Figure 58. Structure of the buildings sector model

Starting from socio-economic assumptions, the buildings sector model determines demand drivers and related useful energy demands, which are then applied across buildings end uses and technology choices to calculate final energy consumption across the 35 model countries and regions.

Modelling of the transport sector in the MoMo

Overview

The MoMo is a techno-economic database spreadsheet and simulation model that enables detailed projections of transport activity, vehicle activity, energy demand, and well-to-wheel CO2 and pollutant emissions according to user-defined policy scenarios to 2060.

It comprises:

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 2017 (or 1990 to 2017 for certain countries)

a simulation model in five-year time steps, for creating scenarios to 2060 based on “whatif” analysis and backcasting

disaggregated urban versus non-urban vehicle stock, activity, energy use and emissions

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, 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.

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Associated fuel supply options include: 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, such as 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).

The 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 the resultant CO2 emissions.

To ease the manipulation and implementation of the modelling process, the MoMo is split into modules that can be updated and elaborated upon independently. Figure 59 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 detailed bottom-up “what-if” modelling, especially for passenger light-duty vehicles (PLDVs) and trucks (IEA, 2018).

Figure 59. Structure of the MoMo

Notes: PPP = purchasing power parity, km = kilometres, LCV = light commercial vehicle, MFT = medium freight truck, GIS = geographic information system, O&M = operation and maintenance.

The MoMo covers all transport modes and includes modules on local air pollutants and the cost of fuels, vehicles and infrastructure, as well as analysis of the material needs for new vehicles.

Data sources

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.

MoMo modellers have collected historical data series from a variety of public and proprietary data sources for more than a decade. National data are gathered primarily from the following

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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 nongovernmental 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. from 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).

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 Activity = Share x Intensity x Fuel (ASIF) identity (Schipper, Marie-Lilliu and Gorham, 2000):

where F is the total fuel use (megajoules [MJ] per year); A is the vehicle activity (vehicle kilometres [vkm] per year]); I is the energy intensity (MJ/vkm); S is the structure (shares of vehicle activity [%]); and i is an index of vehicle modes and classes (MoMo vehicles belong 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 (vehicles), mileage (km/year) and fuel economy (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 emissions 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 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 world wide. 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. twoand 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. The model supports a comparison of marginal costs of technologies and aggregates to total cost across all modes and regions, for each scenario.

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