Artificial Intelligence Readiness and Carbon Productivity in Freight Transport: Evidence from OECD Countries

Pınar Demir; Ayşe Güngör; Çisil Erkan Bal

doi:10.58905/whoosh.v1i2.789

Authors

Pınar Demir Nigde Omer Halisdemir University, Institute of Social Sciences, Department of Political Science and International Relations, Türkiye
Ayşe Güngör Giresun University, Kadir Karabaş School of Applied Sciences, Department of Logistics Management, Türkiye. ayse.gungor@giresun.edu.tr
Çisil Erkan Bal Karadeniz Technical University, Department of Management and Organization, Araklı Ali Cevat Özyurt Vocational School, Türkiye

DOI:

https://doi.org/10.58905/whoosh.v1i2.789

Keywords:

Artificial intelligence readiness, freight transport, carbon productivity, green logistics, OECD countries

Abstract

This study examines whether national artificial intelligence (AI) readiness is associated with a freight-oriented transport carbon productivity proxy in OECD countries. Using a strongly balanced panel of 28 countries for 2020-2022, the dependent variable relates road-and-rail freight transport work, measured in million tonne-kilometers, to national transport-related CO2 emissions. Government AI Readiness is the main explanatory variable, with GDP per capita, renewable energy use, internet penetration, urbanization, and trade openness included as controls. Pooled OLS, fixed-effects (FE), and random-effects (RE) models are estimated with country-clustered standard errors. Correlated random effects/Mundlak, wild cluster bootstrap, and exploratory interaction analyses are added to assess estimator choice, small-cluster inference, and possible complementarity with renewable energy and internet connectivity. The available 2020-2022 data do not identify a statistically significant contemporaneous association between AI readiness and the carbon-productivity proxy. This conclusion is unchanged under within-between decomposition and wild cluster bootstrap inference, while the interaction terms are also statistically insignificant. These null estimates should not be interpreted as evidence of no effect because the short panel, limited within-country variation, construct and outcome measurement limitations, and potentially lagged effects constrain statistical identification.

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