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redd_case [2020/02/17 23:46]
argemiro created
redd_case [2020/02/21 12:01]
britaldo
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 ====== Projecting deforestation rates based on socioeconomic variables and developing a carbon bookkeeping model ====== ====== Projecting deforestation rates based on socioeconomic variables and developing a carbon bookkeeping model ======
  
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 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.  ​ 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.egoml''​ 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 ''​\Guidebook_Dinamica_5\Models\REDD_case_study''​. This model is composed of three main parts: the input data, pre-calculation,​ and the simulation model itself. ​
  
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-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).+After annual deforestation cells are identified, 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 |}}
  
 [{{ :​tutorial:​redd_12.jpg |Fig. 3 –Infrastructure projects in the MAP region. MAP stands for Madre de Dios (Peru), Acre (Brazil) and Pando (Bolivia).}}] [{{ :​tutorial:​redd_12.jpg |Fig. 3 –Infrastructure projects in the MAP region. MAP stands for Madre de Dios (Peru), Acre (Brazil) and Pando (Bolivia).}}]
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 +===Congratulations,​ you have successfully completed this lesson!===
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 +☞[[:​guidebook_start| Back to Guidebook Start]]
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