IEA Data and modelling for Transport

Transcription

1 Data and modelling for Transport Pierpaolo Cazzola International Energy Agency UN DESA Consultation with Experts on Methodologies for Assessing Transport System Efficiency and Benefits for Development New York,15 June 2009

2 data The has a mandate to collect energy statistics in OECD countries Several databases are managed by the Energy Statistics Division Information published regularly (on line, statistical publications) The collects additional information on energy supply and demand in the non-oecd also collects sectoral data Energy indicators work Information collected to feed the energy models (WEO, ETP, Mobility Model) These data on energy supply and demand, also including other statistics, physical or economic (e.g. production of steel, number of light duty vehicles, value added generated in the pulp and paper industry ) In the case of, the Mobility Model and its related databases contain most of the information collected by the Agency

3 What is the Mobility Model? It is a spreadsheet model of global, energy use, emissions, safety, and materials use analysis of a multiple set of scenarios, projections to 2050 Based on hypotheses on GDP and population growth, fuel economies, costs, travel demand, vehicle and fuel market shares World divided in 11 regions, plus a good number of specific countries (for LDVs and trucks only, being extended to other modes) USA, Canada, Mexico, Brazil, France, Germany, Italy, UK, Japan, Republic of Korea, China, India The model is suitable for handling regional and global issues It is based on the "ASIF" framework: Activity (passenger travel) * Structure (travel by mode, load factors) * Energy Intensity = Fuel use Beyond the ASIF data, it contains a large amount of information on technologies and fuel pathways full evaluation of the life cycle GHG emissions cost estimates for new light duty vehicles estimates for fuels costs and taxes section on material requirements for LDV manufacturing

4 Coverage of modes 2-3 wheelers Light duty vehicles Spark ignition (SI) ICEs Compression ignition (CI) ICEs SI hybrid ICEs (including plug-ins) CI hybrid ICEs (including plug-ins) Hydrogen ICE hybrids (including plug-ins) Fuel cell vehicles Electric vehicles Heavy and duty vehicles Passenger Minibuses Buses Freight Medium freight trucks Heavy freight trucks Rail Air Passenger Freight Water National International

5 Coverage of fuel pathways Liquid petroleum fuels Gasoline Diesel (high- and low-sulphur) Biofuels Ethanol Grain, sugar cane, advanced technologies (lignocellulose) Biodiesel Conventional (fatty acid methyl esters, FAME or biodiesel obtained from hydrogenation of vegetable oil in refineries), advanced processes (BTL, fast pyrolysis, hydrothermal upgrade) Synthetic fuels GTL and CTL CNG/LPG CNG, LPG, biogas Hydrogen from natural gas, with and without CO 2 sequestration from electricity, point of use electrolysis, with and without CO 2 sequestration from biomass gasification advanced low GHG hydrogen production

6 Mobility Model data l l A I S F Following the ASIF requirements, we focused on sales, stocks, fuel economies and travel, aiming to explain the total energy use and GHG emissions Most of the data we looked for to date concern road vehicles

7 : secondary data collector We put together information already collected by others (primary or secondary data providers) General economic and population data (OECD, World Bank, other sources) data (energy use), collected through questionnaires sent to national administrations (energy supply and demand, energy prices) More detailed data, found in publications, studies, statistical databases (some examples in next slide) We do not carry out surveys ourselves We do our best to try to assure consistency We collect information from more than one source, we assess it comparatively, we perform internal consistency checks (e.g. between vehicle stock and registrations, or stock, load factors and total activity) and we select our best estimate on this basis, also bridging across missing information with estimates Our final check is the need to be able to explain the total fuel consumption in a given country/region

8 Mobility Model, data sources Detailed data: examples of some of the sources we use: National and regional Statistical Offices (e.g. Bureau of Transportation Statistics in US, Eurostat in Europe, Australian Bureau of Statistics, Instituto Nacional de Estadísticas in Chile ) National Agencies (e.g. Ministry of Land New Zealand; ADEME in France, EPA in US ) Industrial institutions (e.g. ACEA in Europe; JAMA in Japan; ANFAVEA in Brazil; AMIA in Mexico; FCAI in Australia; ADEFA in Argentina ) Published studies from international institutions (e.g. UN ECLAC, National communications to the UNFCC, Inter American Development Bank, Asian Development bank, ECMT (now ITF), International Road Federation ) Universities, research institutes (e.g. Institute of Transportation Studies, University of California, Davis; the Scientific and Research Institute of Motor Transport for Russia; Tsinghua University, Lawrence Berkeley National Laboratory for China ) Central banks (e.g. Banco Central de Chile) Studies published by consultants (e.g. Ernst and Young for Russia and Eastern Europe) Information released by Companies (e.g. Scania and Volvo in several regions, Bosch for Eastern Europe and India, Severstal Auto for Russia ) GlobalInsight, Polk (aggregated in our databases)

9 Lessons from our experience (1) Sales The best providers are Industrial Associations, OEMs, component providers, consultants Data more readily available for LDVs, but all road modes are relatively well covered More information exist for the OECD, but it is reasonable amount of data available in non-oecd countries Stock We assess comparatively the cumulative sales across years and the data available from National and Regional Statistical Offices, National Agencies The quality of the information varies. In many cases deregistrations are poorly reported (significant discrepancies between the sum of years of sales and the stock). In countries where the import of second hand vehicle is allowed, a proper assessment is more difficult (poor data in used imports: no information on their age, likely lifetime )

10 Lessons from our experience (2) Fuel economies LDVS: the best data providers (new LDVs) are Industrial Associations and some National Agencies Stock FE: data need to be processed on the basis of the FE of new registrations (sales, used imports), and scrappage ages (limited information) Adjustments required to reflect on-road fuel economies and to bridge the test-issue (different tests in different regions) Very poor data for non-oecd countries Other vehicles: poor (if not inexistent) information Some in research publications (e.g. focused on logistics), but very scattered We estimate specific consumption of trucks and buses on the basis of other data (weights, load, technology) Vehicle travel Limited data, generally provided by National and Regional Statistical Offices and National Agencies (notably in the OECD) Some information can be extrapolated from case studies (specific cities/regions) Quality rather poor, notably for heavier vehicles

11 What would we need, ideally (1)? We would want to fill the ASIF equation, also going beyond it Vehicle perspective: Vehicle sales, stocks, used imports Travel and load factors per vehicle by mode and by travel category Information on vehicle travel differentiated by mileage over the vehicle lifetime (especially for LDVs) Fuel economies and pollutant emissions per km for the same vehicles linked to existing regulation Full characterisation of the vehicle stocks by vehicle type, including scrappage curves (need to account correctly for de-registered vehicles) scrappage age, at least for new and imported used vehicles (by mode); Vehicle ownership by vehicle class (for LDVs) and demographic Fuel costs, including details on the share of taxes/subsidies Vehicle cost and share of tax in it (by mode, and possibly by vehicle class for LDVs), would help

12 What would we need, ideally (2)? Travel perspective Distribution of load factors (passenger and freight) in all travel categories, for all modes (% of X, Y, Z passengers or tonnes on a given vehicle, including empty running for freight) Characterisation of passenger travel by distance classes and travel purpose (work, leisure ) Intermodality: characterisation of trips by mode (single mode vs. multiple mode) Information on the availability of travel options for different types of trips (especially urban?) Extension of public (urban and nonurban; road, rail), combined with quality-related parameters (speed, frequency, stops) and usage (also across time); cost/km Frequency of intercity connections by distance and mode, average time required, cost/km by mode Identification of congestion levels (e.g. by illustrating travel with indicators like the share of travel at different average speed on a given mode)

13 What would we need, ideally (3)? Infrastructure perspective Extension of the infrastructure (km of roads, railways, bus lanes, bike lanes, walkways) by capacity classes (e.g. km of motorways and roads by number of lanes, conventional railway lines and high speed) Utilization rates of infrastructure by capacity class (road, rail, airports) Share of roads subject to pricing in different travel areas (urban, non-urban) Average cost/km Availability of parking areas (urban) Share of parking for which it is necessary to pay (urban), average cost of parking Average cost of construction the infrastructure by type Intercity: roads and railways, differentiating by capacity class e.g. amongst conventional roads, highways, conventional railways, high speed railway lines) Urban: bus systems, BRTs, light rail and metro

14 Standardisation Working on international data, we feel the need for a standardisation of vehicle classes across modes Motorbikes and scooters, passenger and freight three wheelers, passenger cars, passenger light trucks, freight light trucks (light commercial vehicles), multipurpose light trucks, at least two classes of heavy trucks, at least two classes of buses (by size), at least two (maybe three) aircraft classes Standardisation needs go beyond the vehicle level Urban, sub-urban, intercity bus services and rail services (different definitions for different sizes of urban areas?). High speed, high capacity for rail. Standardised data lead to comparable statistics, but require additional efforts Need to build on work already done (e.g. ITF glossary), enriching it International organisations can play a key role in this respect

15 What can we get? Better sales, stock, scrappage, travel data for LDVs We can build on what exists (especially for sales), combining it with complementary information taken from traffic observation surveys, especially for stock-related data (can collect vehicle location, type, name plate, age, odometer readings, driverstated fuel efficiency) or travel diary approach Representative case studies for detailed travel information Traffic observation survey (odometer reading, crossed with vehicle age), in combination with national household travel survey (annual travel by purpose/mode covering all types of travel; explanatory data on location, income, car ownership, travel choices available) Relatively aggregated information on infrastructure Length of road network, length of rail network More coherent data across different global regions

16 How to approach this? Come up with a plan that is affordable and can be carried out by countries in a reasonable time frame, with reasonable intermittency Need to identify ways to fund projects that get integrated in existing work programs Common methodology that countries can use around the world International organisations best suited for this, but there is a need to leverage on what exists and to work with primary data providers Need for an initial proposal Need for workshops, meetings to share experiences and discuss methodology, gaining consensus Need for credibility (do the homework ), especially for the project leader Consistency in timing and publication of data and analysis International coordination and systematic, long-term funding support Something to discuss!