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Forecasting Thailand’s Transportation CO 2 Emissions: A Comparison among Artificial Intelligent Models

Author

Listed:
  • Thananya Janhuaton

    (School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand)

  • Vatanavongs Ratanavaraha

    (School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand)

  • Sajjakaj Jomnonkwao

    (School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand)

Abstract

Transportation significantly influences greenhouse gas emissions—particularly carbon dioxide (CO 2 )—thereby affecting climate, health, and various socioeconomic aspects. Therefore, in developing and implementing targeted and effective policies to mitigate the environmental impacts of transportation-related carbon dioxide emissions, governments and decision-makers have focused on identifying methods for the accurate and reliable forecasting of carbon emissions in the transportation sector. This study evaluates these policies’ impacts on CO 2 emissions using three forecasting models: ANN, SVR, and ARIMAX. Data spanning the years 1993–2022, including those on population, GDP, and vehicle kilometers, were analyzed. The results indicate the superior performance of the ANN model, which yielded the lowest mean absolute percentage error (MAPE = 6.395). Moreover, the results highlight the limitations of the ARIMAX model; particularly its susceptibility to disruptions, such as the COVID-19 pandemic, due to its reliance on historical data. Leveraging the ANN model, a scenario analysis of trends under the “30@30” policy revealed a reduction in CO 2 emissions from fuel combustion in the transportation sector to 14,996.888 kTons in 2030. These findings provide valuable insights for policymakers in the fields of strategic planning and sustainable transportation development.

Suggested Citation

  • Thananya Janhuaton & Vatanavongs Ratanavaraha & Sajjakaj Jomnonkwao, 2024. "Forecasting Thailand’s Transportation CO 2 Emissions: A Comparison among Artificial Intelligent Models," Forecasting, MDPI, vol. 6(2), pages 1-23, June.
    • Handle: RePEc:gam:jforec:v:6:y:2024:i:2:p:26-484:d:1418607
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    References listed on IDEAS

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    1. Mizen, Paul & Tsoukas, Serafeim, 2012. "Forecasting US bond default ratings allowing for previous and initial state dependence in an ordered probit model," International Journal of Forecasting, Elsevier, vol. 28(1), pages 273-287.
    2. Shuai Yang & Yu Wang & Wengang Ao & Yun Bai & Chuan Li, 2018. "Prediction and Analysis of CO 2 Emission in Chongqing for the Protection of Environment and Public Health," IJERPH, MDPI, vol. 15(3), pages 1-15, March.
    3. Jiao, Xiaoying & Li, Gang & Chen, Jason Li, 2020. "Forecasting international tourism demand: a local spatiotemporal model," Annals of Tourism Research, Elsevier, vol. 83(C).
    4. Song, Haiyan & Li, Gang & Witt, Stephen F. & Athanasopoulos, George, 2011. "Forecasting tourist arrivals using time-varying parameter structural time series models," International Journal of Forecasting, Elsevier, vol. 27(3), pages 855-869.
    5. Joeri Rogelj & Michel den Elzen & Niklas Höhne & Taryn Fransen & Hanna Fekete & Harald Winkler & Roberto Schaeffer & Fu Sha & Keywan Riahi & Malte Meinshausen, 2016. "Paris Agreement climate proposals need a boost to keep warming well below 2 °C," Nature, Nature, vol. 534(7609), pages 631-639, June.
    6. Sahraei, Mohammad Ali & Duman, Hakan & Çodur, Muhammed Yasin & Eyduran, Ecevit, 2021. "Prediction of transportation energy demand: Multivariate Adaptive Regression Splines," Energy, Elsevier, vol. 224(C).
    7. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    Full references (including those not matched with items on IDEAS)

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