This is a repository providing supporting data for the paper "**Future flooding increases unequal exposure risks to relic industrial pollution**" published in *Environmental Research Letters* (2022). Available at: https://iopscience.iop.org/article/10.1088/1748-9326/ac78f7 **Data:** For each of the study areas, we provide a selection of former industrial sites that have an elevated flood risk over the next 30 years. Flood risk (not shared here) is based on First Street Foundation's property level Flood Factor Scores (https://help.riskfactor.com/hc/en-us/articles/360047585694). **Variables**: - **fsid**: Unique First Street ID. This ID may be used to link site to First Street data on flood risk. - **last_year**: the last year we observe a site in a manufacturing directory - **first_year**: the first year we observe a site in a manufacturing - **tract_fips**: ID of the census tract the site is located in. - **major_group:** Primary 2-digit Standard Industrial Classification (SIC) Code - **prop_long:** longitude - **prop_lat:** latitude - **city:** Geographic area We provide data in two formats: 1. A plain text CSV providing coordinates 2. A GeoJSON file for use in a GIS (EPSG:4326) - *Edit on 07/13/2022 – EPSG updated from 4236 to 4326* **Data Sources as described in the manuscript:** **Data sources as described in the manuscript:** *Relic Site Data* - We collected address-level data on relic industrial sites in sectors that regularly report on-site hazardous waste releases. We used state manufacturing directories covering every 2-5 years from 1953 to 2010, excluding facilities still in operation after 2010 to spotlight relic sites that are no longer visibly active (Frickel and Elliott, 2018; Berenbaum et al., 2019). We exclude addresses containing P.O. boxes to avoid non-industrial headquarter sites. One limitation of focusing on relic sites detected from manufacturing directories is that we do not know the true presence or significance of contamination on respective sites. Furthermore, there may be heterogeneity across cities in toxic risk due to differences in the types of hazardous chemicals left behind and how they were deposited into on-site lands. To minimize this uncertainty, we select only relic sites in industrial sectors identified as having a high probability of historical onsite contamination known to be harmful to humans (Noonan and Vidich, 1992). Those sectors include chemicals and allied products, petroleum and coal products, rubber and plastics products, stone/clay/glass and concrete products, primary metal production, fabricated metal production, machinery and computer products, and transportation equipment manufacturing (Indicated by the variable ‘major_group’). In Noonan and Viditch’s study, the probability of site contamination in these sectors ranges from 80% to 95%. In Providence only, we also include jewelry manufacturing, which dominated local industry during much of the 20th century, utilizing heavy metals as well as polyvinyl chloride (PVC) and other organics. The result is an address-level database of 15,419 unique sites where hazardous industries operated but are no longer active across the six study areas. Directories of manufacturers are print directories produced regularly since the 1950s by a variety of private publishers and public institutions, typically using data from state business tax records. The consistent intent across all directories is to provide a comprehensive inventory for entities looking for manufacturers to produce their goods. Directories include basic firm information such as location, industrial sector, the products produced, and the number of employees. Print manufacturing directories are often available in local libraries and in the Library of Congress. Researchers also increasingly have access to manufacturing and city directories via digitization projects like the Internet Archive. For example, North Carolina’s 1990 manufacturing directory is viewable at http://archive.org/details/northcarolinaman1990nort. For a full description of the methods used for extracting data from directories see the semi-automated approaches described in Berenbaum et al. (2019), Bell et al. (2020) or alternatively, the manual approach of Frickel and Elliott (2018). *Flood Risk Projections Data* – To estimate the future flood risk of relic industrial properties, we use site-specific flood risk projection data from the First Street Foundation (FSF). To produce these projections, FSF uses a combination of 21 global climate models under the middle IPCC RCP 4.5 emissions scenario while considering a range of high (75th percentile) and low (25th percentile) scenarios. They also incorporate a baseline climate period of 1980-2010 and data on historical flood events (First Street Foundation, 2020b). FSF flood modelling methodology was independently reviewed and has since been used by the U.S. Environmental Protection agency in their 2021 report on evaluation of social vulnerability during climate change (EPA, 2021). Compared to FEMA estimates of property flood risk, the FSF identifies many more properties in the United States at risk (First Street Foundation, 2020a). Using this methodology, FSF calculates a Flood Factor (FF) for each property. The FF is an integer between 1 and 10 that captures the likelihood and severity of flooding by 2050, with 1 representing minimal risk and 10 representing extreme risk (First Street Foundation, n.d.). The number increases as the probability increases or as the depth increases, or both. We identify properties with a value of 3 or higher as being at an elevated risk of future flooding (i.e. what FSF characterizes as ‘moderate risk’ or greater). At this threshold, a property at ‘moderate risk’ (FF > 3) has at least a 6-12% chance of flooding by 2050, or a good chance of flooding to a depth of 6-9 inches by 2050. **References** Bell, S. et al. (2020) ‘Automated data extraction from historical city directories: The rise and fall of mid-century gas stations in Providence, RI’, *PLOS ONE*, 15(8), p. e0220219. Available at: https://doi.org/10.1371/journal.pone.0220219. Berenbaum, D. et al. (2019) ‘Mining Spatio-Temporal Data on Industrialization from Historical Registries’, *Journal of Environmental Informatics*, 34(1). Available at: https://doi.org/10.3808/jei.201700381. First Street Foundation (2020a) *1st National Risk Assessment: Defining America’s Growing Risk*. First Street Foundation. Available at: https://firststreet.org/research-lab/published-research/highlights-from-infrastructure-on-the-brink/ (Accessed: 14 October 2021). First Street Foundation (2020b) *First Street Foundation Flood Model (FSF-FM) Technical Documentation*. First Street Foundation, p. 78. Available at: https://assets.firststreet.org/uploads/2020/06/FSF_Flood_Model_Technical_Documentation.pdf. Frickel, S. and Elliott, J.R. (2018) *Sites Unseen: Uncovering Hidden Hazards in American Cities*. New York: Russell Sage Foundation. Noonan, F. and Vidich, C.A. (1992) ‘Decision Analysis for Utilizing Hazardous Waste Site Assessments in Real Estate Acquisition’, *Risk Analysis*, 12(2), pp. 245–251. Available at: https://doi.org/10.1111/j.1539-6924.1992.tb00672.x.