The Bonn Challenge, launched in 2011 in response to the economic, social and environmental challenges facing the world, established the goal to restore 150 million hectares by 2020, subsequently validated and expanded to 350 million hectares of restoration by 2030. The chosen strategy was Forest Landscape Restoration (FLR). As part of its contribution to the global climate agenda, Brazil announced its goal to restore and reforest 12 million hectares of native vegetation over a 20-year period, through the National Policy for the Recovery of Native Vegetation (Proveg, in Portuguese) and the National Plan for the Recovery of Native Vegetation (Planaveg, in Portuguese), whose base strategy is also FLR.
In this context of a global effort to reduce greenhouse gas emissions, in 2012 the World Resources Institute (WRI Brasil) and the International Union for Conservation of Nature (IUCN) created the “Inspire, Support, and Mobilize Forest and Landscape Restoration” project, in partnership with Imazon. One of the objectives of this project was the application of Restoration Opportunity Assessment Methodology (ROAM) to collect key data for forest landscape restoration that can guide decision-makers, specialists and those who implement restorative actions.
In Brazil, the project utilized one of the ROAM components, the diagnostic of key success factors for forest restoration, which was carried out in depth for the municipality of Paragominas and in a more simplified manner for the State of Pará. One of the opportunities present in the state is “Pará 2030”, a plan that aims to improve the state’s social and economic development indicators and it is expected that Paragominas, a municipality that participates in the state Green Municipalities Program (PMV), is one of the areas in Pará where the work demonstrated in this plan will be developed.
This report presents diagnostics carried out in Paragominas and Pará, and suggests strategies to promote restoration through the elaboration of the theory of change, developed by considering the absent factors.