Land-use - BLUES

Model Documentation - BLUES
Socio-economic drivers - BLUES Population - BLUES Economic activity - BLUES Macro-economy - BLUES Production system and representation of economic sectors - BLUES Capital and labour markets - BLUES Monetary instruments - BLUES Trade - BLUES Technological change - BLUES Energy - BLUES Energy resource endowments - BLUES Fossil energy resources - BLUES Uranium and other fissile resources - BLUES Bioenergy - BLUES Non-biomass renewables - BLUES Energy conversion - BLUES Electricity - BLUES Heat - BLUES Gaseous fuels - BLUES Liquid fuels - BLUES Solid fuels - BLUES Grid, pipelines and other infrastructure - BLUES Energy end-use - BLUES Transport - BLUES Residential and commercial sectors - BLUES Industrial sector - BLUES Other end-use - BLUES Energy demand - BLUES Technological change in energy - BLUES Land-use - BLUES Agriculture - BLUES Forestry - BLUES Land-use change - BLUES Bioenergy land-use - BLUES Other land-use - BLUES Agricultural demand - BLUES Technological change in land-use - BLUES Emissions - BLUES GHGs - BLUES Pollutants and non-GHG forcing agents - BLUES Carbon dioxide removal (CDR) options - BLUES Climate - BLUES Modelling of climate indicators - BLUES Climate damages, temperature changes - BLUES Non-climate sustainability dimension - BLUES Air pollution and health - BLUES Water - BLUES Other materials - BLUES Other sustainability dimensions - BLUES Appendices - BLUES Mathematical model description - BLUES Data - BLUES References - BLUES
Corresponding documentation
AIM-Hub BLUES C3IAM COFFEE-TEA DNE21+ GCAM GEM-E3 GRACE IFs IMACLIM IMAGE MESSAGE-GLOBIOM POLES PROMETHEUS REMIND-MAgPIE TIAM-UCL WITCH
Previous versions
Archive of BLUES version 1.0
Model information
Model link	https://www.nature.com/articles/s41558-018-0213-y
Institution	COPPE/UFRJ (Cenergia), Brazil, http://www.cenergialab.coppe.ufrj.br/.
Solution concept	General equilibrium (closed economy)
Solution method	Optimization
Anticipation

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).