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result , the consumption of E85 fuel remains low and the initial federally mandated volumes of ethanol are not consumed by the light-duty passenger vehicle fleet ( see Figure 4 ). While some of this is the result of limited refueling infrastructure , consumers still often choose conventional gasoline even when both are available ( Alliance of Automobile Manufacturers , 2013 ). In general , substantial uncertainty exists with regard to customer acceptance and the corresponding technological development of alternative fuel powertrains . The suite of fuel economy and vehicle regulations means that the automotive industry faces the challenge of improving conventional powertrains , while simultaneously having to introduce a range of alternative powertrains into the market that compete against conventional ones ( Walther , Wansart , Kieckhäfer , Schnieder , & Spengler , 2010 ). Recent attention has turned to using a mid-level super octane ( 94-96 AKI / 99-101 Research Octane Number ) ethanol blend fuel to achieve fuel economy improvements and increases in ethanol consumption .
Considerable research has validated the potential fuel economy benefits of a mid-level super octane ethanol blend fuel ( Anderson , Ginder , Kramer , Leone , Raney-Pablo , & Wallington , 2012 ; Jung , Leone , Shelby , Anderson , & Collings , 2013 ; Leone , 2014 ; Splitter & Szybist , 2014 ; Splitter & Szybist , 2013 ; Stein , Polovina , Roth , Foster , Lynskey , Whiting , T ., . . . VanderGriend , 2012 ). Other research suggests that the Research Octane Number ( RON ) scale be used to specifically align with the properties of ethanol in the fuel blend ( Foong , Morganti , Brear , da Silva , Yang , & Dryer , 2014 ; Speth , Chow , Malina , Barrett , Heywood , & Green , 2014 ). The resulting super octane ( SO ) blends using 20 – 30 % ethanol have the potential to offset increased fuel and vehicle costs with reduced fuel consumption and improved performance . Existing SO system concepts and narratives assume , as they did for the FFV policy design , that the fuel will enter the market and achieve pre-defined market growth on its own as defined by the policy makers or regulators ( Chow , 2013 ; Speth et al ., 2014 ; USDOE , 2010 ).
There is an incomplete appreciation for and understanding of the challenges of bringing a new fuel-powertrain system with secondary fuel choice to market . Empirical observations of complex systems demonstrate that simple linear and simplified feedbacks modeling can give a misleading representation of the true behavior of the system ( Levin , Xepapadeas , Crépin , Norberg , & et al ., 2013 ). The lack of consideration of the full system structure and resulting behavior is a root cause to take into consideration in relation to the problem of ethanol uptake .
Rather than reporting on a full suite of potential system architecture and policy scenarios for the ethanol-gasoline fuel-vehicle system , this paper emphasizes and demonstrates the importance of systems thinking considering the complex stakeholder interactions that drive consumer perception and stakeholder response in energy policy modeling . Within the realm of modeling and analysis techniques , system dynamics ( SD ) is well suited to capture and investigate the behavior and feedback over time of the different system stakeholders . SD provides a framework to understand the interaction of multiple nonlinear feedbacks where simple intuition is insufficient to understand the behavior of the system . Struben and Keith ( S & K ) established effective strategies for the use of SD to explore the market growth of an alternative fuel-vehicle system ( Keith , 2012 ; Keith , Sterman , & Struben , 2011 ; Struben , 2006 ). Their approach was based on a virtuous cycle with the model not considering the potential for vicious cycles , and related balancing effects to occur
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