A policy-sensitive, spatial-dynamic model of logging for the Brazilian Amazon
Soares-Filho, Universidade Federal de Minas Gerais, email@example.com
Merry, Woods Hole Research Center, firstname.lastname@example.org
Rodrigues, Universidade Federal de Minas Gerais, email@example.com
Nepstad, Woods Hole Research Center, firstname.lastname@example.org
The future of the timber industry in the Amazon is at a crossroads. At the same time that the State increasingly moves to curb unrestricted forest destruction, wood demand from growing national and international markets encourages the expansion of logging to new Amazon frontiers. To assess these interacting trends, we have developed a spatially explicit model that simulates the future of the Amazon logging industry. The model produces dynamic rent surfaces based on sawn wood prices, harvest and milling costs collected for 588 milling centers located across the Amazon. For each cell at 2km resolution, the model calculates the transportation cost to the closest milling center, allocating to this center the cell commercial wood volume (estimated from biomass data). Thus each milling center competes against neighboring centers for its area of influence as new roads are built and deforested and logged areas expand. For each time step, a milling center attempts to match its current harvest capacity collecting wood in cells that become profitable after deducting transportation, harvest, milling, tax, and investment costs from sawn wood prices at the milling center gate. These parameters are updated as a milling center ages and road access to it is improved. Also wood volume can be restored after a forest regrowth sojourn time. A centerís harvest capacity is allowed to increase or decrease annually according to the profitable wood volume available within its area of influence. As a result, milling centers have a boom and burst lifecycle as they gradually exhaust profitable forests located in accessible regions. When a center approaches its demise, it gives birth to new centers located in inner Amazon regions, perpetuating the way the logging frontier evolves. Deforestation and infrastructure data come from SimAmazonia 1. In this version, both models run simultaneously exchanging data. As a result, deforestation and road expansion lower logging transportation costs, and in turn logged areas present higher probability of deforestation. Hence this simulation platform is designed to assess the economic cost and benefits as well as environmental impacts in short to long terms from a range of scenarios, including various levels of law enforcement, deforestation trends, infrastructure expansion and improvement, markets for wood, and forest policies, such as the creation of logging concessions in state and federal forests.
Science Theme: LC (Land Use and Land Cover Change)