Better System Needed to Assess Behavior Changes by Energy Efficiency Programs
Lauren Kubiak, Energy Fellow, New York
How are the nation’s utility energy efficiency programs like my 4th-grade California public elementary school classroom?
Let me tell you about Ms. Gulbransen’s strategy to help stretch our class’s resources in the face of budgetary constraints. Our tables of four competed to get the most points tallied on our star-shaped chart each month. We earned points for good behavior, class participation, and the most number of points you could earn at a time – three – for bringing in a bag of aluminum cans that the school could turn in for cash. I diligently brought in empty coke and root beer cans from everywhere I could find them, and forced my tablemates to do the same. With my competitive nature kicked into overdrive, it was a rare month that my table didn’t win.
While I anecdotally recall that the competition aspect was the primary driver in my recycling fundraiser enthusiasm, I have no metrics to prove it, or how big the payoff was for my class.
And neither do many programs that target behavior change to achieve energy efficiency gains.
The American Council for an Energy-Efficient Economy, (ACEEE), recently released a report attempting to change this. In “ACEEE Field Guide to Utility-Run Behavior Programs,” Susan Mazur-Stommen and Kate Farley reviewed nearly 300 different behavioral energy efficiency programs, most of them administered by utilities. Of those, only 10 enabled estimates of the cost of saved energy, a critical metric for regulatory bodies and utilities to assess what types of efficiency programs to run.
Providing feedback on energy use is a type of behavioral program that could be scaled up with better data.
Behavioral energy efficiency benefits and barriers
Energy efficiency, or using less of an energy resource while maintaining the same level of energy service – the things like heating and lighting that keep us warm and productive – is often the cheapest way that utilities can meet growing demand. If customers don’t need as much electricity for the same output, the utilities don’t have to spend as much to buy or make the power and then get it to their residential, business and industrial customers.
Utilities have been running effective efficiency programs for years, many of them focused on technology improvement, like installing more efficient lighting and appliances, and improving buildings. There is evidence that targeting customers’ behavior, and encouraging people to do things like not keeping the heat or lights on when nobody's home or in a room, is effective, and cost-effective, at helping utilities meet their efficiency goals. However, as the study authors point out, there are barriers to proving it.
The first issue arises from defining what differentiates a behavioral program from a technology implementation program, or a market-transformation effort. Because so many utility-run programs have multiple components, untangling the behavioral component – consumers changing their habits – from the technology component is not easy. On top of that, a lack of social science knowledge among program designers, as well as verified data of the programs that are defined as behavioral, impedes many utilities from moving forward with these types of programs.
Need for classification
However, with a better classification system (also known as a taxonomy) for behavioral programs, utilities may be able to take better advantage of behavioral opportunities. ACEEE lays out 20 major categories of programs aimed at helping consumers cut energy waste, and groups them into the three major areas below:
- Cognition programs focus on delivering information to consumers. Categories include general and targeted communication efforts, social media, classroom education, and training.
- Calculus programs rely on consumers making economically rational decisions. Categories include feedback, games, incentives, home energy audits, and installation.
- Social interaction programs rely on interaction among people for their effectiveness. Categories include social marketing, person-to person efforts, eco-teams, peer champions, online forums, and gifts.
Such a classification system would categorize programs based on what drives people to participate in a particular one. By organizing knowledge and identifying relationships between different types of programs, classification presents a strategy to resolve the lack of behavioral efficiency verification data. In case you’re wondering, as a team competition, my 4th grade class recycling competition would be classified as a social interaction program.
NRDC strongly echoes the authors’ support for programs that combine elements of all three “families” of programs: programs that combine multiple strategies are most likely to successfully change behavior. There may be especially rich opportunities in incorporating behavioral science into energy efficiency programs that help customers make efficient purchasing decisions: buying a LED bulb instead of the cheapest bulb on the shelf, for example. NRDC’s Dylan Sullivan, along with Drs. Carrie Armel and Annika Todd, presented a paper at ACEEE’s 2012 Summer Study on Efficiency in Buildings that gives program designers a framework to do just that.
A hint at the potential
As for the latest ACEEE paper, the authors identified 10 programs with estimates of the cost of saved energy, calculating an average of 1.61 cents per kilowatt hour saved. With typical costs of energy efficiency programs averaging around 3 cents per kilowatt hour of saved energy, this preliminary figure, while still subject to high uncertainty, hints at the savings opportunity that behavioral programs may present.
Energy efficiency is an important resource for utilities, and a critical component to meeting our climate and clean energy goals. With such broad savings potential, we should do all that we can to ensure that we are effectively tapping into not only technology improvement and market transformation energy efficiency savings opportunities, but behavioral ones as well. Resolving these data issues is a critical first step in getting us to the head of the class when it comes to achieving behavioral efficiency’s full potential.
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