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Decarbonization of Vietnam’s economy: decomposing the drivers for a low-carbon growth

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Abstract

Vietnam has witnessed a rapid increase in national-level CO2 emissions due to rising urbanization, economic expansion, export growth, and industrial development. Moreover, to support the ambitious economic growth targets, reliance on and consumption of fossil fuels are increasing by each passing year. With this circumstance, this study aims to analyze the key drivers of CO2 emissions in Vietnam from 1990 to 2016 using the Kaya identity and decomposition method. Following this approach, CO2 emissions have been decomposed into five effect categories comprising population, affluence, energy intensity, fuel mix, and emission intensity. As per the results, CO2 emissions in Vietnam were mainly driven by rising affluence (58.5%) and changing fuel mix (33.2%) which have resulted from improved living standards, rapid industrial development, and higher fossil fuel consumption. Moreover, population (13.8%) and emission intensity (3.1%) exhibited a relatively lower impact on CO2 emissions during 1990–2016. However, energy intensity (− 8.7%) was the only negative driver which has resulted in the slowdown of carbon emissions in Vietnam. Based on the analysis of energy policy development, the share of renewable energy resources was still quite low in the national energy mix with higher reliance on traditional fossil fuels (mainly coal and petroleum). Therefore, to make a transition towards low-carbon economic growth, significant improvements in energy efficiency and emission intensity are necessary together with national energy mix restructuring for low-carbon economic growth.

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Abbreviations

ASEAN:

Association of Southeast Asian Nations

BRICS:

Brazil, Russia, India, China, and South Africa

CO2 :

Carbon dioxide

FFC:

Fossil fuel consumption

GDP:

Gross domestic product

IEA:

International Energy Agency

IPCC:

Intergovernmental Panel on Climate Change

LMDI:

Logarithmic Mean Divisia Index

Mtoe:

Million metric tons of oil equivalent

OECD:

Organization for Economic Co-operation and Development

TFC:

Total final consumption

TPES:

Total primary energy supply

UNFCCC:

United Nations Framework Convention on Climate Change

WTO:

World Trade Organization

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Acknowledgments

The authors acknowledge the support from Brain Korea 21 plus (BK21 plus) program from the Ministry of Education, Science, and Technology through the Environmental Engineering Program at the University of Ulsan. We also acknowledge the support from the Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea Government (MOTIE) (P0008421, The Competency Development Program for Industry Specialist).

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, University of Ulsan, Daehak-ro 93, Nam-gu, Ulsan, 680-749, Republic of Korea

    Ta Thi Huong, Izhar Hussain Shah & Hung-Suck Park

  2. Institute of Environmental Sciences and Engineering, School of Civil and Environmental Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan

    Izhar Hussain Shah

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  1. Ta Thi Huong
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Correspondence to Hung-Suck Park.

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Appendix

Appendix

Correlation analysis

We used the regression method (ANOVA) to evaluate the results, which helped increase the reliability of the results. In addition, the correlation between variables was included in the study. Specifically, the R2 values for regression models were high (R2 = 0.9858), which is in reasonable agreement with the adjusted R2 of 0.9823 (i.e., the difference is less than 0.2). Besides, p value is less than 0.05 (p value = 8.766E-18) indicating the robustness of our results. Moreover, a simple linear correlation analysis was performed by selecting two variables from the Kaya identity equation. Every variable on the right side of Eq. 4 was compared with that on the left side and their R2 value was determined. Based on the correlation analysis using second-order regression curves, affluence was highly correlated, whereas energy intensity was least correlated with CO2 emissions. The fifth variable added in this study was the fuel mix variable and it was found to be highly correlated with CO2 emissions in Vietnam having an R2 value of 0.8223, as shown in Fig. 5. The results indicate the very high correlation between all Kaya identity variables, including the substitution effect (∆F); thus, the inclusion of the fifth variable can be termed robust.

Fig. 5
figure 5

Correlation analysis results for the selected Kaya identity variables and CO2 emissions (Y-axis represents CO2 emissions in Mt, and X-axis is based on variables given in Eq. 4. R2 values are based on second-order regression)

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Table 2 Important strategies and policies for sustainable/low-carbon growth in Vietnam
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Huong, T.T., Shah, I.H. & Park, HS. Decarbonization of Vietnam’s economy: decomposing the drivers for a low-carbon growth. Environ Sci Pollut Res 28, 518–529 (2021). https://doi.org/10.1007/s11356-020-10481-0

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