Land-use - MESSAGE-GLOBIOM

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Model Documentation - MESSAGE-GLOBIOM
Corresponding documentation
Model information
Institution International Institute for Applied Systems Analysis (IIASA), Austria, http://data.ene.iiasa.ac.at/message-globiom/.
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 1; Havlík et al., 2014 2). 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 3; Kindermann et al., 2008 4; Gusti, 2010 5). 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 6)

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 7; Schneider, McCarl et al. 2007 8). 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 9). 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.

References

  1. ^  |  Petr Havlík, Uwe A Schneider, Erwin Schmid, Hannes Böttcher, Steffen Fritz, Rastislav Skalský, Kentaro Aoki, Stephane De Cara, Georg Kindermann, Florian Kraxner (2011). Global land-use implications of first and second generation biofuel targets. Energy Policy, 39 (10), 5690--5702.
  2. ^  |  Petr Havlík, Hugo Valin, Mario Herrero, Michael Obersteiner, Erwin Schmid, Mariana C Rufino, Aline Mosnier, Philip K Thornton, Hannes Böttcher, Richard T Conant (2014). Climate change mitigation through livestock system transitions. Proceedings of the National Academy of Sciences, 111 (10), 3709--3714.
  3. ^  |  Georg E Kindermann, Michael Obersteiner, Ewald Rametsteiner, Ian McCallum (2006). Predicting the deforestation-trend under different carbon-prices. Carbon Balance and management, 1 (1), 15.
  4. ^  |  G Kindermann, M Obersteiner, B Sohngen, J Sathaye, K Andrasko, E Rametsteiner, B Schlamadinger, S Wunder, R Beach (2008). Global cost estimates of reducing carbon emissions through avoided deforestation. Proceedings of the National Academy of Sciences, 105 (30), 10302.
  5. ^  |  MI Gusti (2010). An algorithm for simulation of forest management decisions in the global forest model. Штучний інтелект, ().
  6. ^  |  Oliver Fricko, Petr Havlik, Joeri Rogelj, Zbigniew Klimont, Mykola Gusti, Nils Johnson, Peter Kolp, Manfred Strubegger, Hugo Valin, Markus Amann, Tatiana Ermolieva, Nicklas Forsell, Mario Herrero, Chris Heyes, Georg Kindermann, Volker Krey, David L McCollum, Michael Obersteiner, Shonali Pachauri, Shilpa Rao, Erwin Schmid, Wolfgang Schoepp, Keywan Riahi (2016). The marker quantification of the shared socioeconomic pathway 2: a middle-of-the-road scenario for the 21st century. Global Environmental Change, In press ().
  7. ^  |  T Takayama, G G Judge (1971). Spatial and temporal price and allocation models. North-Holland Amsterdam.
  8. ^  |  Uwe A Schneider, Bruce A McCarl, Erwin Schmid (2007). Agricultural sector analysis on greenhouse gas mitigation in US agriculture and forestry. Agricultural Systems, 94 (2), 128 - 140.
  9. ^  |  Bruce A McCarl, Thomas H Spreen (1980). Price Endogenous Mathematical Programming as a Tool for Sector Analysis. American Journal of Agricultural Economics, 62 (1), 87-102.