We spoke with Director Greg Clendenning about NMR’s work in the non-energy impacts (NEI) space, past, present, and future.

Can you share your background?

I’ve been working in evaluation for almost 19 years, including working with the U.S. State Department. Before that I received my PhD from the University of Wisconsin, Madison in Forestry, a combination of environmental sociology and urban and regional planning. I bring that research methodology background to NMR’s NEI research.

Can you break down NEI and NMR’s work in that space?
Non-energy impacts, or NEI, are impacts beyond the energy savings of energy-efficiency measures and programs. For example, if you install a low flow faucet aerator or showerhead, the participant can receive water bill savings. That’s a benefit to them and should factor into cost-effectiveness testing. Other participant impacts include comfort or health and safety, which are a little more difficult to quantify. For income-eligible programs, by making participants’ homes more efficient, they’re also reducing their energy bills. This increased affordability can decrease the likelihood that customers experience difficulties paying their utility bills, which in turn reduces unpaid debt and collection costs, which is a measurable financial benefit for program administrators.

NMR has been doing NEI evaluation since about 2008 or so. We started in earnest around 2010 doing an overall review of all the Massachusetts Program Administrators, focusing on the NEIs attributable to their residential and low-income programs. The PAs needed an evaluator to review and recommend whether there were new or different impacts than what they were claiming at the time. The bulk of our work has been in the northeastern U.S. – for example, Massachusetts, Connecticut, Pennsylvania, Delaware – and almost all of our work has been residential and low-income or income-eligible.

What challenges does NEI work present, and how does NMR address them?
Some NEIs like health and safety are very hard to quantify with primary data, and if you’re not counting these impacts, then you’re undervaluing the program and potentially not funding them to their fullest extent. That could lead to issues of access and equity.

For example, weatherization or electrification can have benefits for participating households with asthma. But something like hospitalizations for asthma are relatively rare in a general population, and in a survey of program participants you may not have a large enough sample size. In those cases, we may rely more on secondary literature and studies. We’ll take peer reviewed journal articles or public health studies that often have larger and more robust samples. If we assume a similar effect from the programs, we can apply those results and pull together an algorithm to estimate the NEI.

What excites you about NMR’s NEI work and its future evolution?
I’m excited about a handful of newer projects. We’re using building energy models to try to estimate the impacts of avoided electricity outages with battery storage. This can be particularly important for a subset of program participants who rely on medical devices like respirators or dialysis machines.

In the past we may have tried to look at studies on outages to find impacts on at-risk populations. In this case, we took a different approach by utilizing state tracking data on utility outages, going back several years. That highlights less common but impactful events like hurricanes and nor’easters. We can then see how many households lost power and for how long. You can then estimate what percentage of outages they might cover or how many hours of power these batteries could supply the typical house. That’s an example of energy modeling going beyond surveys or secondary studies analysis to try to quantify some of these impacts, and it’s something we’re eager to keep exploring in NEI evaluation.