P-graph and Monte Carlo simulation approach for sustainable and risk-managed CDR portfolios.

Negative emission technologies (NETs) that perform carbon dioxide removal (CDR) are now part of the decarbonization strategies to reach net-zero emissions. However, NETs will consume resources that will compete with other societal priorities. Thus, NET portfolios that provide a technology mix are better for sustainability and risk management. This study proposes a two-step approach to optimize and check the robustness of NET portfolios, particularly for industrial-scale applications where resource availability fluctuates due to variations in energy, water, and fertilizer supply. The first step involves the process graph (P-graph) framework to generate optimal and near-optimal solution structures at minimum cost. The second step measures the probability of failure of these solutions against resource availability variations through Monte Carlo simulation. By comparing the cost and probability of failure, decision-makers can select a recommended solution that strikes a balance between robustness and cost. The proposed approach is demonstrated in two case studies involving NET portfolios. The first case study investigates the performance of optimal and near-optimal solutions of NET portfolios, while the second case study focuses on bioenergy with carbon capture and storage portfolios using different feedstocks. In both cases, the results show that near-optimal solutions with higher costs but lower probabilities of failure exist, enabling decision-makers to weigh robustness against cost. This study contributes to developing and analyzing robust decarbonization decision support models, addressing the critical need for sustainable and risk-managed NET portfolios. [Display omitted] • A two-step method for generating robust CDR portfolios is proposed. • The first step uses P-graph to generate solution structures. • The second step subjects the solution structures to Monte Carlo simulation. • The method shows the tradeoff between the robustness and cost. [ABSTRACT FROM AUTHOR]