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tutorial:redd_case_study [2013/07/30 01:06]
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   * Convert gross rates into net rates   * Convert gross rates into net rates
   * How to develop a carbon bookkeeping model   * How to develop a carbon bookkeeping model
-  * Functors: \\  [[:calc_neighborhood|Calc Neighborhood]]\\ [[:​calc_spatial_lag|Calc Spatial Lag]]\\ ​+  * Functors: \\  ​//[[:Calc Neighborhood]]//\\ //[[:​calc_spatial_lag|Calc Spatial Lag]]//\\ 
  
  
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 [{{ :​tutorial:​redd_2.jpg |Fig. 2 –Infrastructure projects in the MAP region. MAP stands for Madre de Dios (Peru), Acre (Brazil) and Pando (Bolivia).}}] [{{ :​tutorial:​redd_2.jpg |Fig. 2 –Infrastructure projects in the MAP region. MAP stands for Madre de Dios (Peru), Acre (Brazil) and Pando (Bolivia).}}]
  
-To perform an ex ante evaluation of the potential for future deforestation under a Business-as-Usual (BAU) scenario, pioneer REDD projects, such as the JUMA conservation PPD – Project Design Document (FAS, 2008) adopted the use of simulation models (Soares-Filho et al., 2006). In this case, a set of assumptions is established to differentiate the BAU from the governance scenario, such as road paving, agricultural expansion, population movements, expansion and consolidation of protected areas and the effectiveness of public policies in curbing deforestation. The model simulates deforestation trajectories under the modeled scenarios and calculates their respective carbon emissions. So, instead of a historical baseline, the potential for reduction is compared by subtracting the accumulated amount of emissions under the governance scenario from the one under the BAU scenario by a specific future year (in general 2050). ​+To perform an ex ante evaluation of the potential for future deforestation under a Business-as-Usual (BAU) scenario, pioneer REDD projects, such as the JUMA conservation PPD – Project Design Document ​[[http://​fas-amazonas.org/​versao/​2012/​wordpress/​wp-content/​uploads/​2013/​06/​FAS_Juma-REDD-Project-summary.pdf|(FAS, 2008)]] adopted the use of simulation models ​[[http://​dx.doi.org/​10.1038/​nature04389|(Soares-Filho et al., 2006)]]. In this case, a set of assumptions is established to differentiate the BAU from the governance scenario, such as road paving, agricultural expansion, population movements, expansion and consolidation of protected areas and the effectiveness of public policies in curbing deforestation. The model simulates deforestation trajectories under the modeled scenarios and calculates their respective carbon emissions. So, instead of a historical baseline, the potential for reduction is compared by subtracting the accumulated amount of emissions under the governance scenario from the one under the BAU scenario by a specific future year (in general 2050). ​
  
 Although simulation models play an important role in modeling the effects of alternative land-use policy scenarios on the landscape dynamics, this methodology should be applied with caution to REDD projects. The need to estimate potential future emissions as a way to measure the contribution of a public policy or conservation initiative to REDD, such as the creation of a protected area, is rapidly disseminating the use of simulation models as a tool for REDD projects. Several commercial and non-commercial packages are available for developing spatial simulation models. However, there is no ready solution for a specific REDD project (despite that some vendors say so). In addition to simulating the effects of spatial determinants on the location of deforestation (see lesson 7), there is a need to model the local, regional, and even international drivers of deforestation. This is far more difficult and relies heavily on the availability of temporal socioeconomic data at several scales as well as wall-to-wall deforestation time series. Those models must be built from the ground (i.e. using bottom-up approaches rather than top-down models), incorporating our knowledge of the proximate and underlying causes of deforestation,​ and must pass through validation not only in terms of their spatial prediction, but also regarding the power to predict the recent deforestation trajectory based on changes in the socioeconomic and political context. Even so, simulation models are no crystal ball, modeled future trajectories must be regarded as a likely possibility only; beyond that it is all speculation. Although simulation models play an important role in modeling the effects of alternative land-use policy scenarios on the landscape dynamics, this methodology should be applied with caution to REDD projects. The need to estimate potential future emissions as a way to measure the contribution of a public policy or conservation initiative to REDD, such as the creation of a protected area, is rapidly disseminating the use of simulation models as a tool for REDD projects. Several commercial and non-commercial packages are available for developing spatial simulation models. However, there is no ready solution for a specific REDD project (despite that some vendors say so). In addition to simulating the effects of spatial determinants on the location of deforestation (see lesson 7), there is a need to model the local, regional, and even international drivers of deforestation. This is far more difficult and relies heavily on the availability of temporal socioeconomic data at several scales as well as wall-to-wall deforestation time series. Those models must be built from the ground (i.e. using bottom-up approaches rather than top-down models), incorporating our knowledge of the proximate and underlying causes of deforestation,​ and must pass through validation not only in terms of their spatial prediction, but also regarding the power to predict the recent deforestation trajectory based on changes in the socioeconomic and political context. Even so, simulation models are no crystal ball, modeled future trajectories must be regarded as a likely possibility only; beyond that it is all speculation.
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 === Developing an econometric projection model that predicts deforestation rates based on changes in the socioeconomic context of municipalities === === Developing an econometric projection model that predicts deforestation rates based on changes in the socioeconomic context of municipalities ===
  
-In this example, an econometric model is coupled to a spatially-explicit simulation model of deforestation. The econometric projection model predicts deforestation rates based on changes in the socioeconomic context of municipalities (Soares-Filho et. al, 2008, Soares-Filho et. al, 2010). A spatial lag regression is applied to compute the influence of five variables on the deforestation trajectory: ​ Crop area expansion, cattle herd growth, percent of protected areas, proximity to paved roads, and migration rates. A spatial neighborhood matrix allows the model to incorporate the influence of the socioeconomic context of neighboring municipalities in the prediction of deforestation rates within a certain municipality.  ​+In this example, an econometric model is coupled to a spatially-explicit simulation model of deforestation. The econometric projection model predicts deforestation rates based on changes in the socioeconomic context of municipalities ​[[http://​www.csr.ufmg.br/​dinamica/​publications/​cap6.pdf|(Soares-Filho et. al, 2008]][[http://​www.pnas.org/​cgi/​doi/​10.1073/​pnas.0913048107|,​Soares-Filho et. al, 2010)]]. A spatial lag regression is applied to compute the influence of five variables on the deforestation trajectory: ​ Crop area expansion, cattle herd growth, percent of protected areas, proximity to paved roads, and migration rates. A spatial neighborhood matrix allows the model to incorporate the influence of the socioeconomic context of neighboring municipalities in the prediction of deforestation rates within a certain municipality.  ​
  
-Load the model simulate_deforestation_under_socioeconomic_scenarios.xml” ​from \ Examples\REDD_case_study. This model is composed of three main parts: the input data, pre-calculation,​ and the simulation model itself. ​+Load the model ''​simulate_deforestation_under_socioeconomic_scenarios.egoml'' ​from ''​\Examples\REDD_case_study''​. This model is composed of three main parts: the input data, pre-calculation,​ and the simulation model itself. ​
  
 {{ :​tutorial:​redd_3.jpg |}} {{ :​tutorial:​redd_3.jpg |}}
  
-In this simplified version of Soares-Filho et al. (2008), the user can modify the scenario by changing the annual rates of crop expansion and cattle herd growth, which are input to the model. Other variables could be also changed by editing the input lookup tables.+In this simplified version of [[http://​www.csr.ufmg.br/​dinamica/​publications/​cap6.pdf|Soares-Filho et al. (2008)]], the user can modify the scenario by changing the annual rates of crop expansion and cattle herd growth, which are input to the model. Other variables could be also changed by editing the input lookup tables.
  
 {{ :​tutorial:​redd_4.jpg |}} {{ :​tutorial:​redd_4.jpg |}}
  
-The pre-calculation group calculates the original forest extent per municipality,​ the municipality area, and the neighborhood matrix (//​Calc ​neighborhood//) that defines which municipalities are neighbors. These are going to be inputs for the projection model. Open the group named “Econometric projection model”.+The pre-calculation group calculates the original forest extent per municipality,​ the municipality area, and the neighborhood matrix (//[[:Calc Neighborhood]]//) that defines which municipalities are neighbors. These are going to be inputs for the projection model. Open the [[:​Group]] ​named “Econometric projection model”.
  
 {{ :​tutorial:​redd_5.jpg |}} {{ :​tutorial:​redd_5.jpg |}}
  
-This Group contains three //For Each// and one //Calc Spatial Lag//. The two first //For Each// update the cattle herd and crop area lookup tables and calculate their annual rates of change, which are input to the spatial lag regression. ​+This //[[:Group]]// contains three //[[:For Each]]// and one //[[:Calc Spatial Lag]]//. The two first //[[:For Each]]// update the cattle herd and crop area lookup tables and calculate their annual rates of change, which are input to the spatial lag regression. ​
  
-<note tip>​**TIP**:​ //For Each// browses the elements of a table allowing its manipulation.</​note> ​+<note tip>​**TIP**:​ //[[:For Each]]// browses the elements of a table allowing its manipulation.</​note> ​
  
-In addition to the lookup tables of the five independent variables, //Calc Spatial Lag// receives as input the lag coefficient,​ the neighborhood matrix, an initial x1 dependent variable table, the regression coefficients,​ and a random error term. This functor represents a spatial lag regression equation as follows (Anselin, 2002):+In addition to the lookup tables of the five independent variables, //[[:Calc Spatial Lag]]// receives as input the lag coefficient,​ the neighborhood matrix, an initial x1 dependent variable table, the regression coefficients,​ and a random error term. This functor represents a spatial lag regression equation as follows ​[[http://​dx.doi.org/​10.1177/​0160017602250972|(Anselin, 2002)]]:
  
 y = pWy+XB+e   ​ y = pWy+XB+e   ​
  
-Where "​p"​ is the autoregressive coefficient,​ "​W"​ is a first order neighborhood matrix, "​y"​ the dependent variable, "​X"​ the matrix of observations for the independent variables, "​B" ​ the vector of regression coefficients and "​e"​ a random error term. In this equation the term "​pW"​ is calculated in an iterative way using moving averages of the "​y"​ responses from the neighboring municipalities. In this case, instead of a classical linear model, a spatial lag regression was adopted since the regression model failed the autocorrelation tests (Anselin, 2002).+Where "​p"​ is the autoregressive coefficient,​ "​W"​ is a first order neighborhood matrix, "​y"​ the dependent variable, "​X"​ the matrix of observations for the independent variables, "​B" ​ the vector of regression coefficients and "​e"​ a random error term. In this equation the term "​pW"​ is calculated in an iterative way using moving averages of the "​y"​ responses from the neighboring municipalities. In this case, instead of a classical linear model, a spatial lag regression was adopted since the regression model failed the autocorrelation tests [[http://​dx.doi.org/​10.1177/​0160017602250972|(Anselin, 2002)]].
 Dinamica EGO does not provide a method to develop a spatial lag regression, but only to solve the equation, which was developed using Geoda (www.geoda.uiuc.edu). ​ Dinamica EGO does not provide a method to develop a spatial lag regression, but only to solve the equation, which was developed using Geoda (www.geoda.uiuc.edu). ​
  
 {{ :​tutorial:​redd_6.jpg |}} {{ :​tutorial:​redd_6.jpg |}}
  
-Finally, the third //For Each// converts gross deforestation rates output from //Calc Spatial Lag// into net deforestation rates using the following formula:+Finally, the third //[[:For Each]]// converts gross deforestation rates output from //[[:Calc Spatial Lag]]// into net deforestation rates using the following formula:
  
 **if t1[v1] / t2[v1] > 1 then 1 else if t1[v1] / t2[v1] < 0 then 0 else  t1[v1] / t2[v1]** **if t1[v1] / t2[v1] > 1 then 1 else if t1[v1] / t2[v1] < 0 then 0 else  t1[v1] / t2[v1]**
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 === Developing a carbon bookkeeping model === === Developing a carbon bookkeeping model ===
  
-This model calculates annual carbon emissions by identifying annual deforestation and then overlaying these areas on a map of forest carbon biomass – figure below (Saatchi et al., 2007), and assuming that carbon content is 50% of wood biomass (Houghton et al., 2001) and that 85% of the carbon contained in trees is released to the atmosphere with deforestation (Houghton et al., 2000).+This model calculates annual carbon emissions by identifying annual deforestation and then overlaying these areas on a map of forest carbon biomass – figure below [[http://​dx.doi.org/​10.1111/​j.1365-2486.2007.01323.x|(Saatchi et al., 2007)]], and assuming that carbon content is 50% of wood biomass ​[[http://​dx.doi.org/​10.1111/​j.1365-2486.2001.00426.x|(Houghton et al., 2001)]] and that 85% of the carbon contained in trees is released to the atmosphere with deforestation ​[[http://​dx.doi.org/​10.1038/​35002062|(Houghton et al., 2000)]].
  
 {{ :​tutorial:​redd_8.1.jpg |}} {{ :​tutorial:​redd_8.1.jpg |}}
  
-In order to calculate annual deforestation,​ the model compares in each time step the current land-use map with the previous one. Dinamica EGO allows the loading of multiple maps using //Load Map// within //Repeat// and passing //Step// as a pointer to the name file that has the model step number as its suffix. Note that in this case the suffix has 6 digits to bear the simulation year (2002-2020).+In order to calculate annual deforestation,​ the model compares in each time step the current land-use map with the previous one. Dinamica EGO allows the loading of multiple maps using //[[:Load Map]]// within //[[:Repeat]]// and passing //[[:Step]]// as a pointer to the name file that has the model step number as its suffix. Note that in this case the suffix has 6 digits to bear the simulation year (2002-2020).
  
 {{ :​tutorial:​redd_9.jpg |}} {{ :​tutorial:​redd_9.jpg |}}
  
-A //Load Map// is placed within a Group to ensure a proper order of execution. The previous land-use map is kept in// Mux Map//, so both //Calculate Map// functors in this container receive the previous land-use map as **i1** and the current as **i2**.+A //[[:Load Map]]// is placed within a //[[:Group]]// to ensure a proper order of execution. The previous land-use map is kept in //[[:Mux Map]]//, so both //[[:Calculate Map]]// functors in this container receive the previous land-use map as **i1** and the current as **i2**.
  
 {{ :​tutorial:​redd_10.1.jpg |}} {{ :​tutorial:​redd_10.1.jpg |}}
  
-After annual deforestation cells are indentified,​ the model picks up the corresponding biomass stocks in the biomass map and convert them into carbon and then into emissions. //Extract Map Attribute// is applied to calculate the total amount of cells and //Calculate Value// integrates those figures on an annual basis. Its output is passed to Set Lookup Table that updates a table with annual carbon emissions (Fig. 3).+After annual deforestation cells are indentified,​ the model picks up the corresponding biomass stocks in the biomass map and convert them into carbon and then into emissions. //[[:Extract Map Attributes]]// is applied to calculate the total amount of cells and //[[:Calculate Value]]// integrates those figures on an annual basis. Its output is passed to //[[:Set Lookup Table Value]]// ​that updates a table with annual carbon emissions (Fig. 3).
  
 {{ :​tutorial:​redd_11.jpg |}} {{ :​tutorial:​redd_11.jpg |}}