Channel simulation is an alternative to quantization and entropy coding for performing lossy source coding. Recently, channel simulation has gained significant traction in both the machine learning and information theory communities, as it integrates better with machine learning-based data compression algorithms and has better rate-distortion-perception properties than quantization. As the practical importance of channel simulation increases, it is vital to understand its fundamental limitations. Recently, Sriramu and Wagner provided an almost complete characterisation of the redundancy of channel simulation algorithms. In this paper, we complete this characterisation. First, we significantly extend a result of Li and El Gamal, and show that the redundancy of any instance of a channel simulation problem is lower bounded by the channel simulation divergence. Second, we give two proofs that the asymptotic redundancy of simulating iid non-singular channels is lower-bounded by $1/2$: one using a direct approach based on the asymptotic expansion of the channel simulation divergence and one using large deviations theory.