Planning Negative Emissions Technologies Portfolios Under Neutrosophic Environment

The deployment of large-scale Negative Emission Technologies (NETs) is now considered a key strategy in climate change mitigation due to their capability to counteract emissions biophysically and economically. However, large-scale NETs will require resources such as land, water, and energy that are limited and uncertainties are present in such technologies. Managing such uncertainties is critical in NET portfolio modeling because they significantly impact the resulting optimized solutions. Existing studies often fail to adequately address these uncertainties, particularly in portfolio optimization, as traditional models often rely on post-optimization sensitivity analysis that does not fully capture the inherent uncertainties in NET performance. This work addresses the research gaps by developing a neutrosophic linear programming (NeLP) model that incorporates membership, non-membership, and indeterminacy components to represent the uncertainties in resource availability, CDR capacities, and synergistic interactions. Unlike previous models, the current novel NeLP model applies different models of uncertainty as neutrosophic sets and adjust expert’s risk tolerance levels providing a more flexible and realistic approach to NET portfolio optimization . The model is demonstrated in two case studies. The results suggest that the carbon dioxide removal (CDR) levels of various options have different behaviors across different risk settings, as illustrated by the two case studies. The changing optimal solutions in response to shifts in risk appetite provide decision-makers with valuable insight into selecting NETs with significant CDR potential for reducing large-scale greenhouse gas emissions.