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Seoul Journal of Economics - Vol. 37 , No. 2

[ Article ]
Seoul Journal of Economics - Vol. 37, No. 2, pp. 99-125
Abbreviation: SJE
ISSN: 1225-0279 (Print)
Print publication date 30 May 2024
Received 08 Aug 2023 Revised 29 Jan 2024 Accepted 01 Feb 2024

The Diverse Impact of Economic Digitalization on Carbon Dioxide Emissions Across Countries
Manh Cuong Dong ; Thuy Linh Cao ; Yeo Joon Yoon ; Keunjae Lee
Manh Cuong Dong, School of Business, British University Vietnam, Hanoi, Vietnam (
Thuy Linh Cao, Faculty of Business, FPT University, Hanoi, Vietnam (
Yeo Joon Yoon, Economics Department, Pusan National University, Busan, Korea (
Keunjae Lee (corresponding author), Economics Department, Pusan National University, Busan, Korea (

Funding Information ▼

JEL Classification: C11, C82, O11, O44


This study examines the impact of economic digitalization on CO2 emissions by using the data of 100 countries from 2008 to 2019. First, we divide our sample into different income-level groups and use the Bayesian panel regression method to examine how economic digitalization can impact CO2 emissions in each group. Second, we conduct Bayesian quantile regression on the whole sample to determine how the different digital economies affect CO2 emissions across the quantile levels. The results obtained by the two approaches are consistent. We find that ICT infrastructure can increase CO2 emissions in the less-developed countries but help reduce CO2 emissions in the developed countries. ICT-related industry activities can help reduce CO2 emissions in nearly all the countries, but the impact differs across the countries. By contrast, ICT product and service exports can lead to an increase in CO2 emissions, but the effect is relatively small and will decrease gradually as the CO2 emissions level rises. Our results can provide helpful information and implications to policymakers to fully employ the advantages of economic digitalization to reduce CO2 emissions.

Keywords: CO2 emissions, Economic digitalization, ICT infrastructure, Bayesian regression


This research was supported by the Ministry of Education of the Republic of Korea, the National Research Foundation of Korea (NRF-2020S1A5B8103268), and the 2022 BK21 FOUR Program of Pusan National University.

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