Tracking sustainability dynamics in EU transport sector: Bayesian latent change-score analysis study

Source abstract

This study examines the structural drivers of greenhouse gas emissions in the European Union’s transport sector by integrating freight activity and logistics performance within a Bayesian Latent Change Score framework. This approach, rarely applied in transport-emissions research, captures dynamic feedback, measurement error, and period-specific effects across three policy intervals (2007–2012, 2012–2018, and 2018–2023). Results show a stable and positive effect of freight activity on emissions, indicating persistent structural inertia in the sector. In contrast, the influence of logistics performance is time-varying, becoming increasingly negative as EU environmental regulation strengthens. This suggests that logistics efficiency contributes to decarbonization only once supportive governance and market conditions are in place. By combining longitudinal EU-27 data with an advanced dynamic modeling strategy, the study provides novel empirical evidence on how logistics systems and policy environments jointly shape transport-sector emission trajectories and informs long-term decarbonization strategies.