Land-use - MESSAGE-GLOBIOM: Difference between revisions

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Land-use dynamics are modelled with the GLOBIOM (GLobal BIOsphere Management) model, which is a recursive-dynamic partial-equilibrium model (Havlík et al., 2011 [[CiteRef::MSG-GLB_havlik_global_2011]]; Havlík et al., 2014 [[CiteRef::MSG-GLB_havlik_climate_2014]]). GLOBIOM represents the competition between different land-use based activities. It includes a bottom-up representation of the agricultural, forestry and bio-energy sector, which allows for the inclusion of detailed grid-cell information on biophysical constraints and technological costs, as well as a rich set of environmental parameters, incl. comprehensive AFOLU (agriculture, forestry and other land use) GHG emission accounts and irrigation water use. Its spatial equilibrium modelling approach represents bilateral trade based on cost competitiveness. For spatially explicit projections of the change in afforestation, deforestation, forest management, and their related CO2 emissions, GLOBIOM is coupled with the G4M (Global FORest Model) model (Kindermann et al., 2006 [[CiteRef::MSG-GLB_kindermann_predicting_2006]]; Kindermann et al., 2008 [[CiteRef::MSG-GLB_kindermann_global_2008]]; Gusti, 2010 [[CiteRef::MSG-GLB_gusti_algorithm_2010]]). The spatially explicit G4M model compares the income of managed forest (difference of wood price and harvesting costs, income by storing carbon in forests) with income by alternative land use on the same place, and decides on afforestation, deforestation or alternative management options. As outputs, G4M provides estimates of forest area change, carbon uptake and release by forests, and supply of biomass for bioenergy and timber. (Fricko et al., 2016 [[CiteRef::MSG-GLB_fricko_marker_2016]])
Land-use dynamics are modelled with the GLOBIOM (GLobal BIOsphere Management) model, which is a recursive-dynamic partial-equilibrium model (Havlík et al., 2011 [[CiteRef::MSG-GLB_havlik_global_2011]]; Havlík et al., 2014 [[CiteRef::MSG-GLB_havlik_climate_2014]]). GLOBIOM represents the competition between different land-use based activities. It includes a bottom-up representation of the agricultural, forestry and bio-energy sector, which allows for the inclusion of detailed grid-cell information on biophysical constraints and technological costs, as well as a rich set of environmental parameters, incl. comprehensive AFOLU (agriculture, forestry and other land use) GHG emission accounts and irrigation water use. Its spatial equilibrium modelling approach represents bilateral trade based on cost competitiveness. For spatially explicit projections of the change in afforestation, deforestation, forest management, and their related CO2 emissions, GLOBIOM is coupled with the G4M (Global FORest Model) model (Kindermann et al., 2006 [[CiteRef::MSG-GLB_kindermann_predicting_2006]]; Kindermann et al., 2008 [[CiteRef::MSG-GLB_kindermann_global_2008]]; Gusti, 2010 [[CiteRef::MSG-GLB_gusti_algorithm_2010]]). The spatially explicit G4M model compares the income of managed forest (difference of wood price and harvesting costs, income by storing carbon in forests) with income by alternative land use on the same place, and decides on afforestation, deforestation or alternative management options. As outputs, G4M provides estimates of forest area change, carbon uptake and release by forests, and supply of biomass for bioenergy and timber. (Fricko et al., 2016 [[CiteRef::MSG-GLB_fricko_marker_2016]])

Revision as of 16:11, 23 June 2020

Model Documentation - MESSAGE-GLOBIOM

Corresponding documentation
Previous versions
Model information
Model link
Institution International Institute for Applied Systems Analysis (IIASA), Austria, http://data.ene.iiasa.ac.at.
Solution concept General equilibrium (closed economy)
Solution method Optimization
Anticipation

Land-use dynamics are modelled with the GLOBIOM (GLobal BIOsphere Management) model, which is a recursive-dynamic partial-equilibrium model (Havlík et al., 2011 MSG-GLB_havlik_global_2011; Havlík et al., 2014 MSG-GLB_havlik_climate_2014). GLOBIOM represents the competition between different land-use based activities. It includes a bottom-up representation of the agricultural, forestry and bio-energy sector, which allows for the inclusion of detailed grid-cell information on biophysical constraints and technological costs, as well as a rich set of environmental parameters, incl. comprehensive AFOLU (agriculture, forestry and other land use) GHG emission accounts and irrigation water use. Its spatial equilibrium modelling approach represents bilateral trade based on cost competitiveness. For spatially explicit projections of the change in afforestation, deforestation, forest management, and their related CO2 emissions, GLOBIOM is coupled with the G4M (Global FORest Model) model (Kindermann et al., 2006 MSG-GLB_kindermann_predicting_2006; Kindermann et al., 2008 MSG-GLB_kindermann_global_2008; Gusti, 2010 MSG-GLB_gusti_algorithm_2010). The spatially explicit G4M model compares the income of managed forest (difference of wood price and harvesting costs, income by storing carbon in forests) with income by alternative land use on the same place, and decides on afforestation, deforestation or alternative management options. As outputs, G4M provides estimates of forest area change, carbon uptake and release by forests, and supply of biomass for bioenergy and timber. (Fricko et al., 2016 MSG-GLB_fricko_marker_2016)

As a partial equilibrium model representing land-use based activities, including agriculture, forestry and bioenergy sectors, GLOBIOM is built following a bottom-up setting based on detailed gridcell information, providing the biophysical and technical cost information. Production adjusts to meet the demand at the level of 30 economic regions. International trade representation is based on the spatial equilibrium modelling approach, where individual regions trade with each other based purely on cost competitiveness because goods are assumed to be homogenous (Takayama and Judge 1971 MSG-GLB_takayama_spatial_1971; Schneider, McCarl et al. 2007 MSG-GLB_schneider_agricultural_2007). Market equilibrium is determined through mathematical optimization which allocates land and other resources to maximize the sum of consumer and producer surplus (McCarl and Spreen 1980 MSG-GLB_mccarl_surplus_1980). As in other partial equilibrium models, prices are endogenous. The model is run recursively dynamic with a 10 year time step, along a baseline going from 2000 to 2100. The model is solved using a linear programming simplex solver and can be run on a personal computer with the GAMS software.

For more information about the land-use part of MESSAGE-GLOBIOM, please visit IIASA's MESSAGE-GLOBIOM documentation.