Title: Will Learning-by-doing Effect Lead California to A Low Carbon Transportation Fuel System? – A Dynamic Programming Approach
Abstract: Transportation sector worldwide contributes to a substantial part of energy-related CO₂ emissions, which is dominated by petroleum fuels of high carbon intensity. Governments are implementing regulations aiming at reducing transportation’s GHG emissions and dependency on petroleum. In the U.S., California adopts a set of regulations to implement its Low Carbon Fuel Standard (LCFS) since 2009, which imposes a cap on GHG emissions from transportation fuels with a purpose to reduce 10% GHG emissions from transportation sector. Fuel providers, whose average carbon intensity is larger than the cap, have to balance the carbon intensity by either producing low carbon fuels or purchasing carbon credits from low carbon fuel producers. The cellulosic ethanol, which is a type of fuel that has advantages in carbon intensity, has immature technology and has potential of playing a big role in the energy portfolio transition process. In this study, the authors aim at building up a dynamic programming model (decomposition techniques are applied to make the problem computationally feasible) to support sustainable transportation energy portfolio design with consideration of learning-by-doing effect in new technology evolvement, and using the model to analyze the optimal fuel portfolios in each year from 2015 to 2020.
Publication Year: 2016
Publication Date: 2016-01-01
Language: en
Type: article
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