(23e) Quantifying Variability in Life Cycle Environmental Footprints of Biofuel Produced from Forest Residues in the United States

As one of the bioenergy sources that expand rapidly, biofuel is regarded as a substitute for the traditional fossil fuel and can provide potential benefits in both environmental and economic aspects. Woody biomass is among the abundant renewable resources in the United States. Traditional wood products contain durable wood products, pulpwood, and other types. In processes like thinning, logging, and product manufacturing, a large amount of forest residues are generated and have a huge potential for biofuel production rather than being left for decay or burning. Annually around 30-108 million dry metric tons of forest residues are generated in the United States.¹ To accelerate the utilization of forest residues for biofuel production, it is important to develop a quantitative understanding of the potential environmental benefits. Life Cycle Analysis (LCA) is a standardized and widely accepted accounting tool for evaluating the life cycle environmental impacts of products. Most of previous LCA studies estimated environmental footprints of converting forest residues to biofuels with deterministic assumptions. Few of them have fully addressed the variations in both biomass production and biomass conversion that may have significant impacts on the results of potential environmental benefits. Examples of variations include those in biomass production (e.g., tree growth rates, management intensities) and in biomass conversion (e.g., carbon content of the biomass). For sustainable utilization of forest residues and biofuel production, it is crucial to quantify the variability of life cycle environmental footprints and identify the main factors contributing to the variations.

In this work, a cradle-to-gate LCA model was developed to cover biomass production, transportation, and biomass conversion in fast-pyrolysis biorefinery in the Southern U.S., the region famous for pine production. The Life Cycle Inventory (LCI) data of biomass production and transportation were collected either from literature. The LCI data of biorefinery was generated by the Aspen Plus simulation models and correlated with biomass quality data such as ash, carbon, and moisture contents. The key parameters with variations in each life-cycle stage were identified by literature review and sensitivity analysis for established models. The range and distribution of each parameter were based on literature data and were used as inputs to Monte Carlo Simulation (MCS). MCS is a widely accepted tool to understand the effects of uncertainties or variations and has been used in many LCA studies before.² Contribution analysis was conducted for the results of MCS to identify key factors driving the variability of life cycle energy and GHG emissions of biofuel derived for forest residues.

Preliminary results showed that the life cycle environmental footprints of biofuel from forest residue have large variations, but the main factors driving the results of energy and GHG emissions are different. For life-cycle GHG emissions, variations are mostly caused by diverse planting strategies, growth rates, rotation lengths, and thinning schedules. For life-cycle energy consumption, feedstock quality and process variations in biorefinery are main contributors. In addition, different methodological choices in LCA, such as allocation methods for products and by-products (e.g., electricity generation) in biorefinery have a large impact on the results.

Reference

1. Perlack, R. D., Eaton, L. M., Turhollow Jr, A. F., Langholtz, M. H., Brandt, C. C., Downing, M. E., Graham, R. L., Wright, L. L., Kavkewitz, J. M., Shamey, A. M., & Nelson, R. G. US billion-ton update: biomass supply for a bioenergy and bioproducts industry. 2011.
2. Sonnemann, G. W., Schuhmacher, M., & Castells, F. Uncertainty assessment by a Monte Carlo simulation in a life cycle inventory of electricity produced by a waste incinerator. Journal of Cleaner Production. 2003, 11(3), 279-292.