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Profiling the Vehicle Parc in a Complex Region where Data is Sparse – The Case of Nigeria

Profiling the Vehicle Parc in a Complex Region where Data is Sparse – The Case of Nigeria. Adrian Stone IEW 20 th June 2012. Research Question. Commissioned by IRENA “Assess the energy services in Nigeria by sector” Data sources – studies & sources in the public domain Transport sector

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Profiling the Vehicle Parc in a Complex Region where Data is Sparse – The Case of Nigeria

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  1. Profiling the Vehicle Parc in a Complex Region where Data is Sparse – The Case of Nigeria Adrian Stone IEW 20th June 2012

  2. Research Question Commissioned by IRENA “Assess the energy services in Nigeria by sector” Data sources – studies & sources in the public domain Transport sector Quantify the technologies Collate activity data Such were the data problems that profiling the vehicle parc – quantifying the vehicle technologies – was a study on its own Nigerian Vehicle Parc

  3. What’s Happening with Vehicle Parcs Elsewhere? Dynamic -The motorcycle phenomenon in Kenya Nigerian Vehicle Parc

  4. Nigeria – A Snapshot 150 million people Federation of 37 states 3rd largest GDP in Africa 26th highest GDP/capita in Africa World’s 11th largest oil producer. Largest in Africa. Producer of LNG for export & piped domestic NG Economically dominant megacity - Lagos Very large component of own generation of electricity – residential, commercial & industrial Largest vehicle sales in Africa in 2007 Nigerian Vehicle Parc

  5. Nigeria – A Snapshot • A rapidly growing vehicle parc and motorisation rate (vehicles/capita) • History of liquid fuel subsidies. Up to half landed gasoline cost. • An extreme consumption ratio in favour of gasoline over diesel (nearly 6 fold in 2008 & 8 fold in 2009?). • Dominance of road freight contrasting deteriorating rail infrastructure. Revival - new locomotive purchases. • Stress on the road infrastructure resulting in congestion and low average vehicle speeds with probable adverse effects on device efficiencies. • Robust growth in air traffic both passenger and freight. Nigerian Vehicle Parc

  6. The Aggregate Data Picture * Passenger Cars Only ** (Jacobs & Aeron-Thomas, December 2000) # (The World Bank, 2010) ## (Gwilliam, Foster, Archondo-Callao, Briceño-Garmendia, Nogales, & Sethi, June 2008) + (Mbawike, 2007) Nigerian Vehicle Parc

  7. The Data Situation – Reg Dbase & Supply Side • National Bureau of Statistics - partial sample new registrations (2004 – 2007) for 25 of 37 states. One third blank. • Limited disaggregation – Only Lagos has Light Trucks, NBS just trucks, cars & MC. • None of these yield a parc size Nigerian National Petroleum Corporation (NNPC) – gasoline & diesel sales. Lagos Bureau of Statistics - new registrations and renewals 1994 – 2009. Most complete source but still partial. Bedfords? Federal Road Safety Corps (FRSC) Partial sample 1 million registrations by state 2004 / 2005. Nigerian Vehicle Parc

  8. The Data Situation – Supply Side Problems Effect of Diesel Shortages? Nigerian Vehicle Parc

  9. Motorcycle Regional Distribution FRSC data shows motorcycle fraction of the parc to be highly skewed by state Nigerian Vehicle Parc

  10. State Motorcycle Fractions – Are these true? The FRSC data is just a sample. Are the highly skewed state motorcycle fractions true? The partial state data correlates well so these data seem more certain than many of our other clues Nigerian Vehicle Parc

  11. Data – Vehicle Category Breakdown Nigerian Vehicle Parc

  12. Data – Modal Share of pkm for Lagos + Other modes sum to 100% # (International Association of Public Transport & African Association of Public Transport, 2010) - This is the share of person trips, not passenger.km * (Tsige, 2009) Nigerian Vehicle Parc

  13. Vehicle Activity Data 1 (International Association of Public Transport & African Association of Public Transport, 2010) 2 (Kumar & Barrett, 2008) 3 (Teravaninthorn & Raballand, 2008) * Average of 15 and 30 seaters Nigerian Vehicle Parc

  14. 1st Pass at the Problem We still don’t even know how many vehicles there are in Nigeria. 1st Idea We have state & national population and GDP data Known that motorisation driven by GDP/Capita Usual model - Gompertz Curve Nigerian Vehicle Parc

  15. GDP/Capita & Motorcycle Motorisation Nigeria in this region (Kenworthy & Townsend, 2002) analysis of motorisation in a number of global cities arranged into clusters according to broad geographical region. Included motorcycle fraction Nigerian Vehicle Parc

  16. GDP/Capita & Motorcycle Motorisation Considered State by state calc using Lagos as calibration BUT state Motorcycle fraction correlates negatively with GDP/Capita. Is this a useful driver where the motorcycle prevails? Besides population of Lagos, alternately estimated in 2006 as 17 552 942 by the Lagos State Government and as 9 113 605 by the National Census Nigerian Vehicle Parc

  17. Option 2 - Simple Linear Fuel Balancing Model • Lagos used as a calibration state. (The population count still needed to be inferred from the data & literature however). • State gasoline and diesel sales used to estimate the size of the parcs in the other states on a proportional basis. • Linear model used to take account of the large fluctuations in motorcycle fraction in each state. The size of a state vehicle parc was therefore dependent on an assumption of the ratio of motorcycle fuel consumption to that of other gasoline fuelled vehicles. • The diesel vehicle fraction in a state was scaled from an assumed 5% of the parc in Lagos based on the diesel consumption of that state relative to that of Lagos State. Nigerian Vehicle Parc

  18. Step 1- Derive Reasonable Utilisation Assumptions for Lagos LBS Data Cleaning Assumed scrapping factors (See full paper) Less Estimated Gasoline & Diesel Use for Off-grid Electricity Generation Calibrate Lagos Total Vehicle Count Validate – Balance Lagos Gasoline & Diesel Sales Fuel Economy VKT Occupancy Assumptions Lagos Vehicle Type Breakdown Validate – Observed Lagos Mode Share Nigerian Vehicle Parc

  19. Step 1- Derive Reasonable Utilisation Assumptions for Lagos Nigerian Vehicle Parc

  20. Step 2 – Derive Model to Take Account of Knowns Fuel Economy VKT Occupancy Assumptions Lagos Total Vehicle Count State Gasoline & Diesel Sales State Motorcycle Fractions MODEL Total Vehicle Count per State Nigerian Vehicle Parc

  21. Step 2 – Derive Model to Take Account of Knowns The gasoline consumption of a state i can be modelled as follows: …Eq. 1 Where • MC = motorcycle • NMGV = non-motorcycle gasoline vehicle (Pass. Cars, LCV & Minibus) • FE = Fuel Consumed per vehicle per annum • Bi = Count of MC in state i • Ci = Count of NMGV in state i • R = / Nigerian Vehicle Parc

  22. Step 2 – Derive Model to Take Account of Knowns If we assume similar energy intensities between states this yields Equation 2: Where L denotes our calibration state Lagos letting µi = and if, θi = Proportion of motorcycles in total parc of state I Di = Count of Diesel Vehicles in state i Then Nigerian Vehicle Parc

  23. Step 2 – Derive Model to Take Account of Knowns Letting, A = And substituting in Eq. 2 yields Equation 3: The count of gasoline vehicles that are not motorcycles as a function of our most certain (approximately known) variables and the count of diesel vehicles in the state. Nigerian Vehicle Parc

  24. Step 2 – Derive Model to Take Account of Knowns The count of diesel vehicles Di was assumed simply fuel consumption proportional, relative to that in Lagos, our calibration state, as follows: …Equation 4 It follows that once we have calculated Ciand Di for state i that the number of motorcycles is: …Equation 5 And the population is: Nigerian Vehicle Parc

  25. Results – Base Year 2009 Vehicle Parc = 7.1 - 9.2 million vehicles of which 50% are motorcycles, 45% passenger cars, LCVs and minibuses and 5% diesel vehicles (midi-buses, large buses and trucks). Nigerian Vehicle Parc

  26. A post-Paper Validation Reasonable activity levels for the gasoline fuelled modes were also attained with the lower estimate for the vehicle parc of 7,114,000 in a MAED model compiled for this project. Nigerian Vehicle Parc

  27. Caveats • Gasoline supply side figures are likely to be over-estimated in Nigeria because of the temptation to declare false imports posed by the long-standing subsidy. • The government is trying to unwind the subsidy and this error will only become evident then. • The diesel under-reporting appears much more severe. The error to the number of vehicles is small in % terms but these heavy vehicles are large consumers. • Combining a MAED type model for transport and a suppressed electricity demand model indicated that actual diesel supplied could be 190 – 280% higher than that reported in official statistics. • Lagos may be very different to rest of country in terms of VKT and FE. Nigerian Vehicle Parc

  28. Conclusions • Profiling the Base Year energy services for complex economies with sparse data requires the modeller to mediate the supply side data and whatever utilisation data exists for the services. • This requires validation of both data sets and comparison with similar markets. • Piecing together clues can improve best guesses but the process is very inefficient • Getting quality data into the public domain remains a challenge for Africa Nigerian Vehicle Parc

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