The Diverse Impact of Economic Digitalization on Carbon Dioxide Emissions Across Countries
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초록
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.
Abstract
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.
참고문헌 (45)
- Ablyazov, T., "Development of the Arctic transport infrastructure in the digital economy", Transportation Research Procedia, 57, pp.1~8 (2021)
- Arain, H., "Investigating the effect of inbound tourism on FDI : The importance of quantile estimations", Tourism Economics, 26(4), pp.682~703 (2020)
- Autio, E., "Strategic entrepreneurial internationalization : A normative framework", Strategic Entrepreneurship Journal, 11(3), pp.211~227 (2017)
- Begum, R. A., "CO2 emissions, energy consumption, economic and population growth in Malaysia", Renewable and Sustainable Energy Reviews, 41, pp.594~601 (2015)
- Chib, S., "Panel data modeling and inference : a Bayesian primer", Advanced Studies In Theoretical And Applied Econometrics, 46, pp.479~515 (2008)
- Dong, X., "Assessing embodied carbon emission and its intensities in the ICT industry : the global case", Frontiers in Energy Research, 9 (2021)
- hierarchical models, "Data analysis using regression and multilevel", pp.2006, Gelman, A.
- Heidari, H., "Economic growth, CO2 emissions, and energy consumption in the five ASEAN countries", International Journal of Electrical Power & Energy Systems, 64, pp.785~791 (2015)
- Hosan, S, "Dynamic links among the demographic dividend, digitalization, energy intensity and sustainable economic growth: Empirical evidence from emerging economies", Journal of Cleaner Production, 330(129858) (2022)
- Hossain, M. S., "Multivariate granger causality between economic growth, electricity consumption, exports and remittance for the panel of three SAARC countries", Global Journal of Management and Business Research, 12(4), pp.41~54 (2012)
- Hsiao, C., "Analysis of panel data" (2022), Cambridge University Press
- Iwami, T., "Economic development and", or environmental quality : emissions of CO2 and SO2 in East Asia, 2004, pp.17(1), 55~83
- Khan, N., "The effect of ICT on CO2 emissions in emerging economies: does the level of income matters?", Environmental Science and Pollution Research, 25(23), pp.22850~22860 (2018)
- Koenker, R., "Regression quantiles", Econometrica, 46(1), pp.33~50 (1978)
- Koenker, R., "Goodness of fit and related inference processes for quantile regression", Journal of the American Statistical Association, 94(448), pp.1296~1310 (1999)
- Kozumi, H., "Gibbs sampling methods for Bayesian quantile regression", Journal of Statistical Computation and Simulation, 81(11), pp.1565~1578 (2011)
- Lee, S. Y., "Evaluation of the Bayesian and maximum likelihood approaches in analyzing structural equation models with small sample sizes", Multivariate Behavioral Research, 39(4), pp.653~686 (2004)
- Li, Y., "Energy structure, digital economy, and carbon emissions : evidence from China", Environmental Science and Pollution Research, 28(45), pp.64606~64629 (2021)
- Li, Z, "The Dynamic Impact of Digital Economy on Carbon Emission Reduction: Evidence City-level Empirical Data in China", Journal of Cleaner Production, 351(131570) (2022)
- Lin, B., "Factors affecting CO2 emissions in China’s agriculture sector : A quantile regression", Renewable and Sustainable Energy Reviews, 94, pp.15~27 (2018)
- Liu, W, "Alleviating corporate environmental pollution threats toward public health and safety: The role of smart city and artificial intelligence", Safety Science, 143(105433) (2021)
- Mardani, A., "Carbon dioxide(CO2)emissions and economic growth : A systematic review of two decades of research from 1995 to 2017", Science of the total environment, 649, pp.31~49 (2019)
- Pouri, M. J., "The digital sharing economy : A confluence of technical and social sharing", Environmental Innovation and Societal Transitions, 38, pp.127~139 (2021)
- Pradhan, R. P., "Short-term and long-term dynamics of venture capital and economic growth in a digital economy : A study of European countries", Technology in Society, 57, pp.125~134 (2019)
- Raudenbush, S. W., "Hierarchical Linear Models: Applications and Data Analysis Methods" (2002), Sage Publications
- Richardson, L., "International Encyclopedia of Human Geography", pp.317~321 (2019), Elsevier
- "CO2 and Greenhouse Gas Emissions" (2020)[URL]
- Sinha, A., "Impact of ICT exports and internet usage on carbon emissions : a case of OECD countries", International Journal of Green Economics, 12(3-4), pp.228~257 (2018)
- Shor, B., "A Bayesian multilevel modeling approach to time-series cross-sectional data", Political Analysis, 15(2), pp.165~181 (2007)
- Shvakov, E. E, "Newest trends and future scenarios for a sustainable digital economy development", Institute of Scientific Communications Conference, pp.1378~1385 (2020)
- Usman, A, "Does ICT have symmetric or asymmetric effects on CO2 emissions? Evidence from selected Asian economies", Technology in Society, 67(101692) (2021)
- Van de Schoot, R, "Analyzing small data sets using Bayesian estimation: The case of posttraumatic stress symptoms following mechanical ventilation in burn survivors", European Journal of Psychotraumatology, 6(1) (2015)
- von Briel, F., "Digital technologies as external enablers of new venture creation in the IT hardware sector", Entrepreneurship Theory and Practice, 42(1), pp.47~69 (2018)
- Watanabe, C., "Digital solutions transform the forest-based bioeconomy into a digital platform industry-A suggestion for a disruptive business model in the digital economy", Technology in Society, 54, pp.168~188 (2018)
- Wu, R., "Nonlinear effect of digital economy on carbon emission intensity based on dynamic panel threshold model", Frontiers in Environmental Science, 10 (2022)
- Wu, Y., "The impact of the development of the digital economy on Sulfur Dioxide emissions: Empirical evidence based on provincial panel data", J. Wuhan Polytech, 20, pp.82~88 (2021)
- Xu, Q, "How does digitalization affect energy? International evidence", Energy Economics, 107(105879) (2022)
- Yang, T. M., "How is information shared across the boundaries of government agencies? An e-Government case study", Government Information Quarterly, 31(4), pp.637~652 (2014)
- Yu, H., "Impact and mechanism of digital economy on China’s carbon emissions: from the perspective of spatial heterogeneity", Environmental Science and Pollution Research, 30, pp.1~16 (2022)
- Yu, K., "Bayesian quantile regression", Statistics & Probability Letters, 54(4), pp.437~447 (2001)
- Yu, K., "Bayesian analysis of a Tobit quantile regression model", Journal of Econometrics, 137(1), pp.260~276 (2007)
- Yue, X. G, "The role of green innovation and tourism towards carbon neutrality in Thailand: Evidence from bootstrap ADRL approach", Journal of Environmental Management, 292(112778) (2021)
- Zhang, S., "Assessing the Impact of the Digital Economy on Green Total Factor Energy Efficiency in the Post-COVID-19 Era", Frontiers in Energy Research, 9 (2021)
- Zhang, W., "Digital economy and carbon emission performance: Evidence at China’s city level", Energy Policy, 165(112927) (2022)
- Zhou, X., "A framework to analyze carbon impacts of digital economy : The case of China", Sustainable Production and Consumption, 31, pp.357~369 (2022)