Global transport modelling in IAMs: learnings from model comparison Bastien Girod, Postdoctoral researcher, Department of Management, Technology and Economics (formerly visiting scholar at Utrecht University and PBL) Laxenburg 19. November 2013 This presentation was held within the project ADVANCE, which has received funding from the European Union’s Seventh Chair of Sustainability and Technology Framework Programme for research, technological development and demonstration under the grant agreement no 308329.
Comparison of IAM transport models: Participating models, structural equation and common assumptions Girod, B., Vuuren, D.P., Grahn, M., Kitous, A., Kim, S.H., Kyle, P., 2013. Climate impact of transportation A model comparison. Clim. Change. Participating models Scenario harmonization ▪ GCAM Baseline scenario ▪ GET ▪ Population projections: OECD Environmental Outlook ▪ TIMER ▪ Income projections: OECD Environmental Outlook ▪ TIMER Emission factors and energy system ▪ IEA/Momo* ▪ Emissions [tCO 2 /TJ]: natural gas: 56, liquid fossil-fuels: 71.5, biofuels 22. ▪ Focus on tank to wheel: Same emissions for fossil-based liquid fuels. *For baseline only ▪ However: Upstream emissions considered in for carbon tax Structural equation and output for comparison ∑ ∑ ∑ ∑ = ⋅ ⋅ ⋅ ⋅ ⋅ ( ( ( ))) CO e Serv Mode Veh Eff Fuel ef 2 r , t m v v ft ft r , t m v ft [ Gt CO 2 ] EnergyEffi ciency Service CarbonInte nsity Chair of Sustainability and Technology | 1
Model assumption Projection GCAM GET IEA/MoMo TIMER POLES ( common , not-common, Approach, A, Input, I) Travel Population, income, Population and income Population, income-based Population, income, travel Population, income, fuel A: Elasticities, trend, TMB, TTB Demand service prices projections vehicle purchase and money budget and service prices for distance, I: Income, population, prices, travel trends prices income for equipment TTB, travel trends. Mode split LM of vehicle costs and Historical shares and their Trends of different LM of vehicle costs and Partial substitution through A: Logit-model, elasticities, time value costs based on connection to GDP growth transport modes time value costs based on fuel price, partial trend. speed and wage rate and travel time speed, travel time budget, autonomous dynamics per I: vehicles cost (incl. fuel price), and travel money budget mode speed, historic shares Freight Total GDP, service prices Total GDP Total GDP Total industrial value Total GDP, fuel prices A: Elasticities Demand added; aircraft connected I: GDP (IVA), fuel prices, to air travel; fuel price connection to air travel Mode split LM of vehicle costs Based on historical shares Based on trends of Autonomous dynamics per Partial substitution through A: Logit-model, historic shares, and their connection to different transport modes. mode fuel price, partial elasticities GDP growth autonomous dynamics per I: Vehicles costs (including fuel mode price), historic shares Fuel use LM for vehicles with Average data for each Trends in vehicle LM for vehicles with Energy efficiency A: Logit-model for vehicles, Energy different fuels and energy mode and region for the composition in fleets and different fuels and energy evolution depends on fuel elasticity, discount rates, trends efficiency efficiency initial year, thereafter load factors. efficiency, income prices and income per I: Vehicle costs, fuel prices, assumptions on annual dependent discount rates capita; LM for vehicle income (discount rate) improvements types on complete costs Determined by vehicle and Determined by cost- Determined by vehicle and Determined by vehicle and Determined by vehicle and A: Determined by vehicle and Fuel mix mode shares; LM for liquid minimizing the entire mode shares mode shares mode shares; LM for liquid modes, logit-model for fuels, fuels global energy system fuels cost-optimization I: Fuel price. Fuel price Endogenous Exogenous Exogenous Endogenous Endogenous A: Endogenous (IAM) Chair of Sustainability and Technology | Very different model approaches for travel demand, mode split and fuel use. 2
Projections for global direct CO 2 emissions from transportation for 2050 Projection of total emissions Main findings: Contribution of different (excluding air and water freight) transport modes • Cars and heavy trucks contribute to int. ship. 16 the largest share of nat. ship. total CO 2 emission 14 air freight • All models project 12 hvy truck Emissions [GtCO 2 ] steep increase from med. truck 10 air travel light truck 8 • Increase in CO 2 rail freight 6 Emissions by 55% air travel to 145% compared 4 car to 2005 train 2 • Structural bus 0 decomposition TIMER GCAM POLES GET IEA TIMER GCAM POLES GET IEA 1990 2005 2020 2035 2050 shows: GCAM & TIMER GCAM POLES POLES high service 2005 2050 GET IEA demand and high efficiency and decarbonization Chair of Sustainability and Technology | 3
Comparison of projections travel demand in the baseline scenarios for 2050 Projection of total travel Main findings: Modal split for 2005 and demand projections for 2050 • rapid increase by 130 – 280% compared to 2005 100% 180 200 air • Decoupling from 180 90% 160 GDP Travel demand [tera pkm] 80% 160 140 hs train • POLES - GET 140 70% Schafer et 120 GDP [106 USD 2005 ] difference explained 60% 120 al. (2010) 100 car by car travel 100 50% 80 • Except for IEA, all 40% 80 60 train models project a 60 30% 40 higher air travel 40 20% 20 growth than car 20 10% bus 0 0 travel demand 0% IEA TIMER GCAM POLES GET IEA TIMER GCAM POLES GET 1975 1990 2005 2020 2035 2050 • But: all project lower growth up to 2050 (3 historic TIMER GCAM 2005 2050 POLES GET IEA – 3.9%) than ICAO GDP for 2006–2036 of 4 to 5.2 % Chair of Sustainability and Technology | 4
Comparison of projections for freight demand in the baseline scenarios Projection of total freigth demand Main findings: (without freight shipping and aircraft) • all models assume stronger decoupling 100 200 from GDP compared to travel demand 90 180 freight demand [tera tkm] • broad range of 80 160 projections can be 70 140 GDP [10 6 USD 2005 ] explained by the 60 120 uncertainty in 50 100 reliable statistics 40 80 • Base year difference 30 60 • heavy trucks (IEA 20 40 lower estimates) 10 20 • rail (POLES 0 0 include vehicle 2005 2020 2035 2050 weight) TIMER GCAM POLES GET IEA GDP Chair of Sustainability and Technology | 5
Comparison of projections for fuel demand in the baseline scenarios Main findings: Projected trends in fuel use and global fuel prices • Up to 2050 all 100% models see fossil electric biofuels fuels dominating 50% hydrogen • Very different gas projections for fossil liquids 0% alternative fuels IEA TIMER GCAM POLES GET IEA TIMER GCAM POLES GET TIMER GCAM POLES GET • Can be explained by regulatory 2005 2050 2095 environment, 2090 2005 2050 consumer choice and fuel prices fuel price [USD2005/ GJ] fossil 60 60 60 • Poor data for fuel prices in non-OECD bio 40 40 40 regions elec. 20 20 20 • Fuel prices explain H2 - - - most changes in fuel mix beyond 2050 • Share of electricity remains small Chair of Sustainability and Technology | 6
Comparison of global energy intensity projections in the baseline scenarios Global energy intensity projections (2005 – 2050) Main findings: • High energy intensity for car, travel freight truck and aviation bus rail car air rail truck 2005 [MJ/pkm] 2005 [MJ/tkm] • Uncertainty in IEA 0.50 0.30 2.05 2.54 0.09 2.82 baseline due to TIMER 0.41 0.33 1.83 2.63 0.37 2.30 occupancy data for GCAM 0.86 0.70 1.65 1.70 0.21 2.48 non-OECD POLES 0.56 0.17 1.69 1.06 0.16 2.76 countries GET 0.58 0.30 1.96 2.08 0.47 3.38 2005 to 2050 [average % change per year] • All models project IEA -0.5% -0.4% -0.5% -0.9% 0.5% -0.5% high efficiency TIMER 0.9% 0.5% -0.6% -1.2% -2.0% -0.2% improvements in the GCAM -0.5% 0.1% -0.9% -0.2% -0.4% -0.4% baseline POLES -0.8% -2.1% -1.2% -2.5% -1.6% -0.8% • Fuel prices and GET -0.8% -0.2% -0.8% -1.0% -0.4% -0.6% 2045 to 2095 [average % change per year] changes in fuel mix TIMER 0.3% 0.5% -1.0% -0.5% -0.3% -0.6% explain most of the GCAM -0.1% -0.1% -0.2% -0.2% -0.1% -0.4% differences in POLES -0.4% -0.5% -0.7% -1.0% -0.5% -0.5% efficiency GET -1.3% -0.2% -0.4% -1.0% -0.4% -0.7% improvements Chair of Sustainability and Technology | 7
Comparison of regional fuel use projections in the baseline scenarios Main findings: Regional fuel use projections for passenger transport and freight All model project (excluding air and water freight). Note: Latin America (LAM), Middle East • stabilization or (MEA) and Former Soviet Union (FSU) decrease of fuel use in OECD regions 400 • decline in the OECD Africa 300 fuel share from fuel use [EJ] Other Asia around 60 % to 20– 200 China 23 % in 2050 • steep increase in LAM, MEA, FSU 100 non-OECD regions OECD they determine - IEA TIMER GCAM POLES GET IEA TIMER GCAM POLES GET TIMER GCAM POLES GET future fuel use • Africa is projected to 2005 2050 2095 amount a similar or larger share of global fuel use than China in the end of the century Chair of Sustainability and Technology | 8
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