G4M

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.   

G4M produces estimates of the:

• forest area change,
• carbon sequestration and emissions in forests,
• emissions from land use change (afforestation and deforestation)
• impacts of carbon incentives (e.g. avoided deforestation, stimulated afforestation, improvements in forest management) and
• supply of biomass for bio-energy and timber. 

The model can incorporate many factors or needs, for instance, the need to provide food security, to increase biodiversity, to understand future urbanization patterns, and to gauge how wildfires or insects might affect forest productivity.

G4M can assess impacts of the land use and land use change and forestry (LULUCF) on climate change mitigation.

G4M can contribute by greenhouse gas emission calculations from different land uses to the Energy Reference Scenario of the EC (Energy Roadmap 2050), to emission trading schemes, to the Reduced Emissions from Deforestation and Forest Degradation (REDD) (an important question to be resolved under the UNFCCC) etc.