Pathways to sustainable transportation in G-20 countries: Unveiling the role of green technology, green energy, green finance and digital economy using panel data and machine learning analyses

This framework gives step‑by‑step guidance for modeling how G‑20 countries can move toward sustainable transportation. It looks at how green technology, green energy, green finance, and the digital economy work together. The method combines advanced panel‑data econometrics with machine‑learning algorithms. It organizes the analysis of macro‑level indicators that affect carbon emissions and energy efficiency in the transport sector. The research targets the non‑linear relationships and complex interactions among financial investments, technological innovations, and digital infrastructure. By applying careful data cleaning, feature engineering, and model training, the framework helps pinpoint the policy tools and technology actions needed to decarbonize transport networks. Implementation support includes building multi‑variable regression models, random‑forest regressors, and

gradient‑boosting machines to forecast sustainability outcomes in different economic settings. Overall, the project helps researchers evaluate real data and craft solid, data‑driven recommendations for sustainable urban mobility and infrastructure, linking theoretical policy ideas with quantitative engineering analysis.

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