The goal of this lab is to learn how to use socioeconomic variables to project deforestation rates, and how to make a carbon bookkeeping model. Gross rates will be converted into net rates, and the functors Calc Neighborhood and Calc Spatial Lag will be used. Two scenarios were created to see if changing the rates of crop expansion and cattle growth affected the rates of deforestation. The amounts of deforestation and CO2 emissions were higher when the rates of crop expansion and cattle growth were increased, but the trend of CO2 emissions had an uphill followed by a downhill trend. Some unaccountable factor may have been in play.
A REDD (reducing emissions from deforestation and forest degradation) program was proposed to financially compensate developing countries in tropical areas for reducing deforestation and thus reducing CO2 emissions into the atmosphere. Predicting deforestation rates in the future would have to factor in several variables including crop expansion, cattle growth, protected land areas, proximity to paved roads, migration rates, and neighborhood matrices. This lesson will do just that by running an econometric model with a spatially-explicit deforestation simulation model. An econometric model uses statistics to specify the relationship between economic variables such as those mentioned above. This is helpful in factoring in the probability of deforestation occurring based on the economic factors. It adds more than just calculating how fast land is cleared in a simulation model. A separate model will map the predicted carbon emissions due to the deforestation.
1) Open the model “simulate_deforestation_under_socioeconomic_scenarios.xml” from the folder “Examples/REDD_case_study”. Notice that there is the input data within the Group “Inputs” located on the far left, the precalculation in the Group “Precalculation” to the right of the inputs, and then the simulation model within the Repeat functor.
The inputs are the annual rates of crop expansion and cattle herd growth. Expand the groups within this input group to look at what they hold. One of the groups has the static variables of deforestation: percent of crop areas, cattle herd density, percent protected areas, proximity to paved roads, and migration rates. Note that the values in each Double is 0.05, reflecting the rate of increase for crop expansion and the growth of the cattel herds. These values will be modified later.
The precalculation group calculates the neighborhood matrix, the original forest extent per municipality, and the municipal areas. This is to define which neighborhoods are municipalities and factor in socioeconimic factors in the neighborhood matrix. With regards to deforestation, how fast a municipality expands for social or economic reasons would equally effect the deforestation rates. Expand the For Each Region functor to see what it contains.
2) Notice that the Group in Repeat has three For Each functors. The first two update and calculate the annual rates of change for crop area and cattle herd. Expand these two to see that they are set up the same way.
This information goes into the Calc Spatial Lag functor, which factors in spatial lag into the annual changes of the variables crop area and cattle herds, percent of protected atres, proximity to paved roads, and migration rates to calculate gross deforestation rates as seen in the input group. Spatial lag is the lag in the use of an area for either of these variables. This lag would lead to a lag in deforestation. This data then goes to the third For Each, which changes the gross deforestation rates into net deforestation rates. Expand the Calc Spatial Lag and the third For Each functor to see what they contain.
3) Notice that the “Number of Iterations” in Repeat is “20” because this model will be running a simulation for each of the years between 2000 and 2020. Run the model. The output landscape maps for each of the years can be found in “Examples/REDD_case_study” as “ers” files. To see how these maps differ if the crops expanded and the cattle grew at a slightly faster rate, change the value in both Doubles from “0.05” to “0.15”. Run the model again under this second scenario.
4) Open the model “carbon_bookeeping_model.xml” from the folder “Examples/ REDD_case_study”.
This model will simulate carbon emission from deforestation by calculating annual deforestation on a map of the area containing forest carbon biomass. It assumes that 50% of wood biomass contains carbon and trees release 85% of their carbon into the atmosphere when deforestation occurs. After that, the model converts the carbon biomass into carbon emission. Click on each input map to see the initial landscape and the forest carbon biomass. The red and yellow areas in the biomass map contain high amount sof carbon while the blue areas in urban spots hardly have any.
5) Notice that within the group in Repeat, there is the same Load Map that has the output maps from the previous model. Since this is the case, go back to the first model and reset the Double values to “0.05” and run the model so that the carbon model will use the results from scenario 1 and its results will thus reflect the first scenario.
These maps are inside the Group to help keep the order of execution in the right sequence.
6) Within Repeat, the identified annual deforestation cells along with their corresponding biomass is converted into carbon and then into emissions. The output tables reflect the annual deforestation and the carbon emissions. Run the model and look at the Excel data of both tables. They can be found as “csv” files under “Examples/REDD_case_study”. Note that the carbon mass has already been converted into units of CO2 in the “annual_carbon_emissions” table. Go back to the first model, change the Double value to “0.15”, run the model, and then run the carbon emissions model again to get results for scenario 2.
The output maps from scenario 1 in step 3 from the years 2001 to 2020 shows a steady increase in land being transformed for socioeconomical usage. In the interest of saving space, the maps for the years 2001, 2005, 2010, 2015, and 2020 respectively are shown below. The dark green shows forested land while the light green shows urban and crop or pasture land.
The output maps from scenario 2 in step 3 from the years 2001 to 2020 also shows a steady increase in land being transformed for socioeconomical usage, only the transformation is faster than in scenario 1. The years 2001, 2005, 2010, 2015, and 2020 are shown respectively below.
The output tables for scenario 1 in step 6. The first table of annual carbon emissions shows a general increase in CO2 emissions from the years 2001 to 2020 and the deforestation shows varying amounts of hectares of land undergoing deforestation occurring from the years 2001 to 2020.
The output tables for scenario 2 in step 6. The first table of annual carbon emissions shows higher CO2 emissions than in scenario 1 from the years 2001 to 2011. From 2001 to 2008 these emissions increased and then in following years decreased until in 2020 the emission is lower than in 2001 for either scenario. The deforestation table shows varying amounts of hectares of land undergoing deforestation occurring from the years 2001 to 2020. Overall the deforestation amounts are higher than in scenario 1 until the year 2020 when it reaches a low value that not even scenario 1 had.
Between scenarios 1 and 2, it was predicted that increasing the crop expansion and cattle growth rates in scenario 2 would lead to an increase in the rates of deforestation as the demand for land for crops and cattle increases and thus also increase CO2 emissions. They were not the only factors for urban expansion and deforestation, but changing the rates of these two factors would supposedly have an effect on net deforestation over consecutive years. The deforestation maps supported this theory by showing urban growth in both scenarios, and the net amount of deforestation in scenario 2 was higher than in scenario 1. The deforestation tables also reflected that more deforestation occurred in scenario 2 than in scenario 1. However, the rate of deforestation varied from year to year in both scenarios and there was no consistent up or downward trend. In general, with each year in both scenarios, CO2 emissions went up if deforestation increased or down if deforestation decreased in comparison with the previous year, but this is just a correlation.
However, the trends of the CO2 emissions were different in each scenario. There was a general upward increase over the 20-year period in the first scenario, but in the second scenario there was an uphill trend to the year 2008 followed by a downhill trend. Since the deforestation data did not have a specific pattern, I cannot map the CO2 trends with deforestation. Perhaps even though the crop expansion and cattle growth rates were increased, other socioeconomic factors might limit how far the expansion of land use for crops and cattle is allowed to go. Maybe the human population growth cannot keep up with these growths, and thus they do not need all the excess food, and so less land is allowed to be deforested for crop and cattle usage. There may also be some other factor that comes into play when these rates are increased, but I cannot account for it. These models do well to display deforestation and CO2 emissions, but they may not account for all factors or explain all possibilities.