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

Abstract

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.

Effects of Production of Woody Pellets in the Southeastern United States on the Sustainable Development Goals

“…The carbon payback time depends on how an analysis is temporally framed and other variables, ranging from the alternative fates for the feedstock to the type and location of sourcing forests [79,80]. If, as many models assume, a forest plot is clear-cut for only bioenergy purposes, the carbon payback time for a typical managed pine plantation in the SE US is calculated to be about 18 years, while mixed pine-hardwood, naturally regenerating forest stands may require three times longer [81,82]. However, forests in the SE US are primarily harvested for higher-value markets.…”

Carbon neutrality should not be the end goal: Lessons for institutional climate action from U.S. higher education

“…34 Recent work suggested that even relative to coal (most institutions are displacing less carbon-intensive natural gas), carbon payback periods for northeastern forests were likely to range from 50 to 100 years. 35 Significantly, even if the ''carbon debts'' are short, front loading of emissions to be offset by later sequestration (i.e., net carbon neutrality over time) still may not be climate neutral. System lags mean that the early pulse of CO 2 will increase the heat trapped in the climate system for years after the pulse has been offset by sequestration, and this short-term warming may push the climate system into feedback loops that accelerate warming.…”

Dynamic life-cycle carbon analysis for fast pyrolysis biofuel produced from pine residues: implications of carbon temporal effects

“…The payback time for the carbon debt was reported to range from 44 to 104 years, depending on forest type and growth. Following the work by Sterman, Siegeland Rooney-Varga [ 48 ], Rolls and Forster [ 51 ] replicated the model and modified the input parameters to predict the forest carbon uptake and payback time of the carbon debt with considering the uncertainty in forest growth. This study showed the payback time largely dependent on the uncertainty of forest growth curves [ 51 ].…”

“…Following the work by Sterman, Siegeland Rooney-Varga [ 48 ], Rolls and Forster [ 51 ] replicated the model and modified the input parameters to predict the forest carbon uptake and payback time of the carbon debt with considering the uncertainty in forest growth. This study showed the payback time largely dependent on the uncertainty of forest growth curves [ 51 ]. Jonker, Junginger and Faaij [ 49 ] calculated the carbon payback period for wood pellets from softwood forest, taking into consideration the temporal profiles of forest growth under several management scenarios.…”

“…However, few of these dynamic GWP studies considered the impacts of variations in forest dynamics and forest management scenarios on the GWP results. Several studies included the carbon dynamics of forest systems in carbon analysis and showed the necessity of including carbon temporal profiles of forest growth [48][49][50][51][52][53][54][55], but these studies used other indicators such as carbon payback time. For example, Sterman, Siegel and Rooney-Varga [48] simulated the replacement of wood for coal in electricity generation and tracked the carbon fluxes in forest, atmosphere, and soil.…”