This lesson shows us how to project
deforestation rates based on socioeconomic variables. An econometric model
specifies the statistical relationship between economic variables pertaining to
a particular economic phenomena, in this case deforestation. It is useful to
use an econometric model to simulate deforestation because it takes into
consideration the many complex dynamic phenomena that produce deforestation as
a result. These types of simulation models are important for evaluation of the
potential for future deforestation under a business-as-usual scenario. In the
case of this example, this is important in order for countries to receive
incentives from reducing their carbon emissions from deforestation. REDD stands
for “reducing emissions from deforestation and forest degradation” and is an
idea that developing countries who do this over time, can receive financial
compensation for doing so. This would require a country’s emission levels to be
lower than historical levels and would require them to know historical emission
levels and whether they were high or not. A model like this is important to
predict future deforestation rates based on multiple economic variables to see
if receiving compensation for REDD is a possibility for certain countries.
The
model used for this lesson contains a spatial lag regression that is applied to
compute the influence of five variables on deforestation: Percentage of crop
areas, cattle herd density, percent of protected areas, proximity to paved
roads, and migration rates. The five variables listed are the five static
variables that are used to determine the probability of deforestation. The spatial
lag regression makes it so that the deforestation does not happen right off in
respect to the spatial variables and instead occurs over time and spreads over
time. This models “spatial neighborhood matrix” allows neighboring cells, in
this case municipalities, to have an influence on the cell in question. This
means that each cell is not independent of the surrounding cells and the
variables in the surrounding cells will effect it. In terms of deforestation,
this means that if one cell becomes deforested, then it is likely that
neighboring cells will become deforested as well over time. Because of this, we
would expect to see deforestation occur in expanding clumps instead of in
random patches.
After
running the model with various cattle herd and crop rates, it was determined
that the smaller the rates the better because this will produce a lower, steady
rate of deforestation and CO2 emissions. A high rate of cattle herd and crops
means a rapid increase of land use and therefore a rapid increase in
deforestation and CO2 emissions and an overall unstable state.
The
model is simulated for 20 years and a map output is produced for each year.
Below are three output maps from the model starting with the first year, the 10th,
and the last year:
At first, it looks like these images are all the same and
there is no difference. But looking at the amount of yellow in the first image
compared to the last one, you can see that the yellow areas are more filled in
and have less green areas between them. This shows that the areas surrounded by
deforestation (yellow) eventually become deforested as well and that
deforestation is similar to the spreading of a disease. The most worrying part
about this is that in the first year, there were very small areas of
deforestation in the middle of green (forested) patches. Those were very small
and may not have caused many problems in the first year, but because of the
spreading of deforestation, these areas have become much larger by the last
year. At this point they will cause problems such as habitat fragmentation for
wildlife species. It also seems that the deforestation has spread faster
between 2010 and 2020 as opposed to 2001 and 2010. This is worrisome because it
means that the rate of deforestation is increasing as time goes on. I could not
get any data to show up for cattle herds, but below is a graph for annual deforestation
and a graph for crops for years 2001, 2010, and 2020:
As you can see, the crop rates are similar every year, but
steadily increase every year. The seasonal changes are always the same, but the
overall annual rate for crops increases. For deforestation, however, the
seasonal changes are not always the same. 2001 and 2010 are similar in that
their peak deforestation rates were at the same time, but in the year 2020, the
peaks were at the times where deforestation was lowest in the past. Overall,
deforestation rates for 2020 do not seem to be higher than they were in
previous years. In 2001 and 2010, deforestation rates seem to be the opposite
of crop rates but in 2020, deforestation rates become a bit more similar to the
seasonal changes of the crop rate.
In the scenario parameters group, I changed the cattle herd
expansion and crop expansion values from 0.05 to 0.5. This simulates a rapid
increase in land use. Below are the output maps for years 2001, 2010, and 2020
again:
As you can see in these
output maps, the spread of deforestation occurs at a much higher rate and by
2020, a larger amount of forested areas have become deforested because of the
demand for crops and cattle herds. It is impossible to see these images as
something that is not a problem. The amount of forested land left after just 20
years is alarming. Below are the graphs for the annual crops and deforestation
rates:
As you can see, for the crop rates, they started out low in
2001, increased a large amount by 2010, and then dropped down to 0 by 2020.
With deforestation, the highest rates were in 2001 and they continually dropped
in 2010 and even more in 2020. Maybe the rates for both the crops and
deforestation decrease by 2020 because there is drastically less land to use
for crops and deforestation because most of it has already been converted.
Next is estimating the carbon losses from the two scenarios
presented above. This will be done with the carbon bookkeeping model which is a
very common approach to estimating carbon emissions. This model calculates
annual carbon emissions by determining annual deforestation (from our previous
model) and overlaying these deforested areas on a map of forest carbon biomass.
This model assumes that carbon content is 50% of wood biomass and 85% of carbon
stored in trees is released into the atmosphere during deforestation. This
model was run for both scenario 1 and scenario 2. The graphs for the annual
carbon emissions in tons for each scenario, CO2 emissions, and the graphs for annual
deforestation in hectares for each scenario are below:
As you can see, these graphs all look the same. With the
small (0.05) cattle herd and crop rates, the annual rates of deforestation and
carbon emissions were fairly steady without much fluctuation. With the rates
much higher (0.5), the rates of deforestation and carbon emissions instantly
were higher and fluctuated much more. Overall, the second scenario emitted much
more CO2 into the atmosphere and much more deforestation took place. Therefore,
it is best if the cattle herd and crop rates remained relatively low so the CO2
emissions and deforestation rates would remain low and steady.
