Using random forest to find the discontinuity points for carbon efficiency during COVID-19

Qu, Y., Lim, M. K. , Yang, M., Ni, D. and Xiao, Z. (2023) Using random forest to find the discontinuity points for carbon efficiency during COVID-19. Soft Computing, 27, pp. 16537-16549. (doi: 10.1007/s00500-023-09179-5)

Abstract

As there is a constant trade-off between carbon dioxide emissions against economic growth for every government, carbon efficiency is a key indicator to guide sustainable development. However, the energy crisis and COVID-19 recovery (declined cases of COVID-19 infection, flight recovery, manufacturing restart, and increasing import and export trading) could affect carbon efficiency. Therefore, this paper combines the fuzzy regression discontinuity and random forest algorithm (RF-FRD method) to estimate the discontinuity of the energy crisis and COVID-19 recovery on carbon efficiency. The findings show that discontinuity points in carbon efficiency were induced by the energy crisis and COVID-19 recovery. The positive treatment effect at the first discontinuity point proves that the “zero-tolerance” policies effectively promote carbon efficiency. Besides, the negative treatment effect at the second discontinuity point proves that electricity rationing has not always improved carbon efficiency during the energy crisis.

• Item Type: Articles
• Additional Information: This work was supported by the National Natural Science Foundation of China [grant numbers 71671019, 72071021], The Fundamental Research Funds for the Central Universities [No. 2020CDJSK02PT13], and the graduate research and innovation foundation of Chongqing, China [grant number CYS21047].
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Lim, Professor Ming

Creator Roles

Lim, M.: Conceptualization, Project administration, Validation

• Authors: Qu, Y., Lim, M. K., Yang, M., Ni, D., and Xiao, Z.
• College/School: College of Social Sciences > Adam Smith Business School > Management
• Journal Name: Soft Computing
• Publisher: Springer
• ISSN: 1432-7643
• ISSN (Online): 1433-7479
• Published Online: 09 September 2023
• Copyright Holders: Copyright: © 2023 Springer Nature
• First Published: First published in Soft Computing 27: 16537–16549
• Publisher Policy: Reproduced in accordance with the publisher copyright policy