TRAINING MANUAL ON METHODOLOGIES FOR DATA …css.escwa.org.lb/sd/esab/ESCWATrainingManual1.pdf ·...

Training Manual about Data Collection Methodologies on the Use of Energy

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TRAINING MANUAL ON METHODOLOGIES FOR DATA COLLECTION ON ENERGY USE BY THE

TRANSPORT SECTOR AND CASE STUDIES FROM THE ARAB REGION1

Part I- Case Studies: Tunisia and Palestine2

Part II- Case Studies: Canada and Morocco3

United Nations ESCWA, 2013

1 Methodological Note: This document has been reproduced in the form in which it was received, without formal editing. It was prepared within the Development Account Project on Energy Statistics and Balance in the ESCWA Region. 2011. Project Manager: Wafa Aboul Hosn, Chief Economic Statistics Section, UNESCWA 2 R. Missaoui, Ph. D. Director General. ALCOR. Email: [email protected]; [email protected] 3 Abdel Aziz BouRahla. Key expert. Medstat III. Prepared part 2 and consolidated parts 1 and 2.

UNITED NATIONS

Economic and Social Commission for Western Asia


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CONTENTS CONTENTS ........................................................................................................................................... 2 Acknowledgements ................................................................................................................................ 4 Acronyms ............................................................................................................................................... 5 Background information ...................................................................................................................... 7 Executive summary ............................................................................................................................... 8 INTRODUCTION ............................................................................................................................... 10

Objective of the guide .................................................................................................................... 10 I. STANDARD TRANSPORTATION ORGANIZATION ..................................................... 11

I.1 Classification of the transportation by type of actors ................................................................... 11 I.2 Classification of the transport sector by mode ............................................................................. 11 I.3 Classification by type of energy ................................................................................................... 12 I.4 Classification of road transportation ............................................................................................ 13

I. 4.1 Passengers Road Transportation ......................................................................................... 13 I. 4.2 Road transportation of goods .............................................................................................. 15

I.5 Transport sector standard actors................................................................................................... 16 I.5.1 A multitude of actors and coordination difficulties ............................................................... 16

II. DATA COLLECTION METHODOLOGIES FOR TRANSPORT SECTOR .................. 17

II-1 - Objectives and results expected from data collection ............................................................. 17 II.1.1 - Characterizing energy consumption ...................................................................................... 17 II.1.2 Energy and environment indicators ......................................................................................... 18 II.1.3- Benchmarking indicators ........................................................................................................ 19 II.2- Data research and needs ............................................................................................................ 19 II.3 – Collection of Energy use data ................................................................................................. 19

II 3.1 Official sources of information: administrative records and business registers ................. 20 II.3.2 Specific surveys .................................................................................................................... 20

III. SURVEY DESIGNS ................................................................................................................ 22

III.1Background ............................................................................................................................ 22 III. 2 Benefits of a harmonized work on final energy consumption surveys for the transport sector ....................................................................................................................................................... 23 III. 4 Survey stages ........................................................................................................................ 25

III.5 Significant variables .................................................................................................................. 26 III.5.1 Transport sector ................................................................................................................. 26 III.5.2 Residential sector ............................................................................................................... 27 III.5.3 Other sectors economic sectors .......................................................................................... 27

III.6 Sample design............................................................................................................................ 28 III.6.1 Definition of population ..................................................................................................... 28 III.6.2 Frames and Statistical unit ................................................................................................. 31 III.6.3 Sampling methods ............................................................................................................... 32 III.6.4 Sampling errors and imputations ....................................................................................... 33 III.6.5 Sample questionnaires ........................................................................................................ 34

IV. MODELS .................................................................................................................................. 36

IV.1. Introduction .............................................................................................................................. 36 IV.1.1 - Top-Down Method ............................................................................................................ 36 IV.1.2 –Bottom-Up Method ........................................................................................................... 37

IV.2 Category of models ................................................................................................................... 37 IV.2.1 Optimization Models ........................................................................................................... 37 IV.2.2 Simulation Models .............................................................................................................. 39


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IV.2.3 Accounting Frameworks ..................................................................................................... 41 IV.2.4 Hybrid Model ..................................................................................................................... 44

V. EXAMPLE OF ENERGY SURVEY IN TRANSPORT SECTOR ..................................... 47

V.1. CANADA experience ............................................................................................................... 48 Fuel Consumption Survey (FCS) ................................................................................................... 48

V.2 Case study of Morocco .............................................................................................................. 50 V.2.1 Survey on the final consumption of energy .......................................................................... 51

V.3 Case study of Palestine .............................................................................................................. 61 V.4.1 Methodology ........................................................................................................................ 61

V.4 Case study of Tunisia ................................................................................................................ 63 V.4.1 Surveys on transportation companies .................................................................................. 67 V.4.2 Surveys in gas stations ......................................................................................................... 69

VI. ANNEX: ......................................................................................................................................... 77

VI. 1 Canada: Quality assurance Framework, Practical checklist and Questionnaire ............... 77 VI.2. Survey on Consumption of Energy in Transport Sector in Morocco .................................... 84 VI. 3. Tunisian Survey on the Road Transportation of Goods: Questionnaire ............................. 87 VI.4 Tunisian Survey on the Road Transportation of Passengers: Questionnaire ........................ 91 VI.5 Tunisian Survey of gas-stations’ users: Questionnaire ......................................................... 96 VI.6 LEAP Demand Modelling Methodologies ............................................................................ 97

References ...................................................................................................................................... 100


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CONTENTS (continued) Page

LIST OF TABLES Table I.1 : Used fuel according to transport mode......................................................................14 Table II.1: Matrix mode of transportation and type of consumed energy...................................18 Table III.1: Correspondence between the energy sector branches and ISIC Rev.4.....................30 Table III.2: Correspondence between the industry sector branches and ISIC Rev.4...................31 Table III.3: Correspondence between the others sectors to ISIC Rev.4......................................32 Table III.4 Example of sample frame and sampling unit according the economic activity sectors........................................................................................................................................33 Table III.5: Respondents needed for 95 percent confidence level.............................................35 Table III.6 : The advantages and inconveniences of open and closed questions......................36 Table V.1 : Sampling approach ................................................................................................55 Table V.2 : Obtained sample size according to selected sectors.................................................57 Table V.3: Quotas adopted at national level by socio-professional category, type of fuel and vehicle age.................................................................................................................................58 Table V.4 : Allocation of the sample of motorcycles according to region..................................58 Table V.5: Main difficulties and uncertainties encountered and the solutions adopted..............60 Table V.6: Number of Vehicles, Employed persons and Main Economic indicators....................64 Table V.7: Barriers hampering the Rational Use of Energy in transports...................................66 Table V.8: Final agenda of the survey according to the selected zone and total of vehicles surveyed....................................................................................................................................68 Table V.9: Population and sample of selected companies according to transport segment.........70

LIST OF GRAPHS Graph I.1: The distribution of energy consumption in Tunisia (2010)..........................................12 Graph I.2: Classification of transportation system......................................................................13 Graph I.3: Classification of Passengers' domestic road transport................................................15 Graph I.4: Classification of Goods' domestic road transport......................................................16 Graph III.1: Cross classification of energy consumers and use by purpose IRES......................23 Graph III.2: Main phases for the implementation of a survey....................................................26 Graph IV.1: The structure of gasoil consumption in Tunisia (2010)............................................38 Graph IV.2: Used model approach for estimating energy consumption in Tunisia.......................38 Graph V.1: Economic branches covered by the survey........................................................................53 Graph V.2: Implementation process of the Moroccan survey....................................................59 Graph V.3: Geographic and temporal distribution of the sample used in the survey ..........................73 Graph V.4: Metadata describing the concept “vehicle” .......................................................................74 Graph V.5: Annual average travelled distance of private cars by fuel type and age.............................76

Acknowledgements

This first methodological document was prepared within the Development Account Project on Energy Statistics and Balance in the ESCWA Region under the responsibility of the Project Manager Dr Wafa Aboul Hosn, Chief Economic Statistics Section, UNESCWA. The authors of the document are Mr Abdelaziz Bourahla, Expert consultant in charge of MEDSTAT III - Energy sector and Dr Rafik Missaoui, General Director of ALCOR.


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Acronyms

ADEME Agency of Energy Conservation and Environment (France) ADEREE Agency of Energy Efficiency and Renewable Energy (Morocco) ANME National Agency of Energy Conservation in Tunisia ATTT Road Transportation Technical Agency (Tunisia) AUGT Grand Tunis Urbanism Agency (Tunisia) CHP Combined Heat and Power DENA DG CLIMA

Agency of Energy Conservation (Germany) Directorate-General for Climate (European Commission)

DG-ENER DG-ENV

Directorate-General for Energy (European Commission) Directorate-General for Environment (European Commission)

DG-TREN DG XVII

Directorate-General for Mobility and Transport (European Commission) Directorate General XVII of the European Commission

DOP Directorate of Observation and Programming (Morocco) DS Direction of Statistics (Morocco) EFOM Energy Flow Optimization Model Energy 20/20 Integrated energy modelling system for electric and gas utilities ENPEP Balance Energy and Power Evaluation Program EPSEM Equal Probability Selection Method ESCWA Economic and Social Commission for Western Asia (United Nations) EU European Union EURELECTRIC Association of the Electricity Industry in Europe: Producers, suppliers, traders

and distributors EUROGAS FYROM

Association representing the European gas wholesale, retail and distribution sectors Former Yugoslavian Republic Of Macedonia

GHG Greenhouse Gas GPM Moroccan Petroleum Group (Morocco) GVWR Gross Vehicle Weight Rating GWh Giga Watt-hour HCP Haut Commissariat à la Planification IAEA International Atomic Energy Agency IDB Islamic Development Bank IDEA Agency of Energy Conservation (Spain) IEA International Energy Agency INS National Statistics Institute (Tunisia) IPCCC Intergovernmental Panel on Climate Change IRES International Recommendations for Energy Statistics ISIC International Standard Industrial Classification of All Economic Activities KT Kilo-Tons LEAP Long-range Energy Alternatives Planning model MAED Model for Analysis of Energy Demand MARKAL MARKet Allocation (Model developed by IEA/ESTAP MEDSTAT Euro-Mediterranean Statistical Cooperation Programme MEM Ministère de l'Energie, des Mines, de l'Eau et de l'Environnement (MEMEE)


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MIDAS Large-scale system of country-specific energy models for medium-term energy planning

MPC Mediterranean partner countries MRV Measure, Reporting and Verification NACE General Name for Economic Activities in the European Union NAMAs Nationally appropriate Mitigation Actions NEEAPs National Energy Efficiency Action Plans ONE National Office of Electricity (Morocco) Passengers-year/veh Number of passengers transported by one vehicle during a year Pass-km/seats Total number of passenger-kms divided by the number of offered seats Pass-km/veh-year Number of passenger-kms operated by one vehicle during a year POLES Prospective Outlook on Long-term Energy Systems PPT Public passenger’s transportation vehicles PRIMES EU Energy system model ( E3MLab – National Technical University of

Athens) PV Private Vehicle RES Renewable Energy Statistics SAMIR Société Anonyme Marocaine de l'Industrie du Raffinage SKO Site – Kilometer Kilometers offered SNCFT National Company of Rail Transport (Tunisia) TCO2e Ton of CO2 Equivalent TKO Ton – Kilometer Kilometers offered Toe Ton of Oil Equivalent Ton-km/veh-year Number of passenger-tons operated by one vehicle during a year Tons-year/vehicle Number of Tons transported by one vehicle during a year Vehicle-km Unit of measurement representing the movement of a vehicle over one

kilometer kilometer WASP Wien Automatic System Planning Package model WB World Bank


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Background information Energy represents a basic input to all sectoral and national development plans, particularly in ESCWA member countries where the energy sector has a vital role in the economic and social development. While the development of national policies is highly dependent on the availability, accuracy and reliability of statistical energy production and sectoral consumption information, the quality of energy statistical information in most of ESCWA member countries still needs capacity building to meet the appropriate statistical requirements for formulating national development plans and international reporting.

The ESCWA region4 provides some 42.5 per cent of global oil reserves and 24 per cent of natural gas reserves, according to 2011 statistics. Total energy production in the ESCWA region, indicating that this region accounted for 28.7 % of world crude oil production and 13 % of natural gas production in 20115.

Meanwhile, data collection on energy is faced by several problems which affect its availability and reliability. As a result, there is an urgent need in the region to upgrade the level of awareness and build statistical capacities in the field of energy statistics as well as for harmonizing the definitions and classification of energy statistical data.

Energy surveys is considered as one essential method in providing data for better energy and environment management especially in the context of climate change mitigation and adaptation which falls under the organizations mandate. Therefore, ESCWA/SD/Economic Statistics Section in cooperation with MEDSTAT seek IDB’s cooperation through providing funding directly national statistical offices to member countries to conduct energy consumption surveys. This will represent an important component to the capacity building process. The regional dimension will be more indicative on the trends and measures that should be taken at the regional levels.

Many of ESCWA member countries have not acquired yet primary data from surveys on energy supply and use which impede their endeavour to design strategic policies towards energy efficiency, the calculation of CO2 emissions to mitigate climate change effects and sustainable development.

ESCWA obtained funds from the Development Account to implement a project which includes many activities on strengthening statistical capacity for the ESCWA countries in energy statistics and energy balance.

ESCWA is providing a training manual on compiling data on energy use of the main energy products in the different sectors mainly transport, with reference to industry and other sectors for the Arab Region. Methodologies will include using surveys and models for estimation (Supply and Use), sample questionnaires and case studies from three Arabic countries (Morocco, Egypt and Palestine) and one developed country (Canada).

4 ESCWA member countries: Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Palestine, Qatar, Saudi Arabia, Sudan, Syria, Tunisia, United Arab Emirates and Yemen 5 UNESCWA 2011 Statistical Abstract of the ESCWA Region Issue 31. E/ESCWA/SD/2011/7


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Executive summary The publication will serve as reference material on methodologies for data collection on energy use by the transport sector, models for estimation (Supply and Use) and case studies from the Arab Region” with case studies on Morocco, Tunisia, Palestine and from one developed country (Canada).

The publication is divided in six main parts:

Starting with a brief introduction of the importance of energy use data in part one, the second part describes the standard transportation organisation based on actors, mode of transport and type of energy. More details are provided about the road transport with distinction between passengers and goods and highlighting difficulties faced between different actors and their coordination process.

The third part explains the commonly used methodologies for data collection of the transport sector focusing more on road transport of passengers and goods and the sector' standard actors.

Fourth part discusses designing surveys in details. It explains the main stage of the guide, then describes the principles that need to be analysed in final energy consumption surveys for the transport sector, which concerns all the economic activity sector and mode of transport, also included the sample design to get data on target variables. Questionnaires samples are attached as part of the annex VI.

The fifth part describes the existing models for estimating energy statistics covering both supply and demand, and highlighting through widely used software forecasting and planning details for transport sector and experiences according to models categories.

Finally, the sixth part presents the case studies from one developed country and other relevant experiences from countries of the Arab region presenting the experience of Morocco which included all energy consumers sector for transport purposes, then the Tunisian one at the level of transportation companies and gas stations, and conclude with the methodology used in Palestine for the public transport companies' survey.

The publication concludes with contribution to proposing a plan for obtaining information on energy use in the different sectors.


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تنفيذي لخصم

استهالكالهدف األساسي من هذا المنشور هو تقديم مواد مرجعية بشأن منهجيات لجمع البيانات المتعلقة ب :ودراسات حالة من المنطقة العربية ) الستهالكالعرض وا(الطاقة في قطاع النقل، ونماذج لتقدير

.اندآ هو و واحد من البلدان المتقدمة بلدالمغرب، تونس، فلسطين و

:أجزاء رئيسيةستة إلى وينقسم المنشور

قطاع مينظتقة، يكرس الجزء الثاني إلى وصف الطا الستهالكبعد مقدمة مختصرة عن أهمية البيانات ن التفاصيل حول النقل البري ع ةدازي ،المستخدمة وسيلة النقل ونوع الطاقة ثم لجهات الفاعلةلالنقل وفقا

وآثرهاالجهات الفاعلة اههئع وتسليط الضوء على الصعوبات التي تواجمع التمييز بين الرآاب والبضا .على التنسيق

أآثر مع قطاع النقل في الطاقة استهالك يشرح الجزء الثالث المنهجيات المستخدمة عادة لجمع بيانات .ز على النقل البري للرآاب والبضائع والجهات الفاعلةيترآ

مسح بعرض و تفسير المرحلة الرئيسية للدليل، ثم بوصف المتغيرات في تصميم ال رابعويتعلق الجزء ال اتلالستهالك النهائي للطاقة لقطاع النقل، الذي يتعلق بكل قطاعالمسوح الدراسات التي ينبغي تحليلها في

وبعد ذلك يشرح تصميم العينة للحصول على البيانات المتعلقة . النشاط االقتصادي ووسائل النقل . المرفق في المستهدفة و ينتهي هذا الجزء بعرض بعض نماذج االستبياناتبالمتغيرات

تغطي جانبي العرض وتي مخصص لوصف النماذج القائمة لتقدير إحصاءات الطاقة الالخامس الجزء بعض التفاصيل البرمجيات المستخدمة للتنبؤ والتخطيط مع لغالالطلب، وتسلط الضوء من خالل است

. التجارب وفقا للفئات من النماذج عضبلقطاع النقل وعرض

من البلدان المتقدمة والخبرات األخرى من بلدان أولى ،وأخيرا، الجزء السادس يعرض دراسات حالة التي ة االقتصادي تالنشاطا و أحدث تجربة في المغرب والتي تغطي آل تقديم أول :المنطقة العربية

وتنتهي مع نيزبنس على المستوى شرآات النقل ومحطات التونتجربة تستهلك الطاقة ألغراض النقل، ثم .المنهجية المستخدمة في فلسطين لمسح شرآات النقل العام

للحصول على معلومات بشأن استخدام الطاقة في مختلف عملويختتم المنشور بمساهمة القتراح خطة .القطاعات


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INTRODUCTION

The design, the implementation and the monitoring of national energy policies require relevant indicators reflecting the energy use performances at macro and sector level. For developing countries the development of databases on energy use and greenhouse gas emissions will be a key factor for the development of new mitigation financing mechanisms (NAMAs, sectoral mechanism, etc.) currently under consideration for the new international climate governance regime. In fact these mechanisms will need Measures, Reporting and Verification systems (MRV) to prove the integrity of these actions. Also, for the League Arab States (LAS) Energy Efficiency Directive, such indicators are crucial for the monitoring and the assessment of the National Energy Efficiency Action Plans (NEEAPs).

Particularly, transport sector is certainly one of the most intricate sectors when it comes to defining and implementing energy efficiency policies, because of the lack and complexity of data collection. Several factors contribute to its complexity, of which mainly:

The crucial social and economic role of this sector in each country daily activities, involving several stakeholders, interest groups and operators;

The lack of information and absence of know-how about the sector’s different dimensions, impeding the execution of reliable analyses and the suggestion of appropriate measures;

The absence of data collection and reporting at national level is a major inhibitor of action.

All these previous concerns stress the importance and necessity to constitute an information system on data and indicators on transportation and its use of energy.

This system will provide a:

• Better understanding and analysis of the sector’s current situation in terms of energy use;

• Assessment of energy efficiency policies and programs in the field of transportation using appropriate indicators;

• Prospective vision about the sector’s future consumption in terms of new measures and policies that are likely to be adopted by the sector’s major stakeholders including the State.

Objective of the guide

The main purpose of this guide can be summarized as follows: • Provide a convenient starting point for those making surveys and survey plans in

government agencies (ministries / institutions / departments). • Create a shared understanding of various terms and concepts related to survey

formulation. • Provide a common framework for reviewing and evaluating FEC surveys. • Summarize the regional experience.

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Energy consumption in the transport sector involves incredibly diversified fleets and activities. The diversification of fleets and activities may be perceived at three preliminary levels:

• First level representing the territory where transportation is performed : domestic or international ;

• Second level representing the nature of the activity : goods or passengers; • Third level: representing the mode of transportation: ground, railways, maritime, or

air.

Graph I.2: Classification of transportation system.

Beyond the third level, the analysis must be made taking into consideration the characteristics of every transportation mode, corresponding activities and the field of operation.

I.3 Classification by type of energy

The transport sector is known for being heavily dependent on oil products, consuming as much as 90% of oil resources. Most fuel used in the transport sector is specific to the transportation mode as shown in the following table:


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Mode Gasoline Diesel LPG Kerosene Fuel Electricity

Road transport x x x Railways x x Maritime x x

Air transport x Table I.1 : Used fuel according to transport mode [5] Gasoline is almost entirely used by car drivers with the exception of a small portion consumed by motorbikes. Air companies use kerosene, while fuel is exclusively used by maritime shipping companies.

However, diesel constitutes an exception due to its wide usages in:

1- All types of vehicles : cars, lorries, trucks, buses, train locomotives, and ships; 2- All economic sectors, mainly transportation, industry, agriculture and fishing.

I.4 Classification of road transportation

Computing the use of energy in the transport sector requires perfect knowledge of the different fleets and infrastructures, as well as of data collection processes.

Energy consumption can be easily calculated for all modes run by organized structures except for the road transport sector. This facilitates the collection of data directly from companies in charge of the corresponding sector.

Hence, it is essential to focus on the road transportation subsector, due to the complexity and diversity of its activities and to its sparse and diluted character, which represents the main constraint standing against the collection of reliable data.

The classification of the fleet of vehicles by type differs between countries. According to the structure of vehicles registration files, the road transportation fleet is generally classified based on the following system:

- Private cars : cars owned by individuals used for the transportation of passengers, with a GVWR (Gross Vehicle Weight Rating) not exceeding 3500 kg;

- Commercial vehicles: vehicles designed for the transportation of goods, with a payload exceeding 500 kg;

- Trucks: vehicles designed for the transportation of goods with a full payload not exceeding the limit of 3500 kg;

- Mixed cars: engine driven cars designed for the transportation of passengers and goods, with a payload not exceeding 3500 kg, and the number of seats ranging between four and nine including the driver’s;

- Lorries: vehicles used for the transportation of goods; - Buses or public passengers transportation vehicles (PPT): vehicles designed for the public

transportation of people, with more than 9 seats; - Road tractors: engine driven vehicles that can be combined with trailers, bearing part of

the overall weight; - Agricultural tractors self-propellant vehicles specifically designed to pull or operate

machines commonly used in agricultural activities. When calculating energy used by the transport sector, considered vehicles are those transporting goods and passengers only.

I. 4.1 Passengers Road Transportation

This includes two major categories based on the type of transportation. They are as follows:


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Individual transportation: motorbikes or cars with less than 9 seats (including the driver’s); (for private /public uses).

Collective transportation: vehicles with more than 9 seats.

Private transportation on the other hand is categorized based on the use of vehicles. It can be summarized as follows:

Graph I.3: Classification of Passengers' domestic road transport.

The first “Own use” group includes:

Private use of cars registered in Tunisia ; Private use of vehicles holding foreign car plates, either by foreign visitors or by

Tunisians residing abroad; Commercial use by companies; Use by administrations and local communities

The second group of uses, named "Service use" of users includes the following service providers:

Car rental companies Tourism transport companies Driving schools Limousines Private taxis Collective taxis Inter-city taxis Rural taxis

It is necessary for this type of transportation mode to further refine analysis in terms of category of vehicles, type of energy and transportation space.


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Collective transportation is generally broken down into four categories according to services they provide:

The first category includes vehicles designed for private transportation (self-profit) generally offered by companies (personnel) or by associations;

The second category concerns vehicles designed for the public transportation of passengers (for others’ benefit);

The third category includes vehicles designed for the transport of tourists provided by travel agencies;

The fourth category concerns vehicles operated by driving schools.

I. 4.2 Road transportation of goods

This activity includes three main service providers:

Transportation of goods for the benefit of others, offered to the public at cost; this service is entirely performed by private operators.

Transportation of goods for self-profit not offered to the public and not serving others. It is performed by public and private operators;

Agricultural transportation is made for self-profit but exclusively related to agricultural products. It is performed by public and private operators.

Graph I.4: Classification of Goods' domestic road transport.

The two first services use mixed vehicles, trucks, lorries, road tractors, trailers and semi-trailers to provide their services.

The third service also uses the same vehicles in addition to specific agricultural vehicles (agricultural tractors, trailers and semi-trailers).


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I.5 Transport sector standard actors

I.5.1 A multitude of actors and coordination difficulties

Planning the rational use of energy in the transport sector is a sinuous process due to the sector’s disparate activities, multiplicity of stakeholders, and the necessity to implement thorough changes in various transportation systems, induced by the search for the sector’s sustainable development.

In fact, transport refers to the mobility of people and goods, of which the organization and management must combine three main responsibilities:

- Responsibility in terms of transportation policy and the organization of sectors and sub-sectors;

- Responsibilities in terms of transportation infrastructure and roads; - Transportation in terms of urban planning and territory development.

Liabilities imply the presence of multiple institutional actors holding different prerogatives, with obvious different objectives and constraints at the national and local levels. The rational use of energy depends on the role and the impact of actions of various stakeholders. For instance, major stakeholders playing a role in the transport sector are:

- Ministry in charge of transport and related institutions (ground transport, sea shipping department, civil aviation, technical inspection agency, cars registration database, etc.)

- Ministry of Equipment, - Ministry of Environment, - Ministry of Territory Planning and Development, - Ministry of Energy, - Local communities, - Transportation operators (public and private), - Oil products distribution companies

During the designing phase of the rational use policies for energy consumption in transport, the multiplicity of stakeholders represent many difficulties for gathering information and data.

For this reason, some countries have created structures to enact as influential drives, moderating stakeholders’ negotiations and interventions to centralize data related to the sector and implement actions impacting the use of energy.

Some of the main existing structures are:

- Energy control agencies (ADEME-France ; IDAE-Spain ; DENA-Germany ; ANME-Tunisia ; ADEREE-Morocco; …) in charge of implementing public policies for the rational use of energy in different sectors and particularly in the transport sector;

- National transport observatories in charge of collecting, processing and managing data related to the sector (economic, energetic, social, supply/demand, etc.);

- Transport organizing authorities (urban, regional, etc.): including representatives of the various public institutions mentioned above working closely with transportation authorities to implement development policies agreed upon by different stakeholders, and ensure complementarities and continuity between public various transportation services.


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II. TRANSPORT SECTOR DATA COLLECTION METHODOLOGIES FOR TRANSPORT SECTOR

II-1 - Objectives and results expected from data collection

The implementation of a development strategy to ensure energy efficiency depends on the knowledge and control of data that may guide priorities of the sectors’ stakeholders and will later evaluate the efficiency of actions and implemented programs.

Due to the complexity of the transport sector, required data may be defined based on its utility, pertinence, facility of use and mainly collection.

In this regard, several data categories may be considered according to the field or type of adopted strategy as follows:

• Macroeconomic data, providing information on the demographic, economic and social context where the transport policy is implemented. These are quantitative data covering several years;

• Data on transportation supply, providing quantitative information for the evaluation of the transportation system’s capacity to respond to the needs of the various economic sectors. They are designed to check whether the country actually holds all means to successfully implement the transportation policy;

• Data on transportation needs are quantitative and include all requests related to transport: modes, means, contexts and environments. They are used to assess the efficiency of activities as well as progress of their performance. They also help to monitor the main activities of the transportation policies and particularly control of their energy efficiency;

• Energy and environment indicators are mainly calculated based on previous data. They help identifying results and can trigger modifications induced by the implementation of an energy efficiency policy in the field of transportation, as well as its effects on the transportation policy’s objectives and also on related socioeconomic aspects.

II.1.1 - Characterizing energy consumption

The characterization of energy consumption will help develop an energy efficiency policy in the field of transportation. In fact, it is necessary to understand the role of transport in the energy balances to develop such policy. Therefore:

• The final energy consumption of the sector by oil product: gasoil, gasoline, kerosene, etc. • The electricity consumption of the sector mainly where there are electrified trains and

tramways.

On the other hand, energy consumption should be estimated according to the mode of transportation and the type of consumed energy, as described in the following matrix:

Gasoline Gasoil Kerosene Electricity …….

Passenger Road transport per mode

Goods transport per mode

Rail transport

Air transport

Maritime transport

Table II.1: Matrix mode of transportation and type of consumed energy.


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In some countries, where energy regulation can target large energy consumer companies, it is necessary that data on energy use should be available at the micro level. For example in Tunisia, energy efficiency law requires that all transportation companies with an annual consumption of 500 Toe to conduct a periodical energy audit at least every 5 years. This way, all companies of the sector are involved in the overall energy efficiency policy.

Finally, the more data characterizing sectors and sub-sectors are available and reliable, the better oriented the energy efficiency strategy is to generate more important energy saving potentials in relatively short periods.

II.1.2 Energy and environment indicators

Indicators are mainly calculated based on previous data. They are used to measure results and modifications induced by the implementation of an energy efficiency policy in the transport sector and to assess the environmental impacts of the transport policy and related socio-economic aspects.

Indicators will also be used according to levels of analysis described above. We suggest for transportation modes, types and contexts, indicators to measure:

1. Transportation supply in terms of seats and payloads;

2. Transportation demand in terms of: Energy specific consumption ; Energy total consumption ; Transported passengers ; Transported tonnage ; Number of kilometer s travelled ; Kilometric seats offered; Kilometric tons offered; Volume of the traffic of passengers in passenger/kilometer ; Volume of the traffic of goods in tons/kilometer ; Volume of the traffic of vehicles in Vehicles/kilometer .

3. Energy efficiency in terms of: Transportation supply : SKO6/energy consumption and TKO7/energy

consumption ; Transportation demand: Travel-km/energy consumption and T-km/energy

consumption.

4. Mobility of the population in terms of : Kilometer /inhabitant Kilometer /schooled inhabitant Kilometer /active inhabitant Ton/inhabitant Traveller/inhabitant

5. Balance of the sector’s energy: Total energy consumption of the transportation system; National balance of energy consumption; International balance of energy consumption.

6. Sector’s energy intensity

7. All energy indicators based GHG emission calculation.

6 Number of available sites x total number of performed kms 7 Available capacity x total number of performed kms


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II.1.3- Benchmarking indicators

These indicators contain elements for reliability check of previous indicators and provide necessary corrections and adjustments. They cover two types of data:

Data related to the national consumption of hydrocarbons distributed according to the type of delivery (retailers: gas stations – direct customers: transportation companies, ports, airports). Data will take into consideration the oil smuggling phenomenon in border regions, often applied in developing countries.

Data related to road traffic counting surveys for all regions and types of roads

II.2- Data research and needs

As previously stated, assessing energy consumption for other types of transport is quite simple due to their organizational and centralized aspect, except for road traffic. We will therefore focus in this section on the issue of collecting data related to road transportation activities.

It is necessary to perfectly know the fleet of vehicles and its evolution in order to be able to assess energy consumption and its evolution in time. This is also a key factor to forecast future energy consumption. However, estimating the energy consumption of any fleet of vehicles requires the compilation of three main data sets:

Composition of the fleet (number, type, power, age, payload, etc…) Average consumption (l/100 km) of every homogenous vehicle family in the fleet Number of kilometer s travelled by vehicles

Though data specified above may seem simple, their collection is quite difficult due to the multiplicity of players, and to the complexity and diversity of the structure of the fleet. In order to assess consumption, three key elements are to be considered to assess consumption:

The study of the fleet evolution should consider cars, trucks and lorries separately ; The evolution of the fleet composition should take into consideration the type of fuel, the

fiscal power category or the payload (capacity); Computation of the fleet should take into consideration the number of vehicles retired

from circulation or wrecked. This type of data is usually not well known by public authorities and may mislead the assessment of energy consumption in the transport sector.

Complexity characterizing data collection led some countries to put in place adapted information systems based on simple models to monitor the evolution of their fleet, its energy performance and use of appropriate data. Such systems will respond to at least two main objectives:

First, this would represent a reliable source of information emanating from a recognized and eligible institution, which can be further used by other institutions;

Second, the survey will highlight missing data and also unreliable or disaggregated data, which would constitute an opportunity for the system evolution and improvement;

II.3 – Collection of Energy use data

Data required to assess energy consumption in the field of transport may be obtained from two different sources:

- Official administrative records, such as ministries in charge of energy, energy observatories which are usually in charge of the preparation of the energy balances, Ministry of Transport, statistics institutes, etc. ;


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- Business registers within large transport companies; such as railways companies, air and maritime transport companies, passengers and goods transport companies, etc.

- Specific surveys, particularly for private car transport.

Nevertheless, existing information published by official sources are very often insufficient or incoherent for the proper definition of the sector’s energy balance, mainly vehicles operated by non-organized structures (private vehicles, commercial vehicles, transport for others’ benefit, etc.).

Therefore, it is necessary to conduct field surveys to set the baseline and validate data, which will help to develop indicators adapted to the local context, and eventually design appropriate energy efficiency programs.

However, surveys are usually expensive, and data handling must be performed according to valid and widely known methods leading to coherent and realistic statistics.

II 3.1 Official sources of information: administrative records and business registers

Official data may be obtained from the following sources:

a) Insurance companies and federations holding extensive data about insured vehicles; b) Transport operators; c) Ministry in charge of energy holding statistics about the sale of hydrocarbons. d) Hydrocarbons distribution companies (Total, Shell, Elf, Exxon Mobil, etc.) e) Ministry of Housing, Equipment and Territory Development holding data concerning road

infrastructures and the traffic of vehicles; f) National Statistics Office (NSO) holding considerable data and information, mainly

socioeconomic information and official sources of data in the country; g) Studies and surveys containing updated data validated by public institutions; h) Specific surveys.

II.3.2 Specific surveys

Several types of surveys can be conducted, of which:

Field work (Highway toll stations, technical check-up centres, gas stations, lobbies of specific buildings, etc.): favourite method to know traffic characteristics (origins/destinations), characteristics of vehicles and goods, hourly/daily traffic, etc.

Postal survey This method is generally preferred when the sample is large and when data sought are mainly quantitative. Posted questionnaires must be rather simple to ensure acceptable response rates; complex questionnaires may be dissuasive and discouraging.

Phone surveys: This method applies to the mixed questionnaire: quantitative and qualitative. Low-cost, it enables the collection of a large number of information. However, it requires people’s availability.

Face-to-face: This method enables the collection of qualitative data by collecting information mainly related to practices. It cannot be exhaustive as it is highly time-consuming;


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Internet-based surveys: This method is becoming more popular, though still hard to conduct due to the difficult access to users email contacts.

Regardless of the type of survey adopted, their execution may be made in-house as it may be out-sourced. In any case, the implementation of surveys requires the elaboration of a planning and organization document to be used as a road map for the survey implementation process (preparation, implementation, processing, and elaboration / analysis) including planning means and resources to use.

It is also beneficial to refer to past experiences of service providers having conducted similar surveys, to learn from strengths and good practices, and avoid weaknesses and difficulties.

On the other hand, in addition to surveys’ appropriate budget planning, their success is usually conditioned by observing the following process:

Constitution of a steering committee including key stakeholders (public and private) to involve them and facilitate data accessibility;

Definition of the study scope of work and objectives as well as the expected results; Definition of the target of the study and the sample characteristics; Planning execution (locations, seasons, deadlines, etc..) to reach out to the sample’s target

audience; Implementation of surveys based on questionnaires designed to this effect. It is

recommended to test the questionnaire before the actual beginning of the survey to refine it when necessary;

Implementation of a quality control and monitoring system: - To Monitor response rates (feedback rates); - To assist and coach surveyors; - To monitor the process progress conditions; - To analyze problems encountered and provide appropriate solutions.


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III. SURVEY DESIGNS

III.1Background

According to the IRES8 recommendations about the organisation of energy data collection process, it is necessary to sketch the production, supply, transformation and consumption flows for each type of fuel, to clarify the processes, procedures and the economic agents involved. Secondly, outline the potential data sources for each stage of the flow to determine whether it is feasible to obtain accurate information on a regular basis and making use of such information for own management purposes. Based on this, it is possible to determine the type of energy data that can be obtained from each industries and organizations producing energy, and through regular programmes of enterprise/establishment surveys and administrative sources, and plan the process accordingly.

The following figure shows how statistics on the use of energy products for transportation, for energy purposes (including transport) and for non-energy purposes are cross-classified with the categories of energy consumers and energy industries.

Graph III.1: Cross classification of energy consumers and use by purpose [18]

The economic units under “energy consumers” may carry out activities of production, transformation and/or distribution of energy as a secondary activity (other producing industries).

The energy data collection relies on a legal and institutional framework for energy statistics and the use of agreed compilation methods, for example statistical business registers, administrative data and census or sample surveys, to obtain comprehensive data. The most appropriate collection method should be selected taking into consideration the nature and specific characteristics of the given energy activity, the availability of the required data, and the budget constraints for the implementation of the collecting strategy.

8 International Recommendations for Energy Statistics

Use of Energy:

Activity (ISIC‐based)

Energy industriesElectricty plants

Coal minesRefineries

Etc.Energy consumers

Iron and SteelConstruction

Etc.HouseholdsAgriculture

Etc.

Tran

sformation

Non

‐Ene

rgy use

Energy use

excl, Transpo

rt

Tran

sport


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The current observations on capacity building in energy statistics and balances conducted by regional and international organisations such as UNSD, IEA, ESCWA, OAPEC, OPEC, IFES, and MEDSTAT give a clear idea about the importance of using international standard in developing comparable and reliable statistics. For instance, the MEDSTAT regional programme helped develop a comprehensive approach for reinforcing energy statistics in the Mediterranean partner countries (MPC), in particular for the development of energy balances according to international standards. The harmonization of statistics is crucial for exploitation and use by various decision makers at both national and regional level and consistency with respect to other data in the energy sector and economy.

Thus, the calculation of indicators of energy efficiency and greenhouse gas emissions (GHG) is essentially based on energy balances: it provides a common basis for comparison between different countries. The work developed by the IEA on indicators of energy efficiency and CO2 emissions, and consequently that of the IPCC GHG emissions are the direct result of national energy balances.

III.1.1 What are the main issues?

The unreliability, lack of metadata and data gaps in renewable energies and final energy consumption present an impediment for the compilation energy balance. Data are mostly obtained from the supply side. Regarding road transport statistics available in some ESCWA countries and mainly for gas/diesel oil, data are not disaggregated according to the usage (residential, industrial, agriculture or others). Similarly, the air transport sector reports only the total quantities of consumed fuel. The deliveries of fuel are not separated according to domestic and international flights. The same applies for shipping (domestic and international) and the fishing sector (which must be integrated into the agricultural sector). Also for the amount of fuel and electricity used in compressors and / or pumping stations and other oil and gas pipelines, the information is usually not reported.

It should be noted that the estimation of quantities consumed by type of use is possible only through direct surveys of consumers or suppliers of fuels (such as gas service stations) by classifying shipments according to the economic activity or type of final consumer.

This is not always used by countries in the region, for many reasons, the gaps in the national data collection system reflects the lack of technical expertise (in term of quality control and assurance), human resources and especially financial ones.

III. 1.2. What can be done?

Based on the above and country partners discussions, a plan proposal consisting of two stages can be developed:

- Completing the first production of energy balances harmonized through technical assistance missions. This activity will assist in producing energy tables, as well as analyse the problems and in particular those related to the estimation of final energy consumption,

- Developing surveys that can capitalize on the consolidation of national capacities to produce harmonised, reliable and timely energy data.

III. 2 Benefits of a harmonized work on final energy consumption surveys for the transport sector

The benefits are primarily to strengthen the scientific nature of compilations of energy statistics with:

- A harmonized method for the collection of data based on international standards; - A comparison with sales statistics and the business structure survey that exist in most

countries based on the ISIC classification; - The development of coefficients to estimate fuel consumption based on the structure of

transport sector. These factors can then be used for several successive years; - A direct improvement of the quality and reliability of national energy balances (substituting

estimates by survey results);


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- Strengthening the comparability of data and indicators of energy.

Therefore the results of the survey will enable national statistical institutes and their partners from the ministries of energy, to:

- Improve the business structure survey , by integrating new variables and by improving the response rate,

- Justify the periodic renewal of the survey (every five years, for example) under the supervision of national statistical office or equivalent,

- Mobilize technical and financial resources and raise the profile of the production of energy statistics in the country.

Also as stated in the IRES, it is fundamental to have a proper survey design before carrying out a survey. To achieve this goal a number of steps are required:

1- Define the scope of survey. 2- Define the purpose of the survey. 3- Identify the probable outcomes of the survey.

III.2. 1 Scope of the survey

Energy consumption should take into consideration all types of consumption and transport types in the country regardless of the economic activity.

The survey should cover the following economic sectors: Transport under the nomenclature of economic activities in Residential, Industry, Commercial, Agriculture, etc.

The main energy products covered by the survey are:

- Gasoline - Gas/Diesel oil - Jet Fuel - LPG (Liquefied Petroleum Gas) - LNG (Liquefied Natural Gas) - Electricity

III.2. 2 Purpose of the survey

The main objectives of the survey are to:

• Determine the final consumption of energy in the transport sector for all related stakeholders; • Calculate the energy efficiency indicators for the transport sector; • Assist in evaluating greenhouse gas emissions from the transport sector; • Elaborate forecasting studies based on energy demand of the transport sector; • Provide stakeholders with reliable statistical information on energy consumption in the

transport sector. • Inform the party on the transport sector in energy balances and associated GHG emissions; • to support the medium and long term forecasts of energy demand in the transport sector ; • Developing better energy policies.

III.2. 3 Probable outcomes of the survey

The main outcome of the survey is to provide the institution in charge of energy statistics with a breakdown of the energy consumption in the transport sector at the national level with:

• Type (Industry, Transport, Residential, etc), • Vehicle type (Touristic, Utility, Motorcycles, etc)


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• Form of energy (Gasoline, Gas/Diesel, LPG, etc).

III. 4 Survey stages

The survey could be divided into five different phases:

Graph III.2: Main phases for the implementation of a survey

For each phase, the Contractor shall provide and perform the following tasks:

For Phase 1 - Status and diagnostic

A set of activities should be undertaken at the first phase of the survey, of which: - Scope and format of the survey - Collection of available data: Ensure that the survey will include sufficient data

request to meet the defined objectives - Evaluation of available data: Review of existing data - Study the needs of the National Office in charge of Energy statistics and its partners

For Phase 2 - Establishment of the survey

A detailed methodology should be prepared for this survey that includes the following elements:

o The general organizational setting including the scope of each type of transport activity; o The instruction manual of the interviewer, the controller and the supervisor; supervision

and interviewing staff o Classifications and other documents and reports used for the preparation of the

methodology; o For each type of activity and transport, the following elements should be considered:

• Planning operations • Size of the target sample by stratum structure and the margin of error relating to

each stratum; • Procedures for determining the sample frames and sampling method; • Conception of the questionnaire; • Statistics options (variables extrapolations, framing ...) • Methods of recovery.


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For Phase 3 - Fieldwork implementation

Before starting the survey, a pilot survey should be performed and evaluate the results in coordination with the Administration in charge of this work, to make the necessary adjustments for a smooth running of survey. In this regard, a detailed report of the pilot survey should be prepared and submitted to the administration.

At the end of this third phase, the outcomes report should reflect:

- The status of implementation of the survey; - The main difficulties and uncertainties encountered; - The proposed solutions; - Detailed proposals methods for the statistical analysis, tailored to address any

shortcomings of the field phase.

For Phase 4 - Statistical Analysis

- Receiving and filing questionnaires - conduct interviews and send out questionnaire - Verification of the completeness of the adopted sample - Control of questionnaires (Encoding validation, consistency, etc.). - Data Entry - Clearance of the file (remove inconsistencies) cleaning data - Treatment of non-response - Extrapolation of the results and possible recovery - Metadata: information about the used method - Statistical tables obtained, analyzed and briefly commented. Construct result tables and write review preliminary interpretation and dissemination of results.

For Phase 5: Final Report

A final report of findings and recommendations conclude the fifth and final phase. Analysis and comments on the report of Phase 4 will be developed. Other elements required for this final report were given above.

III.5 Significant variables The methodological approach of the survey: As a reminder, the energy consumption in the transport sector as defined by this survey should include fuel consumption and different types of energy used for appliances and equipment intended for use by whatever transport activity. Therefore, the survey will cover the following sectors: Transport, Residential, Industrial, Commercial and Agriculture.

III.5.1 Transport sector The transport sector concerns the transportation as an economic activity as defined in the International Standard Industrial Classification of All Economic Activities (ISIC).

In order to meet the objectives of this survey, it will be necessary to distinguish between:

1. Land transportation

It includes the following branches:

- Rail transport With a distinction between passenger rail and freight;


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- Road transport It will be necessary to distinguish between: o Passenger (private and collective/public) and goods transport; o Type of vehicle 2 wheels, 3 wheels, passenger vehicles and commercial vehicles.

These distinctions affect the stratification and the content of questionnaires.

• Passenger transport With a distinction by type of carriers:

a. Public passenger transport: They represent companies of public passenger transport in urban areas. Also we should distinguish between publically owned and privately owned companies.

b. Professionals: This activity will involve only small and large taxis with a license issued by the administrative boundary, regardless of transport medium (urban and / or interurban).

c. Transport companies: Represented by all actors engaged in structured public transport passengers in interurban.

• Transport of goods These are businesses and carriers engaged in a decorated carriage of goods for third parties.

2. Air transport

Air transport includes two branches:

International Aviation These aircraft using "aviation fuel" delivered to aircraft for international aviation. Differentiation between domestic and international aviation should be established according to the geographical location of departure and arrival, and not by the nationality of the airline.

Domestic Aviation These aircraft using "aviation fuel" delivered to aircraft for domestic aviation.

3. Maritime transport

The survey should distinguish between inland and international. The differentiation between these two types of shipping will be determined on the basis of port of departure and port of arrival, and not by the flag or nationality of the ship. A sea crossing is a national crossing that begins and ends in the country, without any stop in a foreign port. Fuels consumed by fishing vessels are not covered by this survey; they are part of the consumption of agriculture.

III.5.2 Residential sector It represents the cars owned by households. This category of transportation will be surveyed with customers' service stations to be selected by occupational category. To this end, the choice of service stations shall be made taking into account the geographical location of these stations: city, highway, toll, etc... The seasonality of this activity (holidays) should also be taken into account. All collected data on this topic will include detailed transportation by vehicle type and fuel used and vehicle age.

III.5.3 Other sectors economic sectors A sample of firms is investigated for each of the economic sectors below. For these units, the consumption of energy reserved for the transport activity in the unit will only be considered.

• For the Energy sector:


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The survey will provide the consumption of energy for transportation. Data will be considered as the own consumption of energy in the energy balance of the International Energy Agency and the consumption of the energy sector in the national nomenclature.

• For Industry sector: The survey will be conducted according to a stratified sampling based on the segmentation of the company's activity branch (business register).

• For the Tertiary sector: In addition to stratification by size, it is important to distinguish the following activities: Administration, hotels, hospitals, education, retail, tourism, car renting and more. It should be noted that companies distributing petroleum products are included in this section.

• For Agriculture: This survey will be conducted by showing the size of operation and the main crops.

All data on energy consumption of transport must be detailed by type of vehicle and use of transport (goods, passengers, staff...). And as recommended by ISIC vehicles and equipments used by the military should not be part of the scope.

III.6 Sample design The final energy consumption surveys are basically conducted by the means of sampling techniques. A small and representative collection of statistical units are selected from a global population and are used to determine the consumption of that population. The idea of applying this approach would save resources (human, financial and time resources) and workload and gives results with known accuracy that can be calculated mathematically.

Therefore, it is essential to follow several steps in the selection of the sample:

• Defining the target population or identification of target population • Specifying a sampling frame: (criteria & parameters need to be defined) a set of items

or events possible to measure • Specifying a sampling method for selecting items or events from the frame • Determining the sample size • Implementing the sampling plan • Determining the type of sampling • Pilot phase to test the sampling plan and adjust it eventually • Sampling • Data collecting

III.6.1 Definition of population

At the preliminary stage, it is important to define clearly the target population. It should indicate clearly which elements are included and/or excluded in the target population.

As advised in the IRES, an efficient data collection requires a good knowledge of the main groups of data reporters, to customize data collection methods as necessary. It is recommended to distinguish, as applicable, at least the following three reporter groups: energy industries, other energy producers and energy consumers.


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As part of the study which covers Final Energy Consumption for Transport in the country regardless of the economic activity sector, the surveys’ target population includes all energy users for transportation purposes:

i. All enterprises engaged in economic activity of the country and which belong to following branches or groups of branches described in detail in ISIC. As recommended in the IRES, the countries should identify, as far as feasible and applicable, the groups of energy consumers as presented in the following tables with their correspondence to ISIC Rev. 4. This correspondence facilitates the link with the national nomenclature and the data collection and the integration of the basic data with other statistics.

Energy industries

Energy industry groups Correspondence in ISIC Rev. 4

Electricity and heat plants Division: 35 - Electricity, gas, steam and air conditioning supply

Pumped storage plants

Coal mines Division: 05 - Mining of coal and lignite

Coke ovens Group: 191 - Manufacture of coke oven products

Coal liquefaction plants Class: 1920 - Manufacture of refined petroleum products

Patent fuel plants Class: 1920 - Manufacture of refined petroleum products

Brown coal briquette plants Class: 1920 - Manufacture of refined petroleum products

Gas works (and other conversion to gases) Class 3520: Manufacture of gas: distribution of gaseous fuels through mains

Gas separation plants Division: 06 – Extraction of crude petroleum and natural gas

Gas to liquid (GTL) plants Class: 1920 – Manufacture of refined petroleum products

LNG plants / regasification plants Class: 0910 - Support activities for petroleum and natural gas extraction Class: 5221 - Service activities incidental to land transportation

Blast furnaces Class: 2410 - Manufacture of basic iron and steel

Oil and gas extraction Division: 06 - Extraction of crude petroleum and natural gas Class: 0910 – Support activities for petroleum and natural gas extraction

Oil refineries Division: 19 - Manufacture of coke and refined petroleum products

Charcoal plants Class: 2011 - Manufacture of basic chemicals

Biogas production plants Class: 3520 - Manufacture of gas; distribution of gaseous fuels through mains

Nuclear fuel extraction and fuel processing

Class 0721 - Mining of uranium and thorium ores Class: 2011 - Manufacture of basic chemicals

Other energy industry not elsewhere specified

Class: 0892 – Extraction of peat ….

Table III.1: correspondence between the energy sector branches and ISIC Rev.4


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Other industries

Industry Correspondence in ISIC Rev. 4

Iron and steel ISIC Group 241 and Class 2431. Consumption in coke ovens and blast furnaces are defined as part of Transformation Processes and Energy Industry Own Use.

Chemical and petrochemical ISIC Divisions 20 and 21. Consumption by plants manufacturing charcoal or enrichment/ production of nuclear fuels (found in ISIC 2011) is excluded, as these plants are considered part of the energy industries.

Non-ferrous metals ISIC Group 242 and Class 2432.

Non-metallic minerals ISIC Division 23. Report glass, ceramic, cement and other building materials industries.

Transport equipment ISIC Divisions 29 and 30.

Machinery ISIC Divisions 25, 26, 27 and 28. Fabricated metal products, machinery and equipment other than transport equipment.

Mining and quarrying ISIC Divisions 07 and 08 and Group 099. This excludes the mining of uranium and thorium ores (Class 0721) and the extraction of peat (Class 0892).

Food and tobacco ISIC Divisions 10, 11 and 12.

Paper, pulp and print ISIC Divisions 17 and 18. Includes production of recorded media.

Wood and wood products (Other than pulp and paper)

ISIC Division 16.

Textile and leather ISIC Divisions 13, 14 and 15.

Construction ISIC Divisions 41, 42 and 43.

Industries not elsewhere specified ISIC Divisions 22, 31, 32 as well as any manufacturing industry not listed above.

Table III.2: correspondence between the industry sector branches and ISIC Rev.4

Other consumers

ii. Transport sector • Domestic aviation • Road transport • Railway • Domestic navigation • Pipeline transport • Transport non elsewhere specified

iii. Public administration and Tertiary sectors iv. Agricultural, Forestry & Fishing sectors v. Households sector


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Others sectors Correspondence in ISIC Rev. 4

Household ISIC Divisions 97 and 98

Commerce and public services ISIC divisions: 33, 36-39, 45-47, 49-51, 52-53, 55-56, 58-66, 68-75, 77-82, 84-88, 90-96 and 99

Agriculture, Forestry ISIC Divisions 01 and 02

Fishing ISIC Divisions 03

Not elsewhere specified ISIC Class 8422 Table III.3: correspondence between the others sectors to ISIC Rev.4

Important: [18] By convention, transport fuels used in fishing, farming and defence (including fuels to military means of transport) are not part of transport in the energy balance, because the main purpose of the fuel use in these activities is not transport, but rather agriculture and defence. Similarly, energy used in lift trucks and construction machineries on the industry sites is considered as stationary consumption, not transport.

III.6.2 Frames and Statistical unit Once the population has been defined, it is necessary to choose the sampling frame. The sample frame is the set of units from which sample has been drawn, given the sampling method that is chosen. Fundamentally, a sampling frame is a complete list of all units of the population under study. From this frame, one can randomly select an appropriate number as representatives of the population that was chosen to take part in the survey. If such a sampling frame is not available, then one is restricted to less satisfactory forms of samples which cannot be randomly selected because not all individuals within that population will have the same probability of being selected for the sample. Thus the sample is a non-probability sample. Simply, three characteristics should be evaluated in selecting sample frame:

1. Comprehensiveness: it is important to assess how completely the sample frame covers or not the target population.

2. Probability of selection: to check if it is possible or not to calculate the probability of selection of each person sampled.

3. Efficiency or the rate at which members of the target population can be found among those in the sample frame.

The sampling unit is basic concept in sampling theory which refers to the observed unit which information is collected and that provides the basis of analysis. In case of multi-stage sample, the sampling unit is composed by a set of units that could be blocks, households, and individuals within the households. Concerning the FEC Transport survey, the sampling frame could be created from mutual exhaustive list of enterprises, administrations and households covering all energy users for transportation purposes in the country. It should contain all the units that are in the survey target population, without duplication or omissions.

N° Sector Branches (*) Sample frame Sampling unit

1 Energy Oil refineries, etc List of enterprises "energy producers"

Enterprise

2 Industry Iron and steel, etc List of companies of industry sectors Enterprise 3 Transport Domestic aviation

Road Railway Domestic

List of companies or vehicle owners operating in the field of transport

Enterprise


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navigation etc

4 Administration List of Administrations Administration 5 Residential Urban/Rural List of households Household /

Vehicle 6 Agriculture List of farms and Individual

producers

Farm Individual producers

Table III.4 Example of sample frame & sampling unit according the economic activity sectors (*) based on the branches defined previously in target population (see tables 1, 2 & 3) As recommended in the IRES, a maintained business register should provide the list of enterprises in the country. If not, the list could be drawn from the latest economic census.

III.6.3 Sampling methods [13] According to the sampling theory, there are several ways to select the subjects who will be responding to the objectives of a research. Essentially, sampling methods falls under two general approaches: the non-probabilistic and probabilistic samples. Non-probability sampling: A non-probability sample is a sample that does not offer all individuals in the population equal chance, or predetermined of being selected. The probability of selection of an individual in the population is therefore unknown. It becomes impossible to calculate the precision of the results and use the results to extrapolate on the entire population. It is possible that respondents may not be representative of the population. This means that results of survey will be biased. Therefore, it is recommended to avoid using these sampling techniques. Probability sampling

A sample is considered to be probabilistic when the probability of being selected is equal for all individuals of the selected population. It is then possible to perform calculations to measure the accuracy of the results of the survey. There are several methods of probability sampling. Four major types of probability sample designs will be examined in this manual.

1. Simple random sampling: The simple random sampling or random sampling method consists in selecting respondents randomly from a population. In this case, each member of the population has an equal chance of being selected: it is an equal probability selection method. The way of selecting the sample is through a table of random numbers. Once a sampling frame is available, each subject unit in the population is assigned a number. It is recommended to apply the random sampling when the variable of greatest interest is randomly distributed within a small population, with little geographical dispersion and when the mode of distribution of the variable of interest is not known.

2. Stratified sampling Stratified random sampling involves dividing the population into different sub-groups that share certain characteristics. Its purpose is to classify populations in subsets or strata based on same


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supplementary information and then a selection of sample from each group or stratum. The aim of stratified sampling is to try to improve the representativeness of the selected sample. The two types of stratified sampling are:

• Proportionate stratified: where the strata sample size are made proportional to the strata population size; and

• Disproportionate stratified: where a varying sampling is used. The stratified sampling is generally recommended for the large populations where it is supposed or known that distribution of the major variable(s) differs between the different subsets or strata.

3. Systematic sampling Systematic sampling gives each subject in the population an equal chance of being selected. It involves the selection of every kth subject for inclusions in the sample. The following two terms are regularly used in the context of systematic sampling: - Sampling interval: that is the standard distance between elements selected in the sample - Sampling rate: is the proportion of elements that are selected in the population

The systematic sampling is generally used when it's impossible to identify every sampling unit within the sample frame or when the access to the sampling is more difficult in the field.

4. Cluster sampling Cluster sampling is a probability sampling procedure in which elements of the population are randomly selected in naturally occurring groupings (clusters). It used when the target population is very large and widely dispersed geographically and/or when the design of a sampling frame of the population is impossible or impractical. Therefore, a random selection by geographical location or any other mean is performed and then selecting randomly the sample.

III.6.4 Sampling errors and imputations

III.6.4.1 Sampling errors Sampling error is due to the examined variation between samples: it represents the portion of the difference between the value of a statistic derived from observations and the value that is supposed to be estimated. This is attributed to the fact that samples represent only a portion of the population. It is possible to use several samples from the same population. Each sample gives a different result. However, the differences between the samples vary only within a range where the percentage is determined by the size of the sample. If many samples are drawn from the same sampling frame, they could potentially generate different results. It is important to note that the sampling errors can be reduced by increasing the size of the sample and/or by stratification. Generally a 5% sampling error is selected. In other words, 95 times out of 100 this sample is representative of the larger population. The following table give an insight to the calculation of the sampling error according to the size of population.

Population Size

Sampling Error ± 3%



100 92 80 49 250 203 152 70 500 341 217 81


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750 441 254 85 1,000 516 278 88 2,500 748 333 93 5,000 880 357 94 10,000 964 370 95 25,000 1,023 378 96 50,000 1,045 381 96 100,000 1,056 383 96 1,000,000 1,066 384 96 100,000,000 1,067 384 96 Table III.5: Calculation of the sampling error according to the size of population [23]

III.6.4.2 Imputation

During the implementation of the survey, many respondents answer partially certain questions. Analysis of datasets with missing data are more problematic than analysis of complete data. Imputation is a method to fill in missing data with plausible values to produce a complete data set. A distinction may be between:

- Deductive imputation: this depends on some redundancy in data so that a missing response can be deduced from auxiliary information.

- Mean imputation overall: which assigns the overall respondent mean to all missing responses (Determinist method).

- Random imputation overall: which assigns each non-respondent the value of respondent sample (Stochastic method).

- Mean imputation within classes: this method divides the total sample into imputation classes according to values on the auxiliary variables.

- Random imputation within classes: which corresponds to the random overall method except that it is applied within imputation classes. Each non-respondent is assigned the value of a respondent randomly selected from the same imputation class.

- Hot-deck method which is similar to the random within class method in which the missing data are replaced by randomly chosen sample from respondents with similar characteristics.

III.6.5 Sample questionnaires The questionnaire design process starts with the formulation of survey objectives and information requirements and continues with the following steps: - consult with data users and respondents; - review previous questionnaires; - draft the questionnaire; - review and edit questionnaire; - test and edit questionnaire; - Finalise questionnaire. The questions must be designed so that can be easily understood and accurately answered by respondents. The questionnaire should be tested before implementation (cognitive test, focus groups, informal test ...)

The type of questions used in a survey have an important impact on the answers quality: The open questions give the opportunity to respondents to use their own words for answering, while the closed questions permit the respondents to choose from a set of answers. The advantages and inconveniences of each of them are summarised in the following table:


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Pros Cons

Open questions Allow respondents to answer in their own words.

Answers are not oriented (no predefined answers).

More rich and diverse answers;

Which help to identify new options for further quantitative research,

And explore new possible aspects (non provided initially).

The answers are more complex to codify.

More time is needed for analysis.

More difficulties in responses comparisons.

Some complex questions needs more time in answering and may be not answered.

Interpretation could be more complex when analyzing data.

Closed questions The answer are easy to code.

Allow for statistical summaries of large number of cases.

If question is well-constructed, can provide more clear-cut categories to measure knowledge, skill, attitude, or behaviour.

Reporting results may be more straight forward.

Limited and forced choices raise the influence risk in the answers.

The response may also be influenced by the order of the questions. Some possible response options can be absent.

The response options "Other" are not always useful.

Table III.6: The advantages and inconveniences of open and closed questions [11, 22]

Also there is big influence of the length of the questionnaires: It's recommended to have the shorter one. Most people did not spent a lot of time in answering questionnaire, and there is risk with a long questionnaire on the response rate. If in some cases a long questionnaire is needed, we should think about incentives for participants to respond.


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IV. MODELS

IV.1. Introduction

Models are among the main methods for estimating energy consumption and activity data for final sectors. They often serve as a framework for consolidating other data sources (administrative, surveys, and other sources). In general, they reflect complex systems in an understandable form, help to organize large amounts of data and provide a consistent framework for testing hypotheses and estimating data. There is a variety of methods used in energy demand forecasting. The World Bank has realised a comparative study of Models in order to help developing countries to choose the right model according to national constraint of data availability. From this study,the economic foundations of the energy demand (household, industrial, commercial and transport energy demand), the energy demand forecasting techniques, the structure of the aggregated and sectoral energy demand forecasting models are displayed.

Two methods are used to compute energy consumption in the field of transportation:

- Top-down evaluation method; and - Bottom-up evaluation method.

IV.1.1 - Top-Down Method

This method is based on calculating fuel deliveries to gas stations and fuel consumed for transportation activities by the industry and the tertiary sector (direct deliveries to companies), then deducting fuel purchased in gas stations for other purposes (public works, agriculture, fishing, etc.). The balance represents energy used in the field of transport. (Graph IV.2)

This method is the simplest as energy consumption is deducted for the energy balance. In fact, it starts by the total final consumption of energy then uses distribution keys by type of energy for different sectors. Distribution is based on surveys, and also very often on estimates and hypotheses. For example in Tunisia the structure of gasoil consumption in 2010 is supposed to be used as the following:

Graph IV.1: The structure of gasoil consumption in Tunisia (2010).


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IV.1.2 –Bottom-Up Method

The consumption of energy in the transport sector is based on the analysis of vehicles in operation. Vehicles are surveyed based on average travelled per year (in kilometer per year), and estimate unitary consumptions by type of vehicles.

This approach requires a large amount of data and hypotheses to compute the final consumption of the transport sector.

Graph IV.2: Used model approach for estimating energy consumption in Tunisia

Source : ANME - ALCOR

IV.2 Category of models

In general, the main four categories of used models are:

IV.2.1 Optimization Models

These models are considered as bottom-up approaches models. They are used for choosing the least-cost configurations of energy systems based on various constraints such as CO2 emissions target, and select among technologies based on their relative costs. Such models can be models MARKAL, TIMES, EFOM and WASP models.

Principals characteristics of MARKAL model

a) Definition of model and type

MARKAL is developed by the International Energy Agency, Energy Technology Systems Analysis Programme (IEA/ETSAP) in cooperative with multinational project over two decades period. The model is widely applied in 37 countries and by more than 77 institutions, including developed, transitional, and developing economies covering energy and environmental issues. It is based on the bottom-up approach, using a dynamic linear programming (LP) and generating energy, economic, engineering, and environmental equilibrium models. Represented as Reference

Car fleet U. ConsumpAnnual kmx x

National consumption

for transport

’ Top-down approach-Based on statistics on fuel deliveries

Bottom-up approach’ Estimation of the fleet, annual km and unitary consumption


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Energy Systems (RES), the model describes an entire energy system from resource extraction, through energy transformation and end-use devices, to the demand for useful energy services.

The MARKAL family of models is unique, with wide applications in a variety of settings such as: Standard MARKAL, MARKAL-MACRO (Standard MARKAL linked to a macro-economic growth model), MARKAL Elastic Demand (Demand is price responsive), TIMES (The Integrated MARKAL-EFOM System) which is gradually expected to replace MARKAL and MARKAL-MACRO.

b) Structure and functionalities of the model

The main function of this demand-driven model is to optimize a linear objective function under a set of linear constraints. The problem is to determine the optimum activity levels of processes satisfying the constraints at the minimum costs.

As with most energy system models, energy carriers in MARKAL interconnect the conversion and consumption of energy. This user-defined network includes all energy carriers involved in primary supplies (e.g. mining, petroleum extraction, etc.), conversion and processing (e.g., power plants, refineries, etc.), and end-use. The demand for energy services may be disaggregated by sector (i.e. residential, manufacturing, transportation, and commercial) and by specific functions within a sector (e.g. residential air conditioning, cooling, lighting, hot water, etc.). The optimization routine used in the model’s solution selects from each of the sources, energy carriers, and transformation technologies to produce the least-cost solution subject to a variety of constraints. The user defines technology costs, technical characteristics (e.g. conversion efficiencies), and energy service demands. As a result of this integrated approach, supply-side technologies are matched to energy service demands.

c) Purposes of the model

MARKAL represents the energy system at the level of the energy supply and demand sides. It provides policy makers and planners in the public and private sectors with extensive details on energy producing and consuming technologies. The model permit to :

• identify least-cost energy systems • identify cost-effective responses to restrictions on emissions • perform prospective analysis of long-term energy balances under different scenarios • evaluate new technologies and priorities for R&D • evaluate the effects of regulations, taxes, and subsidies • project inventories of greenhouse gas emissions • estimate the value of regional cooperation

MARKAL uses a set of matrix forms, parameters and tables to operate on data. These components are supplied by users to represent an energy system from the primary sources of energy to the final energy demand sectors. It is used to describe the technologies that enable resource exchanges with external energy sources (imports and exports) or the supply of energy carriers (mines, biomass, ...); the processes that ensure the transformation of an energy carrier to another; the conversion of electricity or heat production process; and at the end of the energy chain, the technology application that consumes an energy carrier to satisfy final demand.

The model creates optimised solutions by reducing the costs of total energy system during the planning period taking into account all types of constraints (availability of primary energy resources, and technologies, upper bounds on pollution emissions, etc.).


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It can be used with a macroeconomic model to allow interplay between the energy system and the economy, or with a partial equilibrium model where demand levels are endogenously determined (Which is formed inside, without outside intervention).

d) Limitations of the model

MARKAL is data intensive. It picks the solution that provides the lowest costs, consequently the others results with only slightly greater costs are excluded. The use of multiple year planning periods for the calculation of energy balances creates some difficulties for the modelling of renewable energy of which technology implementation can have very short construction times.

e) US Case study [19]

According to the EPA (Environmental Protection Agency) report on Energy modelling with MARKAL [ www.epa.gov/appcdwww/apb/globalchange/], the US environmental protection agency has developed "a first of a kind, nine-region MARKAL model of the US that can be employed by federal and regional decision-makers to explore future scenarios of energy system development and the associated emissions".

The researchers used the MARKAL energy system model to estimate future year technology penetrations and calculate related GHG emissions through 2050. Five economic sectors were concerned: Transformation sector (electricity production), Industry sector, Transport sector and Residential and Commercial sectors. Nine US regions were covered with each region representing a census division.

The energy system structure depicts the primary energy: fossil fuels, biomass, uranium and renewable modules, for the processing and conversion of energy carriers : gasification, refining and processing, combustion based on electricity generation, nuclear power, direct electricity generation (Solar, Hydro and wind), hydrogen generation and carbon sequestration.

The produced MARKAL database and model provided federal and regional decision-makers with valuable tool to explore a variety of future energy scenarios taking into account regional variation in resource availability, transportation costs, and end-use demands. For instance, the following subjects have been used in these scenarios:

- How might a federal renewable portfolio standard be met across regions? - Will the development of a hydrogen economy decrease or increase air pollution and greenhouse

gas emissions? How might hydrogen affect the price of other commodities? - What might be the role of biofuels and biopower in the transportation, power, and industrial

sectors? How might different sectors compete for limited biomass resources? - How do regions differ in their capacity to produce, transport and use biomass feedstock? - What energy technologies could be deployed over the next half-century to meet a low carbon

trajectory in the U.S.? - What are the potential emission consequences of technologies which may be deployed over the

next half-century?

IV.2.2 Simulation Models

These econometric-type energy models simulate behaviour of consumers and producers under various variables such as prices, incomes, policies, etc. This is typically used as an iterative approach to find market clearing demand-supply equilibrium. The energy prices and quantities are adjusted endogenously using iterative calculations to seek equilibrium prices. Such model examples include: ENPEP BALANCE, POLES, MIDAS, Energy 20/20...


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Principals characteristics of ENPEP Balance model


ENPEP is an acronym of “Energy and Power Evaluation Program". It is a set of ten integrated energy, environmental, and economic analysis tools. This section will only focus on the Balance module. This model is a software tool developed at Argonne National Laboratory Centre for Energy, Environmental, and Economic Systems Analysis (CEEESA) and sponsored by the International Atomic Energy Agency (IAEA) and other institutions such as: US Department of Energy (DOE), US Department of State (DOS), US Agency for International Development (USAID), UN Development Program (UNDP), World Bank, Inter-American Development Bank, and private institutions. The ENPEP-Balance is a nonlinear equilibrium module that matches the demand for energy with available resources and technologies. It is based on market simulation approach which allows the model to determine the response of different segments of the energy system to variations in energy prices and demand levels, and permits the users to evaluate the entire energy system (supply and demand sides) and the environmental implications of different energy strategies. The ENPEP-Balance can be run in combination with other ENPEP tools, such as MAED and WASP.


As a demand driven model, ENPEP Balance is based on a decentralized decision-making process in the energy sector. It can be adjusted according to the preferences of energy users and suppliers.

The input parameters include:

- all information about the energy system structure, - the base year energy statistics including production and consumption levels and prices, - the projected energy demand growth, - and any technical and policy constraints. The energy networks are designated graphically to trace the energy flow from the Primary Energy Resources (supply side) to the Useful Energy Demands in the end-use sectors (Demand side). These networks are using nodes and links through the graphical interface of ENPEP Balance module, by doing the following: - Each node type corresponds to a different sub-model: Energy Demands, Conversion Processes,

Resources Processes and Economic Processes. - And the links permit the connection between the nodes and the transfer of information among

them (use of specific equations).

The ENPEP Balance solution is based on simulating the behaviour of energy consumers and producers through a market-sharing algorithm. The equilibrium model represented by the designed energy network is solved by finding a set of prices and quantities that satisfy all relevant equations and inequalities (intersection of supply and demand curves).


The model can project the future energy market penetration through different scenarios. The configuration includes different primary energy resources (supply side) such as coal, crude oil, gas, nuclear and renewables, the transformation and distribution sectors (electricity and petroleum products) and final energy consumption (industry, transport, households, etc.).

The model is configured to evaluate various power system expansion options through the different expansion technologies available. The simulation algorithm uses a nested approach: For instance, the


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model allocates market share between two fuels, then the model decides, within each of these two categories, the share each separate technology will attain.

The model forecasts technology shares based on underlying assumptions on performance, capital cost, operation and maintenance cost, process efficiencies, interest rates, risk premium, and projected fuel prices.

The model projects the balance of energy supply and demand for the entire energy system, up to a 75 year period. The results are a set of prices and quantities for all of the links in the network for every year of the analysis periods. The data generated from ENPEP-Balance can be used by others ENPEP modules such as LDC to transform data and perform calculations necessary to prepare input data on electricity generation requirements for the ELECTRIC module, ELECTRIC calculates an electrical generating system expansion plan that meets demand at the minimum cost, subject to system requirements, and IMPACTS to calculate environmental impacts from the energy supply system.


ENPEP-Balance is data intensive, requiring a significant effort for data collection. The model is complicated and not user friendly. An extensive training and experience are required in order to work successfully with the model.

Also the model does its calculations on a year by year basis. Therefore, it does not make current energy use decisions in conjunction with a projection of what will happen in the future.

e) Romanian Case study [20]

According to the Argonne's Centre for Energy, Environmental and Economics Systems Analysis paper about the "Development of fuel policy for Romania", CEEESA centre assisted the Government of Romania in developing a long-term energy policy under the administration and supervision of the World Bank. An integrated modelling framework was developed by the CEEESA staff specifically tailored to analyze Romania’s energy sector. It included five main modules : - Macroeconomic model (Output : sectoral GDP, population Income), - ENPEP MAED module (Output : energy demand and annual hourly electricity demand), - ENPEP ELECTRIC Module (Output : Hydro capacity and build schedule), - ENPEP Balance module (Output : energy flow and fuel cost), - ENPEP IMPACTS module The configured modelling framework has taken into account all national economic alternative and energy strategies in order to achieve the overall goals and objectives of the country, while satisfying the pollution control requirements. Long term energy supply options and energy balances have been set up until 2020. The different scenarios were also analysed according to the various assumption regarding the development of energy sector and economy of Romania.

This study was used as a long-term energy strategy adopted by the Government of Romania and its final results has provided many recommendations for a sustainable energy policy : liberalisation of the energy market, improving technology efficiency, increasing the use of renewable energy resources, implementing strict pollution measures, improving energy management, diversifying energy supply sources, establishing adequate domestic fuel stocks, increasing research and development, and implementing advanced energy technologies.

IV.2.3 Accounting Frameworks

The account for flows of energy is a system based on simple engineering relationships (e.g. conservation of energy). The model asks users to explicitly specify outcomes than simulating decisions of energy consumers and producers. The evaluation and comparison of policies are largely performed externally by analysts. The model framework serves mainly as a calculator, database or


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reporting tool. Its main function is to manage data and results and is considered as simple, transparent, intuitive and easy to parameterize. Two models can serve as examples: LEAP and MAED. The following section will only focus on the LEAP model.

Principals characteristics of LEAP model


LEAP is an acronym of “Long-range Energy Alternative Planning" system. It was developed at Tellus Institute by the Stockholm Environment Institute (SEI) with support from United Nations Environment Programme (UNEP). The model follows the accounting framework approach. It is an Energy forecasting as part of an integrated model which enables consideration of both demand and supply side technologies and accounts for total system impacts.

The model is available for free. It is a widely used software tool for energy-policy analysis and climate-change mitigation assessment, with flexible application at different geographical levels (city, state, country, region or global).


As demand driven model, LEAP is structured as a series of integrated programs that can be used to develop current energy balances to make projections of supply and demand trends, and to calculate the consequent environmental emissions, etc. It is composed of the following programs:

• Energy scenario program with Demand, Transformation, Biomass, Environment and Evaluation sub-programs;

• Aggregation program to display multi-area analysis results at different geographical levels; • Environmental database to calculate automatically GHG emissions and environmental impacts

according to different scenarios; • and fuel chains program, used to compare the total energy and environmental impacts of fuel

and technology choices. The main data that can be used in LEAP are divided into four categories: - Macro-economic variables:

Sectoral driving variables GDP/ Value added, Population, Household size More detailed driving variables Production of energy intensive materials (tonnes or $ steel);

transport needs (pass-km, tonne-km); income distribution, etc. - Energy Demand Data:

Sector and subsector totals Fuel use by sector/subsector End-use and technology characteristics by sector/subsector

a) Usage breakdown by end-use/device: new vs. existing buildings; vehicle stock by type, vintage; or simpler breakdowns; b) Technology cost and performance

Price and income response (optional)

Price and income elasticities


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- Energy Supply Data:

Characteristics of energy supply, transport, and conversion facilities

Capital and operation and management costs, performance (efficiencies, capacity factors, etc.)

Energy supply plans New capacity on-line dates, costs, characteristics; Energy resources and prices Reserves of fossil fuels; potential for renewable resources - Technology options:

Technology costs and performance

Capital and operation and management costs, foreign exchange, performance (efficiency, unit usage, capacity factor, etc.)

Penetration rates Administrative and program costs

Percent of new or existing stock replaced per year

Emission Factors Emissions per unit energy consumed, produced, or transported LEAP has a function to display results in any desired unit, and in various formats. All reports can be represented in absolute values, growth rates, and percent shares. LEAP is useful in cases where the analysts wish to determine an energy and environment impacts of proposed governmental policies where the initial technology projection has been predetermined.


The forecasts of energy demand in LEAP are performed by multiplying the activity levels by the energy intensities. The simulation is based on the energy demand forecast, energy supply and conversion processes to assess the adequacy of primary resources and to meet the demand and export targets.

LEAP solution is deterministic : the accounting framework approach permit to generate a reliable view of the energy system (demand and supply side) and relies on the scenario approach to develop a consistent actions according to the various possibilities of energy system evolution.

The model does not optimize or simulate the market shares for forecasting the demand. It analyses only the implications of possible alternative market shares on the demand.

The LEAP software is useful and its main advantages is that the training is no time consuming, the program is free, and is presented with a user friendly interface, a detailed manual and training exercises.


The model does not take into account the economic factors in determining energy supply and fuel choices. The shares among fuel usage and fuel substitution between end-uses have to be determined exogenously. Also due to its nature, the model cannot analyze fuel competitiveness between energy products. The synthesis of the future energy systems depend on the choice of future technologies done by the modeller.

e) Irish Case study [15.5]

In the 2009 Energy Forecast report, Sustainable Energy Authority of Ireland SEAI’s Energy Modelling Group identified further specific areas of energy modelling required to improve the evidence base for informed policy decisions. One area identified for future work was to develop a greater degree of sectoral disaggregation of energy demand and to address the potential


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incompatibility of macro-economic assumptions (top-down or sectoral level) underpinning the Baseline data with those underpinning ‘bottom-up’ savings estimates from individual policies. SEAI’s specific aim in this regard was to build a bottom-up Baseline energy demand projection for energy end-use in each sector, based on existing and future stock data. Scenario analysis could then build on this, adjusting the new bottom-up Baseline on the basis of the expected impacts of individual (or groups of) policies and measures. Using this method, the assumptions underpinning the bottom-up Baseline projection and those underpinning scenario analysis would be automatically aligned. The tool identified for this task was the Long-Range Energy Alternatives Planning (LEAP) system. The existing LEAP-Ireland model includes energy use in private-car transport, the residential sector and industry. It is currently being extended to include freight transport. The short-term goal over the next year is to extend this model to cover the entire economy, i.e. to include energy use in the services sector and in aviation. The long-term vision is to use LEAP-Ireland as a planning tool for assessing the future impacts of possible energy efficiency policies and measures, complementing and providing an alternative perspective to ongoing macroeconomic modelling. LEAP works help to inform decision-making on energy efficiency measures that affect technology choices. For the transport sector, a private car stock demographic model has been developed, where:

- The stock, distance travelled and on-road efficiency are calculated for vehicles, disaggregated by technology (fuel type and engine cylinder capacity) and vintage.

- Stock demographics are calculated from vehicle retirement rates derived from historical stock analysis,

- Future car sales (assuming continued growth in sales from 2010), and imports. - The 2009 new-car technology profile is carried forward to 2020.

The distances travelled for different types of petrol and diesel cars were derived from National Car Test (NCT) odometer readings from 2000 to 2008, along with specific fuel consumption data for each new-car type. Fuel consumption is converted to energy demand and aged by 0.3% for each vintage year, and an on-road factor9 of 1.06 for petrol and 1.12 for diesel cars is applied to account for the difference between official test and on-road fuel consumption. Energy demand each year is then calculated in LEAP as the product of stock, distance travelled and specific energy consumption in each technology and age category. Scenario ‘levers’ in the model enable the analysis of efficiency improvements, overall travel reduction and car sharing, modal shift, and efficient driving. The Baseline scenario for private cars comprises a forecast of energy demand where growth in car sales is tied to an assumed recovery of the economy, and the technology profile of new cars remains as it was in previous years.

IV.2.4 Hybrid Model [14] This type models is a mixture between different approaches, combining engineering-orientation with economic market-driven representations: The model examines macroeconomic impacts of energy system on the wider economy, the changes in the energy system can feed-back to effect macroeconomic growth and structure and the production functions allow for substitution among capital, labour and different forms of energy. PRIMES and Markal macro models can serve as examples under this category PRIMES has been used at the level of the EU Directorates for energy system. Principals characteristics of PRIMES model

9 On-road factor = fuel consumption on-road / official test of vehicle's fuel consumption


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The PRIMES energy system model simulates a market equilibrium solution for energy supply and demand. The model determines the equilibrium by finding the prices of each energy form such that the quantity producers find best to supply matches the quantity consumers wish to use. The equilibrium is static (within each time period) but repeated in a time-forward path, under dynamic relationships

The model was developed by the Energy-Economy-Environment modelling laboratory at the National Technical University of Athens in the context of a series of research programmes of the European Commission in order to examine the EU energy system (outlook scenarios for DG TREN and DG ENER) and related energy efficiency and environmental policies (impact assessment studies for DG ENV, DG TREN, DG CLIMA and DG ENER, etc.). The model is updated and extended regularly.


The model is organised in sub-sectors around a modular design representing the energy products flows: supply, transformation and final energy use. The modularity characteristic permit to represent correctly each sector, highlighting the most important issues of the sector. The module can be run independently for stand-alone analysis. The energy production sub-systems, includes: • Supply sectors (supply side): coal, oil products, natural gas, electricity and heat production,

biomass supply, and others; • End-use sectors (demand side): industrial sector, transport sector, residential and commercial

sectors. Some demanders may be also suppliers, for example industrial co-generators of electricity and steam.

The model distinguishes several end-uses and processes:

a) 11 industrial sectors, subdivided into 26 sub-sectors using energy in 12 generic processes (e.g. air compression, furnaces ...);

b) 5 tertiary sectors, using energy in 6 processes (air conditioning, office equipment ...);

c) 4 dwelling types using energy in 5 processes and 12 types of electrical durable goods (e.g. refrigerator, washing machine, television ...);

d) 4 transport modes, 10 transport means and 10 vehicle technologies, 14 fossil fuel types, 4 new fuel carriers (e.g. hydrogen, methanol, biofuels) and 10 renewable energy types,

e) several supply sub-systems: power and steam generation, refineries, gas supply, biomass supply, hydrogen supply, primary energy production. The power generation sub-model represents 150 power and steam technologies, electricity grid with import and export links in the EU internal energy market and details of load curves (typical days and hours) for electricity and steam;

f) 7 types of pollutants emitted from energy processes and a series of associated policy instruments, including emission trading schemes.

The model simulates the European energy system and markets on a country level basis and provides detailed results on energy balances, CO2 emissions, investment, energy technology penetration, prices and costs by 5-years intervals over a time period from 2000 to 2030.

The main variables that can be used in PRIMES are divided in five groups: - Economy system

- GDP, demographics, exchange and interest rates - Activity by sector (18 sectors), income of households


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- Energy demand system - Consumption habits, durable goods and comfort - Manufacturing technology, kind of industry and energy needs - Transportation modes/means and technologies as drivers of energy needs

- Energy supply system - Primary energy supply - Secondary energy supply (power generation, refineries…) - Energy System Balances

- Energy Markets - Competition, price formation and regulation - Import/export

- Environment Impacts - Energy-related emissions - Environmental impacts and pressures, damages - Preventive and corrective measures


The PRIMES model is a general-purpose model. It is conceived for energy outlooks, scenario construction and impact assessment of policies. It covers a medium to long-term horizon. It is modular and allows either for a unified model use or for partial use of modules to support specific energy studies.

More specifically, the following fields can be supported by the model in the policy analysis:

• Standard energy policy issues: security of supply, strategy, costs etc. • Environmental issues including climate change mitigation • Pricing policy, taxation, standards on technologies • New technologies and renewable sources • Energy efficiency in the demand-side • Alternative fuels • Conversion decentralisation, electricity market liberalisation • Policy issues regarding electricity generation, gas distribution and new energy forms.


According to the observations of some users, there is a lack of transparency related to issues of data access. Thus, energy chains were not specified in published scenarios based on the PRIMES model due to the fact that data are often aggregated (for example, in the case of scenarios for the development of bio-energy, it is not clear which sector will be used). Also only the main assumptions underlying the scenarios are available, which makes impossible to know the part of energy products according to the origin (production or importation). This lack of information constitutes a serious difficulty for the life cycle analysis of any energy product in order to reduce its pressure on resources and the environment.

e) EU Case study [21]

The PRIMES model was used to prepare: • the European Union Energy and Emissions Outlook for the Shared Analysis project of the

European Commission, DG XVII. • the report on “European Energy and Transport - Trends to 2030” in the context of the “Long

Range Energy Modelling” framework contract for the Energy and Transport DG.

The model was also extensively used for the Environment DG, the Research DG and EEA as well as at governmental level in the EU. In 2005, the EU Countries in PRIMES were: Ireland, United


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Kingdom, Belgium, Luxembourg, Netherlands, Germany, France, Spain, Portugal, Denmark, Sweden, Norway, Finland, Austria, Italy, Switzerland, Slovenia, Czech, Slovakia, Poland, Hungary, Latvia, Estonia, Lithuania, Croatia, Yugoslavia, Romania, Albania, FYROM, Bosnia, Bulgaria, Greece, Turkey, Malta, and Cyprus.

V. EXAMPLE OF ENERGY SURVEY IN TRANSPORT SECTOR

This chapter presents some case studies to illustrate the afore mentioned methodologies. Four cases will be presented, Canada as developed country and Morocco, Tunisia and Palestine for the Arabic region.

In the Canadian case, Statistics Canada experience on the fuel consumption survey (FCS) will be presented. The main objective is to measure road use by motor vehicles, their fuel consumption and their impact on the environment. This survey is a fully redesigned version of the former Canadian Vehicle Survey (CVS) launched by Statistics Canada in 1999 and terminated at the end of 2009.

The Moroccan example is the most complete and recent. It was conducted under the support program for the energy sector in Morocco by the Directorate of Observation and Programming (DOP) of the Ministry of Energy and funded by the European Commission. The main purpose was to collect detailed and comprehensive data of energy consumption in the transport sector (as defined by energy balance using international standard), and disaggregated by type of vehicle and mode of energy. In addition to residential, the survey covered other economic sectors such as: Transport, Industry, Energy, Agriculture and Tertiary.

The Palestinian case explains the country's survey of the transport sector. The transport survey covered only activities of the outside establishment sector according to (ISIC-3) for passengers and freights transport by road. The objective was to provide information and data about the number of transport vehicles and employed persons by activity, value of output and intermediate consumption, value added components, fixed assets and other selected variables. This survey has been planned and conducted by a technical team from the Palestinian Central Bureau of Statistics (PCBS) using with joint funding by the Palestinian National Authority (PNA) and the Core Funding Group (CFG), represented by the Representative Office of Norway to PNA and the Swiss Development and Cooperation Agency (SDC).

The Tunisian experience was conducted by the national agency for rational use of energy (ANME) in order to update and improve the current information system and to set the structure for a real database on transport and energy. Therefore, the surveys were carried out at two levels: the first covering the major transportation companies and the second one at gas stations level.


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V.1. CANADA experience [15.2]

Canada has realised many final energy consumption surveys of which: - Annual Industrial Consumption of Energy Survey - Households and the Environment Survey - Commercial and Institutional Consumption of Energy Survey - Transport - Canadian Vehicle Survey & Fuel Consumption Survey This example will focus on the most recent survey on fuel consumption in Canada,

Fuel Consumption Survey (FCS) The Fuel Consumption Survey was designed on the basis of the previous "Canadian Vehicle Survey". The purpose of the FCS survey is to measure road use light motor vehicles, their fuel consumption and their impact on the environment. It is conducted by Statistics Canada on behalf of Transport Canada and Natural Resources Canada.

V.1.1 General Description This voluntary survey is considered as the unique source of information on road use light motor vehicles, their fuel consumption and their impact on the environment in Canada. The survey is conducted quarterly and allows for calculation of annual estimates based on the data collected during the four quarters. Its conception is based completely on the former Canadian Vehicle Survey (CVS). The CVS started in 1999 and was implemented until 2009. The only difference between these two surveys resides in the mode of data collection: the engine data logger for FCS replace the used trip logs for the CVS. The aim of using this technology is to reduce respondent burden and improve overall data quality.

V.1.2 Survey design Target population The target population of the FCS is based on the list of all light motor vehicles registered in Canada during the survey reference period. The following vehicles are excluded from the registration lists used in the sample (out-of-scope):

• vehicles based on weight criteria: Ambulances and Fire trucks, Motorcycles, Buses, Tractors, off-road vehicles (e.g., snowmobiles, dune buggies, amphibious vehicles) and special equipment (e.g. cranes, street cleaners, snowplows and backhoes).

• scrapped or salvaged vehicles Instrument design As mentioned previously, this survey uses a new approach for the data collection base on an engine data logger in addition to a short paper questionnaire (Annex VI.1). The engine data logger is a small electronic device that plugs into the on-board diagnostic port of vehicle. The aim of this instrument is to read & store readings from the vehicle parameters in real time (every second). The main measured parameters are:

• vehicle speed, • engine speed, • intake manifold pressure,


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• air flow rate, and • intake air temperature

Sampling The frame is derived from administrative vehicle registration lists from 13 jurisdictions (ten provinces and three territories) in Canada. Then, the lists are shortened, based on the following criteria: 1. Removal of the out-of-scope vehicles (such as buses, trailers, etc.), 2. Removal of the vehicles with expired registrations, 3. Removal of duplicate Vehicle Identification Number (VIN) 4. Removal of records with irregular data. The sampling unit is based on the Vehicle Identification Number which identifies each vehicle, and is produced by the manufacturer and registered in one of 13 jurisdictions. The Sample Universe File (SUF) is the combination of the registration files from the provincial and territorial governments. The SUF is updated every quarter. The vehicles, based on the SUF, are stratified at two levels into 94 strata:

• Census metropolitan area (34 groups, plus 13 non-CMA (census metropolitan area) groups - one in each province or territory)

• and Vehicle type (passenger car or other). The General Sampling System (GSAM) of Statistics Canada is used to select the sample. GSAM is especially useful for managing sample selection and rotation for periodic surveys.

V.1. 3 Data collection The key variables collected are:

• Fuel consumption and • Distance travelled.

These two variables can be collected via the two collection modes: the logger engine or the paper questionnaire. The trip-level information: start time and date, end time and date, duration, time spent idling are collected by the logger engine only. The registered owners of the sampled vehicles are contacted for a Computer-Assisted Telephone Interview (CATI).

V.1. 4 Data edit and imputation Once the necessary information for the survey has been collected, data are subject to a series of computerized and manual verifications to ensure that the records were consistent and that there are no errors as a result of data capture. Then reported data are examined for completeness and consistency using automated edits and manual checks. The outliers are processed manually and the missing values and data found in error imputed using software developed by Statistics Canada.


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V.2 Case study of Morocco [15.4] In the framework of multi-annual surveys programmed by the Direction of Observation and Programming (Department of Energy and Mines), and after completion in 2007/2008 a major survey on the consumption of energy sector who receives a sample of 12 000 units for the six major areas namely: residential, mining, industry, transport, agriculture and the service sector, a survey of consolidation of final energy consumption in the transport sector be launched in 2010.

This will be conducted as part of the support program in the energy sector in Morocco supported by the European Commission. In addition, technical support is also provided by the Department of Statistics (DS) under the Haut Commissariat au Plan (HCP) and the Ministry of Equipment and Transport under the control and monitoring of execution of the survey.

As part of its activities and achieve its objectives declined overall strategy of the Ministry of Energy and Mines, the Department of Observation and Programming programmed multi-year survey, and after the completion in 2007/2008 a major survey on the consumption of the energy sector that has affected a sample of 12 000 units for the six major areas namely: residential, mining, industry, transport, agriculture and the service sector, a survey of consolidation on final energy consumption in the transport sector was launched in 2011.

This will be conducted as part of the support program in the energy sector in Morocco supported by the European Commission.

Through these periodic surveys that Directorate proposes to assess the changing characteristics of energy consumption by the different actors of Moroccan economic fabric to place effective and efficient approaches to reduce the energy consumption in different sectors, including through actions under the sustainable consumption of fuel and reducing emissions. It is, among other things:

• The orientation of the request to the least-cost energy to substantially reduce the consumption of electricity and oil;

• The introduction of some tax incentives for the adoption of emission reduction devices; • Building maintenance and maintenance of equipment used; • Improving knowledge sectoral energy consumption.

Framing perfectly with this, our survey of consolidation on final energy consumption in the transport sector will cover, in addition to residential, the following economic sectors: Transport divided according to sub-sectors: land, maritime, and air transport, and others sectors such as Industry, Energy, Commercial and agriculture (C.f. Graph V.1 ). It will be of interest to the amount of energy consumption in the carriage performed with respect to sectors mentioned above.

The main objective of the survey is to collect detailed and comprehensive to provide reliable estimates of energy consumption in the transport sector. These data will be disaggregated by type of vehicle and power.


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Graph V.1: Economic branches covered by the survey [15.4]

V.2.1 Survey on the final consumption of energy

The main objectives of this survey are : • To align the Moroccan energy statistics with international standards (IEA, Eurostat, UN); • to contribute to the overall work of the Department of Energy through the collection,

management and application of reliable, accurate and complete energy data necessary for the sectoral analysis and prospective studies, and

• to achieve the Ministry of Energy goals declined in the national strategy of energy sector.

The main areas covered are: industries, transport, the service sector, agriculture, mining, and residential, the main products concerned are: electricity, coal, natural gas, biomass, petroleum product and the main outcomes are to:

• Determine the final energy consumption in the transport sector for various relevant stakeholders.

• Calculate energy efficiency indicators for the transport sector. • Assist in evaluating greenhouse gas emissions from the transport sector. • Contribute to forecasting works and establish energy demand projections for the transport

sector based on complete, accurate and reliable data. • Provide stakeholders in the sector with reliable statistics on energy consumption in the

transport sector.

V.2.1.1 Implementation process:

The main steps of the survey are:

o Scope of action


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o Realisation of survey plan Global sample size Sampling by sector

o Creating questionnaires and the sampling plan o Pilot survey o Conducting surveys on the ground o Statistical analysis of data: checks, input, consistency checks, tabulation, etc. o and analysis of results : simple analysis, establishing patterns, projections, etc

The implementation process of the survey followed five main phases, defined as follow:

• Phase 1: Status and diagnostic o A detailed study of the existing o Scoping survey o Master data frame

• Phase 2: Establishment of the survey o Framing intervention o Collection of documents available o Qualification data o Data analysis

• Phase 3: Realization of fieldwork o Sampling by sector o Preparation of questionnaires o Preparation of manuals o Investigation plan

• Phase 4: Statistical evaluation of data o Implementation of the pilot survey o Readjustment of questionnaires survey approach o Training of interviewers o Organization of field collection o Reception and control questionnaires

• Phase 5: Final Report, findings, recommendations and presentation of results. o Preparation of input masks o Entering questionnaires o Control seizures o Clearance File o operating SPSS o Extraction of statistical tables o Analysis and comments

Summary of plans for conducting surveys in sectors:

The following table presents the target population for each transport sub-sector based on companies businesses register (ONDA, ONCF, etc), the used survey methodologies (census, stratified, quotas, etc)and the used administrative mode (fax, face to face, etc). For the first three categories, data on energy consumption are centralized to a limited number of stakeholders, such ONCF for rail, ONDA for domestic and international air transportation, and the National Agency Ports and MARSA MOROCCO on shipping nationally and internationally. Thus, these strata are surveyed exhaustively. Concerning the other strata, the recommended sampling approach are included in this table.


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Table V.1: Sampling approach [15.4]

V.2.1.2 Questionnaires: Several detailed questionnaires were developed to meet the objectives of the survey taking into account the specificities of the target populations in the various sectors covered by the survey.

The development of questionnaires was based the following structure:

• Identification of stratum appearance of the observation unit; • Geographical location of the respondent; • Description of economic units target (business, farm, household, ...); • Information on energy consumption by fuel type and unit of measure (ton for diesel, gasoline

and fuel, GWh for electricity) • Fuel consumption ( amount, value), age and type of vehicle; • Design data; • Additional control information; • General Comments.

Almost all of the questions are closed or semi-closed which helped in facilitating the task of operating the questionnaires to produce relevant qualitative data.

The list of questionnaires is composed by:

• Rail freight and passenger transport questionnaire. • Air transport questionnaire. • Sea transport questionnaire. • Road freight transport questionnaire. • Urban public transport questionnaire. • Certified professionals' questionnaire. • Intercity public transport questionnaire. • Mixed transport questionnaire. • Tourist transport questionnaire. • Transport auxiliaries' questionnaire.

Sector (Transport)

Air transport Sea transport Rail

transport Road Industry transport Construction

Energy Mines Residential Household vehicles. Quotas Face to face

Agriculture Farms Quotas Face to face Taxis Mixed transport Transport Catalogue of transport

companies available from the HCP

Stratified survey

Face to face

Tertiary Companies in the sector concerned

The HCP companies register

Stratified survey

Face to face

State motor vehicle population

SNTL ‐ Full survey Fax/direct contact

Air transport ONDA ‐ Full survey Fax/direct contact

Sector of activity Target population Point of reference Survey method

Administration mode

Sea transport ‐ Full survey Fax/direct contact

Rail transport ONCF ‐ Full survey Fax/direct contact

Central committee of Moroccan ship‐owners (CCAM)

Companies in the sector concerned

The HCP companies register

Stratified survey

Face to face

Vehicles ‐ Quotas Face to face


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• Staff transport for third parties questionnaire. • Construction sector questionnaire. • Industry sector questionnaire. • Residential sector questionnaire. • Mines sector questionnaire. • Energy sector questionnaire. • Tertiary sector questionnaire. • Agriculture sector questionnaire.

The residential questionnaire is presented as example in the Annex VI.2.

V.2.1.3 Sampling plan:

The two type of sampling were used for this survey: Probabilistic and Quotas Methods. 1. Probabilistic selection: For sectors that are subject to probabilistic sampling from the HCP, a stratified survey by profession and activity has been adopted. Areas of study comprising one or several branches have been organised into two strata categories: - Stratum to fully investigate: containing all structured companies with an employment size

beyond a certain threshold. This threshold is determined individually for each area. - Stratum subject to selection: samples are taken based on the employment and activity of the

company. For the transport in industry, the sampling plan was based on the following principles: • Use the register of industrial companies available from the Haut Commissariat au Plan

(HCP) as a basis for selecting the sample. This register is updated and contains an exhaustive list of national industrial companies. In addition to the company's activity, for every unit, this register provides information about employment, the company's branch of activity (economic activity according to Moroccan Nomenclature of Activities), its geographical region and the address of the unit.

• Retain the branch of activity as a stratification variable for industrial companies, while taking into account employment, activity and geographic location.

• Spread the sample obtained proportionally to the number of employees, taking account of the correlation of this auxiliary information with the variables of the study.

• Allocation and selection of the sample. The sample has been distributed proportionally to the weight of each branch or group of branches in terms of employees. At the level of each branch, a random selection, proportional to the size of the company in terms of employment, was used to ensure: • Representativeness of employment classes for units belonging to the same branch or group of

branches. • Geographical representativeness for the whole country. 2. Quota method: The method, based on reasoned choice, sample of same structure as the base population was used mainly, for the transport in households: • The control variables retained to create the quotas are: the socio-professional category, the

type of fuel and age of the vehicle.


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• Furthermore, geographical representation has been assured by taking into account the weight of each region in terms of the number of vehicles based there.

• The choice of these variables is justified by the fact that they correlate highly with characteristics which are of interest to the survey.

• For motorcycles, in the absence of auxiliary information about the characteristics of their owners, we performed a survey by quota, ensuring full representativeness at the regional level.

The quota sheets were filled out in service stations in the main towns of regions (including the periphery areas): • The service stations were chosen as survey locations simply based on the fact that many

vehicles pass through them • This approach facilitates the selection process of respondents according to the criteria

mentioned in the quota sheets • The choice of these stations is made based on the frequency of customers, the distribution

group they belong to and their geographic location (motorway, city and intercity).

Consequently to the implementation of this sampling plan, 4440 units were surveyed distributed according the selected sectors' activities for this survey as synthesized in the following table:

Sector of activity Observation

Unit Sample size

+ Residential Household 1,200

+ Agriculture Farm 400

+ Transport sector: Vehicle

Taxis 360

Mixed transport 120

Auxiliary transport services 289

Freight transport 119

Passenger transport 29

Tourist transport 17

Bus transport 26

+ Secondary: Enterprise

Industry 656

Construction 157

Energy 40

Mines 60

+ Tertiary: Establishment

Office activities 142

Retail 140

Hotels and restaurants 138

Education 160

Health and welfare 159

Public, social and personal services 134

Public administrations 87

Table V.2 : Obtained sample size according to selected sectors [15.4] Transport in households: For passenger cars, the survey sampling was done by the quota method to select a representative sample of vehicles. The control variables used to form the quotas are: socio-professional category, type of fuel and vehicle age. A geographic representation is also provided taking into account the


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weight of each region in terms of number of vehicles it contains. The choice of these variables is justified by the fact that they are highly correlated with the characteristics of interest to the survey. For motorcycles, in the absence of auxiliary information on the characteristics of its owners, the survey by quota was done with a representative at the regional level. For passenger cars, the size determined for each region are then allocated in proportion to the structures provided by the intersection of the professional category, the type of fuel and vehicle age. Structure at the national level is retained for the distribution by age and type of fuel. Leaves obtained quotas are a necessary reference abide by the investigators at the time of data collection to ensure the representativeness of the sample selected. The following quotas sheet shows the distribution of the national sample used as control variables.

Table V.3: Quotas adopted at national level by socio-professional category, type of fuel and vehicle age Concerning motorcycles, the allocation is done in proportion to the size of the regions in terms of bikes available. At the city level (head of regions instead of the regions), has conducted a systematic sampling of the number of bikes with a sample with a step of 5 to master the selection bias. This selection is done at service stations level, selected for the survey..

:

Table V.4: Allocation of the sample of motorcycles according to region [15.4] Implementation of the survey The following synthetic plan of the Moroccan survey summarises all phases and conducted actions:


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Rigorous monitoring system has been set in order to follow each phase based on the following points:

• Organize and conduct daily work of auditors and investigators, responding in a comprehensive manner to the questions on the survey questionnaires and field procedures if necessary.

• Analyse problems and difficulties as well as established solutions. • Hold regular meetings with supervisors to discuss encountered problems in the field and

the progress of planned actions. • Conduct visits to workplace teams. Often this will take the form of monitoring visits

where the supervisor discusses with the teams the results of research. • Follow the schedule of activities developed at the beginning of the operation in

conjunction with the department of energy.

Graph V.2: Implementation process of the Moroccan survey [15.4]

The organisation of work:

Unit Responsibility 1. Investigators Administration of questionnaires 2. Questionnaires reception unit Conformity control of questionnaires

Coding of Questionnaires Sector classification

3. Questionnaires control unit Complementary control 4. Data entry unit Data entry operation under control of data input supervisor 5. Treatment Unit First use of sectoral results to ensure the good performance of

process control and input. If error is detected, return to cell 3.


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V.2.1.4 Difficulties & solutions The main difficulties and uncertainties encountered and the solutions adopted

Problems Solutions At the collection level

Information about samples delivered by the HCP contains a lot of obsolete data which requires a lot of research on the ground. Some companies have disappeared or changed their location, etc.

Find new details for the company in question, make contact by phone to be certain of its existence, update the company's details, then make an appointment in advance with the entity concerned before the surveyor goes there

There is a lack of cooperation on the part of the companies contacted who work in the industrial and construction sector, or sometimes over 50 % of respondents do not fill out the questionnaire

For companies that do not respond, we remind them again so as to meet the survey's requirements. For those which categorically refuse to respond, we replace them with another statistical unit which has the same characteristics

Contact is generally made with a subordinate person, who does not have the authority to complete the questionnaire. Some surveys demand official correspondence addressed to the name of the head of the company concerned

Phoning a few times, in addition to sending a fax, has allowed us to resolve this kind of problem and make contact with the person responsible for the entity in question

Some companies are slow to meet the requirements of the survey.

Sending several reminders has allowed us to address this problem

The time taken to collect a questionnaire that has been left often exceeds two weeks and requires several journeys back and forth.

The direct mode turns completing the questionnaire into indirect mode, where the questionnaire is left to those being surveyed to complete it. The surveyor verifies the data after the document is collected.

Some companies refuse to place a stamp on the questionnaire to authenticate the information provided

Contact the company in question to ensure that the surveyor is granted access

Some companies refuse to communicate certain information which is vital for categorising and stratifying the units surveyed: in the first instance the turnover and permanent employees

Remind the company again, reassuring its heads. Show them the objectives of the survey again, emphasising the confidentiality with which the information is treated.

Several companies do not keep to the appointments made beforehand with the contact person.

Contact the person in question again, emphasising the national interest of the operation

At the service stations, the people to be surveyed are often in a hurry and do not have enough time to answer the questionnaire

Go to places where people will have enough time, such as car washes

In urban areas, the location of the units with mixed transport is not apparent

This type of transport is generally found in rural areas. To survey them, go to the places where they are located

Table V.5: Main difficulties and uncertainties encountered and the solutions adopted


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V.2.1.5 The extrapolation methods: The extrapolation methods used for all surveyed sectors were as follows: 1. Households: Global energy consumption = Residential vehicles * Annual average consumption / vehicle With: Residential vehicles = b * V * P2011*S - b: proportion of households (urban, Rural) with a vehicle. Source HCP - Survey on standard

of living in 2007. - V: Average number of vehicle per households (ECET data estimation) - P2011: Urban and rural population in 2001 (HCP estimation) - S: Structure of residential vehicles (ECET data estimation) Annual average consumption / vehicle: ECET Estimation of coefficient according to Diesel and Gasoline products for tourist vehicles, vans and motorcycles. 2. Industry, construction, energy and Mines: The estimation of final energy consumption by transport in the industry, construction, energy and mines sectors has been based on:

• Technical coefficients of annual energy consumption by type of vehicle and fuel. This information has been estimated directly using the results of the survey.

• The number of vehicles available per company according to the type of vehicle and fuel, and the size of the company in terms of turnover and number of employees.

• The estimation of the number of cars in these sectors has been made on the basis of knowledge of the number of companies which operate in each sector and the aggregated information available for these companies, particularly figures for turnover and employee numbers.

3. Transport The estimation of the final energy consumption in the transport sector took into consideration the specific features of each type of transport.

• For transport by taxi, the estimation was based on technical coefficients for annual energy consumption by type of vehicle and fuel. This information has been estimated directly using the results of the survey. And the number of taxis per category available from the Ministry of Transport. The same method has been applied to mixed transport, bus, freight, passenger transport and tourism.

• For auxiliary transport, calculations took into account the number of companies which operate in this sector and the average number of vehicles per company.

4. Agriculture The estimation of final energy consumption by transport in agriculture has been based on:

• Technical coefficients for annual energy consumption by type of vehicle and fuel. This information has been estimated directly using the results of the survey.

• Data available on the organisation of farmland in terms of surface area. • Data from the survey on life in 2007 related to the availability of cars in rural households.

5. Tertiary The estimation of the final energy consumption of transport in the tertiary sector took into consideration the specific features of each branch making up the sector. For retail, the estimation was based on:

• Technical coefficients of annual energy consumption by type of vehicle and fuel. This information has been estimated directly using the results of the survey.

• The number of vehicles available per company according to the type of vehicle and fuel and the number of employees.


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• The number of employees who worked in the structured retail sector. The main obtained results are:

• Energy consumption of households, tourist, utility vehicles, motorcycles, rail passenger and freight transport, air transport, maritime transport, tourism sector, mixed transport, urban transport by bus, by taxi, staff transport and state motor vehicle population.

• Technical characteristics of energy consumption linked to transport by economic sector according to type of vehicle, type of fuel, age of vehicles, tonnage and seasonal impact on energy consumption


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V.3 Case study of Palestine [12]

The Palestinian Central Bureau of Statistic (PCBS) has carried out in 2010 a survey on transport activities out of the industrial and service companies. This survey is the second onesince conducting the first in 1996.

The main objective of this survey was to provide the following data:

- The number of vehicles working in the transport sector by area - The number of employees in the sector and their classification - The production value - The intermediary consumption including energy - The value added of the activity

This survey does not focus only on energy but on the economic side of the activity of transport as well.

V.3.1 Methodology

Target activities

The Palestinian Central Bureau of Statistics used the classification of economic activities based on the International Standard Industrial Classification (ISIC- third revision 3), issued by the United Nations, for all economic activities. For the transport, the survey covered enterprises following the main activities:

- Transport of passenger with non-specified schedule - Road transport of goods

Target population

The target population of the survey included the following vehicles:

- Public passenger vehicles: These are compounds which allow the transfer of passengers by public transport ministry and transportation. It accounts for 8,319 vehicles.

- Private passenger vehicles: are private vehicles engaged in activities of the public transport of passengers, with a total number of 2,178 vehicles.

- Goods vehicles: trucks of all kinds of small and large sizes working in the public transport of goods with payment. It does not include owned facilities with a fixed address, with the exceptional of transport. Their number is 448 vehicles.

Sampling

The sample used in the survey is a stratified random sample, and the mechanism for the selection of the sample was as follows:

- Sample included all stations that have been identified in the framework. - Comprehensive inventory of vehicles in small stations (stations in which the number of

vehicles is less than or equal to 5 vehicles - Other stations and where layers contain (3-36) vehicles or less, 2 vehicles were selected

from the class.


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- Other layers which contained 37 vehicles and more have considered a sample size of the class, commensurate with the size of the class.

Finally, the sample included 1,731 vehicles on a total of 10,945.

Within the sample, the distribution of the selected vehicles was based on the following criteria:

- The age of vehicles - The weight of vehicles

Data collection

The Survey data were collected based on personal interview with the owners or drivers of vehicles by qualified and trained surveyors who have knowledge of all the concepts of statistical data required using the survey form.

Example of results

The following table presents the main results of the survey.

Table V.6: Number of Vehicles, Employed persons and Main Economic indicators. [12]

Value in Million USD2010 2009 2008 2007 2006 2005 2004 2003

Number of vehicles 10 945 10 791 10 189 10 087 11 337 11 327 11 144 10 434 Number of employees 11 535 11 656 10 846 10 919 11 837 12 072 11 866 11 424 Salaries 17.0 16.9 9.3 11.3 7.4 5.9 5.6 3.9Production 241.5 226.2 177.5 170.8 168.3 162.6 129.6 132.3Intermediary consumption 129.5 120.9 97.7 93.5 86.7 84.5 63.0 60.1Added Value 112.0 105.3 79.8 77.3 81.6 78.1 66.6 72.3


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V.4 Case study of Tunisia [1, 6]

Tunisia was a pioneer in implementing an appropriate institutional framework to support energy control programs. Today, the National Energy Control Agency (ANME)10 aims at being the core of the State’s energy control policy, taking the lead in the design and implementation of strategies, mainly in the field of transportation.

However, the rational use of energy in transportation must of course be directly performed at the level of the Ministry of Transport, but the execution of energy control objectives also depends on policies conducted by other Ministries, such as the Department of Housing and Equipment, which policies may sometimes oppose energy control objectives. Similarly, municipalities which usually lead urban development projects may favour inexpensive options or try to avoid traffic disturbance, rather than favouring energy efficient transportation modes.

ANME has tried to implement the State’s policy and objectives with the cooperation of energy control stakeholders in the field of transportation, starting by identifying different actors and their impact on the sector’s objectives in terms of the rational use of energy (RUE). In this regard, ANME initiated the studies and surveys to assess the sector’s energy consumption rates.

The main stakeholders involved in the implementation of programs are transportation agencies and departments, which role is essential. The role of the Agency is to inform, moderate, promote and incite. Nevertheless, the transport sector is particularly difficult to apprehend, and the implementation of an ambitious plan of action will require additional efforts.

In the same context, the Greater Tunis Urban Agency (AUGT) aims at organizing discussions and consultations about the largest urban centre in Tunisia. Until 1995, the Agency reported to the Ministry of Interior and Contracted experts in urban planning, yet the implementation of the experts activities was often slowed or halted by other stakeholders. In 1995, the Agency was placed under the authority of the Ministry of Equipment losing its executive power, mainly with regard to local communities in charge of managing urban mobility.

Finally, regional authorities in charge of organizing transportation activities were institutionalized by the land transportation law, but were never created.

The following table summarizes a number of barriers hampering the Rational Use of Energy in transports, further to an analysis conducted on existing institutional structures.

Responsibility in the organization of

transportation activities

• Ministry of Transport, key player in the good execution of energy efficiency and control programs in the field of transportation, does not systematically enforce its role;

• Relations between Ministries are difficult in terms of design and development of infrastructures, mainly public transportation structures;

• Municipalities, most concerned by public transportation systems and knowing best local contexts and needs, do not hold institutional skills and capabilities in the field of public transportation;

• ANME has not been able to efficiently play the role of an influential leader in the implementation of actions impacting the use of energy.

Responsibility in terms of • The Ministry of Equipment is not required to comply by the

10 WWW.anme.nat.tn


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infrastructures and road networks

Ministry of Transport plans and schedules; it has full authority on the implementation of transport master plans;

• Municipalities are responsible for secondary road networks, but do not have enough budget to maintain or modify them;

• Consultations on structuring projects involving several municipalities are difficult and complicated due to the absence of a centralized structure moderating and facilitating decision making;

• Public transportation development plans are decided at the level of the Ministry of Transport but municipalities are not always capable of ensuring the availability of locations.

The actions required horizontality

• It is necessary to rely on appropriate human resources for the Agency to accomplish its mission; The Greater Tunis Urban Agency reporting to the Ministry of Equipment does not have enough authority to facilitate decision making processes involving several municipalities.

Table V.7: Barriers hampering the Rational Use of Energy in transports

The Ministry of Transport is the first source of information. It generally holds three databases that constitute the basic evaluation of the national road fleet. Databases are divided to: the car registration database, the exploitation cards database, and the technical check-ups database. The Ministry handling the public accounting database (Ministry of Finance in Tunisia) that will be used to pull together information and identify operating fleet every year. In Tunisia, this information includes all sorts of information about all vehicles (type, power, fiscal power, payload, etc..) obtained by means of tax on road traffic and on heavy oil engines, plus additional tax on vehicles using LPG and the single compensation tax on road transport.

The main used variables/indicators for the transport of passengers and goods were as follow:

a) Traffic of Passengers:

Real consumption of public transportation companies broken down by area (urban and inter-urban);

Companies’ operation data in terms of the number of transported passengers and average travel distance;

The number of individual passengers’ transport vehicles (PV, taxis, inter-city taxis, etc.). This number is held in the national car registration database corrected by the real number of private cars in operation;

Estimate of the unitary consumption of individual passengers’ transport vehicles; Estimate of the annual average mileage run by individual passengers’ transport

vehicles and the share of urban transport in vehicles’ activity PVs in particular); Estimate of the vehicles’ average occupation rate.

b) Transport of goods:

Actual consumption of energy by SNCFT11 on the transportation of goods; Volume of the traffic of goods (in tons/km) transported by SNCFT; Number of road vehicles used for the transportation of goods by small trucks,

trucks, and road tractors. This number is available in the national registration database, corrected by estimates of the actual number of vehicles currently used for the transportation of goods;

11 Tunisian National Company of railway transport


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Estimate of unitary consumption by different types of vehicles used for the transportation of goods;

Estimate of the average annual mileage covered by different types of vehicles used for the transport of goods;

Estimate of the volume of the traffic of goods (tons/km) carried by vehicles.

On the other hand, the collection of data concerning private cars and vehicles used for the transportation of goods for others is a complex and difficult process, it is therefore necessary to conduct specific surveys and gather required data (mileage, unitary consumption, transported tons, power, etc.).

In spite of Tunisia’s efforts in the field of energy control in general and the rational use of energy in particular, previous studies stressed the substantial lack of statistics in the field of transport and the sector’s impacts on energy resources, and also the absence of an observation system to monitor travel and traffic movements.

This relative deficiency badly affects any fair assessment of the current situation, and consequently prevents the definition of decent and straight forecasts of evolution trends and manoeuvring margins.

In order to compensate this shortfall, ANME being the institution in charge of the State’s energy control policy, focused on considerably reinforcing the information system on transports and energy through three specific actions described below.

Compilation existing information of their structuring

In order to identify the most serious deficiencies of the current information system, and set the structure for a real database on transport and energy, it is first necessary to compile any information currently available at the level of various stakeholders: Ministry of Transport, Road Transportation Agency, ANME, Transportation Companies, etc.

Structuring information and identifying the most concerning shortfalls must be made based on data needed for the design of a program on the rational use of energy in the transport sector.

For this reason, an initial inventory of data and studies already available has been made to compile existing information required for the design of an energy assessment report and its different indicators, and also to identify missing data.

Surveys of major transportation companies

One deficiency already identified is the absence of data on energy related activities and consumption rates at the level of energy-intensive industries.

For this reason, direct surveys were carried out on large companies, with the purpose of collecting information regularly compiled by the companies and understanding their updating process, then in a second phase gathering data required to compensate deficiencies and shortfalls already identified.

The ultimate goal of the studies is to set up a data compilation framework and a procedure for the transmission of updated data by different institutions.

Templates of studies conducted with goods and passengers transportation companies are shown in the Annex VI.3.


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Field surveys at the level of gas stations

Most energy used in the field of transport is consumed by households or small companies, whether specialized or not in transportation, but which are not covered by surveys described in the previous paragraph.

The current observation tool still very limited, in fact the conditions of use of vehicles and their own energy consumption are unknown statistically.

In order to start compiling data and compensate this major deficiency, we suggest conducting field surveys at the level of gas stations aimed at collecting three main types of information:

Type of vehicles (age, type, fuel, ownership, function) Annual mileage Specific consumption for 100 km

The sampling of gas stations was made by crossing two criteria aimed at reducing the unavoidable representativeness slants of the fleet of vehicles:

Scope of life basins12 and the population (namely motorized population); Traffic major roads

Crossing these two criteria aims at ensuring that most vehicles operating in Tunisia are well represented, for they constitute the main target of energy control operations.

The choice of survey periods was also made by crossing two criteria, in order to reduce slants of the over-representativeness of major travellers and heavy-traffic seasons, which are classic in this type of study:

Length of each survey ; Spacing of surveys throughout the year.

Crossing these two criteria aims on the one hand at reducing the number of vehicles visiting the same gas station several times during the survey, and also to take in consideration effects of the tourism activity.

The Industrial vehicles (agricultural, public works, specialized machinery, etc.) were not considered by the sector. The survey’s elementary time length has been set for 2 weeks spread over two periods in the year.

The following table summarizes the survey’s final agenda:

Zone Period

Grand Tunis

Sousse Sfax Kasserine Total

April - May 2006 4 stations 1 station 2 stations 1 station 5 000

July - August 2006 2 stations 1 station 1 station ----------- 3 000

September – October 2006 2 stations ---------- ----------- 1 station 2 000

Total 5 500 3 250 1 250 1 0000 Table V.8: Final agenda of the survey according to the selected zone and total of vehicles surveyed [6]

The survey is conducted by specialized surveyors. Due to constraints specific to this type of study (very limited time); the survey was simplified as much as possible:

Vehicle registration number Mileage on meter

12 The life basin is the smallest area in which people have access to equipment and the most common services.


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Quantity and type of fuel purchased from the gas station Reason for using the vehicle.

Along with this survey, it was agreed with the Road Transportation Technical Agency, (ATTT) public institution in charge of the Car Registration Database, to collect information shown on vehicles’ registration documents:

Type of vehicle ; Power ; Date of operation ; Registration date in Tunisia

The survey form is attached in the Annex VI.3.

Constitution of a database

All data generally needed to analyze energy consumption in the transport sector, will be stored in a formalized database. This database is in fact designed to be incorporated in ANME’s general information system. It is developed on EXCEL in the framework of this study.

The database will be made up of two parts:

An exhaustive database including all survey data, based on a predesigned template mainly to rectify survey results and generate average data;

A simplified database, meant to be integrated in ANME’s information system, including rectified average values only.

The exhaustive database includes three main files:

File containing inputs from survey questionnaires filled in gas stations; Files including data collected from car registration documents (car plate numbers

relate the two first files); File containing survey questionnaires conducted at companies specializing in the

transportation of goods and passengers.

V.4.1 Surveys on transportation companies

Target population

This survey targets national transportation companies operating in the following sectors:

Road transportation of goods ; Road transportation of passengers ; Air transportation ; Railway transportation, and Maritime transportation.

Target populations have been defined after analysis of documents collected from various transport stakeholders, mainly including ANME, the Ministry of Transport and its related institutions, and public/private distributors of oil products.

Sampling

The sample we selected includes all companies representing most transport sectors, with the exception of companies specializing in the transportation of goods due to their considerable number, which we sampled based on strata or class. The sample was around 20% of the target population based on the criteria of the size of the companies.


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Segment Companies Population Sample

Road transport of goods

Transport of goods on behalf of third-parties 396 71

International companies specializing in the transport of goods 56 9

Total 452 80

Road transport of passengers

Regional and national companies operating in the urban and inter-urban transport of passengers 14 14

Private companies operating in the urban and inter-urban transport of passengers 5 3

Tourist transportation private companies 2 2

Total 21 19

Air traffic Private and public national air companies 5 5

Railways National railway and metro transport companies 3 3

Maritime Public and private national maritime companies 2 2

Table V.9: Population and sample of selected companies according to transport segment [6]

Survey data

To respond to the survey’s various objectives, there are multiple needs for information and mainly concern consumption of energy and transport companies’ activities. Data are not the same for all segments but share the main pillars defined below:

- Type of activity: differs from one segment to the other, as in the road transport of goods: general transport, dangerous products, agricultural products, construction materials, etc…

- Turnover; - Human resources: number of employees and tasks (supervisors, drivers, …) - Type and characteristics of the fleet:

o Number of vehicles by type of carriage, manufacturer, payload, type of activity, etc… o Number of seats offered o Payload

o Capacity - Annual mileage : number of kilometer s covered per year by type of vehicle and by type of

activity - Actual average age by type of vehicle - Tonnage and/or volume transported by type of vehicle, - Number of passengers transported per year and per type of activity, - Loading rate per type of flights (for air carriers), - Annual energy consumption broken down by:

o Type of energy (diesel, gasoline, fuel, natural gas, electrical power, kerosene and others – to be specified)

o Supply mode: in-house (bulk), vouchers, domestic purchase, purchase abroad, or other (to be specified)

Data are annual and are requested for the five previous years. The survey must also unveil the company’s energy strategy through data referred to as complementary including:

- Is the company subject to a mandatory periodical audit and what is the execution level in terms of energy audits and program contracts?


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- The nature and qualifications of human and technical resources developed for the follow up of the company’s energy performance;

- The Company’s perspectives and future programs in terms of extension and restructuring.

Execution of the survey

The survey was conducted in a hybrid way, involving mailing interviews and field based research. The first phase included reaching out to companies to identify counterparts and explain the survey’s objectives. Later, a copy of the questionnaire and to ANME’s introduction letter were sent by post, fax and/or by email to ask counterparts to prepare and fill in the questionnaires.

Regular reminders were sent to companies to ensure questionnaires were actually received (at the beginning of the survey) and to assess the level of the questionnaire’s difficulty for responders. Clarifications were provided whenever necessary.

When questionnaires were not sent back, surveyors would visit companies to conduct interviews, settle difficulties and encourage counterparts to fill them and send them back.

Data processing

Data processing refers to the calculation of average and percentage rates for each type of information by class of vehicle. According to surveys, processing results are the following:

- Energy unitary consumptions o C/100 km per vehicle category and mode of use o Specific consumption per service unit according to the vehicle category and the type

of use : traveller, passengers/km, ton, ton/km - Usage conditions of road vehicles

o Km/year by vehicle category and mode of use; o Average annual load rate: passengers-year/vehicle; pass-km/veh-km; pass-km/seats-

km, tons-year/vehicle, ton-km/veh-km, ton-km/(CU * km/year), % km-an empty load. o Average age per vehicle category and mode of use

- Fleets and traffics o Vehicle fleets according to categories and modes of use ; o Vehicles-km per vehicle category and mode of use; o Annual services according to the vehicle category and mode of use: passengers

transported per year, passengers-km, tons transported per year; tons-km

V.4.2 Surveys in gas stations

Target population

The survey in gas stations are designed for users of various transportation means, both collective and private, not included in the first survey category (private individuals, small companies, administrations, taxis, etc…) as they represent far the largest energy consumers in the transport sector.

Therefore, the survey’s target population is made up of users of gas stations operated by different oil products distributors (TOTAL, AGIL, SHELL, etc.) spread throughout the national territory.

Sampling gas stations

Gas stations’ samples were made by crossing several criteria in order to reduce representativeness slants of the fleet of vehicles:


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- Volume and structure of fuel products in gas stations ; - Geography; - Traffic main roads; - Scope of life basins and of the population (mainly motorized population); - Volume and structure of the fleet of vehicles operated by gas stations’ customers.

Crossing criteria aims at ensuring appropriate representativeness of vehicles operated in Tunisia which constitute the main target of the rational use of energy project.

Survey periods

The definition of survey periods was made by crossing two criteria in order to limit the over-representation slants of frequent travellers and of seasons, which are common in this type of survey:

- Length of each survey - Spacing of surveys in the course of the year.

Crossing the two criteria aims at reducing the number of vehicles visiting the gas station several times during the survey and also to take in consideration seasonal effects (tourism, beginning of the school year, etc…).

The surveys’ basic time length was set for one month. We chose three periods throughout the year, each in a different season. The total number of surveyed vehicles was 10,000.

Survey data

As vehicles’ passage times in gas stations are very short, we agreed with the Land transportation technical Agency (ATTT) to limit data collection in gas stations to information not available on vehicles’ registration documents. Other data were obtained from ATTT’s vehicle registration files based on car plate numbers recorded by surveyors.

As a consequence, the survey form included the following data only:

- Car plate number ; - Mileage on the meter ; - Quantity and type of fuel purchased at the gas station ; - Type of usage of the vehicle.

Information shown on the car registration documents that can be accessed through the vehicle plate number includes the following:

- Type of vehicle ; - Fiscal power ; - First operation date ; - First registration in Tunisia.

The following graph shows the geographic and temporal distribution of the sample used in the survey.


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Graph V.3: Geographic and temporal distribution of the sample used in the survey

Collection and compilation of metadata

This phase aims at designing a metadata base by compiling information related to each survey, and providing a space for the classification and storage of data generated by surveys.

Metadata are extracted and collected from surveys’ key documents such as: questionnaires, forms, tender documents and other data existing resources. In surveys, metadata constitute structured information sets used to describe given concepts. For example, metadata describing the concept “vehicle” are given in the following graph. The list of defined metadata hence constitutes the data dictionary.

Sousse

Kasserine

Tunis

Sfax Tunis

Station Period

Mohammed V April ‐ May 2006 X

Sokra July‐Agust 2006 X

Ennasr September‐October 2006 X

Charguia Total number of surveyed vehicles 5500

Sousse‐Sfax

Station Period

Oued El Khroub (Sousse) April ‐ May 2006 X

Oued El Khroub (Sousse) July‐Agust 2006 X

Teniour (Sfax) September‐October 2006

Sagaz (Sfax) Total number of surveyed vehicles 3250

Kasserine

Station Period

April ‐ May 2006

July‐Agust 2006

September‐October 2006 X

Total number of surveyed vehicles 1250

(In discussion with the company)


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Graph V.4: Metadata describing the concept “vehicle” [6]

Metadata are compiled than classified in a flowchart made up of several independent but inter-related entities. Every entity represents a well-defined type of information. This flowchart constitutes the basic document used to build and prepare the storage space for survey data.

Survey data are classified and stored in individual EXCEL folders. This type of storage space is used to structure, centralize and share all types of information in well-defined tables that can be easily handled and exploited. They also offer the flexibility to duplicate data and perform complicated analyses without editing basic data.

Data input and preparation

After preparing the space for data classification and storage, agents in charge of the survey performed the following operations:

- Collection of all data files: returned questionnaires, ATTT file (Excel), energy conversion table, etc…

- Input of primary data from survey questionnaires and their archiving in classification files; - Preparation of aggregates, tabs and other secondary or analytical data taken from other

sources or studies; - Drafting all scripts/programs used to produce or transform data files, for the input of data,

correction and imputation, conversation, aggregation, tabulation and analysis.

Data processing

Data processing refers to the calculation of average and percentage rates for each type of information by class of vehicle. According to surveys, processing results are the following:

- Distribution and sale of fuel products in gas stations per car category and type of use: o Gasoline (normal and super), lead-free, diesel, LPG; o Private cars (including private versus professional; <= 4 HP, 5-7 HP, > 7 HP), Taxis,

inter-city taxis, light commercial vehicles, trucks, road tractors, TCP - Annual mileage by type of road vehicle:

o Fuel : gasoline (normal and super), lead-free, diesel, LPG;

Vehicle StationRegistration number NumberGenre GovernorateDate of first registration location

Company

PassageDate TimeMileageFuelAmount


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o Private cars (including private versus professional; <= 4 HP, 5-7 HP, > 7 HP), Taxis, inter-city taxis, light commercial vehicles, trucks, road tractors, TCP

o Age : < 1 year, 1-2 years, 2-3 years, 3-5 years, 5-7 years, 7-10 years, 10-15 years, 15-20 years, > 20 years.

- Structure of the fleets of vehicles o Fuel : gasoline (normal and super), lead-free, diesel, LPG for each vehicle category:

Private cars (including private versus professional; <= 4 HP, 5-7 HP, > 7 HP), Taxis, inter-city taxis, light commercial vehicles, trucks, road tractors, TCP

Rectifying data/Results

As is the case in all surveys, rectifications aim at improving the quality of data and the reliability of analyses. In our survey, we applied the following slant sources:

- Obvious input errors and distortions; - Multiple passages; - Seasonal aspect; - Over-representation of frequent travellers.

The rectification of input errors is a difficult and complex task. It includes the installation of automatic filters, or reading through data series in order to detect errors or aberrations by comparing data of every series to averages or to reference thresholds/levels, and hence eliminating extreme series or not respecting selected references (ex. Eliminate the passage of vehicles with mileage shown on meters below 3000 km/year and aged more than one year).

Multiple passages imply an over-representation problem of some individuals, which may distort the sample’s statistical value. They provided information about refuelling practices and were identified by recurrence of the same plate numbers. They were eliminated.

The seasonal aspect refered to the seasonal use of some vehicles (holidays, agriculture, tourism, etc.) which may distort a survey not covering the entire year. This phenomenon provided information about tourism activities and the type of car usages. This type of distortion was noted through the considerable seasonal variations in the sale of fuel at the level of gas stations. Data were rectified by conducting several surveys spread over various time periods.

The over-representation of frequent travellers was explained by the fact that the probability to survey a given vehicle (passage at the gas pump) was higher than the annual mileage. The problem was then to highly overestimate the average annual mileage of the fleet of vehicles. It was rectified by setting up a set of coefficients that was applied to fleets surveyed for each age class, defined according to km/year/vehicle reports depending on their age class.

Example of results

The survey in gas stations allowed particularly to determine the average annual distance travelled according to the type and the age of the car. The following chart presents a typical kind of such results:

Training Man

Graph Vage [6]

ual about Data Co

V.5: Annu]

ollection Methodo

ual average

logies on the Use

e travelled

of Energy

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distance oof private ccars by fue

el type andd


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RECOMMANDATION FOR SURVEY PLAN Contribution to proposing a plan for obtaining information on sectoral energy use.

It is recommended that national Energy Information System (EIS) should be developed or improved if it exists. It should be based on the "distributed model" in which compatible information and data management systems are hosted by institutions with statutory mandates for the respective datasets. The concept founded on the sharing of common data infrastructure which guarantees the harmonisation of the different set of data required for national energy statistics planning.

The on-going initiatives should constitute the basic framework for the collaboration and co-operation among produces and users relevant to the management of the energy. However, there is an urgent need for co-ordination to ensure that systems being developed are compatible and data generated are harmonised using international standard.

It is recommend that institutions currently developing information system or databases should form the nucleus of the EIS community. A consensus building is critical ingredient to ensure that collaborating institutions recognise the benefits of working together to improve the availability of, and access to, good quality data and information on the energy.

In order to reach this goal, and as first proposal for the national a work plan, we advise the following actions:

Short term period: This preliminary phase will be dedicated to:

- Setting up thematic working of energy use statistics according to the structure flow of energy balances (international standard): Industry, Transport, Public Administration, Tertiary, Residential, Agriculture, etc.

- Select transport as pilot project during the preliminary phase in order to structure the team-work and to gain experience for the next ones.

- Organise national Energy Information System Workshop in order to reach on the establishment of framework for managing energy use statistics in the country. The framework has to set-up the following major element:

o Creation of steering committee at level of EIS stakeholders o Establishment of EIS technical committee o Develop or improve and formalise national network of EIS institutions (Data Centres) o To nominate the lead agency, preferably joint institutions between National Statistical

Office and Ministry of Energy or Equivalent - Inventory of existing data and new data being generated and related metadata for energy use in

transport - Diagnostic of national register / census for the preparation of list frame: Enterprises, Households,

Vehicles, etc - Diagnosis of the state of energy consumption statistics and related indicators & GHG emissions

inventories - Study analysis on methodology of work for the national "Technical control of vehicle" - Research funds for the transport survey

Medium & long term period: This second phase will be dedicated to the implementation the Energy use survey for transport sector:

- Formulation of sampling design - Questionnaire preparation (Pre-test, revision, printing & manuals) - Field operation & Data processing - Report and release of survey results - Aggregate data according ISIC/NACE Integration of data in EB (international standard)

o Generalise the use of international nomenclature ISIC/NACE - Calculation of GHG emissions & related EE indicators


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- Training LEAP Model - Forecasting & planning team within energy & transport institutions - realisation of sectoral publications

In parallel launch a second sector using the same process until to cover all sectors

Challenges/obstacles

The more frequent obstacles and challenges could be summarised as follow:

In term of budget, the financial resources that accompany the project may be limited: it could compromise the complete and perfect implementation of the project and its sustainability.

The human resources meet partially the needs of the project with specific risk about:

- Lack of qualification and experience of engaged staff - Inadequate profile of participants in training and workshops with non-transmission of documents,

training materials, knowledge and expertise with colleagues - Problem of autonomy and limited initiative due to the administrative burden - Problem of turnover of engaged staff - Lack of work appreciation and respect of hierarchical administration and colleagues

On the work program and its implementation, there is a risk of:

- Problem of confidentiality and/or sharing of information and data breasts authorities concerned and the institutions involved in the project

- Lack of project results indicators or inadequate evaluation system of the project - Project ranked at second priority compared to traditional or basic activities in the involved

institutions - Lack of strategic visibility and decision about the interest final energy consumption surveys

Finally, in terms of inter-institutional coordination:

- Lack of visibility of the roles of institutions involved in the project and the complementarities of their activities.

In order to overcome this challenge/obstacle and to achieve the goals initiated in the work plan, it's recommended to develop quality control system at each step of the EIS implementation process, which should address issues including the following:

- Definition, understanding and acceptance of the role of data custodian - Development and adoption of a common database "architecture" - Harmonisation of classification, coding system and use of standard nomenclature - Establishment and adoption of standard data cataloguing procedures - Development and adoption of common data quality and meta-data standards - Issues regarding privacy, confidentiality and freedom of information - Conditions of access to data - Priorities for investments to build and maintain the data infrastructure - Cost-recovery mechanisms and private sector involvement - Priorities for personnel development, including training.


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VI. ANNEX:

VI. 1 Canada: Quality assurance Framework, Practical checklist and Questionnaire

VI. 1.1 Statistics Canada’s Quality Assurance Framework:

The Quality Assurance Framework used in Canadian surveys is multidimensional: • One dimension is the legal framework within which Statistics Canada functions; Statistics Canada,

Canada’s central statistical agency, was created by parliamentary statute, the Statistics Act, and is authorized thereby to collect and publish statistical information. Statistics Canada’s data collection activities as well as fulfillment of its publication mandate are governed by the provisions of that Act.

• The second dimension is that, as an agency ultimately accountable to the Parliament of Canada,

Statistics Canada has developed corporate accountability policies, standards and guidelines applicable to its activities. Statistics Canada’s Policy Manual consolidates these policies, standards and guidelines under five main themes: External Relations, Content of Products, Dissemination, Confidentiality and Internal Management.

• The third dimension is Statistics Canada’s Quality Assurance Framework that is the set of

management, operating and consultative practices, procedures, and mechanisms that are used by Statistics Canada to manage the quality of its information products.

In applying the principles of Statistics Canada’s Quality Assurance Framework, Manufacturing, Construction and Energy Division strives to meet the six important criteria of “quality” or "fitness for use": the relevancy of data and, its accuracy, timeliness, accessibility, interpretability and coherence with respect to the Division’s surveys of energy production, trade and consumption and related publications. The first three of these quality criteria are the direct concern of survey managers:

‘Relevancy of data’: This criterion of “quality” addresses user needs in relation to the Agency’s budgetary possibilities. User needs are identified through bilateral and multilateral liaison with major users, through information and advice provided by statistical organizations and consultative groups and, through user feedback on existing products and services. Regular reviews of all programs are conducted and budgetary possibilities are balanced against user needs. The consultation and budgetary possibilities processes described under relevance determine which programs are going to be carried out, their broad objectives, and the resource parameters within which they must operate.

Accuracy: Management of accuracy requires particular attention during the design and implementation, and assessment phases of a statistical activity, each one built on the others. Statistics Canada’s Policy on Informing Users of Data Quality and Methodology requires that at least the following four primary areas of accuracy assessment to be considered in all programs: assessment of the coverage of the survey; assessment of sampling error where sampling was used (standard errors, or coefficients of variation, should be provided for key estimates); non-response rates and estimates of the impact of imputation; and descriptions or measures of other serious accuracy or consistency problems with the survey results. Measures of accuracy are also an important input for Program Review for assessing whether user requirements are being met, and for allowing appropriate analytic use of the data.


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Regarding the methodological aspect of ‘accuracy’, related information is publicly accessible in an Integrated Meta Data Base, available on the departmental website. It contains information on a survey’s methodological design parameters such as the objectives of a survey; its target universe; the sample design and estimation procedures applied; methods of collection used; editing procedures and, non-response information.

Timeliness: Timeliness of information refers to the length of time between the reference point, or the end of the reference period, to which the information relates, and its availability to users. Information available to users well within the period during which it remains useful for its main purposes is considered to be timely. Timeliness should be monitored over time to warn of program deterioration, and across programs, to recognize extremes of tardiness, and to identify good practices. However, it should be borne in mind that ‘timeliness’ can often be improved only with trade-offs between accuracy and cost. In response to a small number of unfortunate quality control incidents in recent data releases under several high profile statistical programs, Statistics Canada’s Policy Committee struck a senior-level taskforce to undertake a review of the quality assurance practices of nine key statistical programs. The results including conclusions and recommendations of the taskforce’s review were released to the public in the Daily as Quality Assurance Review - Summary Report. The Policy Committee’s Record of Decision with respect to the foregoing report is also available. The purpose of section is to provide a review of Canada’s best practices concerning quality control and assurance in the context of Statistics Canada’s surveys of energy production and consumption and, in publications associated with those surveys. The following is a “Practical Checklist” used by Statistics Canada in various stages of a survey with the objective of assisting and assessing employees in succeeding on the Quality front. VI. 1.2 Practical Checklist of Quality Control and Assurance Activities of an existing survey:

A. Front End of the Survey: Even before survey questionnaires are mailed, key activities must be conducted: 1. Sample Verification:

o Universe and sample counts of entities are verified on industry, geography and stratum levels;

o Research into births and deaths of reporting entities are well examined and confirmed; o Some surveyed entities may report on a consolidated basis; confirm what is included or

excluded; o Changes to the industrial and/or geographical classification of the sample are verified; o Review the existing sample for names changes.

2. Data Collection:

o Training: Interviewers and data capture operators are critical to the success of most data collection and capture operations. Statistics Canada also ensures that they have appropriate training and tools. Training should be more than a onetime training session and should include continuous, ‘live’ monitoring of their skills;

o Pre-contact of new survey units: contacting NEW respondents before a survey is conducted is beneficial in several ways:

- Opportunities to identify changes in mailing address or telephone numbers; - Opportunities to identify the proper contact person in an organization and to establish a rapport with that person


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o Ongoing verification of coverage and activity: for a survey collected on a monthly basis, it is important to verify your existing sample in term of its coverage and activities on a regular basis.

o Historical and consistency editing of units: having a collection processing system that has built-in historical and consistency edits of the responding units is another key factor.

o Capture relevant respondent comments: what are companies or respondents telling you in the ‘COMMENTS AREA’? In several of our surveys, these comments have been used to analyse the data and help write the analytical text.

o Feedback mechanisms: if the data collection is conducted by another part of your organization or region, a feedback mechanism between the two parts of the organization helps to clarify data and coverage issues. Video conferencing can be used to bridge the communication gap between the collection staff and analysts.

B. Back End of the Survey:

The respondents are asked to complete the questionnaire and certify the information contained in their responses to be “complete and correct”. The next steps are the editing, imputation and estimation activities of Quality Control and Assurance. 3. Editing of Responses:

Data editing is the application of checks to detect missing, invalid or inconsistent entries or to point to data records that are potentially in error. Some of these checks involve logical relationships that follow directly from the concepts and definitions. Others are more empirical in nature or are obtained as a result of the application of statistical tests or procedures (e.g., outlier analysis techniques).

o Consistency and historical edits to identify outliers: Confirm the data in the questionnaire is consistent within itself and with previously historical reported data by the respondent.

o Examine large contributors to an estimate: Who are the top 5, 10, 25 contributors in an industry; Who are the top contributors to a specific fuel; Who are the top contributors to a specific region o Examine other complimentary data; enterprise financial and operating reports,

administrative data such as tax data, licenses for exploration, royalty payments, newspaper clippings etc.

o For business surveys, put in place a strategy for selective follow-up with the organization.

4. Imputation:

Imputation is the process used to determine and assign replacement values for missing, invalid or inconsistent data that have failed edits. This is done by changing some of the responses or assigning values when they are missing on the record being edited to ensure that estimates are of high quality and that a plausible, internally consistent record is created.

o Uses tested and standardized methods, and examine the impact of these imputed estimates on the FINAL estimates. Although imputation can improve the quality of the final data by correcting for missing, invalid or inconsistent responses, care must be exercised in choosing an appropriate imputation methodology. Some methods of imputation do not preserve the relationships between variables and can actually distort underlying distributions.

Imputation examines the previous month, the previous year; look at donor group with the same industry, same geography, and hierarchy (strata); Identify and monitor outliers generated at this level;


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o Good imputation attempts to limit the bias caused by not having observed all of the desired values, has an audit trail for evaluation purposes and ensures that imputed records are internally consistent. Good imputation processes are automated, objective, reproducible and efficient. Changes should be made to the minimum number of fields to ensure that the completed record passes all of the edits.

5. Estimation:

Estimation is a process that approximates unknown population parameters using only that part of the population that is included in a sample. Inferences about these unknown parameters are then made, using the sampled data and associated design.

o Total survey error in the estimate is the amount by which the estimate differs from the true value of the quantity for the survey population and equals the sum of the sampling error and non-sampling error.

o The sampling error represents the error associated with estimating a parameter of interest using data from only a sample.

o Non-sampling errors reflect other reasons for having an imperfect estimator. These include coverage errors (imperfect survey frame), measurement errors and non-response errors.

o The estimation method and the sampling design determine the properties of the sampling error. Criteria to evaluate the magnitude of the sampling error include the sampling bias and the sampling variance. Estimation methods that result in both the smallest bias and the smallest sampling variance should be chosen.

o Proper estimation conforms to the sampling design. To that end, incorporate sampling weights in the estimation process. This implies that aspects of the sampling design such as stratification, clustering, and multi-phase or multi-stage information are reflected in the estimation of parameters and their associated variance estimators.

o Use auxiliary data whenever possible to improve the reliability of the estimates.

6. Quality Control & Assurance Analysis at Macro Level One:

Steps 1 to 5 of this Check List relate to editing, imputation and estimation as applied to individual survey responses. When these steps are completed, analysis for quality control moves to the level of aggregates produced from survey returns. o Compare data to that of previous periods; o Confront your data with data from other surveys: production of an energy commodity has

increased; what do the surveys of exports and imports of that commodity tell you; are changes in retail sales consistent with increased commodity production?

o If you use data from another source, make certain these estimates are final; o Examine your quality measures: what are the results of the coefficients of variation; what

is the response rate, by size, by geography? o If the survey is conducting seasonal adjustments, are there unusual events or number of

reporting days?

7. Quality Control & Assurance Analysis at Macro Level Two:

The energy model is just one component within the larger framework of Statistics Canada’s System of National Accounts (SNA); energy must be coherent with the other parts of the SNA. The Energy Statistics Program in Canada has instituted a process called “Work-in-Progress”. This process allows the subject matter people to obtain expert knowledge from engineers and industry officers in other federal policy departments (i.e. Environment Canada and Natural


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Resources Canada) prior to the official release of the data. These experts help us validate the aggregate data and assure its accuracy. o Canada’s central statistical agency has the power under the Statistics Act to enter into

“data sharing agreements” with other federal departments and regional governments. Those agreements have been most useful in identifying and improving the accuracy of the data.

o Outside peer review: for some surveys, it could be useful to meet with industry representatives to confront or validate our final findings. A data package with the industry’s fuel consumption, trends and key energy indicators could be prepared to ask industry representatives to help validate the estimates by examining industry trends and specific industry intelligence.

8. Quality Control & Assurance Analysis at the Product Dissemination Phase:

In the last phase, dissemination of the data, whether as part of news release, a publication or a public database, an error can be so critical. There are many more eyes looking and analysing the data from students, to university professors, consultants, industry associations, other interested domestic government departments and international agencies such as United Nations and the International Energy Agency. There are three main steps that need attention in this Phase: o Have your subject matter experts present the day before and after the release for any

questions; o Make sure all your text, tables etc. are proofread; check the percentage changes and your

units of measurement, scales etc. If your release is in more than one language, check the consistency between the two languages;

o Dealing with the media is an art; make sure you and your staff are trained to deal with them; prepare answers to questions that you are likely to be asked; monitor what is printed in the newspaper and, respond to mistakes or erroneous statements and misinterpretation of data.

Statistics Canada undertakes extensive communication with our key users in order to establish partnerships. This ensures that we are aware of their needs and of the key issues for which they may need data now or, in the future. Our key users are familiar with our surveys and processes so that they understand the data (i.e. no surprises) and able to respond to media questions by commenting on the data’s meaning as well as its integrity. We are all partners and all on the same side. A major challenge one has to confront, as a statistician, is ‘time’. If you have less time, you are simply number crunching, producing tables, reacting and recording what has happened rather than creating ‘value added’. If you have more time, you are trying to get a more accurate measure, estimating additional data points, producing more useful graphic indicators; you are creating value. A reasonable compromise on timeliness can allow the production of more relevant, accurate and interpretable data, i.e. data of better overall quality.


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VI. 1.3 Fuel Consumption Survey Questionnaire


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VI.2. Survey on Consumption of Energy in Transport Sector in Morocco

Questionnaire 'Residential sector'

I ‐ Information about the survey

1‐Identification

1-1 Name of respondent............................................................................................................. 1-2 Sex ........................................................................................................................................ 1-3 Age : ……………………………………….………….……………………………………….…… 1-4 Identification of the investigated station The station name:... ......................................................................................................... Area: ................................................................................................................................ Address: .......................................................................................................................... Commune: …………………………………………............................................................. Prefecture or Province: …………………………………………..........................................

1-5 Matrimonial status Single Married Divorced Widower

1-6 Socio-professional category Farmers Craftsmen, traders, entrepreneurs Executives, managers and higher intellectual professions Professionals Inactive

2 Characteristics of the household

2.1 Sex of head of household:... 2-2 Age of head of household:... 2-3 Category socio professional of the head of household

Farmers Craftsmen, traders, entrepreneurs Executives, managers and higher intellectual professions Professionals Inactive

2-4 Educational attainment of household head Without level Koranic school Primary Secondary High scool

2-5 Household size:...


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2-6 Type of habitat Villa Apartment Modern Moroccan House Traditional Moroccan House Summary habitat

2-7 Number of vehicles owned by the household:

2.7.1 Tourism : ……….…………… 2.7.2 Utility : ……….……………… 2.7.3 Motorcycle : ……….…………

II‐TRANSPORT

Please respond to questions relating to the consumption of energy for transport within your household

1 Characteristic of vehicles

1-1 Please indicate the energy consumption by passenger vehicle available to your household

1-2 Please indicate the energy consumption by utility vehicle available to your household

Category of tonnage : 1 - PTC ≤ 8 t, 2 - 8 t < PTC ≤ 14 t, 3-14 t < PTC ≤ 19 t, 4-19 t < PTC ≤ 26 t, 5 - 19 t < PTC ≤ 26 t, 6 - PTC > 26 t.

1-3 Please indicate the energy consumption per motorcycle available to your household

The vehicle of tourism Mark

Year of entry into

service

Tax power

Fuel type

Consumption

l/100 km

Average number

km per month

Quantity

consumer

per month in litre

Quantity consumed in

litre

in 2010

Value of Dhs consumption

in 2010

…………..

…………..

…………..

…………..

The vehicle registration

Mark

Year of entry into

service

Category of

tonnage

Tax power

Fuel type

Consumption

l/100 km

Average number

km travelled

per month

Quantity

consumption in litres per month

Intake in litres in 2010

Value of Dhs

consumption in 2010

………..

………..

………..

………..

Total


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2 Rate of energy consumption

2-1 Please indicate the rate of consumption of energy by type of vehicle

Comments:

Motorcycle registration Mark

Year of entry into

service

Tax power

Fuel type

Consumption

l/100 km

Average number

km travelled

per month

Quantity

consumer

per month in

litre

Intake in

litres in

2010

Value of Dhs consumption

in 2010

…………..

…………..

…………..

Type of vehicle Average number of months of use

Average consumption in

summer


autumn


winter

Average consumption in the

spring

Tourism

Utility

Motorcycle


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VI. 3. Tunisian Survey on the Road Transportation of Goods: Questionnaire

Identification of the Institution

General Data

Corporate Status

Manager’s Full Name :

Quality:

Full name of POC for additional information

Function :

Creation Date Legal status : LC PC Other

Capital MTD

Address

Phone : Fax : Mail :

Type of transport activity

General transport Dangerous products Others

Agricultural products Construction materials

Activity data

Number of vehicles per type

Vehicle Year 1 Year 2 Year 3 Year 5 Average age(*)

Light trucks (PL< 10T)

Bearing truck (10 <PL< 19 T)

Road tractor

Trailer

Semi‐ Platform


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trailer Tank

Dumpster

Total

General Total (*)Average age = Sum of the age of vehicles of the same category / number of vehicles of subject category

Payload of the entire fleet in tons

Vehicle Year 1 Year 2 Year 3 Year 5

Light trucks (PL < 10T)

Bearing truck (10 < PL < 19 T)

Road tractor

Trailer

Semi‐trailer

Platform

Tank

Dumpster

Total

Total

Total number of kilometers covered in a year

Vehicle Year 1 Year 2 Year 3 Year 5

Small trucks (PL< 10T)

Bearing truck (10 < PL< 19 T)

Road tractor

General total

Rate of empty loads

Tonnage or volume transported per year

Vehicle Unit Year 1 Year 2 Year 3 Year 5

Small trucks (PL< 10T)

Camion porter (10 < PL< 19 T)

Trailer

Semi‐trailer

Platform

Tank

Dumpster

Total


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General Total

Expenses and operation costs

Expenses Year 1 Year 2 Year 3 Year 5

Fuel

Tires

Lubricants

Spare parts

Institution’s energy consumption(*)

Supply mode Units Year 1 Year 2 Year 3 Year 5

Gasoil

In house (bulk)

Vouchers

Others

Total diesel (*) Vehicles used for the transportation of goods + Maintenance vehicles and machinery (excluding office and executive cars)


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Additional data

Follow up of energy consumption

‐ Is your institution subject to a mandatory periodical audit? yes no

‐ Has your institution already conducted an energy audit? yes no

If yes, what was the year?

If yes, have you signed a program contract with the National Energy Control Agency?

yes no

If yes, have you already conducted actions from the program‐contract? yes no

‐ Does your institution employ an Energy Manager? yes no

‐ On a 1 to 10 scale, how much would you grade the energy consumption monitoring system applied in your institution?

/ 10

‐ Is there an energy management system? yes no

If yes, specify: Men‐Energy

Equipment (Tachographs, opacimeters, etc.)

Others ( )

Perspectives and future programs

Extension program

‐

Energy conversion

‐

Restructuring plan

‐

Survey on energy consumption in the road transportation of passengers

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VI.4 Tunisian Survey on the Road Transportation of Passengers: Questionnaire

Identification of the Institution

General Data

Corporate Status

Manager’s Full Name : Quality :

Full name of POC for additional informationFunction :

Creation Date Legal status : LC PC Other

Capital MTD

Address

Phone : Fax : Mail :

Type of transport activities

Urban Rental

Inter‐urban Others ( )

Regional

Human resources on current date (indicate date)………………

Administrative Technical

Exploitation Total

Drivers Cashiers

Permanent

Managers Temporary

Total

Permanent

Supervisors Temporary

Total

Permanent

Execution Temporary

Total

General Total


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Activity Data

Number of vehicles by type of carriage

Carriage Year 1 Year 2 Year 3 Year 5

Office duty vehicles

Mini bus

Standard bus

Articulated bus

Standard bus

Articulated bus

Number of total km covered per year and by type of carriage

Carriage Year 1 Year 2 Year 3 Year 5

Office duty cars

Mini bus

Standard bus

Articulated bus

Standard coaches

Articulated coaches


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Structure of the fleet on current date (indicate date)………………

Brand Type

Number of vehicles by type of carriage Total

vehicles

Seats offered Current average age*

(date……) Duty car Mini bus

Standard bus

Articulated bus

Standard coach

Articulated coach

Standing Sitting Total

General total

*Average age= Total sum of the age of vehicles of the same category/number of vehicles of the same category


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Activity Data

Activities Year 1 Year 2 Year 3 Year 5

Receipts in MTD

Number of travelers

Km covered

Receipts in MTD

Number of travelers

Km coveredReceipts in

MTD Number of travelers

Km covered

Receipts in MTD

Number of travelers

Km covered

Urban

Commercial sales

School bus pass

Civil pass

Others

Regional

Inter‐urban

General Total

Total number of kilometers covered by the entire fleet ………..…………………………………..kms

Effective mileage rate∗ : …………………………%

Rate of vehicles equipped by functional mileage meters: …………………………%

∗Effective mileage rate = Number of kilometers registered on functional meters of the fleet’s vehicles / (Number of km registered on functional meters of the fleet’s vehicles + Estimated mileage of vehicles not equipped with functional meters)


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Institution’s Energy Consumption

Energy Global Consumption

Units Year 1 Year 2 Year 3 Year 5

Diesel*

Total Gasoline*

Including lead‐free gasoline

Fuel‐oil*

Electrical power

Natural gas

Others…………………………

* Real consumption taking in consideration annual purchases and stock variation

Detailed consumption for exploitation vehicles*

Supply mode Unit Year 1 Year 2 Year 3 Year 5

Diesel

In house (bulk)

Vouchers

Others

Total diesel

Gasoline

In house (bulk)

Vouchers

Others

Total gasoline

* Vehicles used for the transportation of passengers + Maintenance vehicles and machinery (excluding office and executive vehicles)


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VI.5 Tunisian Survey of gas-stations’ users: Questionnaire

File n°………..

1. Car plate number

Number ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ TU ‐‐‐‐‐‐‐‐‐‐

Others ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐

2. Fuel Amount ‐‐‐‐‐‐‐‐‐,D‐‐‐‐‐‐‐‐‐‐‐‐

Type :- Normal gasoline - Super gasoline - Lead free gasoline - Diesel - LPG

3. Passage Date ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ Time ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐4. Vehicle Mileage on the meter

‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ Do you use this vehicle for:

- Private use - Professional use

In case of professional use: please specify ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐


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VI.6 LEAP Demand Modelling Methodologies [14] 1. Final Energy Analysis: e = a . i

Where: e=energy demand, a=activity level, i=final energy intensity (energy consumed per unit of activity)

Example: energy demand in the cement industry can be projected based on tons of cement produced and energy used per ton. Each can change in the future.

2. Useful Energy Analysis: e = a . (u / n)

Where u=useful energy intensity, n = efficiency

Example: energy demand in buildings will change in future as more buildings are constructed [+a]; incomes increase and so people heat and cool buildings more [+u]; or building insulation improves [-u]; or as people switch from less efficient oil boilers to electricity or natural gas [+n].

3. Transport Stock Turnover Analysis: e = s . m / fe Where:

s= number of vehicles (stock), m = vehicle distance, fe = fuel economy

This part allows modelling of vehicle stock turnover and allows also pollutant emissions to be modelled as function of vehicle distance. Example: model impact of new vehicle fuel economy or emissions standards.

For the transport analysis in LEAP, the energy consumption is estimated according to the following formula:

Energy consumption = stock of vehicles * annual vehicle mileage * fuel economy With this Demand Analysis methodology, energy consumption is calculated as the product of the number of vehicles, the annual average mileage (i.e. distance travelled) and fuel economy (e.g. liters per km or 1/MPG). The base year stock of vehicles is either entered directly or calculated from historical vehicle sales data and a lifecycle profile describing survival rates as vehicles age. In scenarios, projections can be computed for future sales of vehicles, and for future levels of fuel economy, vehicle mileage and environmental loadings of newly added vehicles. Other lifecycle profiles are used to describe how mileage, fuel economy and environmental loadings change as vehicles age. LEAP then calculates the stock average values for fuel economy, mileage and environmental loadings across all vintages and hence, ultimately, the overall level of energy consumption and environmental loadings. The transport analysis variables include: - Stocks - Sales - Mileage - Mileage Correction Factor - Fuel Economy - Fuel Economy Correction Factor


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For a given branch and as example, the following equations describe the transportation calculations to estimate Energy Consumption: Step 1: Stock Turnover and Stock Rollover

Stockt,y,v = (Salest,v*Survivalt,y-v) - RollOvert,y + ∑ f=1..T RollOverf,y

RollOvert,y = Salest,v*Survivalt,y-v*(1-RollSurvivalt,y-v)

Stockt,y = ∑ v=0..V Stocky,v

Where: t is the type of vehicle (i.e. the technology branch) v is the vintage (i.e. the model year) y is the calendar year T is the number of types of vehicles Sales: is the number of vehicles added in a particular year: entered as an expression. Stock is the number of vehicles existing in a particular year: either entered as an expression for Current Accounts or calculated internally based on historical sales. Survival is the fraction of vehicles surviving after a number of years: entered as a lifecycle profile. V is the maximum number of vintage years: determined automatically from the survival lifecycle profile, with a maximum of 30 years. Rollover is the number of vehicles that get "rolled over" (i.e. sold) from government or business fleets into the private vehicle stock.

Step 2: Fuel Economy

FuelEconomy t,y,v = FuelEconomy t,y * FeDegradation t,y-v

Where: FuelEconomy is fuel use per unit of vehicle distance traveled (i.e. 1/MPG). Entered as an expression. FeDegradation is a factor representing the decline in fuel economy as a vehicle ages. It equals 1 when y=v. Entered as a lifecycle profile.

Step 3: Mileage

Mileage t,y,v = Mileage t,y * MlDegradation t,y-v

Where: Mileage is annual distance travelled per vehicle. Entered as an expression.

MlDegradation is a factor representing the change in mileage as a vehicle ages. It equals 1 when y=v. Entered as a lifecycle profile.

Energy Consumption

Energy Consumption t,y,v = Stockt,y,v * Mileage t,y,v * FuelEconomy t,y,v


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The LEAP software has several functionalities to estimate GHG emissions and to make forecasting calculation according to different scenarios. We can present for this, the Irish experience using LEAP model


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References

List of references

1. Low carbon transport development strategy in Tunisia. Ministry of Transport. Enerdata/Alcor, 2010.

2. Case study of transport and climate change Morocco: improvement of energy efficiency of vehicles. World Bank. Delsey. 2008.

3. Urban Mobility in Tunis: trends and perspectives, Plan Bleu, 2009. 4. Survey on energy consumption in industry and transport sector. ANME. Alcor, 2006. 5. Development of energy efficiency in transport sector. ANME. Enerdata/Alcor. 2008. 6. Survey on energy consumption in private car sector in Tunisia. ANME. Alcor. 2008 7. Status of energy statistics and indicators in the ESCWA region. ESCWA. 2009 8. Fuel efficient road vehicle non-engine components: Potential Savings and Policy

Recommendations. IEA. 2007 9. IEA vehicle efficiency drive new vehicle policy approaches. Thomas Guéret and Paul Waide,

IEA. 2008. 10. Review of international policies for vehicle fuel efficiency. IEA. 2008 11. Manual on energy statistics. IEA/Organisation for Economic Development. 2005. 12. Survey on Transport, Palestinian Central Bureau of Statistics, 2010. 13. Sampling Essentials Practical Guidelines for Making Sampling Choices, Johnnie Daniel

Howard University 14. LEAP, MARKAL, ENEP-BALANCE and PRIMES user guides. 15. MEDSTAT II & III Materials specifically:

[15.1] Methodological note for the final energy consumption survey in transport sector [15.2] Fuel Consumption Survey, Statistics Canada http://www23.statcan.gc.ca/imdb/p2SV.pl?Function=getSurvey&SDDS=2749&Item_Id=136724 [15.3] Morocco, Egypt, Slovenia, Ireland experiences [15.4] Enquête sur la consommation énergétique dans le secteur des transports: Aspects méthodologiques et résultats, Ministere de l’Energie, des Mines, de l’Eau et de l’Environnement - Barcelone du 18 au 21 février 2013 [15.5] Energy Forecasts for Ireland to 2020, SEAI 2010 Report

16. Use of odometer readings in defining road traffic volumes and emissions, Tuuli Järvi, 2013. http://www.cros-portal.eu/sites/default/files/NTTS2013fullPaper_170_0.pdf

17. An Overview of Different Methods Available to Observe Traffic Flows Using New Technologies, Irina Yatskiv1, Alexander Grakovski, Elena Yurshevich, Transport and Telecommunication Institute Latvia http://www.cros-portal.eu/sites/default/files/NTTS2013fullPaper_221.pdf

18. International Recommendations for Energy Statistics (IRES) 19. Energy Modeling with MARKAL, US Environmental Protection Agency (EPA) 20. Development of a Fuel Policy for Romania: An Energy Supply and Demand Study, Argonne

National Laboratory, 2007 21. PRIMES MODEL E3M lab of ICCS/NTUA, version used for the 2010 scenarios for the

European commission including new sub-models 22. Evaluation toolbox,

http://evaluationtoolbox.net.au/index.php?option=com_content&view=article&id=58&Itemid=154

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