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A multi-period model for optimizing negative emission technology portfolios with economic and carbon value discount rates

Maria Victoria Migo-Sumagang, Raymond R. Tan and Kathleen B. Aviso

Energy, 2023, vol. 275, issue C

Abstract: Regionalized Negative Emission Technology (NET) portfolios can sustainably support climate change mitigation. However, current studies focus on individual technologies that may be unsustainable on the large scale. The existing studies also do not consider multiple environmental footprints, technological readiness, and ecological discount rates, which are critical for addressing the future environmental damages of climate change. This study addresses these research gaps by using mixed integer linear programming to develop a multi-footprint, multi-period, optimization model for NET portfolios. The model is illustrated with a case study in the Southeast Asian region until 2100. The results show varying portfolios, with the net present value ranging from USD 516–2783 B, and the negative emissions from 30.42 to 79.56 Gt CO2. The cheaper, technologically ready options like afforestation and biochar are selected earlier in the portfolio, while the initially expensive, emerging technologies like enhanced weathering and direct air capture come later. Land and water are the limiting resources. Both carbon value discounting and using a high initial carbon price support the early deployment of NETs for climate change mitigation. The model currently does not consider synergistic/antagonistic interactions between NETs, multi-objective optimization, and parametric uncertainties, which can be included in future works.

Keywords: Carbon dioxide removal (CDR); Decarbonization; Discounting; Mixed integer linear programming (MILP); Negative emission technologies (NETs); Optimization (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:275:y:2023:i:c:s0360544223008393

DOI: 10.1016/j.energy.2023.127445

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