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End date: 2024 × Start date: 2020 × Publisher: Dordrecht [u.a.] : Springer Science + Business Media B.V × Subject: Integrated assessment models × WGL Institute: PIK ×

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    Implications of climate change mitigation strategies on international bioenergy trade
    (Dordrecht [u.a.] : Springer Science + Business Media B.V, 2020) Daioglou, Vassilis; Muratori, Matteo; Lamers, Patrick; Fujimori, Shinichiro; Kitous, Alban; Köberle, Alexandre C.; Bauer, Nico; Junginger, Martin; Kato, Etsushi; Leblanc, Florian; Mima, Silvana; Wise, Marshal; van Vuuren, Detlef P.
    Most climate change mitigation scenarios rely on increased use of bioenergy to decarbonize the energy system. Here we use results from the 33rd Energy Modeling Forum study (EMF-33) to investigate projected international bioenergy trade for different integrated assessment models across several climate change mitigation scenarios. Results show that in scenarios with no climate policy, international bioenergy trade is likely to increase over time, and becomes even more important when climate targets are set. More stringent climate targets, however, do not necessarily imply greater bioenergy trade compared to weaker targets, as final energy demand may be reduced. However, the scaling up of bioenergy trade happens sooner and at a faster rate with increasing climate target stringency. Across models, for a scenario likely to achieve a 2 Â°C target, 10–45 EJ/year out of a total global bioenergy consumption of 72–214 EJ/year are expected to be traded across nine world regions by 2050. While this projection is greater than the present trade volumes of coal or natural gas, it remains below the present trade of crude oil. This growth in bioenergy trade largely replaces the trade in fossil fuels (especially oil) which is projected to decrease significantly over the twenty-first century. As climate change mitigation scenarios often show diversified energy systems, in which numerous world regions can act as bioenergy suppliers, the projections do not necessarily lead to energy security concerns. Nonetheless, rapid growth in the trade of bioenergy is projected in strict climate mitigation scenarios, raising questions about infrastructure, logistics, financing options, and global standards for bioenergy production and trade. © 2020, The Author(s).
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    Bioenergy technologies in long-run climate change mitigation: results from the EMF-33 study
    (Dordrecht [u.a.] : Springer Science + Business Media B.V, 2020) 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.
    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. © 2020, The Author(s).