  The socioeconomic impact of pollution naturally comes with uncertainty due
to, e.g., current new technological developments in emissions' abatement or
demographic changes. On top of that, the trend of the future costs of the
environmental damage is unknown: Will global warming dominate or technological
advancements prevail? The truth is that we do not know which scenario will be
realised and the scientific debate is still open. This paper captures those two
layers of uncertainty by developing a real-options-like model in which a
decision maker aims at adopting a once-and-for-all costly reduction in the
current emissions rate, when the stochastic dynamics of the socioeconomic costs
of pollution are subject to Brownian shocks and the drift is an unobservable
random variable. By keeping track of the actual evolution of the costs, the
decision maker is able to learn the unknown drift and to form a posterior
dynamic belief of its true value. The resulting decision maker's timing problem
boils down to a truly two-dimensional optimal stopping problem which we address
via probabilistic free-boundary methods and a state-space transformation. We
completely characterise the solution by showing that the optimal timing for
implementing the emissions reduction policy is the first time that the learning
process has become ``decisive'' enough; that is, when it exceeds a
time-dependent percentage. This is given in terms of an endogenously determined
threshold function, which solves uniquely a nonlinear integral equation. We
numerically illustrate our results, discuss the implications of the optimal
policy and also perform comparative statics to understand the role of the
relevant model's parameters in the optimal policy.
