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Journal Article

Bioenergy technologies in long-run climate change mitigation: results from the EMF-33 study

Authors

Daioglou,  Vassilis
External Organizations;

Rose,  Steven K.
External Organizations;

/persons/resource/Nicolas.Bauer

Bauer,  Nicolas
Potsdam Institute for Climate Impact Research;

Kitous,  Alban
External Organizations;

Muratori,  Matteo
External Organizations;

Sano,  Fuminori
External Organizations;

Fujimori,  Shinichiro
External Organizations;

Gidden,  Matthew J.
External Organizations;

Kato,  Etsushi
External Organizations;

Keramidas,  Kimon
External Organizations;

/persons/resource/david.klein

Klein,  David
Potsdam Institute for Climate Impact Research;

Leblanc,  Florian
External Organizations;

Tsutsui,  Junichi
External Organizations;

Wise,  Marshal
External Organizations;

van Vuuren,  Detlef P.
External Organizations;

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No external resources are shared
Fulltext (public)

24782oa.pdf
(Publisher version), 2MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Daioglou, V., Rose, S. K., Bauer, N., Kitous, A., Muratori, M., Sano, F., Fujimori, S., Gidden, M. J., Kato, E., Keramidas, K., Klein, D., Leblanc, F., Tsutsui, J., Wise, M., van Vuuren, D. P. (2020): Bioenergy technologies in long-run climate change mitigation: results from the EMF-33 study. - Climatic Change, 163, 3, 1603-1620.
https://doi.org/10.1007/s10584-020-02799-y


Cite as: https://publications.pik-potsdam.de/pubman/item/item_24782
Abstract
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.