Effective Carbon Tax Learning via Cap and Trade

Global warming warrants the global adoption of carbon reduction schemes, among which the two most popular ones are carbon tax (CT) and the cap-and-trade (CAT) program with quite different implementations. CT levies taxes on the energy generation while CAT directly limits the total carbon emission in the system. Hence, in practice, CAT is more adjustable than CT due to the long legislation process for altering tax rate, though CT is much easier to implement. In this paper, we seek to learn the most effective tax rate through CAT programs by establishing the equivalence between CT and CAT. Through bridging the two schemes, we use numerical study to highlight the different dynamics for the schemes to converge to the equivalent point.