Urban Energy

Urban Energy

Transition and Technology Adoption:

the Case of Tigrai, northern Ethiopia

Zenebe Gebreegziabher Alemu Mekonen

Menale Kassie

Gunnar Kohlin

Outline

• Motivation

• Objectives

• Fuel use, urban energy transition and deforestation

• Theoretical model

• Empirical model and data

• Results and discussion

• Conclusions

Motivation

• Deforestation in Ethiopia has resulted in growing fuel scarcity and higher firewood prices in urban centers

• Urban centers have long been dependent on the rural hinterlands for their fuel and this dependence

aggravated the deforestation

• The use of biofuels of rural origin covers about 90% of the urban fuel use

• One response to reducing the pressure of urban centers on their rural hinterlands could be through substitutions between or switching from one fuel to another, i.e.,

through energy transition

Motivation (Cont’d)

• For example, through substituting away or switching from fuelwood to electricity

• Electricity as cooking fuel is cleaner and do not cause deforestation

• Hence, switching from fuelwood to electricity leads to reduced pressure on the forest resources and lower indoor air pollution

• However, such a transition is conditioned by the

adoption rate of the relevant cooking appliance or stove technology

• That is, it requires that the majority of the households adopt the innovation

Objective of the Study

• To investigate urban energy transition and technology adoption as the possible means of reducing the pressure of urban centers on the rural hinterlands

• More specifically, the objectives are:

(i) assess the electric mitad cooking appliance holding (adoption) and how it conditions urban energy transition

(ii) to analyze factors explaining fuel choice of urban households’ for the various fuels

• Finally, we draw consequences or implications of findings in terms of broader policy issues

Fuel Use, Urban Energy Transition and Deforestation

• Traditional fuels (wood, tree residues, and

charcoal) sole/dominant fuel sources for urban households (about 90%)

• Modern fuels have very limited role

• Structure of urban fuel demand is biomass dominated means greater pressure on local forests

• Traditional use of wood as cooking fuel in stoves with very low efficiency of heat use intensifies

the pressure on local forests

Fuel Use, Urban Energy Transition and Deforestation (Cont’d)

• Whether or not the transition is occurring? How rapidly it is occurring? The scope for affecting the rate and scale of energy transition are issues of very important policy relevance both in terms of energy planning and

environmental restoration

• In the literature, the transition from traditional to modern fuels has often been conceptualized as a relatively

straightforward three-stage process

• The argument in here is that it might not be that simple and that the extent of the environmental and health

effects (positive externalities) generated thereof is conditioned by technology adoption

Theoretical Model

• Economic theory of the demand for consumer durables suggests that such demand arises from the flow of

services provided by durable ownership

• The utility associated with a consumer durable is hence best characterized as indirect

• Durables may differ in capacity, efficiency, versatility, and of course correspondingly in price,

• However, the consumer will ultimately utilize at an intensity level that provides the ‘necessary’ service

• Corresponding to this usage will be the cost of the

derived demand for the fuel that the durable consumes

Theoretical Model (Cont’d)

• Consider a consumer who faces a choice of m mutually exclusive , exhaustive cooking appliance portfolios,

which can be indexed i= 1, …, m.

• Appliance portfolio i has a rental price (cost) r_i.

• Given appliance portfolio i, the consumer has a

conditional indirect utility function (Dubin and McFadden) (1)

where p₁ is price of electricity, p₂ is price of alternative energy source (i.e., fuelwood), y is income, z_i is

observed attributes and ε_i is unobserved attributes of appliance portfolio i, r_i is price (cost) of appliance

portfolio i, η is unobserved characteristics of consumer

) , , , , , ,

(i y r_i p₁ p₂ z_{i i}  V

u   

Theoretical Model (Cont’d)

• Electricity and alternative energy (fuelwood) consumption levels, given appliance portfolio i, are (by Roy’s identity):

(2) (3)

• Therefore, the probability that appliance portfolio i is chosen is:

(4)

• Once the function V satisfies the necessary and sufficient properties of an indirect utility function, it can be used to construct the

econometric model

y z

p p r y i V

p z

p p r y i x V

i i i

i i i

















 

/ ) , , , , , ,

(

/ ) , , , , , ,

(

2 1

1 2

1

1 



y z

p p r y i V

p z

p p r y i x V

i i i

i i i

















 

/ ) , , , , , ,

(

/ ) , , , , , ,

(

2 1

2 2

1

2 



 V j y r p p z j i

z p p r y i V ob

P

j j j

i i i

m i



















for ) , , , , , ,

(

) , , , , , ,

( : ) , ,..., (

Pr

2 1

2 1 1







Empirical Model

• Let S be an indicator variable indexing whether the household owns an electric mitad cooking appliance (stove) (S=1) or not (S=0).

• Hence, the probit model of electric mitad cooking appliance adoption can be specified as:

(5) prob (S_i=1) =

where Ф is the standard normal distribution function, x_i a vector of regressors and  is a vector of parameters to be estimated.

• Note that prob (S_i = 0) = 1-

) (x_i



) (x_i



Study Area and Data

Study Area and Data (Cont’d)

• Dataset of 350 urban households in Tigrai (northern Ethiopia)

• One period data referring to the year 2003, questionnaire

• Data on food and non-food non-fuel expenditure,

expenditure on the different fuels (firewood, charcoal, electricity, kerosene)

• Data on income, and types of cooking appliance (stove) technologies used were collected

• In addition, responses on fuel preferences, reason for not using specific cooking appliance or stove type, etc was also collected

• Five enumerators were trained and used for the data collection

Study Area and Data (Cont’d)

• As far as Ethiopia is concerned injera

baking is the major consumer of fuelwood and accounts for over 50 percent of the

total household fuel consumption

• Hence, it makes sense that the focus of

the study is on injera cookers

Results and Discussion

Table1 Cooking appliances/ injera baking stoves (n=350)

Stove type HHs

involved

%

Open hearth (three-stone stove) ² 0.57 Tigrai-type (trad’l clay enclosed)

324

Tehesh ⁴ 1.14

Mirte ¹ 0.29

Electric mitad ⁷¹ 20.29

Results and Discussion (Cont’d)

Table2

The decision to consume fuel F

variable wood charcoal kerosene electricity

Price of wood (+)**

Price of charcoal (+)*** (+)***

Price of kerosene (+)**

Price of electricity

HH income (+)*** (+)***

Family size (-)**

Age (+)** (-)** (+)**

Education (-) *** (+)**

Occupation

Results and Discussion (Cont’d)

Table3 Cooking appliance/ electric mitad adoption

Variable coefficient

Price of wood

Price of Charcoal Price of Kerosene

HH Income (+)***

Family size (+)**

Age (+)***

Education (+)***

Occupation

Conclusions

• Price of substitute good, household income,

characteristics age and education important on the decision to consume fuel F

• Household income and characteristics education, age and family size most important factors on adoption of modern cooking appliances

• Combining all the model results household income and education have significant role.

• More educated households and households with higher income substitute away from wood to electricity

Conclusions (Cont’d)

• Only about 20 percent of the sample households were found to have adopted the electric mitad

cooking appliance

• In fact, it turns out that electricity is mainly used for lighting, and a lot need to be done if

considerable gains are to be made

• The transition to modern fuels and the extent of the environmental and health effects (positive

externalities) generated thereof is conditioned by

technology adoption

Thank you