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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. 

Flooding occurs at different scales and unevenly affects urban populations based on the broader social, ecological, and technological system (SETS) characteristics particular to cities. As hydrological models improve in spatial scale and account for more mechanisms of flooding, there is a continuous need to examine the re- lationships between flood exposure and SETS drivers of flood vulnerability. In this study, we related fine-scale measures of future flood exposure—the First Street Foundation’s Flood Factor and estimated change in chance of extreme flood exposure—to SETS indicators like building age, poverty, and historical redlining, at the parcel and census block group (CBG) scales in Portland, OR, Phoenix, AZ, Baltimore, MD, and Atlanta, GA. We used standard regression models and accounted for spatial bias in relationships. The results show that flood exposure was more often correlated with SETS variables at the parcel scale than at the CBG scale, indicating scale dependence. However, these relationships were often inconsistent among cities, indicating place-dependence. We found that marginalized populations were significantly more exposed to future flooding at the CBG scale. Combining newly-available, high-resolution future flood risk estimates with SETS data available at multiple scales offers cities a new set of tools to assess the exposure and multi-dimensional vulnerability of populations. These tools will better equip city managers to proactively plan and implement equitable interventions to meet evolving hazard exposure.