Tweaking model improves ethanol's footprint

CALIFORNIA has led the charge in enforcing stricter air emissions requirements.

For ethanol, an initial analysis from Purdue University made ethanol seem less attractive at reducing greenhouse gas (GHG) emissions compared to other alternatives. Now, a revision to the Purdue economic analysis is showing that ethanol could be a somewhat better option than previously thought.


Wally Tyner, a Purdue agricultural economist and the report's lead author, said revisions to the Global Trade Analysis Project (GTAP) model better reflect market conditions and land productivity than a 2009 report showing that corn-based ethanol wouldn't significantly lower GHG emissions over gasoline.


"The difference between this report and previous reports is advances in science," Tyner said. "With any issue, your first cut may not be the best, but when you get new data and new methods, you improve."


The report considers land use changes when calculating total GHG emissions from biofuels based on the U.S. program to increase corn ethanol production to 15 billion gal. by 2015. Those changes include emissions created by converting forests or pastures to cropland. The new analysis predicts emissions related to land use change to be 35% lower than previous analyses.


Purdue economists ran three new simulations through the GTAP model. The first used 2001 economic data as a base, the second updated the data through 2006 and the third used the updated 2006 data and assumed growth in population and crop yield through 2015.


The 2009 report showed total -- including with land use changes -- carbon dioxide emissions per megajoule (MJ) for ethanol to be 86.3 g, while the three simulations in the new report predicted 84.4 g, 81.1 g and 77.5 g, respectively (Table).


The third simulation, which Tyner said is probably the most accurate, reduced the amount of carbon dioxide that would be emitted by about 10%, but he warned that there is still uncertainty with the numbers because the model contains many complex relationships, covers the entire globe and includes data and parameters from diverse resources.