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09-09-2023 | Data analytics and machine learning

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

Authors: Yingchi Qu, Ming K. Lim, Mei Yang, Du Ni, Zhi Xiao

Published in: Soft Computing | Issue 22/2023

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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.

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Title: Using random forest to find the discontinuity points for carbon efficiency during COVID-19
Authors: Yingchi Qu
Ming K. Lim
Mei Yang
Du Ni
Zhi Xiao
Publication date: 09-09-2023
Publisher: Springer Berlin Heidelberg
Published in: Soft Computing / Issue 22/2023
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-023-09179-5

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