This paper investigates the elements affecting dividend yield in developing Southeast Asian countries—more specifically, Thailand, Malaysia, and Singapore. Examined here are the roles of financial information including debt to equity ratio, free cashflows, property, plant, and equipment (PPE) and total sales with controlling factors of size, institutional ownership, and firm age using both short-run and long-run analytical frameworks including the Error Correction Model and Engle and Granger’s approach. The results reveal different trends in the three nations. Higher debt and free cashflows lower dividend yield in Thailand; institutional shareholders benefit from maintaining greater dividend payouts. Aging companies in Malaysia are more likely to pay more dividends while rising revenues are linked to smaller short-term payouts. Leveraged and asset-heavy companies are more likely to keep paying dividends in Singapore. These discoveries have important ramifications for investors and business management trying to maximize dividend policies and improve shareholder value in developing economies.

The maize commodity is of strategic significance to the South African economy as it is a stable commodity and therefore a key factor for food security. In recent times climate change has impacted on the productivity of this commodity and this has impacted trade negatively. This paper explores the intricate relationship between climatic factors and trade performance for the South African maize. Secondary annual time series data spanning 2001 to 2023, was sourced from an abstract from Department of Agriculture, Land Reform and Rural Development (DALRRD) and World Bank’s Climate Change Knowledge Portal. Autoregressive Distributed Lag (ARDL) cointegration technique was used as an empirical model to assess the long-term and short-term relationships between explanatory variables and the dependent variable. Results of the ARDL model show that, average annual rainfall (β = 2.184, p = 0.056), fertilizer consumption (β = 1.919, p = 0.036), gross value of production (β = 1.279 , p = 0.006) and average annual surface temperature (β = −0.650, p = 0.991) and change in temperature for previous years, (β = −0.650, p = 0.991) and the effects towards coefficient change for export volumes, (β = 0.669, p = 0.0007). In overall, as a recommendation, South African policymakers should consider these findings when developing strategies to mitigate the impacts of some of these climatic factors and implementing adaptive strategies for maize producers.

Electricity consumption in Europe has risen significantly in recent years, with households being the largest consumers of final electricity. Managing and reducing residential power consumption is critical for achieving efficient and sustainable energy management, conserving financial resources, and mitigating environmental effects. Many studies have used statistical models such as linear, multinomial, ridge, polynomial, and LASSO regression to examine and understand the determinants of residential energy consumption. However, these models are limited to capturing only direct effects among the determinants of household energy consumption. This study addresses these limitations by applying a path analysis model that captures the direct and indirect effects. Numerical and theoretical comparisons that demonstrate its advantages and efficiency are also given. The results show that Sub-metering components associated with specific uses, like cooking or water heating, have significant indirect impacts on global intensity through active power and that the voltage affects negatively the global power (active and reactive) due to the physical and behavioral mechanisms. Our findings provide an in-depth understanding of household electricity power consumption. This will improve forecasting and enable real-time energy management tools, extending to the design of precise energy efficiency policies to achieve SDG 7’s objectives.