United States transportation, economic growth, and CO2 emissions: A Kernel Transfer Entropy analysis

This research analyzes the complex interplay between transportation, economic growth, and CO2 emissions in the United States. Using Transfer Entropy (TE), a method that quantifies directional information flow by measuring the reduction in uncertainty about one time series given the past of another, we examine the bidirectional influences among GDP, CO2 emissions, and the Transportation Service Index (TSI), which includes both freight and passenger transport. Our findings reveal significant causal relationships between CO2 emissions and passenger transport, highlighting the limitations of traditional correlation analysis. TE's ability to uncover causality beyond correlation is a key methodological contribution. This study advances our understanding of these intricate interdependencies by demonstrating bidirectional causality. By examining data from 2000 to 2023, this work contributes to the ongoing discourse on sustainable transportation and its impact on economic development and environmental sustainability. The identified causal link between CO2 and passenger movements suggests targeted interventions for emission reduction. This research emphasizes the importance of moving beyond simple correlation to understand the complex dynamics at play. The use of TE provides a valuable tool for analyzing these relationships and it serves as a foundation for future research in this area.