Climate Change by dfhdhdhdhjr


									      Designing Climate Change
    Scenarios in a Global Economic
                                     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                                                                          1
• 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)

          Structure of Presentation

•   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

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

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

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

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)

           Figure 1: GDP, Energy and Emissions for US and Japan
                            Index Numbers, 1965=1
             US GDP             Japan GDP

             US Energy          Japan Energy
             US Emissions       Japan Emissions




    1965          1970       1975           1980     1985         1990
• 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.

Theoretical Issues in Forecasting Growth
• Sources of output growth within a country
   – Increases in the supply capital, labor, energy,
   – 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

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
• Many energy models use assumption about emissions
  per capita converging or energy efficiency converging
  autonomously and then overlay this with aggregate GDP
• 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.

Do Carbon Emissions per Capita converge?

   Some models assume this either as fact or as
               a desired target

                Figure 1: Summary Measures of Spread
                         Emissions Per Capita





      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

          Figure 3: The Cross-Sectional Distribution of Emissions per Capita
                                      Sample A

2.5                                               1950         1960

2.0                                               1970         1980

                                                  1990         1999



      0            1          2          3          4            5         6

                            Metric Tons of Carbon Per Capita
  Does GDP per capita converge?

Some models assume this either as fact or as
            a desired target

          Figure 10: The Cross Country Distribution of GDP Per Capita
                               Density Estimates
                                   1971       1980        1990       2000




      0         1           2             3          4           5          6

                                Relative GDP Per Capita
The G-Cubed Approach

  McKibbin & Wilcoxen

              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             20
   • Other non Oil Exporting Developing Countries
              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      21
           Features of the G-Cubed Model

•   Dynamic
•   Intertemporal
•   General Equilibrium
•   Multi-Country
•   Multi-sectoral
•   Econometric
•   Macroeconomic

 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%)

       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

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)

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.

               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

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

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?

        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)

• 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
• 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.

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?
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
• 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
   – This gives us another 30 years of data to add to the climate
      projections with a different degree of confidence.

• 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

Background Papers


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