Determinants of Innovations in Clean Coal Technologies

1. Determinants of Innovations in Clean Coal Technologies Sebastian Voigt Centre for European Economic Research (ZEW) Mannheim Joint work with Ivan Hascic (OECD), Nick Johnstone (OECD), Andreas Löschel (ZEW) International Energy Workshop Venice, June 17, 2009

2. Outline • Introduction • Background • Variables and Data • Results • Conclusions

3. Introduction • 70% of electricity generation from fossil fuels – more than half of that share from coal • „clean coal technologies“ to reduce emissions • econometric analysis: determine impact factors of innovations of these combustion technologies • patents as indicator of innovation output

4. Background – Pulverized Coal Combustion • oldest coal combustion technology • subcritical, supercritical and ultra-supercritical steam conditions – only the latter two considered as clean coal technologies • Efficiency degrees: • subcritical: 38% • supercritical: up to 43% • ultra-supercritical: up to 45%

5. Background – Fluidized Bed Combustion • used in coal combustion as well as for combustion of biomass (among others) • fluidized bed consisting of granular solid material • solid fuel suspended on upward-blowing jets of air  mixing of gas and solids • combustion at approx. 800oC • reduction of SOx and NOx emissions • efficiency up to 44%

6. Background – IGCC • Integrated Gasification Combined Cycle • gas as well as steam turbine used in combustion process • gasification of coal leads to creation of mixture of gases consisting of H2 and CO – syngas • syngas powers gas turbine  waste heat used for steam turbine • so far: efficiency degrees between 40% and 43%  IEA predicts degrees above 50% • capable of pre-combustion CCS

7. Background –Carbon Capture and Storage CCS: • capture, transportation, storage • post-combustion and pre-combustion capture • transportation covered by pipeline systems and ships • storage: geological, deep ocean, mineral carbonation

8. Background – Patents • patent data as output measure of innovative activities • national patent offices and EPO • conditions: • invention has to be novel • invention has to involve non-obvious step • potential problems: • not each invention leads to innovation • different patent regimes and propensity to patent • choice of patent classes • apply International Patent Classification (IPC) of World Intellectual Property Organisation (WIPO)

9. Clean Coal Patents

10. Variables dependent variable: • number of patents of specific technologies (EPO) explanatory variables: • energy-related R&D expenditures (coal combustion) • coal share in total energy production • share of renewables in total energy production • dummy for period after Kyoto • electricity consumption • total patent counts (EPO)

11. Model and Data PATi,t = a + b1R&Di,t + b2CONSi,t + b3KYOTOi,t + b4COALi,t + b5RENi,t + b6EPOi,t + gi + ei,t • fixed country effects • estimation using negative binomial model • panel data for 23 countries from 1974 to 2005 • data sources: OECD Patent Database, IEA Energy Technology R&D Database, IEA Energy Balances

12. Results – Fixed Effects (1)

13. Results – Fixed Effects (2)

14. Results – Random Effects (1)

15. Results – Random Effects (2)

16. Results • clear significance of • Kyoto dummy • share of renewables • partly: electricity consumption, coal share • results for R&D expenditures different than expected  not significant for any technology  no technology-specific data available • Kyoto results can be explained by policy signals  innovations mainly shifted to renewable energy sources • results for renewables share indicates policy impact

17. Conclusions • empirical analysis shows interrelations between patents of all clean coal technologies and coal combustion R&D • identified impact of Kyoto Protocol and renewable energy sources • Outlook: • alternative specification of certain variables • examination of knowledge transfer

18. Thank you for your attention!