Marginal Emissions Methodology

Using an Empirical Model

The fundamental approach of all such models in the literature is very similar: they start with Continuous Emissions Monitoring System's data reported through the EPA CAMPD program on hourly electricity generation and emissions at every major fossil fuel fired power plant in the United States. A similar approach applies in other countries where data is available. Each then applies regression-based modeling to ask, every time a rise or fall in electricity demand occurs in a given place and time, which power plants actually increase or decrease their output in response?

This allows a modeler to compare how marginal emissions rates vary by time and place. For example, by running such regressions separately for day and night periods, such models can compare the marginal emissions caused by using electricity during the day or during the night.

WattTime believes strongly in such empirical modeling techniques because they are driven almost purely by the data and require almost no assumptions. This is part of a growing trend in economics and social sciences towards increasingly questioning the validity of assumption-driven models, and replacing them instead with almost purely data-driven causal models, a trend known as the “credibility revolution”.  The most notable proponent of this approach, Esther Duflo of MIT, won the 2019 Nobel prize in economics for this innovation.   

WattTime staff have run numerous tests of how much Esther Duflo’s fundamental insight of questioning assumption-driven models applies to the energy sector and shown that most basic assumptions often fail to hold up in the real world. These and other insights have deepened the WattTime team’s conviction that empirical models are essential for accurate marginal emissions analysis.

Improving on the Published Literature

WattTime staff over the past decade have extended the basic techniques in the peer-reviewed published literature to keep the fundamental reliability of causal, empirical modeling, but also made them more useful for active emissions reductions and controlling devices in real-time.

For example, it is one thing to say emissions are cleaner in the daytime, but quite another to measure slight variations every five minutes. So, WattTime staff also completed improvements to leverage real-time power grid data from individual grid operators as well as the Energy Information Administration to detect very fine-grained changes in power grid behavior that are highly predictive of patterns when marginal emissions are higher or lower. Furthermore, because CEMS only measures fossil fuel plants, standard models cannot detect the increasingly common moments when non-fossil (e.g. solar) plants are marginal. So, WattTime integrated additional datasets that measure moments of renewable energy curtailment, allowing us to build predictive models for when curtailment is occurring. 

No model is perfect, and WattTime expects to keep improving its models as new information and techniques become available. Our approach is to never stray from the fundamental accuracy and reliability of peer-reviewed empirical marginal emissions analysis techniques, but to constantly improve the precision, granularity, and feature set of such models to enable ever-greater emissions reductions. 

Academic References and Validation

The method for determining marginal emissions rates builds on the approach summarized in these academic papers, including one written by our founder, Gavin McCormick. Additionally, RMI conducted further validation of this work.

Applications in Research

WattTime’s data has been used in academic and applied research to evaluate the emissions reduction potential and real-world impacts for various devices and strategies.