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In 2019, the National Assembly of the Republic of Korea passed a law identifying particle pollution (also called particulate matter, PM) as a “social disaster” (Framework Act on the Management of Disasters and Safety 2019). It was a response to nationwide attention to particle pollution from 2017, when apocalypse-like particle pollution occurred. It is not uncommon to characterize pollution as a disaster. Pollution is often described in damage-based narratives like disasters because environmental pollution becomes visible when a certain kind of damage occurs (Nixon 2011). PM is a mixture of extremely small particles and liquid droplets (EPA 2023). An established method for assessing the health risks associated with PM is the utilization of government or World Health Organization (WHO) air quality indices. These indices reflect the potential harm to human health based on PM concentrations. However, due to the limitations of the available monitoring data and the assumption of a certain normality according to the air quality index, its utility is diminished for bodies that fall outside this assumed range of normality. The existing practices and knowledge in pollution control had individualized pollution by presuming certain states of normalcy and excluding others. To challenge this, the anti-PM advocates in South Korea have defined, datafied, perceived, and adjusted the toxicity of particulate matter in various ways. They refer to the air quality index given by the WHO or the government, but they also set their own standards to match their needs and ways of life. They actively measure the air quality of their nearest environment and share, compare, and archive their own data online. The fact that the severity of air pollution is differently tolerated by individuals challenges the concept of the toxicity index that presupposes a certain normalcy. Describing pollution as a disaster contributes to environmental injustice by obscuring the underlying context and complexities of pollution. With the values of care, solidarity, and connectivity, capturing different perspectives of living with pollution and listening to stories from different bodies can generate alternative knowledge challenging environmental injustice. Drawing upon the stories of different bodies and lives with pollution, we can imagine other ways of thinking about the environment and pollution that do not externalize risks nor individualize responsibility. 

Kim, Seohyung. 2023. "Beyond the Index: Stories of Otherized Bodies Crafting Resistant Narratives against Environmental Injustice in South Korea." In 4S Paraconference X EiJ: Building a Global Record, curated by Misria Shaik Ali, Kim Fortun, Phillip Baum and Prerna Srigyan. Annual Meeting of the Society of Social Studies of Science. Honolulu, Hawai'i, Nov 8-11.

In her article, Scharenberg (2023) provides methodological reflections on politically engaged or militant social science research. In one section, she discusses the challenge that social movements act as knowledge producers in their own right, often working independent from or outside of academic institutions (2023, 15). This raises questions about what social scientiss add to the mix. I've had similar questions working with and alongside activists in the global anti-plastics movement. Building on Casa-Cortes, Osterweil, and Powell (2013), Scharenberg points out that one response for scholars is to act as "editors" or "curators" of collective knowledge. This argument resonates with the way that I and other collaborators have thought about the engaged ethnographic archive projects:

Activist ethnographers thus become editors of collective knowledges rather than the sole producers of scientific theory. Like a literary editor, the ethnographer works from a position, which does not create knowledges from scratch, but collects the perspectives of others and assembles them with reference to the given context. In this view, objectivity might be achieved, to borrow an expression from Haraway, by assembling “partial views and halting voices” into what she calls a “collective subject position” (1988: 590). Alternatively, we might think of the editor-ethnographer as Berger’s “clerk of the records” (Scheper-Hughes, 1995: 419) who compiles the history of a group of people. Scheper-Hughes understands this position as a kind of witness. (Scharenberg 2023, 16). 

During my thesis project, Tim has served as a collaborator and mentor while he studied data use among activists opposing Formosa Plastics Group (FPG). In addition to connecting me with activists and interview candidates, he also introduced me to a small network of American and Taiwanese students in Taiwan and the United States studying FPG. This community can share resources and knowledge to further our individual studies. Could this academic network serve as a parallel to the transnational activist alliances I am studying? Are the strengths and barriers of research alliances reminiscent of the strengths and barriers of activist alliances?

The SVI has been used to assess hazard mitigation plans in the southeastern US, evaluate social vulnerability in connection to obesity, explore the impact of climate change on human health, create case studies for community resilience policy, and even to look beyond disasters in examining a community’s physical fitness. 

The SVI was also used by public health researchers to explore the association between vulnerability and covid-19 incidence in Louisiana Census Tracts. Previous research examining associations between the CDC SVI and early covid-19 incidence had mixed results at a county level, but Biggs et al.’s study found that all four CDC SVI sub-themes demonstrated association with covid-19 incidence (in the first six months of the pandemic). Census tracts with higher levels of social vulnerability experienced higher covid-19 incidence rates. Authors of this paper point to the long history of racial residential segregation in the United States as an important factor shaping vulnerability and covid-19 incidence along racialized lines, with primarily Black neighborhoods typically most disadvantaged relative to primarily white neighborhoods. The compounding factors shaping vulnerability along racialized lines—high rates of poverty, low household income, and lower educational attainment—are identified as shaping the likelihood of covid-19 infection. The authors encourage policy initiatives that not only mitigate covid-19 transmission through allocation of additional resources and planning, but that also “address the financial and emotional distress following the covid-19 epidemic among the most socially vulnerable populations” (Biggs et al., 2021).

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Biggs, Erin N., Patrick M. Maloney, Ariane L. Rung, Edward S. Peters, and William T. Robinson. 2021. “The Relationship Between Social Vulnerability and COVID-19 Incidence Among Louisiana Census Tracts.” Frontiers in Public Health 8. https://www.frontiersin.org/article/10.3389/fpubh.2020.617976.

Lehnert, Erica Adams, Grete Wilt, Barry Flanagan, and Elaine Hallisey. 2020. “Spatial Exploration of the CDC’s Social Vulnerability Index and Heat-Related Health Outcomes in Georgia.” International Journal of Disaster Risk Reduction 46 (June): 101517. https://doi.org/10.1016/j.ijdrr.2020.101517.

The CDC SVI has been acknowledged to be limited in capturing accurate representations of small-area populations that experience rapid change between censuses (e.g. New Orleans in the years following Hurricane Katrina).

The Index is also limited, like other mapping tools, by the lack of homogeneity within any census tract or county/parish. There may very well be more vulnerable communities and individuals living in overall less vulnerable areas. Homeless populations may also specifically not be represented within studies that rely on geocoding by residential address. Length of residence within a geographic area may also impact results.  

The index is also limited by calculations that account for where people live, but not necessarily where they work or play. The lives of individuals are not necessarily restricted to the boundaries of a census tract or county/parish. 

Lastly, vulnerability is only one component of several components that are important for public health officials and policymakers to consider—the hazard itself, the vulnerability of physical infrastructure, and community assets and resources are other elements that must be taken into account for reducing the effects of a hazard.

This data resource has also been critiqued by Bakkensen et al. for not having been explicitly tested and empirically validated to demonstrate that the index performs well (a problem they identify as characterizing multiple indices).

Bakkensen, Laura A., Cate Fox-Lent, Laura K. Read, and Igor Linkov. 2017. “Validating Resilience and Vulnerability Indices in the Context of Natural Disasters.” Risk Analysis 37 (5): 982–1004. https://doi.org/10.1111/risa.12677.

There is a national data set that ranks all counties or census tracts within the entire data set (useful for a multi-state analysis). The user also has the option to utilize a state data set, which ranks counties or census tracts only within the state selected.

The CDC/ATSDR SVI draws on the U.S. Census Bureau’s American Community Survey (ACS), 5-year estimates. The most recently available SVI dataset is for 2018, which utilizes the ACS 2014-2018 data.

Users must select the ranking variable for either the overall vulnerability index score or for one of the four sub themes: Socioeconomic Status, Household Composition & Disability, Minority Status & Language, or Housing Type & Transportation.

A dictionary of terms used in this data resource are available at the bottom of this webpage: https://www.atsdr.cdc.gov/placeandhealth/svi/documentation/SVI_documentation_2018.html.

The CDC/ADSDR SVI is designed to help public health officials and local planners with preparing and responding to emergency events like hurricanes, disease outbreaks, or exposure to dangerous chemicals. The SVI databases and maps can be used to estimate the amount of supplies need (e.g. food, water, medicine, etc.), to identify areas in need of emergency shelters, to estimate the number of emergency personnel need, to create evacuation plans, and to “identify communities that will need continued support to recover following an emergency or natural disaster” (https://www.atsdr.cdc.gov/placeandhealth/svi/fact_sheet/fact_sheet.html).

The SVI determines the social vulnerability of every census tract in the United States. The index ranks each tract on 15 factors grouped into four related themes (see below).

Each census tract/county has a percentile ranking that represents the proportion of tracts/counties for which the tract/county of interest is equal to or lower in terms of social vulnerability. Higher percentile ranking values indicate greater vulnerability. For instance, ranking of 0.85 indicates that the tract/county of interest is more vulnerable than 85% of tracts/counties but less vulnerable than 15% of tracts/counties.

The CDC defines social vulnerability as the extent to which certain social conditions might affect a community’s capacity to respond to a disaster and prevent human suffering and financial loss.

Starting in 2014, the CDC has also added a database for Puerto Rice, as well as for Tribal Census Tracts, which are defined independently of standard county-based tracts.

Overall Vulnerability

1. Socioeconomic Status

• Below Poverty
• Unemployed
• Income
• No High School Diploma

2. Household Composition and Disability

• Aged 65 of Older
• Aged 17 or Younger
• Civilian with a Disability
• Single-Parent Household

3. Minority Status and Language

• Minority
• Speaks English “Less than Well”

4. Housing Type and Transportation

• Multi-Unit Structures
• Mobile Homes
• Crowding
• No Vehicle
• Group Quarters

In 2018, two adjunct variables (not included in the overall SVI rankings) were added: 2014-2018 ACS estimates for persons without health insurance, and an estimate of daytime population taken from LandScan 2018.