Klaassen et al 2005 Impact of R&D on wind innovation

Klaassen, G., A. Miketa, K. Larsen, T. Sundqvist (2005). "The impact of R&D on innovation for wind energy in Denmark, Germany and the United Kingdom," Ecological Economics 54, 227-240.

• 227-8 Policy intervention to help RE justified on basis of "technological learning" - phenomenon of cost decreasing as cumulative installation increases.
• Extend this argument to learning effects of R&D.
• 228 Uses two-factor learning curve (2FLC) model to look at effe3cts of cumulative capacity AND knowledge stock (from past R&D expenditures.
• 229 Review of Denmark policy: Test station that tested every wind turbine for market release, and timing of shift of funds from R&D to market introduction through FITs important.
• 229-30 Germany: Says early R&D programs generally considered failures. Small Germany windmill manufacturers benefited from Danish expertise.
• 230 UK: Seems not to recognize that the RO and the NFFO were two different systems.
• These policy reviews seem poorly researched.
• Commonly used formulation of learning curve: SPC = A*CC^-alpha where SPC is investment cost of technology per unit (1990US$ per kW); CC is cumulative capacity (MW); -alpha is learning index; A is specific cost at unit cumulative capacity.
• Implies that for each doubling of CC there is constant percentage decrease in costs, called the learning rate. Typical learning rates calculated from studies on wind turbines range from 4% to 32%.
• This formulation ignores that CC is a function of demand, which itself depends on factor prices and total output produced.
• Implies that costs are function only of installed capacity, and subsidies should be used to this end.
• KS(t) = (1-delta)KS(t-1) + RD(t-x) Knowledge stock at time t is the knowledge stock at time (t-1), adjusted for depreciation, plus R&D expendtures in time (t-x).
• New two-factor learning curve (2flc) is SPC = A*CC^-alpha*KS^-beta where -beta is learning-by-searching index.
• 233 Data: Public R&D expenditures for wind energy from IEA. Wind capacity installed from various sources. Average investment costs per kW from various sources. Investment cost data for UK only for one project (!)
• 233 "...the non-turbine part of the investment costs might amount to 10-40% of the overall investment costs."
• 234 Assumptions about depreciation of knowledge stock (3%) and lag with which R&D expenditures contributed to knowledge stock (2 years).
• Uses the data and specifications above to econometrically estimate alpha, beta, and A (for each country). Finds that beta (the knowledge stock exponent) is almost 2.5 times as big as alpha (the CC component), indicating that R&D more important for cost reductions than installed capacity.
• 236 Find that results robust wrt alternative depreciation rates and time lags.
• PROBLEMS: Does not take into account private R&D. (notes on p237 that private expenditures on wind energy in 1974-1999 might have been 75% more than public). Spillover effects! Most of UK's turbines, for example, come from Denmark.