The paper appraised the nexus between electricity consumption, agriculture, GDP, oil production, and carbon dioxide (CO₂) emissions in Nigeria using a decoupling approach. The result showed that agriculture, electricity, and GDP were predictive variables for CO₂ emissions in the Granger causality analysis. The relationship between GDP and CO₂ emissions also indicated that the amount of CO₂ released tends to rise as the economy’s output and industrial sectors grow, making GDP and CO₂ emissions increasingly relevant indicators as a driver of CO₂ emissions. Modern agriculture is reliant on large-scale use of fossil fuels and fertilizer production, as well as GHG emissions from crop and livestock production. However, increasing per capita real production can help to enhance quality of the environment, and speed up the uptake of renewable energy which can consequently help to ameliorate global warming. As a result of this study’s policy implications, policies in the agricultural sector that could combat CO₂ emissions, including deforestation, land clearing, fertilization with highly environmentally destructive chemicals, neglected integration of agroforestry, and social forestry practices, can help reduce CO₂ emissions in the agricultural sector. In addition, the study recommends that the financial markets’ monetary policy should regulate the GDP to charges to compensate for their various sectors’ contributions to CO₂ emissions. This investigation might help policymakers in Nigeria to define the CO₂ emission monetary and fiscal strategies. In addition, more alternative energy sources such as biofuels, hydropower, solar energy, and other renewable resources should be embraced in Nigeria as sustainable substitutes for fossil fuels.

Following the global financial crisis, an increasing amount of attention has been directed towards examining the Beveridge curve (BC), which indicates the relationship between unemployment and vacancy rates. This research analyzes the unemployment–vacancy rate dynamics in the Turkiye labor market during both the global financial crisis and COVID-19 periods. The findings from this study demonstrate that the labor market exhibits deteriorating efficiency, as evidenced by movement of BC away from the origin. The unemployment and vacancy rates both increase over time, with a leftward (rightward) shift of BC during the global financial crisis (COVID-19) period. The study also reveals that both crises had no significant effect on unemployment–vacancy rate dynamics. In the Turkish labor market, there exists a situation where the vacancy rate is in shortfall of the unemployment level in Turkiye. This creates a positive relationship between these two factors. The labor market in Turkiye experiences inefficiencies as it struggles to generate a sufficient number of jobs to meet the demand from job seekers.

This paper investigates the impact of the Covid-19 pandemic in predicting the profitability of the stock market of the ten most hit countries at the beginning of the pandemic. The study employed the Artificial Neural Network models for the analysis. Specifically, the Backward Propagation (BP) and Feed-Forward (FF) Neural Network models are used to predict the profitability of the stock market on a daily time frame. Taking Covid-19 into account, the estimation result shows that the Neural Network built is resilient in its ability to forecast the profitability of the stock market in Brazil and China. However, in the case of Germany, Russia, Turkey, and the United States, the Neural Network is partly resilient in its forecasting ability; predicted profitability deviated from the actual profitability in some of the periods. For the remaining countries in the sample, the Artificial Neural Network is found to have a weak prediction power.