The work of Will Rolls and Prof Piers Forster follows research by Sterman et al. who developed a simple Dynamic Life Cycle Analysis (DLCA) model, to predict carbon payback periods for a range of forest types in the USA. The results published by Sterman et al. attracted widespread reporting, but during their work Rolls and Forster identified substantial uncertainty in the results that was not explored in the original study.
In their original model, Sterman et al. include equations for a life cycle calculation, describing emissions from fuel production and use, and a forest site model to calculate atmospheric and carbon associated with forest management. Rolls and Forster recreated the model in an open-source framework to estimate emissions over time attributed to biomass production, use, and forest site recovery when compared to an equivalent fossil fuel scenario. Their study examines alternative curve fitting approaches to identify possible improvements to the model’s description of forest and soil carbon over time and the effect of these changes on the carbon payback period.
While exploring alternative parameterisations of the model, Rolls and Forster identified a number of settings which improved the model’s output, but which resulted in very large differences in carbon payback period. In each case, the payback periods estimated by Sterman et al. were found to sit at the top end of the range of possible outcomes.
Rolls and Forster show that in making small changes to parameterisations to improve the fit of the data, it is possible to achieve substantial changes to estimates of carbon payback period (up to 54 years). They conclude that this variation in possible results reveals large uncertainties in both the time taken for carbon stocks to reach maturity, and the amount of carbon stored in forest sites when using the Sterman et al. model.
The full paper can be accessed here.
Rolls, W. and Forster, P.M., 2020. Quantifying forest growth uncertainty on carbon payback times in a simple biomass carbon model. Environmental Research Communications, 2(4), p.045001.