Zambia - Urban household energy demand study

World Bank/UNDP/Bilateral Aid Energy Sector Management Assistance Program PROJECT WORKING DOCUMENT ZAMBIA URBAN HOUSEHOLD ENERGY DEMAND STUDY DECEMBER 1990 Household Energy Unit Industry and Energy Department The World Bank Washington, D.C. 20433  TABLE OF CONTENTS A. STRATIFICATION . . . . . . . . . . . . . . . . . . . . . . . 1 . TOWN SIZE . . . . . . . . . . . . . . . . . . . . . . . 2 . ELECTRIFICATION . . . . . . . . . . . . . . . . . . . . 3-INCOMELEVEL . . . . . . . . . . . . . . . . . . . . . 4-THESAMPLINGUNIT . . . . . . . . . . . . . . . . . . . B . THE LISTING . . . . . . . . . . . . . . . . . . . . . . . . . C . SAMPLE SIZE . . . . . . . . . . . . . . . . . . . . . . . . . D . CONFIDENCE LEVELS AND TESTS OF SIGNIFICANCE OF THE SAMPLE . . 1 CONFIDENCE INTERVAL FOR A PROPORTION . . . . . . . . . 2 . CONFIDENCE INTERVAL FOR A MEAN . . . . . . . . . . . . 3 . SAMPLE SIZE AND THE ACCURACY OF ESTIMATION OF THE MEAN E . PREPARATORYWORK . . . . . . . . . . . . . . . . . . . . . . 1 . ADMINISTRATIVE PROCEDURE . . . . . . . . . . . . . . . 2 . QUESTIONNAIRE . . . . . . . . . . . . . . . . . . . . . 3 . TRAINING . . . . . . . . . . . . . . . . . . . . . . . F . PROGRESSIONOFWORK . . . . . . . . . . . . . . . . . . . . . 1 . THE FIELD WORK . . . . . . . . . . . . . . . . . . . . 2 . THEOFFICEWORK . . . . . . . . . . . . . . . . . . . . 3 . EXTRAPOLATION OF THE RESULTS TO THE POPULATION . . . . 4. COMPLEMENTARYSURVEY . . . . . . . . . . . . . . . . . I11 . MAJOR FINDINGS OF THE URBAN HOUSEHOLD ENERGY DEMAND STUDY . . . . . 23 A. ECONOMIC AND SOCIO-DEMOGRAPHICASPECTS . . . . . . . . . . . . 23 1 . POPULATION GROWTH . . . . . . . . . . . . . . . . . . . . 23 2 . DISTRIBUTION OF POPULATION BY SIZE OF TOWN . . . . . . . 23 3 . DISTRIBUTION OF HOUSEHOLDS BY STANDARD OF AREA . . . . . 24 4 . INCOME AND EXPENDITURE . . . . . . . . . . . . . . . . . 25 B. ENERGY BALANCE FOR THE RESIDENTIAL SECTOR . . . . . . . . . . . 27 C. MAJOR DETERMINANTS OF ENERGY PATTERN . . . . . . . . . . . . . 29 IV . ANALYSIS OF THE ENERGY DEMAND BY FUELS AND END-USES . . . . . . . . 36 A . INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . 36 B . CHARCOAL: MAJOR FUEL FOR URBAN HOUSEHOLDS . . . . . . . . . . 36 C . ELECTRICITY: AN EXPENSIVE FUEL FOR COOKING BECAUSE OF VERY HIGH UP-FRONTCOSTS . . . . . . . . . . . . . . . . . . . . . . . 41 1 . ELECTRICITY AND CHARCOAL . . . . . . . . . . . . . . . . 42 2 . NON-ELECTRIFIED HOUSEHOLDS . . . . . . . . . . . . . . . 43 3 . THE CERAMIC ELECTRIC STOVE . . . . . . . . . . . . . . . 44  D . KEROSENE: A CHEAP AND WIDESPREAD FUEL . . . . . . . . . . . . . FIREWOOD: AN IMPORTANT FUEL FOR MEDIUM AND SMALL TOWNS . . . . E. F. COOKING PRACTICES AND EATING HABITS . . . . . . . . . . . . . . V. ENERGY DEMAND FORECASTING FOR URBAN HOUSEHOLDS . . . . . . . . . . A. THE MODEL: AN ANALYTICAL APPROACH BASED ON THE INTERNAL COHERENCY OF THE DATA BASE . . . . . . . . . . . . . . . . . B. OPERATION OF THE MODEL . . . . . . . . . . . . . . . . . . . . C. CENTRAL FORECAST . . . . . . . . . . . . . . . . . . . . . . . ANNEXES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The following energy conversion factors have been used in this report. ENERGY CONVERSION FACTORS Product Heat content density (GJ/tonne) (tonne/m3) Charcoal 31.00 Firewood 16.00 Crop residues 13.70 Coal 25.60 Kerosene 43.34 0 . 7 9 6 ( 3 4 . 5 0 GJ/t) Electricity 3 , 6 0 0 GJ per GWh TOE 42.74 SOURCE: Mission Estimate  I. BACKGROUND 1.1 The energy demand p i c t u r e i n Zambia i s dominated by households. According t o 1988 e s t i m a t e s of energy consumption, households accounted f o r 58% of f i n a l energy consumption, p r i m a r i l y i n t h e form of fuelwood and c h a r c o a l . S e r i o u s concern about household energy i s s u e s h a s only r e c e n t l y come a b o u t , i n s p i t e of t h e f a c t t h e households occupy such a prominent p o s i t i o n i n t h e n a t i o n a l energy consumption p i c t u r e . Consequently, t h e r e h a s been a l i m i t e d knowledge on t h e energy supply and demand balance and i t s a s s o c i a t e d problems for this residential sector. 1.2 Households l a r g e l y depend on woodfuel f o r t h e i r energy. Rural households use fuelwood from dead wood c o l l e c t e d from t h e f o r e s t f l o o r while c h a r c o a l produced from f e l l e d t r e e s i s t h e main energy s o u r c e f o r t h e urban households. The h i g h u r b a n i s a t i o n r a t e , h a s c r e a t e d a s i t u a t i o n where t h e m a j o r i t y of households i n t h e b i g c i t i e s have no a c c e s s o r cannot a f f o r d modern e n e r g i e s such a s e l e c t r i c i t y , and must, t h e r e f o r e , depend on c h a r c o a l f o r cooking. 1.3 I n g e n e r a l , t h e r e h a s been a l a c k of r e l i a b l e i n f o r m a t i o n on t h e household energy s e c t o r upon which t o base any p o l i c y o r p r o j e c t i n i t i a t i v e . There has been an u r g e n t need t o e s t a b l i s h a r e l i a b l e d a t a b a s e , which can h e l p c l a r i f y t h e s i t u a t i o n and a d d r e s s t h e v a r i o u s i s s u e s a t hand. Households a r e t h e major energy consumers i n Zambia and a s such t h i s s e c t o r deserves more a t t e n t i o n i n energy planning t h a n has h i t h e r t o been t h e c a s e . 1.4 This s t u d y f o c u s e s on urban households f o r it i s c o n s i d e r e d t h a t t h e energy problems of t h e s e households r e q u i r e more u r g e n t a t t e n t i o n . 1.5 According t o t h e 1980 census t h e r e s i d e n t i a l s e c t o r was composed of approximately 400,000 urban households (35.5% of t o t a l ) , l i v i n g i n 57 towns. The urban households p e r town ranged from 700 i n Senanga t o 100,000 i n Lusaka. The average number of people p e r household was 5 . 6 and according t o t h e C e n t r a l S t a t i s t i c a l O f f i c e p r o j e c t i o n f o r 15 y e a r s a f t e r t h e c e n s u s , " i t i s expected t h a t t h e number of households i n Zambia would i n c r e a s e a t a f a s t r a t e of 3 . 3 % p e r annum" .  11. METHODOLOGY 2.1 The method used t o conduct t h e energy demand survey i n urban a r e a s i n Zambia employed random survey techniques and s t r a t i f i e d sampling procedures t a k i n g i n t o c o n s i d e r a t i o n t h e o b j e c t i v e s of t h e s t u d y w i t h i n t h e budget and schedule l i m i t s . A. STRATIFICATION 2.2 I t was assumed, t a k i n g i n t o account t h e Zambian c a s e , t h a t t h e f o l l o w i n g t h r e e c r i t e r i a may i n f l u e n c e t h e energy consumption p a t t e r n i n t h e urban a r e a s : a. t h e s i z e of town, b. t h e availabilityofelectricityindwellings, c. t h e income l e v e l . 1 - TOWN SIZE 2.3 The towns of Zambia were c l a s s i f i e d i n t o seven groups a c c o r d i n g t o t h e i r s i z e . Only towns w i t h a p o p u l a t i o n of more t h a n 5,000 people have been r e t a i n e d i n t h e sample l i s t ( i . e 42 towns o u t of 5 7 ) . Hence t h i s f i r s t s t r a t i f i c a t i o n i n v o l v i n g t h e s i z e of towns ensured t h a t d i f f e r e n t s i z e s were r e p r e s e n t e d i n t h e f i n a l sample (Table 1 ) . TABLE 1: NUMBER OF TOWNS BY CLASS SIZE AND PROPORTION OF HOUSEHOLDS Class s i z e Number of Number of towns s e l e c t e d towns Urban House- holds (%) 500 & More 1 1 200 - 500 2 1 I Source: CSO , 1980 Census 2.4 I n 1980 t h e t o t a l number of households i n t h e s e 42 towns was about 380,000 which r e p r e s e n t s 95% of t h e t o t a l urban households i n t h e 57 towns. Taking i n t o c o n s i d e r a t i o n t h e budget c o n s t r a i n t s i t was decided t o s e l e c t randomly one town from each c l a s s ( s e e t a b l e above). The towns drawn were: Lusaka -Mazabuka - Kitwe - Luanshya - Livingstone - Chipata -Lukulu. 2.5 The town of Mansa was however added t o t h e sample because i t was thought t o have a d i f f e r e n t energy consumption p a t t e r n i . e f i s h smoking, and Kafue ( i n i t i a l l y drawn) w a s r e p l a c e d by Mazabuka because t h e CSO was n o t a b l e , due t o t e c h n i c a l o b s t a c l e s , t o provide a r e l i a b l e up-dated mapping. 2.6 A s a second s t e p t h e 7 c l a s s e s were aggregated i n t o o n l y 3 c l a s s e s a s follows: l a r g e towns with a p o p u l a t i o n above 200,000 p e o p l e ; medium towns w i t h a p o p u l a t i o n ranging between 50,000 and 200,000 people ; small towns w i t h a p o p u l a t i o n ranging between 5,000 and 50,000 people. 2.7 I n 1980 t h e d i s t r i b u t i o n of t h e households by s i z e of town according t o t h e above c l a s s i f i c a t i o n was roughly 50% i n l a r g e towns, 30% i n medium towns and 20% i n small towns. I t was apparent t h a t t h e s e l e c t e d towns could be aggregated i n t o 3 zones according t o t h e main language spoken. Teams were chosen from people speaking t h e s e main language. MAN LANGUAGE I I Kitwe - Luanshya - Mansa Bemba II Lusaka - Chipata Nyanj a I11 Livingstone -Lukulu - Mazabuka Lozi -Tonga  - 4 - FIGURE 1: SELECTED TOWNS FOR ENUMERATION AND MAIN LANGUAGE SPOKEN 2 - ELECTRIFICATION 2.8 The second c r i t e r i o n t h a t can be used t o s t r a t i f y t h e sample i s t h e a v a i l a b i l i t y of e l e c t r i c i t y i n dwellings which may be an important household energy s u b s t i t u t e because of t h e c o n s i d e r a b l e s u r p l u s of i n s t a l l e d hydro- e l e c t r i c i t y c a p a c i t y . The number of e l e c t r i f i e d and non e l e c t r i f i e d households i n t h e sample should be l a r g e enough t o provide u s e f u l 1 i n f o r m a t i o n on t h e electricity issue. 2.9 A v e r y important module on e l e c t r i c i t y end-uses and e l e c t r i c a p p l i a n c e s a s w e l l a s t h e behaviour of e l e c t r i f i e d andnon-electrifiedhouseholds was i n c o r p o r a t e d i n t h e f i n a l energy demand q u e s t i o n n a i r e . However t h e main o b s t a c l e i n u s i n g t h i s parameter t o s t r a t i f y t h e sample i s t h e n o n - a v a i l a b i l i t y of a c c u r a t e and r e l i a b l e information i n terms of number of domestic consumers. The 1980 census showed t h a t about 37% of urban households were e l e c t r i f i e d (149,000 households), whereas ZESCO r e c o r d s f o r t h e f i n a n c i a l y e a r 1987-88 showed only 99,400 domestic customers. 3 - INCOME LEVEL 2.10 Because of l a c k of r e l i a b l e information concerning t h e d i s t r i b u t i o n of p o p u l a t i o n by income c l a s s , income l e v e l cannot be used a s a strata. T h e r e f o r e , it was planned t h a t t h e breakdown of t h e urban p o p u l a t i o n i n t o c l a s s e s of income ( o r c a t e g o r i e s of s t a n d a r d of l i v i n g ) should come o u t a s a r e s u l t of t h e survey i t s e l f . For t h a t r e a s o n , s p e c i a l c a r e was needed t o have a l l s o c i o - economic c a t e g o r i e s w e l l r e p r e s e n t e d i n t h e sample. N e v e r t h e l e s s , i t i s p o s s i b l e t o have t h e s t a n d a r d of l i v i n g r e f l e c t e d by d i v i d i n g each town i n t o homogeneous a r e a s ( q u a r t e r s ) . However, it i s important t o n o t e t h a t i n high c o s t a r e a s n o t a l l households a r e wealthy ( i . e s e r v a n t s ' q u a r t e r s ) and i n low c o s t a r e a s some wealthy households were found ( e . g a barman e a r n i n g K30,000 p e r month). The s t r a t i f i c a t i o n b y a r e a i s mainly aimed a t e n s u r i n g t h a t d i f f e r e n t socio-economic c a t e g o r i e s of households can be well r e p r e s e n t e d i n t h e f i n a l sample. A c t u a l l y t h i s s t r a t a can be aggregated with t h e a v a i l a b i l i t y of e l e c t r i c i t y i n dwellings because of t h e high r e l a t i o n s h i p e x i s t i n g between e l e c t r i f i c a t i o n and l e v e l of income r e f l e c t e d by t h e type of r e s i d e n t i a l a r e a . 2.11 A s l a r g e towns a r e g e n e r a l l y i n t e r n a l l y very heterogeneous compared t o s m a l l e r towns, they have been s t r a t i f i e d t o ensure t h a t d i f f e r e n t s t a n d a r d s of l i v i n g a r e w e l l r e p r e s e n t e d ; Towns over 50,000 i n h a b i t a n t s were d i v i d e d i n t o 3 zones:  High cost area or low density area with high electrification proportion Medium cost area or Medium density area Low cost area or High density area with low electrification proportion 2.12 Each selected town was mapped and broken down into quarters using the CSO definition of Census Supervisory Areas (CSA) and Standard Enumerator Areas (SEA) ; then for large and medium towns the CSA's were sorted into the three above mentioned areas (Table 2). 2.13 Each town was divided into several CSA's; and each CSA was subdivided into SEA with very clear boundaries between each other. In 1980 the urban population in Zambia (400,000 Households) was housed within 545 CSA's which were composed of 2,860 SEA'S making an average of 140 Households per SEA. However, because of non-existence of clear boundaries in certain areas one SEA may be much larger than a standard one; in that case it is called a composite SEA.  TABLE 2: DISTRIBUTION OF THE AREAS BY STANDARD OF LIVING FOR THE 8 SELECTED TOWNS - - - - - - - - - - - - AREAS TOTAL NUMBER High c o s t Medium c o s t Low c o s t OF NUMBER OF : CSA SEA CSA SEA CSA SEA CSA SEA -- . ----- Large towns Lusaka 28 144 32 196 87 539 147 879 Kitwe 6 31 30 176 13 73 49 280 Medium towns Luanshya 4 27 15 92 3 15 22 134 Livings tone 5 19 13 57 1 4 19 80 Small towns Chipata na na na na na na 8 26 Mansa na na na na na na 7 34 Mazabuka na na na na na na 6 26 Lukulu na na na na na na 2 6 4 - THE SAMPLING UNIT 2.14 The sampling u n i t i s t h e household. For t h e purpose of t h i s s t u d y t h e d e f i n i t i o n used by t h e CSO was adopted s o t h a t t h e r e l i a b i l i t y of t h e sample may be checked t o some e x t e n t . A household is a s p e c i f i c number of people ( i t may be o n l y one person) l i v i n g and e a t i n g t o g e t h e r i n t h e same dwelling who s h a r e t h e same budget. More t h a n one household may l i v e i n t h e same d w e l l i n g .  FIGURE 2: STRATIFICATION CHART Vl + 200,000 People 50 - 200,000 People 5 - 50,000 People High cost Medium cost Low cost area area area d ( * ) S m a l l towns are not stratified by the standard of living B. THE LISTING 2.15 After choosing the sample towns, the major problem was the non- existence of up-dated information on the distribution of the population (i.e by town, by CSA, by SEA. . . . ) . The latest data-base remains the 1980 National Census which limits considerably the results of the survey and the possibility of generalising its findings to the urban population as a whole. To surmount this difficulty and despite its high cost in money and in time, a complete listing of households in the total number of selected SEA's was decided. This listing constituted the master sample from which the final sample was drawn. 2.16 The procedure consisted of using, wherever possible, the up-dated maps of the the selected CSA's and SEA's using the preliminary results of the 1990 census mapping project which is presently being carried out by the CSO. The results of the listing were expected to provide a good estimate of the present population in urban centers (its distribution by town size and by standard of living) ; and the electrification percentage (as a note was made whether the listed household has electricity). 2.17 In fact the listing consisted of firstly identifying the areas and verifying their boundaries from the maps. In some cases composite SEA's housing more than one thousand households were re-demarcated into 3 to 4 areas if it happened that new roads had been built since 1980, the date of the last mapping. This procedure was done in 9 SEA's where it would have been extremely time consuming to conduct a full listing (one composite SEA could comprise of up to 10 single ones). 2.18 Secondly and once the identification of the area was done, the enumerator was shown from where to start the listing and how to move within his area of operation using the census techniques. The enumerators visited all households in selected SEA's (a dwelling may house more than one household) gathering the following information: Street Address Name of Head of the Household The Household Size If the household had electricity Number of year of occupation of the House If the household is a homestead charcoal\firewood trader 2.19 This information was collected from any member of the household and in some cases from the neighbors if member of the households could not be met after several visits. Then a serial number was given to the listed households and a numbered sticker was put on the main door. Servant's quarters were listed as separate households. 2.20 After completing the listing,the listed households Ni excluding those who have been in the dwelling for less than a year 1 / , were divided by the households that were to be enumerated n,. The figure (Ni/n,) denoted as K, represents the sampling interval; in other words, every K, th household was visited by the enumerator. A random start was chosen from 1 to K,. Then the enumeration started the following day and the lister had to play a major role to help his colleagues locating the selected households. The period between the listing and the enumeration should not exceed a few days (say a week) as there is a risk of losing the numbered stickers put on the doors. C. SAMPLE S I Z E 2.21 The sample size is a very important component in the data collection process. The larger the sample the higher the significancy, especially when there are several levels of stratification. However, some other factors should be taken into consideration before fixing the sample size. The budget constraint, the schedule limit, the availability of personnel and supervision are very important in determining the optimum size. Once the sample size is fixed, the confidence level can be calculated to check if it is acceptable. 2.22 It was planned that each zone (I, 11, 111) could be covered by 2 teams of 4 enumerators and one supervisor each. Two other enumerators were seconded to the teams; the first was in charge of surveying homestead woodfuel traders, and the second was to investigate food processors and restaurants. Therefore, a team of 36 persons was required to conduct the field work. It was assumed that an enumerator could handle 6 households per day, therefore the duration of the field work was to depend on the size of the selected SEA'S to be listed, the logistical means used and the sample size. To determine the sample size the following aspects were considered: l/ These households were excluded because they could not provide information about yearly variations in energy consumption in that dwelling.  Large towns have very heterogeneous population; thus a larger sample was required to ensure better representation of all socio-economic categories. Small towns generally have a more homogeneous population and a smaller sample size could be tolerated without reducing the quality of collected data. - The number of households to be selected in each town should be a multiple of 4 8 - representing one working day for 2 teams of 4 enumerators each - So all team members would be used in an efficient way. 2.23 However it is generally wise to draw a larger sample than what is needed to cover the eventual discarding of some questionnaires. The sample size retained for this survey was 1,200 households representing a sampling density of approximately 0.24% assuming that the total number of urban households was about 500,000 in 1 9 8 8 . Therefore, the distribution of the sample by the selected 8 towns was planned to be as follows: Number of Number of Total number SEA'S to be Households of households visited per SEA to be enumerated Lusaka 20 12 240 Chipata 8 12 96 Kitwe 16 12 192 Luanshya 8 24 192 Mansa 8 12 96 Livingstone 16 12 192 Lukulu 4 24 96 Mazabuka 8 12 96 88 1,200 D. CONFIDENCE LEVELS AND TESTS OF SIGNIFICANCE OF THE SAMPLE J 2 2.24 Random sampling is a method of drawing a sample such that any member of the population has an equal chance of appearing in the sample, independetly of other members that happen to fall in the sample. This method is a very useful tool and has the advantage of providing an acceptable estimate at a reasonable cost, of what the reality might be for the whole population. However the method is based on several concepts and assumptions that have to be born in mind during the analysis. 2.25 A sample consists of a small collection from some large population about which we wish to obtain some information; in other words it means that it is the sample we observe but it is the population we seek to know. A sampling distribution is a theoretical probability distribution that shows the functional relation between the possible values of a given statistic based on a sample of n cases , and the probability associated with each value ( for all possible samples of size n drawn from a particular population). The normal distribution or "Gaussian" distribution is one of a vast number of mathematical functions one might use for a distribution but it is also by far the most used distribution in statistical inference which is concerned with attempts to make quantitative statements about characteristics of a population from a knowledge of the results given by a sample. Many sampling distributions based on large n (above 30) can be approximated by the normal distribution even though the population distribution itself is definitely not normal (Central limit theorem). 2.26 Nevertheless, it is clear that the sample value will not be exactly equal to the true population value because of sampling error. It is necessary to qualify the estimate in some way to indicate the general magnitude of this error. Usually this is done by showing a confidence interval, with an estimated range of values which have a given high probability of covering the true population value. When there is a large degree of sampling error the confidence interval calculated from any sample will be large; the range of values likely to cover say the population mean is wide. On the other hand, if sampling error is small the true value is likely to be covered by a small range of values. For the purpose of this study we will concentrate on the confidence intervals related to the following two values that will be used very often: 2/ This section was largely compiled from the following two refrences: 1. STATISTICAL METHODS - G.W Snedecor & W.G Cochran 6th edition 2. STATISTICS - W.L Hays 3rd edition -proportion (say , % of households using certain type of fuel); -mean ( say, average consumption of a certain type of fuel by households). 2 -27 This will help in determining how accurate is a sample mean or a sample proportion as an estimator of the population mean or the population proportion. 1 - CONFIDENCE INTERVAL FOR A PROPORTION 2.28 If r members out of a sample of size n are found to possess some attribute, the sample estimate of the proportion in the population possessing this attribute is =r/n. In large samples, the binomial estimate is approximately normally distributed about the population proportion p with standard deviation (pq/n) (where q-1-p). For the true but unknown standard deviation (pq/n) we substitute the sample estimate (n). The confidence probability P is the probability that lies between the limits: Z, permits to find the confidence interval corresponding to any confidence probability P and can be read from the cumulative normal table. The most , are frequently used probabilities are 95% and 99% for which the correspondent Z respectively 1.96 and 2.58. 2 - CONFIDENCE INTERVAL FOR A MEAN 2.29 If the data are a random sample from a population , the sample mean is used to estimate the corresponding average over the population. If repeated random samples of size n are drawn from any population that has mean p and standard deviation a , the frequency distribution of the sample means in these repeated samples has mean .u and standard deviation a/ n. In other words the sample mean is an unbiased estimator of r . 2.30 If we apply the same princippe described above we will find the probability P that will lie between the limits: p -Z,a/n and p + Z,a/n (2) 3 - SAMPLE SIZE AND THE ACCURACY OF ESTIMATION OF THE MEAN 2.31 The width of any confidence i n t e r v a l f o r t h e mean 1 depends upon a , , , t h e s t a n d a r d d e v i a t i o n ( o r s t a n d a r d e r r o r ) , and any t h i n g t h a t makes a,, p r o p o r t i o n a t e l y s m a l l e r reduces t h e width of t h e i n t e r v a l . T h u s , any i n c r e a s e i n sample s i z e , which reduces a,, , makes t h e confidence i n t e r v a l s h o r t e r . A p r a c t i c a l r e s u l t of t h i s r e l a t i o n between t h e s t a n d a r d e r r o r of t h e mean and t h e sample s i z e i s t h a t t h e p o p u l a t i o n mean may be e s t i m a t e d w i t h i n any d e s i r e d degree of p r e c i s i o n i f it can be s t a t e d i n p o p u l a t i o n s t a n d a r d d e v i a t i o n u n i t s ( x r r ) , t h e r e q u i r e d n is t h e n easy t o f i n d u s i n g t h e f o l l o w i n g e x p r e s s i o n s : 2.32 I n t h e c a s e of t h i s s u r v e y , t h e sample s i z e was d i c t a t e d by f i n a n c i a l means and time schedule c o n s t r a i n t s , and t h e n t h e accuracy was worked o u t backwards u s i n g e x p r e s s i o n s ( 3 ) and ( 4 ) above. 2.33 A t 99% confidence l e v e l and w i t h a sample s i z e of 1213, t h e sample mean would l i e w i t h i n 0.074 of t h e t r u e mean. I f we wish t h a t t h e sample mean l i e s w i t h i n O . O L of t h e t r u e mean t h e sample s i z e should be 66,564 f o r 99% confidence l e v e l and 38,416 f o r 95% . E. PREPARATORY WORK 1. ADMINISTRATIVE PROCEDURE 2.34 A c o n s i d e r a b l e p r e p a r a t o r y a d m i n i s t r a t i v e work had t o be accomplished b e f o r e t h e a c t u a l f i e l d work could s t a r t . L e t t e r s were s e n t t o v a r i o u s n a t i o n a l and r e g i o n a l a u t h o r i t i e s t o inform them about the planned s u r v e y , t h e a r e a s t o be covered, t h e timing and t h e names of t h e p a r t i c i p a n t s . A p e r s o n a l v i s i t was a l s o r e q u i r e d b e f o r e t h e a c t u a l s t a r t and meetings had t o be h e l d with t h e ward chairmen of t h e s e l e c t e d C SA t o i n t r o d u c e them t o t h e teams t h a t were going t o be o p e r a t i n g i n t h e i r a r e a s . Also, a n o t i c e was p u b l i s h e d i n t h e Government Gazette i n o r d e r t o make t h e survey o f f i c i a l . P u b l i c announcements were made p e r i o d i c a l l y on Radio, TV and i n Newspapers b e f o r e and d u r i n g t h e f i e l d work. Maps of t h e 62 CSA's were p r i n t e d by t h e C SO and o f f i c i a l l y s t a m p e d i n o r d e r t o be used i n t h e f i e l d . 2. QUESTIONNAIRE 2.35 The datawere collectedby using pre-coded structured questionnaires. Ten drafts of the questionnaire were made before the final version was adopted. However, during the enumeration some specific behaviour, in terms of energy consumption, was encounterd in Livingstone which could not be captured by the final version and an adjustement in the field was necessary. The questionnaire comprised of 6 sections and focused on the use of charcoal and electricity on one hand and cooking practices and fuel substitution on the other. With such a questionnaire it was possible to collect the following information: 1. General information on household location and socio-economic factors including an attempt to determine the income and the total monthly expenditure. Information about the type and the quality of the dwelling was also recorded. 2. Information on consumption of different fuels used by the household for domestic purposes (i.e charcoal, electricity, firewood, crop residues, dung, gas and paraffin). 3. The following information was gathered: a. Frequency of use of the fuel i.e daily, several times a week or just as stand-by fuel; b. The quantity of fuel consumed during the 1988 cold season (June to September) and the daily consumption of traditional fuels was weighed by interviewers using pocket scales of 50 Kg capacity ; c. The cost of fuel consumed during the same period; d. Seasonal variations in consumption and prices; e. Sources of supply, availability, -means and cost of fuel transportation; f. Estimation in percentage of the distribution of fuel consumption per end-use (if possible). g. Typeofappliancesusedandfrequencyofuse.  5. Information on cooking practices, consumption and major staple food and the use of different kinds of stoves together with type of fuel used. 6. Information on fuel substitution. Households were asked if they had switched from another fuel over the last 2 years and if they were willing to switch to a new one and the reasons of such an actual or proposed change. 3. TRAINING 2.36 A team of 30 enumerators and 6 supervisors were trained in Lusaka with the assistance of a sociologist during the week of 14-20 August,1988. The training consisted of: 1. Explaining the issues of the whole project and the objectives of the households energy demand survey; 2. Carrying out a detailed explanation and discussion of the questionnaire; 3. Dividing the enumerators into groups according to the language spoken in the different areas that were to be covered by the survey, and practicing among themselves in their respective languages; 4. Taking the enumerators for field practice to test their ability in dealing with the questionnaire and the respondents - this was also used to test the questionnaire itself; 5. Revising the questionnaire and discussing the feedback from the field test. F - PROGRESSION OF WORK I. THE FIELD WORK 2.37 The field work started simultaneously in Kitwe (Bemba Zone) and Lusaka (Nyanja Zone) on the 1st of September and was finished on the 3rd of October, 1988. The Lozi-Tonga team (Livingstone, Mazabuka and Lukulu) had to wait until one of the vehicles was available from the other teams, and consequently work could only start on the 26th of September and finished on the 10th of November 1988. Information was gathered only from the head of the household or hisher spouse. Several visits were made to the households if the respondent was not present. After getting used to the questionnaires the enumerators were able to complete an interview within 45 - 60 minutes. During the field work, a spot-check was performed by the project co-ordinators to ensure that the enumerators carried out their duties correctly (3 enumerators were dismissed). All questionnaires were checked in the field by the supervisors under the project co-ordinator's supervision and enumerators were sent back to the households if mistakes were discovered. FIGURE 3: HOUSEHOLD ENERGY DEMAND SURVEY SCHEMATIC REPRESENTATION OF THE COVERAGE OF THE DEMAND SURVEY ( 1st round) 20,291 Households 240 Homestead woodfuel traders - -=- ENUMERATION 1,213 Households Non-household 121 woodfuel traders energy users Food processing Food preparation 24 Establishments 99 Establishments 2.38 In total 20291 households were listed and 1213 enumerated. Out of these ,listed households, 240 sold woodfuel from their houses and 121 were enumerated as homestead traders to supplement the charcoal marketing and distribution study. It was found that the majority of these traders were located in low cost areas and they are dealing in very small quantities. 2.39 Food processors and restaurants were also visited and enumerated during the household energy demand survey in the 8 selected towns in order to determine the importance of this sector and its eventual influence on household energy demand. 2.40 Ninety nine establishments specialised in food preparation (i.e restaurants, canteens etc . . . ) and 24 food processors (i.e milling establishments bakeries etc . . . ) were visited and enumerated (Table 3 ). TABLE 3 NUMBER OF LISTED AND ENUMERATED HOUSEHOLDS IN THE SELECTED SEA'S FOR THE 8 SELECTED TOWNS Number of Number of Number of selected listed enumerated SEA'S households households LARGE TOWNS Lusaka 29 5044 242 Kitwe 34 4590 196 MEDIUM TOWNS Luanshya 11 1565 202 Livingstone 17 3307 188 SMALL TOWNS Chipata 7 1449 102 Mansa 4 1606 104 Mazabuka 4 2031 101 Lukulu 2 699 78 TOTAL 108 20291 1213 2. THE OFFICE WORK 2.41 The office work at this stage consisted of several indispensable steps namely: checking the questionnaires upon their arrival from the field, data entry into the computers, data cleaning, calculation of extrapolation factors and applying them to the sample in order to make it representative of the target population, cross checking the results with some existingbut reliable information, and finally performance of the analysis. The software used in data entry and analysis was taking into consideration while designing the questionnaire. The questionnaire was broken down into several files (less than 200 variables each according to SPSS/PC constraints). Each piece of information was given a specific name and declared as a variable with definite charac- teristics such as type, length and location. 2.42 The local supporting staff was trained and familiarised with the software. Upon arrival from the field, the questionnaires were cheked against mistakes in coding then organised into files and entered into the computers. To speed up this process three micro-computers (640K RAM and 20 Mbytes fixed disk each) were used for data entry and an IBM PS/2 (2 MBytes RAM and 70 MBytes fixed disk supplemented with a maths co-processor) was used for joining files, data checking and cleaning, aggregating, computing, data analysis and tabulation. 3. EXTRAPOLATION OF THE RESULTS TO THE POPULATION 2.43 Before any analysis can take place it is necessary to check if the sample as it is represents the true picture of the population. This is done by checking if the different strata are proportionally represented in the sample; if not (which is the most usual case due to over representing of some minority groups for better significancy) then a correction of the sample must be done by introducing appropriate weighting factors. 2.44 This exercice supposes that the structure of the present population is already known which is not actually the case here. However it was possible to perform an estimation of the basic information needed, thanks to the listing that was carried out during this survey. The listing which covered 20,291 households helped to estimate the following: i. The total population of urban Zambia and its distribution by type of town (large, medium or small),  ii. The electrification percentage of urban households by type of town and also according to the standard of area (high, medium or low cost area). As the results of this study were aimed at being significant at the level of the type of town, the following two steps were carried out: i. Calculating of the average number of households per SEA for the three types of towns taking into consideration the standard of the areas in the case of large and medium towns, ii. estimating the total number of urban households for the three types of towns by multiplying the average number of households per SEA by the total number of SEA's. TABLE 4: ESTIMATE OF NUMBER OF HOUSEHOLDS FOR URBAN ZAMBIA BY TYPE OF TOWN (FOR TOWNS OVER 5,000 PEOPLE) Total number Average number Total number Per- of SEA'S of households of households cent per SEA in Zambia (a) Large towns 1538 186 286,068 56.8 Medium towns 907 147 133,329 26.5 Small towns 382 221 84,422 16.7 2827 178 503,819 100.0  FIGURE 4: STRUCTURE OF THE URBAN POPULATION ACCORDING TO THE DIFFERENT STRATA URBAN ZAMBIA 100% High Med. Low High Med. Low cost cost cost cost cost cost area area area area area area Elect- r i f ied Non - elect- r i f ied 4. COMPLEMENTARY SURVEY 2.45 A second round of interviews was carried out during early March 1989 among those who, according to the Main Survey, had been found to be charcoal- user households. This complementary survey covered households in Lusaka and concentrated mainly on those who used charcoal on a daily basis. The aim was to find out how the households had reacted to the scarcity and high prices of charcoal during this rainy season characterized by an exeptionaly heavy rains. The price of charcoal increased from K30 to as much as K150 per a "90-kg" bag in some localities in Lusaka. Altogether 118 households out of 242 interviewed during the Main Survey were covered. 2.46 To extrapolate the result of this survey, a separate weighting was necessary as its findings may be not generalizable to urban Zambia. The results of the listing conducted in 1988 in Lusaka was used. It was found that there are about 180,000 households distributed as follows: . 12% live in high cost areas, . 11% live in medium cost areas and 77% live in low cost areas 2.47 In 1980 the total number of urban households in Lusaka was about 102,000. Thus the annual growth rate is 7.4% made up of 3.4% population increase and 4.0% urbanization.  111. MAJOR FINDINGS OF THE URBAN HOUSEHOLD ENERGY DEMAND STUDY A. ECONOMIC AND SOCIO-DEMOGRAPHIC ASPECTS 1. POPULATION GROWTH 3.1 According t o 1980 c e n s u s a b o u t 400,000 h o u s e h o l d s were l i v i n g i n 57 towns and of t h e s e 95% were l i v i n g i n towns o v e r 5 , 0 0 0 p e o p l e (42 towns) which r e p r e s e n t s t h e b a s i s o f t h e sampling method u s e d . 3.2 The d i s t r i b u t i o n o f t h e households by s i z e o f town i n 1980 w a s r o u g h l y 5 0 % , 31% and 19% r e s p e c t i v e l y i n l a r g e , medium and small towns 3 / . According t o t h e l i s t i n g c a r r i e d o u t d u r i n g t h i s s u r v e y t h e u r b a n p o p u l a t i o n i n c r e a s e d from 387,000 h o u s e h o l d s i n 1980 t o 504,000 i n 1988 making a n a v e r a g e growth r a t e of 3 . 4 % p e r y e a r . 2. DISTRIBUTION OF POPULATION BY SIZE OF TOWN 3.3 According t o t h e above l i s t i n g t h e d i s t r i b u t i o n o f t h e h o u s e h o l d s by s i z e o f town would i n 1988 be 5 7 % , 26% and 17% f o r r e s p e c t i v e l y l a r g e , medium and s m a l l towns. 3.4 I t a p p e a r s from t h e above r e s u l t s t h a t l a r g e towns have i n c r e a s e d , i n terms of number of h o u s e h o l d s , much f a s t e r ( 5 . 2 % p e r y e a r ) t h a n medium and s m a l l towns which had a n a v e r a g e growth r a t e o f o n l y 1 . 8 % p e r y e a r ( T a b l e 5 ) . TABLE 5: DISTRIBUTION OF HOUSEHOLDS BY SIZE OF TOWN AND THE GROWTH RATES BETWEEN 1980 AND 1988 Large Towns Medium Towns 1980 Number o f HHolds 190.000 116,000 % 50 31 (Rounded f i g u r e s ) 1988 Number o f HHolds 286,000 133,000 % 57 26 Annua 1 T o t a l growrh p o p u l a t i o n rate 5.2% 1.7% 1!88 1,702,400 1 Small Towns 72,000 19 85,000 17 1.9% T o t a l (*) 378,000 100 504,000 100 3.4% (*) For towns o v e r 5 , 0 0 0 p e o p l e 3/ Large towns house o v e r 200,000 p e o p l e , medium towns house between 50.000 and 200,000 p e o p l e s m a l l towns house between 5 , 0 0 0 and 5 0 , 0 0 0 . 3. DISTRIBUTION OF HOUSEHOLDS BY STANDARD OF AREA 3.5 A stratification of houses in large and medium towns which house 83% of the urban population, was done according to the standard of living in the area. The distribution of the 1988 households by standard of area gave the following results: 15.6% of the house-holds lived in high cost areas, 34.5% in medium cost areas and 49.9% in low cost areas (Table 6). It appears from this distribution and by comparison to the 1980 distribution that the number of households in low cost areas has increased much faster (by 8.2% per annum) than in high and medium cost areas (less than 1% per annum). TABLE 6: DISTRIBUTION OF HOUSEHOLDS BY STANDARD OF AREA OF LARGE AND MEDIUM TOWNS IN 1980 AND 1988 \ Ltla) STANDARD OF AREA 1980 1988 1 High Cost areas 16.1 Medium cost areas 47.4 Low cost areas 36.5 Total 100.0 4. INCOME AND EXPENDITURE 3.6 The demand survey questionnaire included two questions on household budget, one concerning the total monthly income of the household from all sources and the other the total monthly expenditure. 3.7 Although information on income and expenditure is very sensitive and generally is very difficult to capture accurately even by specialised surveys such as household budget surveys, it was thought that the collection of this information was important. A lot of care and attention was given to ensure that the information was collected with reasonable accuracy. The importance of this information lies in the fact that its analysis and inclusion in the report should help assess better the energy situation of the residential sector and put it in its proper context. 3.8 The average income of urban households i n Zambia is K1,094 p e r month f o r An average number o f e a r n e r s o f 1 . 6 . The average income i s masked by a very l a r g e i n e q u a l i t y o f income d i s t r i b u t i o n a s about 45% of t h e households have an income l e s s o r e q u a l t o K600 p e r month 4J. 3.9 Using t h e Ginni concept of income d i s t r i b u t i o n t o determine t h e c o n c e n t r a t i o n of income i n urban households, i t appears t h a t 50% of t h e t o t a l number o f households have only 20% of t h e t o t a l income and only 20% of t h e t o t a l households who a r e s i t u a t e d i n t h e upper income c a t e g o r i e s have almost 60% o f t h e t o t a l income e a r n e d by t h e whole s e c t o r (Figure 5 ) . FIGURE 5: DISTRIBUTION OF THE TOTAL INCOME BY TOTAL NUMBER OF URBAN HOUSEHOLDS curnula tive percent of the population &/ The mode i s K600 and t h e median i s K 6 8 7 . These s t a t i s t i c s may give a b e t t e r i n d i c a t i o n than t h e mean a s t h e s t a n d a r d d e v i a t i o n which i s r e l a t e d t o the mean i s very h i g h ( 2 0 4 5 ) . 3.10 The average monthly expenditure is K527 per household which is 48.2% of the. monthly income. Again this percentage does not show the differences existing between different groups of income classes. Households who are earning K600 or less per month spend over 90% of their income on basic commodities, while those who earn K5,000 or more (and who represent 2% of the total number of households) spend only 14.7% of their income per month (Table 7). TABLE 7: DISTRIBUTION OF HOUSEHOLDS BY CLASS OF INCOME AND THE CORRESPONDENT AVERAGE INCOME AND AVERAGE EXPENDITURE Classification Proportion Average Average Proportion of of monthly monthly of income households income expenditure income in kwacha (%> in K in K Spent ( % ) 400 & less 23.3 294 285 96.9 401 - 600 21.3 522 436 83.5 601 - 800 19.2 723 474 65.5 801 - 1200 14.2 1014 603 59.5 1201 - 1600 10.1 1389 636 45.8 1601 2000 3.2 1782 883 49.6 2001 - 2500 2.2 2273 951 41.8 2501 - 3000 2.6 2825 996 35.3 3001 - 5000 2.0 3944 1061 26.9 5001 & above 2.0 11651 1710 14.7 TOTAL 100.0 1094 527 48.2 L B. ENERGY BALANCE FOR THE RESIDENTIAL SECTOR 3.11 In 1988 urban households utilised almost 640,000 TOE of energy dominated by: a. two sources of energy, i.e charcoal and firewood representing respectively 60.6% and 23.6% of the total consumption; and b. three end-uses,cooking, waterheating and space heatingrepresenting respectively 48.7%, 16.3% and 22.5% of the total consumption of the sector. 3.12 Electricity which represents only 7.2% of the total energy consumed by urban households is mainly used for cooking and lighting purposes, respectively accounting for 58.1% and 19.8% of the total electricity consumed. 3.13 Kerosene is the fourth main fuel for urban households (5.3% of the total consumption) and it is mainly used for lighting and fire ignition which represents respectively 52% and 22.8% of the total kerosene consumed.  TABLE 8: ZAMBIA 1988: ENERGY BALANCE FO R URBAN HOUSEHOLDS a/ Units TOE (PJ) --- - - - - - - - - - - - Water Space Fire Cooking Heating Heating Cooling Ironing TV Fridge Lighting Ignition Other Total X Charcoal 186,600 (7.98) Firewood b/ 86,880 (3.71) Crop 14,330 Residues (0.6l Electricity 25,650 (1.10) Kerosene 3,110 (0.13) TOTAL d/ 316,570 100,500 138,840 130 39,450 630 2,830 27,090 9,000 5,010 640,050 100 (13.53) (4.30) (5.93) (0.00) (1.69) (0.03) (0.12) (1.16) (0.38) (0.21) (27.35) Percentage 50 16 22 0 6 0 0 4 1 1 100 a/ 528,000 households; 2,966,000 people - 40% o f Zambia's population. b/ Excluding firewood used i n funerals estimated a t 16,000 T O E (43,000 tons) uhich uhen added b r i n g s the t o t a l household energy c o n s u p t i o n t o about 656,000 TOE. g/ Geysers f o r water heating only. d/ This t a b l e does not take i n t o consideration end use e f f i c i e n c y . C. MAJOR DETERMINANTS OF ENERGY PATTERN 3.14 Intuitive reasoning supplemented with a few results from descriptive statistics suggests that numerous more or less strong correlations exist between energy variables on the one hand and social-economic variables on the other, 3.15 However, analysis of the data and the associated correlation tables tend to be a complex and painstaking task, and results are difficult to interpret especially when no similar work which may give some indications, has been performed before. 3.16 A better approach, one that is almost always possible, is to simplify the analysis (without losing essential information) by identifying a subset of social-economicvariables that largely explains the energy consumption structure in the residential sector, discarding the less important variables. 3.17 As a first step the correlation matrix and associated statistics for 42 variables was computed in order to identify the variables that do not appear to be related to other variables. The second step consisted of eliminating all variables that have a coefficient of determination less than 0.3 when related to other variables one by one. Therefore, there remained 20 variables which could be used to describe the residential sector by identifying the underlying dimensions, or factors, of communities. 3.18 For this simplification it is suggested that multivariate analysis techniques be used which, in comparison with conventional statistical methods, afford a better overall view of large quantities of data, revealing the relations, similarities and differences of interest. One of these techniques, is the Principal Component Analysis which identifies new, synthetic determinants as linear combinations of the basic variables. 3.19 This technique has the advantage of simplifying the correlation table and supplyingvalid approximationby considering that the survey results describe the co-ordinates of a cluster of data points (1213 households in this case) in a space with a number of dimensions equal to the number of studied variables (the first selected subset has 65 variables). 3.20 As a result of this exercise, 4 factors were retained, as almost 60% of the variance is attributable to these first four factors. The first factor alone explains more than 34% of the variance. 2/ The 20 selected variables were then projected in the main plan called the correlation circle involving the first and second factor which explains 45.5% of the variance. I/ The KMO measure of sampling adequacy which is an index for comparing the magnitudes of the observed correlation coefficients with the magnitudes of the partial correlation coefficients, is excellent i.e 0.89.  - FIGURE 6: C i r c l e of Correlation Main Plan Correlation between a ) Conventional Fuels (Factor 1 ) b ) Traditional Fuets (Factor 2) I HIGH COST AREA I 3.21 The main conclusions t h a t emerge from t h i s a n a l y s i s can be summarised a s follows : The energy s i t u a t i o n of t h e urban households i s dominated by 4 s o u r c e s of energy ( n o t i n terms of q u a n t i t i e s b u t i n terms of b e h a v i o u r ) ; namely c h a r c o a l and firewood r e f l e c t e d by f a c t o r 2 , and e l e c t r i c i t y and Kerosene r e f l e c t e d by t h e f a c t o r 1 . F a c t o r 1 can be considered a s r e p r e s e n t i n g t h e use of conventional f u e l s and f a c t o r 2 a s r e p r e s e n t i n g t h e use of t r a d i t i o n a l f u e l s . There i s a h i g h p o s i t i v e c o r r e l a t i o n s between: a. t h e use of firewood and t h e s i z e of town The s m a l l e r t h e town t h e h i g h e r t h e p r o p o r t i o n of households who use firewood. The p r o p o r t i o n of households who use firewood i s 22.4% i n l a r g e towns, 4 8 . 0 % i n medium towns and 7 2 . 2 % i n small towns. b. t h e use of c h a r c o a l and t h e s t a n d a r d o f a r e a s . 9 8 . 0 % of households l i v i n g i n low c o s t a r e a use c h a r c o a l compared t o 8 4 . 7 % i n medium c o s t a r e a s and 6 5 . 9 % i n h i g h c o s t a r e a s . There i s a h i g h n e g a t i v e c o r r e l a t i o n s between: a. t h e use of e l e c t r i c i t y and t h e use of kerosene. The use o f e l e c t r i c i t y excludes o r reduces c o n s i d e r a b l y t h e use of kerosene e s p e c i a l l y f o r l i g h t i n g ( t h e main use of k e r o s e n e ) . About 5 5 % of t h e e l e c t r i f i e d house-holds t h a t s t i l l use kerosene use i t mainly f o r o t h e r purposes such a s f i r e i g n i t i o n and cooking. b. t h e use of c h a r c o a l and t h e use of firewood. Firewood and c h a r c o a l a r e s u b s t i t u t a b l e and t h e use of c h a r c o a l excludes o r reduces c o n s i d e r a b l y t h e use of firewood o r v i c e - v e r s a . 0 n l y 3 2 . 7 % of households who use c h a r c o a l use firewood a l s o , b u t 6 3 % of them use i t a s s t a n d - b y f u e l o r r a r e l y . The above a n a l y s i s shows c l e a r l y t h a t t h e energy p a t t e r n i n small towns i s dominated by t h e use of firewood and t h e energy p a t t e r n of medium and l a r g e towns i s dominated by c h a r c o a l i n low and medium c o s t a r e a s and e l e c t r i c i t y i n high c o s t a r e a s . However, c h a r c o a l i s s t i l l used by a r e l a t i v e l y l a r g e p r o p o r t i o n of a l r e a d y e l e c t r i f i e d households i n medium and high c o s t a r e a s : on a d a i l y b a s i s f o r t h o s e who have no e l e c t r i c s t o v e / h o t - p l a t e ; a s a s t a n d - b y f u e l f o r use i n c a s e of power f a i l u r e by those a l r e a d y u s i n g e l e c t r i c i t y f o r cooking.  - Although the income seems not to play a major role in the energy pattern and was eliminated from the analysis after the first selection, the expenditure is positively correlated with the possession of electric appliances and the use of electricity. This fact seems to indicate that expenditures rather than income explains better the standard of living of the households. From table 7 page 29, it was shown that households spend on the average only 48% of their income and even less than 15% in the higher income classes. These two facts combined seem to indicate that: in low income classes the situation is dominated by the affordability of certain items. in high income classes, it is rather the availability of certain goods which determines the situation. 3.22 These conclusions are very important as they show what are the important determinants of the energy pattern that any statistical analysis should concentrate on without the risk of losing essential information. 3.23 Finally, from the correlation circle, it is easy to see how the interfuel substitution process can operate and how one fuel affects another. Any increase of the electrification rate will reduce considerably the consumption of kerosene as more than half of the kerosene consumption is used for lighting purposes. The increase of electrification rate has no direct impact on charcoal or firewood consumption. The unavailability of electric stoves at an affordable cost is the major barrier. Still more than 40% of electrified households are not using electricity for cooking and 67% of them declared that it is because stoves/hot plates are too expensive to purchase. Cheap and available electric hot plates combined with any new electrification f ~ /will have considerable impact on: Charcoal consumption which is mainly used for cooking, water heating and space heating Kerosene consumption which is mainly used for lighting and fire ignition. This means availability of stoves/hot plates at affordable prices and also availability of spare parts in case of breakdown. The survey results show that 11% of electrified households were not able to repair their stoves and shifted back to charcoal. 3.24 It is useful to represent schematically on the circle of correlation how iterfuel substitution is being done and relate it to the main determinants. The substitution between fuels is being done in a continuous spectrum anti- clockwise round the cercle (figure 7) starting from firewood and ending at electricity. However, there are two major determinants to this substitution process: a. The substitution between firewood and charcoal is mainly determined by the increase of the town size and can be linked to the increase of the urbanisation rate; b. The substitution between electricity and charcoal for cooking is exclusively determined by the income and the availabilty of cooking appliances at a reasonable cost. FIGURE 7: CIRCLE OF CORRELATION - MAIN PLAN Fuel Substitution Process SUBSTITUTION 3.25 During t h e 1988/89 r a i n y season t h e p r i c e s of c h a r c o a l i n Lusaka went up from K30 p e r l a r g e bag (90-kg bag) t o Kl50 i n some c a s e s due t o s h o r t a g e of s u p p l y because o f t h e e x c e p t i o n a l l y heavy r a i n s . 3.26 Using t h e s u r v e y r e s u l t s which i n d i c a t e d t h a t d u r i n g r a i n y season t h e average consumption o f c h a r c o a l - u s e r household i s 3.7kg p e r day and assuming t h a t t h e l a r g e bag was s o l d on t h e average f o r K120, i t a p p e a r s c l e a r l y t h a t 64% of t h e households ( t h o s e who e a r n l e s s t h a n K800 p e r month) had t o spend on t h e average 72% o f t h e i r t o t a l income t o buy c h a r c o a l . 3.27 The r e s u l t s o f t h e second round o f t h e demand s u r v e y conducted i n Lusaka a f t e r t h e heavy r a i n s , showed t h a t about one- t h i r d o f t h e households which used t o u s e c h a r c o a l h a s s h i f t e d e i t h e r t o kerosene (23%) o r t o firewood ( 6 % ) b u t v e r y few have s h i f t e d t o e l e c t r i c i t y (1%) -Figure 8. T h i s r e s u l t i n d i c a t e s t h a t t h e s u b s t i t u t i o n p r o c e s s can be r e v e r s e d compared t o t h e one d e s c r i b e d above. FIGURE 8 : C i r c l e of C o r r e l a t i o n - Main P l a n E f f e c t of high charcoal p r i c e s USE OF ELECTRlCrrY  - 36 - IV. ANALYSIS OF THE ENERGY DEMAND BY FUELS AND END-USES A. INTRODUCTION 4.1 In 1988 on average urban households spent K116.4 per month to purchase their fuels, this represents 11% of their monthly income and 22% of their expenditure. Charcoal expenditure takes the major share with 55% of the total fuel expenditure followed by firewood with 25%, whereas the expenses on electricity and kerosene represent only 11% and 9% respectively. This is detailed in table 11. ZAMBIA URBAN HOUSEHOLDS 1988: TABLE 11: MONTHLY CONSUMPTION OF FUELS AND THE CORRESPONDING COSTS TO HOUSEHOLDS a FUEL USERS AVERAGE PER USER AVERAGE ALL HHOLDS UNIT PRICE 1 AVERAGE COST 1 % (K) Charcoal 82.7 99.0 Kg 81.9 Kg 0.78 Electricity 41.7 209.0 Kwh 87.0 Kwh 0.15 Kerosene 80.5 8.4 L 6.8 L 1.60 Firewood 37.5 177.0 Kg 66.4 Kg 0.431 4.2 The above figures for firewood assume all of it is purchased. In fact about half is collected so no money is spent on this quantity and the average cost will be about K14. The average user only buy about 87Kg out of L77Kg so they save about K39 per month by collecting. B. CHARCOAL: MAJOR FUEL FOR URBAN HOUSEHOLDS 4.3 Charcoal is the main fuel in urban Zambia. About 83% of the households use it and, taking into consideration the seasonal variation, the daily consumption per household is 3.3 Kg, giving an annual consumption of about 500,000 tonnes for the year 1988 z/. During the cold and the rainy seasons the average consumption per charcoail-userhousehold is 3.7 kg per day and during the hot season the average consumption is 2.6 kg per day. 7/ For towns over 5,000 people which represented 95% of the urban population in 1980. 4.4 Out of the 83% of households which use charcoal, 75% use it on a daily basis and consume on the average 3.8 kg per day while 17% use it as a standby fuel and consume 1.3kg per day and only 8% use it several times a week in conjunction with other types of fuels consuming 2.7kg per day as shown in the following table. TABLE 12: Distribution of households using charcoal by frequency of use and the respective average consumption Daily Several times Stand-by Total/ per week Average proportion of house- holdsusing.charcoal 75.1 7.6 17.3 100.0% Average daily 3.8 2.7 1.3 3.3 consumption (in Kg) 4.5 The proportion of households using charcoal varies significantly according to the size of town. In large towns (over 200,000 people) 91% of the households use charcoal compared to 82% in medium towns and 54% in small towns. This phenomenon can be explained by the fact that for both small and medium towns firewood is usually within collecting distance and firewood is the cheapest fuel to use. 4.6 In large and medium towns where a stratification was done according to the standard of area , it was found that charcoal use is much more intense and widespread in low cost areas (in terms of quantities consumed and percentage of users) although unexpectedly it is also quite an important fuel even in high cost areas as shown in the table below.  TABLE 1 3 : Proportion of households using charcoal by standard of area and the respective average consumption for large and medium towns in urban Zambia High Cost Area I Medium Cost Area 1 Low Cost Area 4 TOTAL Proportion of Charcoal users 65.9 84.7 98.0 in % Av . Consumption %/day 2.3 3.0 3.6 4.7 Charcoal is mainly used for cooking, space heating and water heating; these account for more than 92% of the charcoal consumed by urban households (Table 14). TABLE 14: Distribution of charcoal consumption by end-uses T- n I11 ti- Space Water Ironing Cooking Heating Heating and Other Total 48.5 25.6 18.2 8.0 100 4.8 This end-use information is not based on actual measurements but on estimates by the householders, it merely reflects their feelings which may differ from reality. However, for the purpose of this study, this information is interesting and gives an indication on the importance of different end-uses. 4.9 With regards to seasonal use again this was estimated by the consumer. From our recent survey of rainy season charcoal consumption, it was found that people switched to other fuels and/or consumed less charcoal when the price went up. Amongst the total urban households, 85% declared that their consumption was higher in the cold season (98%) and in the rainy season (83%). Therefore actual consumption should be checked by surveys. 4.10 The average daily consumption during the cold and rainy season is 42% higher than the consumption occuring in the hot season. This is to be compared to the proportion of charcoal consumed for water heating and space heating which represents 44% of the consumption of a household. 4.11 On average a household spends K77.50 per month on charcoal. This represents 14% of its income and 21% of the expenditure. However, these ratios do not reflect the disparities existing between different income classes (Table 15). To better capture this aspect, the 10 income classes described above (page 29) were aggregated into only 3 classes namely: a. Low income class; regrouping households who are earning K800 or less per month. This class represents 64% of total urban households. b. Medium income class; consists of households who are earning between K800 and K1,600 per month and who represent 24% of urban households. c. High income class ; for households earning more than K1,600 per month, the remaining 12%. TABLE 15: Proportion of households using charcoal by income class and according to the frequency of use and the proportion of income and expenditure allocated to the purchase of charcoal % of Frequency of use (in % ) Income class % of house - percentage spent In Kwacha house- holds daily Several Stand- Total on charcoal holds using times/ by fuel charcoal week of of income expend. 800 or less 801 - 1,600 1,601 6 above TOTAL 63.8 24.2 12.0 100.0 84.5 81.9 74.7 82.7 78.0 68.8 62.3 ~~~-~ 74.0 7.5 11.1 3.3 7.9 14.5 20.1 34.4 18.1 100.0 100.0 100.0 100.0 19.3 2.7 14.4 I 23.8 16.3 12.5 20.7 1 I 4.12 Households in low income class spend 19% of their income on charcoal whereas households in medium and high income classes spend respectively 7% and only 3%. 4.13 Generally, households buy charcoal in bags (93% of charcoal users). The most common unit is the "90-Kg" bag which contains on the average 40 Kg of charcoal (57% of the households buy in this unit) followed by the "50-Kg" bag containing 30kg of charcoal (27%). Of the remainder 8% buy in "25-Kg" bag (25 Kg of charcoal) and 7% buy in tins or piles. This information is detailed in the following table 16. 4.14 On average a person pays K 0.74 per Kg of charcoal by the bag and K1.33 per Kg by the tin. However a "90-Kg" bag of charcoal contains about 4Kg of fines which are usually discarded, therefore the price of lump charcoal in a sack is K0.77 per Kg some 42% less than the Kg price per tin. It is usually the poor who purchase by the tin because they have insufficient savings to purchase a bag. TABLE 16: AVERAGE WEIGHTS AND PRICES FOR DIFFERENT UNITS OF CHARCOAL AND PROPORTION OF HOUSEHOLDS PURCHASING IN THESE UNITS AVERAGE AVERAGE AVERAGE % OF HOUSE WEIGHT PRICE PRICE HOLDS BUYING OF PER KG IN THESE UNITS CHARCOAL UNITS 90-kg bag 40.0 28.0 0.72 57.2 50-kg bag 30.0 23.7 0.77 27.4 25-kg bag 25.0 18.1 0.76 8.1 Average per bag 0.74 92.7 Large tin 3.0 4.6 1.48 0.9 Medium tin 2.0 2.5 1.34 5.3 Heap 1.5 1.6 1.13 Average per tin/heap 1.33 7.3 C. ELECTRICITY: AN EXPENSIVE FUEL FOR COOKING BECAUSE OF VERY HIGH UP-FRONT COSTS. 4.15 From the 1988 listing it was found that 42% households use electricity and consume on the average 2508 kwh per year which represents 525 Gwh or 7% of the total electricity produced during the financial year 1987-88. 4.16 All houses that are connected use electricity for lighting but with regards to actual consumption most electricity is used for cooking. This represents 57% of the total electricity consumed by urban households. The second major use is lighting accounting for 20% of electricity consumption. Refrigeration, space heating, ironing and water heating have almost the same weight in the electricity balance of households (about 5% for each end-use). This described in detail in table 17. TABLE 17: Distribution of the electricity consumption by end-use and percentage of households using electricity for these end-uses % of electricity % of households END - USES consumption Cooking Lighting Refrigeration Television ,Water heating Space heating Space cooling Ironing 1 TOTAL 1 100.0 % I n/a I 4.17 As shown in the previous section the use of electricity is highly correlated with the standard of area. In large and medium towns the electrification percentage is 87.3% in high cost area, 71.5% in medium cost area and only 10.7% in low cost area. In small towns 31% of the households have electricity. 4.18 The total consumption of electricity varies very little between households living in medium cost areas, low cost areas and small towns. The average annual consumption is respectively 1850 Kwh, 1630 Kwh and 1770 Kwh per year per electrified household; whereas in high cost area the average consumption is about 4370 Kwh (Table 18).  TABLE 18: Electrification percentage and average electricity consumption by standard of area and for small towns Small Large and medium towns Urban towns house - Low cost Medium Cost High Cost holds area area area Electrification in % 31 10.7 71.5 87.3 41.5 Average Annual Consumption in 1770 1630 1850 4370 2508 Kwh per Electrified households 4.19 The large consumption observed in high cost areas by comparison to the other areas is mainly due to the number of electric appliances owned by these households. In high cost areas about 81% of electrified households use electricity for cooking compared to an average of about 48% for the other areas (including small towns). Electrified households living in high cost area are very well equipped in electric appliances; 73% have refrigerators, 81% have geysers and 44% have TV sets. 4.20 The very low ownership of equipment observed in medium and low cost areas as well as in small towns is mainly the result of very high prices of electric appliances. 1. ELECTRICITY AND CHARCOAL 4.21 More than 60% of electrified households who do not use electricity for cooking declared that it is because stoves/hot plates are too expensive for them to purchase and 20% of households in this category declared that if they had cookers they could stop charcoal consumption completely while more than 55% would reduce considerably their charcoal consumption. However, reliability of supply is also a problem and until this is rectified stand-by fuels must still be purchased. Not only that, there can be large voltage fluctuations which shortens the life of appliances and light bulbs. A first priority must be to substantially irn~rovethe reliability of s u ~ p l vand to eleminate l a r ~ evoltage fluctuations. 4.22 Seventy five percent of electrified households use charcoal and consume on the average 2.7 Kg per day, while 88% of non-electrified households use it and consume 3.6 kg per day. These findings are detailed in table 19. TABLE 19: Percentage of charcoal users and the daily average consumption for electrified and non-electrified households by frequency of use of charcoal All By frequency of use of charcoal charcoal users Daily Sev.tim/wk Stand-by ELECTRIFIED: Users ( % ) 75.2 53.8 10.8 35.4 Consumption ( kg/da~ ) 2.7 3.7 2.5 3.1 NON-ELECTRIFIED: Users ( % ) 88.0 88.0 5.6 6.4 Consumption ( Kg/day ) 3.6 3.9 2.8 1.2 4.23 About 65% of households who use electricity for cooking still use charcoal but mainly as a stand-by fuel in case of power failure (66% of the above mentioned percentage). The main reason for not using electricity for cooking by electrified households is the very high cost of purchasing and maintaining stoves/hot-plates. About 67% of the households belonging to this category declared that stoves/hot-plates are too expensive to purchase. An additional 11% could not afford to repair or replace their stoves which brokedown. 2. NON-ELECTRIFIED HOUSEHOLDS 4.24 Out of the 58.5% of the households who do not have electricity 39.3% are within 50 meters of the ZESCO grid wheras 25.8% are situated between 50 and 200 meters and 34.9% are more than 200 meters from the grid. Less than 2% of non-electrified households had electricity before and were disconnected because they could not afford the bill which amounted on the average to about K 20.0. About one-third of non-electrified households declared that the connection cost is very high 8 / . On the other hand it was noted that only about 14% of electrified households had themselves directly paid the connection costs. 3. THE CERAMIC ELECTRIC STOVE 4.25 A question about a possible "low cost" ceramic electric stove was inserted in the demand questionnaire and was asked to electrified households who do not use electricity for cooking. Households were questionned if they are interested in purchasing a ceramic electric stove (several drawings were shown) and the amount they would be willing to pay for it. Almost all of these households were interested in having one (97 % ) and would pay on the average K330 (ranging from K20 to K1800). However this information should be used with caution as from marketing experience studies, households may have a different attitude when it comes to the actual decision of purchasing the device. D. KEROSENE: A CHEAP AND WIDESPREAD FUEL 4.26 In terms of frequency, kerosene is almost as important as charcoal; 80% of urban households use it, and the main use of kerosene is for lighting and fire ignition. The average consumption is 0.28 liter per day per household using kerosene. This represents about 41.2 million liters for the year 1988 (or 32,770 tonnes). This quantity represents approximately 80% of the total kerosene sold in Zambia. On average a household spends K13.4 per month on kerosene, representing 2.7% of the total monthly expenditure of kerosene users and 1.3% of their total monthly income. 4.27 Lighting and fire ignition are the main end-uses; they represent respectively 52% and 23% of the total kerosene consumption. Cooking accounts for 16% of the consumption and water heating represents only 1%. The category "other" represents especially the use of kerosene for polish making (table 20). The use of kerosene is strongly correlated with the availability of electricity and the use of the later for cooking purposes (see section I11 page 31). Almost all non-electrified households use kerosene but only 54% of those who have electricity still use kerosene. The main use for non-electrified households is lighting (61% of the total consumption) whereas the main use in electrified households is for fire ignition (39% of total use). t 3/ Wiring cost may also be considered by households as part of the connection cost.  TABLE 2 0 : Distribdtion of kerosene consumption by end-uses in electrified and non-electrified households (in 2 ) Lighting Fire Cooking Water Other Total Average ignition heating consump. 1iter/day Non-electrified 60.6 18.4 17.1 1.6 2.3 100.0 0.31 Electrified 20.0 39.0 14.0 1.0 26.0 100.0 0.20 - Total 1 0 0 . 0 ~ 4.28 Electrified households use on the average 36% less kerosene than non- electrified households (respectively 0.20 liter per day and 0.31 liter per day per household), mainly due to a considerable reduction (79%) in consumption for lighting. 4.29 The use of charcoal has also an important impact on kerosene consumption as 23% of it is used for igniting the charcoal. 4.30 The use of electricity combined with the non-use of charcoal has a considerable impact on kerosene use. The average consumption of an electrified household which does not use charcoal is 0.05 liter of kerosene per day compared to 0.32 liter for a non-electrified household that uses charcoal. E. FIREWOOD: AN IMPORTANT FUEL FOR MEDIUM AND SMALL TOWNS 4.31 In urban areas firewood is still commonly used for domestic purposes; this is an unexpected result. It was found that 38% of urban households use firewood and consume on the average 5.9 kg per day giving an annual consumption of about 407,000 tonnes. This figure does not take into consideration the firewood consumed for funerals. To estimate this quantity it was necessary to collect information on the number of urban burials, to estimate the average length of mourning and the quantity of firewood used during that period 9J. Information was obtained from the Lusaka Urban District Council on the total number of burials and a ratio of number of burials to number of people was calculated and used to extrapolate the total number of burials for total urban Zambia. It was also estimated that at least two pick-up loads of firewood are required for one funeral. 4.32 Assuming a half a tonne per pick-up load of firewood; it is estimated that the annual urban consumption of firewood for this purpose is about 43,000 tonnes. This quantity represents over 10% of the firewood used for domestic purposes, bringing the 1988 total consumption of urban households to 450,000 tonnes . 4.33 The main uses of firewood are for cooking, water heating and space heating, these account respectively for 44%, 16% and 23% of the consumption (Table 21) . TABLE 21: HOUSEHOLD CONSUMPTION OF FIREWOOD BY END-USES Cooking Water Space Ironing Funerals Other Total Heating Heating 43.8 16.5 23.2 6.5 9.6 0.4 100.0 4.34 The following analysis of firewood consumption focused on domestic end-uses and excludes the quantity used for funerals as the occurrence of funerals per household is very rare compared to daily tasks such as cooking. 4.35 The main firewood users are households who live in medium and small towns because the biomass resource is still available within relatively short distance of the urban centers. In small and medium towns the percentages of households using firewood are respectively 72% and 48% whereas only 22% of households in large towns use it. However, in terms of actual number of users the three types of towns are relatively equivalent i.e 61,370 households using firewood in small towns, 63,840 in medium towns and 64,060 in large towns. 9/ Although a question about the use of firewood for funerals was included in the demand survey questionnaire,it was found that it is a very sensitive question for both respondents and enumerators. 4.36 About 70% of firewood-users i n s m a l l towns use i t on a d a i l y b a s i s by comparison t o 47% i n medium towns and o n l y 14% i n l a r g e towns - t a b l e 2 2 . TABLE 2 2 : PERCENTAGE OF HOUSEHOLDS USING FIREWOOD BY S I Z E OF TOWNS AND FREQUENCY OF USE in % Small towns Medium towns 1 Large towns Total urban Daily b a s i s S e v e r a l times/week Stand-by f u e l and other Percentage of Firewood u s e r s 4.37 Households buy wood i n a v e r y l a r g e v a r i e t y o f u n i t s r a n g i n g from b u n d l e s , h e a p s , l o g s , c o r d s , pick-up l o a d s t o o x - c a r t l o a d s e t c . Also h a l f of t h e firewood ( 5 1 % ) i s c o l l e c t e d n o t bought. 4.38 For a b e t t e r understanding and t o s i m p l i f y t h e a n a l y s i s , t h e s e u n i t s were aggregated i n t o two c a t e g o r i e s : s m a l l u n i t s ( b u n d l e s , h e a p s , l o g s ) and l a r g e u n i t s (cord, pick-up loads, o x - c a r t l o a d s . . . ) . The r e s u l t s from t h e Demand Survey showed t h a t 96% of urban households who purchase firewood buy i t i n s m a l l u n i t s and pay on average K0.45 p e r kg and o n l y 4 . 5 % buy i t i n l a r g e u n i t s and pay about K0.09 p e r kg. T h e r e f o r e , t h e average p r i c e of firewood i s about KO .43 p e r kg. However, t h i s p r i c e does mask t h e d i f f e r e n c e s e x i s t i n g between towns. I n l a r g e towns t h e average p r i c e i s over twice (K0.90/kg) t h a t observed i n medium and s m a l l towns (K0.43/kg). P a r t of t h i s d i f f e r e n c e i n p r i c e s may be e x p l a i n e d by a n e x t r a - c o s t o f t r a n s p o r t a t i o n a s t h e biomass supply a r e a s o f l a r g e towns a r e a t g r e a t e r d i s t a n c e s compared t o medium and small towns supply a r e a s . This f a c t a l s o i s confirmed by t h e r e l a t i v e l y h i g h percentage of households who c o l l e c t t h e i r firewood i n s m a l l and medium towns by comparison t o l a r g e towns. These p e r c e n t a g e s a r e r e s p e c t i v e l y 6 0 % , 45% and 32% of households who use firewood - t a b l e 23. 4.39 Also as mentioned above it is interesting to note that 51% of the firewood used by households is collected which does not require direct. disbursement of money 10/. Therefore, out of the 407,000 tonnes consumed every year (excluding funerals), 198,500 tonnes are purchased and 208,500 are collected. TABLE 23: PERCENTAGE OF HOUSEHOLDS USING FIREWOOD BY SIZE OF TOWN AND SOURCE OF ACQUISITION Small Medium Large Total towns towns towns Urban Source of acquisition Purchased 27.7 47.1 51.5 42.3 Collected 60.2 44.8 32.0 45.5 Purchased & Collected 12.0 8.1 16.0 12.2 Total 100.0 100.0 100.0 100.0 F. COOKING PRACTICES AND EATING HABITS 4.40 The demand survey questionnaire included a section on practices being carried out by households whilst they are cooking. The interpretation of the recorded information should be treated with caution as the enumerators, for this specific section, were listing various practices and the respondents were requested to answer yes or no. The results may be checked at a later stage by real observations of a selected sub-sample. 4.41 It appears from the answers that households tend to have an energy- saving behaviour. Any action concerning energy conservation in food preparation should concentrate on improving the efficiency of the cooking appliances and training households how to use these appliances properly. m/ During interviews it was pointed out that some households buy licences from the Forestry Department to cut firewood. The stumpage fee is K0.50 per head load but it is understood by households as K0.50 per day of cutting. A household spent one month to cut one year's consumption of firewood and paid K15.00 for 8.5 m3 of stacked wood. 4.42 The m a j o r i t y o f r e s p o n d e n t s d e c l a r e d t h a t t h e y c o v e r p o t s w h i l e cooking b u t o n l y a b o u t o n e - f i f t h o f them have t h e h a b i t o f warming w a t e r by p l a c i n g a c o n t a i n e r o f w a t e r n e x t t o t h e s t o v e . I t was a l s o n o t e d t h a t g e n e r a l l y households assemble a l l i n g r e d i e n t s p r i o r t o c o o k i n g , b u t o n l y a b o u t o n e - t h i r d soak legumes such a s b e a n s , one o f t h e s t a p l e f o o d . About 62% o f t h o s e who u s e c h a r c o a l o r firewood a s t h e main cooking f u e l e x t i n g u i s h t h e f i r e immediately a f t e r cooking. However, o n l y 39% o f them declared t h a t they use f i r e c o n s e c u t i v e l y i n s t e a d o f r e - l i g h t i n g a l t h o u g h a l m o s t 80% o f h o u s e h o l d s who b e l o n g t o t h i s c a t e g o r y cook l a r g e r amounts o f food which a r e r e - h e a t e d f o r t h e n e x t meal. These r e s u l t s a r e i n d i c a t e d i n t a b l e 2 6 ) . 4.43 About 15% o f t h e p o p u l a t i o n g e n e r a l l y have lunch o u t o f t h e i r homes mainly f o r r e a s o n s o f work and spend on a v e r a g e o v e r K220 p e r month ( r a n g i n g from K50 t o K600 i n t h e c a s e o f Lusaka). On a v e r a g e t h e r e a r e 0 . 9 members p e r household who r e g u l a r y e a t away from home. However, t h i s r a t i o v a r i e s a l o t a c c o r d i n g t o t h e s i z e o f town. I n l a r g e towns 1.1 p e o p l e p e r household t a k e lunch away from home (18% o f t o t a l p o p u l a t i o n i n t h i s c a t e g o r y o f town), compared t o 0 . 8 p e o p l e p e r household i n medium towns (15% of t h e r e s p e c t i v e t o t a l p o p u l a t i o n ) and 0 . 4 p e o p l e p e r household i n s m a l l towns ( 7 % o f t h e r e s p e c t i v e t o t a l p o p u l a t i o n ) a s shown i n t h e f o l l o w i n g t a b l e : TABLE 24: NUMBER O F PEOPLE REGULARLY HAVING LUNCH AWAY FROM HOME AND THE AVERAGE NUMBER PER HOUSEHOLD ACCORDING TO T HE SIZE TOWN Av. number of T o t a l number o f % of people p e o p l e p e r HHold people e a t i n g e a t i n g away e a t i n g away from home away from home from home -- Large towns Medium towns Small towns TOTAL URBAN 0.95 i 460,000 4.44 A s e p a r a t e s u r v e y was conducted i n r e s t a u r a n t s , c a n t e e n s and e a t i n g p l a c e s i n t h e same towns where t h e household energy demand s u r v e y took p l a c e . The main o b j e c t i v e o f t h i s s u r v e y was t o f i n d o u t t h e q u a n t i t y o f energy consumed by household members o u t s i d e o f t h e i r homes and t o what e x t e n t t h i s a f f e c t s t h e household energy consumption. 4.45 The r e s u l t s show t h a t cooking a t home r e q u i r e s about 4 t i m e s more e n e r g y , t h a n cooking i n r e s t a u r a n t s and i n o t h e r e a t i n g p l a c e s . For cooking on average a household member needs 0.33kg of c h a r c o a l , 0.28kg of firewood and 0.28kWh of e l e c t r i c i t y p e r day; whereas i n e a t i n g p l a c e s o n l y 0.084kg of c h a r c o a l , 0.063kg of firewood and 0.07kWh of e l e c t r i c i t y a r e r e q u i r e d p e r customer p e r day. 4.46 Taking i n t o c o n s i d e r a t i o n t h e f a c t t h a t 460,000 p e o p l e r e g u l a r i l y eat outside t h e i r d w e l l i n g d u r i n g a n e s t i m a t e d 250 days p e r y e a r , t h e t o t a l energy consumption f o r t h i s s e c t o r would be 9 , 6 6 0 tonnes of c h a r c o a l , 7 , 2 4 5 tonnes of firewood and 8 , 0 5 0 MWh of e l e c t r i c i t y . 4.47 I f t h e q u a n t i t y of energy consumed f o r cooking i s p r o p o r t i o n a l t o t h e number of people e a t i n g , t h e energy saved a t household l e v e l would be 37,950 tonnes o f c h a r c o a l and 32,200 tonnes of firewood and 32,200 MWh of e l e c t r i c i t y . The d i f f e r e n c e i s t h e energy saved by t h e Nation. These r e s u l t s a r e i n d i c a t e d i n t h e following t a b l e : TABLE 2 5 : AVERAGE DAILY CONSUMPTION OF ENERGY I N EATING PLACES AND I N HOUSEHOLDS Average d a i l y consumption p e r customer per c a p i t a Ratio i n eating places i n households (B)/(A) FUEL (A) (B) Charcoal (kg) 0.084 0.33 3.9 Firewood ( k g ) 0.063 0.28 4.4 E l e c t r i c i t y (kWh) 0.070 ( * ) 0.28 4 . 0 (*) ( * ) For e a t i n g p l a c e s t h e r a t i o 4 . 0 was assumed a s t h e i n f o r m a t i o n g a t h e r e d was n o t r e l i a b l e due t o t h e unknown and v a r i a b l e p e r i o d s of e l e c t r i c i t y b i l l s . 4.48 I n much o f Zambia, b o t h urban and r u r a l , maize meal o r mealie meal i s t h e s t a p l e food. Formerly whole maize was t h e s t a p l e d i e t and t h i s took about t h r e e h o u r s t o cook whereas mealie meal t a k e s twenty minutes. T h e r e f o r e t h e s w i t c h from maize t o maize meal h a s brought about a c o n s i d e r a b l e s a v i n g of energy even t a k i n g i n t o c o n s i d e r a t i o n t h e energy used i n m i l l i n g . Likewise o t h e r energy s a v i n g foods such a s wheat f l o u r and bread a r e s o l d i n l a r g e q u a n t i t i e s i n urban a r e a s a l l of which h a s reduced energy consumption f o r cooking a t t h e household leve1 .  TABLE 26: KITCHEN PRACTICES UNDERTAKEN BY HOUSEHOLDS DURING COOKING in % e holds - 1 Households who use as main fuel for cooking - COOKING PRACTICES Charcoal/ Electricity Kerosene Firewood PERCENTAGE 1.Tend fire more carefully 2.Extinguish fire immediately after cooking 3.Assemble all ingredients prior to cooking 4.Use aluminum 5.Cover pan with lid while cooking 6.Warm water by placing container next to stove 7.Use fire consecutively instead of re-lighting 8.Soak legumes 9.Serve food that take less time to cook 10.Cook larger amount initially and re-heat 11.Serve cold cooked food 12.Serve fewer meals 13.Simmer food instead of cooking at full boil  - 52 - V. ENERGY DEMAND FORECASTING FOR URBAN HOUSEHOLDS A. THE MODEL: AN ANALYTICAL APPROACH BASED ON THE INTERNAL COHERENCY OF THE DATA BASE 5.1 The method that is used for forecasting the energy demand of urban households in Zambia is an analytical approach which relies exclusively on the quality of the data base, its internal coherency and level of disaggregation. These conditions have been fulfilled through the demand survey. 5.2 The model developed uses the defined major determinants i.e the size of towns and the standard of living as well as the availability of electricity which is considered important in this context (see section C). 5.3 Urban households are distributed into a number of classes, each of which is uniform in terms of energy consumption and associated uses (see figure 9). However, two aggregations were done in order to increase the number of households within each class and to obtain higher statistical significance i.e: (i) the number of end-uses was limited to four major ones namely: cooking and water heating on a stove, lighting, space heating and fire ignition (the minor end-uses were gathered under the category "other"), (ii) large and medium towns were regrouped together. FIGURE 9: STRUCTURE OF THE MODEL URBAN HOUSEHOLDS LARGE 61 MEDIUM TOWNS SMALL TOWNS m HIGH COST MEDIUM COST LOW COST AREA AREA AREA Each of the above areas were grouped into Electrified and non-electrified householders.  TOTAL ENERGY CONSUMPTION OF URBAN HOUSEHOLDS Each ciass of households is characterized by the following matrices: - k ~ ~ ' j Average consumption in class i of energy j for use K. H1 - Number of households in class i. For a given class i, C'jk is the energy balance per energy and per end-use: To obtain the energy balance for the entire urban household, the cijkvalues are summed over all classes of i. B. OPERATION OF THE MODEL The model involves four steps: 1. The data base is processed to supply the basic data for forecasting, i.e k H' matrices for each class of household (8 ~U'jk and 8 H') the ~ ~ ' jand 2. The variation of the major determinants is estimated to obtain a central forecast, these determinants fall into two groups: i. The first group involves the distribution of urban households by size of towns on one hand and by standard of area for large and medium towns on the other. Their evolution is fairly easy to predict using the available information from 1980 census and the 1988 listing. The basic assumption is that the growth rates of urban centres observed between 1980 and 1988 would remain the same until the year 2000. This means that large towns will grow at the rate of 5.2% whereas medium and small towns would have a growth rate of respectively 1.7% and 1.9%. The distribution of households living in large and medium towns by standard of area is also assumed to follow the same trend. For the period 1988-95 households in low income areas would grow as fast as 7.2% per annum whereas the annual growth rate of households in high cost areas would be about 3.3% . For medium cost areas the number of households would decrease siightly by 0.8% every year. There would be a slight slowing down in the period 1995-2000, the expected growth rates would be - high income household 2.7% per year, medium minus 1.6% per year and low income households 6.5% per year. This decrease in size of medium income households means that more people are moving into the higher income class than are being recruted from the lower income class.  ii. The second group concerns the availability of electricity to urban households for the 8 different classes i (see figure 9 page 56). The evolution of this variable is difficult to predict due to lack of reliable past information. From the 1980 census it was found that 37.3% of urban households had access to electricity (about 149,000 households). The 1988 listing revealed that about 40% of all urban households have electricity (i.e 210,000 households) although ZESCO records showed that for the financial year 1987/88 there were consumers. After consultations with ZESCO and through a tentative assessment carried out in March 1989 ll/ , the following observations were pointed out: - The number of ZESCO domestic consumers excludes those who are supplied by some companies such as the Mines, Zambia Railways, etc. or by some institutions such as the insurance company, government agencies, institutions etc. - These companies buy electricity from ZESCO as industries, commercial entreprises or even domestic customers and distribute part or all of it to their employees free of charge or according to a fixed charge regardless of their actual consumption. The large amount of the fixed contribution in some cases seems to indicate that the employer tries to recover the initial investment of connection and wiring. - Some companies, mainly industries, have installed load limiters in the dwellings of their employees. However, to surmount this obstacle the following assumptions have been made: a. The electrification rate observed between 1980 and 1988 (i.e 4.4% per year) would be maintained for the period of 1988-2000. This means that 11,900 households have to be electrified each year compared to 7,600 households connected every year for the period of 1980 - 1988. b. In the year 2000 all households living in high cost areas would have access to electricity and medium cost areas would have the same electrification percentage as that of the high cost areas in 1988 (i.e 88%). The small towns would have the same electrification rate of 4.4% .The remainder in terms of number of households has been assigned to low cost areas and the electrification percentage worked out afterwards (19%). u/ Ref is made to one page questionnaire sent to companies and institutions.  The 1995 c a s e has been c o n s i d e r e d a s a n i n t e r m e d i a t e s t a g e between 1988 and 2 0 0 0 . T h i s s c e n a r i o l e a d s t o a t o t a l e l e c t r i f i c a t i o n p e r c e n t a g e of 4 2 . 7 % i n t h e y e a r 2 0 0 0 ; j u s t above t h e observed one i n 1988 ( i . e 4 0 % ) . 3. Using t h e d a t a b a s e , s i m u l a t i o n s can be made around t h e c e n t r a l f o r e c a s t , by a d j u s t i n g some o r a l l of t h e parameters t h a t were f i x e d i n t h e preceeding s t e p s s o t h a t new energy p o l i c i e s o r programs such a s i n t r o d u c t i o n of new improved s t o v e s , can be r e f l e c t e d on t h e energy demand f o r e c a s t s and t h e i r impacts r a p i d l y ass'essed. 4. A t a f i n a l s t a g e t h e coherency of t h e model r e s u l t s can be checked consumptions: by summing t h e ~ ' j k Over i n d i c e s i and j t o check t h a t supply c a n match t h e f o r e c a s t demand. Over i n d i c e s i and k t o check t h e r e a s o n a b l e n e s s o f f o r e c a s t s concerning t h e p e n e t r a t i o n o f v a r i o u s t y p e s of household energy equipment. C. CENTRAL FORECAST TOTAL ENERGY CONSUMPTION TOWN :ALL AREA :ALL END-USE Cooking Lighting + Water Space Fire Other Total Heating Heating Ignition FUEL with stove Electricity (Gwh) 143 396 31 0 130 700 ----- Kerosene ' 0 0 0 m3 31 7 0 14 Charcoal ' 0 0 0 tonnes 0 45 1 178 0 51 680 Firewood ' 000 tonnes 0 376 133 0 44 55 3 Crop Residues ' 0 0 0 tonnes 0 87 0 0 0 87 TOTAL ENERGY CONSUMPTION TOWN: ALL AREA: ALL END - USE Cooking Lighting + Water Space Fire Other Total Heating Heating Ignition FUEL with stove Electricity (Gm) 179 49 7 44 0 160 8 74 Kerosene ' 0 0 0 m3 38 18 0 17 6 69 Charcoal ' 0 0 0 tonnes 0 548 219 0 61 828 Firewood ' 0 0 0 tonnes 0 436 155 0 50 641 Crop Residues ' 000 tonnes 0 102 0 0 0 102  ANNEXES ZAMBIA URBAN HOUSEHOLD ENERGY DEMAND STUDY  ENERGY CONSUMPTION STRUCTURE IN CLASS 1 TOWN': Large and Medium AREA: High cost ELECTRICITY: Available END-USE Cooking Lighting + Water Space Fire Other Total Heating Heating Ignition FUEL Electricity - with stove (Kwh) 1090 2050 250 0 1050 4440 Kerosene (Liter) 8.4 7.4 0 11 3.8 30.6 Charcoal (Kg) 0 365 159 0 18 542 Firewood (Kg) 0 183 54 0 18 255 Crop Residue::: (Kg) 0 0 0 . 0 0 0 ENERGY CONSUMPTION STRUCTURE IN CLASS 2 ~ C Ujk TOWN: Large and Medium AREA: High cost ELECTRICITY: Not available END-USE Cooking Lighting + Water Space Fire Other Total Heating Heating Ignition FUEL with stove I Electricity (Kwh) .- Kerosene (Liter) 47.1 18.9 0 20 0 86 Charcoal (Kg) 0 720 236 0 78 1034 Firewood (Kg) 0 675 188 0 78 941 Crop Residues (Kg) 0 324 0 0 0 324  ENERGY CONSUMPTION STRUCTURE IN CLASS 3 TOWN: Large and Medium AREA: Medium cost ELECTRICITY: Available END-USE Cooking Lighting + Water Space Fire Other Total Heating Heating Ignition FUEL with stove P Electricity (Kwh) 300 1332 102 0 234 1968 Kerosene Charcoal (Kg) 0 649 183 0 54 886 Firewood (Kg) 0 498 167 0 75 740 P.PPPP Crop Residue::; (Kg) 0 30 0 0 ENERGY CONSUMPTION STRUCTURE IN CLASS 4 TOWN: Large and Medium AREA: Medium cost ELECTRICITY: Not available END-USE Cooking Lighting + Water Space Fire Other Total Heating Heating Ignition FUEL with stove 7 Electricity (Kwh) 0 0 0 0 0 0 Kerosene (Liter) 60.3 10.8 0 23.2 5.1 99.4 ----- . - Charcoal (Kg) 0 805 280 0 78 1163 Firewood (Kg) 0 614 26 4 0 73 951 - - - - - - . - Crop Residues (Kg) 0 102 0 0 1 0 , 1 0 2 ~  ENERGY CONSUMPTION STRUCTURE IN CLASS 5 CU5jk TOWN: Large and Medium AREA: Low cost ELECTRICITY: Available END - USE Cooking Lighting + Water Space Fire Other Total Heating Heating Ignition FUEL with stove Electricity ------- (Kwh) 270 915 50 0 113 1348 Kerosene (Liter) 8.1 2.8 0 22.4 11.6 44.9 Charcoal (Kg) 0 649 129 0 85 863 Firewood (Kg) 0 497 121 0 152 770 Crop Residues (Kg) 0 297 0 0 0 297 ENERGY CONSUMPTION STRUCTURE IN CLASS 6 TOWN: Large and Medium AREA: Low cost ELECTRICITY: Not available END-USE Cooking Lighting + Water Space Fire Other Total Heating Heating Ignition FUEL with stove I Electricity (Kwh) 0 0 0 0 0 0 Kerosene (Liter) 73.7 22.7 0 22.9 2.3 121.6 Charcoal (Kg) 0 843 373 0 97 1313 Firewood (Kg) 0 751 264 0 96 1111 Crop Residues (Kg) 0 133 0 0 0 133  ENERGY CONSUMPTION STRUCTURE IN CLASS 7 Cu7jk TOWN: Small AREA: Not applicable ELECTRICITY: Available END - USE I Space Fire Ignition 1 Other 1 Total 1 FUEL (Kwh) 327 Kerosene 1 1 - - 1 Firewood (Kg) 0 43 ( 560 Crop Residue (Kg) 126 ENERGY CONSUMPTION STRUCTURE IN CLASS 8 TOWN: Small AREA: Not applicable ELECTRICITY: Not available END-USE Cooking Lighting + Water Space Fire Other Total Heating Heating Ignition FUEL with stove P Electricity (Kwh) 0 0 0 0 O I O Kerosene (Liter) 54.9 11.1 0 10.8 77.4 - - - - . - - , Charcoal (Kg) 0 410 138 ' 0 52 600 Firewood (Kg) 0 568 183 0 70 821 Crop Residue- (Kg) 0 281 0 0 0 281  TOTAL ENERGY CONSUMPTION cUjk (1988) TOWN :ALL AREA :ALL END - USE Cooking Lighting + Water Space Fire Other Total Heating Heating Ignition FUEL with stove Electricity (Gn) 108 302 24 0 99 533 Kerosene '000 m3 24 5 0 10 3 42 Charcoal '000 tonnes 0 342 132 0 39 513 Firewood '000 tonnes 0 307 107 0 34 448 Crop Residues '000 tonnes 0 67 0 0 0 67  - 63 - ENERGY CONSUMPTION STRUCTURE IN ELECTRIFIED HOUSEHOLDS C Uj~ k TOWN; All AREA: All ELECTRICITY: Available END-USE Cooking Space Fire Other Total Ignition FUEL with stove Electricity (Kwh) Kerosene (Liter) Charcoal -- (Kg) Firewood 1 1 123 (Kg) II Crop Residues I 0 I O ENERGY CONSUMPTION STRUCTURE IN NON-ELECTRIFIED HOUSEHOLDS TOWN: All AREA: All ELECTRICITY: Not available END-USE Cooking Lighting + Water Space Fire Other Total Heating Heating Ignition FUEL with stove Electricity 0 0 Kerosene (Liter) 66.6 13.1 0 20 2.3 102 Charcoal (Kg) 0 712 305 0 88 1105 Firewood (Kg) 0 70 5 25 3 0 76 1034 Crop Residues (Kg) 1 0 168 O 1 0 0 168  DOE/ESMAP URBAN HOUSEHOLD STUDY - APRIL 1989 Population Forecasts (Tota1,Forecastas per written text census Vol V P7) Urban households are divided into peri-urban areas and urban areas. Peri-urban areas are large villages and small towns with less than 5,000 people and rural people living near towns. In these areas the principal cooking fuel is firewood. From survey results it is estimated that about 75% of households use firewood 20% use charcoal and the remaining 5% electricity and Kerosene. Urban areas are towns larger than 5,000 people. 1980 Urban population and household numbers are taken from P228 of Vol I with household number rounded down to 400,000. Zambia Population 1980 - 2000 (Units 000) Year Total Rural Peri-urban Urban Peri-Urban and Urban - 1980 5661.8 3425.8 90.0 2146.0 2236.0 1985 6692.0 4024.0 107.0 2561.0 2668.0 1988 7441.1 4475.1 119.0 2847.0 2966.0 1990 7986.5 4802.5 128.0 3056.0 3184.0 1995 9577.5 5741.5 154.0 3682.0 3836.0 2000 11540.9 6906.9 187.0 4447.0 4634.0 Zambia Urban & Peri-urban household numbers by Town Size (Units 000)* Number Year Large Medium Small Total Popula- per tion HHolds 1980 190 116 94 400 2236 5.59 1985 247 126 103 476 2668 5.60 1988 286 133 109 528 2966 5.62 1990 316 138 113 567 3184 5.62 1995 409 150 124 683 3836 5.62 2000 525 163 137 825 4634 5.62 * Large Town >200,00 : Medium Town 50,000 to 200,000 : Small Town <50,000 + peri-urban  STRUCTURE OF THE URBAN HOUSEHOLDS ACCORDING TO THE DIFFERENT STRATA 1980 TOTAL NUMBER OF HOUSEHOLDS 400 I High c o s t I Medium c o s t I Low c o s t area area area % (16) (47) (37) 49 144 113 (37) Electrified U N I T : 000' Households  STRUCTURE OF THE URBAN HOUSEHOLDS ACCORDING TO THE DIFFERENT STRATA TOTAL NUMBER OF HOUSEHOLDS High c o s t Medium c o s t Low c o s t area area area % (16) (34) (50) 67 142 2 10 (40 Electrified U N I T : 000' Households  STRUCTURE OF THE URBAN HOUSEHOLDS ACCORDING TO THE DIFFERENT STRATA TOTAL NUMBER OF HOUSEHOLDS % (60) (22) (18) 409 150 124 High cost Medium cost I Low cost area area area % (15) (24) (61) (37) ? C C Electrified Non-electrified U N I T : 000' Households  STRUCTURE OF THE URBAN HOUSEHOLDS ACCORDING TO THE DIFFERENT STRATA 2000 TOTAL NUMBER OF HOUSEHOLDS 825 525 163 137 High c o s t Medium c o s t Low c o s t area area area % (14) (18) (68) (43) Electrified U N I T : 000' Households  Locality 1 ~lectrif ied on ied ~lectrif 1 Missing - 1 Total ) Number % Number % Number % Number % 1. River Side 178 98.3 2 1.1 1 0.6 181 2. Nkana East 206 100 0. 0.0 0 0.0 206 100 3. Parklands 270 98.2 2 0.7 3 1.1 275 100 ------- 4. Nkana East 105 100 0 0.0 0 0.0 105 5. Nkana West 142 82.1 31 17.9 0 0.0 173 TOTAL 901 95.8 35 3.7 4 0.5 940 ' 100 1. Wusakile 141 71.9 0 0.0 196 100 Police Camp 2. Chamboli 82 100 0 0.0 0 0.0 . 82 100 3.Miseshi Mindolo 341 98.8 0 0.0 4 1.2 345 100 1 I I 4. Kwacha T/ship 3 0.9 318 99.1 0 0.0 321 100 5. Ndeke 335 61.4 211 38.6 0 0.0 546 100 1 I - 6. Chimwemwe 1 359 1 51.81 334 (48.2 ) 0 O.O( 693 1I 100 / 7. Chamboli 415 99.8 0 I 0.0 1 0.2 I 416 100 1 8. Kwacha 59 12.0 432 88.0 0 0.0 491 100 TOTAL 1735 56.1 1350 43.7 5 0.2 3090 100 1. Muchele 0 0.0 249 99.6 1 0.4 250 ------ 2. Wusakile Mine T/ship 1 309 100 0 0.0 0 0.0 309 TOTAL 309 55.3 249 44.5 1 0.2 559 100 GRAND TOTAL 2945 64.2 10 0.2 4589 100  LUSAKA Electrified Non Electrified Missing Total Locality Number % Number % Number % Number % 1. Maluba I 71 82.6 4 4.6 11 12.8 86 100 2. Maluba I1 192 85.0 8 3.5 26 11.5 226 100 3. Olympia 143 88.8 9 5.6 9 5.6 161 100 4. Ridgeway 109 77.4 17 16.7 5 5.9 131 100 1 5. Makeni 175 67.6 69 26.6 15 5.8 259 100 TOTAL 690 80.0 107 12.4 66 7.6 863 100 1. Libala 117 98.9 0 0.0 2 1.1 119 100 2. Chelston 144 91.7 0 0.0 13 8.3 157 100 3. Chilenje S. 132 90.4 13 8.9 1 0.7 146 100 4. Lilanda 277 89.4 1 0.3 32 10.3 310 100 5. Kamwala 86 46.2 95 51.1 5 2.7 186 100 6. Matero 17 10.2 146 88.0 3 1.8 166 100 TOTAL 773 71.3 255 23.5 56 5.2 1084 100 1. Mtendere 15 2.7 537 96.2 6 1.1 558 100 2. John Howard 0 0.0 716 98.6 10 1.4 3. Kaunda Sq. 10 6.6 139 91.4 3 2.0 4. Marapodi 11 5.3 195 94.2 1 0.5 5. Garden 37 12.8 249 85.6 5 1.7 ---- 6. Kalingalinga 1 0.2 499 98.2 8 1.6 508 100 7. Chibolya 0 0.0 646 98.9 7 1.1 653 100 TOTAL 74 2.4 2981 96.3 40 1.3 3095 100 GRAND TOTAL 1537 30.5 3343 66.3 162 3.2 5042 100  LUANSHYA Electrified Non E l e c t r i f i e d Missing % Locality Number % Number % Number % 1. Mine Area 129 82.2 28 17.8 0 0.0 157 100 TOTAL 129 82.2 28 17.8 0 0.0 157 100 1. Mikomfwa 0 0.0 127 99.2 1 0.8 128 100 2 . Roan Mine I 116 85.3 20 14.7 0 0.0 136 100 3. Mpatamantu I 129 100 0 0.0 0 0.0 129 100 4 . Roan 362 99.7 0 0.0 1 0.3 363 100 ----- 0 0.0 2 TOTAL 897 85.6 147 14.0 4 0.4 1048 100 - 1. Kawarna 0 0.0 272 100 0 0.0 272 100 2. RoanMine I1 86 100 0 0.0 0 0.0 86 100 TOTAL 86 24.0 272 76.0 0.0 358 100 GRAND TOTAL 1112 71.1 447 28.6 4 0.3 1563 100  LIVINGSTONE Electrified Non E l e c t r i f i e d Missing % Locality Number % Number % Number % Number % 1. H o s p i t a l Area 113 84.9 19 14.3 1 0.8 133 100 2. Nottiebroad 456 78.8 110 19 13 2.2 579 100 TOTAL 569 79.9 129 18.1 14 2.0 712 loo 1. D.L.T.T.C 123 95.3 0 0.0 6 4.7 129 100 2. Linda 1 35 69.6 59 30.4 0 0.0 194 100 3 . R a i l w a y s Comp. 188 65.7 98 34.3 0 0.0 286 100 4 . Libuyu 149 57.3 107 41.2 4 1.5 260 100 -- TOTAL 595 68.5 264 30.4 10 1.1 869 100 1. Maramba 1 56 9.0 1525 88.4 45 2.6 1726 100 TOTAL 156 9.0 1525 88.4 45 2.6 1726 100 GRAND TOTAL 1320 39.9 1918 58.0 69 2.1 3307 100  - 73 - CHIPATA Electrified Non E l e c t r i f i e d Missing Total Locality Number % Number % Number % Number % 1.Kalongwezi 141 29.5 328 68.6 9 1.9 478 100 2 . P o l i c e Camp 60 18.0 252 75.4 22 6.6 334 100 3 . Kapata North 56 20.7 206 76.0 9 3.3 271 100 4 . Kapata/London 212 58.6 138 38.1 12 3.3 362 100 TOTAL 469 32.5 924 63.9 52 3.6 1445 100 MANSA Electrified Non E l e c t r i f i e d Missing Total Locality Number % Number % Number % Number % 1 . Kapesha 8 0.9 92 3 99.1 0 0.0 931 100 2. Minikula 4 2.9 134 97.1 0 0.0 138 100 3 . Chiko twe/Chimes 0 0.0 311 100 0 0.0 311 100 4 . Suburbs 98 42.8 130 56.8 1 0.4 229 100 TOTAL 110 6.8 1498 93.1 1 0.1 1609 100 MAZABUKA Electrified Non E l e c t r i f i e d Missing Total Locality Number % Number % Number % Number % 1. Kabika 0 0.0 98 98 2 2 100 100 2 . Zesco 248 95.0 0 0.0 13 5.0 261 100 Compound 3. Malozilozi 280 37.1 4 55 60.3 19 2.5 754 99.9 Compound 4 . Njomona 620 67.5 29 1 31.7 8 0.8 919 100 TOTAL 1148 56.4 844 41.5 42 2.1 2034 100  URBAN HOUSEHOLD ENERGY DEMAND SURVEY . SUMMARY SHEET: Total Number 1 9 8 8 C.S.A number of C E N S U S L I S T I N G S T R A T A of selected SEA'S in Total number Total number Nber selected (*I- CSA's HHolds 1 Pop A.HIGH COST 1.Parklands 2.Riverside 3.Nkana East I 4.Nkana East I1 5.Nkana West SUB-TOTAL I B.MEDIUM COST 1.Chimwemwe 2.Kwacha I 3 Kwacha I1 4.Ndeke 5.Chamboli I 6.Chamboli I1 7.Police Camp 8.Museshi SUB-TOTAL I1 C .LOW COST 1.Muchele 2.Wusakile SUB-TOTAL I11 GRAND TOTAL * Only number of households and population listed within the selected SEA'S  URBAN HOUSEHOLD ENERGY DEMAND SURVEY SlJMMARY SHEET : LUSAKA Total Number 1 9 8 0 1 9 8 8 C.S.A number of C E N S U S L I S T I N G S T R A T A of selected SEA'S. in Total number Total number Nber selected (*I CSA' s S.E.A1s HHolds Pop HHolds A.HIGH COST 1.Ridgeway 21 2 1 316 1651 135 661 2.Maluba (11) 26 4 1 468 1880 231 900 3 .Maluba (I) 30 8 1 817 3548 86 404 4.Chiwalamabwe 58 6 1 688 3864 160 847 (olympia) 5.Makeni (I) 130 6 2 622 3070 262 1101 SUB-TOTAL 1 5 26 6 2911 14013 874 3913 B.MEDIUM COST 1.Chilenje South 5 7 1 688 4291 145 1013 2.Libala 111 10 7 1 721 4087 119 811 3.Kamwala 15 4 1 559 3600 186 908 4.Chelston 47 6 1 849 5410 157 1018 5 .Matero 96 7 2 842 5353 166 1089 6.Lilanda 101 5 5 515 3601 312 1803 SUB-TOTAL 2 6 36 11 4174 26342 1085 6642 C .LOW COST 1.Kalingalinga 31 5 2 1160 5339 508 2380 2.Mtendere 40 6 2 619 3490 548 2572 3.Kaunda Suare 52 5 1 1019 5914 152 870 4.Garden 65 5 1 318 2797 290 1538 5.Marapodi 83 4 2 366 1985 207 1143 6.Chbolya 134 7 2 1102 4438 656 2644 7.John Howard 154 6 2 733 3742 724 3556 SUB-TOTAL 3 7 38 12 5518 27705 3085 14703 GRAND TOTAL 18 100 29 13603 18060 5044 25258 * Only number of households and population listed within the selected SEA's  UBRAN HOUSEHOLD ENERGY DEMAND SURVEY Y - S SHEET : LUANSHYA Total Number 1 9 8 0 1 9 8 8 C.S.A number of C E N S U S L I S T I N G S T R A T A of selected SEA'S Total number Total number Nber in the (*) selected CSA's S.E.A's HHolds Pop HHolds Pop A.HIGH COST l.Municipa1 10 8 1 1341 6401 158 959 I SUB-TOTAL I 1 8 1 1341 6401 158 959 B.MIDIUM COST 1.Mikomfwa 4 6 1 810 5674 128 857 2.South Mine 11 4 2 647 4431 363 2242 3 .Roan A 15 7 1 767 5495 136 701 4.Mpatamatu I 19 5 1 597 3795 129 863 5.Mpatamatu I1 21 6 2 707 4595 292 2113 SUB-TOTAL I1 5 29 7 3600 23631 1048 6776 C .LOW COST 1 .Roan I11 14 8 1 839 5136 86 405 2.Kawama 22 7 2 537 2647 273 1224 SUB-TOTAL I11 2 14 3 1304 8142 359 1629 GRAND TOTAL 8 51 11 6245 38174 1565 9364 * Only number of households and population listed within the selected SEA'S  URBAN HOSEHOLD ENERGY DEMAND SURVEY SUMMARY SHEET : MAZABUKA Total Number 1 9 8 0 1 9 8 8 C.S.A number of C E N S U S L I S T I N G o f SEA's s e l e c t e d L O C A L I T Y i n the T o t a l number T o t a l number Nber selected > (* CSA's S.E.Ars HHolds Pop HHolds Pop 1. Kaleya 009 3 1 955 5068 753 3451 2. N j omona 027 6 1 1297 6768 918 4924 3 . Nkabika 029 1 1 6 43 99 395 4 . Z e s c o Compound 034 1 1 228 1066 261 1552 (Kafue Gorge) TOTAL 4 11 4 2486 12945 2031 10322 URBAN HOUSEHOLD ENERGY DEMAND SURVEY SUMMARY SHEET : MANSA Total Number C.S.A number of o f SEA's s e l e c t e d 1 Nber i n the selected CSA' s S.E.A's T o t a l number *;a T o t a l number 1. Kapesha 38 2. M i n i k u l a 16 3 . Chimese 19 I 4 . Suburbs 17 5 1 TOTAL 4 19 4 - * Only number of h o u s e h o l d s and p o p u l a t i o n l i s t e d w i t h i n t h e s e l e c t e d SEA's  URBAN HOUSEHOLD ENERGY DEMAND SURVEY SUMMARY SHEET : CHIPATA Total Number C.S.A number of of SEA'S selected L O C A L I T Y in the Nber selected (*I- CSA' s HHolds Pop 2.Chimwemwe/Lunk 100 2 Kalongwezi/ Little Bombay 3.Kapata North TOTAL I1O21 I4.~a~ata/~ondon1y3 1 1 URBAN HOUSEHOLD ENERGY DEMAND SURVEY SUMMARY SHEET : LUKULU Total Number C.S.A number of of SEA'S selected L O C A L I T Y in the Nber selected CSA's S.E.A1s Hospital Area 15 2 1 912 4495 282 1505 TOTAL 2 5 2 1267 6131 699 3458 * Only number of households and population listed within the selected SEA's  URBAN HOUSEHOLD ENERGY DEMAND SURVEY SUMMARY SHEET : LIVINGSTONE Total Number 1 9 8 0 1 9 8 8 C.S.A number of C E N S U S L I S T I N G S T R A T A o f SEA'S s e l e c t e d i n the Nber selected CSA's S.E.A1s HHolds Pop HHolds Pop A.HIGH COST 1.Low Density/ 19 5 1 496 3252 133 873 Hosp .Area 2 . N o t t e Broad 21 4 4 521 2843 SUB-TOTAL I 2 9 5 1017 6095 713 4196 B.MEDIUM COST 1.Libuyu 5 6 2 723 4115 261 1404 2 . Linda 10 4 1 656 4064 194 1218 3.Railways Comp. 11 4 2 479 2797 286 1715 4.D.L.T.T.C 15 4 2 635 4449 SUB-TOTAL I1 4 18 7 2493 15425 870 4961 . C LOW COST Maramba T/Shp 7 5 5 1340 6184 1724 6291 ---- SUB-TOTAL I11 GRAND TOTAL 7 32 17 4850 27704 3307 15448 * Only number o f households and p o p u l a t i o n l i s t e d w i t h i n t h e s e l e c t e d SEA's  - 80 - LUSAKA CSA SEA High Cost Area : 5 SEA'S Selected 1- Ridgeway 2- Maluba 3- Maluba 4- Chiwalamabwe (Olympia) 5- Makeni Medium Cost Area: 6 SEA'S Selected 1-Chilenj e South 2 -Libala 3- Kamwala 4-Chakunkula (Chelston) 5 -Matero 6-Lilanda Low Cost area: 9 SEA'S Selected 1-Kalingalinga 2-Mutendere 3-Kaunda Square 4 -Garden 5-Garden 6-Marapodi 7-George 8- Chibolya 9-John Howard  Locality/Area CSA SEA High Cost area: 4 SEA'S Selected (1nterval:S) 1-Parklands Township 28 05 2-Riverside Township 29 04 3-Nkana Township 31 01 -$-Mine Plant Area/Nkandabwe/AMCO 42 02-03 2. Medium Cost Area: 9 SEAts Selected (Inteval:5) 1-Chimwemwe Township 2-Kwacha Township 3-Kwacha Township 4-Nkana East Township 5-Ndeke Township 6-Chamboli Township 7-Chamboli Township 8-Natwange Township 9-Nkana West Township 3. Low Cost Area: 3 SEA'S Selected 1-Muchele/Malembeka 2-Wusakile 3-Chachacha/Museshi Townships  LUANSHYA CSA SEA 1. High Cost Area 1-Luanshya Municipal Township 2-Roan Township 2. Medium Cost Area: 6 SEA'S Selected (Interval:14) 1-Mikomfwa Township 2-Luanshya South Mine Township 3-Roan Township 4-Mpatamatu Township 5-Mpatamatu Township 3. Low Cost Area: 2 SEA'S Selected (Interval: 8) 1-Kawama Township 22 01 LIVINGSTONE CSA SEA 1. High Cost Area: 2 SEA'S Selected 1-Low Density/Hospital Area 2-North-end (Notte Borad) 2. Medium Cost Area: 5 SEA'S Selected (Interval : 4) 1 - Libuyu Township 2-Linda Township 3-Railway Comp/R.Quarters/Zesco Comp 4-L/Trades Institute Area 3. Low Cost Area: 1 SEA Selected 1-Maramba Township  CHIPATA CSA SEA 1-Police/IRDP/Lutembwe Sect. I&II Muchini I 2-Chimwemwe/Lunkwankwa/Kalongwezi/ Little Bombay 3-Kapata North Compound 4-Kapata/London/Navutika Comps. MANSA Locality Area CSA SEA 1-Benard Minikula/ZBS 2 - Surburbs 3 -Chimese 4-Kapesha - MAZABUKA 1-Kaleya Holdings Area 2 -Njomona Township 3-Kamwala/Nkabika Comp. 4-Zesco Compound LUKULU 1-Lukulu Township 2-Lukulu Township  1980 URBAN POPULATION AND HOUSEHOLD DISTRIBUTION No. of No. of No. of Province District C.S.As S.E.As H/Holds Population Kabwe Urban 31 160 22412 Kabwe Rural 2 12 1945 Central Mkushi 2 5 718 Mumbwa 1 5 765 s Serenje 2 8 1229 Sub-Total Chi1ilabobwe Chingola Kalulushi Copperbelt Kitwe Luanshya Muful ira Ndola Urban Ndola Rural Sub-Total 1 / 219 1 1275 1 - 169983 - I / p~ Chadiza 4 415 Chama 4 336 Eastern Chipata Katete Lundazi B 2 2; 6090 1170 961 Petauke 1 6 908 Sub-Total 6 15 57 9880 Kawambwa 3 9 1319 Mansa 7 33 3412 Luapula Mwense 1 4 585 Nche lenge 3 10 1679 Samfya 3 16 2835 Sub-Total 5 17 72 9830 Luangwa 1 6 196 Lusaka Lusaka Urban 159 897 102907 Lsk/rural(Kafue) 10 11 6805 Sub-Total 3 170 914 109908  No. of No. of No. of Province District C.S.As S.E.As H/Holds Population Chinsali 1 8 742 4238 Isoka 2 6 1630 9016 Kaputa 4 8 2356 10288 Northern Kasama 2 11 5978 32360 Luwingu 1 2 643 3796 Mbala 2 9 2549 13805 Mp ika 2 13 2837 17375 Mporokoso 3 5 641 3326 Chilub i 15460 Sub-Total 9 17 62 17376 94204 Chizera 1 2 156 822 Kabornpo 1 4 939 5310 Northwestern Kasempa 1 6 908 4908 Mwinilunga 1 1 587 3377 Solwezi 2 16 2820 15022 Zambezi 1 9 1535 8094 Sub-Total 6 7 38 6945 . 37533 Choma 4 22 3831 20657 Gwembe 1 8 1069 5347 Kalomo 4 24 1778 10027 Livingstone 19 80 10833 61296 Southern Mazabuka 6 16 3059 16302 Monze 4 19 2122 11272 Namwala 1 3 495 3009 Siavonga 1 6 1427 7135 Sinazongwe 1 1 294 1905 Sub-Total 9 41 179 24908 136950 Kalabo 3 6 1385 6972 Kaoma 3 9 1540 7574 Western Lukulu 2 5 1267 6131 Mongu 10 44 5743 28715 Senanga 1 1 698 4121 Sesheke 3 8 1202 6171 Sub-Total 6 22 73 11835 59684 GRAND TOTAL 57 545 2860 387734 2183059 Source: 1980 Popn. & Housing Census Print-out. Data Processing Unit CSO HQ.  DISTRIBUTION OF URBAN POPULATION BY TOWNS (1980 CENSUS) TOWN HOUSEHOLDS POPULATION LARGE TOWNS 1. Kitwe 2 . Ndola 3 . Lusaka Sub-Total I MEDIUM TOWNS 1. Kabwe Urban 2. Chililabombwe 3. Chingola 4. Kalulushi 5. Luanshya 6. Mufulira 7. Livingstone Sub-Total I1 SMALL TOWNS Kabwe Rural (Kapiri) Serenje Chipata Katete Pe tauke Kawambwa Mansa Nchelenge Samfya Lusaka Rural (Kaf ue ) Isoka Kaputa Kas ama Mbala Mpika Kabompo Solwezi Zambezi Choma  TOWN HOUSEHOLDS % POPULATION % 20. Gwembe 1,069 5,347 21. Kalomo 1,778 10,027 22. Mazabuka 3,059 16,302 23. Monze 2,122 11,272 24. Siavonga 1,427 7,135 25. Kalabo 1,385 6,972 26. Kaoma 1,540 7,574 27. Lukulu 1,267 6,131 28. Mongu 5,743 28,715 29. Sesheke 1,202 6,171 30. Chilubi 15,460 Sub-Total I11 72,459 19.14 379,839 17.82 GRANDTOTAL I 378,594 100.00 2,132,058 100.00 OTHER SMALLER TOWNS 1. Mkushi 715 4,127 2. Mumbwa 765 3,890 3. Chadiza 415 2,241 4. Chama 336 1,936 5. Lundazi 961 4,933 6. Mwense 585 3,394 7. Luangwa 196 978 8. Chinsali 742 4,238 9. Luwingu 643 3,796 10. Mporokoso 641 3,326 11. Chizera 156 822 12. Kasempa 908 4,908 13. Mwinilunga 587 3,377 14. Namwala 495 3,009 15. Sinazongwe 294 1,905 16. Senanga 698 4,121 17. Ndola Rural 468 2,370 GRAND TOTAL I1 9,137 51,001 GRAND GRAND TOTAL 387,731 2,183,059