Reforestation Management to Prevent Ecosystem Collapse in Stochastic Deforestation

dc.contributor.authorChong, Fayuen
dc.contributor.committeechairAmacher, Gregory S.en
dc.contributor.committeecochairCobourn, Kelly M.en
dc.contributor.committeememberSullivan, Jayen
dc.description.abstractThe increasing rate of deforestation, which began decades ago, has significantly impacted on ecosystem services. In this context, secondary forests have emerged as crucial elements in mitigating environmental degradation and restoration. This study is motivated by the need to understand the reforestation management in secondary forests to prevent irreversible ecosystem damage. We begin by setting the drift and volatility in stochastic primary forests. However, it is more manageable to take control of replantation. We employ a dynamic programing approach, integrating ecological and economic perspectives to assess ecosystem services. To simulate a real-world case, we investigate the model in the Brazil Amazon Basin. Special attention is given to the outcome at the turning point, tipping point, and transition point, considering a critical threshold beyond which recovery becomes implausible. Our findings suggest that reducing tenure costs has advantages, while substitution between primary and secondary forests is not necessarily effective in postponing ecosystem collapse. This research contributes to a broader goal of sustainable forest management and offers strategic guidance for future reforestation initiatives in the Amazon Basin and similar ecosystems worldwide.en
dc.description.abstractgeneralDeforestation has been drawing attention from institutions since the 1940s, and this global issue has been discussed for its negative impacts and the ways to restore what has been lost. Reforestation initiatives introduced by global environmental organizations consider forest plantations essential in re-establishing trees and the natural ecosystem. This study aims to investigate how different techniques target the growth of secondary forests to mitigate the irreversible damage of ecosystem services. Our research begins by defining the uncertain primary forests. Primary forests and deforestation face long-term climate changes and immediate shocks like fires, droughts, and human activities, meanwhile, policymakers have difficulties predicting and fully controlling them. We integrate considerations of ecology and economy to the ecosystem functioning, introducing stochasticity in deforestation into our dynamic optimization problem. We apply our models to the Brazil Amazon Basin, a region known for its diverse tropical forests and vast cases of deforestation. We pay close attention to the timing of tipping point that leads to ecosystem collapse, the turning point where reforestation rate catches up with deforestation rate, and the moment of forest type transition. Through simulation and sensitivity analysis, we gain a better grasp on guiding the management of secondary forests under uncertain conditions. Our results indicate that reforestation approaches that lower tenure costs can be beneficial, but merely substituting primary forests cannot necessarily delay an ecosystem collapse. This paper provides practical insights for policymakers, local communities, and international organizations.en
dc.description.degreeMaster of Scienceen
dc.publisherVirginia Techen
dc.rightsAttribution-NonCommercial 4.0 Internationalen
dc.subjectecosystem collapseen
dc.subjectstochastic dynamic programmingen
dc.subjecttenure costen
dc.titleReforestation Management to Prevent Ecosystem Collapse in Stochastic Deforestationen
dc.typeThesisen Polytechnic Institute and State Universityen of Scienceen


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