Forecast energy demand, CO2 emissions and energy resource impacts for the transportation sector

M. Emami Javanmard, Y. Tang, Z. Wang and P. Tontiwachwuthikul

Applied Energy, 2023, vol. 338, issue C, No S0306261923001940

Abstract: Managing energy demand and reducing greenhouse gas emissions are among the most significant challenges ahead for many countries. Accurate prediction of energy demand and CO2 emissions is an important method to tackle the challenge. In addition, as a major source of energy consumption and CO2 emissions, the transportation sector is at the center of the problem. This study employs a hybrid approach integrating a multi-objective mathematical model with data-driven machine learning algorithms to predict energy demand and CO2 emissions in the transportation sector with improved accuracy. Sensitivity analyses are conducted to evaluate the impact of individual and joint consumptions of different energy resources on CO2 emissions. Case studies are conducted with countrywide statistics in Canada where the prediction indicates that energy demand and CO2 emissions will increase by 34.72% and 50.02% from 2019 to 2048 in the Canadian transportation sector. Results also indicated the varied impacts of different energy resources such that a 5% increase in energy demand in oil, gas, electricity, and renewable energy will result in + 4.8%, +0.315%, +0.28%, and −0.51% changes in CO2 emissions respectively. The proposed framework with integrated prediction model and the sensitivity analyses can help identify critical factors impacting CO2 emissions in the transportation sector and provide quantitative references for energy demand management and CO2 emissions reduction.

Keywords: CO2 emissions forecasting; Energy demand forecasting; Transportation sector; Data-driven; Machine learning algorithms; Multi-objective model (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (13)

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DOI: 10.1016/j.apenergy.2023.120830

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