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IPCC SRES

IPCC SRES. 4 qualitative storylines 6 IA model frameworks 40 Scenarios 6 Illustrative Scenarios representative of uncertainty range no single, best guess scenario probabilities/likelihoods not assigned

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IPCC SRES

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  1. IPCC SRES 4 qualitative storylines 6 IA model frameworks 40 Scenarios 6 Illustrative Scenarios • representative of uncertainty range • no single, best guess scenario • probabilities/likelihoods not assigned Impact of technological change of similar importance as population and economic growth combined

  2. MAJOR CLIMATE CHANGE UNCERTAINTIESSocioeconomic (Future Cumulative Emissions SRES Scenarios)Carbon Cycle (Resulting CO2 Concentration) andClimate Sensitivity (ºC for 2CO2) ° C global mean temperature change 1 2 3 4 5 6 0 2.5 7 ppm CO2 300 600 1000 6 4 2 0 2000 3000 <100 1000 SRES scenarios cumulative emissions 1900 - 2100, GtC low high Vulnerability:

  3. Emissions from 130,000 Scenarios of Technological Uncertainty 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 5 10 15 20 25 30 Set of 520 technology dynamics Relative frequency (percent) Optimal set of 53 technology dynamics Emissions by 2100, GtC Gritsevskyi&Nakicenovic, 2000 Energy Policy 38:907-921

  4. Endogenous Technological Change • Future characteristics (e.g. costs) depend on intervening actions (R&D & investments) • Improvements through accumulation of experience (learning) • Interactive rather than linear model (learning by doing and using; supply push and demand pull) • Uncertainty 1: outcomes of R&D and investment strategies (learning) • Uncertainty 2: market environment (demand, environmental constraints, etc.)

  5. Technological Uncertainties: Learning rates (push) and market growth (pull)

  6. Implementation • Integration of stochastic draws • Objective function incl. “risk term” (Y. Ermoliev) • Non-convex, stochastic optimization (A. Gritsevskii) • parallel computing (IIASA network to CRAY-T3E) • Result: optimal diversification portfolio

  7. Global Carbon Emissions: Four Models of Increasing Treatment of Uncertainty 25 20 15 global carbon emissions, GtC 10 (1) no uncertainty 5 (2) uncertain demand, resources, costs (3) = (2) plus uncertain C-tax (4) uncertain demand, resources, techn. learning, C-tax 0 1990 2010 2030 2050 2070 2090

  8. Summary Endogenous technological change through anticipation of (uncertain) increasing returns Non-convex, stochastic optimization problem solvable (within limits) Interpretation: Innovation and niche market development (exploration of learning potentials) economically rational in view of pervasive uncertainties Info:http://www.iiasa.ac.at/Research/TNT/WEB/index.htm

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