Environmental Value Added - Ein neues Maß zur Messung der Öko-Effizienz. (Environmental Value Added. A new measure for the measurement of eco-efficiency)

To increase eco-efficiency environmental information needs to be integrated into corporate decision making. For decision makers the interpretation of eco-efficiency as a ratio can however be quite difficult in practice. One of the reasons for this is, that eco-efficiency as a ratio is measured in a unit, that is difficult to interpret. This article therefore suggests an alternative measure for eco-efficiency. The Environmental Value Added, the measure proposed in this paper, reflects the excess economic benefit, resulting from the difference between the eco-efficiency under consideration and a benchmark eco-efficiency. It is measured in a purely monetary unit and is thus easier to interpret and integrate than eco-efficiency as a ratio.

A fast method for fuzzy neural modeling and refinement

In the identification of complex dynamic systems using fuzzy neural networks, one of the main issues is the curse of dimensionality, which makes it difficult to retain a large number of system inputs or to consider a large number of fuzzy sets. Moreover, due to the correlations, not all possible network inputs or regression vectors in the network are necessary and adding them simply increases the model complexity and deteriorates the network generalisation performance. In this paper, the problem is solved by first proposing a fast algorithm for selection of network terms, and then introducing a refinement procedure to tackle the correlation issue. Simulation results show the efficacy of the method.