Data from: Current inequality and future potential of US urban tree cover for reducing heat-related health impacts
Data files
Feb 26, 2024 version files 2.96 GB
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Figure_1_Annual_Avoided_Mortality_and_Its_Value_final.csv
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Figure_2_DC_data.csv
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Figure_3_Inequality_in_Mortality.csv
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Figure_4_Carbon_and_Health_Benefits_By_City.csv
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Figure_5_additional_trees_planted_versus_reduction_in_mortality.csv
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README.md
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SM_Final_Tree_Cover_Pixel-Level.zip
Abstract
Excessive heat is a major and growing risk for urban residents. Here, we estimate the inequality in summertime heat-related mortality, morbidity, and electricity consumption across 5,723 US municipalities and other places, housing 180 million people during the 2020 census. On average, trees in majority non-Hispanic white neighborhoods cool the air by 0.19 ± 0.05⁰C more than in POC neighborhoods, leading annually to trees in white neighborhoods helping prevent 190 ± 139 more deaths, 30,131 ± 10,406 more doctors’ visits, and 1.4 ± 0.5 terawatt-hours (TWhr) more electricity consumption than in POC neighborhoods. We estimate that an ambitious reforestation program would require 1.2 billion trees and reduce population-weighted average summer temperatures by an additional 0.38 ± 0.01⁰C. This temperature reduction would reduce annual heat-related mortality by an additional 464 ± 89 people, annual heat-related morbidity by 80,785 ± 6110 cases, and annual electricity consumption by 4.3 ± 0.2 TWhr, while increasing annual carbon sequestration in trees by 23.7 ± 1.2 MtCO2e yr-1 and decreasing annual electricity-related GHG emissions by 2.1 ± 0.2 MtCO2e yr-1. The total economic value of these benefits, including the value of carbon sequestration and avoided emissions, would be USD 9.6 ± 0.5 billion, although in many neighborhoods the cost of planting and maintaining trees to achieve this increased tree cover would exceeds these benefits. The exception is areas that currently have less tree cover, often majority POC, which tend to have a relatively high return-on-investment from tree planting.
README: Data from: Current inequality and future potential of US urban tree cover for reducing heat-related health impacts
https://doi.org/10.5061/dryad.zgmsbcckf
This data archives contains datasets generated the analysis of McDonald et al. 2024. Note that all input datasets to the analysis are publicly available (see the section on Data Sources in the Methods).
Description of the data and file structure
Figure 1- CSV file showing data corresponding to Figure 1 of the paper. Column names are in the first row, and are self explanatory. Figure 1 caption from the paper: "Figure 1. Mortality and urban tree canopy cover for our sample of 5,723 US municipalities. Shown are the current annual reduction in mortality due to trees, as well as the additional reduction in mortality possible under the ambitious planting scenario. This quantity can be expressed not just in annual lives saved (left axis) but also the estimated annual value of avoiding this mortality (right axis)."
Figure 2- Data corresponding to Figure 2 of the paper. The generated variables are shown in a CSV file. GISJOIN and GEOID are variable to link to the US Census Bureau blocks (included for convenience of users, in the Block_Data.gdb geodatabase generated from ArcGIS Pro), while the other column names are in the first row and are self explanatory. Figure 2 caption from the paper: "Figure 2. Inequality in the Washington, DC urbanized area. **a) Zoom-in of purple rectangle shown in other panels. Tree cover varies significantly from block to block. Neighborhoods that are predominately people of color (POC) are shown in grey and are primarily east of Rock Creek Park, while majority non-Hispanic white neighborhoods west of the park. Note that Rock Creek Park itself has no residents. b.) Land surface temperature in summer by census block, as observed by satellite imagery. For reference, the extent of the zoomed in panel in the upper left is outlined in purple. c.) The potential reduction in heat risk due to additional tree planting (increase in protective rate, in avoided annual deaths per million people). d.) Potential additional carbon sequestered due to new tree planting within census blocks. While tree planting in the city center occurs at higher population densities, and so benefits more people with heat reduction, the greatest number of potential trees can be planted in suburban areas."
For convenience, we also supply the high-resolution tree cover rasters for all urbanized used in our analysis. These are publicly available as part of: McDonald, R. I. et al. The tree cover and temperature disparity in US urbanized areas: Quantifying the association with income across 5,723 communities. PLoS ONE 16, e0249715 (2021).
Figure 3- CSV file showing data corresponding to Figure 3 of the paper. Each row is an urbanized area. Column names are in the first row, and are self explanatory. Figure 3 caption from the paper: "Figure 3- Protective value of urban tree canopy for large US urbanized areas. The size of the circle is proportional to the avoided annual mortality due to urban tree canopy in the urbanized area. The color of the circle indicates the difference in the protective rate (annual deaths avoided due to urban tree canopy per million population) between people-of-color (POC) majority neighborhoods and non-Hispanic white majority neighborhoods. Negative values indicate that POC neighborhoods have a lower protective rate than white neighborhoods."
Figure 4- CSV file showing data corresponding to Figure 4 of the paper. Each row is an urbanized area. Column names are in the first row, and are self explanatory. Figure 4 caption from the paper: "Figure 4. Ambitious reforestation potential for large US urbanized areas. The size of the circles is proportional to the potential additional carbon storage under the ambitious reforestation scenario. The color of the circle indicates the increase in the protective rate (annual deaths avoided due to urban tree canopy per million population) under the same reforestation scenario."
Figure 5- CSV file with data corresponding to Figure 5 of the paper. Column names are in the first row, and mean:
POC= Whether the block is majority people of color (POC) or not
priority = Whether the block is a priority or not, or all blocks.
DelExcessDeaths_FC0 and similar = The avoided deaths due to achieving the target. FC0 is the current tree cover, and shows the number of avoided deaths due to current tree cover. FC5, FC10, etc. are the decrease in annual mortality due to the additional tree cover in the 5%, 10%, etc. target.
DelExcessDeaths_FC0_error95 = The error in the estimation of the above DelExcessDeath calculation.
TreesFC5 and similar = The number of trees planted in a given tree planting scenario.
Figure caption for Figure 5:"Figure 5: Mortality reductions as a function of planting ambition. Blocks are classified as either majority people of color (POC) or majority non-Hispanic white. For each census block, additions of tree cover of 5%, 10%, etc., up to an Ambitious Scenario, the maximum possible given our assumptions. Each point along the curve thus represents an additional 5% increase in tree cover, wherever this is possible. Error bars are the 95% confidence interval of reduction in annual heat-related mortality. a.) Results for all census blocks. For instance, planting 200 million additional trees in white blocks for a 5% increase in tree cover reduces annual heat-related mortality by an additional 62 lives. b.) Results for high ROI census blocks, defined as in the top 5% of ROI (see methods for details). Note the very different scales in the two plots. For instance, planting 0.4 million additional trees in white blocks for a 5% increase in tree cover reduces annual heat-related mortality by an additional 2 lives."
Code/Software
Our code is archived on a linked Zenodo archive. The analysis is a series of codes for SAS 9.4, run in this order:
1.Basic_analysis_v2_urbanNCS_variant.sas
2.UrbanNCS_v2_1.sas
3.Air_temperature_regression v2_14.sas
4.benefits_v1_03.sas
5.Carbon_calculations_v2_10.sas
6.electricity_v1_0.sas
7.carbon_plus_health_plus_electricity_v4_11.sas
Input Data needed to run these scripts are in the InputData.zip file. To ease use, we have also included intermediate SAS data files that were used to pass data between the scripts.
While there are extensive comments throughout the scripts detailing the analytic methods, the origin of the code (sometimes from previous manuscripts), and version changes, we caution that this code sequence is saved here for archival purpose. It will likely be complex for those not involved in writing the code to understand and use it, especially since the comments were written for our external use not to be used by others. Please email if you have questions and we will do our best to explain technical points as time allows: rob_mcdonald@tnc.org
Methods
Our analysis proceeded in four phases. First, we assembled spatial data from multiple sources and compiled them to a common analysis unit. Second, we developed an algorithm that would set a plausible ambitious reforestation target, given other land-use constraints. Third, we estimated the heat mitigation-related benefits of current tree canopy and of future planting scenarios, up to the ambitious planting scenario. Benefits evaluated were avoided mortality, avoided morbidity, avoided electricity consumption, avoided release of greenhouse gases from avoided electricity consumption, and carbon sequestration in aboveground tree biomass. Fourth, we valued these benefits in monetary terms. See McDonald et al. 2024 in npj Urban Sustainability for Details.