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redd_case [2020/02/18 10:54]
argemiro
redd_case [2020/03/21 20:54]
britaldo
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 <note important>​We do not support the application of deforestation modeling to fix REDD baselines for crediting purpose. <note important>​We do not support the application of deforestation modeling to fix REDD baselines for crediting purpose.
-See http://www.csr.ufmg.br/dinamica/redd/redd.html</​note>​+See https://www.redd-monitor.org/wp-content/uploads/2016/​02/​REDD_CIFOR.pdf</​note>​
  
 ===== What will you learn? ===== ===== What will you learn? =====
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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. ​
  
 {{ :​tutorial:​redd_3.jpg |}} {{ :​tutorial:​redd_3.jpg |}}
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 {{ :​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 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 |}}