Global Forest Model

G4M was developed at International Institute for Applied Systems Analysis (IIASA) in the mid-2000s for modelling afforestation in Latin America under the name of DIMA. Over time, it evolved to a global land use change and forestry scenario analysis framework. G4M operates on regular 0.5x0.5 degree grid. The model uses input data on the grid, country and world region scales. The results can be output on the grid, country or regional scales as well. There are two branches of the model – global and European. The European branch uses additional spatial data on tree species and wood production, species-specific biomass expansion factors, age structure, and other country specific data. The global model usually is solved using 5-year time step while the European model applies 1-year step, the time span is from 1990 (2000) up to 2100.

The G4M model computes and compares the income derived from forests with the income that could be derived from an alternative use of the same land, for example, to grow grain for food or biofuel. To do this, G4M computes the amount of the net income currently being derived from forests by calculating the amount and value of wood produced minus the harvesting costs (i.e., logging and timber extraction), and estimates the potential income from the carbon storage in forests (sequestration). Taking these values into account, G4M allows to assess whether it would be more profitable to grow agricultural crops or bioenergy crops, or whether forestry is the best option for the land use.

G4M can be used for ex-ante impact assessments. It produces estimates of the forest area change, carbon sequestration and emissions in forests, impacts of carbon incentives (e.g. avoided deforestation, stimulated afforestation, and forest management aimed at production of demanded amount of wood and enhancing carbon storage in forest biomass at the same time) and supply of biomass for bio-energy and timber.

The G4M model is composed of four parts: environmental (natural conditions and forest parameters; the model incorporates empirical forest growth functions – generic in global case and for major tree species in case of EU); economic (estimation of local - cell specific - wood and agricultural land prices, net present value (NPV) of forestry and agriculture, forest harvesting and planting costs); decision making (decisions on forest management parameters and the land use change); and CO2 emission estimation.

The model consists of six major modules: virtual forest, forest initialisation, forest management decisions, land use change decisions, forest dynamics and GUI output. The virtual forest module simulates forest growth and management on a forest scale. This module describes forest in terms of the stem wood dynamics. It consists of two parts: an increment function and a forest age cohort simulator. The forest initialisation module is run only once at the beginning. The module creates two types of virtual forest in each cell (forest existing in the year 2000 (named “old forest”) and forest that has been planted after 2000 (named “new forest”) and sets initial parameters of forests according to observed values. The forest management decisions module is run every year to adjust the forest rotation length and thinning to match the wood demand and residues demand on a country or global world region scale taking into account carbon sequestration policies. The land use change decisions module is run every year to estimate the NPV of forestry and agriculture in order to set the cell to one of the three states – afforest/deforest/no change, and estimate the rates of afforestation and deforestation. The forest dynamics module applies forest management and land use change with estimated parameters to virtual forests: afforestation adds new forest to the “new forest”, deforestation decreases the area of “old forest” while the forest management affects both types of forests. The module also estimates CO2 emissions relative to afforestation, deforestation and forest management. In particular, the land use change emissions are estimated for the above and belowground biomass, dead organic matter, (mineral) soil and peat; the forest management emissions are estimated for the above and belowground biomass, and deadwood, and soil emissions due to residue extraction. The GUI output module forms geographic maps, country and global world region aggregated tables in a binary format that can be viewed with special software or extracted to csv format.

By executing the six modules, the G4M model estimates the annual above ground wood increment and harvesting costs, where increment is determined by a potential Net Primary Productivity (NPP) map and translated into the net annual increment (NAI). The increment map can be either static or with a dynamic growth component which reacts to changes in temperature, precipitation or CO2 concentration. The age structure and stocking degree are used for adjusting the NAI. By comparing the income from the managed forest (difference of wood price and harvesting costs, income by storing carbon in forests) with the income from alternative land uses of the same land, a decision of afforestation or deforestation can be made. The main forest management options considered by G4M are species selection, variation of thinning and choice of rotation length. The rotation length can be individually chosen, though also the model itself can endogenously compute optimal rotation lengths to maximise increment, stocking biomass or harvestable biomass.

To model forest growth, the G4M requires static net primary production (NPP) data. The model can apply a dynamic NPP model to simulate how growth rates are affected by changes in temperature, precipitation, radiation, or CO2 concentrations.

G4M also uses information from other models or databases, for example:

• wood prices on country or regional scale (GLOBIOM)
• agriculture land prices on country or regional scale (GLOBIOM)
• wood demand on country or regional scale (GLOBIOM)
• harvest residues demand on country or regional scale (GLOBIOM)
• spatially explicit data on prescribed demand for agriculture land from (GLOBIOM)
• population and GDP development scenarios on country scale or spatially explicit (e.g., IIASA Scenario Database)

to produce forecasts of land-use change, carbon sequestration and/or emissions in forests, the impacts of carbon incentives (e.g., avoided deforestation), and supply of biomass for bio-energy and timber. 

G4M can use parameters based on a country’s own statistics to check their accuracy, for example

• forest cover,
• tree species composition,
• age class distribution, and
• live biomass.