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redd_case [2020/02/18 10:54] argemiro |
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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://csr.ufmg.br/dinamica_old/redd/redd.html</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 |}} | ||