Transcript Slide 1

Economics and the Geosciences
William D. Nordhaus
Yale University
AAAS Annual Meetings
February 18, 2011
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Outline of presentation
1. Economics and geography (GEcon)
2. Economics and luminosity
3. Integrated modeling of economics of climate change
(DICE/RICE)
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The GEcon project
• Purpose is to develop matched geophysical and economic
data at geophysically scaling
• Purposes:
– Many processes are geophysically based (e.g., climate)
– Much higher resolution (circa 100x): like Hubble telescope
– Can be matched with geophysical, environmental data
(climate, elevation, distance from coast or market, pollution,
etc.)
Nordhaus, Macroeconomics and Geography, PNAS,
2007; Nordhaus and Chen,
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Derivation of Data Set
National or regional
gross output,
population data
Regional (e.g., county)
estimates of
output per capita
National and provincial
GIS grid data (RIG,
area, boundaries)
GPW grid cell
estimates of
population, area, RIG
Proportional
allocation
from political
to geophysical
boundaries
GEcon gross cell
product (GCP)
data
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Countries and grid cells for Europe
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Luminosity as a Proxy for
Output
Xi Chen
William Nordhaus
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Combining socioeconomic and luminosity data
Economic data on developing countries is very weak.
Question for this project: Can we use luminosity (nighttime
lights) data as a proxy for standard accounting data for
low-quality regions?
Allows use of regional GEcon data for rich regional data
set.
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Key elements in evaluating luminosity as a proxy
The key elements in determining the value of a proxy are:
1. The quality of the luminosity data
2. The errors of measurement of the standard GDP data
3. The statistical relationship between luminosity and GDP
The background paper shows the optimal weighting as a
signal-extraction statistical problem.
Chen and Nordhaus, The Value of Luminosity Data as a
Proxy for Economic Statistics, NBER Working Paper,
2010
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Problems illustrated for southern New England
Bleeding
Saturation
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Stable lights and output by 1° x 1° grid cell (n = 14,287)
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Results on optimal weight on luminosity
Optimal fractional weight on luminosity
1.0
All regions
0.8
Low-density regions
0.6
0.4
0.2
0.0
A
B
C
D
E
Country statistical quality grade (A = best; E = worst)
Chen and Nordhaus, in process.
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Main Results
1. For most countries, luminosity is essentially useless as a
proxy for GDP and output measures.
2. Possible information value in statistical basket cases.
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Economic Integrated Assessment (IA) Models
in Climate Change
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Integrated Assessment (IA) Models in Climate Change
What are IA models?
These are models that include the full range of cause and
effect in climate change (“end to end” modeling).
Major goals of IA models:
Project trends in consistent manner
Assess costs and benefits of climate policies
Estimate the carbon price and efficient emissions reductions
for different goals
Nordhaus, “Copenhagen Accord,” PNAS, 2010.
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Fossil fuel use
generates CO2
emissions
The emissionsclimate-impactspolicy nexus:
The
RICE-2010
model
Carbon cycle:
redistributes around
atmosphere, oceans, etc.
Climate system: change
in radiative warming, precip,
ocean currents, sea level rise,…
Impacts on ecosystems,
agriculture, diseases,
skiing, golfing, …
Measures to control
emissions (limits, taxes,
subsidies, …)
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RICE-2010 model structure*
Economic module:
- Standard economic production structure
- GHG emissions are global externality
- 12 regions, multiple periods
CO2/Climate module:
-
Emissions = f(Q, carbon price, time)
Concentrations = g(emissions, diffusion)
Temperature change = h(GHG forcings, time lag)
Economic damage = F(output, T, CO2, sea level rise)
* Nordhaus, “Economics of Copenhagen Accord,” PNAS (US), 2010.
Policy Scenarios for Analysis
using the RICE-2010 model
1. Baseline.
2. Economic cost-benefit “optimum.”
3. Limit to 2 °C.
4. Copenhagen Accord, all countries.
5. Copenhagen Accord, rich only.
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Temperature profiles: RICE -2010
6.0
Temperature
Global mean temperature (degrees C)
Optimal
Baseline
5.0
Lim T<2
Copen trade
Copen rich
4.0
3.0
2.0
1.0
0.0
2005
2025
2045
2065
2085
2105
2125
2145
2165
2185
2205
Source: Nordhaus, “Economics of Copenhagen Accord,” PNAS (US), 2010.
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An interesting byproduct: CO2 shadow prices
Shadow prices (social costs) were discovered by developers of
linear programming techniques (Kantorovich and Koopmans,
Nobel 1974). Originally thought useful for central planning
prices.
Today, useful because they reflect the marginal cost, or prices, of
a constraint when efficiently imposed.
For example, IA models can calculate the price associated with
the 2 °C temperature target as a byproduct of the economic
models.
Can be used as guidelines for setting CO2 taxes or prices.
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Carbon prices for major scenarios from RICE-2010 model
350
Carbon price (2005 $ per ton CO2)
300
T < 2 °C
Kyoto Trade
250
200
150
100
50
0
2005
2025
2045
2065
2085
2105
Source: Nordhaus, “Economics of Copenhagen Accord,” PNAS (US), 2010.
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Where are we today?
50
Carbon price (2005 $ per ton CO2)
45
T < 2 °C
40
Kyoto Trade
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Actual
equivalent
global carbon
price = $1 /
tCO2
30
25
20
15
10
5
0
2005
2025
Source: Nordhaus, “Economics of Copenhagen Accord,” PNAS (US), 2010.
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A new scientific renaissance of
social and natural sciences?
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