Integrating Environmental, Social, and Governance Factors in Megaproject Front-End Design: A Quantitative Framework with Prediction and Uncertainty Assessment

Megaproject Front-End Engineering Design (FEED) crucially impacts lifecycle value, yet often lacks systematic methods to integrate multi-dimensional value drivers, including Environmental, Social, and Governance (ESG) factors, using advanced analytics. This research aimed to develop foundational knowledge and a methodological framework to address this gap. This study employed a quantitative approach using panel data (c. 2009-2023), merging country-level ESG indicators (World Bank) and house price indices (HPI) from OECD countries as an economic performance proxy. Analyses included multicollinearity assessment (Variance Inflation Factor), panel data regression (Pooled OLS, Fixed Effects, Random Effects with cluster-robust errors), and the development of a machine learning-based Automated Valuation Model (AVM) using Random Forest with lagged predictors. Uncertainty quantification (UQ) for the AVM was performed using Conformal Prediction. The Fixed Effects model (preferred via diagnostic tests; within-R² = 0.59) identified significant within-country correlations between HPI and specific ESG and economic factors (e.g., coastal protection, literacy rate, economic/social rights performance, energy imports/use, internet adoption, demographics). The Random Forest AVM achieved strong predictive performance on test data (R² = 0.87, RMSE = 6.88), with lagged indicators contributing significantly. Conformal Prediction reliably generated 90% prediction intervals with 90.8% empirical coverage. The study demonstrates the feasibility of a quantitative framework integrating diverse ESG and economic factors using panel regression and machine learning with UQ for analysis relevant to megaproject FEED. This provides essential groundwork for developing future automated, data-driven decision support tools to enhance holistic value assessment.