Greenhouse Gas Emissions and Digital Competitiveness in CEE countries

2nd International Conference on Chemo and Bioinformatics ICCBIKG 2023 (116-119)

АУТОР(И) / AUTHOR(S): Jelena Živković, Nevena Veselinović

Е-АДРЕСА / E-MAIL: 

Download Full Pdf   

DOI: 10.46793/ICCBI23.116Z

САЖЕТАК / ABSTRACT:

This research is focused on the relationship between greenhouse gas emissions and digital competitiveness at the macro level because of the importance of climate conditions and the use of new technologies, especially digital technologies. Greenhouse gas emissions were measured using the composite index (CI) conducted by the DEA method. Digital competitiveness is measured using the Digital Competitiveness Index (DCI) calculated by the International Institute for Management Development (IMD). The research covered 11 CEE countries: The latest available data on greenhouse gas emissions were from 2019. Correlation analysis results showed that there was no correlation between these variables. Explanations for these results can be found at the DCI level. These index values show that CEE countries do not have a high value of digital competitiveness, which indicates that digital technology adoption is not at a high level, so it still has effects on gas emissions, the environment, and climate change.

КЉУЧНЕ РЕЧИ / KEYWORDS:

greenhouse gas emissions, digital competitiveness, composite index, DEA approach

ЛИТЕРАТУРА / REFERENCES:

  • United Nations Framework Convention on Climate Change (UNFCCC). The Paris Agreement.
  • Usman, A., Ozturk, I., Hassan, A., Zafar, S.M., Ullah, S. (2021). The Effect of ICT on Energy Consumption and Economic Growth in South Asian Economies: An Empirical Analysis. Telematics and Informatics, 58(29), 101537.
  • Li, L., Zheng, Y., Zheng, S., Ke, H. (2020). The New Smart City Programme: Evaluating the Effect of the Internet of Energy on Air Quality in China. The Science of the total environment, 714, 136380
  • Lin, R., Xie, Z., Hao, Y., Wang, J. (2020). Improving High-Tech Enterprise Innovation in Big Data Environment: A Combinative View of Internal and External Governance. International Journal of Information Management: The Journal for Information Professionals, 50, 575–585.
  • Lange, S., Pohl, J., Santarius, T. (2020). Digitalization and Energy Consumption. Does ICT Reduce Energy Demand? Ecological Economics, 176, 106760
  • https://databank.worldbank.org/source/world-development-indicators#
  • IMD (2019). IMD World Digital Competitiveness Ranking 2019. International Institute for Management Development
  • Zhou, P., Ang B. W., & Poh, K.L. (2007). A mathematical programming approach to constructing composite indicators. Ecological Economics, 62(2), 291-297.
  • Cherchye, L., Moesen W., Rogge, N. & Puyenbroeck, T. V. (2007). An introduction to ‘benefit of the doubt’ composite indicators. Social Indicators Research, 82(1), 111-145.
  • Fusco, E. (2015). Enhancing non-compensatory composite indicators: A directional proposal. European Journal of Operational Research, 242(2), 620-630.
  • Cherchye, L., Moesen W., Rogge, N., Puyenbroeck, T. V., Saisana, M., Saltelli, A., Liska R. & Tarantola, S. (2008). Creating composite indicators with DEA and robustness analysis: The case of technology achievement index. The Journal of Operational Research Society, 59 (2), 239-251.