Search Results (901)

Multistakeholder partnerships (MSPs) are central to the 2030 Agenda for Sustainable Development, yet the UN Partnership Platform suffers from an extreme validation asymmetry: fewer than 5% of registered projects undergo independent verification. This study examines whether discourse patterns distinguish validated from non-validated MSPs. Applying BERTopic neural topic modeling to 3807 project descriptions in the GENESIS WP4 Database, we identify three substantive thematic clusters, spanning climate, sanitation, and health; marine and fisheries; and sustainable textiles, alongside a combined language–artifact cluster excluded from thematic interpretation. The target topic count was fixed to ensure reproducibility after an initial automatic-selection step proved unstable across runs; a multi-seed check confirms stable topic counts with moderate assignment-level agreement (mean Adjusted Rand Index = 0.63). We introduce the Validated-Discourse Similarity Index (VDSI), a leave-one-out cosine similarity measure comparing each project’s textual embedding to a centroid of validated MSPs shown to be more homogeneous than random samples of non-validated projects (p = 0.002). VDSI analysis indicates that 3538 of the 3807 projects (92.9%) exhibit Inconsistent Non-Validated language resembling validated MSPs despite lacking independent verification, though raw semantic similarity alone only modestly discriminates validation status (AUC = 0.686), indicating a real but partial signal rather than a proxy for validation. Partner count is the strongest structural discriminator of validated MSPs (r = −0.389, p < 0.001), remaining significant after adjusting for SDG scope, description length, duration, topic, and language (adjusted OR = 1.38 per SD, p < 0.001), and consistent across six SDG-level subgroups. These findings extend SDG-washing scholarship to the UN multilateral voluntary commitment ecosystem and offer a provisional, discourse-informed basis for partnership evaluation. Full article

Food loss and waste (FLW) constitutes a structural challenge for agri-food systems. However, current analytical tools for assessing management and valorisation options remain fragmented across disciplines. Existing frameworks rarely capture within a single structure the environmental, social, and economic–institutional feasibility dimensions needed to compare alternative recovery pathways. This study introduces the ESG-FLW Index, a multidimensional composite index designed to support comparative evaluation of FLW valorisation strategies, with particular attention to recovery for human consumption. The index draws on a comparative analysis of eight established frameworks. It integrates Life Cycle Assessment for the environmental pillar; Techno-Economic Assessment together with compliance, data-quality, scalability, and replicability indicators for the governance pillar; and Social Return on Investment for the social pillar. These components are aggregated through Multi-Criteria Decision Analysis, following methodological guidance for robust composite indicators. The framework aligns explicitly with the Food Waste Hierarchy and SDG Target 12.3. It also includes hurdle criteria to limit the risk that high aggregate scores obscure critical shortcomings. As a proof of concept, the index was applied to the CiboAmico redistribution programme. Redistribution scored substantially higher than composting (85.1 versus 30.7). The ranking remained stable under ±20% weight variations and Monte Carlo simulations (n = 1000). The ESG-FLW Index offers a transparent and replicable decision-support tool. It is best suited to relative comparison among options and to making sustainability trade-offs explicit. Full article

Sustainable public procurement (SPP) has emerged as a crucial strategy for advancing environmental management and socioeconomic development. Life Cycle Cost (LCC) is widely regarded as a quantitative measure of long-term economic viability. However, integrating LCC analysis into SPP remains a major challenge. This study addresses this gap by identifying and selecting LCC indicators that align with sustainable procurement. This study develops new indicators for LCC assessment through a mixed-methods approach that combines a systematic literature review, an institutional policy review, and expert stakeholder consensus via a fuzzy Delphi method. A panel of 12 experts evaluated the indicators, and of the 173 potential items generated from the literature review, consensus was reached on 88 indicators, with an average threshold value (d) = 0.182. The findings establish a systematic framework that highlights the cost indicators most critical for guiding public procurement decisions. The findings also suggest indicators that are priorities for LCC in sustainable public procurement. Selecting appropriate LCC indicators in the SPP will help the government achieve several Sustainable Development Goals (SDGs) targets. Full article

This paper illustrates the theoretical framework, the methodological approach, and the primary findings of a project titled MASSTER (Managing(South)Africa-and-Senegal-Sustainability-Targets-through-Economic-diversification-of-Rural-areas), in which higher educational institutions (HEIs) and organizations from Senegal, South Africa, and Europe collaborate with the aim of addressing the nexus between migration, agriculture and development in Sub-Saharan Africa by co-creating evidence-based training, tools, and actions to foster development and co-development while targeting several Sustainable Development Goals (SDGs), namely Zero-hunger, Good-Health-and-Wellbeing, Gender-Equality, Quality-education, Decent-work-and-economic-growth, Responsible-consumption-and-production, all within the frame of Partnership-for-the-Goals. The MASSTER project employs a transdisciplinary and bottom-up approach based on a cross-sectional study conducted in South Africa and Senegal to identify actual challenges, needs, and resources from farmers (n = 737) and students enrolled in agricultural courses (n = 1.013). Findings underpinned the co-creation of the primary project outcomes: training on agritourism development, farm management, income-generating activities, climate change resilience, and food value chain; toolkits designed to boost the adoption of the Whole of Society Approach and to strengthen cooperation between HEIs and local communities; MASSTER Student-and-alumni-tracking-procedure-with-early-warning-mechanism-for-brain-drain; and a MOOC on critical-thinking-and-empowerment. The MASSTER project can have a relevant impact at local and international levels, providing evidence-based tools to be used by HEIs, stakeholders/policymakers, and the scientific community to actively foster development and co-development. Full article

With the deep integration of artificial intelligence technology into education, AI literacy has emerged as a core competence indispensable for pre-service teachers. However, its formation mechanisms and sustainable cultivation pathways remain to be further explored. This study integrates the Technology Acceptance Model (TAM) and Innovation Diffusion Theory (IDT) to construct a theoretical model, in which Individual Innovation (II) and Self-Efficacy (SE) serve as antecedents, Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) as mediators, Behavioral Intention (BI) as a proximal variable, AI literacy as the outcome variable, gender and major as moderating variables, and grade and AI exposure time as control variables, exploring the influencing factors and mechanisms of pre-service teachers’ AI literacy. Through a questionnaire survey of 778 pre-service teachers, Partial Least Squares Structural Equation Modeling (PLS-SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA) were employed for sequential empirical analysis. The PLS-SEM results reveal that II and SE were significantly and positively associated with AI literacy through the serial mediation of PU, PEOU, and BI. The fsQCA further identified four distinct equifinal configurations associated with high AI literacy: “High-efficacy Practice-Oriented”, “High-Behavioral-Intention-Oriented”, “High-Innovativeness-Oriented”, and “Long-Term-Development-Oriented”. The findings demonstrate that the improvement of pre-service teachers’ AI literacy follows multiple equifinal mechanisms, necessitating a shift beyond the single-training mindset. Accordingly, this study proposes differentiated cultivation pathways, providing theoretical foundations and practical references for normal universities to deliver targeted and sustainable AI literacy training. It also offers empirical evidence and strategic support for the sub-goals of SDG 4 concerning teacher capacity-building and the digital transformation of education, which aim to ensure inclusive and equitable quality education and promote lifelong learning opportunities for all. Full article

National development programs increasingly integrate digital transformation, responsible business practices, and sustainable management to achieve governance outcomes aligned with the Sustainable Development Goals (SDGs). However, the literature remains divided on whether institutional quality is a prerequisite for or an outcome of digital transformation, with Gulf-specific empirical evidence remaining sparse. This study develops and applies an indicator-based evaluation framework—the Saudi Triple Nexus Model (STNM)—to assess how the three dimensions of digital transformation, responsible practices, and sustainable management co-evolved within Saudi Arabia’s National Transformation Program (NTP, 2016–2025). A longitudinal documentary–analytical design was adopted, combining systematic classification of 78 strategic performance indicators, gap analysis drawn from the NTP Annual Report 2025, and cross-national benchmarking against UAE, Qatar, and digitally advanced economies. Findings reveal that all three nexus dimensions recorded significant improvements simultaneously, with Saudi Arabia advancing from rank 36 to rank 6 globally on the UN E-Government Development Index and achieving rank 1 in cybersecurity—a pattern descriptively consistent with concurrent rather than sequential institutional development, and with the moderation configuration identified in the broader governance literature. These findings contribute the STNM as a replicable indicator-based framework for evaluating national transformation programs in high-income developing economies, while identifying civic participation and R&D investment as persistent gaps requiring targeted policy intervention. Full article

Satellite -derived environmental features can predict county-level life expectancy (LE) across the contiguous United States with a mean absolute error of 1.08 years over two decades, without using any census or sociodemographic inputs. We assembled 61,680 county-year observations across 3084 counties from 2000–2019, integrating features from 11 satellite and gridded data streams. The data streams include the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature and vegetation indices, Sentinel-1 synthetic aperture radar, Sentinel-2 and Landsat optical imagery, the United States Department of Agriculture (USDA) Cropland Data Layer, the European Commission Joint Research Centre (JRC) Global Surface Water layer, the Copernicus Digital Elevation Model, the European Space Agency Climate Change Initiative (ESA CCI) soil moisture record, and the Food and Agriculture Organization (FAO) gridded livestock densities. After a supervised pruning step that removed low-importance variables, a Random Forest regressor was trained and evaluated using 5-fold cross-validation grouped by county. The grouping places all 20 years of each county exclusively in either the training set or the test set, which prevents spatial information leakage between folds. Coefficient of determination, mean absolute error, and root mean squared error are reported as $R^2=0.631\pm 0.013$, MAE $=1.08\pm 0.02$ years, and RMSE $=1.48\pm 0.04$ years. Moran’s I, a measure of residual spatial autocorrelation, is $0.0988$ ($p=0.001$), which supports geographic generalisation. Multimodal fusion reduces unexplained variance by approximately one-third relative to the strongest single-modality baseline (MODIS land surface temperature alone, $R^2=0.442$). TreeSHAP attribution analysis reveals a feature hierarchy in which nighttime land surface temperature features carry roughly $6.16\times$ the cumulative attribution weight of all daytime channels combined. The model response shows a protective inflection near a minimum overnight temperature of about 7.5 °C. Because all input streams are globally available, the framework is architecturally extensible to regions where civil registration and vital statistics systems are incomplete; however, the trained model and its thresholds require recalibration against local mortality data before application outside the contiguous United States. With that caveat, the approach supports satellite-based monitoring of United Nations Sustainable Development Goal (UN SDG) Target 3.9. Full article

Tuberculosis, malaria, and HIV are infectious diseases that have become major global health problems. Efforts to reduce the incidence and mortality of tuberculosis have undergone a long process, resulting in a significant annual decrease of up to 2%. In a single year, malaria cases can reach nearly 230,000,000, with up to 400,000 deaths worldwide. Meanwhile, approximately 37,000,000 people were living with HIV worldwide in 2020, with about 690,000 deaths due to AIDS reported in the same year. Within the framework of the Sustainable Development Goals (SDGs), particularly Goal 3 on good health and well-being, one of the key targets is to end the epidemics of tuberculosis, malaria, and HIV. This research examines the effects of various interventions on tuberculosis, malaria, and HIV coinfection. The interventions considered include preventive measures, mosquito nets, insecticides, contraception, tuberculosis treatment, malaria treatment, and antiretroviral (ARV) therapy for HIV. The mathematical model of tuberculosis, malaria, and HIV coinfection is well-defined, as it is proven to have non-negative solutions, to be bounded, and to remain within the positive invariant region. The tuberculosis, malaria, and HIV sub-models each have an asymptotically stable equilibrium when the basic reproduction number is less than one. Based on the results of numerical simulations of the sub-models, it can be observed that when the basic reproduction number exceeds one, the disease spreads throughout the population. Full article

Road transport is a leading source of urban nitrogen oxides (NO_x) and fine particulate matter (PM_2.5)—a public-health and urban-sustainability challenge—and Low-Emission Zones (LEZs) are Europe’s principal response. Yet most are governed statically, unable to track conditions changing by the hour and the street. A digital twin, treated as decision infrastructure rather than a 3D model, recasts the LEZ as an adaptive decision infrastructure: a closed loop of sensing, modelling and rule-based adjustment. We develop a scalable, five-phase lifecycle methodology with auditability and GDPR-by-design built in, and derive three falsifiable hypotheses—efficiency, data integration, responsiveness—defining a research agenda. We test only the first. A diagnostic reading of London’s ULEZ shows its unimplemented phases are precisely those that close the loop. A proof-of-concept on real hourly NO₂ from five London sites (2023–2024) tests efficiency: at equal abatement effort, adaptive targeting avoids significantly more elevated-pollution hours than a uniformly stricter policy (about 37% versus 27%; 95% CI excludes parity), the advantage rising with forecast quality. This demonstrates the mechanism in reduced form, not a generalizable figure for a deployed system. By making regulation more responsive and accountable, it advances the Sustainable Development Goals on health, sustainable cities and climate (SDGs 3, 11, 13). Full article

Paclitaxel (PTX), a taxane-derived chemotherapeutic agent, is frequently used in the treatment of breast cancer (BC). Its anticancer effects are primarily associated with microtubule stabilization, disruption of cell-cycle progression, and triggering of apoptotic cell death. In the present study, we investigated the effects of PTX on the expression of genes involved in cancer-related pathways, energy metabolism, and drug resistance in four molecularly distinct BC cell lines: MCF-7, BT-474, SK-BR-3, and MDA-MB-231. The half-maximal inhibitory concentrations (IC₅₀) of PTX in BC cell lines and the non-tumorigenic hTERT-HME1 breast epithelial cell line were determined by the MTT assay to assess cell cytotoxicity. BC cells were exposed to nine different concentrations of PTX for 24, 48, and 72 h to evaluate concentration- and time-dependent effects. Following treatment, total RNA was isolated and converted into cDNA, and RT-qPCR analysis was performed to investigate PTX-mediated alterations in the expression of genes associated with cancer-related pathways. The impact of PTX on the cell-cycle phase distribution and apoptotic cell death was evaluated by flow cytometry. Treatment with PTX for 48 h at concentrations of 12.60 nM in MCF-7, 5.09 nM in BT-474, 16.09 nM in SK-BR-3, and 36.66 nM in MDA-MB-231 cells reduced cell viability and increased apoptosis. PTX treatment also altered the expression of genes involved in apoptosis, cell-cycle regulation, angiogenesis, epithelial–mesenchymal transition, hypoxia-related signaling, energy metabolism, telomere maintenance, and therapy resistance. Collectively, these findings demonstrate that PTX elicits heterogeneous molecular and cellular responses across molecularly distinct BC cell lines, particularly in cell viability, apoptosis, metabolic regulation, and treatment response. These in vitro findings suggest potential molecular mechanisms that could explain why some cells are more sensitive to PTX than others, but further experimental and clinical validation is needed to confirm this. Full article

Background: Global logistics generates 16–25% of greenhouse gas emissions, yet small and medium-sized enterprises (SMEs) in developing economies lack the digital infrastructure to measure and improve their sustainability performance. Business intelligence (BI) systems can support data-driven sustainability decisions, but their application in SME logistics remains poorly understood, and no prior review has examined this intersection with a developing-economy focus. Methods: We conducted a systematic literature review following PRISMA 2020 guidelines, searching Scopus, Web of Science, and IEEE Xplore for peer-reviewed articles published between 2015 and April 2026. Two reviewers independently screened 412 records; 67 studies met inclusion criteria. Results: Five thematic clusters emerged: BI tools for logistics, sustainability KPI frameworks (triple bottom line [TBL], environmental–social–governance [ESG], Sustainable Development Goals [SDGs]), ERP integration, SME-specific barriers, and geographic gaps. Of the studies, 92% originate from developed economies; Africa and MENA remain almost entirely absent. No study combines open-source ERP with a validated TBL KPI framework for logistics SMEs. Conclusions: We propose a six-priority research agenda targeting empirical validation in developing economies and open-source BI solutions for SME logistics sustainability. Full article

Establishing an equitable, evidence-based mechanism for allocating flood prevention funding is critical to mitigating the risk of flash floods. However, existing research seldom accounts for the multi-dimensional nature of vulnerability or achieves an appropriate balance between efficiency and equity. To address this gap, we propose the Multi-dimensional Vulnerability-based Flood Disaster Fund Allocation Optimization Model (MD-FAOM), which integrates the coupling effects of exposure, sensitivity, adaptive capacity, and equity into allocation strategies using the NSGA-II algorithm, TOPSIS method, and geographical detectors. The model prioritizes funding for ecologically targeted flood prevention. We apply this framework to southern Tibet to derive optimal fund allocations and quantitatively assess the resulting benefits. Our results show that areas characterized by negative vulnerability account for 25.22% of the study region, mainly concentrated in Lhasa and Shannan. Under equivalent conditions, MD-FAOM delivers benefits across an area of 85,915 km², achieving an improvement rate of 30.11%. These findings demonstrate that integrating vulnerability science with distributive equity can optimize the allocation of limited resources, thereby enhancing both flood resilience and ecosystem conservation. This approach advances ecohydrological disaster management and supports the achievement of Sustainable Development Goals (SDGs) 13 (Climate Action) and 15 (Life on Land). Full article

The 15-minute life circle has become a critical planning paradigm for developing age-friendly cities; however, its implementation in small and medium-sized cities (SMSCs) is often constrained by topographical heterogeneity and the oversimplified use of facility density as a proxy for service efficiency. This study challenges the conventional assumption that a higher facility density automatically leads to better service outcomes and proposes a dynamic “space–demand–policy” analytical framework to identify the mechanisms underlying spatial mismatch. Using Zhifu District, Yantai, a rapidly aging urban area with complex topography, as a case study, we integrated kernel density estimation (KDE), slope-adjusted network analysis, a modified Gaussian-based two-step floating catchment area (2SFCA) method, and standard deviational ellipse (SDE) analysis. Our results reveal three key findings. First, a “high-density, low-efficiency” paradox is prevalent: older urban cores contain clusters of facilities but have supply–demand ratios below 0.5 because of limited service diversity and slope-induced reductions in accessibility, with the effective service radius decreasing to 750 m. Second, newly developed areas achieve service coverage rates below 50% when terrain constraints are considered, highlighting the limitations of static planning radii. Third, a 90% overlap between the directional distributions of residential areas and elderly care facilities, as indicated by their SDE major axes, supports the spatial feasibility of community-embedded aging-in-place models; however, physical proximity alone does not guarantee effective service delivery. By adapting generic spatial algorithms to create a terrain-sensitive planning tool, this study provides a transferable framework for evidence-based and targeted elderly care planning in SMSCs facing similar demographic and geographic constraints. The proposed framework contributes to the global sustainability agenda and advances SDG 11 (Sustainable Cities and Communities) and SDG 3 (Good Health and Well-being) by promoting more equitable resource allocation and supporting the development of age-friendly, walkable, and sustainable communities in topographically complex urban areas. Full article

Background: Whether financial deepening and trade integration support or hinder environmental sustainability in hydrocarbon-dependent economies remains contested. Methods: This study examines the relationships among financial inclusion, energy intensity, trade openness, and CO₂ emissions per capita in Saudi Arabia for 1980–2020. The empirical strategy combines ARDL bounds testing, FMOLS, DOLS, CCR robustness, Toda–Yamamoto causality, and a battery of structural-break tests comprising Zivot–Andrews unit-root tests, Bai–Perron sup-F tests, and Chow tests. To address the mechanical correlation between carbon productivity and GDP, the per capita emissions specification (LNCP) is used as the primary outcome; carbon productivity (LNES) is reported for robustness. The small-sample sub-period results are stress-tested using ridge regression, residual-bootstrap confidence intervals, a GDP-augmented (scale-control) specification, and a break-date sensitivity analysis. Results: Cointegration is established. The Chow test identifies a significant break in the cointegrating relationship at 2001 (F = 7.36, p < 0.001 for LNCP), supported by the Zivot–Andrews endogenous-break dates for the financial-inclusion series (2000) and trade-openness series (2005), and by the Bai–Perron sup-F test (sup-F = 26.37 at 1990, exceeding the 1% Andrews critical value). Sub-sample re-estimation around 2001 shows that energy intensity, urbanisation, and trade openness are robust drivers of per capita emissions only after the break, while financial inclusion is statistically insignificant in both regimes once the GDP–carbon-productivity mechanical relationship is removed. Conclusions: The Saudi finance–environment relationship is structurally unstable, and policy assessments based on full-sample averages can be misleading. The evidence is best read as describing regime-dependent, conditional long-run associations rather than as identifying structural causal effects. By exposing the interactions, synergies, and trade-offs among financial deepening (SDG 8), energy efficiency (SDG 7), sustainable consumption and production (SDG 12), and climate action (SDG 13), the study shows how this descriptive quantitative evidence can inform—rather than directly identify—an instrument-level policy discussion. The findings are consistent with a Vision 2030 mix that prioritises energy efficiency and green-finance reform, with implications for SDG Targets 7.3, 8.10, 12.2, and 13.2 across oil-exporting economies. Full article

This study conducts a comprehensive investigation into prospective policy alternatives within Pakistan’s power sector using the PAK-TIMES model, targeting the critical challenges of energy scarcity and environmental degradation. Focused on the period from 2022 to 2050, the research evaluates the impact of various policies on energy consumption, supplies, carbon emissions, and expenditures in alignment with Pakistan’s Nationally Determined Contributions (NDC) directed at combatting climate change. The study explores three distinct scenarios: a business-as-usual (BAU) scenario, along with five policy (5% Eff, 10% Eff, 15% REN, 30% REN, 50% REN) scenarios categorized into energy efficiency and renewable integration. The first scenario concentrates on the deployment of energy-efficient devices, while the second scenario delves into diverse levels of renewable energy integration. Key results reveal that energy demand is projected to surge substantially under the BAU scenario, increasing significantly from 3459 PJ in 2022 to 7912 PJ by 2050. In contrast, scenarios prioritizing energy efficiency can potentially curb the total energy supply by 2.3%, while renewable energy integration can expand up to 1.3% compared to business-as-usual by 2050. These alternative scenarios also exhibit the potential to slash greenhouse gas (GHG) emissions from the power sector by up to 15%. Notably, the PAK-TIMES model emerges as a valuable decision support tool for the Pakistani government to facilitate the execution of energy efficiency and renewable energy policies aimed at fulfilling its NDCs, while also contributing to the fulfillment of Sustainable Development Goals (SDGs) 7 (affordable and clean energy) and 13 (climate action). The study underscores the pivotal role of policy interventions in simultaneously mitigating energy challenges and combatting climate change for sustainable development. Full article