While sparse PCA does help to make our transformed features more interpretable by regularizing the coefficients than if we were working with standard PCA, I think we’re still faced with combinations of features that aren’t as interpretable as if we were working with the original features themselves. For example, a one-unit increase in a newly-transformed feature is still difficult to interpret in terms of our original variables.