A scenario-based optimization model integrating MILP and uncertainty modeling for CO2 reduction investments in construction

Abstract

With stricter sustainability and reporting requirements, construction firms face increasing pressure to reduce carbon emissions while maintaining cost efficiency. This study examines a European contractor’s company-specific objective of reducing scope 1 and 2 carbon dioxide (CO2) emissions by 50% by 2030. We develop a scenario-based mixed-integer linear programming (MILP) framework in which Monte Carlo sampling generates alternative realizations of uncertain investment costs and emissions-reduction effects. The joint formulation determines scenario-specific investment plans and minimizes their average discounted net cost subject to an empirical scenario-coverage criterion. The resulting scenario-specific plans are summarized through investment frequencies and their empirical scenario coverage. Under the case assumptions, the 50% target did not meet the required 95% empirical scenario-coverage criterion. A 45% target was the highest tested target that met this criterion, with an average discounted net cost of €55,535. In case-specific descriptive comparisons, the average reported cost was 65.7% lower than that of the Marginal Abatement Cost (MAC) schedule and 88.9% lower than that of the heuristic approach. The framework supports internal investment planning and the documentation of actions relevant to reporting and certification contexts, but it does not, by itself, establish regulatory compliance.