Fuel switching in Harare: An almost ideal demand system approach

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Energy Policy 35 (2007) 2538–2548 www.elsevier.com/locate/enpol

Fuel switching in Harare: An almost ideal demand system approach Muyeye Chambweraa, Henk Folmerb,c, a

WWF Southern Africa Regional Programme Office, P.O. Box CY1409 Causeway, Harare, Zimbabwe Faculty of Spatial Sciences, University of Groningen, P.O. Box 800, 9700 AV Groningen, The Netherlands c Department of Social Sciences, ECH, Wageningen University, P.O. Box 8130, 6700 EW Wageningen, The Netherlands b

Received 12 December 2005; accepted 22 September 2006 Available online 13 November 2006

Abstract In urban areas several energy choices are available and the amount of (a given type of) fuel consumed is based on complex household decision processes. This paper analyzes urban fuel (particularly firewood) demand in an energy mix context by means of an Almost Ideal Demand System based on a survey carried out among 500 households in Harare in 2003. Using a multi-stage budgeting approach, the model estimates the share of energy in total household expenditure and the shares of firewood, electricity and kerosene in total energy expenditure. Using the model results simulations show that the main policy handles to reduce the demand for firewood and to mitigate environmental degradation such as deforestation include decreasing prices of alternative fuels, notably kerosene. Moreover, in the long run sound economic policy will positively impact on the energy budget whereas education and the degree of electrification will contribute to a reduction of the use of firewood. r 2006 Elsevier Ltd. All rights reserved. Keywords: Urban fuelwood demand; Firewood; Deforestation; Energy mix; Multi-stage budgeting; Almost Ideal Demand System; Harare

1. Introduction The widespread use of firewood in Africa, and in the developing world in general, has been linked to several environmental problems. In Africa and Asia Pacific, deforestation, watershed disturbance, indoor air pollution and loss of biodiversity, woodland structure and scenic beauty are some of the most common environmental impacts of fuelwood use (Barnes et al., 2005; Chidumayo, 1997; FAO, 1997). According to FAO data (Amous, undated), firewood consumption accounts for about 90% of total African energy consumption. This makes firewood consumption a major local and global environmental problem in Africa with international ramifications (Agyei, 1998).

Corresponding author. Department of Social Sciences, ECH, Wageningen University, P.O. Box 8130, 6700 EW Wageningen, The Netherlands. Tel.: +31 317 485455; fax: +31 317 485373. E-mail addresses: [email protected], [email protected] (M. Chambwera), [email protected] (H. Folmer).

0301-4215/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.enpol.2006.09.010

The current consumption trends indicate that the deforestation problem is likely to continue in the foreseeable future on a continent with the highest per capita fuelwood consumption in the world of 0.89 m3 per year. Between 1980 and 1996 firewood consumption in Africa increased by about 106 million m3 (Amous, undated) and is likely to increase further in the future as population and poverty continue to grow. Chidumayo (1997) and CIFOR (2003) have attributed the environmental impacts of firewood harvesting in Africa to the energy requirements of urban areas in particular. Thus, urban firewood consumption presents a typical linkage between urban economic activity and the environment. Urban household energy systems are characterized by amongst others the availability and prices of fuels. In Harare, electricity, firewood and kerosene are the main urban fuels while others such as gas, take on a minor role. Electricity is supplied by a government-controlled parastatal and the price is subsidized. The price of kerosene is also controlled by government. However, because the product is scarce, it is mostly available to consumers on the black market at prices much higher than the official price.

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The market for firewood is largely uncontrolled with many players involved in the acquisition, transportation and retailing of the product. Retail firewood traders source it either directly from sources or from middlemen and wholesalers. Although urbanization usually results in greater use of modern fuels such as electricity, and less use of firewood (Hosier, 1993), this has not happened in many African cities. In Zimbabwean urban areas, where electrification rates are quite high compared to most African countries, firewood is still an important energy source with about 25% of households in e.g. Harare using it as the main cooking fuel (Campbell and Mangono, 1994). Moreover, Campbell et al. (2003) conclude that declining economic conditions in Zimbabwe could eventually lead to an increase in the role of firewood in the urban household sector. As the demand for firewood in Harare increases, pressure is going to increase on the environment, especially in areas surrounding the city. As woodland cover gets depleted, problems such as erosion and silting up of rivers and dams will ensue. The agricultural productivity of land will also be affected as well as the scenic quality of the city and its surroundings. The use of firewood also has negative impacts on the users arising from indoor air pollution (UNEP, 2006). Biomass smoke contains a large number of chemicals, many of which have been associated with adverse health effects (Naeher et al., 1995). The circumstances under which urban firewood demand will change guide energy and environmental policy making and planning which, in Zimbabwe and elsewhere, has been crippled by lack of data and economic analyses. This paper analyzes urban firewood demand in Harare by means of an Almost Ideal Demand System. In a next step the estimation results will be used to simulate some policy handles to develop guidelines for environmental policy. Section 2 presents the conceptual framework; Section 3 the theoretical models that form the basis of the empirical analyses, while Section 4 gives the estimation and simulation results. Section 5 concludes and summarizes the policy implications of the findings.

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electricity, and at the bottom are traditional fuels such as firewood, dung and crop wastes. As a household’s economic well-being increases, it is assumed to move up the ladder to more sophisticated energy carriers and to move to less sophisticated energy carriers as economic status decreases through either a decrease in income or an increase in fuel price. The analytical power of the energy ladder model is limited due to the assumption that energy transition is assumed to be a simple and linear progression from traditional to modern fuels driven by income only. In reality, however, there are complex partial fuel adoption and relinquishment processes in energy transition and the paths followed are not linear. The literature confirms the fact that households use multiple fuels (see e.g. Campbell et al., 2003; Kebede, 2002; Foster et al., 2000 and Masera et al., 2000). Moreover, households do not necessarily consider some fuels to be inferior, but may use different fuels for different purposes as shown by, amongst others, Masera and Navia (1996). Very often even those fuels considered inferior still remain in the household consumption set and are used for special occasions or when the main fuel is not available. On the basis of these realities we adopt the energy mix model and hypothesize that a household consumes different fuels for different purposes and in different quantities to suit its energy requirements and budget. Even minor or secondary fuels are present in the mix, and, when aggregated over many households, represent significant demand. In this scheme the total household energy mix is shaped by household characteristics and factors pertaining to the fuels themselves, which are determined outside the household (Fig. 1). Hence, managing the environmental impacts of the use of firewood on e.g. woodlands needs to take place in the context of the energy mix model. The energy mix model as specified above has the flexibility that it enables one to incorporate the factors that influence household energy consumption behavior as found by, amongst others, Masera et al. (2000), Masera and Navia (1996) and Hosier and Kipondya (1993). 3. Theoretical model

2. The conceptual framework 3.1. General considerations The understanding of urban household energy consumption in developing countries has been mainly based on the energy ladder hypothesis (Hosier and Dowd, 1987; Hosier and Kipondya, 1993; Masera and Navia, 1997; Masera et al., 2000 and Campbell et al., 2003). The energy ladder model hypothesizes that as households become wealthier and gain socio-economic status, they abandon energy technologies that are considered outdated, and start using more modern technologies (Masera et al., 2000). The underlying assumption of the model is that households are faced with an array of energy choices which can be arranged in order of increasing technological sophistication (Hosier and Dowd, 1987). At the top of the ladder is

Following amongst others Deaton and Muellbauer (1980b), we postulate that households first decide how much of their total incomes to allocate to energy among other consumption goods, and then how much of their total energy budgets to allocate to individual fuels, thereby implementing a two-stage budgeting process. The twostage budgeting process presumes separability of preferences (Szulc, 2001; Elsner, 2001; and Deaton and Muellbauer, 1980b). Separability partitions commodities into groups such that preferences within groups can be described independently of those in other groups (Deaton and Muellbauer, 1980b; Edgerton et al., 1996). The

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HOUSEHOLD CHARACTERISTICS Household size, income, appliances, dwelling ownership, etc

TOTAL HOUSEHOLD ENERGY CONSUMPTION MIX POLICY LEVERS

Electricity Firewood

Kerosene

ENVIRONMENTAL IMPACTS Gas

Natural woodlands depletion

FUEL CHARACTERISTICS Prices, availability, convenience, etc

Fig. 1. Conceptual framework of the energy mix model.

consumer can then rank different commodity bundles within one group in a well-defined ordering that is independent from the other groups. The sub-utilities from consuming individual commodities in a group can be aggregated to give total utility for the group (Deaton and Muellbauer, 1980a). The foregoing is represented by a utility tree (Fig. 2). Each stage of the multi-stage budgeting process can be regarded as corresponding to a utility maximization problem of its own. Particularly, different fuels are chosen so as to maximize an energy sub-utility function subject to an energy budget constraint. For separable groups, i.e. energy, food and other goods, the utility function is written as (Deaton and Muellbauer, 1980a) u ¼ vðuÞ ¼ f ½vE ðuÞ; vF ðuÞ; vO ðuÞ, where f[] is the total utility function and vE ðuÞ, vF ðuÞ and vO ðuÞ are sub-utility functions associated with energy, food and other goods, respectively. The energy mix demand problem is analyzed using a system of demand as the modeling framework. The actual demand model that is used here is the Almost Ideal Demand System (AIDS) which conforms to the conceptual and theoretical considerations presented above. The AIDS model was first developed by Deaton and Muellbauer (1980a); it gives an arbitrary first-order approximation to any demand system (Deaton and Muellbauer, 1980b; Thomas, 1987). The AIDS demand functions in budget share form (wi) with P being a price index and x total expenditure is X wi ¼ ai þ gij log pj þ bi log ðx=PÞ. (1) j

With Pk the price of good k Deaton and Muellbauer (1980a) and Berck et al. (1997) define P as X 1XX log P ¼ a0 þ ak log pk þ g log pk log pj . 2 j k kj k (2) The restrictions of adding-up, homogeneity and symmetry are imposed on the parameters of the AIDS equations (Deaton and Muellbauer, 1980a). From an econometric point of view, (2) is very close to being linear and can be estimated equation by equation using ordinary least squares (Deaton and Muellbauer, 1980a). The following own price, cross-price and income elasticities can be derived from (1) and (2) (Berck et al., 1997): ii ¼ 1 þ

gii  bi ; wi

ij ¼

gij bi wj  ; wi wi

iy ¼ bi þ 1.

(3)

For further details on the theoretical and econometric aspects of the AIDS demand functions see Deaton and Muellbauer (1980a, b), Thomas (1987) and Elsner (2001). 3.2. Model specification At the first stage, total energy expenditure for household t is estimated as a function of total household expenditure.1 An Engel function that represents the share of household energy expenditure in total household expenditure in logarithm form is estimated (see Elsner (2001) for details on estimation of an Engel function). We extend this function to include other household characteristics as well 1

For ease of exposition the index t is omitted in the equations below.

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Stage 1 budgeting

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All household goods

Stage 2 budgeting

Food

Energy

Electricity

Firewood

Other goods

Kerosene

Fig. 2. Utility tree and multi-stage budgeting.

such that the specification has the form wTEE ¼ a þ b ln TE þ fX þ ml þ ,

Table 1 Household characteristics

(4)

where wTEE is the share of energy expenditure in total household expenditure; TE is total household expenditure; X is a vector of household characteristics; l is the Inverse Mills Ratio2; a, b, m and f are parameters and parameter vector, respectively, to be estimated; e is the error term. At the second stage, a linear specification of the Almost Ideal Demand System (LA/AIDS) is estimated (Elsner, 2001; Berck et al., 1997; Edgerton et al., 1996). For that purpose, we specify a system of demand equations with the value shares of the energy carriers being functions of total energy expenditure, fuel prices and household characteristics. The budget shares to be estimated are those of electricity, firewood and kerosene, since, as argued above, only these are consumed to any significant levels in Harare (Campbell et al., 2003). The model to be estimated therefore is X wi ¼ ai þ bi ln TEE þ di X þ gij ln pj þ Zi l þ ui , (5) j

where wi is household expenditure on fuel i as share of total energy expenditure3; TEE is total household expenditure on energy; X is a vector of household characteristics; pj is the price of fuel j (including own price of fuel i); l is the Inverse Mills Ratio; a, b, d, g, Z are parameters or 2 The inverse Mills ratio in Eqs. (4) and (5) results from Heckman’s twostage procedure to correct for sample selection bias (see Section 4 for further details). 3 Observe that in Eq. (5) it is assumed that fuel prices are constant which is typical for a cross-sectional study. However, for a given fuel there may exist within price variation over space due to differences in ease of accessing the same fuel. These differences impose different costs to consumers. Moreover, where informal and illegal markets exist, price variations may also emerge. In the present study the focus is on price variation between fuels rather than within fuels. The price variation between fuels outweighs the within fuel price variation such that the latter can be ignored for the purpose of this study.

Characteristic

Description

Household expenditure

Total household expenditure per month in Zimbabwe dollars (Z$) Number of individuals in the household Square of household size Number of rooms used by household Value of energy using appliances owned (Z$) Educational level of household head (years) Number of households sharing a property

Household size Household size squared Rooms Assets Education Occupancy

parameter vector to be estimated for each fuel type i; ni is the error term. System (5) is also subject to the homogeneity, additivity and symmetry conditions. The vector X made up of explanatory variables is defined in Table 1. We briefly discuss the rationale for including each variable in the analyses as well as the expected sign. Before going into detail, we observe that the sample analyzed consists of two sub-samples related to electrified and non-electrified households (i.e. households living in neighborhoods with and without access to electricity, respectively) which is relevant for the estimations. In the basic AIDS model household expenditure is a key variable (see e.g. Elsner, 2001 and Deaton and Muellbauer, 1980a). Following this train of thought, we include total expenditure in the first stage model (4) as a key explanatory variable (Attwell et al., 1989; Campbell et al., 2000; Dzioubinski and Chipman, 1999; Foster et al., 2000). Since energy is a necessity, we expect its share in total expenditure to decline as expenditure increases. At the second stage, total energy expenditure is used in the AIDS specification. At this stage, the shares of the most preferred fuels (electricity for electrified households and firewood for non-electrified households) are expected to increase in the

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energy budget while those of the alternative fuels are expected to decrease for increasing energy expenditure. Following Deaton and Muellbauer (1980b), we hypothesize that for the same level of total expenditure larger households spend a larger share of their budgets on necessities and inferior goods than smaller households. For luxuries a reduction is expected. Another aspect of household size that needs to be taken into account is economies of scale. Following Elsner (2001) and Deaton and Paxson (1998), economies of scale are likely to exist in the consumption of energy as well in the consumption of individual fuels. To capture non-linearities due to both above types of effects, both household size and its square (divided by 10) are included in the model. A positive sign of the former and a negative sign of the latter indicates that as household size increases, initially the share of a fuel increases and then decreases (inverted U-curve type of effect). Conversely, a negative sign of household size and a positive sign of its square represent a U-type of effect. Observe that an inverted U-curve type of effect may also occur due to fuel switching when initially the share of a given fuel increases (e.g. electricity) and then decreases when it is substituted for by an inferior fuel (e.g. firewood) due to a tightening of the per capita budget as a consequence of an increase in household size. The number of rooms used by a household is included in the model as an indicator of energy demand for such purposes as heating, lighting, etc. Chow (2001), amongst others, confirms that the amount of space available to a household positively affects energy demand. The number of rooms is also expected to influence the shares of individual fuels in the total energy budget. Firewood is expected to be relatively less demanded because it is less suitable for lighting. Access to appliances determines whether or not households are able to use the corresponding fuel (Linderhof, 2001; Nesbakken, 1999). Ownership of electrical appliances in particular has been shown by amongst others Aburas and Fromme (1991) and Dzioubinski and Chipman (1999) to increase electricity consumption. In this paper, the value of appliances owned by a household is used since the types owned are unknown. As the value of appliances increases, the share of energy in total expenditure is expected to increase. Since appliances that use electricity are by far the most expensive, we furthermore hypothesize that for electrified households increasing values of appliances increase the share of electricity while those of firewood and kerosene decrease. For un-electrified households we expect a positive impact of appliances on firewood and a negative effect on kerosene since the former can be used for more purposes than the latter. We incorporate educational level, measured by years of schooling of the household head, as an indicator of social status (Huang and Lin, 2000; Lippit, 1959). Educational achievement is assumed to affect psychogenic needs and to stimulate energy use (Thomas, 1987) as well as the demand

for the most appreciated fuel (electricity for electrified households and firewood for non-electrified households). The number of households living at the same property is included in the analyses as this is common practice in the urban areas of Harare. This practice allows for combined payment of energy bills, combined purchase of fuels, particularly firewood, and other cost saving strategies. However, the electricity billing system in Harare is such that it charges higher prices beyond a subsistence consumption level per property and makes households sharing the same premises pay higher electricity bills compared to households who do not share dwelling places. Moreover, when the number of households drawing power from the same meter increases, the supply capacity is often exceeded which may induce households to diversify to other fuels, especially during peak demand periods. So, for electrified households, as the number of households living at the same premise increases, the share of energy in total expenditure is expected to increases due to cost saving. At the second stage, increases in the number of households living together is expected to decrease the share of electricity as households diversify to other fuels to make up for the constrained supply of electricity and for its price hikes. Non-electrified are expected to be moderately affected by occupancy levels. The household characteristics and their definitions are summarized in Table 1. 4. Empirical results The models specified above were estimated using household survey data collected from a sample of 500 households in Harare, September 2003. A stratified/random sampling method was used. A list of all wards in Harare and list of households in each ward was obtained using census data. Next, the wards were categorized by income. Two wards were randomly selected from each income group whereas from each selected ward grid blocks and blocks were randomly selected. Finally, from the blocks households were drawn randomly. For further details see Chambwera (2004). Descriptive statistics of the sample are given in Table 2. The most important results are the following. 89% of the households in the sample live in dwellings connected to electricity while 11% are not connected. The average household size is 4.9; the respective household sizes for electrified and non-electrified households being 4.8 and 5.2, respectively. The number of rooms per household is higher for electrified than for non-electrified households (3.9 vs. 2.2). Similarly for the level of education of the head of household, measured in terms of years of schooling (11.3 vs. 9.2). An average of 3 households lives at the same property; this number is higher among non-electrified households than electrified. Average monthly total expenditure for the entire sample is Z$18,400; the figure for electrified households being more than double that for nonelectrified households. In terms of energy appliances, electrified households own assets whose total value is

ARTICLE IN PRESS M. Chambwera, H. Folmer / Energy Policy 35 (2007) 2538–2548 Table 2 Descriptive statistics

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Table 3 Energy use characteristics

Household characteristic

All households Electrified combined households

Unelectrified households

Sample size % electrified and non-electrified households Household size

500 100

57 11

443 89

4.9 4.8 (2.6) (2.5) Rooms 3.7 3.9 (2.5) (2.5) Sex of household head (% of households) Male 82 83 Female 18 17 Employment status of household head (%) Employed 89 91 Unemployed 11 9 Education of 11.0 11.3 household head (3.6) (3.6) (years of schooling) Ownership of residence (% of households) Owning 58 59 Renting 42 41 Occupancy 3 2.9 (1.9) (1.9) Household total $18,400 $19,700 expenditure (Z$ per ($20,100) ($20,900) month) Min $500 $600 Max $142,500 $142,500 Value of energy-using $64,500 $75,100 appliances (Z$) ($82,300) ($84,200) Min 0 0 Max $432,000 $432,000

5.2 (3.0) 2.2 (1.2) 75 25 75 25 9.2 (3.0)

47 53 3.4 (2.3) 7,900 ($5,400) $500 $30,000 $8,500 ($17,400) 0 $83,000

Figures in parentheses are standard deviations.

about 9 times the values of those owned by unelectrified households. As explained in the previous section, this is likely to be due to ownership of electrical appliances. The use of different fuels is summarized in Table 3. Of all the households surveyed, 79% mention electricity as their mostly used fuel followed by 18% and 2%, respectively, for firewood and kerosene. Eighty-nine percent of the electrified households use electricity as their dominant fuel, 9% firewood and 1% kerosene. For non-electrified households firewood is the dominant fuel (86%) whereas kerosene accounts for 14%. The energy expenditure patterns show that electrified and non-electrified households allocate 13% and 11% of their total expenditures to energy, respectively. However, in terms of actual energy expenditure, electrified households on average spend more than double the amount spent by non-electrified households. The shares of the different fuels in total energy expenditure show that, on average, electrified households allocate most of their energy budgets towards electricity, while firewood and kerosene receive about equal shares. Among non-electrified households, firewood and kerosene account for 55% and 45% of the total energy budget, respectively.

Energy use characteristic

Most dominant fuel (%) Electricity Firewood Kerosene Others Total energy expenditure Energy expenditure as share of total expenditure (%) Share of electricity expenditure in energy budget (%) Share of firewood expenditure in energy budget (%) Share of kerosene expenditure in energy budget (%)

All households combined

Electrified households

Unelectrified households

79 18 2 1 Z$1,600

89 9 1 1 Z$1,800

0 84 14 2 Z$800

13

13

11

73

81

0

14

9

55

13

10

45

4.1. Total energy expenditure Before going into detail, we observe that separate models were estimated for electrified and non-electrified households because of the substantial differences in their energy consumption patterns.4 This, however, led to the possibility of sample selection bias which was confirmed by a Hausman test (Greene, 2000). We applied Heckman’s two-stage procedure to correct for this (Greene, 2000; Heckman, 1979). The probit model estimated in this context gave the probability that a household is electrified or not with total expenditure, employment status, educational level and gender of the household head, and ownership of house as explanatory variables.5 This procedure generated an Inverse Mills Ration (IMR), l, which was used as one of the explanatory variables in the estimation of the energy models at both stages of decision-making. This implies that the first stage estimation to obtain the IMR involved all households (electrified and un-electrified). The second stage estimation separated electrified and un-electrified households into two different sub-samples. The models estimated for the first stage of decisionmaking explain the shares of total energy expenditure for electrified and non-electrified households (Table 4). From Table 4, it follows that the share of energy decreases as total expenditure (TE) increases. Hence, energy is a necessity for both groups of households. The coefficients for both groups of households are statistically significant, but that of electrified households is substantially larger (in absolute value). Apparently, non-electrified households are 4 5

Separate models were strongly supported by a test for model pooling. The estimated probit model is available upon request.

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Table 4 Total energy expenditure shares for electrified and unelectrified households Variable

Electrified households

Unelectrified households

Constant

.866*** (.114) .118*** (.013) .020*** (.006) .024** (.011) .001* (.0008) .021*** (.005) .012** (.005) .001 (.003) .186* (.114) .266 .000 .874 .073 164 109 3.712

.603*** (.186) .052** (.261) .015** (.005) .010 (.016) .00003 (.0010) .003 (.016) .012 (.007) .001 (.006) .122** (.054) .380 .024 1.872 .070 49 39 4.703

ln TE ln Asset Hhsize Hhsize2

Rooms Occupancy Education l R2 Significance level Akaike Information Criterion Pearson’s Rho Diagnostic log–likelihood Restricted log–likelihood Log Amemiya prediction criterion

Figures in parentheses are standard errors. *, ** and *** represent significance at 10*, 5% and 1%, respectively.

fuels in the energy mix. For this purpose we estimated a system of equations, determining the share of each fuel in the energy mix on the basis of total energy expenditure, the prices of the fuels and household characteristics. Estimations were carried out separately for electrified and unelectrified households. Again the two-stage Heckman procedure was used to correct the estimators for sample selection bias (which was confirmed by a Hausman test). All the equations in the system have the same regressors and, due to this feature, equation-by-equation estimation of the system using OLS yields the same results as the GLS estimator in the seemingly unrelated regression (SUR) estimation procedure (Greene, 2000; Deaton and Muellbauer (1980a, b); Edgerton et al., 1996). 4.2.1. Electrified households The results are presented in Table 5. Among electrified households, the shares of firewood and kerosene decline as total energy expenditure goes up, while the share of electricity goes up which confirms the hypothesis that electricity is more preferred than firewood and kerosene. Table 5 Shares of individual fuels for electrified households Variable Constant ln TEE ln Asset

less sensitive to changes in income because some of their basic energy needs are hardly met. For both types of households energy expenditure shares are positively and significantly impacted by investments in appliances (ln Asset),6 although the coefficient for electrified households is larger than that for non-electrified households. Household size has a positive impact for both types of households, though the coefficients are only significant for electrified households. The inverted U-type pattern hints at economies of scale (see Section 3). The number of rooms has a positive impact on the share of energy expenditure but is significant only for electrified households. A similar pattern holds for the number of households living at the same property. Educational level is not statistically significant and the sign of the coefficient is negative for both electrified and non-electrified households. Apparently, at the first budgeting stage the hypothesis of psychogenic needs (Section 3) does not work. 4.2. Allocation of energy budget to individual fuels The second stage of household energy decision-making involved allocating the total energy budget to individual 6

Ln denotes logarithm.

Hhsize Hhsize2 Occupancy Rooms Education Pe Pf Pk l R2 Significance level Akaike Information Criterion Pierson’s Rho Diagnostic log–likelihood Restricted log–likelihood Log Amemiya prediction criterion

wf

we

wk

.838*** (.190) .096*** (.018) .058*** (.012) .055*** (.020) .004*** (.001) .004 (.009) .013* (.008) .019*** (.005) .470*** (.143) .281*** (.079) .222* (.122) .330* (.194) .444

.775*** (.147) .021* (.014) .044*** (.009) .015 (.015) .0007 (.001) .013* (.007) .009 (.006) .017*** (.004) .191* (.111) .172*** (.061) .226** (.095) .544*** (.150) .318

1.063*** (.151) .076*** (.015) .014* (.009) .071*** (.016) .005*** (.001) .016** (.008) .004 (.007) .002 (.004) .279** (.114) .109* (.063) .004 (.097) .214 (.155) .320

.000 .105

.000 .616

.000 .558

.0002 91 42 3.454

.182 83 34 3.400

.236 25 50 2.942

Figures in parentheses are standard errors. *, ** and *** represent significance at 10*, 5% and 1%, respectively.

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An increase in the value of appliances has a positive impact on the share of electricity and a negative impact on the share of firewood and kerosene indicating substitution away from the inferior fuels. Household size follows an inverted U-type of effect for electricity, a U-type of effect for kerosene and is insignificant for firewood. These opposing patterns show that for larger households kerosene is substituted for electricity.7 In a similar vein, the number of households living at the same property positively impacts on the share of firewood, negatively on the share of kerosene and is insignificant for the share of electricity. The impact for household size and occupancy are likely to be due to the higher prices for electricity beyond the subsistence level of consumption and the supply problems during peak demand. Moreover, economies of scale due to common purchase seem to apply to firewood only and, moreover, to induce substitution away from kerosene. The share of electricity goes down when the number of rooms increases while those of firewood and kerosene increase, though the latter impacts are not statistically significant. A possible explanation is the following. When the number of rooms increases housing costs go up which are compensated by amongst others saving on electricity expenditures by substituting the cheaper fuels firewood and kerosene for electricity. In line with the expectations formulated in Section 3, we find that the share of electricity in total energy expenditure increases as the level of education of the household head increases while the shares of firewood and kerosene decrease. The share of electricity decreases when its own price and the price of kerosene increase, while it increases when the price of firewood increases. The share of firewood decreases only when its own price increases, but increases when the prices of electricity and kerosene increase. Finally, the share of kerosene decreases when its own price and that of firewood increase, though the coefficients are not significant, but increases when the price of electricity increases. All these results are in line with standard theory. 4.2.2. Non-electrified households The energy choice set of unelectrified households is made up of firewood and kerosene only. Hence, these fuels are direct substitutes. Regarding the estimation results (presented in Table 6) we refer to the very small number of significant coefficients, which is probably due to the very small sample size. In spite of their insignificance, we nevertheless discuss the impacts of all the variables, since in larger samples they may turn out to be significant. Increases in total energy expenditure are associated with an increase in the share of firewood and a reduction in the share of kerosene. Apparently, an increase in the energy budget does not only lead to an increase in the purchase of the cheaper fuel, firewood, but also to substitution away 7 This effect is in line with the fact that electricity is not generally used for cooking in developing countries.

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Table 6 Shares of individual fuels for unelectrified households Variable

wf

wk

Constant

R2

.315 (.676) .088 (.071) .041 (.032) .076 (.058) .003 (.004) .010 (.025) .064 (.058) .005 (.022) .159 (.018) .659* (.371) .225 (.214) .316

1.236** (.599) .074 (.063) .052* (.028) .096* (.052) .004 (.003) .010 (.022) .096* (.051) .009 (.020) .365 (.282) .721** (.328) .255 (.189) .415

Significance level Akaike information criterion Pierson’s Rho Diagnostic log–likelihood Restricted log–likelihood Log Amemiya prediction criterion

.308 .713 .121 3 10 2.108

.087 .469 .043 2 8 2.352

ln TEE ln Asset Hhsize Hhsize2 Occupancy Rooms Education Pf Pk l

Figures in parentheses are standard errors. *, ** and *** represent significance at the 10*, 5% and 1%, respectively.

from kerosene.8 The substitution effect could be related to investments in more, or more advanced, firewood appliances. After all, firewood has more use applications than kerosene whose use is just restricted to the kerosene stove and lamp. This assumption is supported by the finding that the share of firewood increases as the value of assets increases while the share of kerosene decreases. The impact of household size on firewood share follows an inverted U curve while that of kerosene follows a U curve. Furthermore, the number of rooms impacts positively on the share of kerosene and negatively on that of firewood. Both effects could be due to the fact that for the present type of households in terms of energy demand the additional number of family members and rooms only lead to an increase in the demand for lighting for which purpose kerosene is more adequate than firewood. The number of households living at the same premise leads to a slight increase in the share of firewood and a decline in that 8 Observe that in most urban areas firewood is not a cheaper fuel after energy efficiency is taken into account. Moreover, the price of firewood usually follows the price of petroleum fuels. However, in the Harare region the relatively low price of firewood comes from its undervaluation at source. Moreover, kerosene is presently very expensive because of its extreme scarcity.

ARTICLE IN PRESS M. Chambwera, H. Folmer / Energy Policy 35 (2007) 2538–2548

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Table 7 Effects of changing values of selected variables on fuel expenditure shares, total firewood consumption and the contributions of electrified and unelectrified households to total firewood demand Direction of change

Fuel expenditure shares

Total firewood consumption in Harare

Electrified households

Base scenario (this study) TEE increased by 10% Education increased by 10% pe increased by 10% pe decreased by10% pf increased by 10% pf decreased by10% pk increased by 10% pk decreased by 10%

Unelectrified households

Actual (ton/yr)

we

wf

wk

wf

wk

0.81

0.09

0.10

0.55

0.45

120,600

0.83

0.08

0.09

0.56

0.44

0.83

0.07

0.10

0.56

0.77

0.10

0.13

0.86

0.07

0.84

% Change

% contribution

Electrified households

Unelectrified households

0

49

51

132,200

10

48

52

0.44

107,400

11

42

58

0.55

0.45

133,100

10

53

47

0.07

0.55

0.45

106,800

11

42

58

0.07

0.09

0.53

0.47

107,600

11

44

56

0.79

0.10

0.11

0.58

0.42

134,900

12

53

47

0.79

0.11

0.10

0.62

0.38

142,400

18

51

49

0.83

0.07

0.10

0.48

0.52

96,500

20

44

56

of kerosene. The coefficient of education indicates that firewood is more preferred by more educated households than kerosene. As shown in Table 2, the educational level of un-electrified households is substantially lower than for electrified households, which is a possible explanation for the differences in psychogenic needs and preference orderings for fuels for both sub-samples. For both kerosene and firewood, an increase in own price leads to a reduction in the share of the commodity concerned, while an increase in the price of the other fuel has the opposite effect. This is in line with theoretical expectations. However, the price of firewood is not significant for both fuels, which is likely to be related to the practice of large scale own firewood collection by these households. 4.3. Policy impacts The estimation results were used to simulate responses in terms of fuel shares to changes in selected explanatory demand factors (total energy expenditure (TEE), education, fuel prices). The values of these key demand variables were increased and decreased by 10% and the responses are presented in Table 7. Restricting ourselves to firewood, we find that an increase by 10% in TEE, the years of schooling and the price of firewood lead to slight declines in the share of firewood whereas a 10% increase in the prices of electricity and of kerosene lead to increases in the share of firewood.

Next we turn to the quantities of firewood consumed. Using the formula q ¼ wf :TEE=pf and the average values of the relevant variables, we estimate q, the average quantity of firewood consumed by a household in Harare at 185 kg and 695 kg for electrified and non-electrified households, respectively. Using population statistics for Harare, it follows that all households together consume about 120,600 tons of firewood annually. According to Frost (1996), average harvestable wood (on a dry matter basis, excluding leaves and twigs) from Miombo woodlands is 66.43 tons per hectare. Therefore the annual average area required to meet the total demand of firewood for Harare is 1,800 ha.9 Although firewood consumption per electrified household is substantially less than that per un-electrified household, electrified households demand approximately 49% of total of firewood and non-electrified 51% in the base case. The largest reduction in total firewood demand from 120,600 to 96,500 tons (20%) is achieved by decreasing the price of kerosene. In that scenario the shares of firewood fall to 7% and 48% of total fuel expenditure for electrified and non-electrified households, respectively. An increase by 10% in total energy expenditure increases total firewood demand by 10%: from 120,600 to 132,200 tons. This is because while an increase in total energy expenditure reduces firewood demand among electrified households, it 9 These figures refer only to wood that is purchased and does not include wood that households collect on their own.

ARTICLE IN PRESS M. Chambwera, H. Folmer / Energy Policy 35 (2007) 2538–2548 Table 8 Effects of changing electrification status on total energy demand % of electrified households in total population

Total annual firewood demand (tones)

% change in total firewood demand

78 (Base case) 75 80 85 90 95 100

120,600 126,800 116,500 106,100 95,800 85,400 75,100

0 5 3 12 21 29 38

increases its demand among the non-electrified group. When the educational level of the household head increases by 10%, there is a more than a proportionate decline (11%) in total firewood demand in Harare (from 120,600 to 107,400 tons). The effects of changes in the proportions of electrified and non-electrified households on total firewood demand was also investigated (Table 8).10 In the baseline scenario, 78% of households are electrified. If this proportion increases to 80%, firewood demand decreases by 3% to 116,500 tons per year. If all households in Harare are electrified total firewood demand goes down by 38% to 75,100 tones per year.

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prime short run policy handle whereas electrification and education are long term policy handles to manage the demand for firewood. Decreasing the prices of alternative fuels may involve subsidies or, in the case of kerosene, ensuring that its is readily available through the formal system so as to avoid illegal markets. Another important result relevant for policy making is that bad economic policy is bad environmental policy. Put differently, economic policies stimulating a positive economic environment is probably the most important tool to reduce the use of firewood in the long term. Although it is expected to boost the energy budget and the demand for firewood in the short run, it will create opportunities for further electrification in the long run. It will also further enable households to access and increase their shares of modern fuels in their energy mixes, as well as creating employment that reduces the tendency by unemployed urban dwellers to engage in the environmentally damaging firewood business. Particularly, many unemployed urban residents engage in informal business activities such as collecting and selling firewood. The present economic situation in Zimbabwe including Harare seriously hampers the policy recommendations above. Particlarly:



5. Summary and conclusions

The recommendation to expand electrification fits into the existing policy context of Zimbabwe where the government has stimulated the widespread availability of electricity in urban areas. However, when urban areas continue to grow whereas the economy stagnates, the capacity to install and supply will be limited. In such a setting electrification policy and subsidies on electricity are futile. Because of poor economic performance leading to amongst others shortages of foreign earnings, kerosine has become very scarce and most of it is only available at the illegal market where the official price controls do not work. The firewood market has largely remained unregulated. Unemployment has forced many people to get involved in small business such firewood collection and vending. As they compete for the market, prices have remained low.

The main conclusion from this paper is that the overall approach in managing the use of firewood as an energy source in urban areas in a bid to, for instance, mitigate its impact on deforestation, should target overall energy consumption rather than firewood alone. An integrated approach relating to all types of fuels is needed in ensuring significant results. We also find that differences in energy expenditure patterns of electrified and non-electrified households need to be taken into account since most policies will have different outcomes for the two groups. Another conclusion is that while total electrification would reduce firewood demand substantially, it does not totally eliminate it. In fact, at present about half of total firewood demand in Harare is accounted for by electrified households. It is therefore recommended that policies look beyond just electrification and address other factors that reduce the share of firewood in the energy mix of households. Regarding the policy instruments that can be implemented we distinguish between the short term and in the long term.11 Decreasing the consumer price of kerosene is the

The upshot of this is that, as some past studies in Zimbabwe have pointed out (e.g. Campbell et al., 2000), the current negative economic trends will only lead to an increase of the share of firewood in household energy mixes, i.e. fuel back-switching, or going down the energy ladder.

10 The calculations are based on the (strong) assumption that the households who migrate from un-electrified to electrified have the same household characteristics as the present electrified households, such as income and educational levels, household size, etc. 11 Observe that although the underlying analysis is cross sectional and no explicit distinction is made between the long and the short run, it

(footnote continued) nevertheless makes sense to distinguish these two types effects. The impacts of policy handles such as price management could materialize on short notice, e.g. a year, whereas the impacts of improving the educational level would take several decades.





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