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Economics 331b
Integrated Assessment Models
of Economics of Climate Change
Integrated Assessment (IA) Models of Climate
Change
•
What are IA model?
–
These are models that include the full range of cause and
effect in climate change (“end to end” modeling).
–
They are necessarily interdisciplinary and involve natural
and social sciences
•
Major goals:
–
Project the impact of current trends and of policies on
important variables
–
Assess the costs and benefits of alternative policies
–
Assess uncertainties and priorities for scientific and
technology research
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Person or
nation 1
Person or nation 2
Inefficient
initial (no

policy)
position
Bargaining
region (Pareto
improving)
Pareto Improvement from
Climate Policy
Elements of building/using an IAM
1.
Economics
–
Population
–
Inputs: energy, capital, land, …
–
Technology (total factor productivity)
2. Emissions of CO
2
and other GHGs
3. Carbon cycle, forcings, temperature, other geophysical
4. Impacts or damages
5. Policies
–
Emissions controls, taxes, regulations, subsidies
–
International strategies for global externalities
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Basic economic methodology of IA models
We will use a very simple IA model to illustrate
–
the Yale
“DICE” model.
Last published version is 2007 in your assignment
Also:

Regional version (RICE

2010)

Experimental or beta DICE

2010 in Excel format
Lint will give overview of IAM in section this week.
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Simplified
Equations of
DICE Model
(
QoB
, pp. 205

209)
Basic structure of IAM
Economic sectors (more or less elaborate):
Q = A F(K, L) = C + I
plus:
•
Energy sector
•
Emissions
•
Abatement
•
Climate damages
Geophysical sectors:
•
Carbon cycle
•
Climate model
•
Impacts
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I. Economics: DICE/RICE model example
Population exogenous: use UN and IIASA projections.

Should we have endogenous fertility?
Total factor productivity exogenous

Problem that technological change is endogenous,
particularly with large changes in energy prices
Savings rate optimized by country

Use Solow

Ramsey model of optimal economic growth
Put all these together (for 12 regions j=US, EU, …)
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Per capita GDP: history and projections
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Emissions trajectories:
Start with data on Q, L, and E of CO2 for major countries
Estimate population, productivity, emissions growth
Project these by decade for future
Then aggregate up by twelve major regions (US, EU, …)
Constrain by global fossil fuel resources
This is probably the largest uncertainty over the long run.
Modeling Strategies (
II):
Emissions
CO
2

GDP ratios: history
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Decarbonization projections
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Climate model
Idea here to use “reduced form” or simplified models.
As we have seen, large models have very fine resolution and
require supercomputers for solution and cannot be used
in economic modeling.
We take two

layers (atmosphere, deep oceans) and decadal
time steps.
Calibrated to ensemble of models in IPCC science reports.
Modeling Strategies (
III):
Climate Models
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Actual and predicted global temperature history

.6

.4

.2
.0
.2
.4
.6
1840
1880
1920
1960
2000
Y
E
A
R
T_DICE2007
T
_Hadley
T
_GISS
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T projections multi

model comparison
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Modeling Strategies (IV): Impacts
•
Central difficulty is evaluation of the impact of climate
change on society
•
Two major areas:
–
market economy (agriculture, manufacturing, housing, …)
–
non

market sectors
•
human (health, recreation, …)
•
non

human (ecosystems, fish, trees, …)
Summary from Tol Survey
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Richard Tol, “The Economic Impact of Climate Change,”
Journal of Economic
Perspectives,
Vol. 23, No. 2, Spring 2009
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Modeling Strategies (V): Abatement costs
These are the abatement cost functions we discussed in energy
economics.
–
Some use econometric analysis of costs of reductions
–
Some use engineering/mathematical programming
estimates
–
DICE model generally uses “reduced form” estimates of
marginal costs of reduction as function of emissions
reduction rate
–
We will return to this later.
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Outcome of efficient
competitive market
(however complex
but finite time)
Maximization of weighted
utility function:
Economic Theory Behind Modeling
=
1. Basic theorem of “markets as maximization” (Samuelson, Negishi)
2. This allows us (in principle) to calculate the outcome of a market
system by a constrained non

linear maximization.
How do we solve IA models?
The structure of the models is the following:
We solve using various mathematical optimization techniques.
1.
GAMS solver (proprietary). This takes the problem and solves it
using linear programming (LP) through successive steps. It is
extremely reliable.
2.
Use EXCEL solver. This is available with standard EXCEL and
uses various numerical techniques. It is not 100% reliable for
difficult or complex problems.
3.
MATHLAB. Useful if you know it.
4.
Genetic algorithms. Some like these.
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Can also calculate the “shadow prices,”
here the efficient carbon taxes
Remember that in a constrained
optimization (Lagrangean), the
multipliers have the
interpretation of
d[Objective Function]/
dX
.
So, in this problem, interpretation
is MC of emissions reduction.
Optimization programs
(particularly LP) will generate
the shadow prices of carbon
emissions in the optimal path.
For example, if we look at the
DICE model, the carbon
shadow price might be $30 per
ton carbon ($7 per ton CO2)
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Applications of IA Models
I will give an example that compares different policies and
scenarios.
1. No controls ("baseline"). No emissions controls.
2. Optimal policy. Emissions and carbon prices set for
economic optimum.
3. Various international agreements (Strong Kyoto ≈
Obama proposals and Copenhagen Accord)
For these, I will use latest modeling results (RICE

2010,
Nordhaus,
PNAS
, 2010).
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Emissions Trajectories for RICE

2010
Source:
Nordhaus, “Economics of Copenhagen Accord,”
PNAS (US),
2010.
Concentrations profiles: RICE

2010
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Temperature profiles
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IPCC AR4 Model Results: History and Projections
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RICE

2010
model
Policy outcomes variables
Overall evaluation
Two major policy variables are

emissions with controls

carbon tax
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Carbon prices for major scenarios
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Source:
Nordhaus, “Economics of Copenhagen Accord,”
PNAS (US),
2010.
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Where are we today?
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Actual
equivalent
global carbon
price = $1 /
tCO
2
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