Lesson 8- REDD Case Study

Abstract

This lesson uses an econometric model as a tool for the REDD inititive which compensates developing countries for carbon sequestration. The model accounts for socioeconomic factors, using a spatial lag regression and a set of given parameters. A basic scenario of business as usual and a new, dramatic scenario are tested against each other in the model. The first model simulates the deforestation based on the econometrics for 2001-2020. The second model provides a carbon bookkeeping account of CO2 emmissions and deforested hectares, leading to conclusions for stakeholders in the region.

Introduction/Background

An econometric model is a a tool used to determine future aspects of the economy. It uses past relationships between parameters to statistically simulate the future, changing variables to create a variety of results.  An econometric model can be useful to simulate deforestation because socioeconomic factors on a local, regional, and international scale heavily influence the rate of deforestation. Some benefits of this type of modeling is that it takes into account a more compex, anthropogenic system. Economics cannot be ignored in environmental modeling, therefore econometric models can incorporate a more diverse aray of parameters. Some challenges are that it requires a lot more data collection, more statistical and mathematical effort, and a specific understanding of how local economics influence the environment.

REDD is an initiative on Reducing Emissions from Deforestation and forest Degradation in developing countries. The program was developed to provide incentives to developing countries to stop deforestation. If countries could reduce their deforestation to below a historical baseline they would be eligible for financial compensation from the international community, specifically from carbon credits. Modeling REDD projects would require an econometric model because as a country initiates policies to reduce deforestation, they are then making money that is going into projects that do not degrade the land. This is a relationship between economics and environment that requires complex, multi-level datasets.

A spatial lag is when neighboring cells affect one another in the model, due to the influence on one variable to the other. Deforestation results in more crop areas and denser cattle herds, while precent of protected areas decreases deforestation. Deforestation creates more roads and increases migration rates. The latter two may have a feedback loop that will continue to increase. To summarize, increasing protected  areas will decrease deforestation, while growth in the other variables results from and continues due to deforestation.

Figure 1- Simulation Model

The spatial neighborhood matrix incorporates the influence of the socioeconomic context of neighboring municipalities in the prediction of deforestation rates of a certain municipality. This means that the matrix is designed for each municipality to respond to the changing socioeconomic patterns around them. No municipality works in isolation, instead they react to one another. Deforestation will not occur within a single municipality; however it is most likely to create a spreading pattern between municipalities. The model will run for 20 iterations to represent 2001-2020.

Results

The final landscape has a slight increase in deforestation (light green) that fills in a lot of the patches from 2001. There is not a huge difference, but there is a bit of land use change occuring around the edges of where it was already occuring. It shows there is pressure to protect the land, and pressure to develop the land. The municipalities develop and deforest slowly, one affecting the other resulting in a spreading pattern, like a river delta. Intermediate years did not help show much of a trend because 2010 decreased deforestation and then after that it increased again. 

Figure 2- 2001 Land use map (left), 2020 Land use map (right)

The scenario I created was “0” growth in crop expansion and “1” or 100% growth in cattle expansion. This was so dramatic so that I could see the result of such a drastic change; however it was too much and made it difficult to observe the results. The new scenario results in a lot of cattle land in every municipality. Deforestation is vast and unruley. This is unrealistic, but demonstrates the potential for unlimited cattle expansion.

Figure 3- Land use map for scenario of 100% cattle expansion, 2001 map (left), 2020 map (right)

In the carbon bookkeeping model, the model uses 85% for the biomass emmission factor and 50% for the Biomass-Carbon conversion factor.  This model results in excel tables that display deforestation in hectares and CO2 emissions for each year. 

Figure 4- CO2 (top), Deforestation  (bottom)

I repeated it for scenario 2. It is clear in both examples that the CO2 emissions mirror the deforestation to some extent. 

Figure 5- CO2 (top), Deforestation (bottom)

They follow a similar rise and fall pattern, although the CO2 emissions continue to rise after deforestation ends due to a lag in the ecosystem processes. Because of the dramatic choices I made for scenario 2, the model stops at 2004. The deforestation declines and then stops because there is nothing left to deforest; meanwhile the CO2 emissions increase to a massive number of more then nine million tons. I recommend stakeholders do NOT expand at a rate of 100% for cattle or for crops. This will result in complete degradation of the land.