Narrow Results

Biofuel mandates are being widely used by countries to achieve multiple objectives of energy security and climate change mitigation. The Renewable Fuel Standard (RFS) in the US specifies arbitrarily chosen volumetric targets for different types of biofuels in the US based on their greenhouse gas intensity only. Cellulosic biofuels from high yielding energy crops like miscanthus have the potential to co-generate multiple environmental impacts, including reducing nitrate runoff, being a sink for Greenhouse Gas (GHG) emissions and providing a given volume of biofuel with less diversion of land from food crop production than corn ethanol, but at a significantly higher cost. This paper quantifies the tradeoffs between profitability, food and fuel production, GHG emissions and nitrate runoff reduction with different types of biofuels in the Sangamon watershed in Illinois and analyzes the optimal mix of biofuels as well as the policies that should supplement the mandate to achieve multiple environmental outcomes. We find that a two-thirds share of cellulosic biofuel in the mandated level could reduce nitrate run-off by 20% while reducing GHG emissions by 88–100% but would reduce profits by 15–27% depending on whether a GHG policy or a Nitrate policy is used relative to the case where the mandate is met by corn ethanol alone. Additionally, the ratio of corn stover to miscanthus used to produce cellulosic biofuels is higher under a GHG policy compared to a Nitrate policy that achieves the same level of nitrate reduction. Our results show that the optimal mix of different types of biofuels and the policy to induce it depend on the environmental objectives and the tradeoffs that society is willing to make between low cost energy security, food production and various environmental benefits.

Two essential components of systems modeling are i) representation and ii) inference. We review recent developments in fuzzy systems modeling from a perspective of: a) knowledge representation with fuzzy sets including measurement and acquisition of membership functions for a system parameter identification, as well as combination of knowledge with fuzzy sets for the formation of rules in a system structure identification, and b) approximate reasoning with fuzzy logic including properties of reasoning, combination of rules and/or their consequences, and three heuristics that have been proposed during the course of development. This review is restricted to point-valued fuzzy sets and logics.