This is the sort of question that seems like it should have an easy answer, but it is deceptively complex. Regression just doesn’t decompose variance in a way that provides straightforward answers to such questions. In machine learning, the notion of variable importance is common, but less common when dealing with “statistical” models. I would suggest checking out these papers (among many others) for some additional information:
Nathans, L. L., Oswald, F. L., & Nimon, K. (2012). Interpreting multiple linear regression: A guidebook of variable importance. Practical Assessment, Research, and Evaluation , 17 (1), 9.
Grömping, U. (2015). Variable importance in regression models. Wiley interdisciplinary reviews: Computational statistics , 7 (2), 137-152.