Transcript Climate Change

Designing Climate Change
Scenarios in a Global Economic
Model
Warwick J McKibbin
ANU, Lowy Institute and Brookings
Prepared for the OECD conference on “Global Convergence Scenarios: Structural and Policy Issues” to be
held in Paris, January 16, 2006
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• Based on 2 papers:
“Long Run Projections for Climate Change Scenarios”
McKibbin, Pearce and Stegman (2004)
“Convergence and Per Capita Carbon Emissions”
McKibbin and Stegman (2005)
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Structure of Presentation
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Overview
– Why Emission projections matter
– Are Projections Useful?
What do We Know about projecting the future?
– Looking for Empirical regularities
• Some Theoretical Issues
– Sources of growth
• Convergence (of what?) Across countries
A common approach used in energy models
The G-Cubed Economic Approach of making projections
– Sensitivity to PPP versus MER convergence assumptions
Is there a Better Way to make projections for climate policy?
Conclusion
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Why emission projections matter
• Critical input into climate change debate
– Policies have been and are being conditioned on the
baseline and initial conditions
• Emission projections feed into climate models to make
temperature projections
• Temperature projections feed into impact models to
assess – environmental/ecological/economic/health
impacts over the next century
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Are Projections Useful?
• Yes but
– They are but we shouldn’t believe too much outside of
the next 30 years or so
– They should be based on the best empirical evidence
and best practice
– They should reflect the underlying uncertainty that is
manifest in projecting the future
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What do We Know?
• We have about 60 years of data to look for patterns in
the data and test hypotheses
– Economy wide responses to changes in energy prices
– Determinants of growth
– Patterns of convergence
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What do we Know?
Oil price shocks of the 1970s generated important
information for estimating the impacts of energy
prices on economic behavior
- Supply (substitution, technical change)
- Demand (conservation, substitution)
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Figure 1: GDP, Energy and Emissions for US and Japan
Index Numbers, 1965=1
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4
US GDP
Japan GDP
US Energy
Japan Energy
US Emissions
Japan Emissions
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2
1
0
1965
1970
1975
1980
1985
1990
Interpretation
• Economic Modelers use this as evidence that relative
prices matter – (and estimate the effects)
• Energy modelers tend to use the data post 1975 to
calculate “Autonomous Energy Efficiency Improvements”
• In projecting the future, it matters a great deal which
approach is followed.
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Theoretical Issues in Forecasting Growth
• Sources of output growth within a country
– Increases in the supply capital, labor, energy,
materials
– Increase in the quality of these inputs
– Improvements in the way the inputs are used
(technical change)
– Improvements in the way inputs are allocated across
the economy
– Improvements in the way inputs are allocated across
the world
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Theoretical Issues in Forecasting Global Growth
• Convergence across countries
– What converges?
• Incomes per capita
• GDP per capita
• Aggregate level or rate of technical progress
• Sectoral level or rates of technical progress
– The empirical literature examines conditional versus
unconditional convergence of income per capita and
to a lesser extent output per worker (productivity)
– Little empirical evidence of unconditional
convergence across large numbers of countries
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Approaches
• Many energy models use assumption about emissions
per capita converging or energy efficiency converging
autonomously and then overlay this with aggregate GDP
projections
• Hence the reason why the assumptions about economic
growth and the PPP debate don’t matter much in these
models. You just change the numeraire.
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Do Carbon Emissions per Capita converge?
Some models assume this either as fact or as
a desired target
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Figure 1: Summary Measures of Spread
Emissions Per Capita
2.0
1.5
1.0
STDEV
VAR
0.5
CV
MEAN
0.0
1950
1955
1960
1965
1970
1975
1980
1985
1990
1995
• Need to be careful how data is interpreted
• McKibbin and Stegman (2004) use dynamic kernel
estimation to explore convergence
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Figure 3: The Cross-Sectional Distribution of Emissions per Capita
Sample A
3.0
f
2.5
1950
1960
2.0
1970
1980
1990
1999
1.5
1.0
0.5
0.0
0
1
2
3
4
Metric Tons of Carbon Per Capita
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Does GDP per capita converge?
Some models assume this either as fact or as
a desired target
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Figure 10: The Cross Country Distribution of GDP Per Capita
Density Estimates
1.0
f
1971
1980
1990
2000
0.8
0.6
0.4
0.2
0.0
0
1
2
3
4
Relative GDP Per Capita
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The G-Cubed Approach
McKibbin & Wilcoxen
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The G-Cubed Model
– Countries
• United States
• Japan
• Australia
• New Zealand
• Canada
• Rest of OECD
• Brazil
• Rest of Latin America
• China
• India
• Eastern Europe and Former Soviet Union
• Oil Exporting Developing Countries
• Other non Oil Exporting Developing Countries
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The G-Cubed Model
– Sectors
– (1) Electric Utilities
– (2) Gas Utilities
– (3) Petroleum Refining
– (4) Coal Mining
– (5) Crude Oil and Gas Extraction
– (6) Other Mining
– (7) Agriculture, Fishing and Hunting
– (8) Forestry and Wood Products
– (9) Durable Manufacturing
– (10) Non Durable Manufacturing
– (11) Transportation
– (12) Services
– (Y) capital good producing sector
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Features of the G-Cubed Model
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Dynamic
Intertemporal
General Equilibrium
Multi-Country
Multi-sectoral
Econometric
Macroeconomic
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G-Cubed Approach of Generating Future Projections
• Make assumptions about labor augmenting technical
change (LATC) for each sector in the US
• Calculate economy wide gaps between LATC within
each sector relative to the US sector such that the TFP
gap across sectors is approximately equal to the PPP
GDP per worker gap
• Assume that the gap in LATC between each country and
the US closes by x% per year (we vary this between 0
and 2%)
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Process of Generating Future Projections
• Assume that labor supply grows at the rate of the mid
range UN population projections from 2002 to 2050 and
then gradually converges across countries to zero
population growth in the long run.
• Other exogenous inputs include tax rates per country per
sector, tariff rates per country per sector, monetary and
fiscal regimes
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Process of Generating Future Projections
• Given initial capital stocks in each sector, the overall
output growth rate of an economy depends;
– the growth on LATC (exogenous),
– labor force (exogenous in the long run);
– the accumulation of capital (endogenous)
– the use of materials input by type (endogenous)
– the use of energy inputs by type (endogenous)
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Key Points
• The projection of carbon emissions will depend on the
growth of the demand for carbon intensive inputs (oil,
natural gas, coal).
• There is no reason for a fixed relationship between
growth in GDP and growth in carbon emissions
• The is no need for carbon emissions per capita to
converge unconditionally
• The outcomes depend on the trend inputs and the
structural change in the economy induced on the supply
side and demand side of all economies.
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Key Points
• For global emissions it matters which sector in which
country experiences productivity growth
• Models that assume constant ratios or linear trends
between GDP and emissions are likely to be problematic
if the actual growth process involves structural change
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Theoretical Issues
• PPP versus market exchange rates
– Castles and Henderson argue that if the rate of
growth of developing countries are measured based
on the initial differences in income per capita then it is
critical to measure this gap using PPP
– Many studies use market exchange rates and so
growth is likely to be overestimated.
– Does this matter?
• An empirical question
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PPP versus Market Exchange Rates
• G-Cubed uses a PPP concept for GDP to benchmark the
initial productivity gap between sectors in each country
relative to the US
• The rate of economic growth and emissions outcomes
are then determined simultaneously by the model
• Suppose we use market exchange rates to benchmark
initial gaps between countries – what difference does
this make to emission projections?
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How much does PPP versus MER Matter?
• The ratio of the productivity of China to the US is 0.2
based on PPP
• The ratio of the productivity of LDCs to the US is 0.4
based on PPP
• Suppose we assume
– China has an initial gap of 0.1 (from MER)
– LDCs have a gap of 0.13 (from MER)
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Implications
• PPP versus market exchange rates makes a big
difference to the projections of economic growth and the
projections of future carbon emissions
• The errors affect both developing and developed
countries
• Does this matter for temperature?
– Manne and Richels argue that temperature is based
on the stock of cumulative emissions and flows take
time to have any impact
– BUT these magnitudes are too large for the IPCC to
dismiss the way they have to date.
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The Response of the SRES Authors to Critiques
• Convergence is not assumed in most scenarios (not
clear what is assumed)
• Doesn’t matter whether convergence is defined in PPP
or MER one can always convert between the two
(problem is that there is no empirical relationship)
• Even where convergence is assumed, it is not clear that
assuming high growth in developing countries would
cause emissions to be overestimated because with
higher income there would be more investment in
technology and more emissions reductions
– How plausible is this argument?
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Is there a better way to undertake projections of the
world economy for climate evaluation?
• Focus on the time frames we understand better and separate clearly
the types of uncertainty
– The past | the near future | the distant future
• Climate models can give some indication of what the actual
emissions in the past until now would do to the climate in future
years
• Economic models based on the past 60 years of data allow us to
project about 20-30 years into the future with some empirical basis
(rather than pure speculation) and allows statistical uncertainty to be
computed
– This gives us another 30 years of data to add to the climate
projections with a different degree of confidence.
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Finally
• Projecting the world economy over the time horizons
required for making temperature projections is not easy
• It is a big mistake to rely on the accuracy of these
projections in formulating and conditioning policy
• A better approach in the McKibbin-Wilcoxen Blueprint
which focuses on costs and benefits rather that target
and timetables
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Background Papers
www.gcubed.com
www.sensiblepolicy.com
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