IDEAS home Printed from https://ideas.repec.org/a/eee/trapol/v136y2023icp21-46.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0967070X23000306
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tranpol.2023.02.005?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Prakash, Abhijith & Ashby, Rohan & Bruce, Anna & MacGill, Iain, 2023. "Quantifying reserve capabilities for designing flexible electricity markets: An Australian case study with increasing penetrations of renewables," Energy Policy, Elsevier, vol. 177(C).
    2. Yang, Shiyu & Oliver Gao, H. & You, Fengqi, 2022. "Model predictive control in phase-change-material-wallboard-enhanced building energy management considering electricity price dynamics," Applied Energy, Elsevier, vol. 326(C).
    3. Pilotti, L. & Colombari, M. & Castelli, A.F. & Binotti, M. & Giaconia, A. & Martelli, E., 2023. "Simultaneous design and operational optimization of hybrid CSP-PV plants," Applied Energy, Elsevier, vol. 331(C).
    4. Xu, Jiacheng & Liang, Yingzong & Luo, Xianglong & Chen, Jianyong & Yang, Zhi & Chen, Ying, 2023. "Towards cost-effective osmotic power harnessing: Mass exchanger network synthesis for multi-stream pressure-retarded osmosis systems," Applied Energy, Elsevier, vol. 330(PA).
    5. Mittelman, Gur & Eran, Ronen & Zhivin, Lev & Eisenhändler, Ohad & Luzon, Yossi & Tshuva, Moshe, 2023. "The potential of renewable electricity in isolated grids: The case of Israel in 2050," Applied Energy, Elsevier, vol. 349(C).
    6. Hechelmann, Ron-Hendrik & Paris, Aaron & Buchenau, Nadja & Ebersold, Felix, 2023. "Decarbonisation strategies for manufacturing: A technical and economic comparison," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    7. Liang, Tao & Zhao, Qing & Lv, Qingzhao & Sun, Hexu, 2021. "A novel wind speed prediction strategy based on Bi-LSTM, MOOFADA and transfer learning for centralized control centers," Energy, Elsevier, vol. 230(C).
    8. Lu, Yu & Xiang, Yue & Huang, Yuan & Yu, Bin & Weng, Liguo & Liu, Junyong, 2023. "Deep reinforcement learning based optimal scheduling of active distribution system considering distributed generation, energy storage and flexible load," Energy, Elsevier, vol. 271(C).
    9. Ahmadian, Amirhossein & Ghodrati, Vahid & Gadh, Rajit, 2023. "Artificial deep neural network enables one-size-fits-all electric vehicle user behavior prediction framework," Applied Energy, Elsevier, vol. 352(C).
    10. Lefeng, Shi & Shengnan, Lv & Chunxiu, Liu & Yue, Zhou & Cipcigan, Liana & Acker, Thomas L., 2020. "A framework for electric vehicle power supply chain development," Utilities Policy, Elsevier, vol. 64(C).
    11. Paolo Lazzeroni & Brunella Caroleo & Maurizio Arnone & Cristiana Botta, 2021. "A Simplified Approach to Estimate EV Charging Demand in Urban Area: An Italian Case Study," Energies, MDPI, vol. 14(20), pages 1-18, October.
    12. Duc Nguyen Huu & Van Nguyen Ngoc, 2021. "Analysis Study of Current Transportation Status in Vietnam’s Urban Traffic and the Transition to Electric Two-Wheelers Mobility," Sustainability, MDPI, vol. 13(10), pages 1-27, May.
    13. Lian, Renzong & Peng, Jiankun & Wu, Yuankai & Tan, Huachun & Zhang, Hailong, 2020. "Rule-interposing deep reinforcement learning based energy management strategy for power-split hybrid electric vehicle," Energy, Elsevier, vol. 197(C).
    14. Thanh Tung Ha & Thanh Chuong Nguyen & Sy Sua Tu & Minh Hieu Nguyen, 2023. "Investigation of Influential Factors of Intention to Adopt Electric Vehicles for Motorcyclists in Vietnam," Sustainability, MDPI, vol. 15(11), pages 1-16, May.
    15. Xinyu Han & Rongrong Li, 2019. "Comparison of Forecasting Energy Consumption in East Africa Using the MGM, NMGM, MGM-ARIMA, and NMGM-ARIMA Model," Energies, MDPI, vol. 12(17), pages 1-24, August.
    16. Penghui Qiang & Peng Wu & Tao Pan & Huaiquan Zang, 2021. "Real-Time Approximate Equivalent Consumption Minimization Strategy Based on the Single-Shaft Parallel Hybrid Powertrain," Energies, MDPI, vol. 14(23), pages 1-22, November.
    17. Reema Bera & Bhargab Maitra, 2021. "Analyzing Prospective Owners’ Choice Decision towards Plug-in Hybrid Electric Vehicles in Urban India: A Stated Preference Discrete Choice Experiment," Sustainability, MDPI, vol. 13(14), pages 1-24, July.
    18. Wang, Xuewei & Wang, Jing & Wang, Lin & Yuan, Ruiming, 2019. "Non-overlapping moving compressive measurement algorithm for electrical energy estimation of distorted m-sequence dynamic test signal," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    19. Dongming Wu & Liukai Yu & Qianqian Zhang & Yangyang Jiao & Yuhe Wu, 2021. "Materialism, Ecological Consciousness and Purchasing Intention of Electric Vehicles: An Empirical Analysis among Chinese Consumers," Sustainability, MDPI, vol. 13(5), pages 1-19, March.
    20. Zbigniew Bohdanowicz & Jarosław Kowalski & Cezary Biele, 2022. "Intentions to Charge Electric Vehicles Using Vehicle-to-Grid Technology among People with Different Motivations to Save Energy," Sustainability, MDPI, vol. 14(19), pages 1-10, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:trapol:v:136:y:2023:i:c:p:21-46. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/30473/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.