Modelling of the transport sector in the Mobility Model
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 greenhouse gas (GHG) and pollutant emissions according to user-defined policy scenarios to 2060.
Figure A.6 - Structure of MoMo
Key Point: MoMo covers all transport modes and includes modules on local air pollutants and on the cost of fuels, vehicles and infrastructure, as well as analysis of the material needs for new vehicles.
• 27 countries and regions, which are aggregated into four Organisation for Economic Co operation and Development (OECD) regional clusters and 11 groups of non-OECD economies
• Historical data from 1975 to 2014 (or 1990 to 2014 for certain countries)
• A simulation model in five-year time steps, for creating scenarios to 2060 based on "what-if" analysis and backcasting
• Disaggregated urban versus non-urban vehicle stock, activity, energy use and emissions (for methodological details, see ETP 2016 Annex F)
• All major motorised transport modes (road, rail, shipping and air), providing passenger and freight services
• A wide range of powertrain technologies (internal combustion engines, including gasoline, diesel, and compressed natural gas (CNG) and LNG, as well as hybrid electric and electric vehicles [including plug-in hybrid electric and battery-electric vehicles], and fuel-cell electric vehicles)
• Associated fuel supply options (gasoline and diesel, biofuels [ethanol and biodiesel via various production pathways] and synthetic alternatives to liquid fuels [coal-to-liquid and gas-to-liquid], gaseous fuels including natural gas [CNG and liquefied petroleum gas] 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 and elaborated upon independently. Figure A.6 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 (PLDVs) and trucks (Fulton, Cazzola and Cuenot, 2009
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.
Historical 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. Japan Automobile Manufacturers Association, Korea Automobile Manufacturers Association and National Association of Automobile Manufacturers of South Africa); 4) public research institutions (e.g. peer-reviewed papers and reports from universities and national laboratories); 5) private research institutions (e.g. International Council on Clean Transportation); and 6) private business and consultancies (e.g. IHS Automotive/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 for estimating average and aggregate energy consumption for a given vehicle class i can be neatly summarised by the ASIF identity (Schipper, Marie-Lilliu and Gorham, 2000):
where: F = total fuel use (megajoules [MJ] per year]; A = vehicle activity (vehicle kilometres [vkm] per 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 (kilometre [km] per year). The energy used by each mode and vehicle class in a given year (MJ per 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, 2016c).
Vehicle platform, components and technology costs
Detailed cost modelling for 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 or air-conditioning controls), 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. 2- and 3-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
MoMo estimates future (2017-60) 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 into urban and non-urban regions according to the location of the investments. 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 from 2012. Price and income elasticities of fuel demand, for light-duty (passenger) road 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. ETP 2016 included updates for 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.5