Quantifying forest growth uncertainty on carbon payback times in a simple biomass carbon model

In 2018 Sterman et al (2018a) published a simple dynamic lifecycle analysis(DLCA) model for forest-sourced bioenergy. The model has been widely cited since its publication, including widespread reporting of the model’s headline results within the media. In adapting a successful replication of the Sterman et al (2018a) model with open-source software, we identified a number of changes to input parameters which improved the fit of the model’s forest site growth function with its training data. These relatively small changes to the input parameters result in relatively large changes to the model predictions of forest site carbon uptake: up to 92 tC.ha−1 or 18% of total site carbon at year 500. This change in estimated site carbon resulted in calculated payback periods (carbon sequestration parity) which differed by up to 54 years in a clear-fell scenario when compared with results obtained using previously published parameters. Notably, this uncertainty was confined to forests which were slower growing and where the model’s training dataset was not sufficiently long for forests to reach maturity. We provide improved parameterisations for all forest types used within the original Sterman et al (2018a) paper, and propose that these provide better fits to the underlying data. We also provide margins of error for the generated growth curves to indicate the wide range of possible results possible with the model for some forest types. We conclude that, while the revised model is able to reproduce the earlier Sterman et al (2018a)results, the headline figures from that paper depend heavily on how the forest growth curve is fitted to the training data. The resulting uncertainty in payback periods could be reduced by either obtaining more extensive training data (including mature forests of all types) or by modification of the forest growth function.