  Integrated Assessment Models (IAMs) of the climate and economy aim to analyze
the impact and efficacy of policies that aim to control climate change, such as
carbon taxes and subsidies. A major characteristic of IAMs is that their
geophysical sector determines the mean surface temperature increase over the
preindustrial level, which in turn determines the damage function. Most of the
existing IAMs are perfect-foresight forward-looking models, assuming that we
know all of the future information. However, there are significant
uncertainties in the climate and economic system, including parameter
uncertainty, model uncertainty, climate tipping risks, economic risks, and
ambiguity. For example, climate damages are uncertain: some researchers assume
that climate damages are proportional to instantaneous output, while others
assume that climate damages have a more persistent impact on economic growth.
Climate tipping risks represent (nearly) irreversible climate events that may
lead to significant changes in the climate system, such as the Greenland ice
sheet collapse, while the conditions, probability of tipping, duration, and
associated damage are also uncertain. Technological progress in carbon capture
and storage, adaptation, renewable energy, and energy efficiency are uncertain
too. In the face of these uncertainties, policymakers have to provide a
decision that considers important factors such as risk aversion, inequality
aversion, and sustainability of the economy and ecosystem. Solving this problem
may require richer and more realistic models than standard IAMs, and advanced
computational methods. The recent literature has shown that these uncertainties
can be incorporated into IAMs and may change optimal climate policies
significantly.
