Comment on ‘Does replacing coal with wood lower CO
                    <sub>2</sub>
                    emissions? Dynamic lifecycle analysis of wood bioenergy’

Prisley, Stephen P.; Gaudreault, Caroline; Lamers, Patrick; Stewart, William C.; Miner, Reid; Junginger, H.M.; Oneil, Elaine; Malmsheimer, Robert W.; Volk, Timothy A.

doi:10.1088/1748-9326/aaf203

Cited by 9 publications

(6 citation statements)

References 15 publications

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“…This is because plantation forests can surpass mature forests carbon storage after multiple rotations [109]. Harvested and replanted forests represent continued carbon reductions [85], and result in a forest carbon equilibrium [118]. Lippke et al [85] asserted that demolished steel and concrete structures cannot recover used energy (carbon), but wood-based buildings can with each sequential wood-based building replacing a traditional steel and concrete building.…”

Section: Sawtimber Utilizationmentioning

confidence: 99%

Climate Smart Forestry in the Southern United States

Shephard

Narine

Peng

et al. 2022

Forests

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In the United States, Climate Smart Forestry (CSF) has quickly become a popular topic within the academic, political, and industry realms, without substantial delineation of what exactly CSF is. In this review, the aim is to provide a broad overview of CSF by highlighting one of the most productive and prolific forest systems in the United States, loblolly pine (Pinus taeda L.) plantations. One major objective of CSF is to increase forest carbon storage to combat rising atmospheric carbon or climate change mitigation. Fortuitously, increased forest carbon storage can work harmoniously with on-going Southern pine plantation forestry. With a Southern commercial focus, we show (1) traditional plantation practices such as genetic improvement, site preparation, weed control, and fertilization have aided increased forest carbon storage; (2) forest products and forest product carbon are essential to increase carbon storage beyond the stand-carbon baseline; (3) forest carbon data collection must be improved to realize climate change mitigation goals; and (4) additional avenues for future CSF research.

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Section: Sawtimber Utilizationmentioning

confidence: 99%

Climate Smart Forestry in the Southern United States

Shephard

Narine

Peng

et al. 2022

Forests

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“…We acknowledge the argument by Prisley et al (2018) that the lack of more nuanced silvicultural systems is a weakness in the model. We suggest that this, when combined with the uncertainty inherent in the slower growing 'natural' forests, raises important questions about the ability of the model to accurately predict payback times in these forest types.…”

Section: Discussionmentioning

confidence: 90%

“…This publicity has included widespread reporting of the model's results within the media (Moomaw 2018b, Beeler and Morrison 2018, Smith 2020, Beswick 2018. Given the lack of clarity, agreement, and the degree of uncertainty which already exists in attempting to estimate payback periods, and the widespread acceptance of the findings of the Sterman et al (2018a) model: described as 'well documented and thorough' (Prisley et al 2018), we developed a replication of the Sterman et al (2018a) model in an open source framework to serve as a common foundation for future work, addressing the effects of differing assumptions, parameterisations, and scenarios.…”

Section: Introductionmentioning

confidence: 99%

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

Rolls¹,

Forster²

2020

Environ. Res. Commun.

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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.

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“…44 The challenge of carbon accounting across such a complex supply chain, however, has provoked controversy over the ultimate climate impact of operations such as Drax's. [45][46][47] Similar controversies will likely arise with respect to adaptive carbon removal projects. Scientists can work with NGOs and policymakers to ensure that monitoring, reporting, and verification is as robust as possible across scales and jurisdictions.…”

Section: Putting These Principles Into Action: Governance Needsmentioning

confidence: 97%

Adaptation and Carbon Removal

et al. 2020

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Comment on ‘Does replacing coal with wood lower CO ₂ emissions? Dynamic lifecycle analysis of wood bioenergy’

Cited by 9 publications

References 15 publications

Climate Smart Forestry in the Southern United States

Climate Smart Forestry in the Southern United States

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

Adaptation and Carbon Removal

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Comment on ‘Does replacing coal with wood lower CO 2 emissions? Dynamic lifecycle analysis of wood bioenergy’

Cited by 9 publications

References 15 publications

Climate Smart Forestry in the Southern United States

Climate Smart Forestry in the Southern United States

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

Adaptation and Carbon Removal

Contact Info

Product

Resources

About

Comment on ‘Does replacing coal with wood lower CO ₂ emissions? Dynamic lifecycle analysis of wood bioenergy’