Determinants of Carbon Intensity in Vietnam: Evidence from an ARDL Model

This study aims to examine the factors influencing the environmental performance of Vietnam’s economy by analyzing the relationship among economic activity, energy transition, technological progress, and emissions per unit of output. Annual time-series data for the period 1990-2021 were collected from the World Bank’s World Development Indicators and the annual reports of the Intellectual Property Office of Vietnam. The autoregressive distributed lag model is employed to investigate both the short-run and long-run effects among the research variables. The empirical results indicate the existence of a long-run cointegration relationship among the variables. The estimation results show that economic activity increases emissions per unit of output, reflecting the economy’s relatively strong dependence on high-emission energy sources. In contrast, the expansion of clean energy use significantly reduces environmental pressure in both the short and long run. Meanwhile, the proxy for technological progress does not exhibit a statistically significant effect on the environmental indicator during the study period. In addition, the error correction coefficient is negative and statistically significant, confirming the existence of an adjustment mechanism toward long-run equilibrium after short-run shocks. Based on these empirical findings, the study proposes several policy implications to promote energy transition, improve the quality of economic growth, and support the achievement of sustainable development goals in Vietnam.

greenhouse gas emissions; energy transition; green technology; sustainable development; cointegration

1. Ahmed, K., Rehman, M. U., & Ozturk, I. (2017). What drives carbon dioxide emissions in the long-run? Evidence from selected South Asian countries. Renewable and Sustainable Energy Reviews, 70, 1142-1153. https://doi.org/10.1016/j.rser.2016.12.018 [Google Scholar] [Crossref]

2. Apergis, N., & Payne, J. E. (2014). Renewable energy, output, CO₂ emissions, and fossil fuel prices in Central America: Evidence from a nonlinear panel smooth transition vector error correction model. Energy Economics, 42, 226-232. https://doi.org/10.1016/j.eneco.2014.01.003 [Google Scholar] [Crossref]

3. Bowen, A., & Hepburn, C. (2014). Green growth: An assessment. Oxford Review of Economic Policy, 30(3), 407-422. [Google Scholar] [Crossref]

4. Chien, F., Hsu, C. C., Sibghatullah, A., Hieu, V. M., Phan, T. T. H., & Tien, N. H. (2022). The role of technological innovation and cleaner energy towards the environment in ASEAN countries: Proposing a policy for sustainable development goals. Economic Research-Ekonomska Istraživanja, 35(1), 4677-4692. https://doi.org/10.1080/1331677X.2021.2016463 [Google Scholar] [Crossref]

5. Grossman, G. M., & Krueger, A. B. (1995). Economic growth and the environment. The Quarterly Journal of Economics, 110(2), 353-377. [Google Scholar] [Crossref]

6. IEA. (2023). World Energy Outlook 2023. International Energy Agency. https://www.iea.org/reports/world-energy-outlook-2023 [Google Scholar] [Crossref]

7. Kirikkaleli, D., & Adebayo, T. S. (2021). Do renewable energy consumption and financial development matter for environmental sustainability? New global evidence. Sustainable Development, 29(4), 583-594. https://doi.org/10.1002/sd.2159 [Google Scholar] [Crossref]

8. Le, H. P., & Sarkodie, S. A. (2020). Dynamic linkage between renewable and conventional energy use, environmental quality and economic growth: Evidence from emerging market and developing economies. Energy Reports, 6, 965-973. https://doi.org/10.1016/j.egyr.2020.04.020 [Google Scholar] [Crossref]

9. Mai, A. N. T., Xuan, V. N., Mai, H. L., Xuan, H. P., & Thi Phuong, T. N. (2024). Population, carbon dioxide emissions and renewable energy consumption nexus: New insights from Vietnam. Energy Exploration & Exploitation, 42(5), 1763-1798. https://doi.org/10.1177/01445987241252453 [Google Scholar] [Crossref]

10. Nguyen, K. H., & Kakinaka, M. (2019). Renewable energy consumption, carbon emissions, and development stages: Some evidence from panel cointegration analysis. Renewable Energy, 132, 1049-1057. https://doi.org/10.1016/j.renene.2018.08.069 [Google Scholar] [Crossref]

11. OECD. (2011). Towards green growth. OECD Publishing. [Google Scholar] [Crossref]

12. Pearce, D. W., & Turner, R. K. (1990). Economics of Natural Resources and the Environment. Baltimore: Johns Hopkins University Press. [Google Scholar] [Crossref]

13. Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289-326. https://doi.org/10.1002/jae.616 [Google Scholar] [Crossref]

14. Pham, X. T., Nguyen, C. N., & Nguyen, T. A. L. (2024). The impact of innovation and renewable energy on CO2 emission in Asian developing countries. Journal of International Economics and Management, 24(3), 42-58. https://doi.org/10.38203/jiem.024.3.0093 [Google Scholar] [Crossref]

15. Pigou, A. C. (1920). The Economics of Welfare. London: Macmillan. [Google Scholar] [Crossref]

16. Popp, D. (2010). Innovation and climate policy. Annual Review of Resource Economics, 2, 275-298. [Google Scholar] [Crossref]

17. Rennings, K. (2000). Redefining innovation-eco-innovation research and the contribution from ecological economics. Ecological Economics, 32(2), 319-332. [Google Scholar] [Crossref]

18. Stern, D. I. (2004). The rise and fall of the environmental Kuznets curve. World Development, 32(8), 1419-1439. https://doi.org/10.1016/j.worlddev.2004.03.004 [Google Scholar] [Crossref]

• A Study on Customer Awareness on Green Banking Intiatives
• International Financial Reporting Standards and Earnings Management: A Global Research Landscape Analysis
• Analysis of Financial Performance and Operational Efficiency of State Bank of India Using the CAMEL Framework
• Roles and Performance of India Post Payment Bank (IPPB) With Special References to Shivamogga District
• Enhancing Transactional Security in U.S. Banking Systems by Implementing OTP-Based Two-Factor Authentication to Mitigate Debit and Credit Card Fraud