Blog Post: Quantifying the Accuracy of Commonly Used Marginal Emissions Data

November 6, 2024

Nodal granularity matters

Authored by Adam Reeve, SVP, Customer Experience, REsurety

Two common questions that REsurety gets about its Locational Marginal Emissions (LME) data are: “How accurate is it? How does it compare with other emissions data sources?”

Answering these questions is important because more and more companies are shifting their electricity decarbonization strategy to maximize emissions impact, for example through building or purchasing power from renewables in higher-impact locations. Research^1-7 has shown that this strategy can enable faster and more cost-effective decarbonization relative to alternative approaches. At REsurety, we’re proud that our emissions data helps drive this sort of impactful action.

As our LME data and other emissions data sets get increasingly relied upon to guide decarbonization strategies, validating these emissions data sets becomes of growing importance. That’s why we teamed up with researchers at WattTime to study the accuracy of a number of different emissions data sets against real-world emissions impacts.

In this paper, Validating Locational Marginal Emissions with Wind Generation, the authors quantify, for the first time, the accuracy of a number of different commonly used models for estimating the emissions impacts of consumption or generation. The study uses granular data from wind farms in Texas to evaluate how changes in generation result in real-world changes in emissions on the grid.

At REsurety, we are particularly excited to see that the study found that our LME model performed the best across key metrics of accuracy, including best correlation and lowest margin of error. The paper also found that nodal granularity matters: the accuracy associated with using nodal data can result in up to 50% higher impact than lower-resolution models. While data granularity will vary around the world for a number of reasons, this underscores the importance of using nodal data in markets where it is available (which includes all U.S. ISOs).

If you are interested in getting access to this data to more confidently make impactful clean energy decisions, reach out to us at https://resurety.com/contact/.

¹ https://www.sciencedirect.com/science/article/abs/pii/S1040619021001196
² https://www.pnas.org/doi/10.1073/pnas.1221978110
³ https://www.nber.org/papers/w18462
⁴ https://www.jstor.org/stable/26544571
⁵ https://dl.acm.org/doi/pdf/10.1145/3447555.3466582
⁶ https://www.sciencedirect.com/science/article/abs/pii/S0959652616322065
⁷ https://pubs.acs.org/doi/abs/10.1021/es505027p

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