The Future Technology Transformations (FTT) simulation models can be used to indicate which policies, individually and in combination, will likely help deploy new technologies cost effectively.
FTT modeling studies as well as lessons from past technological transitions and recent country experiences from the low-carbon transition indicate that the most cost-effective policy combinations differ at different stages of the transition, and that policy combinations can achieve more or less than the sum of their parts.
- Simulation models such as FTT models are complementary to cost-optimization models. While the latter can identify the lowest cost technology mixes for a sector, the former can identify the relevant policies to support the transition.
- Three examples where the FTT model has been used illustrate the different kinds of policies likely to be effective at each stage of the transition.
– Emergence (early) stage: targeted investment, for example, via subsidies and public procurement after viable technologies emerge from R&D, is most likely to be effective (illustrated by a case study on steel).
– Diffusion (middle) stage: regulatory policies are especially likely to be effective to support further diffusion and cost reduction as new technologies begin to compete against incumbents; subsidies and taxes can also help (illustrated by a case study on road transportation).
– Reconfiguration (late) stage: market reforms, infrastructure investments, and more general support for integration into social and economic systems are important as new technologies become established and price-based measures have less effect (illustrated by a case study on the power sector).
- The general findings from studies using the FTT model are broadly consistent with findings from studies of technological transitions in the past, and with the theoretical understanding described by the multilevel perspective on transitions, which is based on such studies.
- FTT models can be coupled with macroeconomic models to ascertain the implications of simulated policy scenarios and associated sectoral outcomes for macroeconomic indicators such as GDP and employment. An example of this approach using the E3ME model and FTT results from technology scenarios in the power sector is included in the full contribution.
As policy combinations can achieve more or less than the sum of their parts, MoFs need to work closely with other parts of government and consider policies as packages, not just individually. Deliberate alignment of fiscal and regulatory policies will likely lead to greater cost-effectiveness.
Keywords
combining approachesdata-drivenmodelspolicy designtechnology
