Carbon Balance and Management(8)

Table 1: Coefficients for equation (25) – Deforestation speed

CoefEstimateStd. Errorc00.05

-c1-1.799e+004.874e-01c2-2.200e-019.346e-02c3-1.663e-015.154e-02c44.029e-021.712e-02c5-5.305e-041.669e-04c6

-1.282e-04

3.372e-05

Ftdec 0

Fsi=

i=0∨AgSi=0 xi

Fsi>0∧AgSi>0

(25)

The deforestation rates (Ftdec) were taken from [2], wherethe forest area from 1990, 2000 and 2005 for each countrywas given. For the estimation of the model parameters thearea difference between 1990 and 2005 was used to inferthe deforestation rate. All values which showed anincrease of the forest area have been set to 0, because themodel should only predict the deforestation. Countrieswith an increasing forest area have a deforestation rate of0. It should be mentioned that the change rate is based onthe total land area in the grid i and not on the current for-est area.

By using c2/Fs the model can only be used on grid's wherethere is some share of forest. This makes sense, because onplaces where there is no forest, no deforestation canappear. The model will only be usable on grids where for-ests occur. Therefore, for parameterization, the averageagricultural suitability and the population density of acountry are also only taken from grids which indicate for-est cover.

Development of forest share

After calculating the deforestation rate, the forest sharehas to be updated each year with equation (27) assuringthat the forest share stays within the permissible range of0–1.

Fs fsxi,year≤1 (Buli+Crli)i,year=

fsxi,year

1 (Buli+Crli)fsxi,year>1 (Buli+Crli)(27)

fsxi, year = Fsi, year - 1 - Fi, dec (28)

Aboveground carbon in forest biomass

The model describes the area covered by forests on a cer-tain grid. It can also describe the forest biomass if the aver-

Pr(> |t|)-0.000310***0.019865*0.001529**0.019852*0.001789**0.000206

***

age biomass on a grid is known and the assumption wasmade, that the biomass in forests on the grid is propor-tional to the forest area.

For this reason a global carbon map of aboveground car-bon in forest biomass, was created, based on country val-ues from [2]. By dividing the given total carbon, for eachcountry, with the forest area of the country, the averagebiomass per hectare can be calculated. Now the assump-tion was made, that the stocking biomass per hectare onsites with a higher productivity is higher than on sites witha low productivity. Not for every country with forests [2]gives values of the stocking biomass. So a regression,describing the relation between tC/ha and NPP, was cal-culated and the biomass of grids of missing countries havebeen estimated to obtain a complete global forest biomassmap.

Simulations

In the simulations the effect of different carbon-pricesand/or incentives, for keeping forest, have been tested.The simulation period started in the year 2000 and endsin 2100. The decision, whether deforestation takes placeor not and how fast it goes on, was done in one year timesteps. Scenario drivers, available on coarser time resolu-tion (e.g. population density), have been interpolated lin-early between the given years.

Outputs of the simulations are trajectoria of forest cover,

changes in carbon stocks of forests, and financialresources required to cut emissions from deforestationunder varying scenario assumptions.

Data

The model uses several sources of input data some availa-ble for each grid, some by country aggregates and othersare global. The data supporting the values in table 2 areknown for each grid. Some of the values are also availablefor time series.

Beside the datasets, available at grid level, the purchasingpower parity PPP [7] from 1975–2003, the discount rates[8] for 2004, the corruption in 2005 [5] and the fraction