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The outbreak of the COVID-19 pandemic seriously affected railway businesses. The motivation of this study is to provide vital information to railway company management and policymakers by quantitatively assessing the cost efficiency of railway operations. We examine the efficiency of Japanese listed railway companies by applying stochastic frontier analysis to their operational and financial data from 2005 to 2020. Then, we classify the companies into four groups by cost efficiency levels and identify the characteristics of the best-practice companies. Furthermore, we analyze the factors influencing cost efficiency before and during the pandemic. Finally, we discuss the sustainable business practices and measures of digital transformation (DX) that can be applied to improve efficiency and survive severe events like the pandemic. From the results, we reveal that cost-efficient companies succeeded in securing profits through the creation of new services by proactive DX investments. The practical contributions of this study are threefold: quantifying the deterioration in efficiency due to the pandemic; identifying characteristics of best-practice companies; and examining the relationship between cost efficiency levels and concrete measures and investments for sustainable business practices. This study proposes a new analytical framework that combines conventional methods. [ABSTRACT FROM AUTHOR]

The 2015 Paris Agreement accelerated the trend toward international decarbonization. Climate change initiatives, such as Science Based Targets, Renewable Energy 100%, and Task Force on Climate‐related Financial Disclosure are rapidly gaining attention as mechanisms that encourage voluntary efforts by companies to limit emissions. However, participation in these initiatives creates a conflict with the pursuit of short‐term profits. Understanding the firms' motivation for initiative participation contributes to the realization of sustainable development. This study empirically examines the motivations for large‐scale Japanese firms to participate in climate change initiatives. For this purpose, first, we update the conventional model of corporate motivations for taking environmental actions to fit the contemporary context. We then quantify individual firms' motivations based on various data and conduct regression analyses to identify the factors contributing to the firms' participation in the initiatives. The results indicate that firms have competitive and legitimate motives to participate in these initiatives. [ABSTRACT FROM AUTHOR]

Since the 1990s, market liberalization of the electricity industry has advanced all around the world. To survive in the drastically changing business environment, incumbent electric utility companies have conducted operational reforms, including Mergers and Acquisitions (M&As), to enhance and/or complement existing business capabilities. The purpose of this study was to measure the operational efficiencies of 31 of the world's largest electric utility companies using data from 2010 to 2020 and examine regional differences in and the impacts of M&As on the efficiencies. For this purpose, we applied a new type of Data Envelopment Analysis (DEA) and Tobit model regression. We provide findings from the empirical analyses and discuss the business implications of M&As for electric utility companies. The operational efficiency measures were different among regions, but did not show statistically significant changes over the study period from 2010 to 2020. Furthermore, the results of regression analyses indicate that the increasing number of M&A buying transactions and M&A total transactions has a negative marginal impact on the operational efficiency or leads to a lower operational efficiency for utility companies. Since electricity utility companies have not received gains in operational efficiency from increasing the number of M&A transactions, they need to be more cautious about whether M&A transactions can provide value to the operation and technology management. [ABSTRACT FROM AUTHOR]

This study analyzes how the electricity demand and supply constitutions affect electricity independence and power trading within a community and between a community and a grid through simulation analysis. To that aim, we create a simulation model equipped with a community-building function and trading capability. We first construct a community consisting of various types of residential and industrial consumers, and renewable power plants deployed in the community. Residential and industrial consumers are characterized by a state of family/business and ownership and the use of energy equipment such as rooftop solar PV and stationary battery storage in their homes/offices. Consumers' electricity demand is estimated from regression analyses using training data. Using the hypothetical community constructed for the analysis, the simulation model performs rule-based electricity trading and provides outputs comprising the total electricity demand in the community, the state of use of battery storage and solar PV, the trading volume, and the electricity independence rate of the community. From the simulation results, we discuss policy implications on the effective use of renewable energy and increasing electricity independence by fully utilizing battery and trading functions in a community. [ABSTRACT FROM AUTHOR]