End-use energy efficiency is expected to play an important role as states develop plans to comply with EPA’s Clean Power Plan. The agency’s draft guidance on evaluation, measurement, and verification provides a useful basis for moving forward.
End-use energy efficiency is expected to play an important role in the development of state plans to comply with the US Environmental Protection Agency’s (EPA’s) Clean Power Plan and could be particularly important in those states that adopt an emissions rate-based approach to comply with EPA’s carbon dioxide (CO2) emissions guidelines. Under such an approach, entities including utilities, community groups, energy agencies, and others that take certain actions to increase investments in end-use energy efficiency (EE) are entitled to earn emissions reduction credits (ERCs) for every demonstrated MWh of electricity saved. In the aggregate, crediting energy savings in this way is expected to reduce demand for electricity and the need for generation from all sources, including emitting sources. ERCs can be used to augment the denominator in an affected generator’s calculated emissions rate, thereby bringing the generator closer to compliance with the emissions rate standard. By supplementing the denominator in this way, efficiency-associated ERCs lessen the need for a generator to reduce its own emissions directly.
Under such a rate-based regime, the veracity of energy savings and associated ERCs are very important to the environmental integrity of the policy; thus, EPA rightly requires in the final Clean Power Plan rule that entities wishing to earn ERCs for energy savings submit an ex-post evaluation conducted by an independent third party to verify that their actions or policies have yielded the claimed amount of energy savings. The draft evaluation, measurement, and verification (EM&V) guidance document issued in conjunction with the final rule and proposed model rule/federal plan offers a very useful description and guide for the states on how efficiency evaluations are currently conducted and provides a useful basis for moving forward. EPA is to be commended for putting the guidance document together and providing the opportunity to comment.
Summary of Comments
The primary conclusions of these comments are as follows:
- EM&V of energy efficiency measures, programs, and policies are both methods of measuring savings from different approaches to reducing energy use and means of identifying the most effective and cost-effective ways to encourage energy savings.
- The EPA EM&V guidance should be a living document that accommodates future advances in EE programs, policies, and technologies as well as new developments in program evaluation.
- EPA should advocate and provide incentives for greater use of comparison approaches (including randomized control trials, or RCTs, quasi-experiments and empirical approaches using state-of-the-art matching techniques) for evaluating EE measures, programs, and policies within the states.
- EPA should establish a centralized database of RCT and quasi-experimental evaluations of EE programs that includes information about the programs, the revealed energy savings, and, whenever possible, the underlying data used to conduct these analyses. This revealed savings database should be made available to the states and would form the basis of a collection of savings estimates that could be applied to similar measures and programs in other settings with appropriate adjustments as necessary.
- EPA, in conjunction with the US Department of Energy (DOE), should establish a task force or working group that includes representatives from states, utilities, and the efficiency community and experts in experimental and empirical methods to identify a set of high priority RCTs of EE programs and measures for evaluation. One priority for early evaluation would be EE programs in low-income neighborhoods, which are incentivized under the Clean Energy Incentive Program (CEIP).
- Once priorities are established, EPA and DOE should launch a series of EE RCTs and other research experiments to begin to populate the revealed savings database and help to facilitate a collection of savings estimates that can help inform future evaluations.