining inventory enumeration, spatial classification, SDG-based sustainability assessment, and qualitative coding (open, axial, selective). A total of 74 coworking and flexible workspaces were identified across the six national planning regions, evaluated according to six analytical criteria (accessibility, seasonality, specialization, municipal administrative district, urban planning zone, building function) and assessed against five SDG-aligned dimensions (SDG 8, 9, 11, 12, 13). The results reveal uneven territorial distribution, strong concentration in major cities outside the capital, and emerging sustainable models in peripheral areas. Comparative SDG scoring and typological interpretation demonstrate three recurring models—Sustainable Reuse, Nature-Oriented, and Innovative/Experimental—each associated with distinct spatial and environmental characteristics. A metropolitan benchmarking exercise further contextualizes the strongest sustainability profiles. Based on these findings, a conceptual sustainable coworking model is developed for a nationally significant spa and climatic resort, illustrating how coworking can address regional disparities, support green transition policies, and reinforce territorial cohesion. The article concludes by outlining research directions related to digitalization, circular construction, environmental performance indicators, and feasibility assessments for non-metropolitan coworking development. Full article

The successful attainment of the Sustainable Development Goals (SDGs) necessitates robust monitoring frameworks capable of tracking progress toward tangible outcomes while capturing dynamic sustainability trajectories. However, existing SDG evaluation methods suffer from three critical limitations: (1) misalignment between global targets and national priorities, which undermines contextual relevance; (2) fragmented assessments that neglect holistic integration of social, economic, and ecological dimensions, thereby obscuring systemic interdependencies; and (3) insufficient longitudinal analysis, which restricts insights into temporal patterns of sustainable development and hinders adaptive policymaking. To address these gaps, we employed China’s 31 provinces as a case study and constructed an SDG indicator framework comprising 178 metrics—harmonizing global SDG benchmarks with China’s national development priorities. Using official statistics and open-source data spanning 2006–2021, we evaluate longitudinal SDG scores for all 17 goals (SDGs 1–17). Additionally, we developed a composite SDG index that considers the coupling coordination degree of the social–economic–ecological system and evaluated the index value under different economic region settings. Finally, we developed a two-threshold model to analyze the dynamic evolution of SDG conditions, incorporating temporal sustainability (long-term development resilience) and action urgency (short-term policy intervention needs) as dual evaluation dimensions. This model was applied to conduct a longitudinal analysis (2006–2021) across all 31 Chinese provinces, enabling a granular assessment of regional SDG trajectories while capturing both systemic trends and acute challenges over time. The results indicate that China’s social SDG performance improved substantially over the 2006–2021 period, achieving a cumulative increase of 126.53%, whereas progress in ecological SDGs was comparatively modest, with a cumulative growth of only 23.93%. Over the same period, the average composite SDG score across China’s 31 provinces increased markedly from 0.502 to 0.714, reflecting a strengthened systemic alignment between regional development trajectories and national sustainability objectives. Further analysis shows that all provinces attained a status of “temporal sustainability with low action urgency” throughout the study period, highlighting China’s overall progress in sustainable development. Nevertheless, pronounced regional disparities persist: eastern provinces developed earlier and have consistently maintained leading positions; central and northeastern regions exhibit broadly comparable development levels; and western regions, despite severe early-stage lagging, have demonstrated accelerated growth in later years. Our study holds substantial significance by integrating multi-dimensional indicators—spanning ecological, economic, and social dimensions—to deliver a holistic, longitudinal perspective on sustainable development. Full article

This study examines the relationship between national open educational resource (OER) policies and Sustainable Development Goal 4 (SDG4) outcomes across 187 countries between 2015 and 2024, with particular attention to the moderating role of artificial intelligence (AI) readiness. Despite widespread optimism about digital technologies as catalysts for universal education, systematic evidence linking formal OER policy frameworks to measurable improvements in educational access and completion remains limited. The analysis employs fixed effects and difference-in-differences estimation strategies using an unbalanced panel dataset comprising 435 country-year observations. The research investigates how OER policies associate with primary completion rates and out-of-school rates while testing whether these relationships depend on countries’ technological and institutional capacity for advanced technology deployment. The findings reveal that AI readiness demonstrates consistent positive associations with educational outcomes, with a ten-point increase in the readiness index corresponding to approximately 0.46 percentage point improvements in primary completion rates and 0.31 percentage point reductions in out-of-school rates across fixed effects specifications. The difference-in-differences analysis indicates that OER-adopting countries experienced completion rate increases averaging 0.52 percentage points relative to non-adopting countries in the post-2020 period, though this estimate remains statistically imprecise (p equals 0.440), preventing definitive causal conclusions. Interaction effects between policies and readiness yield consistently positive coefficients across specifications, but these associations similarly fail to achieve conventional significance thresholds given sample size constraints and limited within-country variation. While the directional patterns align with theoretical expectations that policy effectiveness depends on digital capacity, the evidence should be characterized as suggestive rather than conclusive. These findings represent preliminary assessment of policies in early implementation stages. Most frameworks were adopted between 2019 and 2022, providing observation windows of two to five years before data collection ended in 2024. This timeline proves insufficient for educational system transformations to fully materialize in aggregate indicators, as primary education cycles span six to eight years and implementation processes operate gradually through sequential stages of content development, teacher training, and institutional adaptation. The analysis captures policy impacts during formation rather than at equilibrium, establishing baseline patterns that require extended longitudinal observation for definitive evaluation. High-income countries demonstrate interaction coefficients between policies and readiness that approach marginal statistical significance (p less than 0.10), while low-income subsamples show coefficients near zero with wide confidence intervals. These patterns suggest that OER frameworks function as complementary interventions whose effectiveness depends critically on enabling infrastructure including digital connectivity, governance quality, technical workforce capacity, and innovation ecosystems. The results carry important implications for how countries sequence educational technology reforms and how international development organizations design technical assistance programs. The evidence cautions against uniform policy recommendations across diverse contexts, indicating that countries at different stages of digital development require fundamentally different strategies that coordinate policy adoption with foundational capacity building. However, the modest short-term effects and statistical imprecision observed here should not be interpreted as evidence of policy ineffectiveness, but rather as confirmation that immediate transformation is unlikely given implementation complexities and temporal constraints. The study contributes systematic cross-national evidence on aggregate policy associations while highlighting the conditional nature of educational technology effectiveness and establishing the need for continued longitudinal research as policies mature beyond the early implementation phase captured in this analysis. Full article

This paper presents the Living Lab Assessment Method (LLAM), a context-sensitive framework for assessing impact and value creation in Living Labs (LLs). While LLs have become established instruments for Open and Urban Innovation, systematic and transferable approaches to evaluate their impact remain scarce and still show theoretical and practical barriers. This study proposes a new methodological approach that aims to address these challenges through the development of the LLAM, the Living Lab Assessment Method. This study reports a five-year iterative development process embedded in Ghent’s urban and social innovation ecosystem through the combination of three complementary methodological pillars: (1) co-creation and co-design with lead users, ensuring alignment with practitioner needs and real-world conditions; (2) multiple case study research, enabling iterative refinement across diverse Living Lab projects, and (3) participatory action research, integrating reflexive and iterative cycles of observation, implementation, and adjustment. The LLAM was empirically developed and validated across four use cases, each contributing to the method’s operational robustness and contextual adaptability. Results show that LLAM captures multi-level value creation, ranging from individual learning and network strengthening to systemic transformation, by linking participatory processes to outcomes across stakeholder, project, and ecosystem levels. The paper concludes that LLAM advances both theoretical understanding and practical evaluation of Living Labs by providing a structured, adaptable, and empirically grounded methodology for assessing their contribution to sustainable and inclusive urban innovation. Full article

Microplastic quantification in marine vegetated ecosystems remains analytically demanding, yet little is known about the environmental footprint of the laboratory procedures required to isolate and measure these particles. This study applies Life Cycle Assessment (LCA) to laboratory analytical workflows for microplastics quantification, focusing exclusively on sample preparation and analytical procedures rather than natural environmental absorption or fate processes, in two ecologically relevant matrices: (i) pelagic algae (Sargassum) and (ii) seagrass biomass. Using the openLCA 2.5 and the ReCiPe Midpoint (H) v1.13 methods, the analysis integrates foreground inventories of digestion, filtration, drying, and spectroscopic identification, combined with background datasets from OzLCI2019, ELCD 3.2 and USDA. Results show substantially higher impacts for the algae scenario, particularly for climate change, human toxicity, ionising radiation and particulate matter formation, largely driven by longer digestion times, increased reagent use and higher energy demand during sample pre-treatment. Conversely, the seagrass scenario exhibited lower burdens per functional unit due to reduced organic complexity and shorter laboratory processing requirements. These findings highlight the importance of matrix-specific methodological choices and the influence of background datasets on impact profiles. This study provides the first benchmark for the environmental performance of microplastic analytical workflows and underscores the need for harmonised, low-impact laboratory protocols to support sustainable monitoring of microplastic pollution in marine ecosystems. Full article

About 16% of typhoons making landfall in China strike Hainan Island, where near-surface extreme winds in dense urban areas exhibit a strong spatiotemporal heterogeneity that is difficult to capture with current observations and mesoscale models. Focusing on Haikou during Super Typhoon Yagi (2024)—the strongest autumn typhoon to hit China since 1949—we developed a multiscale ERA5–WRF–PALM framework to conduct 30 m resolution large-eddy simulations. PALM results are in reasonable agreement with most of the five automatic weather stations, while performance is weaker at the most sheltered park site. Mean near-surface wind speeds increased by 20–50% relative to normal conditions, showing a coastal–urban gradient: maps of weighted cumulative exposure to strong winds (≥Beaufort force 8) show much longer and more intense events along open coasts than within built-up urban cores. Urban morphology exerted nonlinear effects: wind speeds followed a U-shaped relation with both the open-space ratio and mean building height, with suppression zones at ~0.5–0.7 openness and ~20–40 m height, while clusters of super-tall buildings induced Venturi-like acceleration of 2–3 m s⁻¹. Spatiotemporal analysis revealed banded swaths of high winds, with open areas and islands sustaining longer, broader extremes, and dense street grids experiencing shorter, localized events. Methodologically, this study provides a rare, systematically evaluated application of a multiscale ERA5–WRF–PALM framework to a real typhoon case at 30 m resolution in a tropical coastal city. These findings clarify typhoon–city interactions, quantify morphological regulation of extreme winds, and support risk assessment, urban planning, and wind-resilient design in coastal megacities. Full article

Background: Tumor necrosis factor-α (TNFα) inhibitors have significantly improved outcomes in children with non-systemic juvenile idiopathic arthritis (JIA), achieving long-term clinical remission for many patients. However, the optimal strategy for TNF-α inhibitor withdrawal remains unknown, whether through abrupt discontinuation, gradual dose reduction, or interval extension. Objective: We aim to identify patient-, disease-, and treatment-related predictors of successful TNF-α inhibitor withdrawal in children with non-systemic JIA. Methods: In this prospective, randomized, open-label, single-center study, 76 children with non-systemic JIA in stable remission for ≥24 months on etanercept or adalimumab were enrolled. At the time of TNF-α inhibitor discontinuation, all patients underwent a comprehensive evaluation, including a clinical examination, laboratory tests (serum calprotectin [S100 proteins] and high-sensitivity C-reactive protein [hsCRP]), and advanced joint imaging (musculoskeletal ultrasound and magnetic resonance imaging [MRI]) to assess subclinical disease activity. Patients were randomized (1:1:1, sealed-envelope allocation) to one of three predefined tapering strategies: (I) abrupt discontinuation; (II) extension of dosing intervals (etanercept 0.8 mg/kg every 2 weeks; adalimumab 24 mg/m² every 4 weeks); or (III) gradual dose reduction (etanercept 0.4 mg/kg weekly; adalimumab 12 mg/m² every 2 weeks). Follow-up visits were scheduled at 3, 6, 9, 12, and 18 months to monitor for disease relapse. Results: Higher baseline Childhood Health Assessment Questionnaire (CHAQ) scores (≥2), elevated serum calprotectin [S100 proteins] and hsCRP levels at withdrawal, imaging evidence of subclinical synovitis, and a history of uveitis were all significantly associated with increased risk of flare. No significant associations were found for other clinical or demographic characteristics. Conclusions: Early significant clinical response, absence of subclinical disease activity, and concomitant low-dose methotrexate therapy were key predictors of sustained drug-free remission. These findings may inform personalized strategies for biologic tapering in pediatric JIA. Full article

Composting represents a sustainable and effective strategy for converting organic waste into nutrient-rich soil amendments, providing a safer alternative to raw manure, which poses significant risks of soil, crop, and water contamination through pathogenic microorganisms. This study, conducted under semi-arid Moroccan conditions, investigated the efficiency of co-composting green garden waste with sheep manure in an open window system, with the objective of assessing pathogen inactivation and evaluating compost quality. The process, conducted over 120 days, maintained thermophilic temperatures exceeding 55 °C, effectively reducing key pathogens including Escherichia coli, total coliforms, Staphylococcus aureus, and sulfite-reducing Clostridia (SRC), while Salmonella was not detected throughout the composting period. Pathogen reductions exceeded 3.52-log despite moderate temperature fluctuations, indicating that additional sanitization mechanisms beyond heat contributed to inactivation. Compost quality, assessed using the CQI, classified Heap 2 (fallen leaves + sheep manure) as good quality (4.06) and Heap 1 (green waste + sheep manure) as moderate quality (2.47), corresponding to differences in microbial dynamics and compost stability. These findings demonstrate that open windrow co-composting is a practical, low-cost, and effective method for safe organic waste management. It supports sustainable agriculture by improving soil health, minimizing environmental and public health risks, and providing guidance for optimizing composting protocols to meet regulatory safety standards. Full article

Diabetes self-management education (DSME) programs are evidence-based interventions that improve glycemic control and self-care behaviors, yet their effectiveness may be limited by unaddressed health-related social needs (HRSN) (e.g., food insecurity, housing or utility instability, transportation barriers). This qualitative multiple case study examined the feasibility of integrating HRSN assessments into DSME delivery within three community-based organizations (CBOs) across urban and rural U.S. settings. Guided by the Consolidated Framework for Implementation Research, semi-structured interviews were conducted with 15 DSME facilitators and program leadership to identify contextual factors influencing implementation. Findings revealed that while DSME’s structured, manualized design promotes fidelity and client autonomy, it constrains responsiveness to the client’s HRSN. Facilitators expressed openness to integrating HRSN screening, particularly during intake, yet cited limited infrastructure, role clarity, and training as key barriers. CBOs were recognized as trusted, accessible spaces for holistic care, but growing expectations to address HRSN without adequate resources for referral created sustainability concerns. Participants recommended a parallel support model involving navigators or community health workers to manage HRSN screening and referrals alongside DSME sessions. Integrating HRSN assessment processes into DSME may enhance engagement, reduce attrition, and extend the reach of diabetes education to populations most affected by HRSN. However, successful implementation requires dedicated funding, workforce development, and cross-sector coordination. Findings underscore the importance of supporting CBOs as critical partners in bridging diabetes education and social care to advance whole-person, chronic disease management. Full article

Real-time emotion monitoring is increasingly relevant in healthcare, automotive, and workplace applications, where adaptive systems can enhance user experience and well-being. This study investigates the feasibility of classifying emotions along the valence–arousal dimensions of the Circumplex Model of Affect using EEG signals acquired from a single mastoid channel positioned near the ear. Twenty-four participants viewed emotion-eliciting videos and self-reported their affective states using the Self-Assessment Manikin. EEG data were recorded with an OpenBCI Cyton board and both spectral and temporal features (including power in multiple frequency bands and entropy-based complexity measures) were extracted from the single ear-channel. A dual analytical framework was adopted: classical statistical analyses (ANOVA, Mann–Whitney U) and artificial neural networks combined with explainable AI methods (Gradient × Input, Integrated Gradients) were used to identify features associated with valence and arousal. Results confirmed the physiological validity of single-channel ear-EEG, and showed that absolute $\beta$- and $\gamma$-band power, spectral ratios, and entropy-based metrics consistently contributed to emotion classification. Overall, the findings demonstrate that reliable and interpretable affective information can be extracted from minimal EEG configurations, supporting their potential for wearable, real-world emotion monitoring. Nonetheless, practical considerations—such as long-term comfort, stability, and wearability of ear-EEG devices—remain important challenges and motivate future research on sustained use in naturalistic environments. Full article

The development of sustainable smart cities is closely linked to the implementation of artificial intelligence in urban services, which opens up new possibilities for efficient resource management, improving the quality of life and strengthening the participation of citizens. At the same time, the question arises as to how legal and strategic frameworks can support the use of artificial intelligence in a way that contributes to environmental, social and economic sustainability in line with the objectives of the European Union. The aim of this scientific study is to examine the interdisciplinary use of artificial intelligence, data management and sustainability at the European Union level, including support instruments such as regulatory initiatives and funding programs, and to assess their implementation in relation to smart cities. Methodologically, the research is based on a legal analysis of key European and national documents, supplemented by descriptive statistics and visualizations of indicators of digitalization and urban sustainability. In the scientific study, we use the methods of synthesis, comparison and abstraction. The results suggest that the legislative and support framework of the European Union can be a significant impetus for the transformation of individual smart cities, but requires effective coordination and strategic management at the level of local governments. The research highlights the need for an integrated legal-managerial approach that will enable the full use of the potential of artificial intelligence in supporting sustainable urban development of cities. Full article

Climate change and increasing greenhouse gas emissions are driving the global transition to clean energy, with solar energy experiencing the fastest growth among renewable sources in 2024. Solar PV for energy generation in Kenya is gaining momentum as the country moves towards achieving 100% clean energy by 2030. As solar PV penetration in the grid grows, it is necessary to evaluate its impact on system costs to inform policy decisions on capacity expansion options in the Least-Cost Power Development Plan (LCPDP). This study investigates the effect of large-scale solar PV expansion on electricity costs using the Open-Source Energy Modelling System (OSeMOSYS), a modular, bottom-up capacity expansion model. Four scenarios were developed to assess different levels of solar PV penetration: business-as-usual (BAU), moderate-solar-PV expansion (MSPV), high-solar-PV expansion (HSPV), and very-high-solar-PV expansion (VHSPV). The results indicate that, while overall solar PV expansion significantly contributes to decarbonising Kenya’s electricity mix by displacing fossil-based generation, it also increases annual investment obligations and, consequently, total system costs. The system-levelised cost of electricity (LCOE) is shown to rise by 0.2%, 5.7%, and 14.0% under MSPV, HSPV, and VHSPV, respectively, compared to BAU. Analysing the various cost components against sustainability indicators reveals that the least-cost scenario is BAU while the most favourable scenario based on sustainability indicators is VHSPV, which performs best across technical, environmental, and institutional dimensions but less favourably on economic and social aspects, thereby highlighting a trade-off between sustainability and cost minimisation, at least in the short term. Full article

Rapid climate change and its anticipated impacts trigger significant worry and distress among vulnerable groups, including young adults. Little is known about how Dutch young adults experience and cope with climate change within their specific social and environmental context. This study examines Dutch young people’s emotional responses to climate change, their perceived emotional and psychological needs arising from these experiences, and the barriers they encounter in engaging in pro-environmental behaviour, with the aim of informing public health strategies to better support and empower this vulnerable group. Data were drawn from a large online survey among a representative sample of 1006 Dutch young adults (16–35 years; 51% women). The questionnaire included fixed-answer sections assessing emotional responses to climate change, as well as two open-ended questions exploring participants’ perceptions of their emotional and psychological needs related to climate change and the barriers they perceive to pro-environmental behaviour. Descriptive statistics were used for the fixed-response items, and thematic analysis was applied to the open-ended responses. Many Dutch young adults reported worry and sadness about climate change and its impacts, with approximately one third experiencing feelings of powerlessness. A large percentage of respondents attributed responsibility to large companies, and nearly half indicated that they still had hope for the future. One third (31%) felt that nothing could make them feel better about climate change, and another third (36%) reported to experience no climate-related emotions. Key emotional needs included more action at personal, community, and governmental levels, and more motivating positive news. Almost half (46%) of young adults said they already lived sustainably, while perceived barriers to pro-environmental behaviour were mainly financial (21%), knowledge-related (8%), and time-related (7%). This exploratory study highlights key practical and emotional barriers to pro-environmental behaviour reported by Dutch young adults 16–35, who expressed diverse emotional needs while coping with climate change. The findings underscore the need for a multi-level public health response to climate-related emotions, that simultaneously addresses emotional needs, structural barriers, and opportunities for meaningful engagement. Lowering barriers to pro-environmental behaviour and fostering supportive environments that enable sustainable action among young adults may enhance wellbeing and strengthen their sense of agency. Public health supports this by reducing barriers to pro-environmental behaviour in young adults, through targeted support, clear information, and enabling social and structural conditions that promote wellbeing and sustained engagement. Full article

Background/Objectives: Wearable fitness trackers (WFTs) are growing in popularity as tools to motivate physical activity and support healthier lifestyles. Evidence suggests that they can have both positive and negative effects on user behaviour and well-being. However, little is known about their impact in Saudi settings, considering its unique cultural context. This study aims to investigate the perceived positive and negative impacts of WFTs on adults’ health behaviours and well-being in Saudi Arabia. Methods: A cross-sectional survey was conducted among Saudi adults aged 18 years or older who currently use or have previously used WFTs, using an online questionnaire distributed via social media platforms in May 2025. The survey was developed based on evidence from the literature. It included demographic items, five-point Likert-scale questions assessing positive (9 items) and negative (10 items) effects of WFTs, and an open-ended question. Responses were analysed using descriptive statistics, independent samples t-tests, and one-way ANOVA. Results: A total of 154 adults participated. The mean composite score for positive effects was 3.26 (SD = 0.73), indicating general agreement on the benefits of WFTs, while the negative effects score was 2.15 (SD = 0.66), showing low endorsement of adverse outcomes. No significant differences appeared between gender (positive: p = 0.34; negative: p = 0.24) or age groups (positive: p = 0.56; negative: p = 0.19). However, users of over two months had higher positive scores (M = 3.43, SD = 0.66) than newer or former users (p = 0.01). Open responses showed 62% positive experiences; 27% reported stress, guilt, or obsessive monitoring. Conclusions: This study provides initial insights into WFT use and perceptions in Saudi Arabia. However, its cross-sectional nature limits the ability to draw causal conclusions. While most users experienced beneficial health outcomes, a significant proportion reported negative psychological experiences. These findings highlight WFT users’ dual experiences and the need for further longitudinal research and diverse recruitment strategies to better understand sustained engagement and psychological effects. Full article

Urban stormwater management presents significant challenges for municipalities seeking to balance environmental resilience with financial considerations and social equity. This study investigates the factors shaping residents’ willingness to pay (WTP) for a proposed stormwater management fee in Norwalk, Connecticut, within the context of local sustainability plans. A survey of 457 residents assessed demographics, personal beliefs, perceptions of benefits, risks, and WTP. Since participation was voluntary and open, an exact response rate could not be calculated, and the resulting respondent profile differed from city benchmarks. The results were analyzed using descriptive and inferential statistics alongside a Random Forest machine learning model assessing two payment scenarios, achieving classification accuracies above the majority-class baseline (approximately 60–68%). Across both scenarios, expectations of tangible and locally visible outcomes, including infrastructure upgrades and climate resilience improvements, were the strongest determinants of WTP. When respondents evaluated a specific fee amount rather than a general modest fee, concerns about affordability and program effectiveness became more influential and revealed the conditional nature of financial support. The findings illustrate the value of machine learning for analyzing public attitudes toward environmental finance and highlight how policy framing, transparency, and communication shape acceptance of sustainability measures. These insights provide a data-driven foundation for future research on public engagement and equity in local environmental policy and stormwater plan development. Full article