Promoting Pro-Environmental Behavior in Policymaking: A Text-Mining Approach for Literature Review.

The impact of pro-environmental behavior on policymaking has been an exciting area of research. While the relationship between pro-environmental behavior and policymaking has been explored in numerous studies, there needs to be more synthesis on this topic. This is the first text-mining study of pro-environmental effects in which policymaking is a significant factor. In response, this study, for the first time, takes a novel approach by using text mining in R programming to analyze 30 publications from the Scopus database on pro-environmental behavior in policymaking, highlighting major research themes and prospective research areas for future investigation. Results from text mining yielded 10 topic models, which are presented with a synopsis of the published research and a list of the primary authors, as well as a posterior probability via latent Dirichlet allocation (LDA). Additionally, the study conducts a trend analysis of the top 10 journals with the highest impact factor, considering the influence of each journal's mean citation. The study offers an overview of the impacts of pro-environmental behavior in policymaking, showing the most relevant and frequently discussed themes, introduces the scientific visualization of papers published in the Scopus database, and proposes future study directions. These findings can help researchers and environmental specialists better understand how pro-environmental behavior can be fostered more effectively through policymaking.

Volatility spillovers from the Chinese stock market to the U.S. stock market: The role of the COVID-19 pandemic.

The COVID-19 pandemic, which originated in Wuhan, China, precipitated the stock market crash of March 2020. According to published global data, the U.S. has been most affected by the tragedy throughout this outbreak. Understanding the degree of integration between the financial systems of the world's two largest economies, particularly during the COVID-19 pandemic, necessitates thorough research of the risk transmission from China's stock market to the U.S. stock market. This study examines the volatility transmission from the Chinese to the U.S. stock market from January 2001 to October 2020. We employ a variant form of the EGARCH (1,1) model with long-term control over the excessive volatility breakpoints identified by the ICSS algorithm. Since 2004, empirical evidence indicates that the volatility shocks of the Chinese stock market have frequently and negatively affected the volatility of the U.S. stock market. Most importantly, we explore that the COVID-19 pandemic vigorously and positively promoted the volatility infection from the Chinese equity market to the U.S. equity market in March 2020. This precious evidence endorses the asymmetric volatility transmission from the Chinese to the U.S. stock market when COVID-19 broke out. These experimental results provide profound insight into the risk contagion between the U.S. and China stock markets. They are also essential for securities investors to minimize portfolio risk. Furthermore, this paper suggests that globalization has carefully driven the integration of China's stock market with the international equity markets.