The Value of US Urban Tree Cover for Reducing Heat-Related Health Impacts and Electricity Consumption

Leveraging Generative AI Models in Urban Science

Since the late 2000s, cities have emerged as the primary human habitat across the globe, and this trend is anticipated to continue strengthening in the coming decades. As we increasingly inhabit human-designed urban spaces, it becomes crucial to understanding better how these environments influence human behavior and how individuals perceive the city. In this chapter, we begin by examining the interplay between urban form and social behavior, highlighting key indicators of urban morphology, and presenting state-of-the-art methodologies for data collection. Subsequently, we harness the computational capability of foundation models, the latest Artificial Intelligence (AI) generation, to simulate interactions between individuals and urban built environments in a diverse group of 21 cities across the globe. Through this exploration, we scrutinize the models' capacity to encapsulate the intricate complexities of how individuals behave and perceive cities. These examples demonstrate the potential of advanced AI systems to assist urban scientists in understanding cities, emphasizing the necessity for a meticulous evaluation of their capabilities and limitations for the optimal application of Generative AI in urban research and policymaking.

Street trees provide an opportunity to mitigate urban heat and reduce risk of high heat exposure

Climate change is exacerbating the need for urban greening and the associated environmental and human well-being benefits. Trees can help mitigate urban heat, but more detailed understanding of cooling effects of green infrastructure are needed to guide management decisions and deploy trees as effective and equitable climate adaptation infrastructure. We investigated how urban trees affect summer air temperature along sidewalks within a neighborhood of Tacoma, Washington, USA, and to what extent urban trees reduce risks of high summer temperatures (i.e., the levels regulated by state outdoor heat exposure rules intended to reduce heat-related illnesses). Air temperature varied by 2.57 °C, on average, across our study area, and the probability of daytime temperatures exceeding regulated high temperature thresholds was up to five times greater in locations with no canopy cover within 10 m compared to those with 100% cover. Air temperatures decreased linearly with increasing cover within 10 m, suggesting that every unit of added tree cover can help cool the air. Our findings highlight the value of trees in mitigating urban heat, especially given expected warming with climate change. Protecting existing urban trees and increasing tree cover (e.g., by planting street trees), are important actions to enhance climate change resilience of urban areas.

The Global Forest Health Crisis: A Public-Good Social Dilemma in Need of International Collective Action

Society is confronted by interconnected threats to ecological sustainability. Among these is the devastation of forests by destructive non-native pathogens and insects introduced through global trade, leading to the loss of critical ecosystem services and a global forest health crisis. We argue that the forest health crisis is a public-good social dilemma and propose a response framework that incorporates principles of collective action. This framework enables scientists to better engage policymakers and empowers the public to advocate for proactive biosecurity and forest health management. Collective action in forest health features broadly inclusive stakeholder engagement to build trust and set goals; accountability for destructive pest introductions; pooled support for weakest-link partners; and inclusion of intrinsic and nonmarket values of forest ecosystems in risk assessment. We provide short-term and longer-term measures that incorporate the above principles to shift the societal and ecological forest health paradigm to a more resilient state.

Variation in estimates of heat-related mortality reduction due to tree cover in U.S. cities

Heat-related mortality is one of the leading causes of weather-related deaths in the United States. With changing climates and an aging population, effective adaptive strategies to address public health and environmental justice issues associated with extreme heat will be increasingly important. One effective adaptive strategy for reducing heat-related mortality is increasing tree cover. Designing such a strategy requires decision-support tools that provide spatial and temporal information about impacts. We apply such a tool to estimate spatially and temporally explicit reductions in temperature and mortality associated with a 10% increase in tree cover in 10 U.S. cities with varying climatic, demographic, and land cover conditions. Two heat metrics were applied to represent tree impacts on moderately and extremely hot days (relative to historical conditions). Increasing tree cover by 10% reduced estimated heat-related mortality in cities significantly, with total impacts generally greatest in the most populated cities. Mortality reductions vary widely across cities, ranging from approximately 50 fewer deaths in Salt Lake City to about 3800 fewer deaths in New York City. This variation is due to differences in demographics, land cover, and local climatic conditions. In terms of per capita estimated impacts, hotter and drier cities experience higher percentage reductions in mortality due to increased tree cover across the season. Phoenix potentially benefits the most from increased tree cover, with an estimated 22% reduction in mortality from baseline levels. In cooler cities such as Minneapolis, trees can reduce mortality significantly on days that are extremely hot relative to historical conditions and therefore help mitigate impacts during heat wave conditions. Recent studies project highest increases in heat-related mortality in the cooler cities, so our findings have important implications for adaptation planning. Our estimated spatial and temporal distributions of mortality reductions for each city provide crucial information needed for promoting environmental justice and equity. More broadly, the methods and model can be applied by both urban planners and the public health community for designing targeted, effective policies to reduce heat-related mortality. Additionally, land use managers can use this information to optimize tree plantings. Public stakeholders can also use these impact estimates for advocacy.

Estimating Heat-Related Exposures and Urban Heat Island Impacts: A Case Study for the 2012 Chicago Heatwave

Accelerated urbanization increases both the frequency and intensity of heatwaves (HW) and urban heat islands (UHIs). An extreme HW event occurred in 2012 summer that caused temperatures of more than 40°C in Chicago, Illinois, USA, which is a highly urbanized city impacted by UHIs. In this study, multiple numerical models, including the High Resolution Land Data Assimilation System (HRLDAS) and Weather Research and Forecasting (WRF) model, were used to simulate the HW and UHI, and their performance was evaluated. In addition, sensitivity testing of three different WRF configurations was done to determine the impact of increasing model complexity in simulating urban meteorology. Model performances were evaluated based on the statistical performance metrics, the application of a multi-layer urban canopy model (MLUCM) helps WRF to provide the best performance in this study. HW caused rural temperatures to increase by ∼4°C, whereas urban Chicago had lower magnitude increases from the HW (∼2-3°C increases). Nighttime UHI intensity (UHII) ranged from 1.44 to 2.83°C during the study period. Spatiotemporal temperature fields were used to estimate the potential heat-related exposure and to quantify the Excessive Heat Factor (EHF). The EHF during the HW episode provides a risk map indicating that while urban Chicago had higher heat-related stress during this event, the rural area also had high risk, especially during nighttime in central Illinois. This study provides a reliable method to estimate spatiotemporal exposures for future studies of heat-related health impacts.

Urban Heat Island and Its Regional Impacts Using Remotely Sensed Thermal Data—A Review of Recent Developments and Methodology

Many novel research algorithms have been developed to analyze urban heat island (UHI) and UHI regional impacts (UHIRIP) with remotely sensed thermal data tables. We present a comprehensive review of some important aspects of UHI and UHIRIP studies that use remotely sensed thermal data, including concepts, datasets, methodologies, and applications. We focus on reviewing progress on multi-sensor image selection, preprocessing, computing, gap filling, image fusion, deep learning, and developing new metrics. This literature review shows that new satellite sensors and valuable methods have been developed for calculating land surface temperature (LST) and UHI intensity, and for assessing UHIRIP. Additionally, some of the limitations of using remotely sensed data to analyze the LST, UHI, and UHI intensity are discussed. Finally, we review a variety of applications in UHI and UHIRIP analyses. The assimilation of time-series remotely sensed data with the application of data fusion, gap filling models, and deep learning using the Google Cloud platform and Google Earth Engine platform also has the potential to improve the estimation accuracy of change patterns of UHI and UHIRIP over long time periods.

Natural climate solutions for Canada

Alongside the steep reductions needed in fossil fuel emissions, natural climate solutions (NCS) represent readily deployable options that can contribute to Canada's goals for emission reductions. We estimate the mitigation potential of 24 NCS related to the protection, management, and restoration of natural systems that can also deliver numerous co-benefits, such as enhanced soil productivity, clean air and water, and biodiversity conservation. NCS can provide up to 78.2 (41.0 to 115.1) Tg CO₂e/year (95% CI) of mitigation annually in 2030 and 394.4 (173.2 to 612.4) Tg CO₂e cumulatively between 2021 and 2030, with 34% available at ≤CAD 50/Mg CO₂e. Avoided conversion of grassland, avoided peatland disturbance, cover crops, and improved forest management offer the largest mitigation opportunities. The mitigation identified here represents an important potential contribution to the Paris Agreement, such that NCS combined with existing mitigation plans could help Canada to meet or exceed its climate goals.

The tree cover and temperature disparity in US urbanized areas: Quantifying the association with income across 5,723 communities

Urban tree cover provides benefits to human health and well-being, but previous studies suggest that tree cover is often inequitably distributed. Here, we use National Agriculture Imagery Program digital ortho photographs to survey the tree cover inequality for Census blocks in US large urbanized areas, home to 167 million people across 5,723 municipalities and other Census-designated places. We compared tree cover to summer land surface temperature, as measured using Landsat imagery. In 92% of the urbanized areas surveyed, low-income blocks have less tree cover than high-income blocks. On average, low-income blocks have 15.2% less tree cover and are 1.5⁰C hotter than high-income blocks. The greatest difference between low- and high-income blocks was found in urbanized areas in the Northeast of the United States, where low-income blocks in some urbanized areas have 30% less tree cover and are 4.0⁰C hotter. Even after controlling for population density and built-up intensity, the positive association between income and tree cover is significant, as is the positive association between proportion non-Hispanic white and tree cover. We estimate, after controlling for population density, that low-income blocks have 62 million fewer trees than high-income blocks, equal to a compensatory value of $56 billion ($1,349/person). An investment in tree planting and natural regeneration of $17.6 billion would be needed to close the tree cover disparity, benefitting 42 million people in low-income blocks.

Warming from tropical deforestation reduces worker productivity in rural communities

The accelerating loss of tropical forests in the 21st century has eliminated cooling services provided by trees in low latitude countries. Cooling services can protect rural communities and outdoor workers with little adaptive capacity from adverse heat exposure, which is expected to increase with climate change. Yet little is still known about whether cooling services can mitigate negative impacts of heat on labor productivity among rural outdoor workers. Through a field experiment in Indonesia, we show that worker productivity was 8.22% lower in deforested relative to forested settings, where wet bulb globe temperatures were, on average, 2.84 °C higher in deforested settings. We demonstrate that productivity losses are driven by behavioral adaptations in the form of increased number of work breaks, and provide evidence that suggests breaks are in part driven by awareness of heat effects on work. Our results indicate that the cooling services from forests have the potential for increasing resilience and adaptive capacity to local warming.

It is expected that tropical deforestation and related increases in heat exposure have negative impacts on labour productivity, but the size of the effect is not well known. Here, the authors show that deforestation reduces productivity by 8.22% in rural Indonesia and causes behavioural adaptation responses like more work breaks.

The impact of heat waves on daily mortality in districts in Madrid: The effect of sociodemographic factors

Although there is significant scientific evidence on the impact of heat waves, there are few studies that analyze the effects of sociodemographic factors on the impact of heat waves below the municipal level. The objective of this study was to analyze the role of income level, percent of the population over age 65, existence of air conditioning units and hectares (Ha) of green zones in districts in Madrid, in the impact of heat on daily mortality between January 1, 2010 and December 31, 2013. Seventeen districts were analyzed, and Generalized Linear (GLM) Poisson Regression Models were used to calculate relative risks (RR) and attributable risks (RA) for the impact of heat waves on mortality due to natural causes (CIEX:A00-R99). The pattern of risks obtained was analyzed using GLM univariates and multivariates of the binomial family (link logit), introducing the socioeconomic and demographic variables mentioned above. The results indicate that heat wave had an impact in only three of the districts analyzed. In the univariate models, all of the variables were statistically significant, but Ha of green zones lost significance in the multivariate model. Income level, existence of air conditioning units, and percent of the population over age 65 in the district remained as variables that modulate the impact of heat wave on daily mortality in the municipality of Madrid. Income level was the key variable that explained this behavior. The results obtained in this study show that there are factors at levels below the municipal level (district level) that should be considered as focus areas for health policy in order to decrease the impact of heat and promote the process of adaptation to heat in the context of climate change.

The Role of Individual and Small-Area Social and Environmental Factors on Heat Vulnerability to Mortality Within and Outside of the Home in Boston, MA

Climate change is resulting in heatwaves that are more frequent, severe, and longer lasting, which is projected to double-to-triple the heat-related mortality in Boston, MA if adequate climate change mitigation and adaptation strategies are not implemented. A case-only analysis was used to examine subject and small-area neighborhood characteristics that modified the association between hot days and mortality. Deaths of Boston, Massachusetts residents that occurred from 2000–2015 were analyzed in relation to the daily temperature and heat index during the warm season as part of the case-only analysis. The modification by small-area (census tract, CT) social, and environmental (natural and built) factors was assessed. At-home mortality on hot days was driven by both social and environmental factors, differentially across the City of Boston census tracts, with a greater proportion of low-to-no income individuals or those with limited English proficiency being more highly represented among those who died during the study period; but small-area built environment features, like street trees and enhanced energy efficiency, were able to reduce the relative odds of death within and outside the home. At temperatures below current local thresholds used for heat warnings and advisories, there was increased relative odds of death from substance abuse and assault-related altercations. Geographic weighted regression analyses were used to examine these relationships spatially within a subset of at-home deaths with high-resolution temperature and humidity data. This revealed spatially heterogeneous associations between at-home mortality and social and environmental vulnerability factors.