Unemployment remains a major cause for both developed and developing nations, due to which they lose their financial and economic impact as a whole. Unemployment rate prediction achieved researcher attention from a fast few years. The intention of doing our research is to examine the impact of the coronavirus on the unemployment rate. Accurately predicting the unemployment rate is a stimulating job for policymakers, which plays an imperative role in a country's financial and financial development planning. Classical time series models such as ARIMA models and advanced non‐linear time series methods be previously hired for unemployment rate prediction. It is known to us that mostly these data sets are non‐linear as well as non‐stationary. Consequently, a random error can be produced by a distinct time series prediction model. Our research considers hybrid prediction approaches supported by linear and non‐linear models to preserve forecast the unemployment rates much precisely. These hybrid approaches of the unemployment rate can advance their estimates by reproducing the unemployment ratio irregularity. These models' appliance is exposed to six unemployment rate statistics sets from Europe's selected countries, specifically France, Spain, Belgium, Turkey, Italy and Germany. Among these hybrid models, the hybrid ARIMA‐ARNN forecasting model performed well for France, Belgium, Turkey and Germany, whereas hybrid ARIMA‐SVM performed outclass for Spain and Italy. Furthermore, these models are used for the best future prediction. Results show that the unemployment rate will be higher in the coming years, which is the consequence of the coronavirus, and it will take at least 5 years to overcome the impact of COVID‐19 in these countries.

The research examined the influence of the fundamental exchange rate misalignment and Least Developed Countries (LDCs) in Asia and Africa’s financial development on CO₂ emissions in Asian countries using panel data from 1970 to 2021. The methodology consists of ARDL bound testing and PMG/ARDL estimators with dynamic OLS estimators. The results reveal that the long-run real exchange rates for least developed countries (LDCs) are expected to rise in CO₂ emissions in Asian and African countries with improved trade and net foreign asset positions. The relative productivity and trade openness also increase the exchange rate, which also plays a vital role in the growth of CO₂ emissions. Except for Egypt, all least developed countries (LDCs) currencies are overpriced throughout the research period at the same time; it would be harmed by increased openness, foreign direct investment inflows, and currency misalignment. Overvaluation harms Bahrain’s economic growth. In comparison, undervaluation helps Egypt that currency misalignment does not affect financial growth in any LDCs over the long run. In the short-run, more real investment, net foreign assets, and official assistance inflows would enhance financial growth in Qatar, Bahrain, Singapore, and South Korea. In contrast, trade openness would slow it down in Egypt and Kuwait. The study suggested that the poor economic performance is due to RER misalignment, which occurs when exchange rate policies are improper and causes a rise in CO₂ emissions in many developing countries.

The study draws attention to the associations between monetary and economic elements and their potential environmental impacts. The study uses time series data from 1960 to 2022 to examine the connection between CO₂ emissions, industrial growth, GNE, and inflation in China. The researchers utilized the well-known econometric technique of nonlinear autoregressive distributed lag (NARDL) to examine nonlinear correlations between these variables. The results reveal that GDP, inflation, and economic development influence long-term CO₂ emissions. The strong positive correlation between gross national expenditures and economic activity increases CO₂ emissions. In the short run, CO₂ emissions are positively and statistically significantly affected by inflation. While inflation temporarily affects CO₂ emissions, this effect dissipates with time. Industrial activity increases CO₂ emissions, and China’s fast industrialization has damaged the environment. The energy-intensive fertiliser manufacturing process and fossil fuels increase CO₂ emissions. The research shows how government officials and academics may collaborate to create tailored measures to alleviate the environmental impacts of economic activity.

The study investigates the impacts of financial development, electricity use, and technology innovation on CO₂ emissions. International trade also plays an essential role in the economic development of Pakistan. Studying the relationship between ecological parameters, technological innovation, electricity use, and CO₂ emissions is necessary to formulate the country's reasonable and practical energy policies. Based on the study's use of data from 292 Pakistani firms from 2006 to 2021, the paper investigates the mechanism of the role between financial development, electricity use, technological innovation, international trade, and CO₂ emissions using the spatial Durbin model. The results reveal that the effect of economic growth, electricity use, and technological innovation on CO₂ emissions has a spatial spillover effect. The results verify that international trade and the transport sector promote the country's carbon emissions. The typical speculation spike on technology innovation enhanced and financial development should concentrate more on protective ecological parameters. The research provides theoretical guidance for solving the contradictory problem of renewable energy use growth and CO₂ emission limitation while promoting green and low-carbon development in the country.

The objective of the study is to determine the electricity use, and tourism industry environmental impacts, and increase in CO₂ emissions in Pakistan. What are the linkages of foreign direct investment, intercountryal trade, gross domestict product, and CO₂ emissions. The study has applied the Autoregressive distributed lag (ARDL) method to analysis the data set from 1985 to 2023. The robustness test is applied using Dynamic Ordinary Least Square (DOLS), and Fully modified ordinary least squares (FMOLS). The results reveal that the increase in the electricity use, and tourism industry has significant negative impacts on CO₂ emissions in both short- and long-run. The increase in intercountry trade effects the Domestic Product growth (GDP) growth and causes to increase in use of fossil fuels, which are the major source of CO₂ emissions. The increase in foreign direct investment (FDI) increase the GDP growth, and FDI also increase the CO₂ emissions in Pakistan. The results suggests that the incresae in the renewal energy consumption for the electricity production and transportation can help to decrease the CO₂ emissions in Pakistan.

Many academics and experts focus on portfolio optimization and risk budgeting as a topic of study. Streamlining a portfolio using machine learning methods and elements is examined, as well as a strategy for portfolio expansion that relies on the decay of a portfolio’s risk into risk factor commitments. There is a more vulnerable relationship between commonly used trademarked portfolios and neural organizations based on variables than famous dimensionality decrease strategies, as we have found. Machine learning methods also generate covariance and portfolio weight structures that are more difficult to assess. The least change portfolios outperform simpler benchmarks in minimizing risk. During periods of high instability, risk-adjusted returns are present, and these effects are amplified for investors with greater sensitivity to chance changes in returns R.