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Medium-term projections of vehicle ownership, energy demand and vehicular emissions from private road transport in India

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Abstract

Rapid growth of private vehicle ownership in emerging economies like India has major implications on transport infrastructure, energy demand and emissions targets. This study attempts to model the relationship between income and vehicle ownership for two-wheelers and cars in India. Further, the study aims to project various medium-term scenarios of vehicle ownership, fuel demand, and vehicular emission based on economic growth rates and electric vehicle adoption scenarios. Road infrastructure requirements and subsequent CO2 emissions are also forecasted. Using time series data from 1960 to 2019, a nonlinear Gompertz function is estimated. Subsequently, a bottom-up methodology is used to forecast energy demand and emissions. This study proposes and utilises an incremental addition to vehicle stock approach to estimate on-road vehicles. In addition, it also incorporates longer time series to include the exponential growth of private vehicles in the previous decade. Further inclusion of electric vehicles and its related electricity demand and emissions are presented. The results indicate an addition of 107–145 million vehicles to existing fleet by 2030. During this decade 200–250 million vehicles are projected to ply on-road annually resulting in a peak fuel demand of 60 million tons and CO2 emissions of up to 174 million tons. However, with the adoption of EVs and ownership approaching saturation levels, both fuel demand and vehicular emissions are forecasted to peak before they subsequently decline. Lastly, appropriate transport policy measures and investment spheres are required to direct concentrated efforts. Hence, a discussion of how to reduce dependency on private vehicles and regulate vehicular emissions is also suggested.

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Data availability

The datasets generated during and/or analysed during the current study are collected from the Ministry of Road Transport and Highways, Government of India (https://morth.nic.in/road-transport-year-books), World Bank (https://databank.worldbank.org/source/world-development-indicators) and UN World Population Prospect (https://population.un.org/wpp/) which are publicly available.

Notes

  1. Nonlinear functional forms are commonly used for indicating the s-shaped relationship between economic variables and vehicle ownership. Simultaneously, there are metaheuristic optimising algorithms, such as Prairie Dog Optimisation, Astute Black Widow Optimisation and the Global Best-Guided Firefly Algorithm for Engineering Problems, that can be used to determine the optimum pathways to achieve an objective given a set of constraints. However, the present study estimates potential energy and emission scenarios using a bottom-up approach based on the association between economic variables and vehicle ownership. See Ezugwu et al. (2022) and Ahmed et al. (2022) for discussion on optimising algorithms and its application.

  2. The bottom-up is a scenario-based approach that builds on the association between economic variable and vehicle ownership and, projects potential energy and emission scenarios. Simultaneously, there are simulation tools such as RETScreens, PRIMES and EVI-Pro which can be useful in predicting say EV charging stations required given a particular future scenario of vehicle ownership, simulating energy demand and supply, and managing energy usage. A discussion on such tools can be found in Ahmed et al. (2022) and Ahmed et al. (2023).

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Acknowledgements

I would like to thank Centre for Energy Environment and Water, New Delhi, for providing me an opportunity to present an earlier version of this working paper via webinar. I express my gratitude to Dr. Vaibhav Chaturvedi and his team-Low Carbon Pathways (LCP)-CEEW, for sharing their valuable time and offering inputs and suggestions on this working paper. I would also like to thank Madras School of Economics, Chennai, for providing me platform to present an earlier version of this working paper on their 9th retreat seminar where valuable feedbacks on the working paper were received. Further, I would like to extend my special thanks to my supervisor Dr. K.S. Kavi Kumar, Professor, Madras School of Economics for his valuable inputs and constant guidance throughout this process.

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  1. Madras School of Economics, Chennai, Tamil Nadu, India

    B. Ajay Krishna

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Appendices

Appendix A

Annual addition of EVs under various GDP scenarios.

See Fig. 18. 

Fig. 18
figure 18

EVs added each year

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Appendix B

Inventory of local pollutant emissions.

See Table 9.

Table 9 Inventory of local pollutant emissions from private vehicles (in’000 tons)
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Appendix C

Inventory of GHGs.

See Table 10.

Table 10 Inventory of GHG emissions from private vehicles (in’000 tons)
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Krishna, B.A. Medium-term projections of vehicle ownership, energy demand and vehicular emissions from private road transport in India. Environ Dev Sustain (2024). https://doi.org/10.1007/s10668-024-04473-0

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