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Adrian Stone IEW 20 th June 2012. Research Question Commissioned by IRENA “Assess the energy services in Nigeria by sector” Data sources – studies & sources.

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Presentation on theme: "Adrian Stone IEW 20 th June 2012. Research Question Commissioned by IRENA “Assess the energy services in Nigeria by sector” Data sources – studies & sources."— Presentation transcript:

1 Adrian Stone IEW 20 th 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 2 Nigerian Vehicle Parc

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

4 Nigeria – A Snapshot 150 million people Federation of 37 states 3 rd largest GDP in Africa 26 th highest GDP/capita in Africa World’s 11 th 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 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. 5 Nigerian Vehicle Parc

6 The Aggregate Data Picture Source Jacobs et al ** # AICD ## FRSC + Year Population (mil.) Motorisation Pass Cars Motorisation All Vehicles >48 Vehicle Parc Count (mil.) * 2.3* >7.0 6 Nigerian Vehicle Parc *Passenger Cars Only **(Jacobs & Aeron-Thomas, December 2000) #(The World Bank, 2010) ##(Gwilliam, Foster, Archondo-Callao, Briceño-Garmendia, Nogales, & Sethi, June 2008) +(Mbawike, 2007)

7 The Data Situation – Reg Dbase & Supply Side Nigerian National Petroleum Corporation (NNPC) – gasoline & diesel sales. Lagos Bureau of Statistics - new registrations and renewals 1994 – Most complete source but still partial. Bedfords? Federal Road Safety Corps (FRSC) Partial sample 1 million registrations by state 2004 / Nigerian Vehicle Parc 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

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

9 Motorcycle Regional Distribution FRSC data shows motorcycle fraction of the parc to be highly skewed by state 9 Nigerian Vehicle Parc State Motorcycle FractionState Motorcycle FractionState Motorcycle Fraction Bauchi 91% Kebbi 71% Osun 45% Zamfara 91% Borno 68% Abia 42% Akwa Ibom 86% Niger 64% Ogun 35% Jigawa 86% Kogi 64% Delta 28% Nassarawa 82% Kano 63% Kaduna 24% Anambra 81% Taraba 63% Edo 21% Gombe 80% Kwara 62% Bayelsa 17% Benue 80% Ondo 60% Oyo 16% Katsina 78% Ebonyi 56% Lagos 15% Plateau 77% Imo 55% Rivers 14% Cross River 76% Ekiti 53% FCT, Abuja14% Yobe 74% Enugu 53% Sokoto 74% Adamawa 52%

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 10 Nigerian Vehicle Parc Statistic Motorcycle Proportion FRSC Motorcycle Proportion Nig. Stats Bureau Maximum91%86% Minimum15%10% Average of States57%58% Standard Deviation24%22% Pearson Correlation Coefficient (R) 60% Correlation Coefficient (R 2 )36%

11 Data – Vehicle Category Breakdown 11 Nigerian Vehicle Parc LagosNigeriaOther Countries Vehicle Type LBS ( ) * FRSC (2004/ 2005) ** NBS ( ) # FRSC (1996) + FRSC (2004/ 2005) ** South Africa (2008) ## EU (2008) ++ Dehli, India (2007) π Pass. Car70.82% 42.5% 63.25%71%31.1% LCV/Other2.12% 22.74%11.6%2.9% All Trucks3.45% 2.4%4.10%3.81%10.8% Rigid Trucks 3.19% 3.70% 9.8% Tanker 0.02% 2.2% Tractor 0.03% 0.41% 1.0% Tipper 0.20% All Buses8.55% 8.6% 3.87%0.40%0.9% Minibus 8.28% 3.36% Large Bus 0.27% 0.51%0.40% Motorcycle14.98%17%44.4% 53%3.89%6%65.1%

12 Data – Modal Share of pkm for Lagos 12 Nigerian Vehicle Parc Source ModeUITP # Tsige* Average - Motorised Only Bicycle & Walking 40%High + - Private Car16%5%15.8% Private Taxi0%5%2.5% Motorcycle5% 6.7% Large Public Bus 2%10%6.7% Informal Mini & Midi Buses 37%75%68.3% + Other modes sum to 100% # (International Association of Public Transport & African Association of Public Transport, 2010) - This is the share of person trips, not * (Tsige, 2009)

13 Vehicle Activity Data 13 Nigerian Vehicle Parc SourceVehicle Type VKT (km/yr) Average Occupancy (pass. / vehicle) Average Speed (km/h) Region UITP 1 Pass. Car Lagos UITP 1 Midi & Minibus *23Lagos AICD 1 2 Midi & Minibus36 500Lagos UITP 1 Large Bus Lagos AICD 1 2 Large Bus65 700Lagos AICD 2 3 Trucks78 000Nigeria 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

14 1 st Pass at the Problem 14 Nigerian Vehicle Parc

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

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 by the Lagos State Government and as by the National Census 16 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. 17 Nigerian Vehicle Parc

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

19 Step 1- Derive Reasonable Utilisation Assumptions for Lagos 19 Nigerian Vehicle Parc Vehicle Type Proportion of Parc (%) VKT (000 km) - 1 mil. veh. VKT (000 km) mil. veh. Fuel Cons. (km/l) Assumed Occupancy Modal share of (%) Total Fuel (l) MC 15% % NMGV 80% Pass. Car 65% % Minibus 6% % LCV 9% Model Lagos Gasoline Cons.: Est. Lagos Gasoline Consumption (NNPC Data ): Assumed Generator Usage: 10% Est. Lagos Road Sector Gasoline Usage (NNPC Data ):

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

21 Step 2 – Derive Model to Take Account of Knowns 21 Nigerian Vehicle Parc

22 Step 2 – Derive Model to Take Account of Knowns 22 Nigerian Vehicle Parc

23 Step 2 – Derive Model to Take Account of Knowns 23 Nigerian Vehicle Parc

24 Step 2 – Derive Model to Take Account of Knowns 24 Nigerian Vehicle Parc

25 Results – Base Year Nigerian Vehicle Parc Vehicle Parc = million vehicles of which 50% are motorcycles, 45% passenger cars, LCVs and minibuses and 5% diesel vehicles (midi-buses, large buses and trucks). Vehicle Type Lower Estimate Upper Estimate Average Pass. Car (gasoline)28%38%33.1% LCV (gasoline)2%8%5.2% All Trucks2.4%3.9%3.2% Rigid Trucks2.2%3.5%2.9% Artic. Trucks0.2%0.4%0.3% All Buses6%11%8.6% Minibus (gasoline)5%8%6.7% Midi-bus (diesel)0.7%1.4%1.1% Large Bus0.5%1%0.8% Motorcycle40%60%50.0%

26 A post-Paper Validation 26 Nigerian Vehicle Parc Jacobs et al ** # AICD ## FRSC Population (mil.) Motorisation passenger cars Motorisation all vehicles >48 Vehicle parc count (mil.) *2.3* >7.0 Notes: *Passenger cars only **Jacobs & Aeron-Thomas (2000) #World Bank (2010) ##(Gwilliam, 2011) +Mbawike (2007) 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.

27 Caveats 27 Nigerian Vehicle Parc 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.

28 Conclusions 28 Nigerian Vehicle Parc 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

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