Fuzzy mixed-integer linear and quadratic programming models for planning negative emissions technologies portfolios with synergistic interactions

Abstract

Fossil fuels will still be used in the coming decades following the agreement to phase down coal and phase out fossil fuels subsidies in COP26. Negative Emissions Technologies (NETs) can offset the emissions caused by the residual use of fossil fuels. NETs such as Afforestation/Reforestation (AR), biochar application (BC), Soil Carbon Sequestration (SCS) Bioenergy with Carbon Capture and Storage (BECCS), Enhanced Weathering (EW), and Direct Air Carbon Capture and Storage (DACCS) remove CO 2 from the atmosphere and store it in reservoirs. The large-scale implementation of NETs will incur resource footprints; thus, NETs should operate within the Planetary Boundaries. No single NET can sustainably deliver the required CO 2 removal, and optimized regional portfolios of NETs can reduce the biophysical and social impacts of their implementation. In such portfolios, some NETs may have synergistic effects and consume fewer resources when implemented together. In this work, fuzzy mixed-integer programming models are developed to optimize the CO 2 removal (CDR) potential of a NETs portfolio while considering possible synergistic interactions, as well as uncertainties in the resource constraints and negative emission targets. The models are illustrated with a regional case study that demonstrates the changes in the optimal NETs portfolio depending on different scenarios. • Regional NETs portfolios are more sustainable than single NETs. • NETs can have synergistic resource interactions when implemented together. • An optimization model is developed to capitalize on these interactions. • Parametric and scenario constraints lead to different optimal portfolios.