Using Bayesian Networks to Assess Effectiveness of Phosphorus Abatement Measures under the Water Framework Directive

Abstract

The EU Water Framework Directive requires all water bodies within the EU member states to achieve a “good status”. Many economic assessments assume the “good status” is achieved using selected measures and evaluate only associated costs and benefits. In this paper, Bayesian networks are used to test this assumption by evaluating whether the “good status” can be achieved with the selected abatement measures. Unlike in deterministic analysis, Bayesian networks allow effectiveness of measures of the same type to vary, which adds credibility to the analysis by increasing its robustness. The approach was tested on Stanovice reservoir in Czechia using a set of 244 previously designed measures. The results show the target will be met with a probability of 72.4% using the most cost-efficient measures. Based on the results, improvements to the measure selection process are suggested.

Externalities in agriculture: How to include their monetary value in decision-making?

“…Based on all the above, additional methods are proposed that are capable of reducing the risk of uncertainty and the resulting incorrect calculation. In some cases, it is therefore advisable to use the so-called robust decision-making processes and the so-called Bayesian networks (Heckerman et al 1992;Fernandes et al 2012;Brabec and Macháč 2019), generated to reduce the sensitivity of uncertainty regarding future climate conditions. Application of robust processes involves a wide range of scenarios, not individual climate change forecasts.…”