Markov Chain Based Sustainable Risk Management Framework for Infrastructure Projects: A Dynamic Probabilistic Approach

Infrastructure projects in developing economies face complex, interconnected socioeconomic and environmental risks that traditional static risk management frameworks inadequately address. This study develops a novel Markov Chain based Sustainable Risk Management (MCSRM) framework that integrates dynamic risk state transitions with sustainability principles, enabling predictive risk forecasting across project lifecycles. Utilizing data from eight infrastructure projects in Nigeria's Niger Delta region (n=442 stakeholders), we construct project specific transition probability matrices and employ Bayesian integration to address data scarcity. Results demonstrate that the MCSRM framework achieves 78% predictive accuracy (χ²=12.75, p=0.012) in forecasting risk state evolution, with steady state analysis revealing persistent moderate risk dominance (π₂=0.410.43) across all case studies. Comparative analysis shows the framework outperforms traditional models (Bayesian Networks, Monte Carlo simulations, Bow Tie analysis) in temporal risk tracking (F=4.21, p=0.016, η²=0.12). The Bayesian enhanced model addresses data constraints through expert elicitation synthesis (posterior probability convergence within 0.02 tolerance), demonstrating adaptability for resource limited contexts. Findings indicate that proactive intervention at moderate risk states reduces high-risk escalation probability by 29%, supporting long term project sustainability. This research contributes to dynamic risk management theory by bridging stochastic modeling with Triple Bottom Line sustainability frameworks, offering practitioners an evidence-based tool for adaptive decision making in volatile socio-environmental contexts.