A Dynamic Programming Approach for Modeling Low-Carbon Fuel Technology Adoption Considering Learning-by-doing effect

Significance Statement

Most governments around the world are trying to reduce the dependency on petroleum fuels and mitigate greenhouse gas emissions from the transportation sector, by implementing various policies and regulations including adoption of low-carbon fuel technologies in current transportation fuel portfolios. To replace petroleum-based fuel consumption in transportation, an advanced biofuels such as cellulosic biofuels was introduced.

Dr. Yuche Chen and colleagues proposed an economical way of using cellulosic biofuels by establishing an analytical framework to investigate the least-cost cellulosic biofuel technology adoption while considering the learning by doing modeling effect. The research work is now published in Applied Energy.

Cellulosic biofuels are fuels produced from non-edible cellulosic biomass, such as woody crops, or agricultural residues, they have the advantages of producing low-carbon emissions based on lifecycle assessment and of not competing with food crops for land use. There is need for government guidance to ensure enough cellulosic biofuel is produced to develop the technology and to achieve the aim of low-carbon fuel in transportation. To economically promote cellulosic biofuels, the government should know the appropriate timing and approach. However, knowing the appropriate time to promote cellulosic biofuels can be challenging.

The authors introduced dynamic programming and learning by doing modeling approaches to tackle the issue of appropriate timing. The proposed framework was applied in a case study to explore the most economical pathway for California to develop a solid cellulosic biofuel industry under its low carbon fuel standard.

They observed that petroleum-based fuels gasoline and diesel, have 60% higher prices compared with those of sugarcane, ethanol and biodiesel. The biodiesel production significantly increase and remains at a 20% blend rate in the diesel fuels category for all modeling years. The team pointed out that high market penetration of zero Emission Vehicles and plug-in hybrid electric vehicle can lead to a lower production of cellulosic ethanol. The research team also found that learning capabilities in cellulosic biofuels have a significant impact on the overall fuel pathway and adoption of cellulosic biofuels.

This was the first study of its kind to use both dynamic Programming and learning by doing modeling approaches to study transportation energy problems. Their results confirmed that reducing the transportation greenhouse gas emissions requires an approach and the cellulosic biofuel plays a vital role.

A Dynamic Programming Approach for Modeling Low-Carbon Fuel Technology Adoption Considering Learning by doing effect - renewable energy global innovations

About The Author

Dr. Yuche Chen is a senior research scientist at United States National Renewable Energy Laboratory (NREL), United States Department of Energy’s only national lab dedicated to renewable energy research. He actively involves in research development and implementation activities in both vehicle systems analysis and infrastructure analysis. His research addresses transitions to a sustainable transportation system with focuses on alternative fuels, as well as advanced vehicle technologies such as connected and automated vehicles.

Before joining NREL, Chen was a research scientist at Texas A&M Transportation Institute within Texas A&M University System and served as the academic lead for Environmental and Emissions Research Facility. He has also worked for The International Council on Clean Transportation, International Institute for Applied Systems Analysis (IIASA), California Air Resource Board, Lawrence Berkeley National Laboratory, and National Transportation Research Center at Oak Ridge National Laboratory. He has a Ph.D. degree in Civil and Environmental Engineering from University of California, Davis; a Master’s degree in Statistics and a Master’s degree in Agricultural & Resource Economics both from University of California, Davis, a Master’s degree in Management Science and Engineering from Zhejiang University, China; and a bachelor’s degree in Transportation Engineering from Central South University, China.


Yuche Chen1, Yunteng Zhang2, Yueyue Fan2, Kejia Hu3, Jianyou Zhao4, A Dynamic Programming Approach for Modeling Low-Carbon Fuel Technology Adoption Considering Learning-by-doing effect, Applied Energy 185 (2017) 825–835.

Show Affiliations

1 College of Transportation Engineering & The Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai, China.

2 University of California, Davis, 1 Shields Ave., Davis, CA 95616, USA.

3 Kellogg School of Management, Northwestern University, Evanston, IL 60208, USA.

4 School of Automobile, Chang’an University, Xi’an 710064, China.


Go To Applied Energy