One of President Joe Biden’s first executive orders promised that “disadvantaged communities” would receive at least 40 percent of the overall benefits of government spending on infrastructure, clean energy, and other climate-related programs. It’s a historic commitment to reducing pollution and bringing new investment to the areas most in need. But who the “Justice40” program ends up serving rests, in large part, on a deceptively simple question: What defines a disadvantaged community? While little has been released publicly about how this question is being adjudicated at the federal level, environmental justice leaders are currently grappling with it at the state level in New York, where the idea for Justice40 originated. In 2019, a coalition of Empire State environmental groups successfully lobbied for a similar provision to be included in a statewide climate change bill, now know as the Climate Leadership and Community Protection Act. Now, several members of that coalition are participating in a working group that’s developing the state’s official definition for “disadvantaged communities,” or DACs, under the supervision of the state’s Department of Environmental Conservation, or DEC. This isn’t a matter of crafting a statement that you might find in a dictionary. The unpaid advisory group, which includes the leaders of community organizations from across the state, has a much more complicated task. It involves not only deciding on a set of criteria for the definition, but also choosing the data points that will measure that criteria, and then working out how to combine those data points to score and rank every community in the state. These technical decisions will determine which of New York’s census tracts will be prioritized for pollution cleanup, clean energy programs, job training, public transportation improvements, and energy efficiency upgrades that lower utility bills — and which will not. The working group plans to finish its draft definition by September. It will then undergo a 120-day comment period during which at least six public hearings will be held before the definition is finalized. Many working group members are longtime environmental justice advocates who have played advisory roles in past government efforts to engage with communities. Several told Grist that they hope their participation in this foundational work is a break with those previous experiences. “For years what agencies have done is manage our expectations,” said Elizabeth Yeampierre, executive director of the Brooklyn-based nonprofit Uprose. “They have this dog and pony show where they basically cook the solutions, and then bring them to communities to see if we can provide them with input and respond to something that they created without us.” Yeampierre said this working group is an opportunity to demand a different kind of practice. “We’re saying that climate change really demands co-governance — that communities need to be seen as the experts and as a resource,” she explained. But in a state as geographically and socioeconomically diverse as New York, weighing the hardships that communities face and channeling them into a single equation is a tall order. Every decision has the potential to make the policy more or less effective at reaching communities that are the most marginalized, vulnerable, and in need of targeted assistance. The working group has had to wrestle with the limitations of key data sets, a bias toward urban areas in existing metrics, and the reality that even the best definition cannot alone overcome local resource and capacity constraints that might prevent the most disadvantaged communities from accessing funding.
During a working group meeting in June, Amanda Dwelley, director of quantitative research at the consulting firm Illume, compared the group’s project to baking a cake.
First, they need ingredients: in this case, geographic data sets that measure different types of disadvantages that communities experience. These might be measures of certain air and water pollutants like benzene, concentrations of health problems like asthma, socioeconomic vulnerabilities like poverty and race, or climate change-related risks like future flood projections. One of the first things the working group did when they began meeting late last summer was brainstorm as many of these “ingredients” as they could. The initial list included more than 150.
Dwelley, who was hired by the state to help guide the working group through this highly technical and data-centric process, said Illume then worked with the DEC to pare that initial list down to about 40 different metrics. Some of the items were eliminated because they were redundant, but many had to be dropped because there was simply no reliable statewide or census tract-level data — or no data at all — to measure them.
For example, though the group wanted to factor “access to public transportation” into their definition, the available data didn’t cover all of the state’s transit systems, making it impossible to compare communities by this metric. Inevitably, the method for identifying DACs will only be as true to life as the data that underlies it.
In some cases, however, there are workarounds for statistical shortcomings. While childhood lead exposure itself can’t be accurately assessed statewide, Illume pulled data on homes built before 1960, which tend to have lead paint, and is still working with the state’s Department of Health to see if there’s a more precise proxy measure.
At times, the group has also been able to use this opportunity to push the state to collect better data. Eddie Bautista, a working group member who is the executive director of the New York City Environmental Justice Alliance, has repeatedly stressed the importance of including land zoned for manufacturing in the criteria — data that exists locally but not in a statewide data set. In response, the DEC began compiling local zoning data from across the state, and the group will be able to include the metric in its definition.
After taking these steps to select their proverbial cake’s ingredients, the working group will also need to decide how to combine them all before baking their final definition.
For guidance on this step, the New York group has looked to California, which created its own definition for DACs in 2014 after launching its cap and trade program. The program requires major greenhouse gas emitters to pay into a climate investments fund, and 25 percent of the fund’s grants must go to DACs.
California developed its own environmental justice mapping tool, called CalEnviroscreen, which can be used to compare the cumulative burdens communities face throughout the state. The state uses that tool to identify DACs, drawing on 20 different criteria and grouping them into two main categories: pollution burden and population characteristics. An average score is calculated for each of the two categories for every census tract in the state, and then those scores are multiplied — the logic being that an individual’s socioeconomic and personal health status can exacerbate the risk of pollution exposures. For example, asthmatics are more sensitive to air pollution than non-asthmatics, and poor people tend to have less access to health care to address pollution-related illnesses.
The New York working group is leaning toward dividing its criteria into two very similar categories to be multiplied together: burdens and vulnerabilities. Burdens would include things like pollution, historical discriminatory practices like redlining, and climate change risks like extreme heat and flooding projections. Vulnerabilities would include socioeconomic factors and health issues like asthma.
There are other, more complicated ways to combine the criteria that might be warranted. For example, if the equation ends up designating DACs in an area like the Hamptons, which faces serious flooding and storm surge risks but is not vulnerable from an environmental justice standpoint, the group could double certain ingredients in the recipe, giving more weight to criteria like income or health disparities. (The median household income in Southampton is $122,000.)
Alternatively, the group could calculate scores for burdens and vulnerabilities separately, eliminate any communities that aren’t in the top percentile of both, and then combine the two scores for the remaining list and include only the highest-scoring out of those.
“There are so many little things we could be doing to guide the definition one way or the other,” said Illume managing director Alex Dunn during a working group discussion in March. “We need to be explicitly transparent about each of them.”
In addition to choosing ingredients and figuring out how to combine them, the third and perhaps most consequential step in this recipe is figuring out how to slice the cake when it comes out of the oven.
In February, Dunn presented a preliminary model identifying DACs based solely on income and racial demographics. As a result, New York City accounted for 69 percent of all DACs, despite containing just 43 percent of the state’s population. The exercise demonstrated that the sheer density of both poverty and people of color in the city are likely to lead to it being overrepresented, even after other criteria are included. Dunn suggested that the group might want to consider slicing the cake in such a way that ensured that DACs were more evenly spread throughout the state.
“Our choices here are really going to matter,” she said. “This is not something that should just be data-driven.”
One way to ensure a more even spread throughout the state would be to assign a fixed share of the DACs to New York City and an equal share to the rest of the state — for example, designating the top 25 percent of highest-scoring census tracts in NYC as DACs, as well as the top 25 percent of census tracts in the rest of the state.
At a later meeting in April, Illume updated its preliminary model to reflect criteria beyond race and income. Dunn showed the group maps that indicated where DACs would be under each scenario — one that strived for more regional parity, and one that just took the top scoring census tracts statewide. The difference was still stark.