New data from EIA shed light on residential energy expenses
Posted December 27, 2012 in Solving Global Warming
Houses in America (and the people, machines, and devices in them….) use a lot of energy, year in and year out. The massive quantity of energy used is often expressed in measures that are abstract to most people -- megawatt hours, “quads” (that is, quadrillion Btus), thousands of tons of coal burned to create the electricity, or trillions of cubic feet of natural gas burned.
The “Rosenfeld” is an elegant and less-abstract unit created to describe the massive amounts of energy available by reducing waste -- one “Rosenfeld” is the annual output of a conventional electric utility power plant.
The simplest metric for most people is probably dollars. The average annual residential utility bill is about $2,000 a year (or about $2,300 for the average homeowner). A simple benchmark. This data comes from the U.S. Energy Information Administration's (EIA) 2009 Residential Energy Consumption Survey (RECS).
But the figure for average annual utility bill – $2,000, or $2,300 for homeowners – is too distilled to convey information about the savings possible. Over the more than 100 million U.S. households(!), energy use and utility expenses vary widely.
Consider this statement from a recent New York Times article: “…while most people see saving money as a good thing, it is not enough by itself to change habits in the long term. The average household spends about 2 percent of its income on electricity, so a 10 percent reduction in power use doesn’t add up to much…” Full NYT article is here.
Enter better data. The RECS data showing wide distribution of utility expenses conveys a sense of the savings available to many American households from reducing wasted energy.
EIA, 2009 Residential Energy Consumption Survey, Results from Microdata.
Note the significant spread -- over $1,500 per year -- between expenses for customers in the top quartile (> $3,292) and expenses for customers in the bottom quartile (< $1,747). In other words, a homeowner could save about $900 per year by moving from the 75th percentile merely to average (50th). Going further to the 25th percentile could mean savings of about $1,500 a year.
Now, to be clear, there are MANY possible reasons any particular house might have low, average, or high energy expenses, such as:
- efficiency level of the house (air sealing, quality of appliances, insulation levels)
- size of house (bungalow or mcmansion?)
- climate zone (is the house in a Pheonix desert or Seattle?)
- fuel type (fuel oil at $4.70 a gallon, or natural gas?)
- number of occupants (any teenagers??)
- behavior (is thermostat on 65 in winter AND summer?)
- local utility rates
- any swimming pools, pottery kilns, extra freezers?
And, such substantial savings for some utility customers might not be practical or economical or worthwhile for many reasons.
But even with all these variables in the mix, we can draw an important directional conclusion: The wide distribution of energy expenses suggests a large number of households could realize savings, in an amount material to the household budget, from reducing wasted energy -- whether through behavioral programs operated by utilities, weatherization, choosing a smaller or better house when moving, replacing appliances with better appliances, etc.
The point made in NYTImes article cited above is correct, as far as it goes: potential savings may be small for some people with average energy expenses and average incomes and average houses. But the wide distribution of expenses points to the opportunity to target those for whom the savings are likely to be material and to make information available that will enable people to make informed decisions about potential savings.
One final observation. More granular data on energy use is essential to better analysis and ultimately to obtaining a better understanding of residential energy use in America. Policymakers deserve access to results that are based on richer, more detailed data. Researchers need information about energy use by address so that it can be matched with information about houses from other data sets, such as property tax records. Making data available for this kind of research raises questions that must be navigated related to privacy, but the issues are manageable and should be addressed.
For this reason we have urged utilities, their regulators, and other stakeholders to focus on better data access for research and analysis. A recent briefing paper from the California Public Utilities Commission on an "Energy Usage Data Center," located here: CPUC Briefing Paper, is worth reading.
Many thanks to Danielle Mayclin at EIA for making the RECS data available.
Two footnotes for sticklers:
1. Full 2009 RECS data and the many useful charts may be found here, at www.eia.gov.
2. Information on homeowner energy use versus renters can be found in the full RECS data at CE2.11. Average/median expentidures for households with income >$75,000 is very close to average for homeowners ($2,300) and may be a proxy in distribution, but I have not examined that.