On gradient boosting and types of encodings I am having a look at this material and I have found the following statement:

For this class of models [Gradient Boosting Machine algorithms] [...] it is both safe and significantly
more computationally efficient use an arbitrary integer encoding [also known as Numeric Encoding] for
the categorical variable even if the ordering is arbitrary [instead of
One-Hot encoding].

Do you know some references that support this statement? I get that Numeric Encoding is more computationally efficient than One-Hot Encoding, but I would like to know more about their supposed equivalence to encode unordered categorical variables in Gradient Boosting Methods.
Thanks!
This is actually a feature of tree-based models in general, not just gradient boosting trees.
Not exactly a reference, but this Medium article explains why ordinal encoding is often more efficient.
On the topic of safety, I think the author should have said that the use of ordinal encoding is more safe compared to linear methods, but still not perfectly safe.  It's possible for decision-tree methods to find spurious rules within ordinal encodings, but they don't have the strong assumptions about numeric semantics that linear methods do.

. . . I would like to know more about their supposed equivalence to encode unordered categorical variables . . .

Any rule derived with one-hot encoding can also be represented with ordinal encoding, it just might take more splits.
To illustrate, suppose you have a categorical variable foo with possible values spam, ham, eggs.  A one-hot encoding would create 3 dummy variables, is_spam, is_ham, is_eggs.  Let's say an arbitrary ordinal encoding assigns spam = 1, ham = 2, and eggs = 3.
Suppose the OHE decision tree splits on is_eggs = 1.  This can be represented in the ordinal decision tree by the split foo &gt; 2.  Suppose the OHE tree splits on is_ham = 1.  The ordinal tree will require two splits: foo &gt; 1 then foo &lt; 3