Market and Policy Dynamics in Forecasting Fuel Cell Vehicle Adoption in Malaysia: The Role of Technology Readiness, Infrastructure Development, and Policy Intervention

This study examines the adoption of fuel cell vehicles (FCVs) in Malaysia in line with the National Energy Transition Roadmap (NETR) and Hydrogen Economy and Technology Roadmap (HETR). Despite strong policy direction, FCV adoption remains low due to high costs, limited hydrogen infrastructure, and low market readiness. Forecasting FCV growth is also challenging due to limited and incomplete local data.
To address this, this study develops a forecasting framework combining regression analysis and logistic growth modelling. Data from 2019 to 2023, including electric vehicle (EV) sales, EV charging infrastructure, gross domestic product (GDP) per capita, and sustainable and responsible investment (SRI) are used as proxy indicators. Missing data are handled using imputation techniques to improve reliability.
Two scenarios are analysed: a market-driven model and a policy-driven model. The market-driven scenario shows slow adoption, reaching only 288 FCVs by 2070. In contrast, the policy-driven scenario, which includes government support such as infrastructure expansion, incentives, and fleet conversion, shows a significant increase to 4,259 FCVs over the same period.
The results show that infrastructure availability and cost are the main factors influencing FCV adoption, while economic indicators and investment trends have a smaller impact. Overall, the findings suggest that without strong policy intervention, FCV adoption in Malaysia will remain limited. This study provides a practical framework to support policy and investment decisions for hydrogen mobility development.

Fuel Cell Vehicle, Forecast Model, Amputation Regression, Logistic Growth, Energy Policy.

1. A. C. Ruoso, J. L. Duarte Ribeiro. (2022). The influence of countries' socioeconomic characteristics on the adoption of electric vehicle. Energy for Sustainable Development. doi:https://doi.org/10.1016/j.esd.2022.10.003 [Google Scholar] [Crossref]

2. A. Kisok. (2023). Malaysia electric vehicles. International Trade Administration. doi:https://www.trade.gov/market-intelligence/malaysia-electric-vehicles [Google Scholar] [Crossref]

3. A. Pamidimukkala, S. Kermanshachi et al. (2023). Adoption of electric vehicles: A state-of-art review. Transportation Research Procedia, 73. doi:https://doi.org/10.1016/j.trpro.2023.11.923. [Google Scholar] [Crossref]

4. A.D. Rafael, W.O. Kenneth. (2020). Predicting on-time delivery in the trucking industry. (MIT education sites) Retrieved from https://ctl.mit.edu/sites/ctl.mit.edu/files/theses/Duarte%2BOhlund%20executive_summary.pdf [Google Scholar] [Crossref]

5. Angelina F. Ambrose, Abul Quasem Al-Amin, Rajah Rasiah, R. Saidur, Nowshad Amin. (2017). Prospects for introducing hydrogen fuel cell vehicles in Malaysia. International Journal of Hydrogen Energy, 42. doi:https://doi.org/10.1016/j.ijhydene.2016.05.122 [Google Scholar] [Crossref]

6. Aryanpur, V., Rogan, F. (2024). Decarbonising road freight transport: The role of zero-emission trucks and intangible costs. Sci Rep, 14. doi:https://doi.org/10.1038/s41598-024-52682-4 [Google Scholar] [Crossref]

7. Burke AF, Zhao J, Miller MR, Fulton LM. (2024). Vehicle choice modeling for emerging zero-emission light-duty vehicle markets in California. Heliyon, 10(12). doi:https://doi.org/10.1016/j.heliyon.2024.e32823 [Google Scholar] [Crossref]

8. C. Andrew, C. Edward, V. Jasmine. (2023). Summary of Malaysia’s NETR - Navigating the transient stage. PwC Malaysia publications. Retrieved from https://www.pwc.com/my/en/publications/2023/malaysia-national-energy-transition-roadmap-netr-summary.html [Google Scholar] [Crossref]

9. C. Domarchi, E. Cherchi. (2023). Electric vehicle forecasts: a review of models and methods including diffusion and substitution effects. Transport Reviews. doi:https://www.tandfonline.com/action/showCitFormats?doi=10.1080/01441647.2023.2195687 [Google Scholar] [Crossref]

10. C. Mendez, M. Contestabile, Y. Bicer. (2023). Hydrogen fuel cell vehicles as a sustainable transportation solution in Qatar and the Gulf cooperation council: a review. International Journal of Hydrogen Energy, 48. doi:https://doi.org/10.1016/j.ijhydene.2023.04.194 [Google Scholar] [Crossref]

11. C. Xia. (2024). Exploring the Determinants of Electric Vehicle Adoption in Canada: A Multi-City Study. Resource Economics and Policy Analysis (REPA) Research. Retrieved from https://web.uvic.ca/~repa/publications/REPA%20working%20papers/WorkingPaper2024-02.pdf [Google Scholar] [Crossref]

12. Department of Statistics Malaysia. (2024). Gross Domestic Product (GDP). Department of Statistics Malaysia. Retrieved from https://open.dosm.gov.my/dashboard/gdp?form=MG0AV3 [Google Scholar] [Crossref]

13. Economic Planning Unit, Prime Minister’s Department. (2022). National energy policy 2022-2040. (Malaysia Ministry of Economy) Retrieved from https://www.ekonomi.gov.my/sites/default/files/202209/National%20Energy%20Policy_2022_2040.pdf [Google Scholar] [Crossref]

14. H. Julia, C.M. Yasin et al. (2023). Prediction of transportation energy demand in Turkiye using stacking ensemble models: Methodology and comparative analysis. Applied Energy, 350. doi:https://doi.org/10.1016/j.apenergy.2023.121765 [Google Scholar] [Crossref]

15. J. Mina et al. (2023). A wide range of missing imputation approaches in longitudinal data: a simulation study and real data analysis. BMC Med Res Methodol, 23. doi:https://doi.org/10.1186/s12874-023-01968-8 [Google Scholar] [Crossref]

16. J. Sammy. (2016). Malaysia aims to have 100,000 electric cars on the road by 2020. (Motion Digest) Retrieved from https://motiondigest.com/2016/10/07/malaysia-aims-to-have-100000-electric-cars-on-the-road-by-2020/ [Google Scholar] [Crossref]

17. J. Yousef, M. Mohammad Zakaria et al. (2024). Review of data imputation techniques in time series data: Comparative Analysis. The Eurasia Proceedings of Science, Technology Engineering and Mathematics, 27. doi:http://dx.doi.org/10.55549/epstem.1518433 [Google Scholar] [Crossref]

18. J. Yousef, M. Mohammad Zakaria et al. (2024). Review of data imputation techniques in time series data: Comparative Analysis. The Eurasia Proceedings of Science, Technology, Engineering and Mathematics, 27. doi:http://dx.doi.org/10.55549/epstem.1518433 [Google Scholar] [Crossref]

19. L. Michael. (2023). The Strategic Value of Regression Analysis in Marketing Research. (Market research blog) Retrieved from https://blog.marketresearch.com/the-strategic-value-of-regression-analysis-in-marketing-research [Google Scholar] [Crossref]

20. L. Nam Soon, R. Abdul Rauf et al. (2022). Trending: Sustainable responsible investment in Malaysia and the region. EY. Retrieved from https://assets.ey.com/content/dam/ey-sites/ey-com/en_my/topics/climate-change/ey-trending-sri-in-malaysia-and-the-region-final-22022022.pdf [Google Scholar] [Crossref]

21. L. Siaw Wan, C. Jack, C. L. Ong. (2023). Malaysia’s NETR: Part 1 (Flagship catalyst projects and initiatives). Christopher & Lee Ong. Retrieved from https://www.christopherleeong.com/media/5521/202307_clo-national-energy-transition-roadmap-part1.pdf [Google Scholar] [Crossref]

22. M. Asghari et al. (2025). Strategic analysis of hydrogen market dynamics across collaboration models. Renewable and Sustainable Energy Reviews, 208. doi:https://doi.org/10.1016/j.rser.2024.115001. [Google Scholar] [Crossref]

23. M. S. Bindhya, N. V. Madhusoodanan Kartha et al. (2025). Electrifying the Road: A Comprehensive Analysis of Factors Influencing Consumer Adoption of Electric Vehicles. International Journal of Energy Economics and Policy. doi:https://doi.org/10.32479/ijeep.17036 [Google Scholar] [Crossref]

24. M. S. Herdem, J. Nathwani. (2024). A Beginner’s Guide for Policymakers. Balsillie Papers. doi:10.51644/BAP64 [Google Scholar] [Crossref]

25. M.L Journey. (n.d.). Bass Diffusion Model: Predicting Product Adoption and Market Success. (M.L Journey) Retrieved from https://mljourney.com/the-bass-diffusion-model/ [Google Scholar] [Crossref]

26. Malaysia Green Technology and Climate Change Corporation. (2011). Green Investment Tax Allowance. (Malaysia Green Technology and Climate Change Corporation (GITA) & Green Income Tax Exemption (GITE)) Retrieved from https://www.mgtc.gov.my/what-we-do/low-carbon-cities-lcc/low-carbon-cities-2030-challenge/?form=MG0AV3 [Google Scholar] [Crossref]

27. Malaysia Green Technology and Climate Change Corporation. (2014). Green Investment Tax Allowance (GITA) & Green Income Tax Exemption (GITE). (Malaysia Green Technology and Climate Change Corporation) Retrieved from https://www.mgtc.gov.my/what-we-do/green-incentives/green-investment-tax-incentives-gita-gite/?form=MG0AV3 [Google Scholar] [Crossref]

28. Malaysia Ministry of Economy. (2023). National energy transition roadmap – Energising the nation, powering our future. Malaysia Ministry of Economy. Retrieved from https://ekonomi.gov.my/sites/default/files/2023-09/National%20Energy%20Transition%20Roadmap_0.pdf [Google Scholar] [Crossref]

29. Malaysia Ministry of Energy, Green Technology and Water. (2011). Low Carbon Cities Framework (LCCF) & Assessment System. Ministry of Energy, Green Technology and Water. Retrieved from https://lccftrack.greentownship.my/files/LCCF-Book.pdf [Google Scholar] [Crossref]

30. Malaysia Ministry of Science, Technology and Innovation. (2023). Hydrogen economy and technology roadmap. Malaysia Ministry of Science, Technology and Innovation. Retrieved from https://www.mosti.gov.my/en/dasar/#flipbook-df_69947/1/ [Google Scholar] [Crossref]

31. Malaysia Prime Minister’s Office. (2019). Green Technology Master Plan Malaysia. (Malaysia Prime Minister’s Office) Retrieved from https://www.pmo.gov.my/2019/07/green-technology-master-plan-malaysia/?form=MG0AV3 [Google Scholar] [Crossref]

32. Ministry of Energy, Green Technology and Water (KeTTHA). (2017). Green Technology Master Plan. (Ministry of Energy, Green Technology and Water (KeTTHA)) Retrieved from https://www.pmo.gov.my/wp-content/uploads/2019/07/Green-Technology-Master-Plan-Malaysia-2017-2030.pdf?form=MG0AV3 [Google Scholar] [Crossref]

33. N. Flori. (2024). Step-By-Step Guide to Using Regression Analysis to Forecast Sales. (Hubspot blog) Retrieved from https://blog.hubspot.com/sales/regression-analysis-to-forecast-sales?form=MG0AV3 [Google Scholar] [Crossref]

34. N. Norafneeza, et al. (2023). Hydrogen application and its safety: An overview of public perceptions and acceptance in Malaysia. Process Safety and Environmental Protection, 180. doi:https://doi.org/10.1016/j.psep.2023.10.019 [Google Scholar] [Crossref]

35. N. Subhashish, M.K. Pabitra. (2025). A ClusteredRF Approach to Data Imputation in Healthcare for Enhanced Data Integrity. Netw Model Anal Health Inform Bioinforma, 14. Retrieved from https://link.springer.com/article/10.1007/s13721-024-00496-9?form=MG0AV3 [Google Scholar] [Crossref]

36. P. Tan. (2023). EV sales in Malaysia increase to 2,631 units in 2022 – up 860% from 2021; sales in 2023 “will be much higher”. (Paul Tan) Retrieved from https://paultan.org/2023/01/19/ev-sales-in-malaysia-increase-to-2631-units-in-2022-up-860-fr-2021-sales-in-2023-will-be-much-higher/ [Google Scholar] [Crossref]

37. Prime Minister’s Office. (2019). National Policy on Climate Change. (Prime Minister’s Office) Retrieved from https://www.pmo.gov.my/2019/07/national-policy-on-climate-change/?form=MG0AV3 [Google Scholar] [Crossref]

38. Q. Zhang, J. Chen, T. Ihara. (2024). Assessing regional variations in hydrogen fuel cell vehicle adoption: An integrative approach using real-world data and analytic hierarchy process in Tokyo. Applied Energy. doi:https://doi.org/10.1016/j.apenergy.2024.123014 [Google Scholar] [Crossref]

39. R. R. Kumar et al. (2022). Comparative assessment and selection of electric vehicle diffusion models: A global outlook. Energy, 238. doi:https://doi.org/10.1016/j.energy.2021.121932 [Google Scholar] [Crossref]

40. R.Sathiyaraj, A. Bharathi. (2020). Short-term traffic prediction model for urban transportation using structure pattern and regression: an Indian context. Applied Science. doi:https://doi.org/10.1007/s42452-020-2946-2 [Google Scholar] [Crossref]

41. S. B. Arzova, B. Ş. Şahin. (2024). Macroeconomic and financial determinants of green growth: an empirical investigation on BRICS-T countries. Management of Environmental Quality: An International Journal. doi:https://doi.org/10.1108/MEQ-07-2023-0210 [Google Scholar] [Crossref]

42. S.P.Shepherd. (2014). A review of system dynamics models applied in transportation. White Rose Research Online. doi:https://doi.org/10.1080/21680566.2014.916236 [Google Scholar] [Crossref]

43. Sustainable Energy Development Authority (SEDA). (2011). Feed-In Tariff (FIT). (Sustainable Energy Development Authority (SEDA)) Retrieved from https://www.seda.gov.my/reportal/fit/?form=MG0AV3 [Google Scholar] [Crossref]

44. Sustainable Energy Development Authority (SEDA). (2011). Large Scale Solar. (Sustainable Energy Development Authority (SEDA)) Retrieved from https://www.seda.gov.my/reportal/large-scale-solar/?form=MG0AV3 [Google Scholar] [Crossref]

45. Sustainable Energy Development Authority (SEDA). (2023). Renewable energy act 2011. (Sustainable Energy Development Authority (SEDA)) Retrieved from https://www.seda.gov.my/policies/renewable-energy-act-2011/?form=MG0AV3 [Google Scholar] [Crossref]

46. Syed Mansor, S. M. F. & Anuar, H. S. (2025). Elements influencing adoption of Electric Vehicle (EV) among Malaysians. Journal of Technology and Operations Management, 20. doi:https://doi.org/10.32890/jtom2025.20.2.2 [Google Scholar] [Crossref]

47. T. Pimonenko, T. Tambovceva. (2021). Green Investments As Drivers of Sustainable Economic Growth In The EU Countries: A Retrospective Analysis. RTU 62nd INTERNATIONAL SCIENTIFIC CONFERENCE ON ECONOMICS AND ENTREPRENEURSHIP SCEE’2021 PROCEEDINGS. doi:https://doi.org/10.7250/scee.2021.0007 [Google Scholar] [Crossref]

48. T. V. Ramadoss , J. H. Lee et al. (2025). Classifying electric vehicle adopters and forecasting progress to full adoption. Sustainable mobility and transport. doi:https://doi.org/10.1038/s44333-025-00049-1 [Google Scholar] [Crossref]

49. United Nations Framework COnvention on Climate Change (UNFCCC). (2016). The Paris Agreement. UNFCCC. Retrieved from https://unfccc.int/sites/default/files/resource/parisagreement_publication.pdf?form=MG0AV3 [Google Scholar] [Crossref]

50. W. Akihiro, L. Jonathan. (2021). Comparative economic and environmental benefits of ownership of both new & used light duty hydrogen fuel cell vehicles in Japan. International Journal of Hydrogen Energy, 46(52). doi:https://doi.org/10.1016/j.ijhydene.2021.05.141 [Google Scholar] [Crossref]

51. W. Guo, W. Xia et al. (2024). Policy, innovation, market or behavior driven effects: Energy green transition and environmental management in Chinese heterogeneous cities. Journal of Environmental Management, 370. doi:https://doi.org/10.1016/j.jenvman.2024.122634 [Google Scholar] [Crossref]

52. Y. Sonia, H.L. Daniel, S. Carol, G. Cynthia. (2006). An integrated assessment of the impacts of hydrogen economy on transportation, energy use, and air emissions. Proceedings of the IEEE, 94. Retrieved from https://escholarship.org/content/qt55g1z6zq/qt55g1z6zq_noSplash_8bea813f770d743a87939a623e29cb5b.pdf [Google Scholar] [Crossref]

53. Z. Chen, R. Kleijn et al. (2026). Fuel cell and electric vehicles: Resource use and associated environmental impacts. Resources, Conservation and Recycling, 226. doi:https://doi.org/10.1016/j.resconrec.2025.108646 [Google Scholar] [Crossref]

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