Is your goal real-world impact? Then use marginal emissions.

Everyone knows you can’t manage what you don’t measure. Less often pointed out? You can’t manage what you measure incorrectly. 

Corporate net-zero targets are at an all-time high, per reporting from The Economist. In fact, fully 75% of the world’s largest corporate greenhouse gas emitters have set net-zero by 2050 (or sooner) targets, as of an October 2022 benchmarking analysis by Climate Action 100. This is good news.

Or… it should be. Of course, these targets will only genuinely decarbonize the atmosphere if they measure the real thing. And, unfortunately, that’s not always what happens.

From South Korea to Europe to the United States, corporations are under more scrutiny for potential greenwashing than at any other time in recent memory. 

At WattTime, we care about this not because we care about catching bad guys. In our experience, the vast majority of corporate emissions miscounting is a genuinely well-meaning mistake. But such scrutiny is also good news nonetheless. Why? Because it is forcing corporations to re-examine their sustainability efforts to better align with true impact that corresponds to real-world emissions reductions, not merely on-paper-only green claims.

And as companies allocate growing sustainability budgets, a heightened focus on actual impact empowers them to identify and pursue strategies that yield the highest real-world decarbonization return on investment (ROI) — and, reciprocally, to avoid strategies that cause a real-world increase in total global emissions.

Using the Right Math Matters

How companies measure the emissions they cause and which math they use to do so matters. A lot. That’s because, let’s face it, climate change is starting to claim lives. And the only thing that will save lives is impact — whether and how much a company’s actions genuinely cause total global emissions to go up, down, or stay the same.

Historically, much carbon accounting was done in terms of average emissions factors (AEFs). AEFs take the overall electricity generation mix for any given power grid, then apply it to a specific company’s load for their facilities. This was a fine solution in the early days, when carbon accounting didn’t actually do much, and most companies were not taking meaningful real-world actions based on these emissions factors. 

Times have changed. Today, companies are actually meeting GHG targets, optimizing their actions, and taking sustainability seriously. This is fantastic news, but it means that today, the connection between carbon accounting and reality actually matters.  

But there’s one big problem. AEFs are the wrong math for measuring impact, because they ignore the basic physics of how power grids operate — including how power grids respond to various influences. Using AEFs assumes that all generation sources on a power grid equally share in outcomes. They don’t. Nuclear power plants are not going to turn on and off in response to what one electricity user does. Neither will always-on baseload plants.

Moreover, simply making AEFs more granular, such as hourly, doesn’t solve the problem, either, because it still ignores fundamental power grid operations.

When a company chooses to site a new facility (and its electricity load) — a data center, a factory, a new corporate campus — in a particular region because that region has a “green” power grid… When a fleet of electric vehicles (EVs) uses smart charging to modulate when those EVs do and don’t charge… When smart thermostats and building energy management systems modulate the flexible portion of a commercial building’s electricity demand to shift load across hours… 

All of these and other examples don’t impact the entire generation mix. Most of the power grid’s generation stack merrily chugs along unaffected, blissfully unaware of these influences.

But the common corporate decarbonization strategies mentioned above do impact a specific subset of generators that respond to the corresponding increases or decreases in electricity demand. It’s precisely these generators — and their emissions — that matter for understanding impact.

They are known as marginal generators. Their associated emissions intensity is known as the marginal emissions factor (MEF). And their emissions are the marginal emissions: those emissions that specifically result from marginal units responding (e.g., turning on, ramping up) in order to meet the next incremental megawatt of electricity demand.

If a company chooses to site a new facility in a particular power grid, it’s the marginal units that must meet that demand — and therefore, the marginal emissions that best measure the impact of that load-siting decision. If a smart thermostat or EV charging software shifts the timing of power demand, it’s the marginal units that are impacted — and also therefore, the associated increase or decrease in marginal emissions that best measure the impact of that load shifting.

The temptation to use AEFs is understandable: they are widely available and the calculations are easy to run.  But this is a well-established area of research. Scientists and grid experts agree that AEFs do not accurately measure impact. The GHG Protocol is clear that one may not use AEFs to measure avoided emissions; rather, they specify use of MEFs for such Scope 2 calculations. The list goes on and on.

Widespread Agreement to Use MEFs for Impact Assessment

More than a decade of robust research and widespread agreement among scientists and grid experts support using MEFs as the right way to measure the environmental impact of electricity system interventions. For example:

• “We compare marginal and average emissions factors, finding that AEFs may grossly misestimate the avoided emissions resulting from an intervention… Both supply- and demand-side interventions will displace energy and emissions from conventional generators. MEFs give a consistent metric for assessing the avoided emissions resulting from such interventions.” — Stanford’s Azevedo et al. in Environmental Science & Technology (2012)
• “These [marginal emissions] estimates have important implications for understanding the environmental consequences of many electricity-shifting policies… [they] are also relevant for understanding the impacts of activities and policies that increase electricity demand.” — Yale’s Kotchen et al. in the Journal of Economic Behavior & Organization (2014)
• “If the load under analysis represents an incremental increase or decrease, marginal ERs can provide an emissions measurement specific to the change. Thus, for consequential LCAs of new loads, marginal factors are recommended.” — University of Michigan’s Ryan et al. in Environmental Science & Technology (2016)
• “...the use of AEFs, which reflect grid-average situations, to estimate the effect of an intervention may be problematic, because not all generating technologies would respond to changes in demand proportionally… AEFs could significantly misestimate the amount of emissions avoided by an intervention. By contrast, MEFs estimate the emission intensity of marginal power generation that responds to a change in demand, and are a more appropriate metric to assess emission implications of policy and technology interventions.” — University of Minnesota’s Smith et al. in Environmental Science & Technology (2017)
• “Increasing (or decreasing) demand at a given time affects the operation of only the marginal units in the dispatch order, and not the infra-marginal units. Therefore, emission implications of energy storage, or any other incremental demand- or supply-side resource that leads to load-shifting, depend on marginal operating emission rates, based on the emission intensity of the marginal units, and not the average operating emission rates, based on the average emission intensity of the entire grid.” — Columbia’s Shrader et al. in SSRN: Social Science Research Network (2020)
• “Marginal Emission Rate provides a mathematically sound and transparent way to quantify the carbon footprint of electricity consumption and production. Mathematically, the MER calculation is similar to the calculation of Locational Marginal Prices (LMP) and system lambda, which are used for economic dispatch of power systems across North America.” — Tabors Caramanis Rudkevich’s Hua He et al. in The Electricity Journal (2021)

Here at WattTime, we’re strong advocates for measuring whatever will affect real-world total emissions. In electricity, that means MEFs. (Within our datasets, they’re referred to as MOERs: marginal operating emissions rates. You can read more about our perspective in our 2022 insight brief about impact accounting.)

In the wake of the UN IPCC’s AR6 final synthesis report about the climate crisis — underscoring the need for rapid, deep decarbonization of the global economy — none of us can afford to base decisions, and impact assessments, on faulty math. We need to make authentic progress reducing global emissions. And for that, we need to use marginal emissions data to honestly and accurately reflect how power grids actually respond to the strategies we implement.