## 3 Results and Discussion
### Uncertainty Analysis  
An uncertainty analysis is beneficial when performing an LCA, as it allows to check the veracity of the LCI data and whether they have a large variability that directly affects the results obtained from the LCA. The way in which data vary is very wide, but commonly this variability can be represented by a type of distribution (range, triangular, normal, lognormal). Therefore, the first thing to be carried out is to define for each consumable/process identified in the LCI which distribution typology will be applied to it. Firstly, those consumables data values identified in the LCI which have been obtained directly from ANEFA [4] have been modelled through a triangular distribution, as the average value (see Table 2), minimum value and maximum value (see Table 2) of these are known. The other case that is addressed are data which have been extracted directly from the Ecoinvent v.3 database, from the process Gravel, crushed {RoW} | Production | Cut-off, U. In this case, the default values in these processes are assumed by default. These follow a logarithmic distribution defined through the square of their standard deviation (\(\sigma^{2}\)) (see Table 3). The methodology used by the SimaPro software for the uncertainty analysis is the Monte Carlo method, which is performed on the basis of the uncertainty data that were entered. Figure 3 shows the results of the uncertainty analysis graphically through the error bars associated with each impact category and Table 4 shows the results of the uncertainty analysis numerically. As can be seen in Figure 3, for certain impact categories such as Ozone Depletion, the variability of the results with respect to the baseline (6.84 \(\times\) 10\({}^{-7}\) kg CFC-11 eq/t) is very high, with increases of 132.5% (3.18 \(\times\) 10\({}^{-6}\) kg CFC-11 eq/t) above and 50% (6.84\(\cdot\)10\({}^{-7}\) kg CFC-11 eq/t) below. This large variability in the results is due to the fact that this impact category is closely related to the internal transport of aggregates from the gravel pit site to the aggregate production plant, and according to the data collection, there is a variation of 1.144 km above and below the average, with peaks of records of distances up to 23.5 km from the gravel pit face to the aggregate production plant [4]. These distance peaks are not distorted from reality--for example, in the research of [PERSON] et al., they establish a transport distance of 20 km between the quarry and the producing plants [29]. All this variation in distance spans is reflected in the transport process being highly variable and particular to each farm and is reflected in this impact category. The same interpretation can be obtained for other impact categories such as Acidification (+47.5%/\(-\)32.5%) or the impact category Photochemical Oxidation (+47.5%/\(-\)32.5%) which are also related to transport. It is not only the transport process that helps to increase the variability of the results obtained since, as demonstrated in Section 3.1 Environmental Results, diesel consumption and combustion are responsible for generating the greatest environmental impacts in these categories. Another category that suffers from high variability is the Water Scarcity category (+55/\(-\)70%) as it is directly dependent on the type of aggregate being produced--for example, aggregates from ophite material only use approximately 22.87 L/t [23] compared to the average value of 200 L/t in this research. Water use in the