Measuring the perceived quality of professors’ teaching effectiveness is a critical issue in higher education. This study involves a large-scale data exploration with a sample of 16,802 professors, in which each professor had received at least 20 ratings from the RateMyProfessors website. We find that perceived difficulty (from the students’ perspective) has a significantly negative effect on perceived quality. However, when professors teach more difficult courses at top colleges, the decline in perceived quality is relatively small when compared to other colleges. In other words, whether professors come from top colleges has a moderating effect on the relationship between perceived quality and perceived difficulty. Furthermore, through a consideration of the characteristic differences among disciplines in terms of the relationship between perceived quality and perceived difficulty, we obtain three specific groups of disciplines. These findings facilitate a better understanding of quality for professors from different disciplines. We suggest that the measurement of teaching effectiveness should avoid the use of a single criterion because differences in courses, disciplines or schools can influence the measurement results, and these factors are beyond the control of professors.

This study employs an effect decomposition regression (EDR) framework to analyze the impact of ESG scores on firm performance, focusing on the importance of firm size. A key finding of this study is that firm size matters in characterizing this relationship. For large firms, ESG scores significantly impact total revenues and are characterized by a U-shaped curve where ESG scores initially reduce firm performance, but later switch to a positive impact as scores rise. On the other hand, smaller firms exhibit a monotonically increasing or incremental benefit from ESG scores. This research underscores the necessity of controlling for firm size and further suggests that examining the impact of ESG scores by firm size groups may be essential for understanding the underlying dynamics. From a policy perspective, it is clear that one must consider firm size before advocating for practices as a universal value-enhancing proposition for all firms.

Energy efficiency has long been an important issue to the global economic and political theaters; however, searching for an effective and concise measure for efficiency remains a contentious and intriguing topic. There are two obvious flaws with the commonly used metrics in existing literature. First, there is a sense of confusion and misunderstanding between the definitions of energy efficiency and efficacy. As a result, the formulae and methods for measuring efficiency are often the subject of criticism. Second, even if the definition of efficiency is clear, the method of estimation can be quite cumbersome, making it difficult to comprehend or implement. This study attempts to address these two issues. With an OECD comparative dataset, it first presents the contradiction between efficiency and efficacy, explains the loss of effectiveness with the existing measurements, and then proposes a new and easy-to-use method for gauging energy efficiency, so that the succinctness and robustness of the measurement can be re-established. The paper serves as a guide to those who are interested in the controversial issues related to measuring energy efficiency. Both practitioners and policy makers will find an easy and reliable tool from this paper for measuring energy efficiency.

In 2007, the Clean Air Act officially included greenhouse gases, making fossil fuel power plants the first of key industries regulated by the Environmental Protection Agency. How do we measure the impact of the regulations on these power plants’ productivity? Previous studies that attempt to answer this question have provided inadequate answers because their samples cover the periods only up to 2007, and they often use greenhouse gases as the only proxy for the undesirable output. This paper collects data from 133 fossil fuel power plants in the United States and covers 2004 to 2013. These power plants are divided into Sun Belt and Frost Belt based on their geographical locations. To measure the undesirable outputs, we used both carbon dioxide and toxic emissions as the proxies. The estimation model includes the construction of a generalized common stochastic frontier (metafrontier) and a Malmquist productivity index. We used the index to measure the change in productivity for the power plants before and after the implementation of the regulation. The results indicate that, since regulation in 2007, the overall production efficiency of the power plants has declined incessantly while productivity has seen a sustained downward trend despite two surges in growth.