METRICS FOR ENERGY EFFICIENCY: OPTIONS AND ADJUSTMENT MECHANSIMS

This research paper argues that regulators should shift from traditional measure-by-measure energy efficiency tracking to outcome-oriented, economy-wide metrics when tying utility compensation to performance. The authors contend that outcome-oriented metrics are more transparent, easier to monitor, and better aligned with broad policy goals, while reducing the administrative burden and regulatory conflict associated with uncertain savings estimates.

• Traditional measure-by-measure savings approaches for energy efficiency introduce significant regulatory conflict because the assumptions used to estimate savings often have "significant levels of uncertainty as well as annual variation."
• Outcome-oriented metrics, such as total sales or kilowatt-hours (kWh) per customer, offer several advantages over measure-by-measure programs: they are easier to monitor, can be more directly tied to state greenhouse gas targets, and substantially reduce the administrative burden on commissions.
• To minimize regulatory disputes, metrics should be designed to reduce the need for ex post adjustment mechanisms. Some metrics inherently account for population or economic changes, such as kWh per unit of GDP or kWh per customer, while others can use running averages of three to five years to smooth out annual weather or economic fluctuations.
• The authors identify seven potential outcome-oriented metrics: kWh per capita, kWh per customer, kWh per household, kWh per square foot, kWh per $ GDP (all intensity metrics), and total sales (kWh) and annual percent improvement (both consumption metrics).
• The paper recommends the kWh per customer metric as the most effective option because it is easy to measure, transparent, and difficult to manipulate. It can be further calibrated by fixing the number of meters to avoid biasing the metric with new customers.
• Regarding weather adjustments, the paper suggests a two-step approach for electric utilities: first, evaluate the extent to which heating and cooling degree days affect energy use, and second, develop adjustment mechanisms, potentially by customer class, to ensure a statistically significant relationship between weather and load.
• The authors argue that focusing too heavily on the "attribution problem"—determining which savings are exclusively due to utility intervention—can distract from achieving actual energy savings and lead to conflict due to uncertain parameters.

APA

ORVIS, R., AGGARWAL, S., & O’BOYLE, M. (2016). METRICS FOR ENERGY EFFICIENCY: OPTIONS AND ADJUSTMENT MECHANSIMS. Energy Innovation. https://energyinnovation.org/wp-content/uploads/EEMetricDesign-white-paper_final.pdf

Chicago

ORVIS, ROBBIE, SONIA AGGARWAL, and MICHAEL O’BOYLE. METRICS FOR ENERGY EFFICIENCY: OPTIONS AND ADJUSTMENT MECHANSIMS. Energy Innovation, 2016. https://energyinnovation.org/wp-content/uploads/EEMetricDesign-white-paper_final.pdf.

Wikipedia

{{cite report |last1=ORVIS |first1=ROBBIE |last2=AGGARWAL |first2=SONIA |last3=O’BOYLE |first3=MICHAEL |title=METRICS FOR ENERGY EFFICIENCY: OPTIONS AND ADJUSTMENT MECHANSIMS |publisher=Energy Innovation |date=April 2016 |url=https://energyinnovation.org/wp-content/uploads/EEMetricDesign-white-paper_final.pdf |access-date=17 August 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{orvis2016metrics, author = {ORVIS, ROBBIE and AGGARWAL, SONIA and O’BOYLE, MICHAEL}, title = {{METRICS FOR ENERGY EFFICIENCY: OPTIONS AND ADJUSTMENT MECHANSIMS}}, institution = {Energy Innovation}, year = {2016}, month = apr, url = {https://energyinnovation.org/wp-content/uploads/EEMetricDesign-white-paper_final.pdf}, urldate = {2026-08-17}, note = {Indexed by Climate Insights Directory} }