ACKNOWLEDGEMENTS We thank Dennis Ojima and Daniel L. Sanchez for their encouragement on this topic. The authors gratefully acknowledge partial support as follows: J.L.F., L.R.L., T.L.R., E.A.H.S., and J.J.S., the Sao Paulo Research Foundation (FAPESP grant# 2014/26767-9); J.L.F., L.R.L., K.P., and T.L.R., The Center for Bioenergy Innovation, a U.S. Department of Energy Research Center supported by the Office of Biological and Environmental Research in the DOE Office of Science (grant# DE-AC05-00OR22725); L.R.L., the Sao Paulo Research Foundation, and the Link Foundation; J.L.F. and K.P., USDA/NIFA (grant# 2013-68005-21298 and 2017-67019-26327); T.L.R., USDA/NIFA (grant# 2012-68005-19703); D.S.L. and S.P.L., the Energy Biosciences Institute. Data availability The DayCent model (https://www2.nrel.colostate.edu/projects/daycent/) is freely available upon request. Specification of DayCent model runs and automated model initialization, calibration, scenario simulation, results analysis, and figure generation were implemented in Python 2.7, using the numpy module for data processing and the matplotlib module for figure generation. Analysis code is available in a version-controlled repository (https://github.com/johnlfield/Ecosystem_dynamics). A working copy of the code, all associated DayCent model inputs, and analysis outputs are also available in an online data repository (https://figshare.com/s/4c14ec168bd550db4bad; note this URL is for accessing a private version of the repository, and will eventually be replaced with an updated URL for the public version of the repository, which will only be accessible after the journal-specified embargo date).Peer reviewedPostprintPublisher PD

Cai, Hao

Field, John L.

Laser, Mark S.

LeBauer,  David S.

Long, Stephen P.

Lynd, Lee R

Paustian, Keith

Qin, Zhangcai

Richard, Tom L.

Sheehan, John J.

Smith, Pete

Smithwick, Erica A. H.

Wang, Michael Q. L.

Proceedings of the National Academy of Sciences

English

Biofuel and bioenergy systems are integral to most climate stabilization scenarios for displacement of transport sector fossil fuel use and for producing negative emissions via carbon capture and storage (CCS). However, the net greenhouse gas mitigation benefit of such pathways is controversial due to concerns around ecosystem carbon losses from land use change and foregone sequestration benefits from alternative land uses. Here, we couple bottom-up ecosystem simulation with models of cellulosic biofuel production and CCS in order to track ecosystem and supply chain carbon flows for current and future biofuel systems, with comparison to competing land-based biological mitigation schemes. Analyzing three contrasting US case study sites, we show that on land transitioning out of crops or pasture, switchgrass cultivation for cellulosic ethanol production has per-hectare mitigation potential comparable to reforestation and severalfold greater than grassland restoration. In contrast, harvesting and converting existing secondary forest at those sites incurs large initial carbon debt requiring long payback periods. We also highlight how plausible future improvements in energy crop yields and biorefining technology together with CCS would achieve mitigation potential 4 and 15 times greater than forest and grassland restoration, respectively. Finally, we show that recent estimates of induced land use change are small relative to the opportunities for improving system performance that we quantify here. While climate and other ecosystem service benefits cannot be taken for granted from cellulosic biofuel deployment, our scenarios illustrate how conventional and carbon-negative biofuel systems could make a near-term, robust, and distinctive contribution to the climate challenge

Field, J.L.

Richard, T.L.

Smithwick, E.A.H.

Cai, H.

Laser, M.S.

LeBauer, D.S.

Long, S.P.

Paustian, K.

Qin, Z.

Sheehan, J.J.

Smith, P.

Wang, M.Q.

Lynd, L.R.

Lancaster E-Prints

Robust paths to net greenhouse gas mitigation and negative emissions via advanced biofuels

ACKNOWLEDGEMENTS We thank Dennis Ojima and Daniel L. Sanchez for their encouragement on this topic. The authors gratefully acknowledge partial support as follows: J.L.F., L.R.L., T.L.R., E.A.H.S., and J.J.S., the Sao Paulo Research Foundation (FAPESP grant# 2014/26767-9); J.L.F., L.R.L., K.P., and T.L.R., The Center for Bioenergy Innovation, a U.S. Department of Energy Research Center supported by the Office of Biological and Environmental Research in the DOE Office of Science (grant# DE-AC05-00OR22725); L.R.L., the Sao Paulo Research Foundation, and the Link Foundation; J.L.F. and K.P., USDA/NIFA (grant# 2013-68005-21298 and 2017-67019-26327); T.L.R., USDA/NIFA (grant# 2012-68005-19703); D.S.L. and S.P.L., the Energy Biosciences Institute. Data availability The DayCent model (https://www2.nrel.colostate.edu/projects/daycent/) is freely available upon request. Specification of DayCent model runs and automated model initialization, calibration, scenario simulation, results analysis, and figure generation were implemented in Python 2.7, using the numpy module for data processing and the matplotlib module for figure generation. Analysis code is available in a version-controlled repository (https://github.com/johnlfield/Ecosystem_dynamics). A working copy of the code, all associated DayCent model inputs, and analysis outputs are also available in an online data repository (https://figshare.com/s/4c14ec168bd550db4bad; note this URL is for accessing a private version of the repository, and will eventually be replaced with an updated URL for the public version of the repository, which will only be accessible after the journal-specified embargo date).Peer reviewe

Aberdeen University Research

https://aura.abdn.ac.uk/bitstream/2164/15109/1/Field_etal_PNAS_Robust_paths_VOR.pdf

Robust paths to net greenhouse gas mitigation and negative emissions via advanced biofuels

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Aberdeen University Research