Portfolio Decarbonization under Incomplete Carbon and Transition Information

We study dynamic portfolio decarbonization under incomplete information about firms' carbon intensities and the underlying transition state. The investor maximizes expected utility of terminal wealth net of a penalty on perceived cumulative financed emissions. Asset returns and carbon intensities depend on a stochastic transition factor, while the investor may need to infer the current transition state, its long-run mean and firms' carbon intensities from market observations and periodic noisy disclosures. The optimal strategy combines a Merton-type component with climate-dependent expected-return adjustments, a direct carbon intensity penalty and intertemporal hedging demands associated with stochastic carbon intensities and learning. In a numerical application, incomplete information has limited effects on welfare and target satisfaction when prior beliefs are well specified. By contrast, biased beliefs generate welfare losses in either direction but asymmetric environmental consequences: optimistic and pessimistic beliefs induce opposite portfolio tilts and therefore affect the probability of exceeding the cumulative emissions target differently.