Relationships between Causal Factors Affecting Future Carbon Dioxide Output from Thailand’s Transportation Sector under the Government’s Sustainability Policy: Expanding the SEM-VECM Model
Pruethsan Sutthichaimethee, Danupon Ariyasajjakorn
Resources · 2018
doi:10.3390/resources7040081 (opens in a new tab)
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Abstract
This research aims to analyze the relationships between causal factors likely to affect future CO<sub>2</sub> emissions from the Thai transportation sector by developing the Structural Equation Modeling-Vector Autoregressive Error Correction Mechanism Model (SEM-VECM Model). This model was created to fill information gaps of older models. In addition, the model provides the unique feature of viable model application for different sectors in various contexts. The model revealed all exogenous variables that have direct and indirect influences over changes in CO<sub>2</sub> emissions. The variables show a direct effect at a confidence interval of 99%, including per capita GDP (<inline-formula> <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mrow> <mrow> <mi>ln</mi> <mo stretchy="false">(</mo> <mi>GDP</mi> <mo stretchy="false">)</mo> </mrow> </mrow> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>), labor growth (<inline-formula> <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mrow> <mrow> <mi>ln</mi> <mo stretchy="false">(</mo> <mi>L</mi> <mo stretchy="false">)</mo> </mrow> </mrow> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>), urbanization rate factor (<inline-formula> <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mrow> <mrow> <mi>ln</mi> <mo stretchy="false">(</mo> <mi>U</mi> <mi>R</mi> <mi>T</mi> <mo stretchy="false">)</mo> </mrow> </mrow> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>), industrial structure (<inline-formula> <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mrow> <mrow> <mi>ln</mi> <mo stretchy="false">(</mo> <mi>I</mi> <mi>S</mi> <mo stretchy="false">)</mo> </mrow> </mrow> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>), energy consumption (<inline-formula> <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mrow> <mrow> <mi>ln</mi> <mo stretchy="false">(</mo> <mi>E</mi> <mi>C</mi> <mo stretchy="false">)</mo> </mrow> </mrow> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>), foreign direct investment (<inline-formula> <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mrow> <mrow> <mi>ln</mi> <mo stretchy="false">(</mo> <mi>F</mi> <mi>D</mi> <mi>I</mi> <mo stretchy="false">)</mo> </mrow> </mrow> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>), oil price (<inline-formula> <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mrow> <mrow> <mi>ln</mi> <mo stretchy="false">(</mo> <mi>O</mi> <mi>P</mi> <mo stretchy="false">)</mo> </mrow> </mrow> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>), and net exports (<inline-formula> <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mrow> <mrow> <mi>ln</mi> <mo stretchy="false">(</mo> <mi>X</mi> <mo>−</mo> <mi>E</mi> <mo stretchy="false">)</mo> </mrow> </mrow> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>). In addition, it was found that every variable in the SEM-VECM model has an indirect effect on changes in CO<sub>2</sub> emissions at a confidence interval of 99%. The SEM-VECM model has the ability to adjust to the equilibrium equivalent to 39%. However, it also helps to identify the degree of direct effect that each causal factor has on the others. Specifically, labor growth (<inline-formula> <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mrow> <mrow> <mi>ln</mi> <mo stretchy="false">(</mo> <mi>L</mi> <mo stretchy="false">)</mo> </mrow> </mrow> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>) had a direct effect on per capita GDP (<inline-formula> <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mrow> <mrow> <mi>ln</mi> <mo stretchy="false">(</mo> <mi>GDP</mi> <mo stretchy="false">)</mo> </mrow> </mrow> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>) and energy consumption (<inline-formula> <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mrow> <mrow> <mi>ln</mi> <mo stretchy="false">(</mo> <mi>E</mi> <mi>C</mi> <mo stretchy="false">)</mo> </mrow> </mrow> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>) at a confidence interval of 99%, while urbanization rate (<inline-formula> <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mrow> <mrow> <mi>ln</mi> <mo stretchy="false">(</mo> <mi>U</mi> <mi>R</mi> <mi>T</mi> <mo stretchy="false">)</mo> </mrow> </mrow> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>) had a direct effect on per capita GDP (<inline-formula> <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mrow> <mrow> <mi>ln</mi> <mo stretchy="false">(</mo> <mi>GDP</mi> <mo stretchy="false">)</mo> </mrow> </mrow> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>), labor growth (<inline-formula> <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mrow> <mrow> <mi>ln</mi> <mo stretchy="false">(</mo> <mi>L</mi> <mo stretchy="false">)</mo> </mrow> </mrow> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>), and net exports (<inline-formula> <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mrow> <mrow> <mi>ln</mi> <mo stretchy="false">(</mo> <mi>X</mi> <mo>−</mo> <mi>E</mi> <mo stretchy="false">)</mo> </mrow> </mrow> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>) at a confidence interval of 99%. Furthermore, industrial structure (<inline-formula> <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mrow> <mrow> <mi>ln</mi> <mo stretchy="false">(</mo> <mi>I</mi> <mi>S</mi> <mo stretchy="false">)</mo> </mrow> </mrow> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>) had a direct effect on per capita GDP (<inline-formula> <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mrow> <mrow> <mi>ln</mi> <mo stretchy="false">(</mo> <mi>GDP</mi> <mo stretchy="false">)</mo> </mrow> </mrow> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>) at a confidence interval of 99%, whereas energy consumption (<inline-formula> <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mrow> <mrow> <mi>ln</mi> <mo stretchy="false">(</mo> <mi>E</mi> <mi>C</mi> <mo stretchy="false">)</mo> </mrow> </mrow> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>) had a direct effect on per capita GDP (<inline-formula> <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mrow> <mrow> <mi>ln</mi> <mo stretchy="false">(</mo> <mi>GDP</mi> <mo stretchy="false">)</mo> </mrow> </mrow> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>) at a confidence interval of 99%. Foreign direct investment (<inline-formula> <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mrow> <mrow> <mi>ln</mi> <mo stretchy="false">(</mo> <mi>F</mi> <mi>D</mi> <mi>I</mi> <mo stretchy="false">)</mo> </mrow> </mrow> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>) had a direct effect on per capita GDP (<inline-formula> <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mrow> <mrow> <mi>ln</mi> <mo stretchy="false">(</mo> <mi>GDP</mi> <mo stretchy="false">)</mo> </mrow> </mrow> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>) at a confidence interval of 99%, while oil price (<inline-formula> <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mrow> <mrow> <mi>ln</mi> <mo stretchy="false">(</mo> <mi>O</mi> <mi>P</mi> <mo stretchy="false">)</mo> </mrow> </mrow> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>) had a direct effect on industrial structure (<inline-formula> <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mrow> <mrow> <mi>ln</mi> <mo stretchy="false">(</mo> <mi>I</mi> <mi>S</mi> <mo stretchy="false">)</mo> </mrow> </mrow> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>), energy consumption (<inline-formula> <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mrow> <mrow> <mi>ln</mi> <mo stretchy="false">(</mo> <mi>E</mi> <mi>C</mi> <mo stretchy="false">)</mo> </mrow> </mrow> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>), and net exports (<inline-formula> <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mrow> <mrow> <mi>ln</mi> <mo stretchy="false">(</mo> <mi>X</mi> <mo>−</mo> <mi>E</mi> <mo stretchy="false">)</mo> </mrow> </mrow> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>) at a confidence interval of 99%. Lastly, net exports (<inline-formula> <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mrow> <mrow> <mi>ln</mi> <mo stretchy="false">(</mo> <mi>X</mi> <mo>−</mo> <mi>E</mi> <mo stretchy="false">)</mo> </mrow> </mrow> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>) had a direct effect on per capita GDP (<inline-formula> <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mrow> <mrow> <mi>ln</mi> <mo stretchy="false">(</mo> <mi>GDP</mi> <mo stretchy="false">)</mo> </mrow> </mrow> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>) at a confidence interval of 99%. The model eliminates the problem of heteroskedasticity, multicollinearity, and autocorrelation. In addition, it was found that the model is white noise. When the SEM-VECM Model was used for 30-year forecasting (2018⁻2047), it projected that CO<sub>2</sub> emissions would increase steadily by 67.04% (2047/2018) or 123.90 Mt CO<sub>2</sub> Eq. by 2047. The performance of the SEM-VECM Model was assessed and produced a mean absolute percentage error (MAPE) of 1.21% and root mean square error (RMSE) of 1.02%. When comparing the performance value with the values of other, older models, the SEM-VECM Model was found to be more effective and useful for future research and policy planning for Thailand’s sustainability goals.
Abstract licence: CC BY
References33
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