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Fine scale data collection on vulnerability metrics is necessary for just policy outcomes. Those most likely to be disproportionately affected by specific climate risks should be identified early so that the needs of vulnerable communities (especially historically marginalized communities) can be addressed and mitigated in accordance with climate justice principles. While there is a growing body of event-specific and place-based studies, systematic studies on coastal populations at risk have typically not applied equity principles and have often ignored attributes such as race and ethnic composition, age structure, urban/rural classification, and housing tenure. Additionally, assumptions about future population trends depend on understanding past spatial patterns of change, as well as demographic and socioeconomic characteristics of the populations at risk, especially considering increasing coastal hazards. Yet, with few exceptions, research on coastal vulnerability has not analyzed changes in exposure over time and has not systematically addressed implications for communities of color over time. This paper seeks to fill these gaps. In this paper, using an equity lens and spatial demographic methods with the finest-resolution data available (census blocks), we estimate the extent of exposure and population change from 1990 to 2020 in the low elevation coastal zone in the continental United States. We find that the population of the LECZ has increased during this period, primarily by the growth of the urban population which has risen from about 22 million to 31 million persons. From 2000 to 2020, the urban population consistently grew at higher rates inside the LECZ than outside of it, reversing the pattern from the decade prior. We also examine changes in the population by race and Hispanic origin, urban and rural status, and a set of more expansive vulnerability themes. Our estimates, tabulated by counties and states, reveal the concentration and characteristics of exposure and changes to it over the past 30 years. Key findings include: residents of the LECZ are much older than average; Black residents are overrepresented in renter-occupied housing units in the urban LECZ; and from 2000 to 2020, Hispanic population growth was much higher in urban LECZ areas than urban areas elsewhere. These systematic insights into the demographic attributes of the populations most at risk of sea-level rise and associated coastal hazards can be used to ensure adaptation, mitigation, and disaster-related policies are tailored to the specific needs of these communities and actors at local, regional, and national scales. It also showcases how spatial methods can be used to understand demographic change and be put in place for future estimates of population in non-traditional units (e.g., coastal zones or other environmentally-vulnerable areas). 

Abstract New York City (NYC) faces many challenges in the coming decades due to climate change and its interactions with social vulnerabilities and uneven urban development patterns and processes. This New York City Panel on Climate Change (NPCC) report contributes to the Panel's mandate to advise the city on climate change and provide timely climate risk information that can inform flexible and equitable adaptation pathways that enhance resilience to climate change. This report presents up‐to‐date scientific information as well as updated sea level rise projections of record. We also present a new methodology related to climate extremes and describe new methods for developing the next generation of climate projections for the New York metropolitan region. Future work by the Panel should compare the temperature and precipitation projections presented in this report with a subset of models to determine the potential impact and relevance of the “hot model” problem. NPCC4 expects to establish new projections‐of‐record for precipitation and temperature in 2024 based on this comparison and additional analysis. Nevertheless, the temperature and precipitation projections presented in this report may be useful for NYC stakeholders in the interim as they rely on the newest generation of global climate models. 

By 2050, two-thirds of the world’s population is expected to be living in cities and towns, a marked increase from today’s level of 55 percent. If the general trend is unmistakable, efforts to measure it precisely have been beset with difficulties: the criteria defining urban areas, cities and towns differ from one country to the next and can also change over time for any given country. The past decade has seen great progress toward the long-awaited goal of scientifically comparable urbanization measures, thanks to the combined efforts of multiple disciplines. These efforts have been organized around what is termed the “statistical urbanization” concept, whereby urban areas are defined by population density, contiguity and total population size. Data derived from remote-sensing methods can now supply a variety of spatial proxies for urban areas defined in this way. However, it remains to be understood how such proxies complement, or depart from, meaningful country-specific alternatives. In this paper, we investigate finely resolved population census and satellite-derived data for the United States, Mexico and India, three countries with widely varying conceptions of urban places and long histories of debate and refinement of their national criteria. At the extremes of the urban–rural continuum, we find evidence of generally good agreement between the national and remote sensing-derived measures (albeit with variation by country), but identify significant disagreements in the middle ranges where today’s urban policies are often focused. 

Abstract. The collection, processing, and analysis of remote sensing data since the early 1970s has rapidly improved our understanding of change on the Earth's surface. While satellite-based Earth observation has proven to be of vast scientific value, these data are typically confined to recent decades of observation and often lack important thematic detail. Here, we advance in this arena by constructing new spatially explicit settlement data for the United States that extend back to the early 19th century and are consistently enumerated at fine spatial and temporal granularity (i.e. 250 m spatial and 5-year temporal resolution). We create these time series using a large, novel building-stock database to extract and map retrospective, fine-grained spatial distributions of built-up properties in the conterminous United States from 1810 to 2015. From our data extraction, we analyse and publish a series of gridded geospatial datasets that enable novel retrospective historical analysis of the built environment at an unprecedented spatial and temporal resolution. The datasets are part of the Historical Settlement Data Compilation for the United States (https://dataverse.harvard.edu/dataverse/hisdacus, last access: 25 January 2021) and are available at https://doi.org/10.7910/DVN/YSWMDR (Uhl and Leyk, 2020a), https://doi.org/10.7910/DVN/SJ213V (Uhl and Leyk, 2020b), and https://doi.org/10.7910/DVN/J6CYUJ (Uhl and Leyk, 2020c). 

Abstract Urban nature—such as greenness and parks—can alleviate distress and provide space for safe recreation during the COVID-19 pandemic. However, nature is often less available in low-income populations and communities of colour—the same communities hardest hit by COVID-19. In analyses of two datasets, we quantified inequity in greenness and park proximity across all urbanized areas in the United States and linked greenness and park access to COVID-19 case rates for ZIP codes in 17 states. Areas with majority persons of colour had both higher case rates and less greenness. Furthermore, when controlling for sociodemographic variables, an increase of 0.1 in the Normalized Difference Vegetation Index was associated with a 4.1% decrease in COVID-19 incidence rates (95% confidence interval: 0.9–6.8%). Across the United States, block groups with lower income and majority persons of colour are less green and have fewer parks. Our results demonstrate that the communities most impacted by COVID-19 also have the least nature nearby. Given that urban nature is associated with both human health and biodiversity, these results have far-reaching implications both during and beyond the pandemic. 

In May 2020, the New York City (NYC) Mayor’s Office of Climate Resiliency (MOCR) began convening bi-weekly discussions, called the Rapid Research and Assessment (RRA) Series, between City staff and external experts in science, policy, design, engineering, communications, and planning. The goal was to rapidly develop authoritative, actionable information to help integrate resiliency into the City’s COVID response efforts. The situation in NYC is not uncommon. Extreme events often require government officials, practitioners, and citizens to call upon multiple forms of scientific and technical assistance from rapid data collection to expert elicitation, each spanning more or less involved engagement. We compare the RRA to similar rapid assessment efforts and reflect on the nature of the RRA and similar efforts to exchange and co-produce knowledge. The RRA took up topics on social cohesion, risk communication, resilient and healthy buildings, and engagement, in many cases strengthening confidence in what was already known but also refining the existing knowledge in ways that can be helpful as the pandemic unfolds. Researchers also learned from each other ways to be supportive of the City of New York and MOCR in the future. The RRA network will continue to deepen, continue to co-produce actionable climate knowledge, and continue to value organizational sensemaking as a usable climate service, particularly in highly uncertain times. Given the complex, rare, and, in many cases, unfamiliar context of COVID-19, we argue that organizational sensemaking is a usable climate service.