Non-Linear Effects of ESG Performance on Corporate Tax Avoidance: A Multi-Algorithmic Analysis via Explainable Artificial Intelligence

This study aims to examine whether and how environmental, social, and governance (ESG) performance is related to corporate tax avoidance in a non-linear and threshold-dependent manner using explainable machine learning. Based on 6461 firm-year observations of publicly listed European firms over the 2018–2023 period, this study employs a multi-algorithmic machine-learning classification framework. Model interpretability is achieved through SHAP, which identifies feature importance, marginal effects, interaction patterns, and ESG-related threshold dynamics. The results demonstrate that the ESG–tax relationship is highly non-linear. While the Country and Industry factors establish baseline tax risks, ESG sub-dimensions act as critical firm-level determinants. Specifically, high Corporate Social Responsibility (CSR) and Human Rights scores effectively constrain tax avoidance. In contrast, exceptionally high Management scores correlate with increased tax-avoidance risk. These findings support the legitimacy buffer argument and show that strong governance may also reflect managerial sophistication and capacity for less visible tax planning. The study contributes by revealing non-linear ESG threshold effects and by demonstrating how XAI/SHAP can distinguish between symbolic and substantive sustainability practices in corporate tax behavior.