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

Megaproject Front-End Engineering Design crucially impacts lifecycle value, yet often lacks systematic methods to integrate multi-dimensional value drivers, including Environmental, Social, and Governance 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 (circa 2009-2023), merging country-level Environmental, Social, and Governance indicators from the World Bank and house price indices from Organization for Economic Co-operation and Development countries as an economic performance proxy. Analyses included multicollinearity assessment using the Variance Inflation Factor, panel data regression (Pooled Ordinary Least Squares, Fixed Effects, Random Effects with cluster-robust errors), and the development of a machine learning-based Automated Valuation Model using Random Forest with lagged predictors. Uncertainty quantification for the Automated Valuation Model was performed using Conformal Prediction. The Fixed Effects model (preferred via diagnostic tests; within-coefficient of determination = 0.59) identified significant within-country correlations between house price indices and specific Environmental, Social, and Governance and economic factors (e.g., coastal protection, literacy rate, economic/social rights performance, energy imports/use, internet adoption, demographics). The Random Forest Automated Valuation Model achieved strong predictive performance on test data (coefficient of determination = 0.87, root mean squared error = 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 Environmental, Social, and Governance and economic factors using panel regression and machine learning with uncertainty quantification for analysis relevant to megaproject Front-End Engineering Design. This provides essential groundwork for developing future automated, data-driven decision support tools to enhance holistic value assessment.