Drivers of Tree Canopy Loss in a Mid-Sized Growing City: Case Study in Portland, OR (USA)

YunJae Ock, Vivek Shandas (), Fernanda Ribeiro and Noah Young
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YunJae Ock: Toulan School of Urban Studies and Planning, Portland State University, Portland, OR 97201, USA
Vivek Shandas: Department of Geography, Portland State University, Portland, OR 97201, USA
Fernanda Ribeiro: Institute for Sustainable Solutions, Portland State University, Portland, OR 97201, USA
Noah Young: Institute for Sustainable Solutions, Portland State University, Portland, OR 97201, USA

Sustainability, 2024, vol. 16, issue 5, 1-20

Abstract: The benefits of the urban tree and tree canopy (UTC) are increasingly crucial in addressing urban sustainability. Yet, increasingly evident from earlier research is the distributional inequities of UTC and active efforts to expand tree plantings. Less is known about the dynamics of UTC loss over time and location. This study aims to understand the dynamics of UTC change, especially canopy loss, and to investigate the drivers of the loss. This study draws on a high–resolution dataset of an urban canopy in Portland, Oregon, USA, assessing changes in UTC from 2014 to 2020. By integrating demographic, biophysical, and policy data with UTC information, we use a spatial autoregressive model to identify the drivers of UTC loss. The results reveal an unexpected spatial distribution of UTC change: less gain in the neighborhoods with the least UTC, and greater loss in the neighborhoods with moderate UTC. This study identifies four primary drivers of UTC loss: socioeconomic characteristics, urban form, activities on trees, and residential status. Factors such as population density, race, and income have an impact on canopy loss, as well as the building footprint and the number of multifamily housing units; residential statuses, such as the proportion of owner-occupied housing and residential stability, impact canopy loss.

Keywords: urban tree canopy (UTC); urban forestry; canopy change; canopy loss; tree preservation; green infrastructure; spatial regression (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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