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

• Establish a generalized framework which is capable for both energy demand and CO 2 emissions forecasting in the transportation sector. • Develop a novel multi-objective mathematical model integrating machine learning algorithms improving the prediction accuracy. • Identify and compare multiple machine learning algorithms with the developed multi-objective mathematical Model. • Quantify effects of energy consumption on CO 2 emissions at different levels and compare them with the various types of the energy resources. • Evaluate relationship between energy consumption and CO 2 emissions by sensitivity analysis with a Canada case study. Managing energy demand and reducing greenhouse gas emissions are among the most significant challenges ahead for many countries. Accurate prediction of energy demand and CO 2 emissions is an important method to tackle the challenge. In addition, as a major source of energy consumption and CO 2 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 CO 2 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 CO 2 emissions. Case studies are conducted with countrywide statistics in Canada where the prediction indicates that energy demand and CO 2 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 CO 2 emissions respectively. The proposed framework with integrated prediction model and the sensitivity analyses can help identify critical factors impacting CO 2 emissions in the transportation sector and provide quantitative references for energy demand management and CO 2 emissions reduction. [ABSTRACT FROM AUTHOR]