Meeting climate targets under distortionary fiscal policy: Directed technical change and learning-by-doing

I study optimal implementation of climate targets in a model with distortionary fiscal policy, learning-by-doing, and directed technical change. The key mechanism is that fiscal constraints link innovation policy to labor allocation, creating a tension between directing research and directing learning-by-doing. Analytically, I show that learning-by-doing shapes the effectiveness of carbon taxation in directing research through an expertise effect: carbon taxes are more effective at steering innovation toward green technologies when green expertise is relatively high. Quantitatively, I calibrate the model to the U.S. economy to characterize the optimal policy mix consistent with climate targets. I find that carbon should be taxed heavily, persistently exceeding the social cost of carbon. While higher carbon prices raise green expertise, they induce an excessively rapid reallocation of researchers from fossil to green technologies, generating persistent innovation misallocation. A welfare analysis shows that learning-by-doing substantially amplifies the cost of distortionary taxation, in particular during the transition to net-zero emissions.