Multi-Agent Reinforcement Learning (MARL) provides a powerful conceptual and computational framework for modeling emergent communication as a way to solve complex problems in sequential environments. However, despite the recent advances in this field, there is still a need to better understand the role of heterogeneous factors, e.g. partial observability and channel reliability, in the emergence of communication systems. An important step has recently been done in this direction by proposing new information-theoretic measures of emergent communication. As of yet, very few contributions have taken advantage of these new measures to perform detailed quantitative studies analyzing how different environmental and cognitive factors can foster the emergence of communication systems. This work quantitatively measures the joint role of partial observability and channel reliability in the emergence of communication systems. To this end, we performed experiments in a simulated multi-agent grid-world environment where agents learn how to solve different cooperative tasks through MARL.