In August, the Environmental Protection Agency, or EPA, finalized the Clean Power Plan to cut carbon pollution from the nation’s power plants and require states to comply with clean energy targets by 2022. As part of the final rule, the EPA included a new program called the Clean Energy Incentive Program, or CEIP, which is designed to incentivize states to begin energy efficiency and renewable energy projects before this date.

The CEIP allows states to give emissions rate credits, or ERCs, for projects and programs that generate zero carbon energy or reduce energy demand by 2020 and 2021, respectively; states need these ERCs to meet the Clean Power Plan’s pollution reduction goals. The EPA will match credits for each megawatt-hour of carbon-based energy use that is avoided, up to the equivalent of 300 million short tons of carbon dioxide. Nationwide, 300 million short tons is roughly the amount of carbon dioxide, or CO2, emitted from 71.4 power plants per year, according to the EPA’s greenhouse gas equivalencies calculator. Notably, EPA will give a two-to-one credit for eligible energy efficiency, or EE, projects in low-income communities.

Many details of the CEIP are still under consideration. This week, the EPA closed a comment period that sought input on the program’s design, including how to define low-income communities for the purpose of rewarding early action on energy efficiency programs. The Center for American Progress recommends that the EPA define these low-income communities not by their geographic location but by their household characteristics, regardless of location in a particular state. This will maximize the number of low-income residents who could benefit from the program.

EPA’s extra incentives for energy efficiency, or EE, programs in low-income communities are particularly important for three reasons. Low-income communities pay a disproportionate burden of energy costs: In fiscal year 2014, low-income households spent an estimated mean of 16.3 percent of their household income on energy costs, compared with 3.5 percent for wealthier households. Low-income communities also disproportionately suffer from coal-fired power plant pollution and the ensuing effects of climate change. Furthermore, without expanding EE—and renewable energy—opportunities beyond upper- and middle-income households, the United States will fail to meet its 2025 carbon pollution reduction goals.

One approach: Defining low-income communities by geography

The EPA specifically sought stakeholder feedback on the definition of low-income communities. The agency faces two distinct choices: whether to define a low-income community as a contiguous geographic area or as a group of people having similar characteristics. While the former might be administratively easier, it would not have the reach and positive impact of the latter.

If the EPA were to use a geographic definition of a low-income community, they could utilize other agencies’ definitions of low-income communities for federal programs. Such agency programs include the U.S. Department of Housing and Urban Development’s Promise Zones and the Small Business Association HUBZone definition and qualifying areas. These programs are each designed to benefit low-income constituencies, but for different purposes.

These and other definitions of low-income communities rely partly on census tract data or equivalent county divisions that demarcate percentages of households living at the poverty rate or below a specific percentage of the median gross income for the rest of the census tract. Such an approach is fairly simple, but using census tract data may not be an accurate way of reaching all low-income communities. For example, a county or census tract that meets a low-income definition threshold may also be home to areas with median or high-income earners. Additionally, census data is relatively static, and may not account for recent changes in a region to the proportion of people living in poverty. Solely relying on census data could result in EE programs inadvertently targeted at middle- or higher-income communities.

If EPA relies on census tract data to determine low-income community eligibility, it will have to determine the specific concentration of income eligibility level within each tract. Other agency programs define low-income areas through a range of concentrated poverty levels from roughly 20 percent to 40 percent. While areas with poverty rates of 40 percent or more are high-poverty areas that should be served, the effects of concentrated poverty begin to appear at the 20 percent rate. However, an area with an overall 20 percent poverty rate may include a significant number of high-wage households that fall within a strictly geographically defined low-income area. Thus, a 30 percent poverty rate—similar to the Promise Zones initiative’s 32.5 percent rate for the set boundary—would be an effective concentrated poverty rate for CEIP EE programs in geographic areas.

Preferred approach: Defining low income by household characteristics

To maximize the efficacy of the CEIP and its low-income EE incentives, EPA should consider a way to define households, not areas, as eligible for CEIP low-income EE programs. Ideally, this eligibility would be at the individual household level. However, that could place a costly and high burden of verification on the EPA. To find an appropriate level of verification for income levels and to maximize outreach to low-income households, EPA could use the existing definition of eligible households for the Low Income Home Energy Assistance Program, or LIHEAP, and the Weatherization Assistance Program, or WAP.

LIHEAP offers financial assistance for heating and cooling expenses to eligible low-income households through the U.S. Department of Health and Human Services. WAP improves EE for low-income households through the U.S. Department of Energy, or DOE. Eligibility rates for LIHEAP participants are set by each state, with overall federal income eligibility limited to households at or below 150 percent of the federal poverty income guidelines, or 60 percent of the state median income, whichever is higher. States can also make LIHEAP assistance available to households where at least one member receives Temporary Assistance for Needy Families, Supplemental Security Income, Supplemental Nutrition Assistance Program, and pre-specified veterans’ programs.

WAP relies on both income and building eligibility. WAP income eligibility includes people at or below 200 percent of the poverty level, or who are eligible for supplemental cash assistance payments under Title IV or Title XVI of the Social Security Act. Additionally, DOE provides a list of multifamily homes that are building and income eligible under WAP. Adopting a definition of low-income communities that currently serve LIHEAP and WAP eligible constituencies would enable the EPA to use existing information and resources, tailored by states, to expand CEIP EE programs to low-income communities.

Employing a LIHEAP standard of outreach to low-income communities would also improve the CEIP program. States under LIHEAP are required to conduct outreach to vulnerable and high-needs populations. States are also required to treat owners and renters equitably, so that eligibility is unaffected by whether renters pay their own utilities or pay the cost to the landlord. States conduct outreach through local agencies such as Community Action Agencies, or CAAs, or can partner with specific demographic agencies such as Offices on Aging and Head Start. If the CEIP low-income EE programs can link to LIHEAP outreach efforts, the potential impact is immense. CAAs serve 16.2 million Americans, averaging 300,000 people per CAA, of whom 37,600 are low income. Under this model, states could generate valuable ERCs both while reducing the coal-fired power plant pollution that disproportionally affects low-income households and while implementing energy efficiency programs to save people money on their energy bills.

Conclusion

The CEIP’s double ERC credit for energy efficiency in low-income communities addresses an important need to protect poor families from burdensome health and energy costs. EPA should ensure that its definition of low-income communities precisely targets the proper demographic. A definition that relies on census tract data may be simple, but could reach unintended recipients. Defining low-income households through existing program definitions designed to improve energy efficiency, such as LIHEAP and WAP, will not only strengthen these existing programs, but will reduce the EPA’s burden of verification.

Danielle Baussan is the Managing Director of Energy Policy at the Center for American Progress. Ben Bovarnick is a Research Assistant with the Energy Policy team at the Center.