Search Results (421)

This study investigates the predictive performance of financial indicators in forecasting stock prices within the automotive sector using an adaptive neuro-fuzzy inference system (ANFIS). In light of the growing complexity of global financial markets and the increasing demand for automated, data-driven forecasting models, this research aims to identify those financial ratios that most accurately reflect price dynamics in this specific industry. The model incorporates four widely used financial indicators, return on assets (ROA), return on equity (ROE), earnings per share (EPS), and profit margin (PM), as inputs. The analysis is based on real financial and market data from automotive companies, and model performance was assessed using RMSE, nRMSE, and confidence intervals. The results indicate that the full model, including all four indicators, achieved the highest accuracy and prediction stability, while the exclusion of ROA or ROE significantly deteriorated model performance. These findings challenge the weak-form efficiency hypothesis and underscore the relevance of firm-level fundamentals in stock price formation. This study’s sector-specific approach highlights the importance of tailoring predictive models to industry characteristics, offering implications for both financial modeling and investment strategies. Future research directions include expanding the indicator set, increasing the sample size, and testing the model across additional industry domains. Full article

Despite growing global momentum to reduce the number of children who never received a dose of any vaccine, i.e., zero-dose (ZD) children, persistent geographic and social inequities continue to undermine progress toward universal immunization coverage. In Madagascar, where routine vaccination coverage remains below 50% in most regions, the non-governmental organization Doctors for Madagascar and public sector partners are implementing the SOAMEVA program: a targeted community-based initiative to identify and reach ZD children in sixteen underserved districts in the country’s south. This paper outlines the equity-sensitive evaluation design developed to assess the implementation and impact of SOAMEVA. It presents a forward-looking evaluation framework that integrates both quantitative program monitoring and qualitative community insights. By focusing at the fokontany level—the smallest administrative unit in Madagascar—the evaluation captures small-scale variation in ZD prevalence and program reach, allowing for a detailed analysis of disparities often masked in aggregated data. Importantly, the evaluation includes structured feedback loops with community health workers and caregivers, surfacing local knowledge on barriers to immunization access and program adoption. It also tracks real-time adaptations to implementation strategy across diverse contexts, offering insight into how routine immunization programs can be made more responsive, sustainable, and equitable. We propose eight design principles for conducting equity-sensitive evaluation of immunization programs in similar fragile settings. Full article

Value-at-Risk (VaR) is a key metric widely applied in market risk assessment and regulatory compliance under the Basel III framework. This study compares two Monte Carlo-based VaR models using publicly available equity data: a return-based model calibrated to historical portfolio volatility, and a CAPM-style factor-based model that simulates risk via systematic factor exposures. The two models are applied to a technology-sector portfolio and evaluated under historical and rolling backtesting frameworks. Under the Basel III backtesting framework, both initially fall into the red zone, with 13 VaR violations. With rolling-window estimation, the return-based model shows modest improvement but remains in the red zone (11 exceptions), while the factor-based model reduces exceptions to eight, placing it into the yellow zone. These results demonstrate the advantages of incorporating factor structures for more stable exception behavior and improved regulatory performance. The proposed framework, fully transparent and reproducible, offers practical relevance for internal validation, educational use, and model benchmarking. Full article

The rubber industry is facing major socio-economic and environmental constraints. Rubber-based agroforestry systems represent a more sustainable solution through the diversification of income and the provision of greater ecosystem services than monoculture plantations. Participative approaches are known for their ability to co-construct solutions with stakeholders and to promote a positive impact on smallholders. This study therefore implemented a participatory research process with stakeholders in the natural rubber sector for the purpose of improving inclusion, relevance and impact. Facilitation training sessions were first organised with academic actors to prepare participatory workshops. A working group of stakeholder representatives was set up and participated in these workshops to share a common representation of the value chain and to identify problems and solutions for the sector in Indonesia. By fostering collective intelligence and systems thinking, the process is aimed at enabling the development of adaptive technical solutions and building capacity across the sector for future government replanting programmes. The resulting adaptive technical packages were then detailed and objectified by the academic consortium and are part of a participatory plant breeding approach adapted to the natural rubber industry. On-station and on-farm experimental plans have been set up to facilitate the drafting of projects for setting up field trials based on these outcomes. Research played a dual role as both knowledge provider and facilitator, guiding a co-learning process rooted in social inclusion, equity and ecological resilience. The initiative highlighted the potential of rubber cultivation to contribute to climate change mitigation and food sovereignty, provided that it can adapt through sustainable practices like agroforestry. Continued political and financial support is essential to sustain and scale these innovations. Full article

The economic impact of health promotion and disease prevention interventions in ageing populations remains debated, as theories of morbidity compression and expansion offer contrasting views on the relationship between life expectancy and duration of morbidity. A MEDLINE search was conducted to identify studies evaluating the economic impact of health promotion or primary or secondary prevention interventions in OECD countries, over a lifetime time horizon. Among the 29 studies included, 16 reported cost-saving interventions (reducing costs while improving health outcomes), 11 reported cost-effective interventions (health gains at an acceptable additional cost based on an established threshold), and two presented cost-ineffective interventions (costs exceeding the threshold for the health benefits achieved). Interventions targeting diabetes and obesity prevention were cost-saving; cancer screening and fall prevention strategies were cost-effective; whereas interventions targeting rare diseases were cost-ineffective. Regulatory interventions were also cost-saving, while most programme-based interventions were cost-effective. Cost-saving or cost-effective interventions generally adopted broader analytical perspectives, while cost-ineffective ones employed narrower perspectives. The four studies that incorporated competing risks—despite using a narrower healthcare sector perspective—still found the interventions to be cost-saving or cost-effective interventions. None of the included studies assessed whether interventions led to morbidity compression or expansion. Only a few studies considered equity impact; those that did reported improved outcomes for disadvantaged groups, in regulatory and community-based interventions. Further research is needed to quantify morbidity outcomes and enhance methodological consistency, particularly with respect to analytical perspectives, the integration of competing risks, and the inclusion of equity analyses. Full article

This study conducts a comprehensive systematic review of the economic impact of Digital Twin (DT) technology within the Architecture, Engineering, and Construction (AEC) industry, following the PRISMA methodology. While DT adoption has been accelerated by advancements in Building Information Modelling (BIM), the Internet of Things (IoT), and data analytics, significant challenges persist—most notably, high initial investment costs and integration complexities. Synthesising the literature from 2016 onwards, this review identifies sector-specific barriers, regulatory burdens, and a lack of standardisation as key factors constituting DT implementation costs. Despite these hurdles, DTs demonstrate strong potential for enhancing construction productivity, optimising lifecycle asset management, and enabling predictive maintenance, ultimately reducing operational expenditures and improving long-term financial performance. Case studies reveal cost efficiencies achieved through DTs in modular construction, energy optimisation, and infrastructure management. However, limited financial resources and digital skills continue to constrain the uptake across the sector, with various extents of impact. This paper calls for the development of unified standards, innovative public–private funding mechanisms, and strategic collaborations to unlock and utilise DTs’ full economic value. It also recommends that future research explore theoretical frameworks addressing governance, data infrastructure, and digital equity—particularly through conceptualising DT-related data as public assets or collective goods in the context of smart cities and networked infrastructure systems. Full article

The building sector remains one of the largest contributors to global energy consumption and CO₂ emissions, yet selecting optimal retrofit strategies is often hindered by inconsistent evaluation practices and limited integration of environmental and social impacts. This review addresses that gap by systematically analyzing how various assessment methods are applied to building retrofits, particularly from a financial and environmental perspective. A structured literature review was conducted across four major scientific databases using predefined keywords, filters, and inclusion/exclusion criteria, resulting in a final sample of 50 studies (green colored citations of this paper). The review focuses on the application of Life Cycle Cost Analysis (LCCA), Cost–Benefit Analysis (CBA), and Life Cycle Assessment (LCA), as well as additional indicators that quantify energy and sustainability performance. Results show that LCCA is the most frequently used method, applied in over 60% of the studies, often in combination with LCA (particularly for long time horizons). CBA appears in fewer than 25% of cases. More than 50% of studies are based in Europe, and over 60% of case studies involve residential buildings. EnergyPlus and DesignBuilder were the most common simulation tools, used in 28% and 16% of the cases, respectively. Risk and uncertainty were typically addressed through Monte Carlo simulations (22%) and sensitivity analysis. Comfort and social impact indicators were underrepresented, with thermal comfort included in only 12% of studies and no formal use of tools like Social-LCA or SROI. The findings highlight the growing sophistication of retrofit assessments post-2020, but also reveal gaps such as geographic imbalance (absence of African case studies), inconsistent treatment of discount rates, and limited integration of social indicators. The study concludes that future research should develop standardized, multidimensional evaluation frameworks that incorporate social equity, stakeholder values, and long-term resilience alongside cost and carbon metrics. Full article

Inclusion in play, physical education, outdoor life, organized sports, and other movement-based activities can promote resilience and support physical, emotional, and social well-being. These arenas are particularly important for reducing health disparities and preventing social marginalization across the lifespan. Yet, children and adolescents from vulnerable or disadvantaged backgrounds encounter persistent barriers to participation, rooted in broader inequalities related to their socioeconomic position, disability, gender, ethnicity, and access to supportive environments. This perspective outlines how inclusive movement contexts, when informed by developmental systems theory and resilience frameworks, can interrupt trajectories of marginalization and promote long-term equity in health, education, and work inclusion. We emphasize the need for interdisciplinary approaches, combining longitudinal and qualitative methods, to uncover how vulnerability and participation interact dynamically over time. By integrating insights from developmental science, education, public health, and spatial ecology, we identify strategic pathways for research and action. Addressing these challenges requires coordinated efforts across sectors and stakeholders to co-create inclusive, context-sensitive interventions. Full article

This study examines the effects of the International Maritime Organization’s (IMO) market-based measures, with a particular focus on the greenhouse gas (GHG) levy and on the financial and operational performance of Korean shipping companies. The analysis estimates that these companies, which play a vital role in global trade, consume approximately 9211 kilotons of fuel annually and emit 28.5 million tons of carbon dioxide. Under the lowest proposed carbon tax scenario, the financial burden on these companies is estimated at approximately KRW 1.07 trillion, resulting in an 8.8% reduction in net profit, a 2.4% decrease in return on equity (ROE), and a 1.1% decline in return on assets (ROA). Conversely, under the highest carbon tax scenario, costs rise to KRW 4.89 trillion, leading to a significant 40.2% decrease in net profit, thereby posing a serious threat to the financial stability and competitiveness of these firms. These findings underscore the urgent need for strategic policy interventions to mitigate the financial impact of carbon taxation while promoting both environmental sustainability and economic resilience in the maritime sector. Full article

Water scarcity, exacerbated by climate change, population growth, and competing sectoral demands, poses a major threat to agricultural sustainability, particularly in irrigated regions such as the Nile Delta in Egypt. Addressing this challenge requires innovative approaches to evaluate irrigation performance despite the limitations in ground data availability. Traditional assessment methods are often costly, labor-intensive, and reliant on field data, limiting their scalability, especially in data-scarce regions. This paper addresses this gap by presenting a comprehensive and scalable framework that employs publicly accessible satellite data to map crop types and subsequently assess irrigation performance without the need for ground truthing. The framework consists of two parts: First, crop mapping, which was conducted seasonally between 2015 and 2020 for the four primary crops in the Nile Delta (rice, maize, wheat, and clover). The WaPOR v2 Land Cover Classification layer was used as a substitute for ground truth data to label the Landsat-8 images for training the random forest algorithm. The crop maps generated at 30 m resolution had moderate to high accuracy, with overall accuracy ranging from 0.77 to 0.80 in summer and 0.87–0.95 in winter. The estimated crop areas aligned well with national agricultural statistics. Second, based on the mapped crops, three irrigation performance indicators—adequacy, reliability, and equity—were calculated and compared with their established standards. The results reveal a good level of equity, with values consistently below 10%, and a relatively reliable water supply, as indicated by the reliability indicator (0.02–0.08). Average summer adequacy ranged from 0.4 to 0.63, indicating insufficient supply, whereas winter values (1.3 to 1.7) reflected a surplus. A noticeable improvement gradient was observed for all indicators toward the north of the delta, while areas located in the delta’s new lands consistently displayed unfavorable conditions in all indicators. This approach facilitates the identification of regions where agricultural performance falls short of its potential, thereby offering valuable insights into where and how irrigation systems can be strategically improved to enhance overall performance sustainably. Full article

This study examines the long-term financial sustainability of commercial banks, emphasizing the crucial role of credit risk management. Given that the core function of credit creation inherently exposes banks to credit risk, this analysis evaluates how five key bank-specific risk variables, namely expected credit losses (ECL_BS), impairment gains or losses (ECL_IS), non-performing loans (NPLs), common equity tier 1 capital (CET1), and leverage (LEV) affect long-term financial sustainability. Applying a panel error correction model on data from listed South African banks spanning 2006 to 2023, the study reveals a stable long-term relationship, with approximately 74% of short-term deviations corrected over time, indicating convergence towards equilibrium. By taking into account the significance of major exogeneous shocks such as the 2009–2010 global financial crisis and the COVID-19 pandemic, as well as regulatory framework changes, the results reveal persistent relationships between credit risk factors and banks’ long-term financial sustainability in both short and long horizons. Notably, expected credit losses, and impairment gains and losses exert significant negative influence on long-term financial sustainability, while higher CET1 and NPLs exhibit positive effects. The study findings are framed within four complementary theoretical perspectives—the resource-based view, institutional theory, industrial organisation, and the dynamic capabilities framework—highlighting the multidimensional drivers of financial resilience. Thus, the study’s originality lies in its integrated approach to assessing credit risk, offering a holistic model for evaluating its influence on long-term financial sustainability. This integrated framework provides valuable, actionable insights for financial regulators, bank executives, policymakers, and banking practitioners committed to strengthening credit risk frameworks and aligning banking sector stability with broader sustainable development goals. Full article

We present a new method for trend analysis that integrates traditional foresight techniques with advanced data processing and artificial intelligence. It addresses the challenge of analyzing large volumes of information while preserving expert insight. The hybrid methodology combines computational analysis with expert validation across four phases: literature review, information systematization, trend identification, and analysis. Tools like Voyant Tools 2.6.18 and NotebookLMare used for semantic and statistical exploration. Among them, we highlight the use of the Defly Compass tool, a natural language processing tool based on semantic projections and developed by our team. The method produces mixed results, including both conceptual conclusions and quantifiable, reproducible outcomes adaptable to diverse contexts. Comparative case studies in agriculture, education, and public health identified key patterns within and across sectors. Cross-domain validation revealed universal trends such as digital infrastructure, data integration, and equity. Designed for accessibility, the method enables small, non-specialized teams to combine computational tools with expert knowledge for strategic decision making in complex environments. Full article

In this paper, we consider the concept of global digital citizenship, particularly as it applies to museums and cultural heritage sectors. In this perspective survey study, we explore current examples of how museums can adjust to the tenets of global digital citizenship that are necessary to navigate and participate in increasingly interconnected digital worlds and to collectively address global challenges. This paper provides a qualitative survey and discussion covering issues concerning open foundations, knowledge co-creation, digital equity and inclusion, and participatory innovation. In conclusion, there are untapped opportunities for museums (e.g., through audience participation and digital tools, especially with recent developments in artificial intelligence), but also constraints that must be considered (e.g., lack of funding, digital inequality, and ethical issues related to the use of AI). Full article

This study explores the presence of hidden market regimes in a sector-specific stock index within the Thai equity market. The behavior of such indices often deviates from broader macroeconomic trends, making it difficult for conventional models to detect regime changes. To overcome this limitation, the study employs a multivariate Gaussian mixture hidden Markov model, which enables the identification of unobservable states based on daily and intraday return patterns. These patterns include open-to-close, open-to-high, and low-to-open returns. The model is estimated using various specifications, and the best-performing structure is chosen based on the Akaike Information Criterion and the Bayesian Information Criterion. The final model reveals three statistically distinct regimes that correspond to bullish, sideways, and bearish conditions. Statistical tests, particularly the Kruskal–Wallis method, confirm that return distributions, trading volume, and open interest differ significantly across these regimes. Additionally, the analysis incorporates risk measures, including expected shortfall, maximum drawdown, and the coefficient of variation. The results indicate that the bearish regime carries the highest risk, whereas the bullish regime is relatively stable. These findings offer practical insights for regime-aware portfolio management in sectoral equity markets. Full article

This paper is based on our engagement in a cross-sector network in Leeds, UK, taking local climate action. It draws on in-depth engagements with participants in this network, to explore how they negotiate being in, while at the same time wanting to push beyond, a climate emergency. We found three emergent trends: a reworked interpretation of the climate emergency through longer-term, holistic, historically grounded, and politicised definitions; novel forms of disruptive, collaborative place leadership that could help respond to this longer emergency; and a value-based focus on a reparative ethics of self-care, people-care, and Earth-care that foregrounds climate justice and accountability to frontline communities. We end by recommending that place-based actors can enhance the effectiveness of their collective action by broadening emergency definitions, developing politics and strategy, and supporting values-based climate justice and equity. Full article