University of Illinois Urbana-Champaign

The effectiveness of protected area funding for forest conservation and human development in the Peruvian Amazon

Nakamura Lam, Katia Sofia

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NAKAMURALAM-DISSERTATION-2022.pdf

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https://hdl.handle.net/2142/116192

Description

Title
The effectiveness of protected area funding for forest conservation and human development in the Peruvian Amazon
Author(s)
Nakamura Lam, Katia Sofia
Issue Date
2022-07-11
Director of Research (if dissertation) or Advisor (if thesis)
Miller, Daniel C.
Doctoral Committee Chair(s)
Brazee, Richard
Committee Member(s)
  • Baylis, Katherine
  • Waldron, Anthony
Department of Study
Natural Res & Env Sci
Discipline
Natural Res & Env Sciences
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
  • Protected Areas
  • Peru
  • Amazon
  • Deforestation
  • Impacts
  • Human Development
  • Poverty
  • Funding
  • Conservation finance
  • biodiversity
Abstract
Protected areas (PAs) are the most prominent conservation tool worldwide. Considerable evidence shows that PAs, from national parks to Indigenous and community conserved areas, effectively protect forests. However, many PAs do not perform at their highest potential due to inadequate access to financial resources. Studies have assessed the effects of protection, but there are no rigorous quantitative studies on the impacts of PA funding. This dissertation addresses this gap in the literature by estimating the impacts of PA funding on avoided deforestation and human development in the Peruvian Amazon. I developed a framework for estimating the impact of PA funding (Chapter 2), then applied it to quantify the effects of PA funding on avoided deforestation at the district level (Chapter 3) and the grid level (Chapter 5) and on the human development index (HDI) at the district level (Chapter 4). Data derive from a new method for identifying within-country conservation funding flows and a range of publicly available sources. I used quasi-experimental (Chapters 2–3) and machine learning (Chapter 5) approaches to reduce estimation bias. The results show generally positive impacts: PA funding avoided forest loss in the grid-level analysis and increased human development within districts while having mixed effects on forest cover at the district level. However, effect sizes were small, shaped by baseline forest characteristics and economic activities. Greater population density, Indigenous lands, non-state conservation areas, and mining areas had significant effects human development indicators. Funding avoided forest loss conditional on a number of biophysical and socio-economic factors, including temperature; baseline forest cover; poverty; and distance to towns, pastures, and mining. At the district level, altitude, forest cover outside PAs, and previous deforestation moderated the effect of funding, with higher effects in Andes-Amazon districts, as well as those with more unprotected forest cover and more deforestation before the treatment. Older PAs, PAs allowing sustainable use, and those that are comanaged showed larger impacts. PAs with greater funding and tourism were able to protect more forests. The most effective PAs were in the Amazon-Andes region, where their greater accessibility has made them more vulnerable to deforestation. On average, these PAs were also the most well-funded, receiving I$1,500–I$3,000/sq km. The least effective PAs were more remote, located on country borders, and received below-average funding. The findings of this dissertation suggest that investing more funds in PAs can lead to greater positive impacts for biodiversity and human development. However, the same amount of funding may have larger effects conditional on local threat levels. Investments addressing threats from illegal activities in highly accessible areas, promoting tourism, and securing forest-related livelihoods are found to be especially effective pathways to protect forests in the Peruvian Amazon.
Graduation Semester
2022-08
Type of Resource
Thesis
Handle URL
https://hdl.handle.net/2142/116192
Copyright and License Information
Copyright 2022 Katia Nakamura Lam

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