National Downscaling in Global Transportation ModelingPage KylePacific Northwest National Laboratory

International Transport Energy Modeling – Meeting 3Paris, FranceOctober 26, 2017

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Background and goalsThe iTEM models disaggregate the world into between 7 and 40 model regions

Boundaries are national; regions are constructed from countries

Goals of downscalingAllowing re-construction of common regions, to allow inter-comparison at a finer resolution than globalProviding nation-level information as requested (e.g., for NDC analysis)

Goals of downscaling methodSimple to implement, explain, replicate, defendEasy to install, review, run, testPerformance!

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The iTEM Project

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The ModelsBPEPPA5 (MIT)ExxonMobilGCAM (PNNL)GET (Chalmers)ITF (OECD)MESSAGE (IIASA)MoMo (IEA)Roadmap (ICCT)ShellStatoilWEPS+ (U.S. EIA)

The regions

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The regions

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The regions

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The regions

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The regions

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The regions

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The regions

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The regions

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The regions

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The regions

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The regions

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The regions

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Bundling of selected countries

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Bundling of selected countries

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Bundling of selected countries

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Bundling of selected countries

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iTEM downscaling

The methods are documented with all code available in a distributable R package called itemIn the equation below, the country-within-region Share of some quantity for country r, mode m, and future year y, is determined from a base-year country-level Proxy which evolves as a function GDP growth, with the GDP responsiveness determined by income elasticity ⍺.

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Current iTEM downscaling, cont’d

Mode-level alpha parameterVariable -> proxy assignments

Currently relying almost entirely on base-year energy because it’s all we have at the country level

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mode elasticity2Wand3W 0.1Aviation 0.8Bus 0.3DomesticShipping 0.5FreightRail 0.7FreightRailandAirandShip 0.7HDT 0.65InternationalShipping 0.4LDV 0.3PassengerRail 0.4Road 0.4Rail 0.6All 0.5

Variable Nation-leveldownscalingproxyCO2Emissions (allsectors) CDIAC: CO2Emissions in2010energy IEA:Transportenergy bymodein2010GHGEmissions (allsectors) CDIAC: CO2Emissions in2010pkm IEA:Transportenergy bymodein2010Population SSPDatabase:Population 2010 - 2100PPP-GDP SSPDatabase:GDP2010- 2100sales IEA:Transportenergy bymodein2010sales_weighted_intensity_new IEA:Transportenergy bymodein2010stock IEA:Transportenergybymodein2010tkm IEA:Transportenergybymodein2010ttw_bc/ch4/co2/pm2.5 IEA:Transportenergy bymodein2010vkt IEA:Transportenergy bymodein2010

Socioeconomic scenario assignment

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Socioeconomic scenario assignment

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BP, GCAM, GET, ITF, MoMo, [Statoil]

MESSAGE, ExxonMobil, Shell (Mountains)

Shell (Oceans) EPPA, Roadmap

Example Results

Full iTEM country-level data currently contains 19 million data values (1.6 GB)[no analysis intended; charts are just a demonstration that the method produces results at the country level]

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2000 2010 2020 2030 2040 2050 2060 2070

billio

npkm/yr

Mozambique- LDVpassengertravel(All)

GCAM- 2ds GCAM- Ref MESSAGE - 2ds

MESSAGE - Ref MoMo- 2ds MoMo- 4ds

Roadmap- Ref Roadmap- Hi-eff WEPS+

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2000 2010 2020 2030 2040 2050 2060 2070

PJ/yr

France- AviationEnergy(All)

BP- Ref GCAM- 2ds GCAM- Ref MESSAGE - 2ds

MESSAGE - Ref MoMo- 2ds MoMo- 4ds Roadmap- Ref

Conclusions and Next Steps

ConclusionsFor this inter-comparison, we’ve developed a distributable R package for downscaling model results from global macro-regions to countriesThe methods rely to a great extent on merging existing datasets, with minimal user assumptions.

Future performance will improve with future availability of country-level socioeconomic scenarios and proxy data.

Next stepsThoroughly test and document the methodsImprove the country-level historical data

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