GWS developed a state of the art algorithm which applies alternative statistical evaluation methods for trend assessments and projections of environmental indicators. The applied evaluation methods facilitate a historical classification of observable trend patterns and enable researchers to examine the empirical significance of identified trend dynamics. Prospective future developments can be automatically assessed from applications of various forecasting methods.

Due to a thorough control for inherent spurious regression risks of commonly applied detrending procedures, this evaluation algorithm advances well-established environmental monitoring routines to notable degrees. The algorithm automatically infers the dynamic properties of any analysed time series from automated applications of econometric testing procedures. This enables users to assess whether an analysed indicator features trend-stationary properties (which represent a basic necessity for usual interpretations of the regression results for trending time series). Furthermore, the algorithm also features an automated assessment of simulated trend projections in light of future target values. For this purpose, prospective target achievements are mapped by newly defined measures. 

The source code of this application was handed over to the Federal Environment Agency at the end of the project together with a separate manual documenting the functionality of the application and relevant technical parameters. As the application was programmed in R it can be installed on any workstation PC of the Federal Environment Agency without major efforts for licence-free use. Thereby, this project contributed an essential tool for future research work towards the establishment of a standardised methodology for indicator evaluation at the Federal Environment Agency.