Estimating the spatial amplification of damage caused by degradation in the Amazon

Rafael Araujo, Juliano Assunção, Marina Hirota and José A. Scheinkman ()
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Rafael Araujo: a Departament of Economics , Fundação Getulio Vargas’ Sao Paulo School of Economics , Sao Paulo 01332-000 , Brazil
Juliano Assunção: b Department of Economics , Pontifical Catholic University of Rio de Janeiro and Climate Policy Initiative , Rio de Janeiro 22451-900 , Brazil
Marina Hirota: c Department of Physics , Federal University of Santa Catarina , Florianopolis 88040-900-SC , Brazil
José A. Scheinkman: e National Bureau of Economic Research , Cambridge , MA 02138

Proceedings of the National Academy of Sciences, 2023, vol. 120, issue 46, e2312451120

Abstract:

The Amazon rainforests have been undergoing unprecedented levels of human-induced disturbances. In addition to local impacts, such changes are likely to cascade following the eastern–western atmospheric flow generated by trade winds. We propose a model of spatial and temporal interactions created by this flow to estimate the spread of effects from local disturbances to downwind locations along atmospheric trajectories. The spatial component captures cascading effects propagated by neighboring regions, while the temporal component captures the persistence of local disturbances. Importantly, all these network effects can be described by a single matrix, acting as a spatial multiplier that amplifies local forest disturbances. This matrix holds practical implications for policymakers as they can use it to easily map where the damage of an initial forest disturbance is amplified and propagated to. We identify regions that are likely to cause the largest impact throughout the basin and those that are the most vulnerable to shocks caused by remote deforestation. On average, the presence of cascading effects mediated by winds in the Amazon doubles the impact of an initial damage. However, there is heterogeneity in this impact. While damage in some regions does not propagate, in others, amplification can reach 250%. Since we only account for spillovers mediated by winds, our multiplier of 2 should be seen as a lower bound.

Keywords: Amazon rainforests; degradation; cascading effects; spatial–temporal autoregression (search for similar items in EconPapers)
Date: 2023
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