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positive finances for the other stakeholders . For example , the early investment in fueling infrastructure and ethanol production infrastructure ahead of the vehicle deployment will increase the supply and availability of SO fuel , improving consumer utility , but will also lead to cash flow and financial risk for the respective stakeholders resulting in delayed price and availability risks . The relative fuel economy and performance of the vehicle can also be adjusted but not simply by policy or marketing levers . It is clear that each of the items on this list belongs to the domains of different stakeholders , meaning such an environment can only be created by all stakeholders acting together and synchronizing their efforts to make sure they do not fall prey to capability traps . These capability traps could include prescribed or mandated sales volumes . From a policy perspective the capability traps in the ethanol-fuel and vehicle system derive from the fact that different government agencies regulate or manage policy for each of the stakeholders . While they depend on the behavior of the other stakeholders they lack the ability to directly control or influence their behavior . This should serve as a warning to policy and regulatory development that if ethanol supply is not closely matched to demand due to aggressive vehicle sales , poor vehicle performance , constraints in ethanol supply , or ethanol price then the outcome may be opposite of what is intended . In effect , the behavior of the multiple stakeholders serves to mitigate the growth rate of ethanol consumption .
Specific to ethanol consumption the analysis demonstrates that SO platforms have the potential to reverse the downward trend of ethanol consumption and achieve a substantial increase compared to the business as usual scenario where the ethanol consumption plummets over time due to fuel economy improvements of the vehicle fleet . The current scenarios tested don ’ t show that it is possible to increase the ethanol consumption to the RFS mandated level of ~ 30 billion gallons by 2022 given the current ethanol content of regular gasoline fuel and the projected 20 – 30 % of ethanol in SO fuel for the vehicle stock used in the model ( using current fuel economy regulations out to 2025 ). Therefore , in order to reach ~ 30 billion gallons of ethanol from today ’ s level of about 15 billion gallons requires other sources or uses of ethanol consumption , such as trucks , and other policies that may incentivize the use of SO ethanol fuels ( such as carbon pricing ). More broadly , the results imply a reorientation of policy from a prescriptive approach to a performance-based approach with incentives . This finding is consistent with the “ narrow ” version of the Porter Hypothesis proven by Lanoie et al . ( 2011 ), which states that flexibility in regulations such as performance based regulations lead to better innovation and outcomes Furthermore , the results support the framework developed by Carlson requiring economic value as the precursor to disruptive technology change in a policy framework that is able to adopt to future unknown conditions and maintain flexibility ( Carlson & Fri , 2013 ). Given the relative long-term uncertainty of the factors affecting relative utility , vehicle demand , and travel demand the current RFS is poorly structured to ensure sustained ethanol market growth . The system behavior and limitations derived from an expanded policy analysis can better inform expectations and help manage risk for the stakeholders by informing more constrained expectations and stakeholder behavior .
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