California has adopted legislation and has executive orders in place laying out aggressive, long-term greenhouse gas (GHG) reduction targets. There are also specific energy policies including those to increase efficiency, generate more renewable electricity, and to reduce fossil fuel use in the transportation sector that in essence support the overarching goal of reducing greenhouse gases. While each of those energy policies can be measured in kilowatt-hours, therms, and kilowatts, one way to normalize them is in terms of their greenhouse gas reduction impacts. This allows for comparison across types of policies and programs and across sectors. In a way, carbon – or carbon dioxide equivalent – is becoming the common currency of California’s energy policies.
As I have written about in previous posts (11/5/16 and 12/1/15), one of the most important variables in estimating greenhouse gas emissions from electricity generation is the rate of emissions – or emissions factor. In this post, I will discuss the different types of emissions factors, focusing on those needed to estimate the greenhouse gas impacts of reducing or displacing a unit of electricity, their policy importance, and the possible need for standard approaches and methods in California.
Not all Electricity Emissions Factors are Created Equal
There are several different types of electricity emissions factors. I will discuss two main types here – average and marginal – and focus in a bit on the latter. The most commonly used emissions factor used to estimate emissions from electricity is the average emissions factor, which represents the average CO2 equivalent emissions per average unit of electricity delivered to customers over a given period. Estimating the average emissions factor, typically expressed in pounds or kilograms of CO2e per megawatt-hour (lbs or kg/CO2e), is straightforward. We typically do this by looking at the actual sources of electricity supplied over the course of one year and estimate the emissions from each. Total emissions divided by total energy supplied equals the average emissions factor. This value could be used to estimate the GHG emissions associated with electricity use by a city, region, or even a company.
A more complicated question is what emissions factor should be used to estimate the GHG impacts of reducing electricity use as a result of energy efficiency programs and policies? This typically requires a marginal emissions factor, which represents the CO2 equivalent emissions associated with the next unit of electricity delivered. This concept is sometimes called an avoided emissions factor. In general, electric sources are dispatched on an economic basis. Those with the lowest marginal cost are dispatched first, followed by sources with marginally higher costs. As an illustrative example, renewable energy sources like wind and solar have high capital costs but relatively low marginal costs, so they would typically be dispatched first. Nuclear might be dispatched next, followed by combined cycle gas turbines, and finally simple cycle gas peaker plants. Figure 1 below demonstrates this concept (see PJM).
Figure 1 Illustration of Marginal Cost Dispatch Concept
Generally speaking, the emissions factors for each source is analogous to the marginal cost; that is, the source with the lowest marginal cost also has the lowest (in this case zero) emissions. So, in theory, if enough customers turn off their lights at 6pm and electric load is reduced, the highest cost or marginal resources will be reduced. These resources normally have an emissions factor higher than the average of all resources. So, to determine the GHG emissions effects of turning off the lights at 6pm in this example, we would use the marginal emissions factor.
To most accurately estimate the effects of energy efficiency would require detailed information about when the efficiency occurred and what the marginal electric resource was at the moment of the electric reduction. This is a complicated analysis and time-differentiated data is typically not available for when electricity reductions occurred. As a result, using an annual marginal emissions factor is most likely sufficient.
Selecting a Marginal Emissions Factor for Electricity
Suppose we want to estimate the potential greenhouse gas reductions from the Governor Brown’s target of doubling the efficiency of existing buildings by 2030(see Gov. Brown’s Inaugural Speech and SB 350). What annual marginal emissions factor would we use? Not such an easy question. While there are several estimated marginal emissions factors for California, currently there is no standard method or value for such calculations in California. Further, there is not guidance on how to project these values to accurately estimate future impacts of such policies. Here are a few examples of current and short-term values put forth by California and federal agencies.
The California Public Utilities Commission (CPUC) has adopted electric emissions factor for various reasons over the years. Most recently, it adopted a marginal emissions factor to help determine eligibility for the Self-Generation Program. In Decision 15-11-027 in Rulemaking 12-11-005, the CPUC adopted a 10-year average value for 2016 of 350 kgCO2/MWh (772 lbs CO2/MWh). The decision states that this value “…reflects the displaced emissions from existing capacity and the avoided need for new capacity.”
With this value the CPUC further broke down the marginal emissions factor into the operating margin, which relates to the existing infrastructure, and the build margin, which relates to the expected additional infrastructure that could be added over time to meet additional needs (WRI 2007). In essence, how would adding a megawatt of self-generation to the system affect the operation of generation resources already on the system and how would an accumulation of self-generation units affect the need for new generation over time. The CPUC also sought to account for the effects of the Renewable Portfolio Standard.
In June 2015 the California Energy Commission (CEC) issued a staff report entitled Proposed Near-Term Method for Estimating Generation Fuel Displaced by Avoided Use of Grid Electricity. This report sought to develop a method “for calculating emissions reduction resulting from avoided generation.” The report seeks to propose “a common method for estimating the amount of generation fuel displaced from avoided use of grid electricity over the next five years…” The Staff Report developed the following 5-year average emissions factors for various examples of how electricity could be reduced or displaced. Note these are illustrative values that were used to demonstrate a particular method of estimating a marginal emissions factor. Table 1 shows a summary of the emissions factors in the report.
Table 1 Summary of Emissions Factors from CEC Staff Report
The US Environmental Protection Agency (US EPA) maintains the Emissions and Generation Resource Integrated Database (eGrid), which contains detailed information about the fuel use and electricity generation of nearly all the generation units in the country. As part of its recent update, it provided “Annual Output Emissions Rates,” which include both annual average emissions factors and “non-baseload” – or marginal – emissions factors for subregions in the US. The 2012 non-baseload emissions factor for the WECC California subregion is 1,063 lbs CO2e/MWh. Not that this value is a few years behind the CPUC and CEC efforts above.
There are many other examples of emissions factors adopted for various regulatory purposes. Also publications that develop methods to estimate marginal electric factors abound, including the following:
- Archsmith et al. (2015) assesses the greenhouse gas benefits of electric vehicles.
- Hawkes (2014) discusses long-run marginal emission factors.
- Harmsen and Graus (2013) evaluate various methods to estimate the greenhouse gas reductions from electricity reductions.
- Graff Zivin et al. (2013) discusses how marginal emissions factors vary by time and location.
There is a lot to consider even from a cursory review of the above. Perhaps the CEC Staff Report best sums up the dilemma in selecting a marginal emissions factor:
“[m]ethods for calculating emissions reduction resulting from avoided generation vary substantially in approach and assumptions. Each method has been developed to fit a specific program or purpose. While these methods are sufficient for some programs, the differences in approaches and assumptions makes program comparison difficult.”
Can One Size Fit All?
A marginal emissions factor is an integral part of greenhouse gas analysis from the state’s scenario planning to better understand how our new 2030 energy policy targets will affect statewide emissions to cities developing climate action plans, from individual projects estimating their emissions under CEQA to determining eligibility of technologies to participate in various programs. So if different agencies are using different emissions factors how do we add up and compare the potential reductions to help plan for our aggressive long-term targets? This would be a bit like identifying ways to cut money from your personal budget but using different values for the dollar to quantify each. This scenario would seem to favor a common method – or at least a core set of guiding assumptions and principles. On the other hand, can one size fit all in this regard? Is there a need for different marginal emissions factor for specific policies and programs? Are the GHG impacts of self-generation technologies fundamentally different from energy efficiency reductions? Could one method yield different emissions factors for different purposes but still be consistent overall? I don’t have any definitive answers to these questions but think they are worth asking.
As time goes on, the methods used to estimate the potential emissions reductions of energy policies are likely to become more important. It might be time to respond in earnest to the CEC Staff Report’s call for a common method. Perhaps a good first step would be an interagency working group with representation from the CA Independent System Operator, CPUC, CARB, and CEC to compare approaches, determine data availability and needs, and evaluate whether a common method is feasible. It certainly would make sense if carbon is to be our common currency.
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