For example, the model contains the rules 'Human health/wellbeing':0.1 :- \+ 'Human health/wellbeing_pos',
'Human health/wellbeing_neg'.

'Human health/wellbeing':0.5 :- \+ 'Human health/wellbeing_pos',
\+ 'Human health/wellbeing_neg'.

'Human health/wellbeing':0.5 :- 'Human health/wellbeing_pos',
'Human health/wellbeing_neg'.

'Human health/wellbeing':0.9 :- 'Human health/wellbeing_pos',
\+ 'Human health/wellbeing_neg'.

that collect positive and negative effects on the receptor *"Human health/wellbeing"*.

These rules express the fact that *"Human health and wellbeing"* **is unlikely if**
there is no positive evidence on it and there is negative evidence on it (first rule). It is very likely if there is positive evidence on it and no negative evidence on it
(last rule). In the other cases, the probability of *"Human health and wellbeing"* is in between (second and third rule).

All the parameters were subjectively estimated and validated by the expert.

## 6 Experiments 6.1 Experimental Results Of Clp(R)

The agency for the environment of the Emilia-Romagna region (Italy) kindly provided us with the coaxial matrices used for assessing eleven types of plans (that we translate into the CLP model) and the data of a regional energy plan: for each of the activities, we have a *"magnitude"* **value. Thanks to the CLP model described** earlier, we are able to compute the corresponding values of impacts **and receptors.**
Initially the results were counterintuitive: the considered plan concerned energy
(aimed at raising the available electrical power in the region), while the **receptor** energy availability **had a lower value than the previous year. These types of results**
may be partially due to the qualitative information contained in the matrices, but also highlight possible human mistakes in the data of the matrices. Indeed, a flaw was found (and fixed) in the matrices, showing how logic-based decision support can contribute to increase the reliability of the environmental assessment.

Once the human mistakes had been corrected, we reran the experiments. The new results were highly appreciated by the evaluator: the decision support system foresaw strong decrease of *quality of air* **(mainly due to the boost on thermoelectric** plants), and *water availability* **(since thermoelectric plants need refrigeration).**
As the plan had a large impact on some receptors, we tried to improve **it from**
an environmental viewpoint: the magnitude of each of the activities **was allowed to** deviate up to 1% with respect to the original plan, and we optimized the *quality of* air receptor. We had an improvement of about 20.3% on this receptor, **which shows** that even by allowing small variations one can get significant improvements. On the other hand, we had a decrease of industrial indicators, such as the availability of productive resources **or the** *availability of energy*.