SimAmazoniaINFRA

The projections used in the modeling of deforestation and greenhouse gas (GHG) emission scenarios were based on publicly accessible spatial and non-spatial databases, including annual deforestation mapping up to 2023 [1,2] and estimates of above-ground biomass density [3], as well as climate action plans aimed at reducing GHG emissions [4] from the nine countries that make up the Pan-Amazon—Bolivia, Brazil, Colombia, Ecuador, Guyana, French Guiana, Peru, Suriname, and Venezuela. In the Brazilian Amazon, the databases from INPE’s PRODES project (annual monitoring of native vegetation suppression) [1] were considered, while for the other countries, the global continuous mapping by Hansen et al. [2] was used, which analyzes time series of Landsat images, due to the lack of national databases with annual monitoring.

Scenarios

In the Baseline scenario, the fixed average deforestation of the last five years (2019 to 2023) was adopted as a reference. Although the observed deforestation database [2] provided information up to 2024, the values recorded in that year, in all analyzed Amazonian regions, were much higher than in previous years. Such a pattern was identified through comparisons with satellite imagery and other national databases, including INPE’s PRODES project [1]. These results suggest possible inconsistencies associated with the mapping or post-processing of the data, in addition to indicating potential interference in future projections of forest vegetation suppression.

In the Tendential scenario, the projection of future values was used based on the historical period corresponding to the last five years (2019 to 2023). The linear trend statistical method uses linear regression via the least squares method to identify behavioral patterns in a dataset, allowing for the tracing of a continuous line that represents the general direction of events.

In the Target scenario, the objectives defined by the respective NDCs (Nationally Determined Contributions) of each country [4] were adopted, which converge on the general goal of climate neutrality (or net zero emissions), with the elimination of deforestation being a fundamental means to achieve this objective.

• In Bolivia, the forestry sector targets differ between areas located inside and outside protected areas (PAs), as established in the NDC document [5]. For areas located within PAs, a 100% reduction in native vegetation suppression was defined, while for external areas, an 80% reduction was stipulated, both with a target by 2035, relative to the respective baselines (2016–2020 and 2016–2021). A linear reduction trajectory was adopted starting from 2023 to 2035, considering the average of the years corresponding to the baselines, with the objective of reaching both targets.
• In Brazil, the established commitment consists of eliminating illegal deforestation by 2030 and reaching net-zero GHG emissions by 2050 in the Amazon biome [6]. Studies conducted by the federal government and UFMG estimated that, in 2022, 90% of the native vegetation suppression observed in the biome was unauthorized [7]. Based on this estimate, this proportion was applied to the deforestation value observed in the same year, and a linear reduction trajectory was calculated starting from the last year with available mapping (2023) until both targets are reached.
• Colombia establishes, in its NDC, the goal of reducing the annual deforestation rate in the forestry sector to a range between 37,500 and 49,999 hectares by 2035 [8]. For calculation purposes, the average between these two values was adopted. Considering that the target applies to the entire national territory, the proportion corresponding to the Amazonian portion of the country was estimated in order to apply the average only to the territorial fraction relative to this region. The reduction trajectory calculation was modeled linearly, based on 2023.
• Ecuador does not establish a specific NDC target for the forestry sector or for the Amazon region, defining instead a total reduction of 7% of its emissions (approximately 8,800 ktCO₂eq) by 2035 [9]. Starting in 2023, the last year with available deforestation data for the country, a linear reduction trajectory until 2035 was defined, based on the emissions value (CO₂eq) converted into deforested area (ha) from the biomass density map and by applying a conversion factor of 3.66. Furthermore, it was assumed that carbon content corresponds to 50% of woody biomass [10] and that 85% of the carbon contained in trees is released into the atmosphere due to deforestation [11].
• The schedule associated with Guyana‘s NDC covers the period until 2025 [12]. The established objective is the mitigation of 48.7 MtCO₂eq annually by 2025, in the timber and mining sectors, provided that adequate incentives are supplied. In the absence of updates to climate action plans aimed at reducing GHG emissions, it was decided to extend this target until 2035. To do so, the value in CO₂eq was converted to hectares, obtaining a value distributed over the period from 2024 to 2035, based on a linear reduction trajectory starting in 2023.
• French Guiana does not have its own NDC, and is therefore subject to the targets established for the French territory (European Union). The document defines as an objective the reduction of net GHG emissions between 66.25% and 72.5% by 2035, relative to 1990 levels [13]. Due to the absence of spatial data referring to the baseline year, the value reported by Citepa (Centre Interprofessionnel Technique d’Études de la Pollution Atmosphérique) for the forestry sector was adopted [14]. This value, expressed in tCO₂eq, was converted into deforested area (hectares), based on the biomass density database, and used as a reference for determining the absolute value of the established target. The average between the percentages defined in the NDC was calculated, and from this value, a linear reduction was applied for the period from 2023 to 2035, which was extended until the elimination of deforestation in 2050.
• In Peru, the target established in the NDC consists of reducing GHG emissions by between 54.0 and 69.9 MtCO₂eq by 2030 in the AFOLU sector (Agriculture, Forestry, and Other Land Use) [15]. To operationalize the calculations, these values were converted from MtCO₂eq to hectares, and subsequently, the average between the two limits was calculated, with the objective of defining an absolute target. Considering the national character of the target, the deforestation value to be reduced by 2030 was proportionalized for the area corresponding to the Peruvian Amazon. Then, a linear reduction trajectory was applied starting from 2023, in order to reach the target established for 2030.
• In Suriname, the main mitigation targets in the AFOLU sector for 2030 and 2035 include maintaining forest cover equal to or greater than 90% [16]. The country is characterized as a net sink of greenhouse gases and has a carbon-negative economy. In this context, a methodological approach was adopted that estimates the forest area subject to deforestation, ensuring the maintenance of forest cover equal to or greater than 90% of the current area. Thus, only the difference between the established target and the current forest cover (93%) was considered for reduction [16]. From this difference, a linear deforestation reduction trajectory was estimated, starting in 2023, until its complete elimination.
• In Venezuela, one of the targets established in the NDC consists of maintaining the rate of native vegetation suppression at 0.20% per year until 2035 in the national territory (equivalent to 90,000 ha/year), taking the year 2020 as a baseline and considering exclusively the forestry sector [17]. Thus, the area was proportionalized for the Venezuelan Amazon region, followed by the application of a linear deforestation reduction trajectory, with the objective of its elimination by 2050, in line with the global climate neutrality commitment provided for in the Paris Agreement.

References:

[1] Instituto Nacional de Pesquisas Espaciais – INPE (2026) Projeto Prodes – Monitoramento de Desmatamento na Amazônia Legal. São José dos Campos, Brasil: INPE. Available at: http://terrabrasilis.dpi.inpe.br/downloads/.

[2] Hansen M.C., Potapov P.V., Moore R., et al. (2013) High-Resolution Global Maps of 21st-Century Forest Cover Change. Science 342, 850-853. Available at: https://glad.earthengine.app/view/global-forest-change. Base de dados atualizada em 2026.

[3] Harris N.L., Gibbs D.A., Baccini A., et al. (2021) Global maps of twenty-first century forest carbon fluxes. Nature Climate Change 11, 234–240. Available at: https://doi.org/10.1038/s41558-020-00976-6.

[4] United Nations Framework Convention on Climate Change – UNFCCC. Nationally Determined Contributions (NDC). Available at: https://unfccc.int/NDCREG.

[5] Estado Plurinacional de Bolivia (2025) Contribución Nacionalmente Determinada (CND 3.0): presentada en el marco del Acuerdo de París y la CMNUCC, para el periodo 2026-2035. La Paz, Bolívia: Ministerio de Medio Ambiente y Agua; Autoridad Plurinacional de la Madre Tierra. Available at: https://unfccc.int/documents/650130.

[6] República Federativa do Brasil (2024) Brazil’s Second Nationally Determined Contribution (NDC). Brasília, Brasil: Governo Brasileiro. Available at: https://unfccc.int/sites/default/files/2024-11/Brazil_Second%20Nationally%20Determined%20Contribution%20%28NDC%29_November2024.pdf.

[7] Brasil, Ministério do Meio Ambiente e Mudança do Clima (2026) Plano Clima Mitigação: plano setorial de mudanças do uso da terra em áreas rurais privadas. Brasília, DF: MMA, MCTI, MAPA, MDA, CC/PR. Available at: https://www.gov.br/mma/pt-br/centrais-de-conteudo/publicacoes/mudanca-do-clima/plano-setorial-mudancas-uso-terra-areas-rurais-privadas.pdf.

[8] República de Colombia (2025) Contribución Determinada a Nivel Nacional (NDC 3.0) de Colombia – Transformaciones para la Vida. Bogotá, Colômbia: Ministerio de Ambiente y Desarrollo Sostenible. Available at: https://unfccc.int/sites/default/files/2025-09/NDC%203.0%20Declarativa%20Colombia%20Transformaciones%20para%20la%20Vida%20V.25.09.2025%20Gob.%20Nacional.pdf.

[9] República del Ecuador (2025) Segunda Contribución Determinada a Nivel Nacional de la República del Ecuador 2026-2035. Quito, Equador: Ministerio del Ambiente, Agua y Transición Ecológica. Available at: https://unfccc.int/sites/default/files/2025-02/Segunda%20NDC%20de%20Ecuador.pdf.

[10] Houghton R.A., Lawrence K.T., Hackler J., Brown L.S. (2001) The spatial distribution of forest biomass in the Brazilian Amazon: a comparison of estimates. Global Change Biology 7:731-746.

[11] Houghton R.A., Skole D.L., Nobre C.A., et al. (2000) Annual fluxes of carbon from deforestation and regrowth in the Brazilian Amazon. Nature 403, 301–304.

[12] Republic of Guyana (2015) Guyana’s Revised Intended Nationally Determined Contribution. Georgetown, Guiana: Government of Guyana. Available at: https://unfccc.int/sites/default/files/NDC/2022-06/Guyana%27s%20revised%20NDC%20-%20Final.pdf.

[13] European Union and its Member States (2025) The nationally determined contribution of the European Union and its Member States. União Europeia: Danish Presidency of the Council of the European Union and European Commission. Available at: https://unfccc.int/sites/default/files/2025-11/DK-2025-11-05%20EU%20NDC.pdf.

[14] Centre Interprofessionnel Technique d’Études de la Pollution Atmosphérique – Citepa (2024) Inventaire national d’émissions de gaz à effet de serre et de polluants atmosphériques en Outre-mer. Ed. 2024. Available at: https://www.citepa.org/en/air-climate-data/data-greenhouse-gas/french-overseas-territories/.

[15] Gobierno del Perú (2025) Tercera Contribución Determinada a Nivel Nacional del Perú. Lima, Peru: Ministerio del Ambiente. Available at: https://unfccc.int/sites/default/files/2025-11/Documento%20NDC%203.0_UNFCCC.pdf.

[16] Republic of Suriname (2025) Republic of Suriname’s Third Nationally Determined Contribution (NDC 3.0). Paramaribo, Suriname: Ministry of Oil, Gas, and Environment. Available at: https://unfccc.int/sites/default/files/2025-11/NDC%203%20report%20Suriname%20251104%20Final%20Publication%20Version.pdf.

[17] República Bolivariana de Venezuela (2021) Actualización de la Contribución Nacionalmente Determinada de la República Bolivariana de Venezuela para la lucha contra el Cambio Climático y sus efectos. Caracas, Venezuela: Ministerio del Poder Popular para el Ecosocialismo. Available at: https://unfccc.int/sites/default/files/NDC/2022-06/Actualizacion%20NDC%20Venezuela.pdf.

SimAmazonia[1] is a unique system for developing spatially explicit projections of future deforestation trends under scenarios involving infrastructure investments (particularly in transportation), public conservation policies, and land management. By enabling comparisons between scenarios, such as the trend or business-as-usual scenario—which assumes the continuation of current deforestation trends and the implementation of planned infrastructure—and the governance scenario, which incorporates conservation efforts and policy interventions, such as the deforestation reduction targets established under the Nationally Determined Contributions (NDCs), SimAmazonia contributes to a better understanding of the challenges and, consequently, of solutions aimed at strengthening the commitment to adopting large-scale conservation policies and actions across the Amazon region.

Reference:

[1] Soares-Filho et al. Modelling conservation in the Amazon basin. Nature, London, v. 440, p. 520-523, 2006.

Species distribution records were compiled from online databases—the Global Biodiversity Information Facility (GBIF) [1,2,3] and the Global Ant Biodiversity Informatics (GABI) [4]—using the Amazon region as a geographic filter and restricting records to specimens from biological collections. The dataset included vertebrates (birds, mammals, amphibians, and reptiles), several groups of invertebrates (terrestrial annelids, bees, spiders, cockroaches and termites, flies, dragonflies, terrestrial gastropods, hemipterans, lepidopterans, orthopterans, trichopterans, and wasps), angiosperms (Asteraceae, Fabaceae, Orchidaceae, Poaceae, and Rubiaceae), and pteridophytes (all families). The data underwent coordinate validation to remove records located in the ocean and records with inconsistencies between coordinates and locality descriptions. When necessary, records were georeferenced using OpenStreetMap [5]. Taxonomic validity was verified using the Catalogue of Life Checklist 2023 [6], and synanthropic species were excluded. Finally, the dataset was cleaned and georeferenced using the BioDinamica cleaning tool [7], resulting in 8,619,185 records representing 300,228 species, from an initial dataset of 23,365,020 records. Two metrics were calculated: species richness, defined as the number of species per hexagon, and endemism, defined as the predominance of species with restricted distributions per hexagon, quantified using the Corrected Weighted Endemism Index (WEIc), which sums, for each cell, the inverse of each species’ distribution area (the more restricted the distribution, the greater its contribution).

References:

[1] GBIF.org (3 April 2024) GBIF Occurrence Download https://doi.org/10.15468/dl.4wcg7z

[2] GBIF.org (6 April 2024) GBIF Occurrence Download https://doi.org/10.15468/dl.mcg2u9

[3] GBIF.org (3 April 2024) GBIF Occurrence Download https://doi.org/10.15468/dl.wvqpbn

[4] Guénard, B., Weiser, M., Gomez, K., Narula, N., Economo, E.P. (2017) The Global Ant Biodiversity Informatics (GABI) database: a synthesis of ant species geographic distributions. Myrmecological News 24: 83-89.

[5] OpenStreetMap contributors. OpenStreetMap database [Online]. OpenStreetMap Foundation. https://www.openstreetmap.org.

[6] Bánki, O., Roskov, Y., Döring, M., Ower, G., Hernández Robles, D. R., Plata Corredor, C. A., Stjernegaard Jeppesen, T., Örn, A., Pape, T., Hobern, D., Garnett, S., Little, H., DeWalt, R. E., Miller, J., Orrell, T., Aalbu, R., Abbott, J., Abreu, C., Acero P, A., et al. (2026). Catalogue of Life (2026-05-15 XR). Catalogue of Life Foundation, Amsterdam, Netherlands. https://doi.org/10.48580/dgxsq

[7] Oliveira U, Soares-Filho B, Leitão RFM, Rodrigues HO. 2019. BioDinamica: a toolkit for analyses of biodiversity and biogeography on the Dinamica-EGO modelling platform. PeerJ 7:e7213 http://doi.org/10.7717/peerj.7213

OtimizaInfra is a route modeling and simulation platform that integrates existing or planned infrastructure, current or projected demand, port capacity, and cargo flows. OtimizaInfra simulates complex network flows, identifying the lowest total-cost routes while allocating cargo volumes based on the actual monthly throughput capacity of each port or terminal. The platform integrates agricultural logistics scenarios using production forecasts generated by Otimizagro. By incorporating new infrastructure projects under development—such as strategic railways, new highways, bioceanic corridors, or port terminal and channel deepening expansions—the system enables the assessment of their synergistic impacts. As a result, OtimizaInfra addresses critical questions such as cargo migration, bottleneck mitigation, and the feasibility of new transport infrastructure under economically and environmentally sustainable alternatives.