Bayes-consistent cost-sensitive classification
I'm not sure if this is what you're looking for, but people have studied consistency of surrogate risk minimization. There, we define a surrogate loss function $L$ and a link $\psi$. We first minimize surrogate loss on our dataset, yielding some surrogate hypothesis $h$. Then we define $f(x) = \psi(h(x))$. This procedure is roughly consistent if, as data $\to \infty$, we have $f \to f^*$.
The question is, given e.g. $C$, what are some nice, consistent surrogate losses? Example: for binary $0-1$ loss, one can use hinge loss or logistic loss as a surrogate, and one can show this is consistent as long as the hypothesis class is rich enough.
Tewari and Bartlett (2007) study multiclass classification, but I think not cost-sensitive, and relate consistency to calibration. A more recent work is Agarwal and Agarwal (2015), which has some more references.
[1] Tewari, Bartlett. On the Consistency of Multiclass Classification Methods. JMLR 2007. https://www.jmlr.org/papers/volume8/tewari07a/tewari07a.pdf
[2] A. Agarwal, S. Agarwal. On consistent surrogate risk minimization and property elicitation. COLT 2015. http://proceedings.mlr.press/v40/Agarwal15.pdf