Modeling Uncertainty of Induced Technological Change

This paper presents a new method for modeling-induced technological learning and uncertainty in energy systems. Three related features are introduced simultaneously: (1) increasing returns to scale for the costs of new technologies; (2) clusters of linked technologies that induce learning depending on their technological "proximity" in addition to the technology relations through the structure (and connections) of the energy systems; and (3) uncertain costs of all technologies and energy sources. The energy systems-engineering model MESSAGE developed at IIASA was modified to include these three new features. MESSAGE is a linear programming model. The starting point for this new approach was a global (single-region) energy systems version of the MESSAGE model that includes more than 100 different energy extraction, conversion, transport, distribution and end-use technologies. A new feature is that future costs of all technologies are uncertain and assumed to be distributed according to the log-normal distribution. These are stylized distribution functions that indirectly reflect the cost distributions of energy technologies in the future based on the analysis of IIASA energy technology inventory. In addition, the expected value of these cost distributions is assumed to decrease and variance assumed to narrow with the increasing application of new technologies. This means that the process of technological learning is uncertain even as cumulative experience increases. New technologies include, for example, fuel cells, photovoltaic, and wind energy conversion technologies