Deep decarbonization and U.S. biofuels production: a coordinated analysis with a detailed structural model and an integrated multisectoral model
Abstract
Scenarios for deep decarbonization involve biomass for biofuels, biopower, and bioproducts, and they often include negative emissions via carbon capture and storage or utilization. However, critical questions remain about the feasibility of rapid growth to high levels of biomass utilization, given biomass and land availability as well as historical growth rates of the biofuel industry. We address these questions through a unique coordinated analysis and comparison of carbon pricing effects on biomass utilization growth in the United States using a multisectoral integrated assessment model, the Global Change Analysis Model (GCAM), and a biomass-to-biofuels system dynamics model, the Bioenergy Scenario Model (BSM). We harmonized and varied key factors—such as carbon prices, vehicle electrification, and arable land availability—in the two models. We varied the rate of biorefinery construction, the fungibility of feedstock types across conversion processes, and policy incentives in BSM. The rate of growth in biomass deployment under a carbon price in both models is within the range of current literature. However, the reallocation of land to biomass feedstocks would need to overcome bottlenecks to achieve growth consistent with deep decarbonization scenarios. Investments as a result of near-term policy incentives can develop technology and expand capacity—reducing costs, enabling flexibility in feedstockmore »
- Authors:
-
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States). Joint Global Change Research Institute
- Independent Contractor, West Lebanon, NH (United States)
- Publication Date:
- Research Org.:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Sustainable Transportation. Bioenergy Technologies Office (BETO)
- OSTI Identifier:
- 2005578
- Report Number(s):
- NREL/JA-6A20-82158
Journal ID: ISSN 1748-9326; MainId:82931;UUID:7bd7bdfe-2e44-4a31-b567-01351ba2bf07;MainAdminID:70733
- Grant/Contract Number:
- AC36-08GO28308; AC05-76RL01830
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Environmental Research Letters
- Additional Journal Information:
- Journal Volume: 18; Journal Issue: 10; Journal ID: ISSN 1748-9326
- Publisher:
- IOP Publishing
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 09 BIOMASS FUELS; 29 ENERGY PLANNING, POLICY, AND ECONOMY; biofuels; climate change; integrated assessment model; land use; system dynamics
Citation Formats
Vimmerstedt, Laura, Atnoorkar, Swaroop, Bergero, Candelaria, Wise, Marshall, Peterson, Steve, Newes, Emily, and Inman, Daniel. Deep decarbonization and U.S. biofuels production: a coordinated analysis with a detailed structural model and an integrated multisectoral model. United States: N. p., 2023.
Web. doi:10.1088/1748-9326/acf146.
Vimmerstedt, Laura, Atnoorkar, Swaroop, Bergero, Candelaria, Wise, Marshall, Peterson, Steve, Newes, Emily, & Inman, Daniel. Deep decarbonization and U.S. biofuels production: a coordinated analysis with a detailed structural model and an integrated multisectoral model. United States. https://doi.org/10.1088/1748-9326/acf146
Vimmerstedt, Laura, Atnoorkar, Swaroop, Bergero, Candelaria, Wise, Marshall, Peterson, Steve, Newes, Emily, and Inman, Daniel. Thu .
"Deep decarbonization and U.S. biofuels production: a coordinated analysis with a detailed structural model and an integrated multisectoral model". United States. https://doi.org/10.1088/1748-9326/acf146. https://www.osti.gov/servlets/purl/2005578.
@article{osti_2005578,
title = {Deep decarbonization and U.S. biofuels production: a coordinated analysis with a detailed structural model and an integrated multisectoral model},
author = {Vimmerstedt, Laura and Atnoorkar, Swaroop and Bergero, Candelaria and Wise, Marshall and Peterson, Steve and Newes, Emily and Inman, Daniel},
abstractNote = {Scenarios for deep decarbonization involve biomass for biofuels, biopower, and bioproducts, and they often include negative emissions via carbon capture and storage or utilization. However, critical questions remain about the feasibility of rapid growth to high levels of biomass utilization, given biomass and land availability as well as historical growth rates of the biofuel industry. We address these questions through a unique coordinated analysis and comparison of carbon pricing effects on biomass utilization growth in the United States using a multisectoral integrated assessment model, the Global Change Analysis Model (GCAM), and a biomass-to-biofuels system dynamics model, the Bioenergy Scenario Model (BSM). We harmonized and varied key factors—such as carbon prices, vehicle electrification, and arable land availability—in the two models. We varied the rate of biorefinery construction, the fungibility of feedstock types across conversion processes, and policy incentives in BSM. The rate of growth in biomass deployment under a carbon price in both models is within the range of current literature. However, the reallocation of land to biomass feedstocks would need to overcome bottlenecks to achieve growth consistent with deep decarbonization scenarios. Investments as a result of near-term policy incentives can develop technology and expand capacity—reducing costs, enabling flexibility in feedstock use, and improving stability—but if biomass demand is high, these investments might not overcome land reallocation bottlenecks. Biomass utilization for deep decarbonization relies on extraordinary growth in biomass availability and industrial capacity. In this paper, we quantify and describe the potential challenges of this rapid change.},
doi = {10.1088/1748-9326/acf146},
journal = {Environmental Research Letters},
number = 10,
volume = 18,
place = {United States},
year = {Thu Sep 28 00:00:00 EDT 2023},
month = {Thu Sep 28 00:00:00 EDT 2023}
}
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