The development of renewable energy technologies depends on many factors, and governments have a large variety of policy measures to stimulate the development and use of these technologies. They can make available funds for research, give subsidies, set targets for minimum use, introduce taxes on carbon emissions, etc.
In this research project, we will analyse historical effects of policies on research and industrial activity, and the resulting development and use of a number of renewable energy technologies. We will compare experiences of a large group of countries, and a long period of time.
With the use of statistical models we can identify, for example, whether more research funding also leads to more installations of wind turbines. Such models can also determine if this is a strong effect, i.e., if twice as much research funding results in twice as much installations or only 10 percent more. Similarly, we can compare whether research funding or carbon taxes have a bigger effect on installations. Such information will be very useful for policy makers. They can better target the current weaknesses of the renewables sectors, and move more quickly towards a fully sustainable energy supply.
This research project will develop improved econometric models of factors that influence when, and how fast renewable energy technologies are deployed, based on panel data from a broad group of countries. The foremost improvement over existing models will be the use of an expanded set of explanatory variables, which will be based on insights from the Technological Innovation Systems literature. This will include indicators of e.g., RD&D activity, the formation of markets and manufacturing industries etc., and the policies that support these processes. Results will provide statistical evidence and
strength of the influence of these processes on renewable energy development.
Results will be highly useful for policy making, as it incorporates processes that are potentially directly influenced by policy intervention. Such information can help inform policy for accelerating sustainability transitions in energy and other technological domains, as well as building and maintaining competitive domestic industries in high-tech clean-tech sectors.
Results can further be used to improve energy systems models and forecasts, and inform discussions on the timeframe required for energy transitions.