We use historical data for the land-based passenger (in passenger-kilometers (km)) across 38 countries and freight transport (in tonne-km) for 43 countries between 1990 and 2018 from the Transport Outlook of the International Transport Forum (ITF) transport database, to investigate the key drivers of transport energy demand source: ITF. (2019). ITF Transport Outlook 2019. ITF Transport Outlook 2019. https://www.oecd-ilibrary.org/transport/itf-transport-outlook-2019_transp_outlook-en-2019-en We collect the historical socioeconomic variables from the World Bank’s global open data bank source: World Bank. (2020). Data Bank: World Development Indicators. https://databank.worldbank.org/source/world-development-indicators For this scenario analysis, we rely on the shared socioeconomic pathways (SSPs) from the IIASA database (Riahi et al., 2017). source: Riahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O’Neill, B. C., Fujimori, S., … Tavoni, M. (2017). The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environmental Change, 42, 153–168. https://doi.org/10.1016/j.gloenvcha.2016.05.009 Available Online: https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=about The lack of data disaggregated by country and end-use sector in countries of interest was a significant drawback in the data collection process. We make a crucial assumption in this modeling exercise that historical demand profiles in developing countries track the global average per capita transport trends. Therefore, the resulting estimates are indicative and must be interpreted within this analysis's scope given the future is unknown and highly uncertain.