Study Objective: - This study explores how uncertainty in trade policy affects firm performance in the Environmental, Social, and Governance (ESG) domains, with an emphasis on the mediating role of supply chain resilience and the moderated role of top management risk preference in this relationship. This is analyzed using a data sample of Egyptian non-financial firms listed on the Egyptian Stock Exchange (EGX) over the period 2015–2024. Methodology:- The study relies on three advanced statistical methodologies in an integrated framework to ensure robust validity and reliability of the conclusions: 1. Two-Stage Least Squares (2SLS) to address endogeneity • Purpose: To mitigate endogeneity problems that arise when the explanatory variable (trade-policy uncertainty) is correlated with the error term in the regression model, resulting in biased and inconsistent coefficient estimates. • Mechanism: 2SLS handles endogeneity by using instrumental variables (IVs) that satisfy two essential conditions: i. Relevance: The instrument is strongly correlated with the endogenous explanatory variable (trade-policy uncertainty). ii. Exclusion: The instrument affects the dependent variable (ESG disclosure quality) only through its impact on the endogenous explanatory variable, and not via other channels. • Application: This approach provides more reliable estimates by addressing endogeneity concerns in estimation. 2. Propensity Score Matching (PSM) to address selection bias • Purpose: To mitigate selection bias arising from non-random exposure to higher trade-policy uncertainty among listed firms. • Rationale: Firms exposed to higher levels of trade-policy uncertainty may differ systematically from those exposed to lower uncertainty in ways that also affect ESG disclosure quality (e.g., size, profitability, ownership structure). PSM constructs a matched control group that is similar to the treated group on observed characteristics, except for exposure to high uncertainty. • Application: Using PSM techniques, a matched control group is created that is comparable to the treated group on observed covariates, thereby reducing selection bias in the estimates. 3. Heckman Correction Procedures to address reverse causality and self-selection • Purpose: To address endogeneity arising from reverse causality and self-selection by jointly modeling the selection decision and the outcome. • Steps: • Stage 1: Estimate a Probit model for the binary selection decision. •Stage 2: Compute the Inverse Mills Ratio (λ) from the selection stage. • Stage 3: Include the Inverse Mills Ratio as an additional repressor in the outcome regression model for disclosure quality. This framework provides a comprehensive solution to endogeneity and selection bias, helping to uncover a potentially nonlinear relationship between trade-policy uncertainty and ESG governance indicators. Study Results: - • The results reveal a steep inverted U-shaped relationship between uncertainty in trade policy and Environmental, Social, and Governance (ESG) indicators, indicating that moderate uncertainty encourages the adoption of sustainable practices, while extreme policy volatility constrains these investments. • Additional analysis shows that supply chain resilience partially mediates this relationship, suggesting that firms facing higher levels of trade-policy uncertainty improve their operational adaptability, thereby enhancing ESG outcomes. • The moderating effect of executive risk preference is also confirmed, implying that risk-averse leadership amplifies the positive impact of trade-policy uncertainty on ESG governance at moderate levels, but exacerbates the decline once uncertainty surpasses a critical threshold. • Variance analysis reveals important institutional differences: non-state-owned enterprises exhibit greater sensitivity to changes in uncertainty compared with state-owned enterprises, while lifecycle analysis indicates that distressed firms display more pronounced responses to changes in policy uncertainty.