Geoscience Reference
In-Depth Information
emissions and climate change without including all the architectural
nuts and bolts. The main advantage of IAMs is that they can represent
the entire process from start to fi nish. The main disadvantage is that
they simplify some of the processes that are analyzed in greater detail
in the more complete models.
Many IAMs—large and small—have been developed by modeling
teams around the world, and they have proven remarkably useful for
understanding the implications of policies to slow climate change.
Throughout this topic, I rely extensively on IA Ms in describing the eco-
nomic aspects of climate change.
Additionally, I often refer to the results of models that have been
developed at Yale University by me, students, and colleagues, known as
the DICE family of models. DICE is short for Dynamic Integrated model
of Climate and the Economy. There is also a more elaborate Regional
version, known as the RICE model.
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The DICE model has a logical structure similar to the circular fl ow
in Figure 1. An energy-economy module generates economic growth
and CO
2
emissions in different regions over coming decades. Small
carbon-cycle and climate modules generate global temperature trends.
The DICE model includes damage calculations, where damages depend
upon the size of the economy and on the temperature increase. Finally,
there is a policy module, where countries can either limit emissions
or put a price on CO
2
emissions, thereby bending down the emissions
trajectory.
In the simplest global version, the model includes only a few equa-
tions and is relatively easy to understand. The more complete RICE
model—with twelve major regions such as the United States, China,
and India—contains thousands of lines of computer code and is more
challenging to grasp. Readers who would like to examine the simple
DICE model are encouraged to look at the online version (DICE-2012).
You can change the parameters and assumptions (such as the long-run
world population or the climate sensitivity) to get a feel for how integrated
assessment models work as well as how sensitive they are to the under-
lying assumptions.
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