Comparative Analysis of Gini Coefficient, GDP, Energy Consumption, and Transportation Modes on CO2 Using NARDL (Nonlinear Distributed Lag Autoregressive Model) for the USA

The significance of the transportation sector, notably in terms of the carbon emission factor, is an undeniable fact. Along with this fact, individuals’ transportation preferences depend on their income levels. In this context, when the issue is considered, the income level in the USA pushes people toward cheap air travel. The main reason for this is that it is cheap, accessible, and transports one to their destinations quickly. Thus, from the perspective of road transportation, bus transportation is popular among the public. The reason why both air and road transportation modes are empirically evaluated together through income distribution is due to the preference of the US people. In this context, the effectiveness of active transportation on both air and highways in the USA from 1975 to 2023 is investigated by taking into consideration the income distribution. Empirical findings obtained through the FMOLS, DOLS, CCR, and NARDL models demonstrate that all independent variables, including GDP, energy use, air transportation, and the Gini coefficient, affect carbon dioxide emissions. In addition, wavelet analysis is performed to comprehend the form of and fluctuations in the series, which are vital to monitoring the periodical changes.