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Neighbourhood-level screening algorithms are increasingly being deployed to inform policy decisions. However, their potential for harm remains unclear: algorithmic decision-making has broadly fallen under scrutiny for disproportionate harm to marginalized groups, yet opaque methodology and proprietary data limit the generalizability of algorithmic audits. Here we leverage publicly available data to fully reproduce and audit a large-scale algorithm known as CalEnviroScreen, designed to promote environmental justice and guide public funding by identifying disadvantaged neighbourhoods. We observe the model to be both highly sensitive to subjective model specifications and financially consequential, estimating the effect of its positive designations as a 104% (62–145%) increase in funding, equivalent to US$2.08 billion (US$1.56–2.41 billion) over four years. We further observe allocative tradeoffs and susceptibility to manipulation, raising ethical concerns. We recommend incorporating technical strategies to mitigate allocative harm and accountability mechanisms to prevent misuse.

 

Exposure to climate hazards is increasing, and the experiences of frontline communities warrant meaningful and urgent attention towards how to mitigate, manage, and adapt to hazards. We report results from a community-engaged pilot (November 2021–June 2022) ofN= 30 participants in four frontline communities of the San Francisco Bay Area, California, USA. The study region is an area where low-income, non-English-speaking residents are inequitably exposed and vulnerable to wildfire smoke, extreme heat, and other climate hazards. Building from a yearslong partnership of researchers, community organizations, and community members, we report the feasibility of a project piloting (1) instruments to monitor indoor air quality, temperature, and participant sleep health, and (2) interventions to improve indoor air quality and support protective behaviors. Data collection included experience-based survey data (via in-person administered surveys and a smartphone application) and interviews about heat and air quality, as well as data from an air monitoring protocol. Results cover the prevalence of hazard exposure and protective actions among participants. We discuss throughout methods for conducting and evaluating a community-engaged pilot, particularly by using a community ambassador program. Implications include the feasibility of community-engaged research projects, including discussion of resources required to accomplish this work.

 

As sea level rises, urban traffic networks in low-lying coastal areas face increasing risks of flood disruptions. Closure of flooded roads causes employee absences and delays, creating cascading impacts to communities. We integrate a traffic model with flood maps that represent potential combinations of storm surges, tides, seasonal cycles, interannual anomalies driven by large-scale climate variability such as the El Niño Southern Oscillation, and sea level rise. When identifying inundated roads, we propose corrections for potential biases arising from model integration. Our results for the San Francisco Bay Area show that employee absences are limited to the homes and workplaces within the areas of inundation, while delays propagate far inland. Communities with limited availability of alternate roads experience long delays irrespective of their proximity to the areas of inundation. We show that metric reach, a measure of road network density, is a better proxy for delays than flood exposure. 

Increasing coastal flooding threatens urban centers worldwide. Projections of physical damages to structures and their contents can characterize the monetary scale of risk, but they lack relevant socioeconomic context. The impact of coastal flooding on communities hinges not only on the cost, but on the ability of households to pay for the damages. Here, we repurpose probabilistic risk assessment to analyze the monetary and social risk associated with coastal flooding in the San Francisco Bay Area for 2020–2060. We show that future coastal flooding could financially ruin a substantial number of households by burdening them with flood damage costs that exceed discretionary household income. We quantify these impacts at the census block group scale by computing the percentage of households without discretionary income, before and after coastal flooding costs. We find that for several coastal communities in San Mateo County more than 50% of households will be facing financial instability, highlighting the need for immediate policy interventions that target existing, socially produced risk rather than waiting for potentially elusive certainty in sea level rise projections. We emphasize that the percentage of financially unstable households is particularly high in racially diverse and historically disadvantaged communities, highlighting the connection between financial instability and inequity. While our estimates are specific to the San Francisco Bay Area, our granular, household‐level perspective is transferable to other urban centers and can help identify the specific challenges that different communities face and inform appropriate adaptation interventions.