The present research assesses the influence of globalization and technological innovation on CO2 emissions in South Korea as well as taking into account the role of renewable energy consumption and energy consumption utilizing datasets between 1980 and 2018. The autoregressive distributed lag (ARDL) bounds testing method is utilized to assess long-run cointegration. The outcome of the ARDL bounds test confirmed cointegration among the series. Furthermore, the ARDL reveals that economic growth, energy consumption and globalization trigger environmental degradation while technological innovation improves the quality of the environment. In addition, the study employed the frequency domain causality test to capture causal linkage among the series. The major advantage of this approach is that causal linkage between series can be captured at the short, medium and long term, respectively. The outcomes of the causality test revealed that globalization, technological innovation, economic growth and energy use can predict CO2 emissions in South Korea.

We propose and estimate several models controlling for firm-specific information, to examine the relation of macroeconomic variables with the probability of default of firms in the Eurozone. The novelty of our approach consists in capturing the informational value of macroeconomic factors on credit default prediction by using data from firms spanning 11 European countries; our

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Inventory leanness requires that firms minimize inventory mistreatment and misuse. A firm performance deteriorates because of high inventory misuse, and because of such an issue, the effect on the firm’s credit rating can also be seen. This study examines the effect of inventory leanness on firms’ credit ratings. It aims to create an understanding of the relationship between inventory leanness and the firm’s financial performance and provides insight into the credit rating system of Pakistan. We analyze secondary Pakistan data between 2008 and 2017. Among the sixty firms on Pakistan Stock Exchange that are rated by PACRA, only thirty-eight have complete data available on their respective websites. By using panel data analysis, the results indicate that inventory leanness and credit ratings are positively related. In an added analysis, we evaluate the financial performance in the context of credit rating by using control variables (size, leverage, and capital intensity ratio) and dummy variables (loss and subordinate debt). Our results are consistent with earlier studies.