Bioenergy technologies in long-run climate change mitigation: results from the EMF-33 study
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...
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creator | 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. |
description | 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. |
doi_str_mv | 10.1007/s10584-020-02799-y |
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(NREL), Golden, CO (United States)</creatorcontrib><description>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.</description><identifier>ISSN: 0165-0009</identifier><identifier>EISSN: 1573-1480</identifier><identifier>DOI: 10.1007/s10584-020-02799-y</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Assessing Large-scale Global Bioenergy Deployment for Managing Climate Change (EMF-33) ; Atmospheric Sciences ; Availability ; bioenergy ; Biomass ; Carbon capture and storage ; Carbon dioxide ; Carbon dioxide removal ; Carbon sequestration ; Climate change ; Climate change mitigation ; Climate Change/Climate Change Impacts ; climate policy ; Costs ; Deployment ; Earth and Environmental Science ; Earth Sciences ; Electromagnetic fields ; Energy ; Energy conversion ; Energy modeling ; Environment and Society ; ENVIRONMENTAL SCIENCES ; integrated assessment models ; Intercomparison ; Mitigation ; Modelling ; Raw materials ; Renewable energy ; scenario analysis ; Specifications ; Storage ; technological change ; Technology ; Technology assessment</subject><ispartof>Climatic change, 2020-12, Vol.163 (3), p.1603-1620</ispartof><rights>The Author(s) 2020</rights><rights>The Author(s) 2020. 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(NREL), Golden, CO (United States)</creatorcontrib><title>Bioenergy technologies in long-run climate change mitigation: results from the EMF-33 study</title><title>Climatic change</title><addtitle>Climatic Change</addtitle><description>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. 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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.</description><subject>Assessing Large-scale Global Bioenergy Deployment for Managing Climate Change (EMF-33)</subject><subject>Atmospheric Sciences</subject><subject>Availability</subject><subject>bioenergy</subject><subject>Biomass</subject><subject>Carbon capture and storage</subject><subject>Carbon dioxide</subject><subject>Carbon dioxide removal</subject><subject>Carbon sequestration</subject><subject>Climate change</subject><subject>Climate change mitigation</subject><subject>Climate Change/Climate Change Impacts</subject><subject>climate 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subjects | Assessing Large-scale Global Bioenergy Deployment for Managing Climate Change (EMF-33) Atmospheric Sciences Availability bioenergy Biomass Carbon capture and storage Carbon dioxide Carbon dioxide removal Carbon sequestration Climate change Climate change mitigation Climate Change/Climate Change Impacts climate policy Costs Deployment Earth and Environmental Science Earth Sciences Electromagnetic fields Energy Energy conversion Energy modeling Environment and Society ENVIRONMENTAL SCIENCES integrated assessment models Intercomparison Mitigation Modelling Raw materials Renewable energy scenario analysis Specifications Storage technological change Technology Technology assessment |
title | Bioenergy technologies in long-run climate change mitigation: results from the EMF-33 study |
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