reduction of model accuracy while using PCA for a regression problem
When you do PCA to project your problem two a 'n' dimensional space, in your case 2 dimensions. So you are fitting a model with just two features. 

If you print your variable 'explained_variance' you will see the percentage of variance respect to the total that you have. 

Ideas to improve your model: search for hyperparameters, do target encoding instead of OHE, dont do PCA...

By the way Decision trees model don't need the data to be standarized.