Evaluating Impacts of Forests and Forest Policy: Methods and Applications using Satellite Data

This defense will be presented in person. Join us in Bren Hall 1414 or watch online using this link and passcode forests

ABSTRACT

Forests provide ecosystem services at a variety of scales, from local to global. Recent attention has focused on forests’ potential to mitigate climate change because of their ability to store carbon. They also provide substantial local benefits, as trees can mitigate pollution, reduce extreme temperatures, and enhance psychological well-being. Unfortunately, threats such as logging and commodity agriculture have led to substantial deforestation of primary forest. Other factors such as drought, fire, and insects also pose a threat, impeding the provision of ecosystem services and undermining climate mitigation potential. In order to design policies that can effectively deliver on the promise of forests, better evidence is needed to quantify the social costs and benefits that forests provide and understand how policy can be best designed to support both forest ecosystems and local actors. 

Advances in earth observation have made more data detailing the dynamics of land cover and land use change available than ever before. In response, a growing body of work has emerged that integrates econometric methods of causal inference with these big data in order to evaluate the effectiveness of various policy designs. This has become particularly true in the context of forest conservation, where these quasiexperimental impact evaluations are increasingly used to inform new policy. However, factors inherent to their structure such as measurement error and irreversibility may affect the performance of common econometric approaches. Further work is needed to help researchers grapple with how these data can be best integrated with econometric methods of causal inference, thereby providing more informative insight into policy design.

This dissertation seeks to ask and answer three core questions: 1) How does the binary and irreversible structure of most deforestation datasets affect the performance of typical panel econometric methods of causal inference; 2) Can policymakers design payments for ecosystem services programs to capitalize on the potential of forest-based climate solutions while supporting livelihoods in the reforestation context? and 3) How does ecosystem degradation, specifically invasive species induced tree cover loss, impact education outcomes?

BIO

Albert is a PhD Candidate in the Bren School of Environmental Science & Management focused broadly on environmental economics. He previously received a B.A. in Mathematics and Economics from Willamette University. Albert's research interests lie in the economics of environmental conservation, land use change, and natural climate solutions. During his time at UCSB, he has been a member of the Conservation Economics Lab and worked as a researcher for the Environmental Markets Lab on multiple projects. When not thinking about environmental economics, Albert can be found hiking on one of the Santa Barbara area trails or casting a fly to a hungry trout.