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

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  • Abdullah-Al-Nahid, Syed
  • Jamal, Taskin
  • Aziz, Tareq
  • Bhuiyan, Ashraf Hossain
  • Khan, Tafsir Ahmed

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.

Suggested Citation

  • Abdullah-Al-Nahid, Syed & Jamal, Taskin & Aziz, Tareq & Bhuiyan, Ashraf Hossain & Khan, Tafsir Ahmed, 2023. "Additive linear modelling and genetic algorithm based electric vehicle outlook and policy formulation for decarbonizing the future transport sector of Bangladesh," Transport Policy, Elsevier, vol. 136(C), pages 21-46.
  • Handle: RePEc:eee:trapol:v:136:y:2023:i:c:p:21-46
    DOI: 10.1016/j.tranpol.2023.02.005
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    References listed on IDEAS

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    1. Ahmed M. Ali & Dirk Söffker, 2018. "Towards Optimal Power Management of Hybrid Electric Vehicles in Real-Time: A Review on Methods, Challenges, and State-Of-The-Art Solutions," Energies, MDPI, vol. 11(3), pages 1-24, February.
    2. Castillo, Victhalia Zapata & Boer, Harmen-Sytze de & Muñoz, Raúl Maícas & Gernaat, David E.H.J. & Benders, René & van Vuuren, Detlef, 2022. "Future global electricity demand load curves," Energy, Elsevier, vol. 258(C).
    3. Kapustin, Nikita O. & Grushevenko, Dmitry A., 2020. "Long-term electric vehicles outlook and their potential impact on electric grid," Energy Policy, Elsevier, vol. 137(C).
    4. El Mehdi Er Raqabi & Wenkai Li, 2022. "An Electric Vehicle Migration Framework," Working Papers EMS_2022_03, Research Institute, International University of Japan.
    5. Sarmad Zaman Rajper & Johan Albrecht, 2020. "Prospects of Electric Vehicles in the Developing Countries: A Literature Review," Sustainability, MDPI, vol. 12(5), pages 1-19, March.
    6. Lebotsa, Moshoko Emily & Sigauke, Caston & Bere, Alphonce & Fildes, Robert & Boylan, John E., 2018. "Short term electricity demand forecasting using partially linear additive quantile regression with an application to the unit commitment problem," Applied Energy, Elsevier, vol. 222(C), pages 104-118.
    7. Nusrat Chowdhury & Chowdhury Akram Hossain & Michela Longo & Wahiba Yaïci, 2018. "Optimization of Solar Energy System for the Electric Vehicle at University Campus in Dhaka, Bangladesh," Energies, MDPI, vol. 11(9), pages 1-10, September.
    8. Zhang, Kequan & Qu, Zongxi & Dong, Yunxuan & Lu, Haiyan & Leng, Wennan & Wang, Jianzhou & Zhang, Wenyu, 2019. "Research on a combined model based on linear and nonlinear features - A case study of wind speed forecasting," Renewable Energy, Elsevier, vol. 130(C), pages 814-830.
    9. Yunsun Kim & Sahm Kim, 2021. "Forecasting Charging Demand of Electric Vehicles Using Time-Series Models," Energies, MDPI, vol. 14(5), pages 1-16, March.
    10. , Yangriani, 2022. "Electronic Customer Relationship Management," OSF Preprints myp8g, Center for Open Science.
    11. Paul L. J. Helgers & James A. H. Stotz & Haruki Sanada & Yoji Kunihashi & Klaus Biermann & Paulo V. Santos, 2022. "Flying electron spin control gates," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
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