Modelling of the transport sector in the Mobility Model (MoMo)
The Mobility Model (MoMo) is a technical-economic database spreadsheet and simulation model that enables detailed projections of transport activity, vehicle activity, energy demand, and well-to-wheel GHG and pollutant emissions according to user-defined policy scenarios to 2050.
- 27 countries and regions, which are aggregated into 4 OECD regional clusters and 11 groups of non-OECD economies
- historic data from 1975 to 2013 (or 1990 to 2013 for certain countries)
- simulation model in five-year time steps, for building scenarios to 2050 based on "what if" analysis and backcasting
- disaggregated urban versus non-urban vehicle stock, activity, energy use and emissions (for methodological details, see Annex F)
- all major motorised transport modes (road, rail, shipping and air), providing passenger and freight services
- a wide range of powertrain technologies (internal combusion engines, including gasoline, diesel, and compressed and liquefied natural gas, as well as hybrid electric vehicles [including plug-in hybrid electrics - PHEVs, and battery-electric vehicles - BEVs], and fuel-cell electric vehicles [FCEV])
- associated fuel suppply options (petroleum gasoline and diesel, biofuels [ethanol and biodiesel via various production pathways] and synthetic fuel alternatives to liquid fuels [coal-to-liquid - CTL and gas-to-liquid - GTL], gaseous fuels including natural gas [compressed natural gas - CNG and liquefied petroleum gas - LPG] and hydrogen via various production pathways, and electricity [with emissions according to the average national generation mix as modelled by the ETP TIMES model in the relevant scenario]).
MoMo further enables estimation of scenario-based costs of vehicles, fuels and transport infrastructure, as well as the primary material inputs required for the construction of vehicles, related energy needs and resultant GHG emissions.
To ease the manipulation and implementation of the modelling process, MoMo is split into modules that can be updated independently. Figure A.5 shows how the modules interact with one another. By integrating assumptions on technology availability and cost in the future, the model reveals, for example, how costs could drop if technologies were deployed at a commercial scale and allows fairly detailed bottom-up "what-if" modelling, especially for passenger light-duty vehicles and trucks (Fulton et al., 2009).
Figure A.5 MoMo structure
Note: LDV - light-duty vehicle.
The MoMo modelling framework relies upon compiling and combining detailed data from various sources on vehicles in each of the countries/regions to estimate aggregate energy consumption, emissions and other energy-relevant metrics at the country/regional level.
Historic data series have been collected by MoMo modellers from a wide variety of public and proprietary data sources for more than a decade. National data are gathered primarily from the following organisations: (1) national and international public institutions (e.g. the World Bank, the Asian Development Bank and Eurostat); (2) national government ministries (e.g. departments of energy and transport, and statistical bureaus); (3) federations, associations, and non-governmental organisations (e.g. JAMA, KAMA and Naamsa); (4) public research institutions (e.g. peer-reviewed papers and reports from universities and national laboratories); (5) private research institutions (e.g. ICCT); and (6) private business and consultancies (e.g. POLK, Segment Y, and other major automotive market research and analysis organisations, in addition to major energy companies and automobile manufacturers themselves). Full details on data sources on a national or regional basis are documented in the regional data files of MoMo.
Calibration of historical data with energy balances
The framework of estimating average and aggregate energy consumption for a given vehicle class i can be neatly summarised by the ASIF identity (Schipper, 2000):
where: F = total fuel use [MJ/year]; A = vehicle activity [vkm/year]; I = energy intensity [MJ/vkm]; S = structure (shares of vehicle activity) [%]; and i is an index of vehicle modes and classes – MoMo models vehicles belonging to several modes. Vehicle activity can also be expressed as the product of vehicle stock [vehicles] and mileage [km/year]. The energy used by each mode and vehicle class in a given year [MJ/year] can, therefore, be calculated as the product of three main variables: vehicle stock (S) [vehicles], mileage (M) [km/year] and fuel economy (FE) [MJ/vkm].
To ensure a consistent modelling approach is adopted across the modes, energy use is estimated based on stocks (via scrappage functions), utilisation (travel per vehicle), consumption (energy use per vehicle, i.e. fuel economy) and emissions (via fuel emission factors for CO2 and pollutants on a vehicle and well-to-wheel basis) for all modes. Final energy consumption, as estimated by the “bottom-up” approach described above, is then validated against and calibrated as necessary to the IEA energy balances (IEA, 2015a).
Vehicle platform, components and technology costs
Detailed cost modelling for passenger light-duty vehicles (PLDVs) accounts for initial (base year) costs, asymptotic (i.e. fully learned-out) costs and an experience parameter that defines the shape of cost reductions. These three parameters define learning functions that are based on the number of cumulative units produced worldwide. Cost functions define various vehicle configurations, including vehicle component efficiency upgrades (e.g. improved tyres, A/C controls, etc.), material substitution and vehicle downsizing, conventional spark- and compression ignition engine improvements, conventional and plug-in hybrid powertrain configurations, batteries, electric motors and fuel cells. These configurations are added to a basic glider cost. The ratios of differences in vehicle technologies deployed in PLDVs are extrapolated to other road vehicle types (i.e. two- and three-wheelers and freight trucks).
The primary drivers of technological change in transport are assumptions on the cost evolution of the technology, and the policy framework incentivising adoption of the technology. Oil prices and the set of policies assumed can significantly alter technology penetration patterns. For each scenario, the model supports a comparison of marginal costs of technologies and aggregates to total cost across all modes and regions.
Infrastructure and fuel costs
As outlined in Box 1.4 of Chapter 1 of ETP 2016, MoMo estimates future (2010-50) infrastructure costs according to scenario-based projections on modal activity and fuel use. Infrastructure cost estimates include capital costs, operations and maintenance, and reconstruction costs – split by geography according to the location of the investments into urban and non-urban regions. Fuel costs are also estimated based on scenario-specific projections of urban and non-urban consumption, and include all fuel types (fossil-derived fuels, biofuels, electricity and hydrogen).
Key elasticities have been included in MoMo since 2012. Price and income elasticities of fuel demand, for light-duty road (passenger) activity as well as road freight, based upon representative “consensus” literature values, are used to model vehicle activity and fuel consumption responses to changes in fuel prices – which are themselves driven by projections and policy scenarios (i.e. GHG or fuel taxes). Elasticities also enable vehicle ownership to vary according to fuel prices and income, as proxied by GDP per capita.
The 2015-16 updates for ETP 2016 include an expanded treatment of the above elasticities to encompass the urban / non-urban split, and to include the potential for municipal level policies to reduce transport energy use.
Further details on the newly added national and municipal policies, the elasticities that are used to model transport activity, stock, and mode share responses to these policies, and the demand generation module can be found in Annex F.
Fulton, L., P. Cazzola and F. Cuenot (2009), "IEA Mobility Model (MoMo) and its use in the ETP 2008", Energy Policy, Vol. 37, No. 10, Elsevier, Amsterdam, pp. 3758-3768.
IEA (2015), "World Energy Balances", IEA World Energy Statistics and Balances 2015, www.iea.org/statistics/, accessed 4 February 2016.
Schipper, L., C. Marie-Lilliu and R. Gorham (2000), Flexing the Link between Transport and Greenhouse Gas Emissions: a Path for the World Bank, OECD/IEA, Paris.