Key Findings
- States or utilities interested in maximizing greenhouse gas (GHG) reductions through their energy efficiency (EE) programs have multiple options, including setting explicit GHG reduction targets, setting targets in terms of quantities correlated with GHG (e.g., all-fuel energy savings, heat pumps sold), or using financial signals or incentives to drive decarbonization.
- Energy savings can be converted into GHG reductions using emission rates, which have three key properties: type (average or marginal), scope (short run or long run), and time granularity. While the best approach for translating energy savings into avoided GHG depends on circumstance and use case, we recommend that states and utilities strive for a consequentialist approach that relies on marginal emission rates that account for both the short- and long-run impacts of energy efficiency.
- At minimum, utilities should track the GHG avoided by their EE programs, preferably using multiple accounting approaches. Furthermore, the value of avoided GHG should be embedded somewhere within EE programs, even if it is difficult to quantify with precision.
- A limited number of states and utilities have begun incorporating GHG reduction goals into their energy efficiency programs. Almost all have been motivated by either legislation or statewide decarbonization plans.
- To date, GHG reduction goals within energy efficiency programs have been too modest to significantly transform utility efficiency portfolios. The most prominent outcomes have been an increased preference for beneficial electrification and measures with long effective useful lives.
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Utilities are under increasing pressure to both decarbonize and account for their achieved GHG reductions. This will require utility EE programs designed to maximize GHG reductions to reliably measure EE’s specific decarbonization impacts. This report will
- provide technical options for converting energy reductions measured in megawatt-hours to avoided tons of GHG emissions, with the benefits, drawbacks, and preferred use cases for each
- identify methods states and utilities can use to maximize GHG reductions through their EE portfolios
- describe the motivations, key policies, programs, and results in states that have begun utilizing these methods
Technical options for converting energy savings into avoided GHG
There is no single best approach for translating energy reductions into avoided GHG. Rather, different approaches offer advantages that make them preferable for certain use cases or circumstances.
Energy efficiency impacts GHG emissions in two ways: (1) by avoiding energy generation that would have otherwise been needed to meet load and (2) by impacting the future numbers and types of power plants, transmission lines, storage facilities, and other assets that will operate on the grid. The carbon intensity of generated electricity can be quantified through the use of emission rates, which reflect the weight of GHG emissions per unit of generated electricity. The GHG impact of energy efficiency can be calculated by multiplying the emission rate by the amount of electricity saved.
Any emission rate has three general properties, as summarized in table ES-1. Each property has use cases or circumstances for which it is most appropriate. States and utilities are free to utilize whichever combination(s) of emission rate properties produce data that help their EE programs meet their specific decarbonization goals. Regardless of which options are chosen, we recommend tracking GHG reductions in multiple ways to produce a more complete picture of decarbonization impact.
Table ES-1. Emission rate properties and their preferred use cases
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Emission rate property
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Options
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Description
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Use cases
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| Type |
Average |
Measures the impact of all generators operating at the time the emissions were produced |
- When EE saves electricity of similar character to average grid electricity
- Marginal emission rates are unavailable or unreliable
- Forecasts in which the future grid mix is known (e.g., renewable portfolio standards)
- Simplicity is preferred
- Low variance in GHG reduction estimates is preferred
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| Marginal |
Measures the impact of the marginal generators operating at the time the emissions were produced |
- When EE is assumed to affect marginal generators
- Forecasts in which the marginal generators are known
- Accounting for EE’s impact on future grid assets (if using the marginal build emission rate)
- Larger aggregations of load savings if appropriately sized or multiple marginal emission rates are used
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| Scope |
Short run |
Represents how a change in load impacts emissions on the grid as it exists today |
- Calculating GHG reduction benefits of incremental or near-term (e.g., <5 years) energy savings
- When the grid is assumed static over the lifetime of the efficiency measure
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| Long run |
Represents how a change in load impacts emissions on the grid as it will exist in the future |
- Undertaking integrated resource or other long-term system planning processes
- Accounting for EE’s impact on power plant builds, retirements, or extensions
- When the grid mix is assumed to not be static
- Evaluating long-term impacts of climate policy
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| Time granularity |
Annual, on-/off-peak, monthly by hour, hourly, |
Time period over which energy and emissions are averaged in order to calculate the emission rate |
- As long as time-based emissions and electric usage/savings data are reliable, always use the most granular data required to meet the precision needs of your application and to avoid systematically biasing (i.e., over- or underestimating) GHG reduction estimates.
- More granular data will typically be available for historic emissions, so high granularity is most appropriate for evaluating past program performance.
- More time granularity is needed when renewable energy or energy savings are highly variable, or when energy savings and grid carbon intensity are correlated.
- In other cases, or where simplicity is preferred, low granularity data may suffice.
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Methods for maximizing GHG reductions
States and utilities can set EE program targets in several ways with the purpose of intentionally reducing GHG emissions:
- Set explicit GHG reduction goals.
- Set goals in terms of quantities that are correlated with GHG reduction (e.g., combined electricity and gas energy savings).
- Provide financial signals or incentives that drive utilities toward decarbonization.
The six non-mutually exclusive approaches to quantifying GHG reductions described in this report are
- Consequentialist: use grid-scale modeling to estimate the short- and long-term GHG impacts with and without EE
- Marginal Emissions: set GHG reduction goals measured in terms of marginal emission rates (MER)
- Fuel Neutral: set energy reduction targets that apply to multiple fuels simultaneously
- Economic: place a value on avoided GHG and allow market-based mechanisms to select for low-carbon solutions
- Average Emissions: set GHG reduction goals measured in terms of average emission rates (AER)
- Proxy Metrics: set targets for actions that are strongly correlated with decarbonization, but which do not involve direct measurement of energy or GHG
State and utility progress has been minimal so far
Most states that have seriously considered embedding GHG abatement goals into their energy efficiency programs have been driven to do so either by legislation or statewide decarbonization plans. Examples include gas-fired appliance rebate phase-outs, requiring GHG reduction to be a component of utility efficiency goals, requiring states to adopt fuel-neutral energy savings targets, and clarifying how GHG should be considered in the context of utility regulation. Legislation can also have the opposite effect, for instance by restricting the purpose of efficiency programs in a way that precludes electrification.
Accounting for GHG reductions within EE programs is still in its early days, and there are limited examples of significant programmatic changes that have occurred as a result. The most notable change has been the additional emphasis GHG accounting has placed on beneficial electrification and EE measures with long effective useful lives.
We recommend that state lawmakers provide adequate legislative support for decarbonization via EE programs, if they have not done so already. States and utilities can begin by simply tracking the GHG reductions achieved by their EE programs. Eventually, the value of avoided GHG emissions should be captured somewhere in the utility’s EE program, such as in explicit targets, cost-effectiveness tests, or through financial incentives. We do not recommend orienting EE programs exclusively around GHG reductions, as that ignores the many other value streams that EE can provide.
