Adrian Stone IEW 20 th June 2012. Research Question Commissioned by IRENA “Assess the energy...

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

Adrian StoneIEW

20th June 2012

Nigerian Vehicle Parc 2

Research QuestionCommissioned by IRENA

“Assess the energy services in Nigeria by sector”

Data sources – studies & sources in the public domain

Transport sectorQuantify the technologiesCollate activity dataSuch were the data problems that profiling the vehicle parc –

quantifying the vehicle technologies – was a study on its own


What’s Happening with Vehicle Parcs Elsewhere?

Dynamic -The motorcycle phenomenon in Kenya

0

20000

40000

60000

80000

100000

120000

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Vehi

cle

Coun

t

Year

Passenger Cars

Motorcycles


Nigeria – A Snapshot150 million peopleFederation of 37 states3rd largest GDP in Africa26th highest GDP/capita in AfricaWorld’s 11th largest oil producer. Largest in Africa.

Producer of LNG for export & piped domestic NG Economically dominant megacity - LagosVery large component of own generation of electricity –

residential, commercial & industrialLargest vehicle sales in Africa in 2007


Nigeria – A SnapshotA 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.


The Aggregate Data Picture

Source

Jacobs et al** data.worldbank.org#

AICD##

FRSC+

Year 1995 2003 2004 2007 2006 2007

Population (mil.)

65.5

133.1

136.4

147.0

144.7

147.0

Motorisation Pass

Cars

16

17

31

Motorisation All

Vehicles

21

31

24 >48

Vehicle Parc Count

(mil.)

1.4

2.1*

2.3*

4.6

3.5

>7.0

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


The Data Situation – Reg Dbase & Supply Side

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.

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


The Data Situation – Supply Side Problems

0

1000

2000

3000

4000

5000

6000

7000

8000

1970 1975 1980 1985 1990 1995 2000 2005 2010

Road

Sec

tor C

onsu

mpti

on (k

toe)

Year

Diesel

Gasoline

1998

Petrol became progressively cheaper relative to Diesel from 2003

Fuel Subsidy Era

Effect of Diesel Shortages?

9

Motorcycle Regional Distribution

FRSC data shows motorcycle fraction of the parc to be highly skewed by state

Nigerian Vehicle Parc

StateMotorcycle

Fraction StateMotorcycle

Fraction StateMotorcycle

FractionBauchi 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, Abuja 14%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


StatisticMotorcycle

Proportion FRSC

Motorcycle Proportion Nig.

Stats Bureau

Maximum 91% 86%

Minimum 15% 10%

Average of States 57% 58%

Standard Deviation 24% 22%

Pearson Correlation Coefficient (R)

60%

Correlation Coefficient (R 2)

36%

11

Data – Vehicle Category Breakdown


Lagos Nigeria Other Countries

Vehicle Type

LBS (1994

- 2009)*

FRSC (2004

/ 2005)

**

NBS (2004 - 2007)#

FRSC (1996)

+

FRSC (2004/ 2005)*

*

South Africa

(2008)#

#

EU (2008)+

+

Dehli, India

(2007)π

Pass. Car 70.82%

42.5% 63.25% 71% 31.1%

LCV/Other

2.12% 22.74% 11.6% 2.9%

All Trucks

3.45% 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 Buses 8.55% 8.6% 3.87% 0.40% 0.9%Minibus

8.28% 3.36%

Large Bus

0.27%

0.51% 0.40%

Motorcycle

14.98%

17% 44.4% 53% 3.89% 6% 65.1%

12

Data – Modal Share of pkm for Lagos


SourceMode UITP# Tsige* Average -

Motorised Only

Bicycle & Walking

40% High+ -

Private Car 16% 5% 15.8%Private Taxi 0% 5% 2.5%Motorcycle 5% 5% 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 passenger.km* (Tsige, 2009)

13

Vehicle Activity Data


Source Vehicle Type

VKT (km/yr)

Average Occupan

cy (pass. / vehicle)

Average

Speed (km/h)

Region

UITP1 Pass. Car 4 260 1.8 23 LagosUITP1 Midi &

Minibus72 000 18* 23 Lagos

AICD 12

Midi & Minibus

36 500 Lagos

UITP1 Large Bus 73 920 43 23 LagosAICD 12

Large Bus 65 700 Lagos

AICD 23

Trucks 78 000 Nigeria1 (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


1st Pass at the ProblemWe still don’t even know how many vehicles there are in Nigeria.1st IdeaWe have state & national population and GDP dataKnown that motorisation driven by GDP/CapitaUsual model - Gompertz Curve


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

R² = 0.55

0

100

200

300

400

500

600

700

0 5000 10000 15000 20000 25000 30000 35000

Vehi

cles /

1000

perso

ns

GDP/Capita

Car & Motorcycle Motorisation vs GDP/capita

R2 = 0.72

R² = 0.00

0

20

40

60

80

100

120

140

0 5000 10000 15000 20000 25000 30000 35000

Vehi

cles /

1000

perso

ns

GDP/Capita

Motorcycle Motorisation vs GDP/capita

Nigeria in this region


GDP/Capita & Motorcycle MotorisationConsidered State by state calc using Lagos as calibrationBUT 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

R² = 0.4819

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 1 000 2 000 3 000 4 000 5 000

Prop

ortio

n of

Mot

orcy

cles

2007 GDP (PPP)/Capita (US$)


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.


Step 1- Derive Reasonable Utilisation Assumptions for Lagos

Fuel EconomyVKT

OccupancyAssumptions

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 CleaningAssumed scrapping

factors(See full paper)


Step 1- Derive Reasonable Utilisation Assumptions for LagosVehicle

TypeProportion of Parc (%)

VKT (000 km) - 1 mil.

veh.

VKT (000 km) - 1.3 mil. veh.

Fuel Cons. (km/l)

Assumed Occupancy

Modal share of Pass.km (%)

Total Fuel (l)

MC 15% 14.0 10.8 34.3 1.4 5% 61 192 049

NMGV 80% 19.2 14.8 8.7 1 766 037 834

Pass. Car 65% 14.0 10.8 12.6 1.8 26% 723 178 808

Minibus 6% 59.5 45.7 5.2 12 69% 685 243 132

LCV 9% 30.0 23.1 7.6 357 615 898

Model Lagos Gasoline Cons.: 1 827 229 887

Est. Lagos Gasoline Consumption (NNPC Data - 2009): 2 030 255 430

Assumed Generator Usage: 10%

Est. Lagos Road Sector Gasoline Usage (NNPC Data - 2009): 1 827 229 887


Step 2 – Derive Model to Take Account of Knowns

State Gasoline & Diesel Sales

State Motorcycle Fractions

Fuel EconomyVKT

OccupancyAssumptions

Lagos Total Vehicle Count

MODEL

Total Vehicle Count per

State



The gasoline consumption of a state i can be modelled as follows:

…Eq. 1

Where

MC = motorcycleNMGV = non-motorcycle gasoline vehicle (Pass.

Cars, LCV & Minibus)FE = Fuel Consumed per vehicle per annumBi = Count of MC in state iCi = Count of NMGV in state iR = /



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



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.



The count of diesel vehicles Di was assumed simply fuel consumption proportional, relative to that in Lagos, our calibration state, as follows:

…Equation 4It follows that once we have calculated Ci and Di for state i that the number of motorcycles is:

…Equation 5

And the population is:


Results – Base Year 2009Vehicle 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).

Vehicle Type Lower Estimate

Upper Estimate

Average

Pass. Car (gasoline) 28% 38% 33.1%LCV (gasoline) 2% 8% 5.2%All Trucks 2.4% 3.9% 3.2% Rigid Trucks 2.2% 3.5% 2.9% Artic. Trucks 0.2% 0.4% 0.3%All Buses 6% 11% 8.6% Minibus (gasoline) 5% 8% 6.7% Midi-bus (diesel) 0.7% 1.4% 1.1% Large Bus 0.5% 1% 0.8%Motorcycle 40% 60% 50.0%


A post-Paper Validation

Jacobs et al**

data.worldbank.org#

AICD#

#

FRSC+

1995 2003 2004 2007 2010 2007Population (mil.) 65.5 133.1 136.4 147.0 151.3 Motorisation passenger cars

16 17 31

Motorisation all vehicles

21 31 52 >48

Vehicle parc count (mil.)

1.4 2.1* 2.3* 4.6 7.828 >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.


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.


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

Adrian Stone IEW 20 th June 2012. Research Question Commissioned by IRENA “Assess the energy...

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Transcript of Adrian Stone IEW 20 th June 2012. Research Question Commissioned by IRENA “Assess the energy...