This study investigates the efficacy of diverse machine learning survival models, including Cox, Kernel SVM, DeepSurv, Survival Random Forest, and MTLR models, employing the concordance index to assess their predictive abilities. The primary objective of this research is to identify the most accurate model for forecasting the time it takes for a country to witness a 10% surge in unemployment within a 120-month timeframe (2013-2022), utilizing variables from the MVI dataset of 28 American countries. Through the comparative evaluation of complex survival models, we discovered that DeepSurv, a sophisticated machine learning algorithm, excels in capturing intricate nonlinear relationships, while conventional models exhibit comparable performance under specific circumstances. The weight matrix, a pivotal element of our analysis, meticulously assesses the economic repercussions of various risk factors, vulnerabilities, and capabilities.

The COVID-19 crisis has led to an unprecedented surge in teleworking. Teleworking during the COVID-19 pandemic has brought both benefits and costs. Yet, its impact on business resilience remains underexplored. This study examines the impacts of teleworking on business resilience from two perspectives: business continuity and employee furlough. Furthermore, we highlight the role of female leadership in shaping these impacts. Using a matched dataset of more than 3,000 global firms, we tested four hypotheses and found empirical support. We found that female leadership enhances the positive effects of teleworking on business continuity while mitigating its negative effects on employment.

Crowdfunding has become an established method of entrepreneurial finance. Despite the growing body of literature in this field, limited research has investigated the impact of macroeconomic variables on crowdfunding activity. This study provides a theoretical framework, developed through an extensive review of the existing literature, to examine the relationship between macroeconomic conditions and crowdfunding. The analysis identifies key macroeconomic factors, such as employment, inflation, interest rates, economic uncertainty, and the business and credit cycles, as significant determinants of crowdfunding dynamics. Notably, crowdfunding exhibits a unique responsiveness to macroeconomic conditions in comparison to traditional financial instruments. Furthermore, evidence suggests a bidirectional relationship, wherein crowdfunding also exerts measurable effects on macroeconomic conditions, an aspect conceptually outlined and discussed in the later sections of the paper. The findings underscore the critical role of the macroeconomic environment in shaping crowdfunding patterns and outcomes. This study contributes to the theoretical understanding of the intersection between macroeconomic and alternative finance, and it offers structured directions for future research. Overall, the study serves as a reference point for scholars and practitioners seeking a comprehensive synthesis of current knowledge on the macroeconomic dimensions of crowdfunding.

This study seeks to investigate how climate change affects actual output in African countries, using greenhouse gas emission as a case study. This paper uses the actual output of Nigeria, Ghana, Liberia, The Gambia, Cabo Verde, and Sierra Leone for the analysis. Climate change is proxied by greenhouse gas emission, which are extracted from the International Monetary Fund (IMF) database, for the period, 1980 to 2022. Descriptive statistics, unit root, and panel System Generalized Method of Moments 1(SGMM1) are estimated, and the result shows that agricultural greenhouse gas emission reduces the output of the selected African Countries, while other greenhouse gases such as building and other sector’s greenhouse gas emission, energy industries greenhouse gas emissions, mineral industry greenhouse gas emission and road transport greenhouse gas emission increase output. For actual output to increase, the paper suggests policy remedies such as sustainable farming practices and adoption of climate-smart technologies.

The determinants of production-based carbon emission (PCE) have been extensively examined in the literature. However, PCE only accounts for emissions generated within the territory of a country and does not capture emissions embedded in imported goods. The rapid growth in Consumption-based Carbon Emissions (CCE) in Sub-Saharan Africa (SSA), driven by increasing imports and economic activities, highlights the need for a comprehensive understanding of these emissions. This motivates us to examine the impact of Renewable Energy Consumption (REC) on CCE in SSA. We employed a two-step system Generalized Method of Moments (GMM) methodology, utilizing data from 1995 to 2020. The results show a negative effect of REC on CCE, suggesting that increases in renewable energy consumption tend to reduce CCE. In contrast, the positive impact of real GDP and population indicates that economic growth and population expansion tend to bolster carbon emissions. These findings underscore the importance of implementing policies harmonizing economic growth with sustainable energy strategies. They provide valuable insights for informed environmental and economic planning decisions.