Integrated Assessment Models of Economics of Climate Change

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Oct 23, 2013 (3 years and 7 months ago)

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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