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Biomass Energy Potentials @ ENERegion Summer School

Biomass Energy Potentials @ ENERegion Summer School. Gilbert Ahamer GIScience, Austrian Academy of Science gilbert.ahamer@oeaw.ac.at. Relevance of „biomass“. BM has several functions for humans: Food Fibre Fuels. Energy – how estimated? (recall “facts III”). % Energy per fuels

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Biomass Energy Potentials @ ENERegion Summer School

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  1. Biomass Energy Potentials@ ENERegion Summer School Gilbert Ahamer GIScience, Austrian Academy of Science gilbert.ahamer@oeaw.ac.at Salzburg, 1.7.09

  2. Relevance of „biomass“ BM has several functions for humans: • Food • Fibre • Fuels

  3. Energy – how estimated? (recall “facts III”) • % Energy per fuels • d/dt (% Energy per fuels) Source: Global Change Data Base

  4. „we“: Modellers and GIS people • Both try to meet the quality criteria of up-to-date models • As demanded by the scientific communities (else risk) • What is the state of the art in modelling? • Major vehicle: climate change exercise • Historically a suite of models was coupled • Consecutively one discipline after the other [IPCC]

  5. Let‘s learn from the „history of modelling“ in climate change business: add viewpoints • Discipline by discipline is added • in order to compose a consistent „multi-disciplinary“ world view • that can be offered to readers, the public, clients Source: IPCC TAR

  6. Approach: Modelling the global carbon cycle & the energy economy • „Combined Energy and Biosphere Model“ • biospheric part: 2433 grid elements • energy part: country scenarios • result: Net C balance for CO2 concentration

  7. Modelling methods: the issues • Two disciplines are merged by the CEBM: • energy economics (E) • biosphere (B) • The depth of description is symbolized by two peaks

  8. Modelling methods: the scope

  9. Modelling methods: the substances

  10. The annual carbon cycle: „old“ & „new“

  11. Modelling scheme: annual carbon mass flows

  12. Carbon flows and carbon pools

  13. Modelling methods: the stylised ocean

  14. Modelling methods: temperature data

  15. Modelling methods: agricultural productivity

  16. Modelling methods: examples for results

  17. Modelling: sensitivity of energy scenarios „%“ = annual growth of CO2 emissions

  18. Modelling: sensitivity of deforestation scenarios left: complete deforestation till 2100 right: including energy plantations

  19. Modelling methods: population projection Source: IIASA

  20. Modelling: scenario for energy intensity E/GDP = „energy intensity“of an economy

  21. Modelling results for energy and CO2 CO2 emission  CO2 concentration

  22. 5 theoretical biomass energy potentials • Agricultural biomass on agricultural areas • Energy plantations on agricultural areas • Agricultural biomass on natural areas • Natural biomass on natural areas (forestry) • Natural biomass on natural areas (short rotat.)

  23. Theoretical biomass energy potentials

  24. Theoretical biomass energy potentials

  25. Theoretical biomass energy potentials

  26. Theoretical biomass energy potentials

  27. Theoretical biomass energy potentials

  28. Comparison of energy demand & supply Link (de, 300 p.): http://www.uni-graz.at/globalstudies/deposit/CO2BM_Dissertation.pdf Link (en, 30 p.): http://www.uni-graz.at/globalstudies/1994_International-Journal-of-Global-Energy-Issues_OriginalLayout.pdf

  29. Theoretical potential depends on ... • ... What you do with the previously standing biomass... • Can scenarios ever come true ;-)

  30. „realistic“ potential ~ 1/10 theoretical pot.

  31. Carbon flows and carbon pools = f(t)

  32. C flows and C pools = f(t) with BM strategies

  33. Results of „B“ = Biomass • Biomass will not „save the world“ • Biomass energy is a valuable and ready-to-use measure as part of a bundle of measures • Do not forget about huge transportation needs • Keep in mind necessity for energy conversion • Take care in order to ensure carbon neutrality • Expand the perception beyond carbon

  34. (C) The case: Austria Salzburg, 1+2..7.09

  35. Several levels of action / legislation • EU (directives) • National (law, §15a) • Regional (energy efficiency in buildings) • Local (climate alliance of cities)

  36. Energy & Emission Balance for a City (Graz) • Start from census data on m² housing • Compute energy demand for heating • Compute emissions • Scenarios to evaluate potential measures

  37. Energy & Emission Balance for a City (Graz)

  38. Energy & Emission Balance for Austria (CORINAIR) for IPCC & EU

  39. „Toronto Technology Program“ for CO2 Reduction in Austria

  40. Thank you for your attention Salzburg, 1+2..7.09

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