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Optimization and decision support models for deploying negative emissions technologies

Author

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  • Maria Victoria Migo-Sumagang
  • Kathleen B Aviso
  • Dominic C Y Foo
  • Michael Short
  • Purusothmn Nair S Bhasker Nair
  • Raymond R Tan

Abstract

Negative emissions technologies (NETs) will be needed to reach net-zero emissions by mid-century. However, NETs can have wide-ranging effects on land and water availability, food production, and biodiversity. The deployment of NETs will also depend on regional and national circumstances, technology availability, and decarbonization strategies. Process integration (PI) can be the basis for decision support models for the selection, planning, and optimization of the large-scale implementation of NETs. This paper reviews the literature and maps the role of PI in NETs deployment. Techniques such as mathematical programming, pinch analysis (PA), process graphs (P-graphs), are powerful methods for planning NET systems under resource or footprint constraints. Other methods such as multi-criteria decision analysis (MCDA), marginal abatement cost curves, causality maps, and machine learning (ML) are also discussed. Current literature focuses mainly on bioenergy with carbon capture and storage (BECCS) and afforestation/reforestation (AR), but other NETs need to be integrated into future models for large-scale decarbonization.Author summary: Radical approaches will be needed to deal with the ongoing climate crisis. In addition to the reduction of greenhouse gas emissions through strategies such as energy conservation or decarbonization of electricity, negative emissions technologies (NETs) that remove carbon dioxide from the atmosphere will also have to be commercialized. These technologies can offset both historical greenhouse gas emissions as well as residual emissions from sectors that are inherently hard to decarbonize. However, the rapid scale-up of NETs poses the risk of unintended consequences due to their need for energy, land, water, nutrients, and other resources. These requirements also translate to incremental cost and social acceptability aspects of carbon drawdown options. The evaluation of many alternatives is also problematic due to the uncertainties inherent in new technologies. This paper surveys the emerging literature on decision support models that have been developed to deal with these issues and facilitate the large-scale deployment of NETs.

Suggested Citation

  • Maria Victoria Migo-Sumagang & Kathleen B Aviso & Dominic C Y Foo & Michael Short & Purusothmn Nair S Bhasker Nair & Raymond R Tan, 2023. "Optimization and decision support models for deploying negative emissions technologies," PLOS Sustainability and Transformation, Public Library of Science, vol. 2(5), pages 1-17, May.
    • Handle: RePEc:plo:pstr00:0000059
    DOI: 10.1371/journal.pstr.0000059
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