AUTHOR=Hsu Angel , Chakraborty Tirthankar , Thomas Ryan , Manya Diego , Weinfurter Amy , Chin Nicholas Jian Wei , Goyal Nihit , Feierman Andrew TITLE=Measuring What Matters, Where It Matters: A Spatially Explicit Urban Environment and Social Inclusion Index for the Sustainable Development Goals JOURNAL=Frontiers in Sustainable Cities VOLUME=2 YEAR=2020 URL=https://www.frontiersin.org/journals/sustainable-cities/articles/10.3389/frsc.2020.556484 DOI=10.3389/frsc.2020.556484 ISSN=2624-9634 ABSTRACT=

The Urban Environment and Social Inclusion Index (UESI) creates a new spatial framework to measure progress toward Sustainable Development Goal 11 (SDG-11). SDG-11 aims for cities to be both sustainable and inclusive by 2030 and conceptualizes this goal in spatially-explicit ways. Few data sources or indices, however, measure its progress in both a comprehensive (global coverage) and detailed (intra-city) manner. To address this gap, we use publicly-available datasets including detailed census data, satellite remote sensing, and crowdsourced data that provide global coverage and regular temporal resolution to develop spatially-explicit indicators to measure neighborhood-level environmental performance in 164 global cities. The UESI framework includes 10 indicators that assess air pollution, urban tree cover, public transit access, and urban heat at the neighborhood scale, and water stress and carbon dioxide emissions from fossil fuels at the city-level. We also present a new method for quantifying distributional equity to measure how evenly or unevenly cities are distributing environmental benefits and burdens across neighborhoods. We find that the majority of the UESI cities disproportionately burden lower-income communities with higher shares of environmental burdens and lower shares of environmental benefits. This finding holds true even in cities that perform highly on environmental indicators. In light of the challenging, rapidly evolving urban contexts, the UESI framework serves as a way of addressing some of the central challenges—data standardization, data gathering, and data localization—around the SDGs.