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# Second-generation purpose-grown biomass from specialized ligno-cellulosic grassy and woody bioenergy crops, such as miscanthus, poplar, and eucalyptus.
# Second-generation purpose-grown biomass from specialized ligno-cellulosic grassy and woody bioenergy crops, such as miscanthus, poplar, and eucalyptus.


To represent supply of purpose-grown bioenergy from the land-use sector, REMIND can either be run in standalone mode or soft-coupled to the land-use model MAgPIE (Model of Agricultural Production and its Impact on the Environment) <ref>Lotze-Campen H, Müller C, Bondeau A, et al (2008) Global food demand, productivity growth, and the scarcity of land and water resources: a spatially explicit mathematical programming approach. Agricultural Economics 39:325–338. doi: 10.1111/j.1574-0862.2008.00336.x</ref>; <ref>Popp A, Lotze-Campen H, Bodirsky B (2010) Food consumption, diet shifts and associated non-CO2 greenhouse gases from agricultural production. Global Environmental Change 20:451–462. doi: 10.1016/j.gloenvcha.2010.02.001</ref>; <ref>Lotze-Campen H, Popp A, Beringer T, et al (2010) Scenarios of global bioenergy production: The trade-offs between agricultural expansion, intensification and trade. Ecological Modelling 221:2188–2196. doi: 10.1016/j.ecolmodel.2009.10.002</ref>, see also Section “Land Use” . In standalone mode, REMIND draws on an  emulator of MAgPIE, which describes bioenergy supply costs and total agricultural emissions as a function of bioenergy demand, as described in detail in Klein <ref>Klein D, Humpenöder F, Bauer N, et al (2014) The global economic long-term potential of modern biomass in a climate-constrained world. Environ Res Lett 9:074017. doi: 10.1088/1748-9326/9/7/074017</ref>. The supply curves capture the time, scale and region dependent change of bioenergy production costs, as well as path dependencies resulting from past land conversions and induced technological changes in the land-use sector, as represented in MAgPIE. Ligno-cellulosic agricultural and forest residues are based on low-cost bioenergy supply options. Their potential is assumed to increase from 20 EJ/yr in 2005 to 70 EJ/yr in 2100 <ref>Chum H, Faaij A, Moreira J, et al (2011) Bioenergy. In: IPCC Special Report on Renewable Energy Sources and Climate Change Mitigation [O. Edenhofer, R. Pichs-Madruga, Y. Sokona, K. Seyboth, P. Matschoss, S. Kadner, T. Zwickel, P</ref>, based on Haberl <ref> Haberl et al. (2010)</ref>.
To represent supply of purpose-grown bioenergy from the land-use sector, REMIND can either be run in standalone mode or soft-coupled to the land-use model MAgPIE (Model of Agricultural Production and its Impact on the Environment) <ref>Lotze-Campen H, Müller C, Bondeau A, et al (2008) Global food demand, productivity growth, and the scarcity of land and water resources: a spatially explicit mathematical programming approach. Agricultural Economics 39:325–338. doi: 10.1111/j.1574-0862.2008.00336.x</ref>; <ref>Popp A, Lotze-Campen H, Bodirsky B (2010) Food consumption, diet shifts and associated non-CO2 greenhouse gases from agricultural production. Global Environmental Change 20:451–462. doi: 10.1016/j.gloenvcha.2010.02.001</ref>; <ref>Lotze-Campen H, Popp A, Beringer T, et al (2010) Scenarios of global bioenergy production: The trade-offs between agricultural expansion, intensification and trade. Ecological Modelling 221:2188–2196. doi: 10.1016/j.ecolmodel.2009.10.002</ref>, see also Section “Land Use” . In standalone mode, REMIND draws on an  emulator of MAgPIE, which describes bioenergy supply costs and total agricultural emissions as a function of bioenergy demand, as described in detail in Klein <ref>Klein D, Humpenöder F, Bauer N, et al (2014) The global economic long-term potential of modern biomass in a climate-constrained world. Environ Res Lett 9:074017. doi: 10.1088/1748-9326/9/7/074017</ref>. The supply curves capture the time, scale and region dependent change of bioenergy production costs, as well as path dependencies resulting from past land conversions and induced technological changes in the land-use sector, as represented in MAgPIE. Ligno-cellulosic agricultural and forest residues are based on low-cost bioenergy supply options. Their potential is assumed to increase from 20 EJ/yr in 2005 to 70 EJ/yr in 2100 <ref>Chum H, Faaij A, Moreira J, et al (2011) Bioenergy. In: IPCC Special Report on Renewable Energy Sources and Climate Change Mitigation [O. Edenhofer, R. Pichs-Madruga, Y. Sokona, K. Seyboth, P. Matschoss, S. Kadner, T. Zwickel, P</ref>, based on Haberl <ref>Haberl H, Beringer T, Bhattacharya SC, et al (2010) The global technical potential of bio-energy in 2050 considering sustainability constraints. Current Opinion in Environmental Sustainability 2:394–403. doi: 10.1016/j.cosust.2010.10.007</ref>.


In REMIND, we assume that the use of traditional biomass (supplied by residues) is phased out, as modern and less harmful fuels are increasingly used with rising  incomes <ref>Sims et al. 2010</ref>. We also assume that first generation modern biofuels are phased out, reflecting their high costs and accounting for concerns about land-use impacts, co-emissions, and competition with food production from first-generation biofuels <ref>Fargione et al. 2008</ref>; <ref>Searchinger et al. 2008</ref>. As a consequence, the main sources of bioenergy in REMIND scenarios are second-generation purpose-grown biomass and ligno-cellulosic agricultural and forestry residues.  
In REMIND, we assume that the use of traditional biomass (supplied by residues) is phased out, as modern and less harmful fuels are increasingly used with rising  incomes <ref>Sims REH, Mabee W, Saddler JN, Taylor M (2010) An overview of second generation biofuel technologies. Bioresource Technology 101:1570–1580. doi: 10.1016/j.biortech.2009.11.046</ref>. We also assume that first generation modern biofuels are phased out, reflecting their high costs and accounting for concerns about land-use impacts, co-emissions, and competition with food production from first-generation biofuels <ref>Fargione J, Hill J, Tilman D, et al (2008) Land Clearing and the Biofuel Carbon Debt. Science 319:1235–1238. doi: 10.1126/science.1152747</ref>; <ref>Searchinger T, Heimlich R, Houghton RA, et al (2008) Use of U.S. Croplands for Biofuels Increases Greenhouse Gases Through Emissions from Land-Use Change. Science 319:1238–1240. doi: 10.1126/science.1151861</ref>. As a consequence, the main sources of bioenergy in REMIND scenarios are second-generation purpose-grown biomass and ligno-cellulosic agricultural and forestry residues.  


To further reflect concerns about the sustainability of large-scale deployment of lingo-cellulosic bioenergy, REMIND assumes an ad valorem tax on bioenergy. The tax increases linearly from 0 to 100% between 2030 and 2100 and is applied to the bioenergy price given by the emulator (see above). Based on the current public debate, we consider this tax to be a reflection of the potential institutional limitations on the widespread-use of bioenergy.
To further reflect concerns about the sustainability of large-scale deployment of lingo-cellulosic bioenergy, REMIND assumes an ad valorem tax on bioenergy. The tax increases linearly from 0 to 100% between 2030 and 2100 and is applied to the bioenergy price given by the emulator (see above). Based on the current public debate, we consider this tax to be a reflection of the potential institutional limitations on the widespread-use of bioenergy.

Revision as of 14:07, 3 February 2017

Model Documentation - REMIND-MAgPIE

Corresponding documentation
Previous versions
Model information
Model link
Institution Potsdam Institut für Klimafolgenforschung (PIK), Germany, https://www.pik-potsdam.de.
Solution concept General equilibrium (closed economy)MAgPIE: partial equilibrium model of the agricultural sector;
Solution method OptimizationMAgPIE: cost minimization;
Anticipation

REMIND models three types of bioenergy feedstocks:

  1. First-generation biomass produced from sugar, starch, and oilseeds (typically small in quantity, based on an exogenous scenario);
  2. Ligno-cellulosic residues from agriculture and forest; and
  3. Second-generation purpose-grown biomass from specialized ligno-cellulosic grassy and woody bioenergy crops, such as miscanthus, poplar, and eucalyptus.

To represent supply of purpose-grown bioenergy from the land-use sector, REMIND can either be run in standalone mode or soft-coupled to the land-use model MAgPIE (Model of Agricultural Production and its Impact on the Environment) [1]; [2]; [3], see also Section “Land Use” . In standalone mode, REMIND draws on an emulator of MAgPIE, which describes bioenergy supply costs and total agricultural emissions as a function of bioenergy demand, as described in detail in Klein [4]. The supply curves capture the time, scale and region dependent change of bioenergy production costs, as well as path dependencies resulting from past land conversions and induced technological changes in the land-use sector, as represented in MAgPIE. Ligno-cellulosic agricultural and forest residues are based on low-cost bioenergy supply options. Their potential is assumed to increase from 20 EJ/yr in 2005 to 70 EJ/yr in 2100 [5], based on Haberl [6].

In REMIND, we assume that the use of traditional biomass (supplied by residues) is phased out, as modern and less harmful fuels are increasingly used with rising incomes [7]. We also assume that first generation modern biofuels are phased out, reflecting their high costs and accounting for concerns about land-use impacts, co-emissions, and competition with food production from first-generation biofuels [8]; [9]. As a consequence, the main sources of bioenergy in REMIND scenarios are second-generation purpose-grown biomass and ligno-cellulosic agricultural and forestry residues.

To further reflect concerns about the sustainability of large-scale deployment of lingo-cellulosic bioenergy, REMIND assumes an ad valorem tax on bioenergy. The tax increases linearly from 0 to 100% between 2030 and 2100 and is applied to the bioenergy price given by the emulator (see above). Based on the current public debate, we consider this tax to be a reflection of the potential institutional limitations on the widespread-use of bioenergy.









  1. ↑ Lotze-Campen H, Müller C, Bondeau A, et al (2008) Global food demand, productivity growth, and the scarcity of land and water resources: a spatially explicit mathematical programming approach. Agricultural Economics 39:325–338. doi: 10.1111/j.1574-0862.2008.00336.x
  2. ↑ Popp A, Lotze-Campen H, Bodirsky B (2010) Food consumption, diet shifts and associated non-CO2 greenhouse gases from agricultural production. Global Environmental Change 20:451–462. doi: 10.1016/j.gloenvcha.2010.02.001
  3. ↑ Lotze-Campen H, Popp A, Beringer T, et al (2010) Scenarios of global bioenergy production: The trade-offs between agricultural expansion, intensification and trade. Ecological Modelling 221:2188–2196. doi: 10.1016/j.ecolmodel.2009.10.002
  4. ↑ Klein D, Humpenöder F, Bauer N, et al (2014) The global economic long-term potential of modern biomass in a climate-constrained world. Environ Res Lett 9:074017. doi: 10.1088/1748-9326/9/7/074017
  5. ↑ Chum H, Faaij A, Moreira J, et al (2011) Bioenergy. In: IPCC Special Report on Renewable Energy Sources and Climate Change Mitigation [O. Edenhofer, R. Pichs-Madruga, Y. Sokona, K. Seyboth, P. Matschoss, S. Kadner, T. Zwickel, P
  6. ↑ Haberl H, Beringer T, Bhattacharya SC, et al (2010) The global technical potential of bio-energy in 2050 considering sustainability constraints. Current Opinion in Environmental Sustainability 2:394–403. doi: 10.1016/j.cosust.2010.10.007
  7. ↑ Sims REH, Mabee W, Saddler JN, Taylor M (2010) An overview of second generation biofuel technologies. Bioresource Technology 101:1570–1580. doi: 10.1016/j.biortech.2009.11.046
  8. ↑ Fargione J, Hill J, Tilman D, et al (2008) Land Clearing and the Biofuel Carbon Debt. Science 319:1235–1238. doi: 10.1126/science.1152747
  9. ↑ Searchinger T, Heimlich R, Houghton RA, et al (2008) Use of U.S. Croplands for Biofuels Increases Greenhouse Gases Through Emissions from Land-Use Change. Science 319:1238–1240. doi: 10.1126/science.1151861
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