SAN FRANCISCO, Ca. –Wildfire is the most influential factor in determining the carbon-payback period for forest biomass energy projects, according to a study published online this month in Global Change Biology – Bioenergy.
Research in the past five years has shown that forest biomass energy projects typically create a carbon debt for a period of time which is gradually erased as new tree growth sequesters the carbon emitted when the energy was produced. This month’s study revealed the most important factors that influence the payback period. First and foremost is natural disturbance, according to David Saah, an Associate Professor and Director of the Geospatial Analysis Lab at the University of San Francisco. Saah was one of the driving forces behind the study.
“Disturbances like wildfire and insect outbreaks are important ecosystem dynamics that affect the amount of carbon stored in and sequestered by forests,” Saah said. “The most influential factor was whether or not the study included the effects of wildfire.” The inclusion of wildfire dynamics typically led to longer payback periods, but when forest material from thinning to reduce fire risk generates biomass energy (and would have otherwise been left onsite or burned without a market) carbon payback periods can be considerably shorter.
Led by Thomas Buchholz of the Spatial Informatics Group, and Matthew Hurteau, assistant professor of Forest Resources at Penn State University, researchers analyzed the results of 38 previously published studies on carbon baselines that included a measure of carbon-payback period.
Researchers identified 20 different attributes to classify these studies. They included factors such as type of forest (plantation or natural), fossil fuel energy source displaced, and whether or not the study included natural disturbance when modeling forests. After they classified all of the studies based on these attributes, they ran an analysis to determine which attributes were most influential for determining the payback period.
The full study can be found here
Buchholz, T., M.D. Hurteau, J.S. Gunn, and D.S. Saah. 2015. A global meta-analysis of forest bioenergy greenhouse gas emission accounting studies. Global Change Biology – Bioenergy doi: 10.1111/gcbb.12245.
Related SIG & SIG-NAL Forest Bioenergy Publications:
Buchholz, T., P. Stephen, G. Marland, C. Canham, N. Sampson. 2014. Uncertainty in projecting greenhouse gas emissions from bioenergy. Nature Climate Change 4: 1045–1047.
Saah, D., Patterson, T., Buchholz, T., Ganz, D., Albert, D., & Rush, K. (2014) Modeling economic and carbon consequences of a shift to wood-based energy in a rural ‘cluster’; a network analysis in southeast Alaska. Ecological Economics, 107, 287-298.
Walker, T., P. Cardellichio, J.S. Gunn, D. Saah, J.M. Hagan. 2013. Carbon Accounting for Woody Biomass from Massachusetts (USA) Managed Forests: A Framework for Determining the Temporal Impacts of Wood Biomass Energy on Atmospheric Greenhouse Gas Levels. Journal of Sustainable Forestry 32 (1-2):130-158.
Gunn, J.S., D. Ganz, W.S. Keeton. 2012. Biogenic vs. geologic carbon emissions and forest biomass energy production. Global Change Biology – Bioenergy 4:239-242.