Excessive heat poses a significant and growing threat to urban populations, exacerbated by climate change. This research addresses the unequal distribution of heat-related health risks and explores the potential of urban tree cover as a nature-based solution (NBS) for mitigation and adaptation. Climate change is increasing average summer temperatures and the frequency and intensity of heat waves, resulting in substantial mortality and morbidity globally and in the US. Epidemiological studies estimate thousands of excess heat-related deaths annually in the US, a number likely underestimated by medical records that may attribute death to other causes. Higher temperatures increase mortality and morbidity through heat stroke, exhaustion, and exacerbation of pre-existing conditions. Projections for future heat-related impacts vary, but general consensus points to increasing frequency and intensity of heat waves, causing increased mortality and morbidity. Urban trees offer a vital strategy for mitigating heat-related risks. Through shading and transpiration, trees reduce ambient air temperature, primarily within a few hundred meters of the canopy. While air temperature is a key factor, other metrics considering humidity and solar radiation also impact heat stress. Heat action planning, including early warning systems, cooling centers, and response plans, is critical, and incorporating urban tree cover as a key component is gaining traction. Existing research demonstrates that unequal distribution of urban tree cover often leaves low-income and POC neighborhoods with less tree cover and higher summer temperatures, compounding existing health disparities. Beyond health, excessive heat also increases peak electricity demand for cooling, leading to higher energy consumption and GHG emissions. Urban trees offer a mitigation strategy by reducing both air temperature and building solar insolation. Furthermore, urban trees provide crucial carbon sequestration and storage, acting as an NBS to address climate change. Previous studies estimated the carbon sequestration potential of US urban reforestation, but these often relied on coarser datasets that underestimated existing tree cover, potentially overestimating reforestation potential. This study aims to quantify the current inequality in the protective value of urban trees, estimate the potential benefits of increased tree cover through various reforestation scenarios, and evaluate the ROI of urban reforestation across different socioeconomic and racial demographics.

Existing literature highlights the significant and growing public health threat of excessive heat, particularly in urban areas, with climate change intensifying this risk. Studies consistently show a correlation between higher temperatures and increased mortality and morbidity, with varying projections for future impacts. The role of urban trees in mitigating urban heat islands and reducing heat-related health risks is well-established, showcasing their ability to lower ambient temperatures through shading and transpiration. However, the distribution of urban tree cover is highly unequal, with disparities often observed along socioeconomic and racial lines, resulting in uneven distribution of the health benefits derived from tree cover. Previous research also demonstrates the link between heat and increased electricity consumption for cooling, and the potential of urban trees to lessen this demand. Studies have explored the potential of urban reforestation for carbon sequestration, yet differences exist in methodologies and estimation of available plantable areas, impacting the accuracy of these estimations. A common thread among previous work is the need for high-resolution data and spatially explicit analysis to accurately assess the potential of urban trees to improve health outcomes, reduce energy demand and mitigate climate change.

This study utilized a four-phase approach. First, spatial data were compiled from various sources, including high-resolution (2m) tree cover maps, US Census data (2020), National Land Cover Database (2019), and Landsat 8 land surface temperature (LST) data (2016-2020). The analysis unit was the US Census block, providing demographic and socioeconomic data alongside land cover and temperature information for 5723 municipalities within 100 urbanized areas (>500 km²). An algorithm was developed to determine a plausible ambitious reforestation target, considering physical constraints (non-impervious surfaces) and social/political/climatic constraints (land use, regulations, and climate). The algorithm set targets at the 90th percentile of observed tree cover in each impervious surface category for each urbanized area. Various reforestation scenarios (5%, 10%, 15%, up to the ambitious scenario) were modeled. The impact of tree canopy increase on LST and air temperature was estimated using a two-step regression approach. The first regression related LST to impervious cover and tree cover, considering mesic and xeric climates; the second related air temperature to LST, allowing slopes to vary by biome. The health impacts (mortality, morbidity) of temperature changes were estimated using published epidemiological studies relating temperature changes to mortality (Bobb et al.) and morbidity (Gronlund et al.). Avoided electricity consumption and associated GHG emissions were calculated based on studies linking temperature increases to electricity use (Santamouris et al.) and using EPA data on carbon intensity of electricity generation. Economic benefits were calculated using the value of a statistical life (VSL) for mortality, a cost-of-illness (COI) approach for morbidity, and the EPA's Social Cost of Carbon (SCC) for carbon sequestration and avoided GHG emissions from reduced electricity consumption. Costs of tree planting and maintenance were considered to determine return on investment (ROI).

The study revealed significant inequalities in current urban tree cover and its associated benefits. Majority POC neighborhoods had 11% less tree canopy cover and 14% more impervious surface than majority white neighborhoods. Trees in white neighborhoods annually helped avoid 632 deaths, compared to 442 in POC neighborhoods, despite similar population sizes. This disparity extended to morbidity (30,131 more cases avoided in white neighborhoods) and electricity consumption (1.4 TWh more avoided in white neighborhoods). A case study of Washington, D.C., illustrated the neighborhood-scale variation in tree cover and heat risk reduction potential, showing that while the greatest reduction in heat risk per capita from additional planting occurred in denser, often POC, neighborhoods, the highest planting potential (measured by stems or carbon sequestration) was in suburban areas, which already had higher tree cover. Nationally, an ambitious reforestation scenario (1.2 billion trees) could reduce population-weighted mean summer air temperatures by 0.38°C, resulting in additional annual avoided mortality (464 deaths), morbidity (80,785 cases), and electricity consumption (4.3 TWh), alongside increased carbon sequestration (23.7 MtCO₂e yr⁻¹) and decreased electricity-related GHG emissions (2.1 MtCO₂e yr⁻¹). The total annual economic benefit of this scenario was estimated at USD 9.6 billion. While planting costs would generally exceed benefits, high-ROI neighborhoods (often those with less tree cover and majority POC populations) showed a positive ROI, particularly for a 5% nominal target (ROI = 1.12). The ROI of tree planting was greater in majority POC neighborhoods than in majority white neighborhoods for all planting ambitions.

This study's findings underscore the significant benefits of urban trees in mitigating heat-related health impacts and reducing energy demand, while also highlighting the stark inequalities in their distribution and associated benefits. The disparity in current tree cover and its consequential unequal distribution of health benefits between white and POC neighborhoods emphasizes the need for equitable urban greening initiatives. The ambitious reforestation scenario demonstrates the substantial potential for reducing heat-related mortality, morbidity, and energy consumption, coupled with significant climate mitigation benefits through carbon sequestration. While the substantial investment required is undeniable, the findings suggest that strategically targeting high-ROI areas, which are often characterized by lower tree cover and majority POC populations, can yield substantial benefits with a potentially positive ROI. This targeted approach could maximize both the environmental and social equity goals of urban greening. This study's results provide compelling evidence to support targeted investments in urban tree cover increases, particularly in areas where this investment would yield the highest ROI. The integration of urban forestry into broader climate adaptation and heat action plans is crucial to achieving both environmental and social justice goals.

This study quantifies the substantial but unequal benefits of urban trees in reducing heat-related health impacts and electricity consumption in the US. Significant disparities exist in tree cover between white and POC neighborhoods, leading to inequitable distribution of cooling benefits. An ambitious reforestation program is shown to provide substantial reductions in heat-related mortality, morbidity, and electricity consumption, along with significant carbon sequestration, but targeted investments are needed to maximize benefits and ensure equitable outcomes. Future research should focus on refining cost estimates for diverse planting scenarios and exploring mechanisms for implementing equitable urban greening initiatives, while incorporating social and cultural dimensions into urban planning for heat mitigation.

This study acknowledges several limitations. Spatial and temporal differences in input datasets (tree cover, impervious surface, LST, air temperature) may affect the accuracy of results. The definition of 'plantable' areas involves some subjectivity. The methodology for setting ambitious reforestation targets implicitly accounts for various factors influencing plantability but does not explicitly model them. Regional variation in certain factors (e.g., planting costs, canopy size) is not fully accounted for due to limitations in available data. Future research could address these limitations through more comprehensive data, refined modelling, and explicit incorporation of social and economic factors into reforestation planning.