Public Policy, Investment, and Improvements in Wind Power in California

Transcription

1 Public Policy, Investment, and Improvements in Wind Power in California Gregory Nemet University of Wisconsin Madison September, 2007 USAEE Meetings

2 The climate-innovation policy problem Reduce GhG emissions by >70% while demand grows by 2 3x Innovation needed: existing low-carbon options expensive or limited in availability, reliability Market failures: climate a public good, knowledge spillovers Government role: correct for under-supply of innovation Policy problem: select, time, and implement instruments Gregory Nemet Public policy and wind power in California 2

3 Policy: demand-pull vs. technology-push debate Technology Push: govt actions that reduce the cost of innovation to private actors Demand Pull: govt actions that increase the payoff to successful innovation for private actors Technology push Demand pull Govt Target: availability of size of market knowledge Examples: govt R&D, tax credits, IPR, tax credits, govt education, demonstration projects, procurement, technology mandates, standards, knowledge networks taxes on substitutes Consensus: both necessary, neither sufficient But how to allocate? first, evaluate demand-pull... Gregory Nemet Public policy and wind power in California 3

4 Research question Theoretical argument: Demand-pull hypothesis argues that an increase in expected future demand increases incentives for firms to invest in innovation. Research question: Did policy-driven demand induce innovation? Hypotheses: H1: Demand-side policies stimulated diffusion of the technology. H2: Policy-led diffusion enabled learning-by-using. H3: Policy-led demand created incentives for investments in patentable inventions. Gregory Nemet Public policy and wind power in California 4

5 Research question and approach Research question: Did policy-driven demand induce innovation? Approach: 1 Historical case study of wind power in California 2 Operationalize policy history to assess demand pull 3 Outcomes: innovation indicators over time H1: Diffusion H2: Learning-by-using H3: Inventive activity/effort 4 Assess influence of policy-driven demand on each indicator Results Gregory Nemet Public policy and wind power in California 5

6 Results: operationalizing demand-pull Figure: Cost of wind power, price of purchased electricity Cost of wind elec. Cost w/ tax credits Cost for best operators Purchase price w/ SO#4 Electricity purchase price $/kWh $/kWh r t (1+r t ) L t LAC t = C t + OM t F th C = capital cost of wind turbines, r = interest rate, L = lifetime of turbines, F = capacity factor, OM = op. & maint. costs and h = # hours in a year. Gregory Nemet Public policy and wind power in California 6

7 Results: policies and diffusion H1: Policy-led demand stimulated diffusion. Figure: Cost of wind power and diffusion 05$/kWh Cost of wind elec. Cost w/ tax credits Cost for best operators Purchase price w/ SO#4 Electricity purchase price 0.05 New cap. (05$m) Gregory Nemet Public policy and wind power in California 7

8 Results: learning-by-using and diffusion H2: Policy-led diffusion enabled learning-by-using Installed capacity (MW) Figure: Installed capacity and learning-by-using Installed capacity Capacity factor 30% 25% 20% 15% 10% 5% Capacity factor % capacity factor = electricity produced (kwh/yr) (capacity installed (kw))(hours/year) Gregory Nemet Public policy and wind power in California 8

9 Results Results: H1: Diffusion responded to demand side policy. H2: Learning-by-using at least partially attributable to increase in cumulative production. H3: Inventive activity only occasionally responsive to growing demand. Gregory Nemet Public policy and wind power in California 9

10 Results: patenting by firms H3: Policy-led demand created incentives for investments in patentable inventions. Figure: Company wind patents and investment in new capacity. New capacity (05$m) Wind power patents Gregory Nemet Public policy and wind power in California 10

11 Results: investment and highly-cited patents H3: Policy-led demand created incentives for investments in patentable inventions. Figure: Investment in new capacity and highly-cited patents. New capacity (05$m) Wind power patents Gregory Nemet Public policy and wind power in California 11

12 Results and outstanding question Results: H1: Diffusion responded to demand side policy. H2: Learning-by-using at least partially attributable to increase in cumulative production. H3: Inventive activity only occasionally responsive to growing demand. why did diffusion and learning-by-using respond to changes in demand but inventive activity did not? Gregory Nemet Public policy and wind power in California 12

13 Why such a weak response by inventors to demand-pull? Possible explanations: 1 convergence on a dominant design 2 time lags + policy uncertainty 3 exhaustion of the technical frontier 4 factors other than demand-pull policy Gregory Nemet Public policy and wind power in California 13

14 Explanation 1: convergence on a dominant design Blades Rotor position Axis orientation Others Gregory Nemet Public policy and wind power in California 14

15 Explanation 1: categorization of highly-cited patents Table: Highly-cited U.S. wind power patents by design n % Dominant design (3-blade, upwind, horizontal axis) Power controllers 11 15% Blade pitch control 7 10% Blade designs 4 5% Drive train 0 0% sub-total 22 30% Alternative designs Vertical axis 15 21% Integrated end-use 9 12% Other alt. designs 27 37% sub-total 51 70% Gregory Nemet Public policy and wind power in California 15

16 Explanation 1: highly-cited patents H3: Policy-led demand created incentives for investments in patentable inventions. Figure: Highly-cited patents by type of design. Gregory Nemet Public policy and wind power in California 16

17 Why such a weak response by inventors to demand-pull? Possible explanations: 1 convergence on a dominant design 2 time lags + policy uncertainty 3 exhaustion of the technical frontier 4 factors other than demand-pull policy Gregory Nemet Public policy and wind power in California 17

18 Explanation 2: time lags + policy uncertainty Policy instruments were rarely in place for more than three years continuously Time until investment payoff Diffusion: 1 3 years L-b-u: <1 year Inventions: >5 years (?) Gregory Nemet Public policy and wind power in California 18

19 Explanation 3: exhaustion of the technical frontier Argument would go: But... the extraordinary burst of R&D investment and discovery in the 1970s exhausted the technological frontier; there was not much left to invent and patent. 1-MW-turbine goal was still a distant prospect. important inventions emerged in the 1990s (variable speed turbine) new needs emerged: reactive power, grid congestion, scaling up patenting rose again in the 2000s. Gregory Nemet Public policy and wind power in California 19

20 Explanation 4: other factors: R&D U.S. federal R&D (05$m) Public R&D Patent filings Wind power patents Gregory Nemet Public policy and wind power in California 20

21 Conclusions Results: H1: Diffusion responded to demand side policy. H2: Learning-by-using associated with increase in capacity. H3: Inventive activity only occasionally responsive to policy-led demand. why did diffusion and learning-by-using respond to changes in demand but inventive activity did not? Explanations: emphasis on incremental uncertainty + lags R&D, energy prices Gregory Nemet Public policy and wind power in California 21

22 Implications: was demand-pull a success? incremental sufficient: 10 1 cost reduction close to competitive Gregory Nemet Public policy and wind power in California 22

23 Implications: was demand-pull a success? incremental sufficient: policy failure: 10 1 cost reduction 30 years to 1 MW close to competitive still expensive limited diffusion Gregory Nemet Public policy and wind power in California 22

24 Future work why was demand-pull for inventive activity so weak in s but appears stronger since late-1990s? New capacity (MW) Worldwide New Capacity Patent filings (% of total) x Patents as a % of total Gregory Nemet Public policy and wind power in California 23