Chen, Jing

This comprehensive study delves into the intricate connections between economic and financial factors and carbon dioxide (CO2) emissions across G20 nations (excluding the European Union) spanning 1994 to 2021. our investigation, utilizing a multiple linear regression model, meticulously examines diverse energy consumption types, financial institutions, life insurance premiums, economic factors, and the aftermath of the 2008 financial crisis. Our preliminary findings reveal robust links between various energy sources, financial institutions, life insurance volumes, and CO2 emissions. Notably, the Financial Institutions Index and Life Insurance Premium Volume unveil novel insights that can add new visions to conventional perspectives. Recognizing the influential role of the G20 on a global scale, our research aspires to inform and guide sustainable policy decisions. Methodologically, after a comparative evaluation of various data transformation methods, we employ a cube root transformation to enhance analytical precision. Also, Principal Component Analysis (PCA) reveals underlying patterns in the data. Granger causality tests shed light on temporal relationships, complementing the robust quantification of each variable's impact on CO2 emissions derived from the linear regression model. Rigorous validation, including Durbin-Watson, Breusch-Pagan, Shapiro-Wilk, RESET, Bonferroni Outlier test, and ADF stationarity tests, ensures the reliability of our results. Our linear model enhances interpretability and provides clear insights into the determinants of CO2 emissions. This research significantly contributes to the field by extending our knowledge of the complex factors influencing CO2 emissions. It unveils unexpected relationships, underscores the pivotal role of financial institutions, explores the repercussions of economic crises, and provides practical policy implications. Methodologically, our study stands out for its advanced statistical analyses. This research yields a valuable understanding of the sustainability framework, presenting a nuanced view for policymakers, researchers, and practitioners alike. This study enhances the academic speech by thoroughly addressing the factors influencing CO2 emissions and delivering a foundation for informed decisionmaking in pursuing a more sustainable future.

We examine the relationship between stock returns and components of idiosyncratic volatility—two volatility and two covariance terms— derived from the decomposition of stock returns variance. The portfolio analysis result shows that volatility terms are negatively related to expected stock returns. On the contrary, covariance terms have positive relationships with expected stock returns at the portfolio level. These relationships are robust to controlling for risk factors such as size, book-to-market ratio, momentum, volume, and turnover. Furthermore, the results of Fama-MacBeth cross-sectional regression show that only alpha risk can explain variations in stock returns at the firm level. Another finding is that when volatility and covariance terms are excluded from idiosyncratic volatility, the relation between idiosyncratic volatility and stock returns becomes weak at the portfolio level and disappears at the firm level.