The impact of air and rail transportation on environmental pollution in Turkey: a Fourier cointegration analysis

• Published in: Management of environmental quality 2024-11, Vol.35 (8), p.1836-1857
• Main Authors: Beşer, Nazife Özge, Tütüncü, Asiye, Beşer, Murat, Magazzino, Cosimo
• Format: Article
• Language: English
• Subjects: Air pollution; Air transportation; Aircraft; Carbon dioxide; Economic development; Economic growth; Emissions; Environmental policy; Environmental regulations; Environmental testing; Greenhouse gases; Impact analysis; Learning algorithms; Locomotives; Machine learning; Pollution; Pollution dispersion; Rail transportation; Robustness; Statistical analysis
• ISSN: 1477-7835; 1758-6119
• DOI: 10.1108/MEQ-02-2024-0079

PurposeThis paper aims to investigate the influence of air and rail transportation on pollution in Turkey from 1970 to 2020.Design/methodology/approachFourier Autoregressive Distributive Lags (ADL) and Fourier Fractional ADL cointegration tests (Banerjee et al., 2017; Ilkay et al., 2021) are employed to analyze the relationship be-tween the variables. Cointegration tests that take into account soft transitions under structural changes are implemented. Structural change issues are crucial for this topic since the changes in countries’ environmental policies and transportation habits are shaped by the decisions taken in relation to environmental regulations. Finally, for robustness purposes, we tested the estimated equation with a completely different methodology. Thus, a Machine Learning (ML) analysis is conducted, through a Ridge Regression (RR).FindingsThe findings obtained by applying Fourier Autoregressive Distributive Lags (FADL) and Fourier Fractional ADL cointegration tests, which can control for structural changes, reveal the existence of a long-term relationship between the variables. In addition, FMOLS estimates emphasize that economic growth and air transport can lead to increased pollution in the long run, while rail transport reduces it. Moreover, the statistically significant trigonometric terms indicate the existence of a smooth structural change among the variables. Robustness checks are performed through a Machine Learning (ML) analysis, which roughly confirms the previous results.Originality/valueTo our knowledge, existing research in Turkey focuses mainly on road transport, while the impact of rail and air transport on pollution has not yet been investigated. As such, this study will be a significant addition to the academic literature.