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

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