What I'm wondering is how I might decide between two options to alleviate this issue. On one hand, I've read that log-transforming the predictor can be appropriate in situations such as this - and doing so does seem to make the regression residuals look better (i.e., random). However, I've also read that nonlinear terms can be useful as well. Including a X and X-squared as predictors improves the R2 over a model containing only X as a predictor.
Thus, am I correct that both strategies are equally appropriate? Is there some reason that a researcher should adopt one over the other? Without having any special knowledge in this topic, it seems like using a log transformation might be better because it is simpler - this approach uses only 1 predictor while the nonlinear regression approach uses 2.