IAMC-Documentation - User contributions [en]

Socio-economic drivers - BLUES

2017-10-05T13:16:48Z

Alexandre Koberle:

{{ModelDocumentationTemplate
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}}
Population and GDP (MER) are the main exogenous socio-economic drivers behind demand projections in the Reference scenario implemented in BLUES. Demand projections are derived from GDP and population projections through econometric and regression techniques.

Economic activity - BLUES

2017-10-05T13:13:45Z

Alexandre Koberle:

{{ModelDocumentationTemplate
|IsDocumentationOf=BLUES
|DocumentationCategory=Economic activity
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GDP growth rate projections are based largely on SSPs projections (Dellink et al 2015), with adjustments made for the short-term to reflect current economic activity in Brazil. Short-term projections through 2020 are based on The Focus Bulletin published by the Brazilian Central Bank (BCB 2017). The GDP projection of the Reference scenario in BLUES is shown in the figure below.

[[File:GDP.jpg|none|750px|thumb|<caption> Population projections for Brazil from IBGE and SSPs/caption>]]

==References==

BCB. (2017). Sistema de Expectativas de Mercado. Retrieved September 01, 2017, from https://www3.bcb.gov.br/expectativas/publico/consulta/serieestatisticas

Dellink, R., Chateau, J., Lanzi, E., Magné, B., 2015. Long-term economic growth projections in the Shared Socioeconomic Pathways. Glob. Environ. Chang. IN PRESS, 1–15. doi:10.1016/j.gloenvcha.2015.06.004

Economic activity - BLUES

2017-10-05T13:11:25Z

Alexandre Koberle:

{{ModelDocumentationTemplate
|IsDocumentationOf=BLUES
|DocumentationCategory=Economic activity
}}

GDP growth rate projections are based largely on SSPs projections (Dellink et al 2015), with adjustments made for the short-term to reflect current economic activity in Brazil. Short-term projections are based on The Focus Bulletin published by the Brazilian Central Bank (BCB 2017). The GDP projection of the Reference scenario in BLUES is shown in the figure below.

[[File:GDP.jpg|none|750px|thumb|<caption> Population projections for Brazil from IBGE and SSPs/caption>]]

==References==

BCB. (2017). Sistema de Expectativas de Mercado. Retrieved September 01, 2017, from https://www3.bcb.gov.br/expectativas/publico/consulta/serieestatisticas

Dellink, R., Chateau, J., Lanzi, E., Magné, B., 2015. Long-term economic growth projections in the Shared Socioeconomic Pathways. Glob. Environ. Chang. IN PRESS, 1–15. doi:10.1016/j.gloenvcha.2015.06.004

File:GDP.jpg

2017-10-05T13:02:57Z

Alexandre Koberle:

Population - BLUES

2017-10-05T12:34:22Z

Alexandre Koberle:

{{ModelDocumentationTemplate
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Population projection through 2050 was taken from the Instituto Brasileiro de Geografia e Estatistica (IBGE 2016). It compares well with the projection for SSP2 (KC & Lutz 2017), as shown in the figure below.

[[File:Population.jpg|none|750px|thumb|<caption> Population projections for Brazil from IBGE and SSPs/caption>]]

==References==

IBGE (2016). Projeção da População do Brasil por sexo e idade: 2000-2060. Instituto Brasileiro de Geografia e Estatistica. Accessed online on July 1 2015 at https://ww2.ibge.gov.br/home/estatistica/populacao/projecao_da_populacao/2013/default.shtm

KC, S., Lutz, W., 2017. The human core of the shared socioeconomic pathways: Population scenarios by age, sex and level of education for all countries to 2100. Glob. Environ. Chang. 42, 181–192. doi:10.1016/j.gloenvcha.2014.06.004

File:Population.jpg

2017-09-28T12:34:55Z

Alexandre Koberle: Population projections from IBGE and SSPs

Population projections from IBGE and SSPs

Population - BLUES

2017-09-28T12:33:39Z

Alexandre Koberle:

{{ModelDocumentationTemplate
|IsDocumentationOf=BLUES
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[[File:Population.jpg|none|750px|thumb|<caption> Population projections for Brazil from IBGE and SSPs/caption>]]

Land-use - BLUES

2017-09-22T08:42:30Z

Alexandre Koberle: /* Land Use Transitions allowed in BLUES */

{{ModelDocumentationTemplate
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==Land Use classes==

A representative set of distinct land use classes were chosen to optimize representation and minimize computational requirements in the MESSAGE framework. The starting point were the CSR-UFMG maps uso_da_terra_2013 representing land use in 2013 as allocated by the land use model OTIMIZAGRO (http://maps.csr.ufmg.br/). The map represents the cultivated area of 14 crops, double crop areas, planted forests and pastures, plus the natural remnants of forests and savannas, both inside and outside of protected areas (Soares-Filho et al., 2016). It also shows urban areas and water bodies which were used to create an exclusion mask for agricultural activities. These land use class were aggregated for our purposes according into 9 base-year land use classes:

Cropland, Double crop areas, Pastures, Planted Forests, Savannas, Savannas in Protected Areas, Forests, Forests in Protected Areas.

Pastures were then divided into two categories of grazing intensity: Low-capacity pastures with <0.8 AU/ha and High-capacity pastures with >0.8 AU/ha.

The spatial allocation and area calculation of the two classes of pastures was derived from the "Lotação Bovina no Brasil" map from LAPIG (http://maps.lapig.iesa.ufg.br/lapig.html).

To these base-year LU classes was added the Integrated Systems LU class. It represents Crop-Livestock-Forest Integrated Agricultural Systems and is not represented in the initial area allocation as it occupied a negligible area in the base year. However, this production system has gained much attention in recent years and is one of the cornerstones of future intensification of Brazilian agriculture, and an important mitigation measure in the Brazilian Low Carbon Agriculture Plan (MAPA, 2011).

Land-use change - BLUES

2017-09-22T08:39:32Z

Alexandre Koberle: /* Land Use Transitions */

{{ModelDocumentationTemplate
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==Land Use Transitions==

The figure below shows the allowed land use transitions in BLUES. Note that a unit area may undergo more than one transition in each time step.

[[File:LU_Transitions_sm.jpg|none|750px|thumb|<caption> BLUES Land Use Transitions diagram</caption>]]

File:LU Transitions sm.jpg

2017-09-22T08:37:56Z

Alexandre Koberle:

Land-use change - BLUES

2017-09-22T08:35:18Z

Alexandre Koberle:

File:LU Transitions.jpg

2017-09-22T08:31:47Z

Alexandre Koberle: Alexandre Koberle uploaded a new version of File:LU Transitions.jpg

Model scope and methods - BLUES

2017-09-21T18:25:18Z

Alexandre Koberle: Blanked the page

Model scope and methods - BLUES

2017-09-21T18:25:02Z

Alexandre Koberle:

{{ModelDocumentationTemplate
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COPPE-MSB (Koberle et al., 2015; Portugal-Pereira et al. 2016; Rochedo et al. 2015) is a development and expansion of the MESSAGE-Brazil model developed by the Cenergia lab at COPPE/UFRJ (Borba et al., 2012; de Lucena et al., 2009; Herreras-Martínez et al., 2015; Lucena et al., 2015; Nogueira et al., 2014). MESSAGE is a mixed integer, perfect foresight optimisation model platform, designed to evaluate different strategies of energy supply development to meet a given demand, which can be exogenous or endogenous. It is included in the category of integrated assessment models (IAMs) that combine techno-economic and environmental variables to generate cost-optimal solutions, which minimize the total cost of expanding the energy system to meet projected energy service demands, subject to constraints that represent real-world restrictions to the full range of the variables in question.

Temporal dimension - BLUES

2017-09-21T18:23:14Z

Alexandre Koberle:

{{ModelDocumentationTemplate
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Model scope and methods - BLUES

2017-09-21T18:20:42Z

Alexandre Koberle:

{{ModelDocumentationTemplate
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COPPE-MSB (Koberle et al., 2015; Portugal-Pereira et al. 2016; Rochedo et al. 2015) is a development and expansion of the MESSAGE-Brazil model developed by the Cenergia lab at COPPE/UFRJ (Borba et al., 2012; de Lucena et al., 2009; Herreras-Martínez et al., 2015; Lucena et al., 2015; Nogueira et al., 2014). MESSAGE is a mixed integer, perfect foresight optimisation model platform, designed to evaluate different strategies of energy supply development to meet a given demand, which can be exogenous or endogenous. It is included in the category of integrated assessment models (IAMs) that combine techno-economic and environmental variables to generate cost-optimal solutions, which minimize the total cost of expanding the energy system to meet projected energy service demands, subject to constraints that represent real-world restrictions to the full range of the variables in question.

Energy - BLUES

2017-09-21T18:20:08Z

Alexandre Koberle:

{{ModelDocumentationTemplate
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Techno-economic parameters that form the input deck of COPPE-MSB were derived from various sources (Koberle et al., 2015; Nogueira et al., 2014; J Portugal-Pereira et al., 2016; Soria et al., 2015). Techno-economic input parameters of IAMs in general, and also of COPPE-MSB, include specific investment costs (CAPEX, in US$/kW), construction times (years), conversion efficiency (%), and any technical or economic specifications that may be required to appropriately model the performance of an energy technology (investment and O&M costs, minimum utilization time, inputs and outputs, auxiliary inputs and secondary outputs among others).

File:LU Transitions.jpg

2017-09-21T18:08:38Z

Alexandre Koberle:

Land-use - BLUES

2017-09-21T18:04:54Z

Alexandre Koberle:

Land-use - BLUES

2017-09-21T17:57:19Z

Alexandre Koberle:

Land-use - BLUES

2017-09-21T17:54:21Z

Alexandre Koberle:

Land-use - BLUES

2017-09-21T17:51:32Z

Alexandre Koberle:

{{ModelDocumentationTemplate
|IsEmpty=No
|IsDocumentationOf=BLUES
|DocumentationCategory=Land-use
}}
Land Use classes
A representative set of distinct land use classes were chosen to optimize representation and minimize computational requirements in the MESSAGE framework. The starting point were the CSR-UFMG maps uso_da_terra_2013 representing land use in 2013 as allocated by the land use model OTIMIZAGRO (http://maps.csr.ufmg.br/). The map represents the cultivated area of 14 crops, double crop areas, planted forests and pastures, plus the natural remnants of forests and savannas, both inside and outside of protected areas (Soares-Filho et al., 2016). It also shows urban areas and water bodies which were used to create an exclusion mask for agricultural activities. These land use class were aggregated for our purposes according into 9 base-year land use classes:

Cropland, Double crop areas, Pastures, Planted Forests, Savannas, Savannas in Protected Areas, Forests, Forests in Protected Areas.

Pastures were then divided into two categories of grazing intensity: Low-capacity pastures with <0.8 AU/ha and High-capacity pastures with >0.8 AU/ha.

The spatial allocation and area calculation of the two classes of pastures was derived from the "Lotação Bovina no Brasil" map from LAPIG (http://maps.lapig.iesa.ufg.br/lapig.html).

To these base-year LU classes was added the Integrated Systems LU class. It represents Crop-Livestock-Forest Integrated Agricultural Systems and is not represented in the initial area allocation as it occupied a negligible area in the base year. However, this production system has gained much attention in recent years and is one of the cornerstones of future intensification of Brazilian agriculture, and an important mitigation measure in the Brazilian Low Carbon Agriculture Plan (MAPA, 2011).

Land-use - BLUES

2017-09-21T17:50:56Z

Alexandre Koberle:

{{ModelDocumentationTemplate
|IsEmpty=No
|IsDocumentationOf=BLUES
|DocumentationCategory=Land-use
}}
Land Use classes
A representative set of distinct land use classes were chosen to optimize representation and minimize computational requirements in the MESSAGE framework. The starting point were the CSR-UFMG maps uso_da_terra_2013 representing land use in 2013 as allocated by the land use model OTIMIZAGRO (http://maps.csr.ufmg.br/). The map represents the cultivated area of 14 crops, double crop areas, planted forests and pastures, plus the natural remnants of forests and savannas, both inside and outside of protected areas (Soares-Filho et al., 2016). It also shows urban areas and water bodies which were used to create an exclusion mask for agricultural activities. These land use class were aggregated for our purposes according into 9 base-year land use classes:

Cropland, Double crop areas, Pastures, Planted Forests, Savannas, Savannas in Protected Areas, Forests, Forests in Protected Areas

Pastures were then divided into two categories of grazing intensity: Low-capacity pastures with <0.8 AU/ha and High-capacity pastures with >0.8 AU/ha

The spatial allocation and area calculation of the two classes of pastures was derived from the "Lotação Bovina no Brasil" map from LAPIG (http://maps.lapig.iesa.ufg.br/lapig.html).

To these base-year LU classes was added the Integrated Systems LU class. It represents Crop-Livestock-Forest Integrated Agricultural Systems and is not represented in the initial area allocation as it occupied a negligible area in the base year. However, this production system has gained much attention in recent years and is one of the cornerstones of future intensification of Brazilian agriculture, and an important mitigation measure in the Brazilian Low Carbon Agriculture Plan (MAPA, 2011).

Land-use - BLUES

2017-09-21T17:49:38Z

Alexandre Koberle:

{{ModelDocumentationTemplate
|IsEmpty=No
|IsDocumentationOf=BLUES
|DocumentationCategory=Land-use
}}
Land Use classes
A representative set of distinct land use classes were chosen to optimize representation and minimize computational requirements in the MESSAGE framework. The starting point were the CSR-UFMG maps uso_da_terra_2013 representing land use in 2013 as allocated by the land use model OTIMIZAGRO (http://maps.csr.ufmg.br/). The map represents the cultivated area of 14 crops, double crop areas, planted forests and pastures, plus the natural remnants of forests and savannas, both inside and outside of protected areas (Soares-Filho et al., 2016). It also shows urban areas and water bodies which were used to create an exclusion mask for agricultural activities. These land use class were aggregated for our purposes according into 9 base-year land use classes:

Cropland
Double crop areas
Pastures
Planted Forests
Savannas
Savannas in Protected Areas
Forests
Forests in Protected Areas

Pastures were then divided into two categories of grazing intensity:
Low-capacity pastures with <0.8 AU/ha
high-capacity pastures with >0.8 AU/ha

The spatial allocation and area calculation of the two classes of pastures was derived from the "Lotação Bovina no Brasil" map from LAPIG (http://maps.lapig.iesa.ufg.br/lapig.html).

To these base-year LU classes was added the Integrated Systems LU class. It represents Crop-Livestock-Forest Integrated Agricultural Systems and is not represented in the initial area allocation as it occupied a negligible area in the base year. However, this production system has gained much attention in recent years and is one of the cornerstones of future intensification of Brazilian agriculture, and an important mitigation measure in the Brazilian Low Carbon Agriculture Plan (MAPA, 2011).

Land-use - BLUES

2017-09-21T17:39:24Z

Alexandre Koberle:

{{ModelDocumentationTemplate
|IsEmpty=No
|IsDocumentationOf=BLUES
|DocumentationCategory=Land-use
}}
Land Use classes
A representative set of distinct land use classes were chosen to optimize representation and minimize computational requirements in the MESSAGE framework. The starting point were the CSR-UFMG maps uso_da_terra_2013 representing land use in 2013 as allocated by the land use model OTIMIZAGRO. The map represents the cultivated area of 14 crops, double crop areas, planted forests and pastures, plus the natural remnants of forests and savannas, both inside and outside of protected areas (Soares-Filho et al., 2016). It also shows urban areas and water bodies which were used to create an exclusion mask for agricultural activities. These land use class were aggregated for our purposes according to Table 3.

Aggregated LU class Symbol Original LU classes
Cropland cropland Single crop areas with: soy, sugarcane, corn, cotton, rice, wheat, beans, coffee, cassava, oranges, bananas, cocoa and tobacco.
Double crop areas dblCrop Double crop areas with: soy/corn, soy/wheat, soy/beans, corn/beans, beans/beans
Pastures pasture Pasture inside and outside protected areas
Planted Forests pltForest Forests planted for wood, paper or bio-energy
Savannas savanna Savannas outside protected areas
Savannas in Prot Areas savanna_PA Savannas inside protected areas
Forests forest Forest outside protected areas
Forests in Prot Areas Forest_PA Forest inside protected areas

BLUES

2017-09-21T17:28:13Z

Alexandre Koberle:

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{{ScopeMethodTemplate
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|Concept=BLUES is a cost-optimization model aiming to represent interactions between human activities and the environment. Such activities include agriculture and industrial activity; energy services demand for mobility, heat and lighting; and environmental protection.
|SolutionMethod=Perfect Foresight Cost Minimization
|Anticipation=Used in conjunction with the global COFFEE model which determines boundary conditions such as global energy costs and emissions budgets. this allows for globally consistent representation of traded commodities in the national context. Currently under development is a CGE model that will provide global macroeconomic consistency.
|BaseYear=2010
|TimeSteps=5 years
|Horizon=2050
|Nr=6
|Region=Brasil, North, Northeast, Midwest, Southeast, South;
|SpatialText=One main region (Brasil) with five sub-regions following the Brazilian geopolitical macro-regions
|PolicyImplementation=Climate policy
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Land use policies (food and bio-energy)
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|modelusers=Alexandre Köberle, Pedro Rochedo, Andre Lucena, Alexandre Szklo, Roberto Schaeffer
|country=Brazil
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BLUES

2017-09-21T17:27:23Z

Alexandre Koberle:

BLUES

2017-09-21T17:16:40Z

Alexandre Koberle:

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|SpatialText=One main region (Brasil) with five sub-regions following the Brazilian geopolitical macro-regions
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Land use policies (food and bio-energy)
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BLUES

2017-09-21T17:11:54Z

Alexandre Koberle:

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|modelusers=Alexandre Köberle, Pedro Rochedo, Andre Lucena, Alexandre Szklo, ROberto Schaeffer
|country=Brazil
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BLUES

2017-09-21T17:11:03Z

Alexandre Koberle:

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{{ScopeMethodTemplate}}
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|GridInfrastructureOption=Electricity; Gas; CO2
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|EnergyServiceSector=Agriculture;
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{{Land-useTemplate
|Land-use=Cropland; Extensive Pastures; Forest; Grassland; Intensive Pastures; Protected land; pasture; Integrated Systems; Double Cropping; Savannas;
}}
{{OtherResourcesTemplate
|OtherResourceOption=Metals; Cement
|OtherResource=Chemicals;
}}
{{EmissionClimateTemplate
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|GHGText=Separated per sector AFOLU, Process Emissions, Combustion
}}
{{InstitutionTemplate
|abbr=Cenergia
|institution=Centro de Economia Energetica e Ambiental/Programa de Planejamento Energetico
|link=http://www.coppe.ufrj.br/pt-br/pesquisa/laboratorios/centro-de-economia-energetica-e-ambiental-cenergia
|modelusers=Alexandre Köberle, Pedro Rochedo, Andre Lucena, Alexandre Szklo, ROberto Schaeffer
|country=Brazil
}}

BLUES

2017-09-21T17:09:36Z

Alexandre Koberle:

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{{ScopeMethodTemplate}}
{{Socio-economicTemplate
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|ExogenousDriver=Learning-by-doing; Population;
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|ElectricityTechnologyOption=Coal; Gas; Oil; Nuclear; Biomass; Wind; Solar PV; CSS
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|GridInfrastructureOption=Electricity; Gas; CO2
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|EnergyServiceSector=Agriculture;
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{{Land-useTemplate
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}}
{{OtherResourcesTemplate
|OtherResourceOption=Metals; Cement
|OtherResource=Chemicals;
}}
{{EmissionClimateTemplate}}
{{InstitutionTemplate
|abbr=Cenergia
|institution=Centro de Economia Energetica e Ambiental/Programa de Planejamento Energetico
|link=http://www.coppe.ufrj.br/pt-br/pesquisa/laboratorios/centro-de-economia-energetica-e-ambiental-cenergia
|modelusers=Alexandre Köberle, Pedro Rochedo, Andre Lucena, Alexandre Szklo, ROberto Schaeffer
|country=Brazil
}}

BLUES

2017-09-21T17:08:42Z

Alexandre Koberle:

{{ModelTemplate}}
{{ModelInfoTemplate
|Name=BLUES
|Version=1.0
}}
{{ScopeMethodTemplate}}
{{Socio-economicTemplate
|ExogenousDriverOption=Exogenous GDP; Energy Technical progress; Materials Technical progress
|ExogenousDriver=Learning-by-doing; Population;
}}
{{Macro-economyTemplate
|EconomicSectorOption=Agriculture; Industry; Energy; Transport; Services
|EconomicSector=other;
|EconomicSectorText=Households
|CostMeasureOption=Energy system costs
|TradeOption=Coal; Oil; Gas; Electricity; Bioenergy crops; Food crops
|Trade=Bioenergy products; Diesel; Gasoline; Agriculture;
}}
{{EnergyTemplate
|ResourceUseOption=Coal; Oil; Gas; Uranium; Biomass
|ElectricityTechnologyOption=Coal; Gas; Oil; Nuclear; Biomass; Wind; Solar PV; CSS
|ConversionTechnologyOption=CHP; Hydrogen; Fuel to gas; Fuel to liquid
|GridInfrastructureOption=Electricity; Gas; CO2
|TechnologySubstitutionOption=Discrete technology choices; Expansion and decline constraints; System integration constraints
|EnergyServiceSectorOption=Transportation; Industry; Residential and commercial
|EnergyServiceSector=Agriculture;
}}
{{Land-useTemplate
|Land-use=Cropland; Extensive Pastures; Forest; Grassland; Intensive Pastures; Protected land; pasture; Integrated Systems; Double Cropping; Savannas;
}}
{{OtherResourcesTemplate}}
{{EmissionClimateTemplate}}
{{InstitutionTemplate
|abbr=Cenergia
|institution=Centro de Economia Energetica e Ambiental/Programa de Planejamento Energetico
|link=http://www.coppe.ufrj.br/pt-br/pesquisa/laboratorios/centro-de-economia-energetica-e-ambiental-cenergia
|modelusers=Alexandre Köberle, Pedro Rochedo, Andre Lucena, Alexandre Szklo, ROberto Schaeffer
|country=Brazil
}}

BLUES

2017-09-21T17:06:50Z

Alexandre Koberle:

{{ModelTemplate}}
{{ModelInfoTemplate
|Name=BLUES
|Version=1.0
}}
{{ScopeMethodTemplate}}
{{Socio-economicTemplate
|ExogenousDriverOption=Exogenous GDP; Energy Technical progress; Materials Technical progress
|ExogenousDriver=Learning-by-doing; Population;
}}
{{Macro-economyTemplate
|EconomicSectorOption=Agriculture; Industry; Energy; Transport; Services
|EconomicSector=other;
|EconomicSectorText=Households
|CostMeasureOption=Energy system costs
|TradeOption=Coal; Oil; Gas; Electricity; Bioenergy crops; Food crops
|Trade=Bioenergy products; Diesel; Gasoline; Agriculture;
}}
{{EnergyTemplate
|ResourceUseOption=Coal; Oil; Gas; Uranium; Biomass
|ElectricityTechnologyOption=Coal; Gas; Oil; Nuclear; Biomass; Wind; Solar PV; CSS
|ConversionTechnologyOption=CHP; Hydrogen; Fuel to gas; Fuel to liquid
|GridInfrastructureOption=Electricity; Gas; CO2
|TechnologySubstitutionOption=Discrete technology choices; Expansion and decline constraints; System integration constraints
|EnergyServiceSectorOption=Transportation; Industry; Residential and commercial
|EnergyServiceSector=Agriculture;
}}
{{Land-useTemplate}}
{{OtherResourcesTemplate}}
{{EmissionClimateTemplate}}
{{InstitutionTemplate
|abbr=Cenergia
|institution=Centro de Economia Energetica e Ambiental/Programa de Planejamento Energetico
|link=http://www.coppe.ufrj.br/pt-br/pesquisa/laboratorios/centro-de-economia-energetica-e-ambiental-cenergia
|modelusers=Alexandre Köberle, Pedro Rochedo, Andre Lucena, Alexandre Szklo, ROberto Schaeffer
|country=Brazil
}}

BLUES

2017-09-21T17:05:09Z

Alexandre Koberle:

BLUES

2017-09-21T17:03:33Z

Alexandre Koberle:

BLUES

2017-09-21T17:00:40Z

Alexandre Koberle:

{{ModelTemplate}}
{{ModelInfoTemplate
|Name=BLUES
|Version=1.0
}}
{{ScopeMethodTemplate}}
{{Socio-economicTemplate}}
{{Macro-economyTemplate}}
{{EnergyTemplate}}
{{Land-useTemplate}}
{{OtherResourcesTemplate}}
{{EmissionClimateTemplate}}
{{InstitutionTemplate
|abbr=Cenergia
|institution=Centro de Economia Energetica e Ambiental/Programa de Planejamento Energetico
|link=http://www.coppe.ufrj.br/pt-br/pesquisa/laboratorios/centro-de-economia-energetica-e-ambiental-cenergia
|modelusers=Alexandre Köberle, Pedro Rochedo, Andre Lucena, Alexandre Szklo, ROberto Schaeffer
|country=Brazil
}}

BLUES

2017-09-21T17:00:05Z

Alexandre Koberle:

{{ModelTemplate}}
{{ModelInfoTemplate
|Name=BLUES
|Version=1.0
}}
{{ScopeMethodTemplate}}
{{Socio-economicTemplate}}
{{Macro-economyTemplate}}
{{EnergyTemplate}}
{{Land-useTemplate}}
{{OtherResourcesTemplate}}
{{EmissionClimateTemplate}}
{{InstitutionTemplate
|abbr=Cenergia/COPPE
|institution=Centro de Economia Energetica e Ambiental/Programa de Planejamento Energetico
|link=http://www.coppe.ufrj.br/pt-br/pesquisa/laboratorios/centro-de-economia-energetica-e-ambiental-cenergia
|modelusers=Alexandre Köberle, Pedro Rochedo, Andre Lucena, Alexandre Szklo, ROberto Schaeffer
|country=Brazil
}}

Model Documentation - BLUES

2017-09-21T11:34:22Z

Alexandre Koberle: Created page with "{{ModelDocumentationTemplate |IsEmpty=No |IsDocumentationOf=IMAGE |DocumentationCategory=Model Documentation |HasLevel=0 |HasSeq=0 }} The Brazilian Land Use and Energy System..."

{{ModelDocumentationTemplate
|IsEmpty=No
|IsDocumentationOf=IMAGE
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The Brazilian Land Use and Energy System (BLUES) model is a perfect-foresight, least-cost optimization model for Brazil. It chooses the energy system configuration with the least total system cost over the entire time horizon of the study, in this case 2010 to 2050. The model minimizes costs of the entire energy system, including electricity generation, agriculture, industry, transport and the buildings sectors. BLUES finds optimized mixes for the energy system as a whole, rather than evaluating sectorial optimal solutions. It includes CO2, CH4 and N2O emissions associated with land use, agriculture and livestock, fugitive emissions, fuel combustion, industrial processes and waste treatment.

BLUES has six native regions. One main overarching region into which five sub-regions are nested following the geopolitical division of the country. The energy system is represented in detail across sectors, with over 1500 technologies available in and customized for each of its six native regions. The representation of the land-use system includes forests, savannas, low- and high-capacity pastures, integrated systems, cropland, double cropping, planted forests, and protected areas. Cropland is made up of Land useis also regionalized and customized for each subregion, with yields and costs varying from region to region. Demand is exogenous but endogenous energy efficiency measures permit demand responses through technological options.