The triple components of energy consumption, carbon dioxide emissions, and economic expansion are important to achieving sustained economic activity and sound ecological advancement. This study aims to estimate the impact of wide-ranging parameters on environmental circumstances in South Asian countries. This analysis required two approaches: 1)quantile autoregressive distributed lag (QARDL) as an econometric model, and 2) data envelopment analysis (DEA) non-parametric comparable composite index to examine concurrently South Asian nations' data for the 2000-2018 period. The underscored category of the parameters were grouped into four key indices, namely financial, fiscal, human, and energy. The DEA's mathematical composite findings reveal varied circumstances regarding environmental self-maintenance in South Asian nations. India and Pakistan are doing quite well; Afghanistan is abysmal. In addition, the QARDL approach findings reveal that energy use and fiscal indicators abate pollution. Furthermore, the correlation between fiscal decentralization and ecological attributes is strengthened by the excellent level of institutions and human capital progress. There is a unidirectional impact emanating from fiscal devolution, gross domestic product, human capital, eco-innovation, and institutional excellence on carbon dioxide pollution, although different from the other correlations obtained.
The subject matter of the article relates to the assessment of the perception of selected types of risk in economic activities of the SME sector, which change their intensity as a result of the outbreak of the COVID-19 pandemic. The current economic downturn is unprecedented and involves many companies and industries that have faced new, previously unknown challenges and threats. The objective of the article is to identify the most important risks and their resources based on the empirical research carried out in small and medium-sized enterprises in Poland. The formulated objective was accomplished using the data collection method, i.e., the survey and reports on the condition of the SME sector in Poland as well as statistical data analysis methods, i.e., structure index and the analysis of variance, using the SPSS system. The process of primary data collection was carried out by means of an electronic survey among selected enterprises of the SME sector, conducting business activities in Poland. In the study, the employment factor was taken into account as a determinant of the perception and assessment of the intensity of selected risks arising from the economic activity in the Polish market in the conditions of the current economic downturn. On the basis of the obtained results, the impact of market, economic, financial and operational risks, depending on their intensity, on the functioning of micro-, small and medium-sized enterprises was identified. Based on the analysis of variance, the effect of the size of the company on the level of individual risks was also examined. As a result of the observations made, it was established that, during the pandemic, the level and type of risk is similar in all the surveyed enterprises. They are most often threatened by strong competition in the industry, an increase in energy prices and insufficient profit. The overall results of the empirical research indicate the importance and the need to manage the key threats to the Polish SME sector.
This comparative study is an attempt to explore the determinants of capital structure for Malaysian firms listed in various sectors level. Within the framework of traditional and moderate dynamic capital structure theories, the key determinants such as fixed assets, current assets, return on equity, size, earning per share and total assets are tested in relation to the debt-equity ratio. The large-scale study entails data collected from 551 listed firms of Bursa Malaysia main market over 12 years period i.e. 2005-2016. Notably, this study combines Time Series econometrics with Panel Data analysis to enhance methodological robustness. Moreover, the comparative analysis approach is designated to recognize the most persistent capital structure determinants. In the first place, the Multiple Regression analysis (MRA) is selected as a baseline estimation method. Subsequently, the Auto Regression Distributed Lag model (ARDL), the Panel Data Static models, and Dynamic model via the Generalized Method of Moments (GMM) are employed to identify the capital structure determinants for the firms listed at Bursa Malaysia. The outcomes are surprising and indicate that the entire market is primarily controlled by the studied determinant total assets, which is significant in both construction and property sectors through MRA, ARDL, and GMM analysis. Technically, the significant role of tangibility and the existence of speed of adjustment across sectors imply that the Dynamic Capital Structure is the most prominent among all, followed by the Dynamic Trade-off theory.