The 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. / Master of Science / Deforestation 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.

The dissertation consists of three essays in forestry resource management, with focuses on investigating the ecosystem collapse and invasive species control problems. The first two papers consider the shift from primary forests to cleared land and secondary forests in the context of deforestation. This process is known to lead to irreversible tipping points that lead to the loss of ecosystem services. The past literature has discussed forest rotations under stochastic prices, timber volume, and amenity values. I extend this body of work to show how stochastic processes concerning primary forests could lead to ecological collapse. Drift and volatility in these processes explain different types of long-term and short-term shocks in tropical forest systems, such as fire, drought, or climate changes, all mechanisms that can drive ecosystem function to collapse. Common examples of severe ecosystem damage include the irreversible change from tropical forests to grassy savanna, fire events, and other climate problems. However, another case of uncertainty happens when ecosystem service production of primary and secondary forests itself is stochastic, so that there is a more complicated relationship between deforestation and reaching a point where ecosystem functions collapse. I compare and contrast these two cases to determine how drift and volatility determines the timing of a tipping point in a deforestation model where primary and secondary forests, as well as agricultural land, influence ecosystem function. I examine the sensitivity of the timing of collapse in both model variants to critical market and land-use parameters. The third chapter of this dissertation explores the connection between landowners' risk preferences, invasive species spread, and optimal control efforts. This study analyzes the control effort involved in neighboring infested and uninfested municipalities, which may have differing risk preferences. In the context of an application to the spread of Emerald ash borer (EAB) in the Twin Cities, Minnesota, I develop a simulation to explore the level of control and spread in a myopic policy scenario versus that in a first-best problem, where the two municipalities may be either risk neutral or risk averse. The results suggest that heterogeneity in risk aversion across the municipalities leads to lower control efforts and a longer time to drive the probability of spread to zero. / Doctor of Philosophy / This dissertation presents three studies that aim to support policy making for sustainable forest resource management. In the first two chapters, I examine the deforestation of native tropical forests, which damages ecosystems and leads to losses that can be irreversible, even with investments in reforestation. At present, the Amazon, Indonesia, and Africa are all witnessing the catastrophic collapse of forest systems as a result of deforestation. Prior work by economists on this topic has not considered how uncertainty in native forest losses may affect the likelihood of ecosystem collapse. Native forest losses may be uncertain when policymakers cannot fully control deforestation (as is often the case in developing countries) or when complex ecosystems are not well understood. I develop a model that incorporates long-term and short-term shocks in forest systems, such as fire, drought, or climate changes, all mechanisms that can drive the primary forest stock function to ecosystem collapse. Using this model, I examine the sensitivity of the timing of collapse to critical market and land-use parameters. In the final chapter of the dissertation, I present a novel model to study how risk preferences affect the management of invasive species. The model is inspired by the Emerald Ash Borer (EAB) infestation in the Twin Cities, Minnesota, which spread from St. Paul into Minneapolis and decimated valuable urban tree cover over the past several decades. I use the model to explore how differences in risk preferences between neighboring municipalities affect the control efforts they undertake, the probability of pest spread, and the consequent economic losses.