Dynamic greenhouse gas accounting for cellulosic biofuels: implications of time based methodology decisions

Purpose Greenhouse gas (GHG) emissions resulting from biofuel production and use often occur over many different years. Nondynamic GHG accounting methods traditionally sum the global warming impacts (GWIs) occurring over a 100-year period for all emissions occurring in the life cycle regardless of emission timing. When examining biofuels from a policy perspective, time horizons are chosen to determine the benefits a policy or action has over a desired time period. It is critical to only account for impacts occurring within the given time period by having consistent temporal boundaries. When calculating the GWI as a function of time, additional assumptions and data are required. These assumptions have implications on the results and are explored herein to determine their influence on the overall conclusions when comparing biofuels made from different cellulosic feedstocks. Materials and methods The time zero assumption of both biomass planting and harvesting was examined. Analytical time horizon choice was also tested by examining results on a 25-, 50-, 100-, and 500-year time horizon. GWIs using dynamic GHG accounting methods were compared to nondynamic GWI method results. Direct land use change (LUC) emissions were determined for the different feedstock conversion scenarios and used to calculate a payback period for switchgrass and sweet sorghum biofuel scenarios. Dynamic biofuel life cycle emissions were also modeled for biofuel scenarios where LUC emissions were negative in the case of converting cropland to forests. Results and discussion Biofuel life cycle emissions and GHG reductions compared to gasoline were highly sensitive to GHG accounting methods and time horizons. The time zero assumption had greater influence on the results when shorter time horizons were chosen and decreased as time horizons approached 500 years. Using the dynamic GHG accounting method, LUC payback periods were determined to be greater compared to a 0 % emission discount method. Payback periods using a discount rate of 2 and 3 % were at times greater and less than dynamic GHG accounting method results. Conclusions The data presented herein suggest that time zero and other temporal emission timing assumptions are important and influence the overall study results. Analytical time horizons were also shown to be important and significantly influence the overall results, as well as be important to achieving carbon mitigation goals. Dynamic GHG accounting was shown to be a more robust method than the traditional static GWI accounting method ensuring consistent temporal boundaries; however, dynamic inventories require additional emission timing details and assumptions that require more effort and resources to model.