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PurposeGender diversity in corporate boards is broadly studied in existing corporate governance literature. However, the role of board gender diversity on environmental, social and governance (ESG) performance of the banks is still unaccounted for. Drawing on resource dependence and legitimacy theory, this study addresses this pressing research issue. Moreover, investigation of ESG controversies as a moderator paves the existing corporate governance research to the new avenues.Design/methodology/approachData were sourced from Refinitiv database on 37 US banks from the period of 2013 to 2017. This study employs static and dynamic panel regression models that include random effects, fixed effects and dynamic generalised method of moments (GMMs) to test the hypotheses. Furthermore, system GMM is used to reduce the issue of endogeneity, measurement error, omitted variables bias and bank-specific heterogeneity.FindingsWe identify a significant positive relationship between board gender diversity and the ESG performance of US banks. However, the result propounds non-significant moderating effect of ESG controversies on the board gender diversity–ESG performance nexus.Originality/valueLiterature on board gender diversity and ESG separately and predominantly explains firm/bank's financial performance. This study is one of the pioneering attempts to explain the role of board gender diversity on ESG performance. Although incremental, however, this study also contributes to the literature on ESG in the US context.

Purpose Earlier firms were evaluated mostly from their financial performance perspective, but with the increasing attention to sustainability goals, environmental, social and governance (ESG) performance of firms became key concerns to stakeholders. The purpose of this paper is to explore the effects of ESG performance of banks on their financial performance, in the context of emerging markets. Design/methodology/approach This study employs the generalised method of moments technique for estimation purpose due to the dynamic nature of the data and to correct for endogeneity. This study uses the ESG performance data of 93 emerging market banks from 2015 to 2018, available in Asset4 ESG database of Refinitiv, formerly known as Thompson Reuters. The accounting and financial data are collected from Refinitiv Datastream database. Findings The findings indicate a positive association of emerging market banks’ environmental and social performance with their financial performance, but governance performance does not influence financial performance. Originality/value While many studies exist on the association of ESG concerns of an organisation with their financial profitability, the literature on in the context of banking is still limited. To the best of the authors’ knowledge, this is the first study that examines the effect of ESG practices of banks on their financial performance in the context of emerging economies.

This study aimed to conduct a comprehensive systematic review and dose-response meta-analysis to summarize available findings on the associations between dietary protein intake and prostate cancer risk as well as the dose-response associations of total, animal, plant, and dairy protein intake with prostate cancer risk. This study followed the 2020 PRISMA guideline. We conducted a systematic search in the online databases of PubMed, Scopus, ISI Web of Science, and Google Scholar to detect eligible prospective studies published to October 2021 that assessed total, animal, plant, and dairy protein intake in relation to prostate cancer risk. Overall, 12 articles containing prospective studies with a total sample size of 388,062 individuals and 30,165 cases of prostate cancer were included. The overall relative risks (RRs) of prostate cancer, comparing the highest and lowest intakes of total, animal, plant, and dairy protein intake, were 0.99 (95% CI: 92–1.07, I2 =12.8%), 0.99 (95% CI: 95–1.04, I2 =0), 1.01 (95% CI: 96–1.06, I2 =0), and 1.08 (95% CI: 1.00–1.16, I2 =38.1%), respectively, indicating a significant positive association for dairy protein intake (P = 0.04) and non-significant associations for other protein types. However, this positive association was seen among men who consumed ≥ 30 gr/day of dairy protein, such that a 20 g/d increase in dairy protein intake (equal to 2.5 cups milk or yogurt) was associated with a 10% higher risk of prostate cancer (Pooled RR: 1.10, 95% CI: 1.02–1.20, I2 = 42.5%). Such dose-response association was not seen for total, animal, and plant protein intake. Overall, dairy protein intake may increase the risk of prostate cancer in men who consumed > 30 gr/day of dairy protein. Larger, well-designed studies are still required to further evaluation of this association. •we found no significant association between total protein intake and risk of prostate cancer.•Each increase of 20 gr/day of dairy protein was associated with a 10% higher risk of prostate cancer.•The significant association between dairy and prostate cancer no longer held after excluding Allen et al. study.

The safety of pedestrians, such as workers who largely walk to and from work, has not been given sufficient attention, especially in the area of traffic safety in developing countries, including Bangladesh. Although the National Road Safety Strategy has a very strong emphasis on road safety education and publicity campaigns, the road safety knowledge may not have reached these vulnerable road users who most needed them. Moreover, little is known about the penetration rate of these campaigns and who have benefited or not benefited from them. On the other hand, the developing country, like Bangladesh, is heavily dependent on its Readymade Garment (RMG) workers for earning foreign currency, and walking is one of the major mode of transports of those workers. The objective of this study is to identify those who are not reach by the safety education. Results from a survey of 1020 RMG workers around Dhaka identified several socioeconomic, demographic, travel characteristics and accident experience that affect the most vulnerable segments who are left out of the system. The findings of this study would help the policy makers to arrange necessary road safety education for the most vulnerable cohorts of pedestrians to encourage the continued use of this sustainable mode of commute.

Probabilistic streamflow models play a pivotal role in quantifying hydrological uncertainty and form the backbone of modern risk management strategies for flood and drought forecasting, water allocation planning, and the design of resilient infrastructure. Unlike deterministic approaches that yield single-point estimates, these models provide a spectrum of possible outcomes, enabling a more realistic assessment of extreme events and supporting informed, sustainable water resource decisions. By explicitly accounting for natural variability and uncertainty, probabilistic models promote transparent, robust, and equitable risk evaluations, helping decision-makers balance economic costs, societal benefits, and environmental protection for long-term sustainability. In this study, we introduce the bounded half-logistic distribution (BHLD), a novel heavy-tailed probability model constructed using the T–Y method for distribution generation, where T denotes a transformer distribution and Y represents a baseline generator. Although the BHLD is conceptually related to the Pareto and log-logistic families, it offers several distinctive advantages for streamflow modeling, including a flexible hazard rate that can be unimodal or monotonically decreasing, a finite lower bound, and closed-form expressions for key risk measures such as Value at Risk (VaR) and Tail Value at Risk (TVaR). The proposed distribution is defined on a lower-bounded domain, allowing it to realistically capture physical constraints inherent in flood processes, while a log-logistic-based tail structure provides the flexibility needed to model extreme hydrological events. Moreover, the BHLD is analytically characterized through a governing differential equation and further examined via its characteristic function and the maximum entropy principle, ensuring stable and efficient parameter estimation. It integrates a half-logistic generator with a log-logistic baseline, yielding a power-law tail decay governed by the parameter β, which is particularly effective for representing extreme flows. Fundamental properties, including the hazard rate function, moments, and entropy measures, are derived in closed form, and model parameters are estimated using the maximum likelihood method. Applied to four real streamflow data sets, the BHLD demonstrates superior performance over nine competing distributions in goodness-of-fit analyses, with notable improvements in tail representation. The model facilitates accurate computation of hydrological risk metrics such as VaR, TVaR, and tail variance, uncovering pronounced temporal variations in flood risk and establishing the BHLD as a powerful and reliable tool for streamflow modeling under changing environmental conditions.

This paper introduces Exponentiated Inverse Exponential Distribution (EIED), a novel probability model developed within the power inverse exponential distribution framework. A distinctive feature of EIED is its highly flexible hazard rate function, which can exhibit increasing, decreasing, and reverse bathtub (upside-down bathtub) shapes, making it suitable for modeling diverse lifetime phenomena in reliability engineering, survival analysis, and risk assessment. We derived comprehensive statistical properties of the distribution, including the reliability and hazard functions, moments, characteristic and quantile functions, moment generating function, mean deviations, Lorenz and Bonferroni curves, and various entropy measures. The identifiability of the model parameters was rigorously established, and maximum likelihood estimation was employed for parameter inference. Through extensive simulation studies, we demonstrate the robustness of the estimation procedure across different parameter configurations. The practical utility of EIED was validated through applications to real-world datasets, where it showed superior performance compared to existing distributions. The proposed model offers enhanced flexibility for modeling complex lifetime data with varying hazard patterns, particularly in scenarios involving early failure periods, wear-in phases, and wear-out behaviors.

Risk management is vital for financial institutions to evaluate and mitigate potential losses. Thunderstorm count modeling with risk analysis is used by various sectors, such as insurance and utility companies, to forecast storm recurrence, analyze risk, and estimate financial losses based on factors like wind speed, hail size, and tornado potential. This paper introduces a novel discrete distribution, the Beta-Gamma Discrete (BGD) distribution, designed for modeling count data that inherently excludes zero values. Developed through the compounding of a discrete gamma distribution with a beta distribution, the BGD offers significant flexibility in handling overdispersion and complex data characteristics. The study derives key statistical properties of the BGD, including its probability mass function, moments, hazard rate function, moment generating function, and mean residual life. A comprehensive characterization theorem is also established. The model’s practical utility is demonstrated through an application to thunderstorm event data from the Kennedy Space Center (KSC), where the frequency of thunderstorms per event is a critical operational concern. The performance of the BGD is thoroughly assessed against established zero-truncated models—namely, the Zero-Truncated Generalized Poisson (ZTGP), Size-Biased Negative Binomial (SBNB), and Zero-Truncated Generalized Negative Binomial (ZTGNB)—using evaluation criteria such as Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Chi-square goodness-of-fit, and the Vuong test. The results consistently show that the BGD provides a superior and more accurate fit for the thunderstorm data, thus help NASA and other space agencies for establishing it as a robust and effective tool for modeling positive count data in meteorological and other applied contexts with risk analysis.