Energy Demand of the Road Transport Sector of Saudi Arabia—Application of a Causality-Based Machine Learning Model to Ensure Sustainable Environment

Muhammad Muhitur Rahman (), Syed Masiur Rahman, Md Shafiullah (), Md Arif Hasan, Uneb Gazder, Abdullah Al Mamun, Umer Mansoor, Mohammad Tamim Kashifi, Omer Reshi, Md Arifuzzaman, Md Kamrul Islam and Fahad S. Al-Ismail
Additional contact information
Muhammad Muhitur Rahman: Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia
Syed Masiur Rahman: Applied Research Center for Environment & Marine Studies, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31260, Saudi Arabia
Md Shafiullah: Interdisciplinary Research Center for Renewable Energy and Power Systems, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
Md Arif Hasan: Climate Change Response Unit, Wellington City Council, 113 The Terrace, Wellington 6011, New Zealand
Uneb Gazder: Department of Civil Engineering, College of Engineering, University of Bahrain, Isa Town P.O. Box 32038, Bahrain
Abdullah Al Mamun: Department of Civil and Environmental Engineering, University of Utah, 110 Central Campus Drive, MCE-1435, Salt Lake City, UT 84112, USA
Umer Mansoor: Department of Civil and Environmental Engineering, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31260, Saudi Arabia
Mohammad Tamim Kashifi: Applied Research Center for Environment & Marine Studies, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31260, Saudi Arabia
Omer Reshi: Applied Research Center for Environment & Marine Studies, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31260, Saudi Arabia
Md Arifuzzaman: Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia
Md Kamrul Islam: Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia
Fahad S. Al-Ismail: Applied Research Center for Environment & Marine Studies, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31260, Saudi Arabia

Sustainability, 2022, vol. 14, issue 23, 1-21

Abstract: The road transportation sector in Saudi Arabia has been observing a surging growth of demand trends for the last couple of decades. The main objective of this article is to extract insightful information for the country’s policymakers through a comprehensive investigation of the rising energy trends. In the first phase, it employs econometric analysis to provide the causal relationship between the energy demand of the road transportation sector and different socio-economic elements, including the gross domestic product (GDP), number of registered vehicles, total population, the population in the urban agglomeration, and fuel price. Then, it estimates future energy demand for the sector using two machine-learning models, i.e., artificial neural network (ANN) and support vector regression (SVR). The core features of the future demand model include: (i) removal of the linear trend, (ii) input data projection using a double exponential smoothing technique, and (iii) energy demand prediction using the machine learning models. The findings of the study show that the GDP and urban population have a significant causal relationship with energy demand in the road transportation sector in both the short and long run. The greenhouse gas emissions from the road transportation in Saudi Arabia are directly proportional to energy consumption because the demand is solely met by fossil fuels. Therefore, appropriate policy measures should be taken to reduce energy intensity without compromising the country’s development. In addition, the SVR model outperformed the ANN model in predicting the future energy demand of the sector based on the achieved performance indices. For instance, the correlation coefficients of the SVR and the ANN models were 0.8932 and 0.9925, respectively, for the test datasets. The results show that the SVR is better for predicting energy consumption than the ANN. It is expected that the findings of the study will assist the decision-makers of the country in achieving environmental sustainability goals by initiating appropriate policies.

Keywords: artificial neural network; causality analysis; energy demand; greenhouse gas emission; sustainable environment; machine learning; road transport; Saudi Arabia; support vector regression (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/23/16064/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/23/16064/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:23:p:16064-:d:990507

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().