IIASA A. Grübler, 2002 IPCC SRES 4 qualitative storylines 6 IA model frameworks 40 Scenarios 6 Illustrative Scenarios representative of uncertainty range.

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Presentation transcript:

IIASA A. Grübler, 2002 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

SRES scenarios cumulative emissions , GtC < ppm CO °C global mean temperature change MAJOR CLIMATE CHANGE UNCERTAINTIES Socioeconomic (Future Cumulative Emissions SRES Scenarios) Carbon Cycle (Resulting CO 2 Concentration) and Climate Sensitivity (ºC for 2  CO 2 ) Vulnerability: lowhigh

Emissions from 130,000 Scenarios of Technological Uncertainty Emissions by 2100, GtC Relative frequency (percent) Set of 520 technology dynamics Optimal set of 53 technology dynamics Gritsevskyi&Nakicenovic, 2000Energy Policy 38:

IIASA A. Grübler, 2002 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.)

IIASA A. Grübler, 2002 Technological Uncertainties: Learning rates (push) and market growth (pull)

IIASA A. Grübler, 2002Implementation 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

IIASA A. Grübler, 2002 Global Carbon Emissions: Four Models of Increasing Treatment of Uncertainty global carbon emissions, GtC (1) no uncertainty (2) uncertain demand, resources, costs (3) = (2) plus uncertain C-tax (4) uncertain demand, resources, techn. learning, C-tax

IIASA A. Grübler, 2002 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: