Greenhouse gas emissions and road infrastructure in Europe: A machine learning analysis

Abstract

This paper explores the determinants of greenhouse gas (GHG) emissions in Europe, focusing on transportation-related variables. By combining classical econometric models with Machine Learning (ML) techniques, we analyze data spanning from 2013 to 2021. The empirical findings highlight the complex relationship between newer passenger cars and GHG emissions, noting the significant impact of their production and increased usage. Conversely, the adoption of alternative fuel vehicles is found to significantly reduce emissions. This is further supported by ML models, which emphasize the critical role of car density and alternative fuel vehicles in determining emissions. Policy implications suggest the need for targeted interventions, including the promotion of electric and hybrid vehicles, enhancements in transportation infrastructure, and the implementation of economic incentives for clean technologies.