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France Projections of Energy related CO2 emissions Methodology and assumptions

France Projections of Energy related CO2 emissions Methodology and assumptions. Projections carried out. 1997 – MIES : Two scenarios up to 2020, base year 1992. Without measures and with measures

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France Projections of Energy related CO2 emissions Methodology and assumptions

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  1. France Projections of Energy related CO2 emissions Methodology and assumptions

  2. Projections carried out • 1997 – MIES : Two scenarios up to 2020, base year 1992. Without measures and with measures • 1998 – MIES: Updating the scenario with measures; base year 1997. Reference for the second national Communication • 2001 – MIES, MATE, ADEME: Three scenarios up to 2020, base year 1997. Without measures, with existing measures, comprehensive National Programme. Input in the 3rd National Communication

  3. Overall organisation SENARIO M I E S MINISTRIES, socio-economic assumptions EXPERTS, technical assumptions SIMULATION Adjustment for price/rebond effects End-Use, bottom-up model MEDEE Energy Demand per end-use, Energy Balances GHG emissions inventory, Common Reporting Format

  4. MEDEE: a bottom-up end-use model to assess policy measures within homogeneous energy demand modules Household social need: intensity, satisfaction degree Useful energy need Final energy demand Utilisation device, energy product, efficiency Production activity Physical, climatic, technological context

  5. MEDEE – Overall structure Final demand Industry Transport Résidential Tertiairy Agriculture Branches Freight Flats Branches Passengers Houses Irrigation Engines LECI’s Hot water End-uses Other Road Fishing Cooking End-uses Individual Products Lighting Collective Appliances Thermal Rail Electric Space heating Waterways Air cond’ Air

  6. MEDEE: how to account for policy measures MEASURES MEDEE INPUT PROJECTIONS Intensity of needs input variables Prices, income Level and structure of the final energy demand of the module Service supply Regulations, voluntary agreements, incentives Technology input variables Budget coefficients

  7. Budget coefficients: an instrument to account for interactions between economic and regulatory instruments Households Budget coefficient MEDEE calculation Trend Price effect rebond effect History Future

  8. Budget coefficients: an instrument to account for interactions between economic and regulatory instruments Producers Budget coefficient MEDEE calculation Trend Price effecton production rebond effect History Future

  9. Budget coefficient: example of motor cars Fuel and lubricants, source INSEE Scénario without measure

  10. Budget coefficients for motor cars: the lessons from historical evolutions • Historical trend downwards: the scenario without measure indicates the bottom line of the budget coefficient in the future • Historical short term fluctuations shows that the budget coefficient never get higher than 25% above the long term trend • The historical elasticity of the budget coefficient to energy prices is roughly 0.85

  11. Example: ACEA agreement combined with fuel tax harmonisation and carbon tax 500F99/tC • As compared to the scenario without measure, MEDEE calculates in 2010: • A decrease of the budget coefficient: 0.07 point • A decrease of the CO2 emissions: 3,8 MtC • This result violates one of the fundamentals of the budget coefficient: it cannot remain permanently below the bottom line

  12. Example: ACEA agreement combined with fuel tax harmonisation and carbon tax 500F99/tC • Medee results are therefore « adjusted », taking into account the rebond effect: • The budget coefficient is assumed to get back to the bottom line in 2010 • This implies that the CO2 emission reduction is limited to 3.1 MtC instead of 3.8 • Burden sharing among the three measures is then: 0,8 MtC (29%), 1,2 MtC (37%) et 1,1 MtC 34%

  13. Findings – Evaluation of measures • The global impact of a package of measures is less than the addition of the impacts of all individual measures • The impact of one particular measure depends on all the other measures taken at the same time: there cannot be a single universal evaluation of this impact • The reference as to the future « natural » trend of the budget coefficient may have a crucial importance in the ex-ante evaluation process

  14. Findings – Modelling • Price elasticities give an accurate assessment of the impact of pricing-taxation measures only in case no regulatory measure is taken simultaneously • Budget coefficients as instruments to evaluate rebond effects and impacts of packages of measures: efficient, but controversial • Simulating feed-backs of budget coefficients on technical and economic determinants of the energy demand is almost impossible: only a global evaluation « off model » on energy demand and CO2 emissions is relevant

  15. Energy-CO2 projections, 3rd National Communication, France • SM - Scenario without policy measure dedicated to green house gas mitigation after 1990 • AME – Scenario with policy measure dedicated to green house gas mitigation implemented before 31-12-1999 • PNLCC - Scenario with all policy measures considered in the « Programme National de Lutte contre le Changement Climatique »

  16. Socio-economic assumptions - 1 • Economic growth • Demography

  17. Socio-economic assumptions - 2 • Energy prices 2010 • Traffic growth, without measure, 1997-2010

  18. Policies and measures - Buildings

  19. Policies and measures - Buildings

  20. Policies and measures - Industry

  21. Policies and measures - Transport

  22. Policies and measures - Transport

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