Increasing Precision in Greenhouse Gas Accounting Using Real-Time Emission Factors: A Case Study of Electricity in Spain

Charlie C. Spork, Abel Chavez, Xavier Gabarrell Durany, Martin K. Patel, Gara Villalba Méndez

Research output: Contribution to journalArticleResearchpeer-review

11 Citations (Scopus)

Abstract

© 2014 by Yale University. For many companies, the greenhouse gas (GHG) emissions associated with their purchased and consumed electricity form one of the largest contributions to the GHG emissions that result from their activities. Currently, hourly variations in electricity grid emissions are not considered by standard GHG accounting protocols, which apply a national grid emission factor (EF), potentially resulting in erred estimates for the GHG emissions. In this study, a method is developed that calculates GHG emissions based on real-time data, and it is shown that the use of hourly electricity grid EFs can significantly improve the accuracy of the GHG emissions that are attributed to the purchased and consumed electricity of a company. A model analysis for the electricity delivered to the Spanish grid in 2012 reveals that, for companies operating during the day, GHG emissions calculated by the real-time method are estimated to be up to 5% higher (and in some special cases up to 9% higher) than the emissions calculated by the conventional method in which a national grid EF is applied, whereas for companies operating during nightly hours, GHG emissions are estimated to be as low as 3% below the GHG emissions determined by the conventional method. A significant error can therefore occur in the organizational carbon footprint (CF) of a company and, consequently, also in the product CF. It is recommended that hourly EFs be developed for other countries and power grids.
Original languageEnglish
Pages (from-to)380-390
JournalJournal of Industrial Ecology
Volume19
Issue number3
DOIs
Publication statusPublished - 1 Jun 2015

Keywords

  • Carbon footprint
  • Greenhouse gas (GHG) emissions
  • Hourly emission factors
  • Industrial ecology
  • National electric grid
  • Power generation mix

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