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Preparedness for adverse events is critical to building urban resilience to climate-related risks. While most extant studies investigate preparedness patterns based on survey data, this study explores the potential of big digital footprint data (i.e. population visits to points of interest (POI)) to investigate preparedness patterns in the real case of Hurricane Ida (2021). We further investigate income and racial inequality in preparedness by combining the digital footprint data with demographic and socioeconomic data. A clear pattern of preparedness was seen in Louisiana with aggregated visits to grocery stores, gasoline stations, and construction supply dealers increasing by nearly 9%, 12%, and 10% respectively, representing three types of preparedness: survival, mobility planning, and hazard mitigation. Preparedness for Hurricane Ida was not seen in New York and New Jersey states. Inequality analyses for Louisiana across census block groups (CBGs) demonstrate that CBGs with higher income have more (nearly 8% greater) preparedness in visiting gasoline stations, while CBGs with a larger percentage of the white population have more preparedness in visiting grocery stores (nearly 12% more) in the lowest income groups. The results indicate that income and racial inequality differ across different preparedness in terms of visiting different POIs.

 

Understanding spatial determinants, i.e., social, infrastructural, and environmental features of a place, which shape infectious disease is critically important for public health. We present an exploration of the spatial determinants of reported COVID-19 incidence across India’s 641 urban and rural districts, comparing two waves (2020–2021). Three key results emerge using three COVID-19 incidence metrics: cumulative incidence proportion (aggregate risk), cumulative temporal incidence rate, and severity ratio. First, in the same district, characteristics of COVID-19 incidences are similar across waves, with the second wave over four times more severe than the first. Second, after controlling for state-level effects, urbanization (urban population share), living standards, and population age emerge as positive determinants of both risk and rates across waves. Third, keeping all else constant, lower shares of workers working from home correlate with greater infection risk during the second wave. While much attention has focused on intra-urban disease spread, our findings suggest that understanding spatial determinantsacrosshuman settlements is also important for managing current and future pandemics.

 

A differentiated urban metabolism methodology is developed to quantify inequality and inform social equity in urban infrastructure strategies aimed at mitigating local in-boundary PM2.5 and co-beneficially reducing transboundary greenhouse gas (GHG) emissions. The method differentiates community-wide local PM2.5 and transboundary GHG emission contributions by households of different income strata, alongside commercial and industrial activities. Applied in three Indian cities (Delhi, Coimbatore, and Rajkot) through development of new data sets, method yields key insights that across all three cities, top-20% highest-income households dominated motorized transportation, electricity, and construction activities, while poorest-20% homes dominated biomass and kerosene use, resulting in the top-20% households contributing more than three times GHGs as the bottom-20% homes. Further, after including commercial and industrial users, top-20% households contributed as much or more in-boundary PM2.5 emissions thanallcommercial ORallindustrial emitters (e.g. Delhi’s top-20% homes contributed 21% of in-boundary PM2.5 similar to industries at 21%. These results enabled co-benefit analysis of various infrastructure transition strategies on the horizon, finding only three could yield both significant GHG and PM2.5 reductions (>2%-each): (a) Modest 10% efficiency improvements among top-20% households, industry and commercial sectors, requiring a focus on wealthiest homes; (b) Phasing out all biomass and kerosene use within cities (impacting poorest); (c) Replacing gas and diesel vehicles with renewable electric vehicles. The differentiated PM2.5 and GHG emissions data-informed social equity in the design of the three co-beneficial infrastructure transitions by: (a)-prioritizing free/subsidized clean cooking fuels to poorest homes; (b)-increasing electricity block rates and behavioral nudging for wealthiest homes; and, (c)-prioritizing electrification of mass transit and promoting electric two-wheelers ahead of providing subsidies for electric cars, where the free-rider phenomenon can occur, which benefits wealthiest homes. The methodology is broadly translatable to cities worldwide, while the policy insights are relevant to rapidly urbanizing Asia and Africa to advance clean, low-carbon urban infrastructure transitions.

 

Developing sustainable, livable and equitable cities is a major policy goal. However, livability metrics are amorphous, emphasizing different dimensions. This paper develops a novel data-driven approach by directly surveying subjective well-being (SWB) of urban residents, alongside satisfaction with key social–ecological–infrastructural–urban correlates to inform livability and equity priorities. Our survey is novel in quantifying SWB (Cantril ladder) of urban residents and evaluating both household- and neighborhood-level correlates while addressing confounding effects of socio-demographics and personality. We propose a three-way typology of provisioning systems—foundational, consistently important and added-bonus—based on their quantitative relationship with SWB. Implemented in the Twin-Cities USA, among 21 attributes, home heating-cooling, neighborhood greenery, access to public transportation and snow removal emerged as foundational in cold Minnesota climates; home size was consistently important and satisfaction with streets an added-bonus. Assessing inequality in foundational and consistently important categories revealed disparities by income and race, informing local infrastructure priorities for livability and equity. Key insights emerged on sufficiency and sustainability.

 

With the goal of informing local food-action planning, this paper develops the first county-level database detailing agrifood consumption and production across 3114 counties in the United States. The database covers 12 070 food items that comprise the entire diet, mapping them to the production demand of 95 agrifood commodities. Agrifood demand is delineated further into fresh and processed components, along with characterization of animal feed, and compared with local food production to yield the current local agrifood capacity (CLC). CLC results are shown for individual agrifoods and for aggregated categories (e.g., on average, 0.03 for fruits and nuts, 0.24 for vegetables, 0.31 for non-meat animal products) across all US counties. CLC results for the entire diet find that a large proportion of US counties can be self-sufficient in individual agrifood commodities (ranging from <0.5% of counties for agrifoods like hops, papayas, and artichokes to 59% of counties for beef), and 23% of US counties can supply over half of their total human dietary demand through local production, but only 9% of the US population resides in these counties. Such granular, subnational baselines are essential to inform future goal-setting for urban agriculture.

 

Cities seek nuanced understanding of intraurban inequality in energy use, addressing both income and race, to inform equitable investment in climate actions. However, nationwide energy consumption surveys are limited (<6,000 samples in the United States), and utility-provided data are highly aggregated. Limited prior analyses suggest disparity in energy use intensity (EUI) by income is ∼25%, while racial disparities are not quantified nor unpacked from income. This paper, using new empirical fine spatial scale data covering all 200,000 households in two US cities, along with separating temperature-sensitive EUI, reveals intraurban EUI disparities up to a factor of five greater than previously known. We find 1) annual EUI disparity ratios of 1.27 and 1.66, comparing lowest- versus highest-income block groups (i.e., 27 and 66% higher), while previous literature indicated only ∼25% difference; 2) a racial effect distinct from income, wherein non-White block groups (highest quintile non-White percentage) in the lowest-income stratum reported up to a further ∼40% higher annual EUI than less diverse block groups, providing an empirical estimate of racial disparities; 3) separating temperature-sensitive EUI unmasked larger disparities, with heating–cooling electricity EUI of lowest-income block groups up to 2.67 times (167% greater) that of highest income, and high racial disparity within lowest-income strata wherein high non-White (>75%) population block groups report EUI up to 2.56 times (156% larger) that of majority White block groups; and 4) spatial scales of data aggregation impact inequality measures. Quadrant analyses are developed to guide spatial prioritization of energy investment for carbon mitigation and equity. These methods are potentially translatable to other cities and utilities. 

If the material intensive enterprises in an urban area of several million people shared physical resources that might otherwise be wasted, what environmental and public benefits would result? This study develops an algorithm based on lifecycle assessment tools for determining a city’sindustrial symbiosis potential—that is, the sum of the wastes and byproducts from a city’s industrial enterprises that could reasonably serve as resource inputs to other local industrial processes. Rather than report, as do many previous papers, on private benefits to firms, this investigation focuses on public benefits to cities by converting the maximum quantity of resources recoverable by local enterprises into an estimate of the capacity of municipal infrastructure conserved in terms of landfill space and water demand. The results here test this novel approach for the district of Mysuru (Mysore), India. We find that the industrial symbiosis potential calculated based on analysis of the inputs and outputs of ∼1000 urban enterprises, translates into 84 000 tons of industrial waste, greater than 74 000 tons of CO₂e, and 22 million liters per day of wastewater. The method introduced here demonstrates how industrial symbiosis links private production and public infrastructure to improve the resource efficiency of a city by creating an opportunity to extend the capacity of public infrastructure and generate public health co-benefits.