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1. New rail transit stations and the out-migration of low-income residents

   https://doi.org/10.1177/0042098019836631  

   Delmelle, Elizabeth ; Nilsson, Isabelle ( January 2020 , Urban Studies)

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   This article tests the hypothesis that low-income residents disproportionately move out of neighbourhoods in close proximity to new rail transit stations. This transit-induced gentrification scenario posits that the development of rail transit will place an upward pressure on land and housing values and that higher-income residents will outbid low-income residents for this new amenity. The most transit-dependent population may therefore be displaced from the most accessible locations, forming a paradox in the investment in new transit systems. We test this hypothesis using the Panel Study on Income Dynamics (PSID) dataset to trace the out-migration of residents across the United States from census tracts within five years of the opening of a new station, between 1970 and 2014. We find that low-income individuals are more likely to move, regardless of their neighbourhood. However, we do not find significant evidence that low-income individuals are more likely to move out of transit neighbourhoods, after controlling for both individual and other neighbourhood characteristics. The odds of moving out of a transit neighbourhood for low-income residents is statistically insignificant. In other words, they do not have a heightened probability of leaving new transit neighbourhoods compared with other residents. Our results are robust across decades, when examining renters alone, for different time spans and for varying definitions of transit neighbourhoods. We further find that those living in transit neighbourhoods are not more likely to live in a crowded dwelling. Our results therefore suggest that, on average, across the nation, low-income residents do not disproportionately exit new transit neighbourhoods. 

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2. Who's Moving In? A Longitudinal Analysis of Home Purchase Loan Borrowers in New Transit Neighborhoods

   https://doi.org/10.1111/gean.12234  

   Delmelle, Elizabeth C. ; Nilsson, Isabelle ; Schuch, Johanna Claire ( March 2020 , Geographical Analysis)

   This article examines the characteristics of residents moving into new rail transit neighborhoods using longitudinal, individual‐level data from the Housing Mortgage Disclosure Act. To disentangle the role of transit from other neighborhood amenities that may give rise to shifts in the socioeconomic or demographic profile of homebuyers, an exploratory text analysis is first performed on property advertisements in transit‐adjacent neighborhoods. This informs the creation of variables for our models that estimate the probability of an applicant applying for a loan by race and income, and highlights where light rail is most prominently advertised as an amenity. We do not find that the announcement of a new light rail line significantly alters the income profile of loan applicants. Rather, proximity to the center city is a more important determinant in attracting higher income applicants. We do find that the announcement of the transit line is significant in explaining changes in the racial profile of applicants. Postannouncement, White applicants are significantly more likely to apply for loans in transit‐adjacent neighborhoods, while Blacks are significantly less likely to. As for other amenities, the walkability of a neighborhood is significant in predicting where White applicants are more likely to apply for home purchase loans.

    

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3. Measuring social equity in urban energy use and interventions using fine-scale data

   https://doi.org/10.1073/pnas.2023554118  

   Tong, Kangkang ; Ramaswami, Anu ; Xu, Corey ; Feiock, Richard ; Schmitz, Patrick ; Ohlsen, Michael ( June 2021 , Proceedings of the National Academy of Sciences)

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   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. 

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

   https://doi.org/https://doi.org/10.1371/journal.pone.0249715  

   McDonald, R.I. ; Biswas, T. ; Sachar, C. ; Housman, I. ; Boucher, T.M. ; Balk, D. ; Nowak, D. ; Spotswood, E. ; Stanley, C.K. ; Leyk, S. ( January 2021 , PloS one)

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   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. 

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5. Socially-differentiated urban metabolism methodology informs equity in coupled carbon-air pollution mitigation strategies: insights from three Indian cities

   https://doi.org/10.1088/1748-9326/ac881e  

   Nagpure, Ajay Singh ; Tong, Kangkang ; Ramaswami, Anu ( September 2022 , Environmental Research Letters)

   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.

    

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