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A Primer on Energy Technologies and Technological Change

A Primer on Energy Technologies and Technological Change. Energy Technologies in the US (estimates in GW). Prime movers based on US DOC, 1975 and 1994, (year 2000 data refer to 1992) All others: zero order estimates 46 TW in 2000 equal 4.6 Trillion US$ (or 40% of US GDP in 2005) at 100 $/kW.

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A Primer on Energy Technologies and Technological Change

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  1. A Primer on Energy Technologies and Technological Change Arnulf Grubler

  2. Energy Technologies in the US(estimates in GW) Prime movers based on US DOC, 1975 and 1994, (year 2000 data refer to 1992) All others: zero order estimates46 TW in 2000 equal 4.6 Trillion US$ (or 40% of US GDP in 2005) at 100 $/kW Arnulf Grubler

  3. An 75% Snapshot of Energy Technologies:Steam & Combined Cycle + Motors Gas turbine Exhaustgases Fuel Heat recovery steam generator Combustion chamber Air Electricity Generator Cooling air boiler electricdrives Steam turbine Water Electricity Generator Condenser fuelsupply Arnulf Grubler

  4. Technology and Energy Economics • Price data, while volatile indicate no resource scarcity/depletion • Depletion mitigated by technological change, substitution, efficiency improvements • Technological change originates both from within energy sector (off-shore oil) as well as from economy at large • Productivity gains and cost declines yield macro-economic benefits Arnulf Grubler

  5. US Energy Prices over the Last 200 Years:Prices do not show depletion, nor can explain energy transitions (wood→coal→oil/gas); Technology as main driver 1860: 300 $/bbl Arnulf Grubler

  6. in constant 2000$ Extent of inflation since 1900: 1900$ = 1$/bbl2000$ = 22 $/bbl in current $ Arnulf Grubler

  7. US Electricity in the 20th Century Falling nominal and realprices(constant 2000$)to 1970 Nominal and realprices vs.cumulative generation

  8. Energy Economics of the 20th Century >1970: Price escalation & volatility of fuel prices Conversion:-- scale and efficiency frontier reached for steam cycles,-- slow diffusion of gas turbines and combined cycles,-- slower demand growth, lack of investment, slow capital turnover -- increasing environmental regulation -- deregulation of natural monopoly markets (e.g. electricity) 1900-1970: Falling real-term fuel prices Conversion:-- Improved efficiency,-- economies of scale,-- rapid demand growth and-- capital turnover Arnulf Grubler

  9. History of US Steam Turbine Generators Drivers of lower costs: Increasing temperature & pressure= higher efficiency. Increasing unit size= economies of scale Source: T. Lee and R. Loftness,1987. IIASA WP-87-54 Arnulf Grubler

  10. US - Scale Frontier of Power Plants Source: T. Lee and R. Loftness, 1987. IIASA WP-87-54 Arnulf Grubler

  11. Declining Costs per kW of German Wind Turbines: Pure Economies of Scale: DMt = (kWt/kWt-1)0.84 x DMt-1 Arnulf Grubler

  12. Nordex N-80: Capacity: 2.5 MW Height: 80 mRotor: 80 mTotal: 120 mHarknessTower: 66m Arnulf Grubler

  13. 101 of Technological Change • Technological change is a process involving many steps and feedbacks. • Uncertainty pervasive at all stages of technology life cycle • Technology = combination of disembodied and embodied knowledge. Embodied TC only via (costly) investments (by technology users ≠ tech producers) • Significant costs downstream “R” (research): Development dominates “R&D”, Deployment investments dominate R&DD • “Value” of technology increases downstream also: Value of patent < private RoR < social RoR of innovation • Returns to adoption: Static technologies: decreasing returns; dynamic technologies, networks: increasing returns (the more deployed the cheaper, better, more acceptable) Arnulf Grubler

  14. The “black box” of Technology Public Sector incentives,standards, regulation,subsidies, taxes funding Market / Demand Pull Learning EmbodiedTechnology(plant,equipment,..) DisembodiedTechnology(Knowledge) BasicR&D AppliedR&D Demon-stration Nichemarkets Diffusion Product / Technology Push investments, knowledge andmarket spillovers funding Private Sector Arnulf Grubler

  15. Stages of Technology Development and the Resource Gap for Innovation The “Valley of Death” Relative Resources Available Sum Industry Government Source: M. Chertow, 2003 technology sellersAND buyers Research Prototype Pilot Production Stages of Technology Development

  16. R&D as % of Net Sales(Source: EPRI, 2005) Arnulf Grubler

  17. US – Energy R&DSource: Nemet and Kammen (in press) Arnulf Grubler

  18. US – Private Sector Energy R&D and Venture CapitalSource: Nemet and Kammen (in press) Arnulf Grubler

  19. US Electricity Sector: Investment as % of Revenues. Source: K. Yeager EPRI, 2005 Arnulf Grubler

  20. The Energy “Valley of Tears” • Declining R&D (public and private) • Declining investments • Declining venture capital • Declining long-term R&D and investment incentives in deregulated markets • Increasing needs for long-term strategic decision making (hedging portfolios to address climate change) Arnulf Grubler

  21. TECH 101 cont’dTechnological Change is… • Uncertain (feasibility, improvement potentials, opposition, env. impacts) • Dynamic (only certainty: Change) • Cumulative (building on past experience, other technologies) • Systemic (no technology is an island: Electricity+telephone+PC+www=Internet) • Actor based: Producers and Consumers • Extreme event like: Majority of benefits from few “big hits” Arnulf Grubler

  22. Innovation Uncertainty: Patented but non-functional smoke-spark arrestors Source: J. White, American Locomotives, 1968. Arnulf Grubler

  23. Scherer’s Rule (compounding uncertainties) Probability an R&D project gets selected** ??Probability of technical success (once selected)* .57Commercialization (given technical success)* .67Financial success (given commercialization)* .74Aggregate probability .27 Magnitude of financial success(private AND social RoR)** ?? * Based on Mansfield et al.’s empirical study of R&D project histories in US enterprises in chemical, pharmaceutical, electronics, and petroleum industries ** Largest uncertainties! Arnulf Grubler

  24. Fat tailed distributions: Pay-offs from US Pharmaceutical Innovations: (similar as in UK and EU “patent value” studies) NCE: New Chemical Entities Source: Scherer, 2000. Arnulf Grubler

  25. Cost Uncertainty: Important Information in Tails! Strubegger&Reitgruber 1995 analysis of near-term technology cost projections

  26. Basic Economics of PV Supply and Demand Source: BP, 2003 Arnulf Grubler

  27. Japan PV: Importance of Supply Push AND Demand Pull Data: Yuji, 2002 Result of R&D Result of niche market filling Arnulf Grubler

  28. Japan - PV Costs (improved knowledge) vs. Expenditures (effort): Cumulative Effects 100,000 1973: 30,000 1976: 16,300 10,000 1980: 4,900 – 4.0 0.54x y = 10 PV costs (1985) Yen per W 2 R = 0.989 1,000 1995: 640 Basic R&D Applied R&D Investment 100 0 0.1 1 10 100 1,000 Cumulative expenditures, billion (1985) Yen 1985: 1,200 Data source: Watanabe, 1995 &1997 →learning curvesPR: 2-b = 2-0.54 = ~0.7 = 30% decline in costs/Wper doubling of cumulative expenditures(total: ~2.5 billion $, ~75% investment, ~25% R&D)

  29. “Learning”: ResolvingTechnological Uncertainties • Innovation: many are called, few are chosen • Diffusion: multiple factors, e.g.-- Technological “Figures of Merit”-- Economics: Use = f (Costs) - Static: Ct - Dynamic: e.g. Ct = f (ΣtU) (LbD) • Feedbacks: e.g. spillovers, “take backs” • Externalities: e.g. networks, knowledge, • Impacts: nonlinear f(U) or“discovery by accident” Arnulf Grubler

  30. LbD • Vast case study literature (with possibilities of statistical interpretation, e.g. mean costs: -20% for 2x cumprod) • Pro-innovation bias: mostly success stories (exceptions: Lockheet Tristar, French reactors) • Quality improvements (+/-) largely ignored • Impossibility to separate R&D (innovation) and learning in manufacturing (2-factor learning curves empirically vacuous and theoretically based on dismissed linear model of innovation) • Intricate measurement challenges (costs vs. prices,…) • Effects of spillovers important but difficult to measure (esp. for inter-industry and internationalspillovers, not to mention inter-technology spillovers) Arnulf Grubler

  31. Learning/Experience Curve Terminology Costs: C Learning Rate: LR(% cost decline per doubling of output) Progress Ratio: PR = 1 – LR(remaining fraction of initial costs after doubling of output) Learning parameter: b Output: O Learning investment: Cumulative expenditures above break-even value Ct = C0 * (Σ0tO)-b PR = 2-b LR = 1 - PR e.g. 30% cost reduction per doubling of output: Co=100 Ct = 70 Oo =1 Ot = 2 LR = .3 PR = .7 b= -.51477 Arnulf Grubler

  32. Gas Turbines at GE Source: MacGregor et al., 1991 and Rogner, 1995. Arnulf Grubler

  33. Improved Economics: Prices vs. Costs Source: Based on Abernathy&Ward, 1975 Arnulf Grubler

  34. Learning Potentials: Number of units sold to date and cum. investments (at current replacement costs) Reminder: LbD Models need to separate economies of scale effects,i.e. consider UNITS rather than capacity

  35. Technology Learning Curves 1993 Windmills (Germany)(learning rate <10%) 1990 1998 2002 Arnulf Grubler

  36. Learning Rates of 108 Technologies Negative learning: Lockheed TristarFrench nuclear reactors Source: Argote&Epple, 1990 Arnulf Grubler

  37. 2 Uncertainties: Learning Rates and Market Growth Technology dynamics in response to R&D outcomes, economies of scale, material costs, etc. Demand growth, incentives, dynamics of competitors

  38. Impacts of Uncertainty, Learning, and Spillovers (IPCC AR4 ,forthcoming in 2007) Figure 2.2. Emissions impacts of exploring the full spectrum of technological uncertainty in a given scenario without climate policies. Relative frequency (percent) of 130,000 scenarios of full technological uncertainty regrouped into 520 sets of technology dynamicswith their corresponding carbon emissions (GtC) by 2100 obtained through numerical model simulations for a given scenario of intermediary population, economic output, and energy demand growth. Also shown is a subset of 13,000 scenarios grouped into 53 sets of technology dynamics that are all "optimal" in the sense of statisfying a cost minimization criterion in the objective function. The corresponding distribution function is bi-modal, illustrating "technological lock-in" into low or high emissions futures respectivelythat arise from technological interdependence and spillover effects. Baseline emissions are an important determinant for the feasibility and costs of achieving particular climate targets that are ceteris paribus cheaper with lower baseline emissions. Source: Adapted from Gritsevskyi and Nakicenovic, 2000.

  39. Cost Dynamics of PVs: 2 Case Studies Arnulf Grubler

  40. US - PV Factors of Cost Declines (in constant $/W)Source: G. Nemet, 2004 Innovation (R&D)Economies of ScaleExperience (LbD)Other/Total Arnulf Grubler

  41. Summary • Technologies have emergent properties that are constructed by “learning” processes (increasing returns!) • Good empirical and theoretical understanding of “routine” innovations (e.g. incremental improvements via industrial R&D) • Need to move beyond proxy drivers and black-box view of technology:Who learns what, when, and how? • Need to move beyond pro-innovation biasin the empirical literature • Technologies of greatest (economic, social, environmental) interest: low probability, extreme events which are difficult to anticipate or to model (→scenario approach) Arnulf Grubler

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