Multi-scale sustainability evaluation of natural resource management systems: Quantifying indicators for different scales of analysis and their trade-offs using linear programming

The purposes of this paper are: (a) to describe a framework designed for multi-scale sustainability evaluation of Natural Resource Management Systems (NRMS), and (b) to illustrate its application for quantitative analysis using linear programming. The framework described here is intended to contribute to the operationalisation of the concept of sustainability by supporting the processes of design, evaluation and implementation of alternative NRMS at different scales. In this paper, Linear Programming is used for the quantitative analysis of indicators and their trade-offs; using a schematised example, the basic characteristics of the Multi-scale Multiple Goal Linear Programming (M-MGLP) method are described. In M-MGLP, indicators pertaining to different scales of analysis can be set as objectives or constraints for the optimisation. In this way, stakeholders interacting in a specific region can be made aware of the consequences of alternative NRMS in terms of the different indicators at the same scale and/or for indicators at other scales of analysis. The paper ends with a discussion of the main strengths and limitations of the framework and, specifically, of linear programming.     

Watershed integrated management: A decision-support model on Sete Cidades Lake (Azores)

The Sete Cidades Lake (437 ha) is located in a volcanic crater of São Miguel Island (Azores). The watershed is 1923 ha, and 25% of it is covered with water. Extreme logging and the use of fertilizers in the cattle grazing areas have caused sedimentation and eutrophication of the main water body (Carlson Trophic State Index TSI = 40–50 mesoeutrophy). To deal with the issue, a linear programming model was created to generate efficient land-use scenarios for different amounts of phosphorus release. The model was calibrated for the present situation and then simulated to obtain 29 scenarios. It allowed us to derive parameters for each scenario. Then a multiobjective program was used to derive an acceptable weight for each indicator. The dominant solutions were discovered and discussed with different stakeholders.