The weight of silent criteria: ESG score performance and investment attractiveness in sub-Saharan Africa.

ABSTRACT Home garden is an ecological interaction system between human beings and nature, such as the plants, animals, soil and water in a specific area around the home. Home gardening faces various challenges, including a lack of care, the problem of pest control, poor maintenance/management, inadequate application of necessary agricultural techniques, improper use of inputs such as water, unhealthy seeds or seedlings and poor soil quality, as well as inconsistent and sporadic involvement by the household. Some individuals in the household lack the necessary regard for the home garden, thereby affecting its sustainability, despite its contributions to income, food security, nutritional security, dietary diversification, recreation, as well as emotional and psychological improvements. However, research is still lacking in crucial aspects, including factors that affect the sustainability of home gardening and the gardeners' willingness/determination to transfer knowledge gained from home gardening to mainstream agricultural participation, which this research investigated amidst other objectives. This research reports its outcome on 104 home gardeners among 360 sampled households in the Eastern Cape, South Africa, using descriptive, ordinary least squares (OLS) and logistic regression as the analytical tools. The results show that some, but few, households were actively engaged in their home gardens by cultivating their plots twice to three times a year. The logistic results show that experience gained from home gardening influences the gardeners' willingness to engage in mainstream agricultural activities. The OLS model indicates that household size, level of education, knowledge of farming systems; including crop rotation, compost and animal manures, as well as the combined use of organic and inorganic manures, significantly influence the sustainability of home gardening. The interview revealed that home gardening makes neglected and Underutilized Crop Species (NUCS) or crops not commonly available in an area accessible to households for health and other benefits. Home gardening enables immigrants to experiment with and cultivate crop species from their place of origin in their new location. Home gardens provide indigenous horticultural crops, even in a foreign land. Home garden awareness should be intensified.

This research is a case study on how disruptions by fintech are included in the financial inclusion effect in Nigeria from 2015 to 2024. Using descriptive statistics, correlation analysis and ordinary least squares (OLS) regression, the study measures the effect of fintech adoption, accessibility, innovation and regulatory frameworks on financial inclusion with GDP per capita introduced as the control variable. The outcomes reveal that fintech adoption, accessibility and innovation have a high positive impact on financial inclusion whereas currently regulatory frameworks have an insignificant impact. Strong positive correlations among the components of fintech and GDP add to the interconnected nature of the fintech ecosystem in promoting access to financial services. These findings indicate that use of digital financial services tuning policies to enhance accessibility and innovative functioning are vital points for increased financial inclusion. The study adds to the literature by presenting current empirical evidence on fintech-led inclusion from an emerging economy context and deliver lessons for policy makers, financial institutions, and researchers on best practises for optimising fintech strategies to support inclusive economic growth.

This study examines the use of the reverse regression (RR) model as a diagnostic instrument for detecting and correcting bias in the digital datasets by employing digitally simulated data from a hypothetical medium-sized online enterprise. Set against the backdrop of the high-frequency data generation by digital platforms, this study tackles the widespread issue of measurement error and endogeneity that arises from self-reported, algorithmically modified, and poorly validated datasets. The primary objective was to assess the effectiveness of RR in detecting bias when the assumptions of ordinary least squares (OLS) are not met, and to evaluate its potential for enhancing the reliability of empirical analysis in datasets derived from various platforms. The results indicate that RR enhances model robustness by identifying distortions in parameter estimates and facilitates more precise causal interpretations when used in conjunction with instrumental variables (IVs) and the generalised method of moments (GMM) framework. Empirical findings show that, ceteris paribus, a one-unit increase in actual digital advertising expenditure leads to a 5.04% rise in digital sales, whereas a similar increase in reported advertising expenditure results in a 4.78% increase in sales. In the reverse regressions, both actual and reported advertising expenditures remain positive and statistically significant, with coefficients of 3.06% and 2.72%, respectively. These consistent findings confirm that investments in advertising have a significant impact on online sales performance and consumer engagement. The study concludes by suggesting a policy-oriented framework aimed at enhancing data reliability and informing the formulation of digital economy policies, particularly in calibrating tax incentives, innovation grants, and digital interventions based on empirically validated metrics.

This study examines the impact of key economic growth indicators on the carbon credit market in India, highlighting how selected macroeconomic variables shape its dynamics in an emerging economy. Using secondary data covering 11 years from the Reserve Bank of India (RBI) and BSE India, the research applies rigorous econometric methods, including the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) Unit Root Tests, the Vector Error Correction Model (VECM), and Ordinary Least Squares (OLS) regression, to explore both short- and long-run relationships among variables. The analysis considers crude oil prices, automobile sales (AMS), the Housing Price Index (HPI), GDP, the Index of Industrial Production (IIP), and the Green Exchange Index—an indicator of sustainable finance performance and investor confidence in carbon markets. These variables capture economic activity, market sentiment, and energy dependence, all of which influence carbon credit pricing and demand. Empirical results indicate that crude oil prices and AMS negatively affect carbon credits, whereas HPI, GDP, and IIP positively impact them. The Wald test indicates no long-run relationship between carbon credits and crude oil, AMS, or HPI. However, GDP, IIP, and the Green Exchange Index significantly boost carbon credits in the short run. A robustness check using 2024 data confirms the consistency and structural stability of these associations over time. The study concludes that macroeconomic and industrial indicators are decisive in shaping carbon credit movements. Strengthening industrial productivity, promoting sustainable finance, and aligning macroeconomic management with green policy objectives can enhance the efficiency of carbon markets. These findings provide valuable insights for policymakers, investors, and environmental economists seeking to harmonise economic growth with emission reduction goals. The Indian experience also offers a strategic reference for other emerging economies pursuing sustainable carbon trading frameworks.

The Shandong section of the Yellow River Basin (SDYRB), a critical zone for ecological security in the lower reaches of the Yellow River, faces multiple ecological challenges including salinization, soil erosion, water scarcity, and anthropogenic pollution. These issues significantly hinder regional sustainable development. To assess eco-environmental quality in the SDYRB accurately, an Improved Remote Sensing Ecological Index (IRSEI) was developed by integrating the Composite Salinity Index (CSI) and Soil-Water Conservation Function Index (SWCFI). Utilizing multi-temporal imagery (2009-2023), this study analyzed spatio-temporal patterns of eco-environmental quality and their driving mechanisms. The results show that: (1) The overall eco-environmental quality exhibits a declining trend, with a spatial distribution pattern characterized as "superior in the west and poorer in the east". High-quality areas were concentrated in western plains and Yellow River riparian zones, versus low-quality areas in eastern/northern coasts. (2) The global Moran's I approached 1 and exhibited a gradual year-by-year decline, indicating persistent spatial agglomeration of ecological quality. Local spatial autocorrelation was predominantly characterized by High-High (H-H) and Low-Low (L-L) agglomerations, with low-value areas exhibiting an outward spread tendency. (3) Ecological quality fluctuated, declining significantly (2009-2014) before recovering (2019-2023). Degradation hotspots were identified in the northeast and southwest, whereas the improved areas were concentrated in the central region. (4) Ordinary Least Squares (OLS) regression and GeoDetector (GD) identified synergistic natural and anthropogenic driving factors: mean annual temperature, evapotranspiration, nighttime light intensity, and land use were dominant. This study improves the applicability and interpretability of IRSEI in salinized and soil-eroded regions by integrating CSI and SWCFI, offering a scientific foundation for ecological conservation and high-quality development in the SDYRB. The approach can also be extended to dynamic monitoring and evaluation of other similarly vulnerable ecological zones.

Gender comparison and supervised learning prediction of functional outcomes after combined MPFL reconstruction and tibial tubercle transfer: Role of radiological parameters and psychological factors.

This study analyzes how Environmental, Social, and Governance (ESG) affects the value of companies listed on the Indonesia Stock Exchange (IDX). Researchers applied a purposive sampling method to select 86 companies that had a clear ESG score from Refinitiv and were listed on the IDX during the 2018–2022 period. It employs Ordinary Least Squares (OLS) for data analysis, to examine the relationships between the variables. Findings show that environmental and governance performance are negatively related to firm value, this tells that companies with high environmental and governance performance will has a higher spendings which will lower the company’s value, while social performance has a positive impact when considered alongside the other ESG components. A key limitation of this study is its exclusive focus on the Indonesian market, where distinctive regulatory frameworks, ESG disclosure practices, cultural factors, and investor preferences may shape the relationship between ESG performance and firm value in ways that differ from other countries, thereby limiting the extent to which the findings can be generalized to broader global contexts.

The innovation of financial technology (fintech) has significantly transformed the global financial landscape. Fintech's impact is particularly crucial for alleviating income inequality, as women facing gender-specific financial barriers experience enhanced economic opportunities through algorithm-based lending decisions and expanded digital access. Concurrently, the income disparity between men and women persists as a significant global challenge that researchers have extensively investigated. This study examines the relationship between fintech adoption and gender income inequality by analyzing provincial panel data from China spanning 2011 to 2021. Employing both Ordinary Least Squares (OLS) and Two-step Generalized Method of Moments (GMM) models, our findings reveal that fintech adoption measured by the breadth and depth of digital finance use and the level of financial inclusion digitization significantly reduces gender income inequality. This study provides a novel analysis demonstrating a significant negative relationship between fintech adoption and gender income inequality at the provincial level across all dimensions: the breadth, depth, and digitization of fintech. This suggests that Fintech serves not only as a transformative tool for the financial industry but also contributes significantly to socioeconomic equity. By evaluating the impact of digital financial services on gender economic outcomes, this research provides valuable implications to policymakers, financial institutions, and Fintech product developers promoting fintech and gender equality. Based on these findings, we recommend promoting fintech as an effective tool for advancing gender pay equity.

Schistosomiasis causes significant morbidity in over 78 countries, including Ghana. In females, untreated urogenital schistosomiasis can progress to female genital schistosomiasis (FGS), with focal prevalence ranging from 11% to 73% in sub-Saharan Africa (SSA). This condition poses complex challenges for healthcare professionals. This study assessed the knowledge, attitudes, and practices of healthcare workers (HCWs) regarding FGS in two schistosomiasis-endemic districts in Ghana. A cross-sectional mixed-method study was conducted in 36 health facilities, involving 252 HCWs from the Lower Manya-Krobo (LMK) and Shai Osudoku (SOD) districts. Quantitative data were analyzed using descriptive statistics, independent t-tests, and Ordinary Least Squares (OLS) models with Huber-White robust standard errors in Stata 18. Additionally, 38 purposively selected HCWs were interviewed, and qualitative data were analyzed thematically (NVivo 20). A joint display analysis was used to integrate findings. HCWs in SOD had significantly higher knowledge scores (M = 55.9, SD = 9.8) than those in LMK (M = 41.4, SD = 17.1; t (250) = - 8.25, p < 0.001), while attitudes or practices did not differ significantly between districts. Robust regression analysis showed knowledge was higher among HCWs with > 5 years of practice (β = 7.21, 95% CI: 3.34-11.08, p < 0.001), general nurses β = 10.59, 95% CI: 5.07-16.12, p < 0.001) and midwives (β = 13.92, 95% CI: 7.46-20.38, p < 0.001); attitudes were lower in clinical settings compared to public health settings (β = - 7.08, 95% CI: - 9.63 to - 4.53, p < 0.001); and practices were among general nurses (β = 9.58, 95% CI: 4.84-14.33, p < 0.001) and midwives (β = 12.48, 95% CI: 7.35-17.61, p < 0.001) but lower among diploma holders (β = - 9.90, 95% CI: - 14.71 to - 5.09, p < 0.001) in clinical settings (β = - 5.96, 95% CI: - 9.49 to - 2.43, p = 0.001). Only 4.8% of HCWs in LMK and 9.5% in SOD reported facility capacity to diagnose and manage FGS. Qualitative findings confirmed a lack of FGS-specific interventions, including clinical guidelines and facility-level support. Substantial gaps exist in HCWs' KAP and readiness to manage FGS, exacerbated by systemic deficiencies in training, and resources. Addressing these gaps requires integration FGS in regular in-service training for frontline HCWs; improved diagnostic and treatment capacity; ensure the availability of resources and tools; and strengthened district-level supervision to facilities.

Tax-aggressive behavior by firms can undermine tax revenues, corporate transparency, and overall economic governance. Corporate governance mechanisms are increasingly recognized as critical tools for mitigating such behavior, particularly in emerging markets such as Morocco. This study investigates how corporate governance structures influence the reduction in tax aggressiveness in a developing-country context, while also assessing the moderating role of audit quality. Using financial data from firms listed on the Casablanca Stock Exchange, the hypotheses are tested through OLS regression with firm and year fixed effects to examine the impact of board characteristics and audit quality on tax aggressiveness. The results show that the separation of the CEO and chairman roles and larger board size significantly reduce tax-aggressive behavior. Moreover, audit quality strengthens the negative relationship between board size and tax aggressiveness, with higher-quality audits further constraining aggressive tax practices. Additionally, ownership concentration is associated with higher tax aggressiveness, reflected in lower effective tax rates, whereas board independence exhibits no significant association with tax aggressiveness (p-value = 0.500879). Overall, the findings suggest that robust corporate governance and high-quality audits effectively mitigate tax-aggressive practices among Moroccan listed firms. This study contributes novel evidence from the Moroccan context, highlighting governance structures and audit mechanisms most effective at curbing such behavior. Policymakers and regulators are encouraged to promote stronger governance frameworks and enhance audit quality standards, while firms should reinforce these mechanisms to improve tax compliance and transparency

This study uses a simulated macroeconomic dataset with 100 observations to compare Ordinary Least Squares (OLS) regression versus Bayesian regression for GDP modelling. Investment, consumption, and government spending are included in the model definition as important explanatory variables. Under stringent distributional assumptions, OLS, which is based on the traditional frequentist paradigm, yields parameter estimates that are solely obtained from the observed sample. In contrast, Bayesian regression produces posterior estimates by integrating prior distributions with the likelihood, providing a probabilistic description of parameter uncertainty. The methodological significance of prior specification was highlighted by the unstable inferences obtained from initial Bayesian estimation using weakly informative priors. However, posterior convergence and predictive alignment with OLS findings were significantly enhanced by the addition of sophisticated, commercially viable priors. While Bayesian regression provided wider credible intervals reflecting uncertainty, OLS produced more accurate (narrower) predicted intervals. The results confirm that Bayesian regression is a rigorous and reliable substitute for OLS when backed by well-informed priors, especially in situations with sparse data or ambiguous model assumptions.

Sustainability in higher education increasingly relies on the clarity with which enacted practices and codified procedures communicate credible governance signals. India's NAAC (National Assessment and Accreditation Council) Criterion-7 offers publicly accessible, comparable evidence of such practices across various colleges. The objective of this study is to determine whether institutional green practices and procedural maturity, as documented in NAAC Criterion- 7 records, are associated with the level of sustainability governance, as reflected in environment and energy audits (7.1.6). A quantitative, cross-sectional research design utilising secondary institutional-level data was employed. Four indicators were operationalised from NAAC AQAR/SSR documents using ordinal coding (A = 4, B = 3, C = 2, D = 1): Green Campus (7.1.5.), Alternate Energy (7.1.2.), Water Conservation (7.1.4), and Environment/Energy Audits (7.1.6) The presence of an e-waste Standard Operating Procedure (SOP) was captured through a binary proxy. The analysis incorporated descriptive statistics, Pearson and Spearman correlation coefficients, and Ordinary Least Squares (OLS) regression predicting 7.1.6. Among twenty colleges (N = 15 after listwise deletion), the breadth of Green Campus practices demonstrated the most substantial partial effect on audit outcomes, followed by the presence of an e-waste SOP. Alternate Energy showed a positive association but was not statistically significant after controlling for other variables. Water Conservation exhibited a slight, non-significant, negative partial coefficient, likely attributable to predictor overlap. The model demonstrated a high degree of fit (R²≈0.94). These findings suggest that visible, campus-wide practices and formalised e-waste procedures are the most evident institutional levers for enhanced sustainability governance, as indicated by audit intensity. The methodology provides a transparent, replicable baseline for benchmarking. Actionable priorities include formalising e-waste SOPs, deepening routine Green Campus practices, and demonstrating energy investments through capacity, coverage, and monitoring metrics. Future research should aim to expand the sample across additional states and over multiple years.

Accurate forecasting of disease progression is vital in glaucoma management. Ordinary least square regression (OLSR) analyses are not appropriate to perform trend analysis on longitudinally collected perimetry data. This study examines the applicability of an irregular autoregressive of order 1 (IAR (1)) method to model mean deviation (MD) series and investigates if IAR (1) improves validity of the model and results better forecasts then OLSR. Longitudinal data from eyes with progressive glaucoma were used. A total of 1200 MD data from forty-two eyes were included in this study. MD series from the eyes were fitted using both OLSR and an IAR (1) methods. A correlogram was used to determine if errors of the fitted OLSR and IAR (1) were correlated. Predictability of the IAR (1) method was then compared with OLSR using forecast Mean Square Error (MSE). Residuals from the OLSR were correlated and did not satisfy the assumption of normality. On the other hand, the IAR (1) model markedly improved the validity of the model as evidenced by insignificant autocorrelation functions (p-value > 0.05) and model’s ability to fit heavy-tailed distribution. Compared to the OLSR fit, significantly higher percentages of eyes resulted smaller MSE (62% vs. 38%, P = 0.02) when fitted with IAR (1) method. The IAR (1) method adequately addresses the shortcomings of OLSR when fitting repeatedly collected perimetry data. The IAR (1) method appears to be statistically more valid method for fitting MD series and more accurately forecasts MD progression when compared with OLSR fit.

The Phillips curve has long pointed out. Inflation and unemployment have a negative relationship. This study checks again. Does this idea work in the United States? The time is from 2000 to 2024. This time had financial crises, pandemics, and supply-side shocks. This study uses monthly inflation and unemployment data from the Bureau of Labor Statistics and the Federal Reserve Economic Database. This study uses an ordinary least squares (OLS) regression. It does this to check if there is a clear linear relationship between the variables people care about. It also does this to see if this relationship is statistically significant. The number that shows how unemployment affects inflation (regression coefficient) is 0.0024. Its p-value is 0.433. The R-squared is 0.001. The results show there is no statistically significant linear relationship between inflation and unemployment in the United States from 2000 to 2024. This challenges the traditional Phillips curve framework. It also suggests its ability to explain things in modern macroeconomics is limited.

Background/Objectives: The relationship between household tobacco expenditure and child health has attracted considerable attention from both academic and policy communities, as tobacco expenditure can influence children’s health, nutrition, and overall well-being in multiple ways, particularly in rural and low-income settings. This study examines the causal impact of household tobacco expenditure on child health outcomes in a transitional economy. Methods: Using nationally representative microdata from the most recent Household Living Standards Survey, the authors employ Ordinary Least Squares (OLS), Random Effects (RE), and Instrumental Variable (IV) estimations to identify the effects of tobacco spending on children’s healthcare utilization and health status. Results: The results consistently show that higher household tobacco expenditure significantly increases the likelihood of hospitalization among Vietnamese children, with the effects being most pronounced for those under six years of age. Moreover, the authors uncover substantial heterogeneity across gender, maternal age at childbirth, and regional contexts, highlighting persistent socioeconomic inequalities in health outcomes. Conclusions: This study provides compelling evidence of the adverse effects of household tobacco expenditure on children’s health in Vietnam. Theoretically, the study contributes to the literature on the economics of health and intra-household resource allocation by providing micro-level causal evidence from a transitional setting. From a policy perspective, the findings underscore the need for targeted fiscal and public health interventions to mitigate tobacco-related welfare losses and to promote equitable access to healthcare among vulnerable populations.

Domestic violence (DV) is now widely recognized as a severe public health problem owing to its health consequences. India has high prevalence rates of physical, sexual, and emotional violence against spouses (28%, 14%, and 6%, respectively). The study uses data from the National Family Health Survey (NFHS-5) to analyze the spatial distribution of different forms of DV in Northeast India. Bivariate analysis, ordinary least squares (OLS), and geographically weighted regression (GWR) were employed for data analysis. Domestic violence in Northeast India stands at 31.3%, with Manipur at 41.5%, followed by Assam and Arunachal Pradesh. Hailakandi in Assam (64.7%) and Bishnupur in Manipur (59.9%) have the highest rates. The local R2 values for domestic violence were notably higher in the southern and eastern regions of the northeast States. Specifically, in the southeastern districts of Nagaland, these values ranged between 0.65 and 0.70. Regional disparities were evident in the prevalence of physical, emotional, and sexual violence, with Manipur, Assam, and specific districts in Arunachal Pradesh and Nagaland frequently highlighted as hotspots. The results highlight the necessity of region-specific strategies and focused interventions to effectively address and prevent DV throughout the Northeast. Prioritizing the mitigation of significant risk factors for DV in hotspot regions should be the government’s top priority.

This study explores the intersection of financial performance and sustainable economic development in Malaysia, emphasizing the role of interdisciplinary education in fostering long-term growth. In light of Malaysia’s 2021 budget priorities and the Sustainable Development Goals (SDGs), particularly economic sustainability, the research investigates how stock market performance influences national output growth. Using Ordinary Least Squares (OLS) regression analysis, the study evaluates five macroeconomic variables—stock market capitalization (MCAP), inflation rate, government expenditure, school enrollment, and openness—over a 23-year period (1989–2011). The findings reveal that MCAP is the most significant variable affecting output growth, highlighting the importance of robust financial systems in driving sustainable development. The study underscores the need for interdisciplinary approaches that integrate finance, education, and policy to enhance economic resilience. School enrollment, though less statistically significant, represents a critical link between education and economic performance, suggesting that financial literacy and education reform are essential for empowering future generations. By bridging economic theory, financial analytics, and educational policy, this research contributes to a holistic understanding of sustainable development. It advocates for finance education as a strategic tool to equip individuals with the knowledge to navigate and contribute to increasingly complex global financial systems. Ultimately, the study supports the integration of interdisciplinary education into national development strategies to ensure inclusive, informed, and sustainable economic growth.

Purpose Digital products often rely on continuous version updates as generational product innovation (GPI) to build and sustain consumer engagement. Unlike traditional settings that emphasize discrete, content-focused launches, digital contexts elevate the temporal structure of innovation. This study examines how the release rhythm influences engagement and explores the moderating roles of brand awareness and the pricing model. Design/methodology/approach We collected data on 416 mobile apps and analyzed 1,071 version updates. We employ negative binomial regressions as the primary estimation method to analyze consumer engagement and use ordinary least squares (OLS) regressions as robustness checks. To address endogeneity, we apply a two-stage control function approach using industry-level GPI rhythm irregularity as an instrumental variable. Findings The analysis reveals that irregular GPI rhythms enhance consumer engagement, while regular rhythms yield weaker effects. Moreover, the positive impact of irregular GPI rhythm is amplified when brand awareness is high and when products are free. Practical implications Managers can strategically use irregular innovation timing to enhance user engagement, especially for free products and strong brands in competitive digital markets. Originality/value The study reframes GPI in digital markets as a temporal signaling process that highlights the pattern of revealing innovation beyond its content or frequency. It identifies irregular rhythm as a novel antecedent of engagement and shows that brand awareness and pricing condition how temporal signals are interpreted, explaining firms’ use of aperiodic releases and informing decisions about when to reveal product generations.

The mental health impacts of cumulative heat exposure remain poorly understood, particularly regarding the role of social mechanisms. This study investigates whether social isolation mediates the relationship between cumulative heat exposure and mental health among older Chinese adults who have experienced previous heat exposure. Using data from the Chinese Longitudinal Healthy Longevity Survey (2005–2018) and corresponding meteorological records (1974–2019), we quantified cumulative heat exposure as the total number of days during the warm season (May–September) exceeding specific wet-bulb globe temperature thresholds (1-day, 2-day, and 3-day). Social isolation was assessed across four dimensions: core networks, peripheral networks, frequency of family visits, and social participation. Employing ordinary least squares (OLS) regression analyses and bootstrap-based parallel mediation models with clustered standard errors, we found that higher cumulative heat exposure was significantly associated with poorer mental health across all definitions. Mediation analyses further indicated that heat exposure was related to an expansion of core networks and a reduction of peripheral ties, both of which were linked to better mental health outcomes. No mediation effects were observed for family visit frequency or social participation. These findings provide novel evidence that cumulative heat exposure among older adults may trigger an adaptive restructuring of social networks, which in turn could help mitigate adverse mental health impacts.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-27446-3.