After a little research, I finally came up with the answer I was looking for.
when using Global Moran's I index (I) with incrementally increasing distance searches (thus, changing the weight matrix at every iteration), only the the z-values are independent from both weight matrices and variable intensity variations, thus, they are comparable across multiple analyses.
The I in Moran's I statistics is not comparable across analyses, i.e, if with distance of 10m I=0.3 and distance 15m I=0.6, we cannot say that with a distance of 15m the clustering strength is double. We could only say that in both cases there is a positive (sign of the I) spatial autocorrelation. For the strengths, we use the z-values.
That is why ESRI plots distances in the x-axis and z-values in the y axis, indicating significant (p-value < than specified signification level) peaks as interesting distances.
For more information, it is clearly explained during a class that Luc Anselin in this Global Autocorrelation class, given in 2016 in Chicago University.
https://www.youtube.com/watch?v=d1WJNBwXfgo&list=PLzREt6r1Nenkr2vtYgbP4hs44HO_s_qEO&index=4
follow from minute 38 when he talks about the permutation approach.
