Sustainable tourism for climate change and environmental sustainability in Tunisia: Evidence from a novel measure, nonlinear modeling, and wavelet coherence.

Abstract

This study examines the asymmetric impact of international tourism on CO2 emissions and environmental sustainability in Tunisia. Using the Nonlinear Autoregressive Distributed Lag (NARDL) model within a Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) framework, we construct a composite index via Principal Component Analysis (PCA). Robustness checks include Fully Modified Ordinary Least Squares (FMOLS), Bias-corrected Wavelet coherence, and asymmetric causality analysis through Vector Autoregression (VAR). Findings reveal that tourism traffic asymmetrically affects environmental sustainability, except for carbon emissions, even under nonlinear Granger causality analysis. Trade openness also exerts asymmetric effects, supporting the Pollution Haven hypothesis. Policy recommendations highlight the need for smart strategies such as mobile applications and taxation to track tourism-related carbon footprints, foster youth-led tourism businesses, address brain drain, and advance a circular economy. Sustainable tourism, alongside key structural factors, plays a vital role in shaping long-term environmental quality. Strategic agricultural development, improved governance, and the efficient use of renewable energy are crucial. Enhancing energy security, reducing fossil fuel dependence, and promoting green technology investments are necessary steps. This study contributes uniquely by compiling and updating a dataset based on six environmental criteria-air quality, energy management, biodiversity, health, population pressure, and water resources-while employing advanced econometric techniques.