Bioenergy Technologies in Long-Run Climate Change Mitigation: Results from the EMF-33 Study

Daioglou, Vassilis; Rose, Steven K.; Bauer, Nico; Kitous, Alban; Muratori, Matteo; Sano, Fuminori; Fujimori, Shinichiro; Gidden, Matthew J.; Kato, Etsushi; Keramidas, Kimon; Klein, David; Leblanc, Florian; Tsutsui, Junichi; Wise, Marshal; van Vuuren, Detlef P.

doi:10.1007/s10584-020-02799-y

Bioenergy Technologies in Long-Run Climate Change Mitigation: Results from the EMF-33 Study

Journal Article · Mon Aug 24 04:00:00 EDT 2020 · Climatic Change

DOI:https://doi.org/10.1007/s10584-020-02799-y· OSTI ID:1665824

Daioglou, Vassilis; Rose, Steven K.; Bauer, Nico; Kitous, Alban; ; Sano, Fuminori; Fujimori, Shinichiro; Gidden, Matthew J.; Kato, Etsushi; Keramidas, Kimon; Klein, David; Leblanc, Florian; Tsutsui, Junichi; Wise, Marshal; van Vuuren, Detlef P.

Bioenergy is expected to play an important role in long-run climate change mitigation strategies as highlighted by many integrated assessment model (IAM) scenarios. These scenarios, however, also show a very wide range of results, with uncertainty about bioenergy conversion technology deployment and biomass feedstock supply. To date, the underlying differences in model assumptions and parameters for the range of results have not been conveyed. Here we explore the models and results of the 33rd study of the Stanford Energy Modeling Forum to elucidate and explore bioenergy technology specifications and constraints that underlie projected bioenergy outcomes. We first develop and report consistent bioenergy technology characterizations and modeling details. We evaluate the bioenergy technology specifications through a series of analyses—comparison with the literature, model intercomparison, and an assessment of bioenergy technology projected deployments. We find that bioenergy technology coverage and characterization varies substantially across models, spanning different conversion routes, carbon capture and storage opportunities, and technology deployment constraints. Still, the range of technology specification assumptions is largely in line with bottom-up engineering estimates. We then find that variation in bioenergy deployment across models cannot be understood from technology costs alone. Important additional determinants include biomass feedstock costs, the availability and costs of alternative mitigation options in and across end-uses, the availability of carbon dioxide removal possibilities, the speed with which large scale changes in the makeup of energy conversion facilities and integration can take place, and the relative demand for different energy services.

Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE)

DOE Contract Number:: AC36-08GO28308

OSTI ID:: 1665824

Report Number(s):: NREL/JA-5400-77566; MainId:27502; UUID:c9cf9bc4-8b69-4463-83c5-cab7206fdba4; MainAdminID:17416

Journal Information:: Climatic Change, Journal Name: Climatic Change

Country of Publication:: United States

Language:: English

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