integrated transport plan for ghana · pdf file · 2017-09-01egis bceom...

Ministry of Finance and Economic Planning

Republic of Ghana

Integrated Transport Plan for Ghana

Volume 4: Socio-economic variables and commodities forecasts for Ghana Final Version

June 2010

Financed by the 9th European Development Fund

Service Contract N° 9 ACP GH019

In association with

Egis Bceom International

Page 1 Integrated Transport Plan for Ghana


Executive Summary

This report presents the forecast of socio-economic factors and production of commodities, necessary to estimate the future demand for transport. This report uses the Economic Analysis and Forecasts (Volume 2 of the Final Report) and conclusions from the Commodities Study (Volume 3 of the Final Report).

Two forecast horizons are provided, 2015 and 2035. 2015 is the horizon for the first year of evaluation (or reference scenario), it corresponds to the horizon when the projects evaluated in the Integrated Transport Plan could be operational. 2035 is the final year for evaluation, it corresponds to an evaluation period of 20 years, required to compute the lifetime benefits under a discount rate of 12%. In addition, the values for 2008 – the calibration year – are provided.

The two main variables guiding the forecast exercise are GDP and population. GDP is estimated at the national level, taking into account the current international crisis and the emerging oil economy in Ghana. Population forecasts are performed on a district basis, since this level of detail is necessary for demand modelling.

Other economic and commodities forecasts mainly rely upon GDP and population, as well as information provided by stakeholders, international comparisons and expert judgement.

The main results are summarised below.

Variable Unit 2008 2015 2035

GDP index 2008=100 100.0 138.0 546.5

Population million inhabitants 22.9 27.2 45.8

Urbanization average Ghana (%) 45.4 49.5 61.9

Tourism million trips 24.3 28.1 43.8

Motorization million of working cars 0.19 0.30 1.06 Soc

io-e

cono

mic

Expenditure million 2005 cedis 5.4 5.7 8.2

Bauxite million tons 0.6 1.0 2.0

Manganese million tons 1.0 1.3 1.6

Cement million tons 1.6 1.9 3.2

Timber million tons 12.1 14.7 24.2

Cocoa million tons 0.7 1.0 1.3

Agriculture million tons 26.0 31.1 52.0

Oil consumption million tons 3.1 4.3 12.6

Com

mod

ities

Other/general cargo million tons 5.0 7.0 27.0




Contents

Chapter 1 Introduction and Context........................... .............................7

1. The planning process ........................................................................7

2. Test of scenarios ...............................................................................8

3. Summary of the transport planning process.....................................10

4. The truth isn’t out there....................................................................11

5. Challenges and limitations of the approach......................................11

Chapter 2 Gross Domestic Product ............................. .........................12

Chapter 3 Population......................................... .....................................13

1. District Level ....................................................................................13

2. Regional and National levels............................................................17

Chapter 4 Urbanization....................................... ....................................19

Chapter 5 Tourism ............................................ ......................................22

Chapter 6 Motorisation....................................... ....................................23

Chapter 7 Household Expenditure .............................. ..........................26

Chapter 8 Passengers Demand .................................. ...........................27

Chapter 9 Minerals........................................... .......................................28

1. Bauxite ............................................................................................28

2. Manganese......................................................................................31

3. Cement............................................................................................32

4. Synthesis on mineral forecasts ........................................................32

Chapter 10 Timber ............................................. .......................................33

1. Woodfuels........................................................................................33

2. Timber for export .............................................................................33

3. Total timber production ....................................................................33

Chapter 11 Cocoa .............................................. .......................................34

Chapter 12 Agriculture ........................................ .....................................35

Chapter 13 Petroleum products................................. ..............................36

1. Oil Production ..................................................................................36

2. Demand for fossil fuels ....................................................................37




Chapter 14 Other / General Cargo .............................. .............................38

Chapter 15 Impact on freight demand........................... ..........................39

1. Minerals ...........................................................................................39

2. Timber .............................................................................................39

3. Cocoa ..............................................................................................40

4. General Cargo .................................................................................40

5. Agriculture .......................................................................................40

6. Oil ....................................................................................................40

Chapter 16 Conclusions and recommendations.................... ................41




List of Tables

Table 1 : Major input and output data for the main modelling processes 10

Table 2 : Population by district 13

Table 3 : Population by region 17

Table 4: Evolution in urbanization between 1982 and 2003 for SSA countries. 19

Table 5 : Urbanization in Ghana 20

Table 6 : Urbanization levels (in % of urban population) 21

Table 7 : Touristic visits 22

Table 8 : Motorisation in the world 23

Table 9 : Motorisation by country income category 24

Table 10 : Number of cars by region 25

Table 11 : Number of cars per 1000 inhabitants 25

Table 12 : Household Expenditure (cedis/capita) 26

Table 13 : Passengers’ traffic growth 27

Table 14 : Bauxite world production (x1000 tonnes, Numbers for 2008 estimated) 29

Table 15 : Bauxite deposits in Ghana 30

Table 16 : Forecasts for Bauxite (x 1000 tonnes) 30

Table 17 : Mineral production forecasts (in million tonnes) 32

Table 18 : Expected timber production 33

Table 19 : Cocoa production in Ghana 34

Table 20 : Cocoa Forecasts 34

Table 21 : Expected agricultural production 35

Table 22 : Oil consumption forecast 37

Table 23 : Forecast of general cargo 38




List of Figures

Figure 1: Future base year reference forecast diagram 8

Figure 2 : Test of scenarios 9

Figure 3 : GDP growth 12

Figure 4 : Population forecast for Ghana 18

Figure 5 : Motorisation X GDP 24

Figure 6: Bauxite production 28

Figure 7 : Manganese production 31

Figure 8 : Oil production 36




Acronyms and Abbreviations

BOST Bulk Oil Storage and Transport

COCOBOD Cocoa Board

GDP Gross Domestic Product

GLSS Ghana Living Standards Survey

GNPC Ghana National Petroleum Corporation

GRC Ghana Railways Company

GSS Ghana Statistical Service

ITP Integrated Transport Plan

LPG Liquefied Petroleum Gas

NDPC National Development Planning Commission

PPP Purchase Power Parity

SSA South Sahara Africa




Chapter 1 Introduction and Context

Put simply, transport demand is the result of spatial separation. People have to move to reach their destinations and goods travel from areas of production to areas of consumption. Transport planning is therefore concerned with the provision of transport to meet the future needs of both goods and people.

The main assumption behind the transport planning techniques used for the ITP is that the demand for transport is determined by socio-economic factors. The role of the planner therefore, is to provide adequate infrastructure and regulatory conditions that best satisfy the future transport needs of society as determined by those factors.

Passenger transport is influenced by a number of factors, including population and income, among others. Freight transport is generated by economic activities, including mining, agriculture and industrial production. An analysis of the likely levels of production is provided in the Commodities Generation Report. The objective of this report is to present forecasts of the main factors affecting the future demand for passengers and freight transport as derived from the key socio-economic factors in Ghana.

1. The planning process

The transport model, calibrated to reconstitute the present supply and demand, will be used to forecast future transport. Traffic forecasts result from a combination of the computed future demand matrices and the definition and assignment of a future year reference network.

The figure below illustrates the planning process.

Two types of data should be input at this stage: � Future socio-economic characteristics, � On-going infrastructure projects.

Future socio-economic characteristics

Independent socio-economic variables used during the calibration process should be forecast thus allowing the construction of future year reference matrices for passengers and vehicles traffic.

Integration of the without-project network

Definition of base year reference transport supply consists in adding to the network defined during the calibration phase, the on-going infrastructure and services projects. The definition of the future reference network and services is essential since it will serve as a base of comparison to test all the other scenarios.




Figure 1: Future base year reference forecast diagr am

The reference year for project evaluation is 2015. This is the horizon when we can expect the projects emerging from the plan to be financed and constructed and therefore contributing to the network.

Future year transport demand is then assigned on the base network taking into account calibrated model parameters. Assignment results will permit the assessment of weaknesses in transport infrastructure and services in the case where no development projects are implemented.

2. Test of scenarios

Once the reference transport demand and supply is modelled, different demand and supply alternatives can be defined and assessed to resolve weaknesses observed during the previous phase.

Definition of scenarios may comprise: � Alternative networks, � Optimal use of various modes (e.g. railways development), � Change capacity of existing modes.

FORECASTING OF FUTURE DEMAND

Socio -economic

UNCHANGED SUPPLY

FUTURE SITUATION WITHOUT

PROJECTS

EVALUATION OF CONGESTION PROBLEMS

SYSTEM WITHOUT PROJECTS




All scenarios should be restricted to: � Corridor study, � Long distance traffic, � Major arterials, � Long term needs.

In order to provide a long term for scenario evaluation (see the Manual for Evaluation of Transport Projects in Ghana), all scenarios are evaluated during a 20-year period using a multi-criteria evaluation methodology. Since the reference year is 2015, the final year for evaluation is 2035.

Once defined, all the scenarios are integrated into the model and tested as illustrated in the following figure:

Figure 2 : Test of scenarios

EVALUATION OF FUTURE DEMAND

VARIOUS SUPPLY SCENARIOS

Road, Rail, Air, Maritime

SITUATION FUTURE

SCENARIO 1

EVALUATION OF THE TRANSPORT

SYSTEM

SCENARIO 1 -

SITUATION FUTURE

SCENARIO 2


SYSTEM

SCENARIO 2 -

SITUATION FUTURE

SCENARIO N


SYSTEM

SCENARIO N -




Traffic figures needed for the socio-economic evaluation are expressed in term of: � vehicle-kilometres, � passenger-kilometres, � vehicle-hours, � passenger –kilometres.

These traffic indicators should be declined: � for the different modes:

• Air • Inland waterway • Rail • Road

� for each scenario, � with and without project.

3. Summary of the transport planning process

Major input and output data necessary to implement the different modelling processes are illustrated in the following table. The calibration of the model (as reported in the “Report on Transport Model Calibration”) is based upon OD surveys, network inventory and various transport and economic statistics.

The model calibration establishes the relationship between socio-economic variables and the demand for transport. In order to forecast future demand for transport, we need then to forecast future socio-economic variables and apply that relationship.

The future demand for transport defines the network needs according to same level of service (congestion). In order to satisfy these needs, infrastructure projects are tested and evaluated.

Table 1 : Major input and output data for the main modelling processes

Process Input Output

Model Calibration Survey Service and Infrastructure Supply Inventory Traffic Indicators Socio-economic indicators

Model parameters (generation, distribution, modal split and assignment) calibrated

Network Reference Forecast

Model parameters; Future socio-economic trends

Future year reference supply and demand and model parameters

Integration and tests of scenarios

Future Scenario Alternatives (Transport Supply and Demand)

Indicators for each scenario




4. The truth isn’t out there

In order to satisfy core principles of the integrated planning methodology defined for the project, it was necessary to collect data from stakeholders in key sectors of the economy from which the future demand for transport would be determined. A series of meetings were held with key sectoral stakeholders as follows:

� Ministry of Energy � Ministry of Food and Agriculture � Ministry of Finance and Economic Planning � Cocobod – Cocoa Board � Energy Commission � Minerals Commission � BOST – Bulk Oil Storage and Transport � GNPC – Ghana National Petroleum Corporation � Ministry of Lands, Forestry and Mines � Forestry Commission � NDPC – National Development Planning Commission

At the outset, it was anticipated that each sector would provide a clear strategy or plan for development in its respective sector, thus providing base data on which to forecast future demand for transport. Whilst the ITP team built a better understanding of the current situation of the different sectors, there were often no development plans available and where available only projected forward a few years.

5. Challenges and limitations of the approach

For passengers, the socio-economic variables that influence the transport needs were determined in the calibration process, where the objective was to represent the existing demand by means of socio-economic factors, namely: population, urbanization, motorisation, expenditure and tourism. Therefore, these are the variables that need to be forecast in order to estimate the future demand for passenger transport.

As the calibration process was based on 2006 figures from the Ghana Living Standards Survey (GLSS) for some variables to estimate 2008 traffic, the forecast for those variables starts in 2008 (instead of 2006). This shortcoming does not affect the quality of results.

Regarding the transport of goods, the situation is much more complex. Each commodity has its own production and consumption drivers, which in most cases depend on international markets. In addition, the production of goods is most often based on sector development plans, which appear not to be available in the format and timescale required.




Chapter 2 Gross Domestic Product

The Economic Analysis and Forecast Report contains GDP forecasts taking into account the international crises of 2008 and 2009 and the emergence of the oil economy in Ghana. These forecasts have been validated by the NDPC.

The forecast is split into 5-year periods and proposes three growth scenarios: low, medium and high. Since the medium scenario is supposed to represent the most likely forecast we only use the medium scenario in order to estimate the future demand for transport. Sensitivity tests have been used in the socio-economic evaluation to test what happens in conditions of higher or lower demand, and higher or lower costs, compared to the central case.

The real1 GDP growth rates used in the transport demand forecasts are: � 2009 – 2010: 4.0 % p.a. � 2011 – 2015: 5.0 % p.a. � 2016 – 2020: 6.0 % p.a. � 2021 – 2025: 7.0 % p.a. � 2026 – 2030: 7.5 % p.a. � 2031 – 2035: 8.0 % p.a.

Since the GDP growth is used as a proxy for the growth of other socio-economic variables, the absolute GDP values are not relevant, only the real growth rates. Indexing the 2008 GDP to 100, the growth is represented in Figure 3 below.

0

100

200

300

400

500

600

2005 2010 2015 2020 2025 2030 2035

year

GD

P in

dex

(200

8=10

0)

Figure 3 : GDP growth

1 Real growth, in opposition to nominal growth, means that the values are adjusted for the inflation. Nominal GDP measures the value of output during a given year using the prices prevailing during that year. Over time, the general level of prices rises due to inflation, leading to an increase in nominal GDP even if the volume of goods and services produced is unchanged. Real GDP measures the value of output in two or more different years by valuing the goods and services adjusted for inflation.




Chapter 3 Population

1. District Level

Population growth rates are based on past trends, and in the forecast for the period 2001-2008 provided by GSS.

The annual average growth rates for each district, as well as the forecasts for 2008, 2015 and 2035 are presented in the table below.

Table 2 : Population by district

DISTRICT REGION Zone Model

Growth Rate POP2008 POP2015 POP2035

Adansi North Ashanti 33 3.07% 120026 148284 271291

Adansi South Ashanti 34 3.09% 147275 182183 334536

Afigya Sekyere Ashanti 29 3.15% 152487 189418 351985

Ahafo Ano North Ashanti 28 2.89% 90623 110626 195585

Ahafo Ano South Ashanti 28 2.38% 160725 189529 303545

Amansie Central Ashanti 34 2.84% 101177 123059 215306

Amansie East Ashanti 33 2.86% 181314 220948 388685

Amansie West Ashanti 27 3.12% 139407 172826 319340

Asante Akim North Ashanti 31 2.88% 158972 193944 342305

Asante Akim South Ashanti 32 2.87% 121688 148296 260918

Atwima Nponua Ashanti 27 3.54% 124312 158621 318259

Atwima Nwabiagya Ashanti 26 3.06% 176496 217944 398183

Bosomtwe Kwanwoma Ashanti 26 0.98% 160837 172243 209489

Ejisu/Juabeng Ashanti 26 3.23% 160837 200916 379401

Ejura-Sekyedumase Ashanti 30 2.99% 102866 126464 228164

KMA Ashanti 26 3.27% 1519305 1903202 3622659

Kwabre Ashanti 26 2.92% 207398 253609 450573

Obuasi Municipality Ashanti 34 3.48% 209308 265911 526910

Offinso Ashanti 29 3.18% 178714 222522 416308

Sekyere East Ashanti 31 2.70% 194593 234410 399001

Sekyere West Ashanti 30 2.95% 181019 221839 396613

Asunafo North Brong Ahafo 19 2.18% 126517 147093 226241

Asunafo South Brong Ahafo 19 2.13% 80052 92762 141327




Asutifi Brong Ahafo 19 2.08% 99731 115226 174084

Atebubu-Amantin Brong Ahafo 25 2.14% 77391 89763 137124

Berekum Brong Ahafo 23 2.18% 110994 129046 198488

Dormaa Brong Ahafo 23 2.28% 180588 211504 332203

Jaman North Brong Ahafo 22 2.18% 98789 114849 176614

Jaman South Brong Ahafo 22 2.29% 78450 91910 144489

Kintampo North Brong Ahafo 21 1.96% 88951 101885 150165

Kintampo South Brong Ahafo 21 1.82% 81333 92253 132222

Nkoranza Brong Ahafo 24 2.13% 152796 177059 269768

Pru Brong Ahafo 25 2.26% 117403 137250 214446

Sene Brong Ahafo 25 2.11% 97140 112433 170727

Sunyani Brong Ahafo 20 2.39% 217252 256273 410840

Tain Brong Ahafo 22 2.10% 103479 119701 181464

Tano North Brong Ahafo 20 2.20% 70650 82276 127143

Tano South Brong Ahafo 20 2.19% 76532 89090 137521

Techiman Brong Ahafo 24 2.45% 212480 251641 408020

Wenchi East Brong Ahafo 22 2.16% 94059 109253 167586

Abura/Asebu/Kwamankese Central 55 1.33% 99961 109671 142927

Agona Central 58 1.60% 180540 201826 277506

Ajumako/Enyan/Essian Central 58 1.34% 102001 111931 145958

Asikuma/Odoben/Brakwa Central 58 1.43% 100116 110611 147062

Assin North Central 57 1.53% 134165 149181 202001

Assin South Central 57 1.41% 86845 95760 126602

Awutu/Efutu/Senya Central 56 1.79% 195957 221874 316399

Cape Coast Central 54 1.82% 136824 155281 222912

Gomoa Central 56 1.57% 220081 245422 335077

Komenda/Edina Eguafo/Abirem Central 54 1.42% 125790 138845 184103

Mfantsiman Central 55 1.48% 171502 190055 254882

Twifo-Hemang/Lower Denkyira Central 53 1.57% 125100 139479 190333

Upper Denkyira Central 52 1.54% 122637 136494 185329

Afram Plains Eastern 36 0.96% 146621 156726 189600

Akwapim North Eastern 44 0.84% 111865 118573 140035

Akwapim South Eastern 41 0.98% 125719 134594 163559

Asuogyaman Eastern 39 1.13% 83131 89962 112729

Atiwa Eastern 35 0.81% 93778 99227 116604

Birim North Eastern 36 0.84% 131799 139754 165230




Birim South Eastern 42 1.01% 194269 208476 255055

East Akim Eastern 37 0.89% 109738 116723 139226

Fanteakwa Eastern 38 0.89% 92420 98348 117465

Kwaebibirem Eastern 36 0.84% 191464 203062 240219

Kwahu South Eastern 38 0.69% 136984 143735 164917

Kwahu West Eastern 35 0.92% 94025 100238 120344

Manya Krobo Eastern 43 0.87% 164418 174749 207983

New Juabeng Municipality Eastern 40 1.38% 152965 168355 221403

Suhum/Kraboa/Coaltar Eastern 41 0.98% 179909 192585 233942

West Akim Eastern 42 0.92% 165682 176660 212198

Yilo Krobo Eastern 43 0.97% 92983 99518 120828

Accra Metropolitan Assembly Greater Acc 61 4.14% 2322056 3084861 6945455

Dangme East Greater Acc 59 3.37% 122127 153990 298645

Dangme West Greater Acc 59 3.61% 129558 166014 337132

Ga East Greater Acc 61 4.18% 227433 302971 687466

Ga West Greater Acc 61 4.21% 546530 729510 1664818

Tema Municipality Greater Acc 60 4.16% 709729 943962 2132289

Bole Northern 6 2.12% 58540 67792 103097

Bunkprugu Yunyoo Northern 10 2.19% 98056 114075 175768

Central Gonja Northern 8 2.22% 90066 105064 163149

East Gonja Northern 14 2.16% 206838 240189 368166

East Mamprusi Northern 10 2.19% 109622 127587 196844

Gushiegu Northern 10 2.13% 81602 94555 144047

Karaga Northern 9 2.17% 66815 77655 119325

Nanumba North Northern 13 2.23% 84913 99121 154216

Nanumba South Northern 13 2.17% 86645 100670 154552

Saboba/Chereponi Northern 11 2.27% 112258 131401 206049

Savelugu Nanton Northern 9 2.09% 106039 122548 185293

Sawla Tuna-Kalba Northern 6 2.29% 92970 108933 171308

Tamale Municipality Northern 9 2.35% 354512 417136 663933

Tolon Kumbungu Northern 9 2.02% 155688 179110 267313

West Gonja Northern 7 2.18% 75702 88058 135631

West Mamprusi Northern 9 2.11% 135827 157253 238975

Yendi Northern 12 2.15% 154641 179524 274951

Zabzugu Tatale Northern 13 2.29% 94871 111166 174848

Bawku East Upper East 2 0.66% 207250 217050 247681




Bawku West Upper East 2 0.63% 85366 89177 101031

Bolgatanga Municipality Upper East 1 0.91% 133084 141773 169852

Bongo Upper East 1 0.79% 83624 88380 103511

Builsa Upper East 1 0.74% 80795 85075 98596

Garu Tempane Upper East 2 0.50% 118500 122753 135761

Kassena Nankana Upper East 1 0.81% 161218 170624 200633

Talensi Nabdam Upper East 1 0.70% 113737 119399 137177

Jirapa Lambussie Upper West 4 1.12% 104293 112740 140833

Lawra Upper West 4 1.13% 94449 102181 127940

Nadowli Upper West 4 1.13% 89262 96566 120895

Sissala East Upper West 5 1.29% 46388 50755 65631

Sissala West Upper West 5 1.18% 46619 50622 64057

Wa East Upper West 3 1.14% 65833 71278 89449

Wa Municipality Upper West 3 1.31% 115285 126313 163987

Wa West Upper West 3 1.13% 62732 67888 85072

Adaklu-Anyigbe Volta 16 1.40% 57194 63037 83236

Akatsi Volta 15 1.33% 103394 113422 147761

Ho Volta 16 1.51% 206854 229786 310301

Hohoe Volta 17 1.45% 171155 189277 252335

Jasikan Volta 17 1.44% 124463 137549 183020

Kadjebi Volta 18 1.43% 58115 64170 85175

Keta Volta 15 1.16% 145906 158216 199414

Ketu Volta 15 1.41% 264142 291436 385971

Krachi East Volta 18 1.45% 83780 92662 123575

Krachi West Volta 18 1.32% 94032 103090 134070

Nkwanta South Volta 18 1.44% 168681 186432 248128

North Dayi Volta 17 1.49% 83719 92863 124873

North Tongu Volta 15 1.51% 146288 162446 219134

South Dayi Volta 16 1.49% 43083 47799 64314

South Tongu Volta 15 1.28% 71247 77872 100395

Ahanta West Western 47 2.85% 119763 145818 255894

Aowin/Suaman Western 48 2.91% 150309 183740 326141

Bia Western 45 2.91% 161639 197609 350848

Bibiani/Anhwiaso/Bekwai Western 51 2.88% 130207 158884 280586

Jomoro Western 49 2.81% 139617 169550 295338

Juabeso Western 45 2.88% 147209 179579 316877




Mpohor Wassa East Western 50 2.46% 149189 176869 287635

Nzema East Western 49 2.69% 177300 213482 362906

Sefwi Wiawso Western 45 2.95% 188706 231313 413820

Shama Ahanta East Western 47 3.11% 474348 587582 1083177

Wassa Amenfi East Western 46 2.65% 110688 132925 224264

Wassa Amenfi West Western 46 2.74% 180227 217752 373810

Wassa West Western 46 2.95% 294935 361556 646973

2. Regional and National levels

For modelling purposes, the population, as well as the other socio-economic data, is aggregated either by region or by zones (shown in the Model Calibration Report). The table below presents the population forecast by region and the national total. These results are simply the sum-up of the district forecasts, for each region.

Table 3 : Population by region

2008 2015 2035

Ashanti 4 589 377 5 656 792 10 329 057

Brong Ahafo 2 164 589 2 521 267 3 900 474

Central 1 801 520 2 006 430 2 731 090

Eastern 2 267 772 2 421 285 2 921 337

Greater Acc 4 057 434 5 381 309 12 065 806

Northern 2 165 606 2 521 838 3 897 465

Upper East 983 575 1 034 231 1 194 241

Upper West 624 861 678 342 857 864

Volta 1 822 054 2 010 057 2 661 702

Western 2 424 139 2 956 658 5 218 269

Total Ghana 22 900 927 27 188 209 45 777 303

Figure 4 provides a graphical representation of the forecast at national level.




0

5 000 000

10 000 000

15 000 000

20 000 000

25 000 000

30 000 000

35 000 000

40 000 000

45 000 000

50 000 000

2000 2005 2010 2015 2020 2025 2030 2035

Population

Figure 4 : Population forecast for Ghana




Chapter 4 Urbanization

African society is rapidly changing from rural to urban, with cities and towns expanding, not only in terms of population growth, but also spatially.

It is estimated that in 1900 about 95% of Africa's inhabitants SSA lived from the primary occupations of farming, hunting & gathering, cattle nomadism, and fishing, meaning that less than 5% were urban. In 1950 (the start of the independence period) 14.7% of Africa's inhabitants were urban, in 2000 it had risen to 37.2% and it is expected to rise to 45.3% in 2015 (UN, 2002). The Nigerian city of Lagos that in 1963 had 665 000 inhabitants (Rakodi, 1997) and 8.7 million in 2000 is expected to become the worlds 11th biggest city by 2015 with 16 million inhabitants (UN, 2002). The urbanization of most of Africa is moving fast forward, especially south of the Sahara.

Table 4: Evolution in urbanization between 1982 and 2003 for SSA countries.

total population (million) urban population (million) % urban pop

1982 2003 %growth 1982 2003 %growth 1982 2003

Benin 3.7 6.7 2.7 1.1 3 4.7 28.7 44.5

Botswana 1 1.7 2.3 0.2 0.9 3.6 22.5 50.3

Burkina Faso 7.3 12.1 2.4 0.7 2.1 4.4 9.6 17.6

Cameroon 9.3 16.1 2.5 3.1 8.2 4.3 33.1 51.2

Côte d’Ivoire 8.8 16.8 2.7 3.2 7.6 4.3 35.8 44.9

Ethiopia 39.9 68.6 2.3 4.3 11.4 4.4 10.9 16.6

Ghana 11.8 20.7 2.3 3.7 7.7 3.1 31.6 37.1

Guinea 4.6 7.9 2.4 0.9 2.3 4.1 20.1 28.9

Kenya 17.9 31.9 2.4 3.1 11.6 5.6 17.6 36.3

Lesotho 1.3 1.8 1 0.2 0.5 4.2 14.6 30.3

Madagascar 9.3 16.9 2.9 1.8 5.3 5.1 19.5 31.4

Malawi 6.6 11 2 0.6 1.7 4.4 9.6 15.9

Mauritania 1.7 2.8 2.6 0.5 1.8 5.2 31 61.7

Mauritius 1 1.2 1.1 0.4 0.5 1.4 42 42.3

Mozambique 12.7 18.8 2.2 1.9 6.7 6.2 14.6 35.6

Niger 5.9 11.8 3.3 0.8 2.6 5.8 13.3 22.1

Nigeria 75.8 136.5 2.7 21.5 63.6 4.9 28.4 46.6

Rwanda 5.5 8.4 1.5 0.3 0.6 3.1 4.8 6.6




Senegal 5.9 10.2 2.6 2.1 5.1 4.2 36.4 49.6

South Africa 29 45.8 2 14 27.1 3.5 48.2 59.2

Tanzania 19.8 35.9 2.6 3.1 12.7 6.4 15.9 35.4

Togo 2.7 4.9 2.6 0.7 1.7 4.2 24.4 35.1

Uganda 13.5 25.3 2.9 1.2 3.9 5.3 9.2 15.3

Zambia 6.1 10.4 2.2 2.4 4.2 2.4 39.8 40.3

In Ghana, the urbanization growth is comparable to other SSA countries. All the regions experience an increase in urbanization, as can be seen in the table below extracted from the 2000 census report.

Table 5 : Urbanization in Ghana

Based on these strong tendencies and on the population forecasts, we forecast the evolution of urban population in Ghana.




Table 6 : Urbanization levels (in % of urban popula tion)

2008 2015 2035

Ashanti 51.3 55.0 67.1

Brong Ahafo 37.4 40.1 48.9

Central 37.5 40.2 49.1

Eastern 34.6 37.1 45.3

Greater Acc 87.7 90.8 96.4

Northern 26.6 28.5 34.8

Upper East 15.7 16.8 20.5

Upper West 17.5 18.8 22.9

Volta 27.0 28.9 35.3

Western 36.3 38.9 47.5

Average Ghana 45.4 49.5 61.9

The increase in urbanization engenders a reduction in long distance trips. Since most of services, products and attractions can be reached within an urban perimeter, there is less necessity to travel long distances.




Chapter 5 Tourism

There was no evidence provided by the tourism sector of significant investments being made to promote internal tourism in the country2. Therefore in the absence of a clear and quantified strategy for the sector we suppose that the per capita number of touristic visits will remain constant. The total number of touristic visits are forecast to follow the same trend as the population.

The table below presents the forecast of the number of touristic visits by region.

Table 7 : Touristic visits

2008 2015 2035

Ashanti 5 533 511 6 820 516 12 453 968

Brong Ahafo 1 707 132 1 988 431 3 076 161

Central 3 042 523 3 388 588 4 612 440

Eastern 6 373 393 6 804 829 8 210 184

Greater Acc 2 087 141 2 768 141 6 206 641

Northern 582 159 677 921 1 047 718

Upper East 254 779 267 901 309 349

Upper West 863 415 937 313 1 185 371

Volta 2 385 286 2 631 404 3 484 485

Western 1 517 536 1 850 898 3 266 690

Total Ghana 24 346 875 28 135 944 43 853 007

It is worthy noting that these figures, according to the GLSS, represent the number or touristic visits and is not necessarily related to foreign visitors or to holidays.

2 International tourism will probably continue to grow, but this tourism has a small impact on transport.




Chapter 6 Motorisation

Increasing private vehicle ownership is clearly a worldwide trend. While in developed countries a very high level of motorisation has been reached, growth is now relatively low. In developing countries there is still large potential for the level of motorisation to increase.

The table below shows the level of motorisation and the annual increase in the different regions of the world:

Table 8 : Motorisation in the world

Continent Region Veh/1000pop increase (%

per year)

North 762 1

Central 117 4 America

South 126 5

Northern 401 2

Western 529 2

Southern 189 3 Europe

Eastern 176 6

Western 99 5

South-Central 10 6

Southeast 46 10 Asia

East 69 7

North 44 4

Middle 21 2

East 8 5

West 17 4

Africa

South 130 3

Oceania 499 3

Sources: International Road Federation (2002) and WorldBank (1991, 2000)




The rate increase tends to be a function of two variables; first the level of motorisation and second, the economic growth. When the level of motorisation is very high, like in the US, the increase rates are low because there is no need for more cars. For developing countries, the motorisation is increasing with the economy.

The figure below shows that the per capita income is the strongly correlated with the level of motorisation in 161 countries.

Figure 5 : Motorisation X GDP


Despite serious congestion problems in urban areas, the motorisation level in Ghana is still very low. It should be noticed, however, that there is a huge discrepancy between the number of registered vehicles (around 800 in 2006) and the number of vehicles stated as working during the household survey. The latter is used in this analysis.

The table below presents the vehicle ownership and the rate of increase by country income category.

Table 9 : Motorisation by country income category

Income Category Vehicle ownership

(veh/1000pop) rate of

increase

Low 12 6.4

Low-Middle 39 6.5

Upper-Middle 175 4.8

High 573 2.4

Average 130 3.2





Based on these figures, we adopt an annual average growth rate of 6.5%.

Table 10 : Number of cars by region

2008 2015 2035

Ashanti 38 872 60 406 212 850

Brong Ahafo 10 740 16 689 58 808

Central 8 046 12 504 44 058

Eastern 13 625 21 173 74 605

Greater Accra 89 005 138 312 487 364

Northern 5 277 8 201 28 897

Upper East 2 238 3 478 12 254

Upper West 1 589 2 470 8 703

Volta 7 527 11 696 41 214

Western 15 881 24 678 86 958

Total Ghana 192 799 299 607 1 055 709

Table 11provides the same forecast, but relates it to the population of each region.

Table 11 : Number of cars per 1000 inhabitants

2008 2015 2035

Ashanti 8.5 10.7 20.6

Brong Ahafo 5.0 6.6 15.1

Central 4.5 6.2 16.1

Eastern 6.0 8.7 25.5

Greater Acc 21.9 25.7 40.4

Northern 2.4 3.3 7.4

Upper East 2.3 3.4 10.3

Upper West 2.5 3.6 10.1

Volta 4.1 5.8 15.5

Western 6.6 8.3 16.7

Average Ghana 8.4 11.0 23.1




Chapter 7 Household Expenditure

Traditionally, the income is used as a good measure for individual and household wealth. In Ghana, it is suggested by GSS, that the expenditure is a better measure, since the measure is based on actual register of household expenditure, while the income is based on stated values.

The average household expenditure is strongly related to the national economy. Estimates for Ghana, realized by the ITP Team suggest that the elasticity of the expenditure with respect to GDP is around 0.9. It means that a 10% increase on GDP generates an 9% increase on expenditure. However, with the economic growth and more accumulation of capital, by private companies and by individual savings, this elasticity will decrease. We therefore adopt a value of 0.9 in 2015, since no huge economic change is expected until then. This value decreases over time and stabilizes around 0.5 in 2035. This stabilization means that the additional income will not be reversed into consumption but in individual savings and on industrial accumulation of capital.

Given this assumption and the current expenditure, we can forecast the future expenditure levels in Ghana. The values are presented in cedis 2005 real values.

Table 12 : Household Expenditure (cedis/capita)

2008 2015 2035

Ashanti 6 817 916 6 872 122 8 278 224

Brong Ahafo 5 144 491 5 487 249 7 801 767

Central 6 763 269 7 544 447 12 191 363

Eastern 6 125 471 7 127 687 12 994 207

Greater Acc 10 500 000 9 835 774 9 648 881

Northern 3 616 290 3 858 169 5 491 019

Upper East 2 289 306 2 704 888 5 152 428

Upper West 1 658 085 1 897 568 3 300 384

Volta 4 907 515 5 526 755 9 180 280

Western 6 482 076 6 602 771 8 228 822

Average Ghana 5 430 442 5 745 743 8 226 738




Chapter 8 Passengers Demand

Entering the socio-economic forecasts presented here, we estimate the total number of long distance trips made by passengers, using the transport model developed for Ghana (see the report on Transport Model Calibration)

It is important to remember that these constitute inter-zonal trips, according to the zone system defined for the transport planning model in Ghana.

The total number of inter-zonal trips increases from 524,600 in 2008 to 643,700 in 2015 and reaches 1.76 million in 2035, which represents a sustained increase of 5% per annum in average. This growth is led essentially by the growth in population and in expenditure

Table 13 : Passengers’ traffic growth

2008 2015 2035

Passenger traffic (million of interzonal trips)

0.52 0.64 1.76

These figures exclude any effect related to induced or disinduced traffic. Traffic induction is the increase in traffic due to a new project, which lower transport costs. Traffic disinduction is related to the increase in transport costs due to congestion, bad network conditions or traffic management measures. See the calibration report for details.




Chapter 9 Minerals

Ghana possesses valuable mineral reserves. The exports (and hence the production) are not driven by the amount of reserves but by the exploration policy and the international market.

The main determinant of minerals production worldwide is the international market, which in turn is driven by three main factors: demand, price and quality.

In this forecast we estimate what the likely trend will be if no new strong policy is implemented in the sector, i.e. if Ghana continues to maintain its market share in the world market.

1. Bauxite

The production of bauxite has been growing by an annual average rate of 6.74% during the last 27 years. It has been experiencing strong, both upwards and downwards variations, which have been more significant in the last 10 years.

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Figure 6: Bauxite production

The production during the 3 first quarters of 2008 was 482 527 tonnes; the annual production is expected to be 643 000 tonnes.




Table 14 shows the Bauxite production of the main producers in the world. We can estimate that the Ghana market share in 2007 was about 0.5%.

Table 14 : Bauxite world production (x1000 tonnes, Numbers for 2008 estimated)

Mine production Country

2007 2008 Reserves Reserve base

Guinea 18,000 18,000 7,400,000 8,600,000

Australia 62,400 63,000 5,800,000 7,900,000

Vietnam 30 30 2,100,000 5,400,000

Jamaica 14,600 15,000 2,000,000 2,500,000

Brazil 24,800 25,000 1,900,000 2,500,000

Guyana 1,600 1,600 700,000 900,000

India 19,200 20,000 770,000 1,400,000

China 30,000 32,000 700,000 2,300,000

Greece 2,220 2,200 600,000 650,000

Suriname 4,900 4,500 580,000 600,000

Kazakhstan 4,800 4,800 360,000 450,000

Venezuela 5,900 5,900 320,000 350,000

Russia 6,400 6,400 200,000 250,000

United States NA NA 20,000 40,000

Other countries 7,150 6,800 3,200,000 3,800,000

World total (rounded) 202,000 205,000 27,000,000 38,000,000

Source: U.S. Geological Survey, Mineral Commodity Summaries, January 2009

As can be seen in the table below, Ghana has huge reserves of bauxite, especially in Aya Nyinahin. There appears to be significant potential for Ghana to increase its participation in the world market.




Table 15 : Bauxite deposits in Ghana

Deposit Name Total Reserves (Million Tonnes)

Afo-Sefwi Bekwai (Awaso) 19

Aya Nyinahin 350

Atewa Range (Kibi) 150 – 180

Mt. Ejuanema 5

Source: Kesse (1985)

A number of forecasts have been performed on the production of bauxite in Ghana. The table below summarizes some of them.

Table 16 : Forecasts for Bauxite (x 1000 tonnes)

GRC

(2009)

Bonifica

(2009)

CarlBro

(2006)

SNC-Lavalin

(2007)

Sudop-Praha (2007)

2009 1500 692 700

2010 1500 708 1000 1000

2011 1500 724 5100

2012 2000 741 5100

2013 2000 757 5100

2014 12000 5100 5100

2015 12000 5120 1500 5100

2016 12000 5120 5100

2017 12000 5120 10600

2018 12000 5120 10600

2019 - 10600 10600

2020 - 10600 10600 5500

2021 - 10600 10600

2022 - 10600 10600

2023 - 10600 10600

2024 - 10600 10600

2025 - 10600 1500 10600

2026 - 10600 10600

2027 - 10600 10600

2028 - 10600 10600

2029 - 10600 10600

2030 - 10600 10600




Sources:

Ghana Railways Company

Proposals for Modernising, Improving and Expanding Ghana's Western Railway Line for the Socio-Economic Development of Ghana. 2009.

Bonifica Feasibility Study of the Western Corridor Infrastructure Project – Diagnosis Report. 2009.

CarlBro Feasibility Studies, Detailed Engineering Design and Construction Supervision of Two Roads: Awaso-Ayamfuri-Bogoso IR8/IR6 and Gambia n°2 – Kyeremaso. 2006.

SNC-Lavalin Pre-Feasibility Study Report, Ghana Railway Corporation Project. 2007.

SUDOP-PRAHA Modernizing and construction of the railway lineTakoradi/Sekondi – boarder with Burkina Faso. 2007.

The main uncertain variable in these forecasts is the role of the reserves in Nyinahin. GRC expects a heavy exploitation from 2010 while CarlBro doesn’t expect any exploitation in the foreseeable future. SNC-Lavalin proposed a gradual exploitation starting in 2011. Bonifica forecasts are based on SNC-Lavalin, delaying the exploitation of Nyinahin reserves.

According to the information provided by the Minerals Commission, including the exploration and mining licences, there is no indication of a likely exploitation of Nyinahin reserves in the near future.

Given the uncertainty around this issue, the ITP Team proposes a conservative approach related to the past trends and current level of exploitation and do not consider the role of Nyinahin in the forecasts, similarly to CarlBro studies. The ITP Team expects the Minerals Commission to validate this approach or to provide additional information.

2. Manganese

The trend in manganese production in Ghana is shown in Figure 7 . The graphic shows that the production has been almost constant until the mid of the 90’s when it experience an dramatic increase until 2005. The production has been decreasing since then.

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Figure 7 : Manganese production




The production during the 3 first quarters of 2008 was 740,852 tonnes; the annual production is expected to be about 1 million tonnes.

World steel production has been growing at an average rate of 1% per year for many years. Although manganese usage in the steel industry has been increasingly efficient, the net demand for manganese in the steel production has grown because of the increase in the steel production itself (Kogel et al, 2006 apud Machamer, 2002).

Also the demand for manganese in batteries of all sorts is increasing, as is the demand for manganese for other chemicals. High value batteries ores have been produced from the enriched deposits of Nsuta.

Supposing that the demand in other sectors balances out the increases in efficiency in steel production, an annual average growth rate of around 1% should be expected.

The participation of Ghana in the world production has been varying between 0.8% 5.2%. We suppose in this forecast that Ghana will be able to recover by 2015 and sustain afterwards a market share of 5%.

3. Cement

Production in 2006 was 1.6 million tonnes. Since the construction sector is expanding in the country we can expect a growth in the consumption of cement. In the absence of any other policy guidance, we shall assume that this growth is proportional to the population growth.

4. Synthesis on mineral forecasts

Table 17 : Mineral production forecasts (in million tonnes)

2008 2015 2035

Bauxite 0.6 1.0 2.0

Manganese 1.0 1.3 1.6

Cement 1.6 1.9 3.2




Chapter 10 Timber

1. Woodfuels

According to the Energy Commission biomass energy consumption was 11.7 million tonnes in 2007.

Following past trends, we shall assume that woodfuel consumption will grow in line with the population growth reaching 14.3 million tons in 2015 and 23.7 million tons in 2035.

It is likely that as consumers have access to a choice of fuels, wood fuel consumption will decline. However, without a quantified strategy on which to base forecasts, and that substitute fuels will most likely require distribution, we shall assume the forecasts above for wood fuels and potential substitutes such as LPG.

Increasing in the per capita energy consumption linked to the increases in income will be most related to electricity (air conditioning, electronic devices, etc) and petroleum (cars) and not woodfuels.

2. Timber for export

573,500 m³ were exported in 2008. Considering an average density of 1.5 m³/ton, the volume exported is 0.382 million tons.

There is no evidence on the development of the sector of timber for exports. We therefore suppose a moderate growth, reaching 0.4 million tons in 2015 and 0.55 in 2035 .

3. Total timber production

Table 18 summarizes the forecasts for woodfuel and timber for export.

Table 18 : Expected timber production

2008 2015 2035

Timber volume (million tons) 12.1 14.7 24.2




Chapter 11 Cocoa

Table 19 shows the evolution of the production of cocoa in Ghana in the last 6 seasons.

Table 19 : Cocoa production in Ghana

Season production (million tons)

2002/2003 0.498

2003/2004 0.736

2004/2005 0.591

2005/2006 0.740

2006/2007 0.582

2007/2008 0.680

The objective of COCOBOD is to reach 1 million tonnes by 2011. Based on current and past production rates, this appears to be very ambitious. Therefore, no further increase is foreseeable in the coming years and it is estimated that production will reach 1 million tonnes in 2015. With adequate intensification of the cocoa production process, we believe that an increase of an additional 25%, i.e. 1.25 million tons by 2035 is a realistic assumption.

Table 20 : Cocoa Forecasts

2008 2015 2035

Cocoa volume (million tons) 0.68 1,00 1.25




Chapter 12 Agriculture

The production of selected crops (Maize, Rice (Milled), Millet, Sorghum, Cassava, Yam, Plantain, Cocoyam, Groundnut, Cowpea) adds up 21.3 million tonnes in 2007, from which 16.2 million are available for human consumption.

Production of industrial crops is estimated at 2.36 million tonnes in 2005.

Non-traditional exports add-up to about 0.3 million tonnes. Imported cereals in 2007 is estimated at 0.77 million tonnes.

Imports of livestock and poultry products are estimated at 0.11 million tonnes and domestic productions represent 0.097 million tons. Fish production accounts for 0.38 million tonnes.

The total volume of agriculture products in the country in 2008 can be estimated at around 26 million tons.

In this study it is proposed that the agriculture productions/consumption will grow in line with the population growth, at an average of 2.6% per annum, reaching 31.12 million tonnes in 2015 and 52 million tonnes in 2035.

Table 21 : Expected agricultural production

2008 2015 2035

Agriculture products (million tonnes)

26.0 31.1 52.0




Chapter 13 Petroleum products

Regarding petroleum products, and in particular fuels, we must differentiate the quantities in terms of production and in terms of national consumption/demand, since their impact on land transport is completely different.

1. Oil Production

The ITP’s Commodities Study presents a forecast of oil production based on information from BOST and NDPC. The new exploitations will increase the current output of 40,000 barrels/day to 120,000 barrels/ day between 2010 and 2016, and reaches 220,000 barrels/day in 2035. This growth is represented in Figure 8.

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Figure 8 : Oil production

The impact of this production on the national transport system will depend on the share of oil and gas which is processed domestically, and if processed in Ghana, on the location of the refinery and the key areas of consumption proposed for products.

Supposing that some oil and gas is refined in Ghana, current plans suggest that Tema refinery will be extended with oil transported in pipelines to the port. Likewise, current plans suggest that gas will be piped ashore to a processing plant on the coast and then supplied by pipeline to a thermal plant, also located on the coast. In these cases, the impact of oil and gas on land-side transport is insignificant.




Only if there are significant plans to distribute gas or oil products to areas of industrial production and consumption, will there be a significant impact on land-based transport. In this latter case, the increase in transportation of petroleum products has been considered in the figures below. In the case where there may be a substitution of LPG for wood fuels, and without a quantified strategy, it is supposed that the quantities already forecast for wood fuels will suffice for forecasting purposes.

2. Demand for fossil fuels

In opposition to the national production, the national consumption, be it produced locally or imported, will have a certain impact on all the transport system around the country.

Currently, the consumption is of 66 000 barrels/day. At 159 litres/barrel and a density of 0.8 gives 3.1 million tonnes/year.

The demand for fossil fuels is strictly related to the level of motorisation, and therefore we can use the increase in motorisation as a proxy for the increase in fuel consumption. In addition, we suppose an efficiency gain of 5% up to 2015 and 25% up to 2035, due to more efficient motors.

Table 22 : Oil consumption forecast

2008 2015 2035

Oil consumption (barrels/day)

66 000 92 000 271 000

Oil consumption (million tons/year)

3.1 4.3 12.6




Chapter 14 Other / General Cargo

The commodities studied so far represent the main generators of freight transport in the country. However, a number of other commodities that, individually don’t have a significant impact on transport, will result in a total high volume. These commodities are considered as general cargo, they include:

� Minor agricultural products and processed food � Electric and electronic equipments � Furniture � Newspapers and books � Other minerals � Etc.

We estimate that they currently represent about 5 million tons. As a proxy for growth, we use the GDP growth, producing the following forecasts:

Table 23 : Forecast of general cargo

2008 2015 2035

General Cargo (million tons)

5.0 7.0 27.0




Chapter 15 Impact on freight demand

The number of tonnes transported is obviously not equal the number of tonnes produced for most products. This is due to the fact that from the production point to the final destination, some intermediate storage or logistic centres are used; this would lead to the same product to be count more than once. On the other hand, some products are consumed locally and don’t require long-distance transport; these are not affecting the transport system.

In order to account for the real impact on long-distance transport from the number of tonnes produced, we estimate how many tonnes for long distance transport, a tonne in production represents for each type of commodity.

This estimate is done by using the handling factor, defined by the ratio:

tonsproducedtransportfortons

.

This handling factor has been extensively studied in Europe, in particular in the study REDEFINE (Relationship between Demand for Freight-transport and Industrial Effects). This study provided some initial indications, which have subsequently been adapted for the Ghanaian context.

1. Minerals

There is no intermediate stock or local consumption. It goes directly from mines to port. Factor 1.0

2. Timber

Most timber is consumed for energy. Therefore, supposing that part of the wood is consumed locally we shall assume a transport factor of 0.75.




3. Cocoa

Most is exported. It can be stocked and some is transformed in the industry (about 10%). A transport factor of 1.1 seems reasonable.

4. General Cargo

A share of it is stocked and used by industry, therefore generating more transport. Most of it is moved to warehouses and then to distribution centers, generating 2 movements. We can suppose an average transport factor of 1.75.

5. Agriculture

A significant part of agriculture products are consumed locally and do not need long-distance transport. We therefore suppose that only 25% of all agricultural products use long distance transport. (factor of 0.25).

6. Oil

Regarding the oil consumed in Ghana, some is transported by pipeline and some by road to refineries and storage. Currently and most likely for the next few years, distribution is made by road. A factor of 2 seems realistic as BOST distribution includes shipping to distribution depots and then ‘lifting’ from those depots by Oil Marketing Companies. The future impact of oil and gas products will, as indicated in Chapter 13, strongly depends the location of production plants and outlets.




Chapter 16 Conclusions and recommendations

This report presents the forecasts for the socio-economic variables, driving passenger transport, and the production of commodities, driving the freight transport.

These forecasts are based on the analysis undertaken of various socio-economic factors and key commodities. The forecasts have been used in the transport planning model to evaluate the impact of future demands for transport on the network. The future demand was evaluated against the base network, in order to indentify the future needs in terms of infrastructure and to evaluate the benefits from the infrastructure in terms of reduction in vehicle operating costs, travel time, environmental impacts, etc.

The key socio-economic variables are GDP and population. They are used as a basis for all the forecasts in this report. GDP forecasts have been validated by the NDPC and the population forecasts are based on actual data and on medium-term forecasts by GSS, which improves the reliability of the forecasts in this report.

The reason why most variables are forecasts based on associations with other variables (in particular GDP and population), international comparisons and expert judgment is the lack of official forecasts for both socio-economic factors and commodities production.

The ITP Team strongly recommends that official long-term forecasts be generated on a periodic basis, in order to provide a more reliable basis for project assessment.

If adopted, the use of common, integrated and official forecasts would enable government agencies to:

� Compare different projects in different sectors, in terms of national development � Evaluate intra- and inter-sector projects � Ensure a integrated planning process, in which the information is generated and shared

with the participation of all stakeholders � Inform the private sector about business opportunities and reduce the risk � Reduce the cost of information acquisition and generation, by both public and private

sectors � Indentify divergences or incoherencies in sectorial planning

The production of an integrated forecast is strongly related to the need and the benefits of an integrated planning process.

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