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This result is taken from Roberts and Tweedie, Thm 5.1 (Biometrika 1996): Geometric convergence and central limit theorems for multidimensional Hastings and Metropolis algorithms.
Proposition. Let be an irreducible Markov kernel with invariant distribution which is not concentrated on a single point, such that is measurable and where the essential supremum is taken wrt the measure . Then the Markov kernel is not geometrically ergodic
The proof works by contradiction. Assume that is geometrically ergodic, then there exists a -small set such that for some constant .
Now, for any , define . We can assume that (since in the assumptions, the esssup is taken wrt ). Then, if , Moreover,
Now, write for any , which is divergent for sufficiently close to 1.