Method to Assess Spatiotemporal Impact of Tree Canopy on Energy Related Carbon Emissions: Unpacking Fine Scale Social-Ecological-Infrastructural Urban Field Data

Abstract:

The national and global trillion-tree program as a part of nature-based solutions aims to create negative carbon emissions. Many cities (e.g., New York City, Los Angeles, Denver, etc.) actively participate in these programs in their climate actions. The impact of urban trees on carbon sequestration has been modeled. However, there is a wide variation when evaluating the impact of urban tree canopy on energy-related carbon emissions. Previous studies, assessing this impact based on modeling or observations of single buildings, have revealed the seasonal and spatial variation of energy and carbon savings related to urban trees at the individual building level. However, there are no explorations on how social-infrastructural-urban features and tree canopy together shape residential buildings’ energy use and carbon to inform urban planning and greenery interventions, due to the rare availability of fine-scale urban energy use data and the lack of method. We developed a partnership with a utility to collect the fine spatial-scale data at a monthly base in a US city. Taking advantage of this unique dataset, we developed a method to assess the spatiotemporal impact of tree canopy on energy-related carbon emissions in this city. We applied a non-parametric matching approach to identify two groups (census block group as the analysis unit) with similar social-demographic-infrastructural and urban form features, but with different tree canopy coverage levels. One group has higher tree canopy coverage (about 50% on average), and the other group has lower tree canopy coverage (about 15% on average). It was found that electricity use intensity in places with higher tree canopy coverage can be up to 26.5% lower than in places with lower tree canopy coverage in the summertime within a city at the block group level. The method developed in this research is applicable to other cities for evaluating how urban trees affect energy-related carbon emissions using data from utilities.