Choices in land representation materially affect modeled biofuel carbon intensity estimates. (15th May 2022)

• Record Type: Journal Article
• Authors: Plevin, Richard J.; Jones, Jason; Kyle, Page; Levy, Aaron W.; Shell, Michael J.; Tanner, Daniel J.
• Is Part Of: Journal of cleaner production. Volume 349(2022)
• Publication Date: 2022-05-15
• Subjects: Biofuel -- Land use change (LUC) -- GCAM -- GTAP -- Carbon intensity -- Climate change mitigation; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2022.131477
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: Estimates of biofuel carbon intensity are uncertain and depend on modeled land use change (LUC) emissions. While analysts have focused on economic and agronomic assumptions affecting the quantity of land converted, researchers have paid less attention to how models classify land into broad categories and designate some categories as ineligible for LUC. To explore the effect of these land representation attributes, we use three versions of a global human and Earth systems model, GCAM, and compute the "carbon intensity of land-use change" (CI-LUC) from increased U.S. corn ethanol production. We consider uncertainty in model parameters along with the choice of land representation and find the latter is one of the most influential parameters on estimated CI-LUC. A version of the model that protects 90% of non-commercial land reduced estimated CI-LUC by an average of 32% across Monte Carlo trials compared to our baseline model. Another version that mimics the GTAP-BIO-ADV land representation, which protects all non-commercial land, reduced CI-LUC by an average of 19%. The results of this experiment demonstrate that land representation in biofuel LUC models is an important determinant of CI-LUC. Graphical abstract: Image 1 Highlights: Models of biofuel-induced land use change differ in how they categorize land. Biofuel effects modeled with alternate land representations in a single model, GCAM. Land representation affects estimates of climate change mitigation by biofuels.