CARBON STANDARD: Carbon Accounting to Redefine Biofeedstock Operation Normality using Sensing Technology Assisted by Numerical and Data Analytics for Reliable Detection

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Exploratory Topics
Berkeley, California
Project Term:
11/01/2020 - 05/01/2024

Critical Need:

This topic works to establish validation sites for field-level emissions quantification of agricultural bioenergy feedstock production. These teams will work towards the development of “ground truth” solutions to establish measurements and protocols for emissions monitoring at the field level to create publically available, open-source, high-resolution datasets to support testing and validation of emerging biofuel production monitoring technologies. The projects will also compliment selections in ARPA-E’s full SMARTFARM program, further supporting and validating the selections made through this full funding opportunity. Ethanol production is one of the largest consumers of domestic grain in the U.S., and developing sustainable production methods for ethanol and bio-based fuels has great potential to both reduce emissions and potentially provide a net emissions-free source of energy. While the economic and emissions impacts of ethanol production nationally are clear, field-level contributions remain unclear. The lack of understanding of field-level feedstock emissions, combined with the absence of economic incentives beyond yield, leaves feedstock producers to estimate and assume risks to their primary revenue stream by new management practices. By establishing sites and protocols for measuring the impact on yield increasing and emissions reducing technologies, these teams will bridge the technology gap between feedstock producers and existing market incentives to de-risk sustainable management practices, defray the cost of monitoring their impact, reduce biofuel feedstock production emissions, and broadly enable a future carbon farming industry.

Project Innovation + Advantages:

The Lawrence Berkeley National Lab (LBNL) CARBON STANDARD team will develop advanced machine learning tools for a cross-scale quantification of carbon intensity (CI) during biofuel feedstock production. LBL will act as the integrator across all SMARTFARM teams to analyze complex, multi-physics, and multi-scale datasets, and develop scaling approaches across the variety of CI monitoring fields.


ARPA-E Program Director:
Dr. Marina Sofos
Project Contact:
Yuxin Wu
Press and General Inquiries Email:
Project Contact Email:


University of Illinois, Urbana Champaign

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