Measuring carbon emissions performance of Japan's metal industry: Energy inputs, agglomeration, and the potential for green recovery reduction

This study measures the total factor carbnon dioxide (CO2) emissions performance of the metal industry, iron and steel, nonferrous metal, and metal processing industries in 39 Japanese prefectures from 2008 to 2019. The true fixed-effects panel stochastic frontier model identifies regional carbon efficiency as well as the inefficiency determinants. The main results are as follows. First, a decrease in the coal ratio and an increase in the electricity ratio in total energy consumption improves efficiency. This result suggests that electrification in the metal industry, especially conversion from blast furnaces to electric furnaces in the iron and steel industry, contributes to reducing carbon emissions. Second, industrial agglomeration improves carbon emissions performance in the metal industry. This implies that agglomeration and decarbonization policies focusing on there are more effective, rather than a uniform national policy. Third, compared to the cumulative CO2 emissions over the sample period, 49,017 × 103 tons, the cumulative CO2 mitigation potential is 29,703 × 103 tons, indicating that CO2 emissions can be reduced by 60.6% without affecting the output. Forth, to examine the green economic recovery with efficiency in Japan's metal industry after COVID-19, we present a simple scenario analysis where a k% replacement coal ratio with an electricity ratio in total energy consumption, assuming that each prefecture will achieve the maximum CO2 emission amount during the sample period. By replacing 10% of the coal ratio with the electricity ratio, CO2 emissions can be reduced by 23.0%. In the case of a 20% replacement, CO2 emissions can be reduced by 33.0%. Our results show that Japan's targets in the post-COVID-19 green recovery process should be a decrease in coal consumption, an increase in electricity, and industrial agglomeration. © 2023 Elsevier Ltd

Control, intervention, and behavioral economics over human social networks against COVID-19

In this short paper, we propose a new direction of cross-cutting research for prediction and control of spreading COVID-19 viruses over a human social network. Such a network consists of human agents whose behaviors are highly uncertain and biased. To predict and control such an uncertain network, we need to employ various researches such as control theory, signal processing, machine learning, and behavioral economics. In this article, we introduce our recent research results and propose future research topics to overcome the COVID-19 pandemic. [ABSTRACT FROM AUTHOR] Copyright of Advanced Robotics is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)