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This thesis explores the relationship between electric vehicle adoption and decarbonisation of electricity, and the energy-technology nexus as a driver of accelerating climate mitigation. Employing panel data for 20 countries and 14 Indian states from 2015 to 2023, it explores if raising the proportion of non-fossil electricity generation affects EV adoption, and what economic, infrastructural, and policy determinants shape this interaction. A fixed-effects econometric model is employed to account for regional heterogeneity and to identify the primary drivers.
The cross-country analysis records a strong, positive relationship between the penetration of EVs and the penetration of non-fossil electricity. The other important variables positively correlated with EV penetration are charging infrastructure, urbanisation, and GDP per capita. These findings indicate that cleaner power systems provide enabling conditions for electric transport, especially if supported by infrastructure and prosperity. On the other hand, evidence from Indian states shows a negative correlation between the proportion of clean electricity in the grid and the adoption of EVs. This contrast results from institutional and infrastructural asymmetries like centralised grid supply, concern for reliability, and the prevalence of electric two—and three-wheelers in rural areas. State EV policies previously had a strong positive effect on adoption, contrary to national policy in the international case.
These results challenge the idea of a global complementarity between clean energy and EV growth. Instead, they highlight the need for coordinated, context-specific policies that link the electricity and transport transitions, especially in emerging economies. This thesis adds to sustainable transition research by providing evidence on how sectors and regions differ and depend on each other so that we can reach our net-zero goals quickly.
Keywords: Electric Vehicle Adoption, Electricity Decarbonization, Energy-Technology Nexus, Renewable Energy Integration, Panel Data Analysis.
