Short-and long-term effects of GDP, energy consumption, FDI, and trade openness on CO2 emissions

tion model, the coefficient of ΔLnGDPt-2 is significant at the 
10% level, which means that in the short-run GDP per capita affects CO2 emission with two-year lags. 
T. V. T. Do and H. L. Dinh /Accounting 6 (2020) 369
Table 5 
The estimated results of Vector error correction model 
Dependent variable: LnCO2 
 Coeficient P_value 
Error Correction -0.6116*** 0.0026 
ΔLnCO2t-1 -0.0544 0.8091 
ΔLnCO2t-2 0.2097 0.3791 
ΔLnGDPt-1 -0.3863 0.2647 
ΔLnGDPt-2 0.5057* 0.0840 
ΔLnECt-1 1.2945* 0.0531 
ΔLnECt-2 0.2075 0.7574 
ΔFDIt-1 -0.1278 0.8897 
ΔFDIt-2 0.7147 0.5175 
ΔTOt-1 0.1518 0.4463 
ΔTOt-2 -0.1338 0.4444 
Constant -0.0160 0.7489 
 R-squared 0.6810 
*, **, and *** denotes significance at 10%, 5%, and 1% level, respectively. 
At the same time, the coefficient of ΔLnECt-1 is significant at the 10% level, indicating that in the short run, energy consumption 
influences CO2 emission with a one-year lag. This result specifies that if energy consumption rises by 1% in the current year, 
then it will contribute to the increase in CO2 emission by 1.2945% next year. These results confirm previous findings in the 
literature of He and Richard (2010), Hamit-Haggar (2012), and Govindaraju and Tang (2013). Likewise, this study conducts 
Tests of Cumulative Sum of Recursive Residuals (CUSUM) and Cumulative Sum of Square of Recursive Residuals 
(CUSUMSQ) to examine the stability of the model. Results of these tests presented in Figure 1, demonstrating that the 
cumulative sum of recursive residuals and the cumulative sum of a square of recursive residuals fall within the range of 5% 
significant level. Therefore, the model is concluded to be stable. 
Fig. 1. Results of Cumulative Sum of Recursive Residuals and Cumulative Sum of Square of Recursive Residuals 
Also, this research carried out the impulse response function to examine the response of CO2 emission to the changes of GDP 
per capita, foreign direct investment as the percentage of GDP, energy consumption per capita, and trade openness. As can be 
seen from Fig. 2, the response of CO2 emission to GDP per capita increases in the first 3 years, then dramatically rises in the 
next 2 years. During the period from 5th year to 10th year, it drops, afterward, rises inconsiderably in the next 5 years, and remains 
stable from the 15th year. According to the response of CO2 emission to FDI, during the first six years, the effect of FDI shocks 
cause the continuous fluctuation of CO2 emission, thereafter, CO2 emission slightly falls from 7th year, and then remains quite 
stable. The response of CO2 emission to EC is positive during the first year. After that, it gradually reduces until the 10th year 
and stabilizes from the 13th year. The response of CO2 emission to trade openness tends to increase during the first year. From 
the 2nd year to the 7th year, CO2 emission slightly fluctuates then remains stable. 
-.04
.00
.04
.08
.12
5 10 15 20
Response of LNCO2 to LNGDP
Response to Cholesky One S.D. Innovations
-.04
.00
.04
.08
.12
5 10 15 20
Response of LNCO2 to FDI
Response to Cholesky One S.D. Innovations
 370
-.04
.00
.04
.08
.12
5 10 15 20
Response of LNCO2 to TO
Response to Cholesky One S.D. Innovations
Fig. 2. A response of CO2 emission to LnGDP, LnEC, FDI, TO 
Finally, the article carried out the variance decomposition analysis to measure the proportion of a variable’s forecast error 
variance that appears as the consequence of a shock from each of the variables into contribution originating from its own and 
the other variables’ variances for 20 years. The results in Table 6 describe that, during the first year, the variance of CO2 depends 
on its own innovation. From 2nd year, the contribution of CO2 to its variance gradually decreases, while other factors’ variances 
increase. Until the 10th year, CO2 approximately accounts for 83.14% to the total variance, whilst the contributions of the 
variances of GDP, EC, FDI, and TO to the total variance are 4.93%, 9.46%, 0.31%, 2.15%, respectively. At the end of 20 years, 
the forecast error variance for CO2 emission, explained by its innovation, is 79.25%, and GDP, EC, FDI, and TO represent 
5.28%, 12.85%, 0.31%, and 2.31% of the variance of CO2, respectively. In the long run, energy consumption per capita and 
GDP per capita considerably affect CO2 emission. The findings are consistent with the empirical results of Akbostanci (2009) 
when examined the data during the period 1968–2003 and 1992–2001 in Turkey or the findings of Ang (2007) in France, Fodha 
and Zaghdoud (2010) in Tunisia. 
Table 6 
Decomposition of variance 
 Period S.E. LNCO2 LNGDP LNEC FDI TO 
 1 0.079889 100.0000 0.000000 0.000000 0.000000 0.000000 
 2 0.125253 97.40316 0.026014 0.875129 0.265010 1.430686 
 3 0.151254 97.45375 0.074772 0.657066 0.256129 1.558287 
 4 0.172815 95.88978 0.800386 1.497271 0.262697 1.549867 
 5 0.190156 93.24998 2.762426 1.856190 0.318626 1.812781 
 6 0.202658 90.45659 3.824912 3.460191 0.284962 1.973340 
 7 0.213744 87.96906 4.383383 5.384615 0.256289 2.006650 
 8 0.224785 85.94548 4.840623 6.872017 0.251667 2.090218 
 9 0.235325 84.25379 4.982570 8.325055 0.281710 2.156877 
 10 0.245880 83.14584 4.929121 9.460523 0.305372 2.159143 
 11 0.256701 82.47130 4.910877 10.11764 0.322010 2.178169 
 12 0.267173 81.97965 4.919229 10.56771 0.328925 2.204482 
 13 0.277241 81.60182 4.919196 10.94010 0.327307 2.211577 
 14 0.286981 81.26082 4.972795 11.21938 0.320717 2.226292 
 15 0.296223 80.88896 5.054443 11.49459 0.314456 2.247551 
 16 0.305023 80.50996 5.117776 11.80163 0.309643 2.260998 
 17 0.313554 80.15003 5.177275 12.09263 0.306285 2.273786 
 18 0.321839 79.81126 5.229755 12.36611 0.304997 2.287871 
 19 0.329911 79.50949 5.262387 12.62549 0.304952 2.297687 
 20 0.337842 79.25261 5.286574 12.84999 0.305143 2.305679 
4. Conclusion 
This study has implemented a Vector error correction model to investigate the long-run and short-run relationships among CO2 
emission, GDP per capita, energy consumption, FDI, and trade openness. Based on the results, in the long run, the changes in 
variables influence the CO2 emission: GDP growth per capita has a negative influence on CO2 emission. According to the 
Kuznets curve for the relationship between carbon dioxide emission and economic growth, the environment pollution 
represented by CO2 emission has risen at the early stages of economic growth; however, CO2 emission began to reduce after 
GDP per capita reached a certain point on the Kuznets curve. The result of the cointegration equation is appropriate with the 
Kuznets curve in the long term. Whereby, the GDP of Vietnam accumulated to a certain level would enable Vietnam to pay 
more attention to environmental issues, which could decrease the CO2 emission. Moreover, in the long term, energy consumption 
-.04
.00
.04
.08
.12
5 10 15 20
Response of LNCO2 to LNEC
Response to Cholesky One S.D. Innovations
T. V. T. Do and H. L. Dinh /Accounting 6 (2020) 371
and trade openness negatively affect CO2 emission. The foreign direct investment as the percentage of GDP in a long time has 
a positive relationship with CO2 emission. Furthermore, in the vector error correction model, the short-run GDP per capita 
affects CO2 emission with two-year lags. At the same time, energy consumption influences CO2 emission with a one-year lag. 
This result specifies that if the energy consumption rises in the current year, then it will contribute to the increase in CO2 emission 
by next year. According to these findings, this paper suggests several issues that should be conducted in Vietnam to design 
policies that encourage FDI inflow and liberalize the level of trade in Vietnam. Firstly, the most important issue in economic 
development is the government needs to issues strict regulations and laws to reduce CO2 emission. To reduce CO2 emission, the 
authority must consider carefully the new wave of FDI inflow to Vietnam in this period to limit the effect of FDI on 
environmental quality. On the other hand, the authority needs to work hard with the non-governmental organizations and 
educational institutions in tackling the issue. The higher rate of taxation on the environment and violate environmental law 
might be taken into account. Secondly, international trade represents a negative impact on the environment. This indicates that 
policymakers should encourage importing high and green technology to replace obsolete technologies. Moreover, policymakers 
must limit the export-oriented industries that damage the environment such as cement and so on. 
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