Everybody wants affordable energy. Energy consumers want to be able to pay their bills and energy providers also want customers to be able to pay their energy bills. What we can’t seem to agree on is how to best measure energy affordability.
Energy burden, the percent of a person’s income spent on energy (electricity, home heating, and transportation), is one of the better metrics we have to measure affordability.
In an earlier blog, I took a look at electricity affordability, looking only at electricity bills, leaving out other energy costs like those to heat a home with a gas or oil furnace. The data was also limited to looking at averages, which can obscure the burden energy costs place on low and moderate-income (LMI) folks. While those households tend to use less energy than the average household, LMI households also make less, so the energy burden for LMI folks tends to be above average.
In this blog, I’m able to expand the scope of my analysis thanks to data from the National Renewable Energy Laboratory, or NREL.
Illuminating the national energy burden
My original post only looked only at electricity. Similarly, the map on the right is the estimated electricity bill of LMI households by county. On the left, is the estimated home heating fuel bill of LMI households by county. LMI is defined here as 0% to 80% of area median income.
Looking at these maps side by side, you might be tricked to thinking the energy burden is distributed evenly across the United States: with a higher electricity burden in the Southeast and a higher home heating burden in the rest of the US, it all balances out. Right?
Energy burden isn’t distributed equally across the US, as the map below shows.
Appalachia lights up on the orange-colored map, with parts of Pennsylvania, West Virginia, Virginia, Kentucky and Tennessee all experiencing high energy burden. Why?
The energy burden issue is complicated.
The data shows significant energy burdens felt by communities in the Gulf Coast and Southeast, with clusters spanning Louisiana, Mississippi, Alabama, Georgia, and north Florida, up through the Carolinas. These areas experience high energy burden driven mostly by electric bills.
The Northeast area also sees hot spots of decreased energy affordability, which is mostly driven by home heating expenses.
Sometimes it is electric bills, sometimes it is home heating (gas and oil) bills, and sometimes it is both.
Bright ideas on what reduces bills
Okay, so energy burden is a problem; what’s a policymaker to do?
The good news is we have two resources that are well equipped to help reduce customers’ bills.
Both these solutions I noted in the original blog and just so happen to have been evaluated by NREL.
Energy efficiency potential
Energy efficiency can take many forms, from replacing incandescent bulbs with LEDs to in installing home insulation. Efficiency reduces home energy consumption and, as a direct result, reduces monthly bills.
In this green-colored map, the darker the green represents greater potential savings for LMI households.
Note the tremendous amount of efficiency potential in states like California, Arizona, New Mexico, Texas, Colorado, Nebraska, Kansas, Oklahoma, Arkansas, Louisiana, Mississippi, Illinois, Indiana, Ohio, Tennessee, Georgia, Florida, Massachusetts, Maryland, New York, and really… everywhere.
The potential for LMI households to save some green by investing in energy efficiency is widespread, with cost-effective savings in nearly every county where there is available data.
Solar panels can be installed on or near LMI households and with the right policies can be used to reduce the bills of LMI households. One great example of such a policy is DC’s Solar For All program, which helps improve LMI households’ access to solar.
Some of the areas with the greatest potential for bill reductions (darker teal) are in places you’d expect, like California, Texas, Arizona, and New Mexico. These are all states where the sun is shining and where households often see higher electricity bills.
Kansas, a state I spent many formative years, lit up, much to my surprise. Also surprising: Michigan’s Upper Peninsula.
The high potential for consumer savings isn’t just a function of the number of sunny days a state has, but also the building stock and energy consumption patterns.
Parts of Maryland, New Jersey, New York (notably Long Island), Connecticut, Massachusetts, Vermont, and New Hampshire also showed up as places where LMI households could reduce home energy bills by hundreds of dollars a month with judicious applications of solar.
Even the places that don’t show up on the map as the hottest of hot spots were still significant. Florida doesn’t look that great when compared to say, California, but, most of Florida’s LMI customers could see savings in the $800-$1,400 per year range. That’s a lot of money.
How will this change with climate change?
One more angle on this to consider is the impact of climate change. A recent analysis (and accompanying graphic) released by National Oceanic and Atmospheric Administration and based on data from the Climate Impact Lab, showed how the US energy burden will worsen over time, in many states, if we don’t rapidly reduce carbon emissions. The southern parts of the United States, already burned by energy bills, will be hit very hard. Communities in these states will have higher energy costs. This analysis suggests that climate change will exacerbate the existing energy burden.
Interestingly, renewable energy and energy efficiency can play a role in staving off both climate change and the energy burden.
“Make things less bad.”
A policy professor of mine was fond of asserting that the objective of most public policy should be to “make things less bad.”
NREL’s latest data confirms what utility practitioners have known for a while: LMI households in most parts of the US are well positioned to reduce their energy bills with energy efficiency and solar. Energy efficiency and solar policy alone won’t solve the issue of energy burden for LMI households, but it will make their energy burden less bad.
Fresh Charts and lots of data
In this blog, I expanded the scope of my original analysis by digging in on some new (to me) data.
I want to thank the great staff of DOE’s office of renewable energy and energy efficiency for pointing me in the data’s direction. Maybe I’ll get my hand on some good transportation cost data, and if you know where I can find some, hit me up on twitter?
The latest data comes from one of the US Department of Energy’s fine national labs (the National Renewable Energy Laboratory, or NREL) and it is all assembled in a great interactive map. A big thanks to the find staff of NREL. NREL’s latest data is aggregated at the county level which made it difficult to turn into a table or provide clear data labels on the maps above. If you’d like to know more or zoom in on the data, you can find the map and the underlining data: here. Have fun digging in.
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