## 5 Discussion
There are processual benefits connected to a procedure based on composites with weights extracted through both PCA and EJ. PCA provides important indications on the structure of the information contained in the indicators and may support the choice of \"objective\" weights. EJ-driven weights convey per se a powerful symbolic meaning, which can increase political awareness and endorsement for the widest possible viability of the composite. This holds also in our case, where the weights triplet obtained with EJ does not diverge much from the EW triplet. Still, it represents the combination of the opinions of many individuals representing sometimes the sentiment of relevant political parties or stakeholders. EJ-driven weighted composites often are accepted with higher appreciation in deliberative contexts, where PCA-based weights may be perceived as abstract constructs of meaningless technicality. As a general message, this paper confirms that the design of a composite indicator is context specific and never a neutral activity and weighting processes are time and resources consuming. The main practical implication is that the cornerstones of the procedure must be clarified step by step so that the audience (institutional partners, decision-makers, stakeholders, etc.) can adhere to and use the composite indicator with full awareness of its impacts. In this respect, the development and correct communication of sensitivity analyses are fundamental. In this study, we have developed the analyses under precise assumptions; thus, the outcomes have some limitations we would like to clarify for departing with further research studies. Firstly, we have tested the method on the CILF, a composite combining three indicators. The method--with the same steps and procedures--can be exported to other composites, but the results may be affected by the different structure of the indicator nesting and pattern and some adjustments may be required. Additionally, PCA statistics vary, in front of a higher number of indicators; and the extraction of EJ-driven weights may lead to diverse patterns when judging the importance of a larger number of indicators. Secondly, we have considered the CILF proposed by [PERSON] et al. [10] as a possible combination of three out of all the available measures of LF. However, we are aware that different formulations of the CILF may be obtained by including a larger number of indicators with a multi-layer nested structure. Thirdly, we have addressed the weighted CILF by choosing the min-max rescaling of the original indicators. Different courses of action would have followed a selection of alternative normalization patterns (Borda distance, standardization, ratio to the maximum, etc.). Similarly, we have focused on the GGM aggregation rule calibrated on a fixed compensation parameter \(\beta\). We are aware that sensitivity analysis can be directed also to the assessment of the interplay between the composite and a changing compensation of the indicators. We will delve into this in future studies. Fourthly, EJ weights are extracted through the method proposed by [PERSON] [46] and the Likert scale. Other methods can be selected for processing expert judgment (see, among others, the regime method by [PERSON] and [PERSON] [49], the analytical hierarchy process by [PERSON] [50], and goal programming and DEA methods [51; 52]). This choice has its impacts on the entire study, which would have implied different data harvesting procedures and outcomes with the selection of a different EJ processing framework. Fifthly, regardless of the EJ processing pattern, we have extracted a unique weight triplet from the consideration of the entire dataset of answers provided by the experts. However, experts can be profiled by profession, age, education, and familiarity with LF issues in different clusters, which may be associated to many corresponding weight triplets. Finer sensitivity analyses may be applied to understand the influence of experts profiling on the weights and, overall, on the composite indicator.