Additive linear modelling and genetic algorithm based electric vehicle outlook and policy formulation for decarbonizing the future transport sector of Bangladesh

Abstract

The transport sector as a whole plays an important role in the economy of Bangladesh as its contribution to gross domestic product (GDP) stood at 10.98 percent in the fiscal year (FY) 2018-19 with a growth rate of 6.88 percent. However, this sector also accounted for 9.92 percent of the country's total greenhouse gas (GHG) emissions in 2020. This GHG emission is projected to grow by 116 percent by 2030, making decarbonization of transport an urgent priority by uptaking the electric vehicle (EV). Hence, the Government of Bangladesh is presently working towards issuing an EV policy to remove the barriers to accommodate this paradigm shift in the transportation sector. In order to develop an effective policy for scaling up EV uptake in Bangladesh, systematic and detailed forecasting needs to be carried out considering various influencing parameters. In this study, an EV outlook for Bangladesh from 2022 to 2040 has been formulated by applying the concepts of the additive linear programming mechanism. The predictive modelling includes linear growth of ICE vehicles, EV price reduction, and inclusion of carbon tax on ICE cars as contributing factors in this paper. It has been estimated that, with the inclusion of a 40% carbon tax, the forecasted number of EVs would reach around 3,000,000 in the year 2040. The outlook has led to a comparative financial analysis between ICE vehicles and EVs through the establishment of financial modelling. The genetic algorithm optimizes total tax incidence (TTI) that limits the payback period, and hence, reduces the price gaps between EVs and ICE vehicles. Based on the findings of the study, a number of policies are recommended on fiscal and financial incentives to pave the way for increased uptake of EVs in Bangladesh.