Planning Negative Emissions Technologies Portfolios Under Neutrosophic Environment

Abstract

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

DOI: 10.5281/zenodo.15127309