This task focused on the key role of technological change in the transition to sustainable economic structures. In particular, the following issues are addressed: Inertia, uncertainty, diffusion; learning curves; and parameterisation.

The key role of technological change in the decline of energy and carbon intensities of aggregate economic activities is widely recognised. This has brought great attention to the issue of modelling endogenous technological change, and, with few exceptions, this is done in a completely deterministic framework. Nevertheless, technological change is a dynamic process which is uncertain by nature. The two main vectors of technological change, learning through R&D investments and learning by doing, evolve and cumulate in a stochastic manner. In addition to this, the effect of technological progress on the processes of decarbonisation and of energy saving is inherently uncertain. This is due to lock-in/lock-out effects, which may produce lag times between new discoveries and the diffusion of the new technology, as well as to cluster diffusion of technology. Overall, this results in the inertia, as well as in the uncertainty, characterising the dynamics of these macro phenomena. Thus, accounting for uncertainty is crucial when designing optimal climate policy strategies, in terms of optimal abatement efforts and of R&D expenditures.

A further issue to be analysed relates to learning or experience curves that describe technological progress as a function of accumulating experience with the production and the use of a technology during its diffusion. Learning curves have been suggested as meaningful presentations of technological change in economy/environment models, and their strengths and weaknesses in the context of a sustainability perspective are to be investigated.

• An assessment of the role of uncertainty and how it can be incorporated in the modelling of endogenous technical change in order to produce more reliable forecast.
• A review of existing research on learning curves and their role for sustainability.
• The identification of key problems related to the parameterisation in order to outline future research needs.

• Technological Change and Sustainability in W8D and IMPEC Models of the Polish Economy
• Uncertain R&D, Backstop Technology and GHGs Stabilization
• International Technology Spillovers and Global Climate Strategies
• Modeling Various Aspects of Sustainability: The Case of Poland
• Optimal Energy Investment and R&D Strategies to Stabilise Greenhouse Gas Atmospheric Concentrations
• Technological Change and Learning Curves in the Context of the TranSust.Scan Modeling Network

FEEM (lead), LIFEA, WIFO