Artificial intelligence and environmental sustainability: assessing the total factor productivity – ecological footprint relationship in MENA economies through the PTAR model

Purpose The objective of this paper is to study the non-linear impact of total factor productivity on the ecological footprint. Design/methodology/approach The study examines data from 13 MENA countries spanning 2000–2023, employing a Panel Threshold Autoregression (PTAR) approach. For robustness checks, both linear and quadratic GMM estimation methods are applied. Findings The results show that the total factor productivity has a non-linear effect on the ecological footprint. Originality/value To our knowledge, this is the first study to empirically analyze the nonlinear impact of total factor productivity (TFP) on the ecological footprint using a Panel Threshold Autoregression (PTAR) approach. In addition, the study examines how the interaction between artificial intelligence and TFP influences the ecological footprint.