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Cellulosic ethanol production: Assessment of the impacts of learning and plant capacity.

Authors :
Aui, Alvina
Wang, Yu
Source :
Technological Forecasting & Social Change; Dec2023, Vol. 197, pN.PAG-N.PAG, 1p
Publication Year :
2023

Abstract

Cellulosic ethanol has great potential to displace fossil fuels and reduce greenhouse gas emissions, but supply is low due to high cost. Learning-by-doing is a strategy to reduce cost with increases in capacity. This study attempts to understand how learning-by-doing affects the growth of cellulosic ethanol in synergy with plant capacity. We modeled a three-phase learning effect using a hybrid general equilibrium model to project the U.S cellulosic ethanol production. The reference case projects an annual production of 5.49 million gallons (MG) from 2030 to 2040. With strong policy support of a high mandate, the U.S. is projected to produce 171 MG of cellulosic ethanol in 2040. However, the learning-by-doing effect cannot be initiated because only 4 large-scale centralized plants will be built. When plant size reduces, learning-by-doing is enabled, and 2040 cellulosic ethanol production can reach 3.27 billion gallons. Similar result is predicted for the cases representing an actual biorefinery. Fast and widespread learning is estimated to greatly increase production when plants have lower capital cost. The findings show that policy incentives are critical to technology development when cost is a significant barrier. Additionally, building small-size plants is a strategy that will enable and facilitate the learning-by-doing effect. • The U.S. annual cellulosic ethanol production is projected to be 5.49 MG in 2030. • A high mandate will drive the U.S. cellulosic ethanol production to 171 MG in 2030. • Learning is not observed for a few large-size plants, thus no reduction in cost. • When cost is a limiting factor, building small plants will facilitate learning. • Building plants at half- and quarter- sizes will boost production by 1.1–19 times. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00401625
Volume :
197
Database :
Supplemental Index
Journal :
Technological Forecasting & Social Change
Publication Type :
Academic Journal
Accession number :
173343857
Full Text :
https://doi.org/10.1016/j.techfore.2023.122923