Modelling Four Neighbourhood-Scale Urban Forest Scenarios for 2050: Vancouver, Canada

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Abstract

Background: Urban forests are increasingly recognized as important tools in climate change mitigation and adaptation, prompting many cities to set tree canopy cover targets. However, current gaps in knowledge include understanding relationships and the feasibility of maximizing benefits between urban greening and other climate actions, such as densification. This study offers a data-driven and manageable framework for assessing current and anticipated future urban forestry conditions using spatial tree and built-form models. Methods: We spatially modelled 4 planting scenarios for increasing tree canopy cover by 2050 in a densifying neighbourhood in Vancouver, Canada, with low (< 10%) existing tree canopy. Results: Based on mortality assumptions, we aged out and replaced 1,853 to 2,445 trees since 2020. We added 6,079 to 11,726 trees across the 4 scenarios (10,228 to 15,823 total), increasing canopy cover from 7% in 2020 to a maximum of 16% by 2050. Despite rigorous tree planting, we were unable to achieve a 30% canopy cover target at neighbourhood scale. Tree replacement due to mortality was a major contributor to decreased canopy cover and volume in future scenarios. The 31% to 34% reduction in future canopy cover due to the replacement of aged-out trees was driven by changes on private parcels. Conclusion: Our systematic framework for generating and spatially modelling trees in a simulated future neighbourhood provides an opportunity for iteratively assessing multiple potential tree planting configurations. Future work for this project includes investigating social-ecological, outdoor shading, and building energy implications of various modelled urban forest strategies.

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APA

Czekajlo, A., Cheng, Z., Barron, S., Girling, C., & Nesbitt, L. (2024). Modelling Four Neighbourhood-Scale Urban Forest Scenarios for 2050: Vancouver, Canada. Arboriculture and Urban Forestry, 50(1), 18–56. https://doi.org/10.48044/jauf.2023.025

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