From average to marginal: estimating emission factors in Europe's electricity markets

2 June 2025

Professors Matthew Brander and Luca Taschini from the Business School, together with colleagues, present their latest research on estimating emission factors in Europe’s electricity markets.

Electricity pylon and overhead wires against a blue sky above a green field

This study introduces a new econometric approach to estimate marginal emission factors (MEFs) across 29 European electricity markets using publicly available data from 2016 to 2022. It highlights that average emission factors (AEFs), which are commonly used, can be unreliable proxies for MEFs when assessing the emissions impact of incremental changes in electricity use, such as load-shifting decisions. The research documents significant variations in MEFs across different markets and times of day, and quantifies substantial discrepancies between MEFs and corresponding AEFs. These differences can lead to underestimations of emissions and miscalculated carbon savings on the order of tens of millions of tons of carbon dioxide annually. Thus, the study highlights the importance of using MEFs for accurate (short term) consequential carbon accounting and effective evaluation of load-shifting interventions.

Key points for decision-makers

AEFs are not a reliable indicator of the emissions impact of changes in electricity consumption. Relying on AEFs for decisions like load-shifting can significantly underestimate (or in some cases, overestimate) the actual change in emissions. For instance, in 22 out of 29 European markets analysed, MEFs were consistently higher than AEFs.
As illustrated by one example of multinational data centers, using AEFs instead of MEFs for load-shifting could lead to underestimating emissions by millions of tons of carbon dioxide and forgoing hundreds of millions of euros in potential carbon cost savings.
MEFs provide a more accurate measure of the environmental consequences of actions that change electricity demand or generation. By understanding when and where shifting electricity load has the greatest impact on marginal emissions, decision-makers can implement more effective strategies.
This study introduces a new econometric approach to estimate MEFs, accounting for real-world complexities such as ramping and cycling constraints of power plants, outages, and redispatch measures.