Differences
This shows you the differences between two versions of the page.
Next revision | Previous revision Last revision Both sides next revision | ||
redd_case [2020/02/17 23:46] argemiro created |
redd_case [2020/03/21 20:54] britaldo |
||
---|---|---|---|
Line 1: | Line 1: | ||
+ | {{ :logo_logo.png?400 |}} | ||
+ | \\ | ||
====== 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 ====== | ||
Line 4: | Line 6: | ||
<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? ===== | ||
Line 35: | Line 37: | ||
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 |}} | ||
Line 85: | Line 87: | ||
{{ :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 |}} | ||
[{{ :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).}}] | ||
+ | \\ | ||
+ | \\ | ||
+ | ===Congratulations, you have successfully completed this lesson!=== | ||
+ | \\ | ||
+ | \\ | ||
+ | ☞[[:guidebook_start| Back to Guidebook Start]] | ||
+ |