Let $g:\mathbb R_+\to\mathbb R$ be a measurable function (which could be supposed to be bounded and Lipschitz if required). Let $\mathcal P$ be the collection of probability measures $\mu$ on $\mathbb R_+$ with finite first moment, i.e.

$$\int_{\mathbb R_+}|x|d\mu(x)~~<~~+\infty.$$

For any fixed $\epsilon\in [0,1)$, denote

$$\mathcal P_y^{\epsilon}~~:=~~\left\{\mu\in\mathcal P:~ \big(1-\epsilon\big)y~\le~\int_{\mathbb R_+}xd\mu(x)~\le~\big(1+\epsilon\big)y\right\} \mbox{ for all } y\in\mathbb R_+.$$

Define further $G^{\epsilon}:\mathbb R_+\to\mathbb R$ by

$$G^{\epsilon}(y)~~:=~~\sup_{\mu\in\mathcal P_y^{\epsilon}}~ \int_{\mathbb R_+}g(x)d\mu(x).$$

I'm interested in the characterization of the function $G^{\epsilon}$. It is well known that, if $\epsilon=0$, then $G^{0}$ turns to be the concave envolope of $g$, and thus the left and right derivatives of $G^{0}$ exist. My question is that could we have a more detailed analysis on $G^{\epsilon}$? Many thanks!