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  • Original article
  • Open Access

Households’ energy preference and consumption intensity in Kenya

  • 1Email author,
  • 2 and
  • 3, 4
Energy, Sustainability and Society20199:20

https://doi.org/10.1186/s13705-019-0201-8

  • Received: 22 May 2018
  • Accepted: 3 May 2019
  • Published:

Abstract

Background

There have been notable joint efforts from the private and public sectors in promoting households’ access to clean and efficient energy sources. Despite the noteworthy progress realized over the years, the consumption and reliance on clean energy sources are reportedly low. This scenario is evident among households practicing multiple energy use, whereby energy proportions consumed from the clean energy sources are much lower compared to non-clean energy sources. As such, reliance on non-clean energy has greatly hindered the projected welfare and productive gains that comes along with access to clean energy sources. To understand households’ energy consumption behavior, this study takes into consideration that energy preference (choice) and intensity (proportions consumed) are two independent decisions. Therefore, a succinct understanding of the factors affecting these decisions acts as a basis for an optimal transition to clean energy sources.

Methods

The study utilized a nationally representative cross-sectional household dataset (3663 households) across Kenya. A series of diagnostic and specification tests were carried out so as to identify the most suitable estimation technique in achieving the underlying objectives of the study. The preference for Cragg’s double-hurdle model was premised on the fact that the model postulates that households must pass two separate hurdles before a positive level of consuption is observed. Maximum likelihood estimations were derived, followed by the marginal effects for the probability of participation and consumption intensity (conditional and unconditional) to unveil the effects of explanatory variables on the dependent variable.

Results

Results show the diversity in magnitude and direction of how various factors affect the preference and consumption intensity among households. For instance, households’ energy preference and consumption intensity are predominantly affected by location (rural or urban), household’s decision maker on energy use, education level, age of the household head, and the average monthly income.

Conclusion

In this regard, the promotion of clean energy use should target households in rural areas and households with lower level of education and lower income brackets. Uptake of clean energy sources such as liquefied petroleum gas should be encouraged among rural and urban poor households through reducing the upfront cost of acquiring cylinders and the refilling costs.

Keywords

  • Energy consumption
  • Clean energy sources
  • Energy preference
  • Consumption intensity
  • Double-hurdle

Introduction

Access and sustained consumption of clean energy sources are essential for a nation’s overall socio-economic development and improved human welfare [1]. Consumption of clean energy among the population is associated with economic prospects and provision of basic needs required for the sustenance of human life including food, housing, health services, and clothing. Hence, sustainable socio-economic development at the household level is directly linked to the preference and intensity of energy consumed [2]. The global statistics indicate that about 2.7 billion people consume solid biomass for cooking, which is associated with 3.5 million deaths annually from indoor air pollution [3]. Further, statistics portray inter and intra-regional disparities in energy consumption patterns. Developed economies and members belonging to Organization for Economic Co-operation and Development (OECD) have nearly universal access and reliance on modern energy sources [3]. Similarly, other regions with remarkable trends in utilization of clean energy include Latin America (95%), North Africa (99%), Middle East (92%), and South East Asia (84%) [4]. On the contrary, the energy consumption patterns in Sub-Saharan Africa (SSA) are of global concern as they dominate the world totals with roughly 80% dependency on biomass [4]. It is estimated that only 43% of the population in Sub-Saharan Africa have access to electricity which is considered as an efficient and clean energy source [5]. It is further projected that if the current scenario persists, nearly 880 million of Sub-Saharan Africa (SSA) population will rely on non-clean energy for domestic use in the year 2020 [5]. The East Africa region is reported as one of the fastest growing regions in Africa but still exhibits a high dependency (80%) on non-clean energy sources [6]. The abovementioned trends clearly indicate the extent to which clean energy potential benefits and opportunities are mislaid especially among the population.

Kenya’s progress in promoting clean energy consumption at the household level has had its own hurdles. For decades now, biomass is reported as the dominant energy source in Kenya, accounting for about 68% of the energy utilized [7]. Nearly three-quarter of Kenya’s population rely on biomass sources in meeting their cooking, heating, and lighting needs [8]. Other sectors which rely on biomass include the industries micro and small enterprises. Apart from biomass, other major energy sources accounting for the total energy consumed include petroleum products and electricity at 22% and 9%, respectively [9]. The proportion of households relying on non-clean energy is projected to rise from the current 26 million to 45 million by the year 2020 [4]. Access and consumption of clean and efficient energy remains one of the fundamental enablers that is implemented through various national plans and programmes such as the Kenya’s Least Cost Power Development Plan 2017–2037 and the Vision 2030 and the medium-term plans incluing; grid extension renewable off-grid solutions, and the last mile connectivity. The above-mentioned projects have set a clear path towards universal access to clean energy sources and remained pertinent in changing the landscape of energy preference and consumption among households. For instance, the number of customers connected to electricity under the Rural Electrification Programme and Last Mile Connectivity in Kenya increased from 2,264,508 in March 2013 to 6,526,987 customers in June 2018 and access rate stands at 73.42% having improved from 32% in 2013 [10].

Considerable efforts have been made in reforming the energy sector in Kenya. However, consumption of clean energy sources remains relatively low at the household level [10]. This scenario is evident among households practicing multiple energy use, whereby consumption intensity for clean energy sources is considerably low. According to [11], electricity consumption has declined for the last 5 years from 2823 kW per hour (kWh) in the year 2013 to 1338 kWh as of 2017. Further, 3.6 million households of the 6.5 million connected to the national grid consume an average of 15 kWh of electricity per month (ERC, 2017). The energy consumption behavior portrayed bears a negative implication to the overall growth of the economy and hinders progress in productive activities such as micro small and medium enterprises and the overall welfare benefits at the household level [1]. More so, indoor pollution from exposure to biomass smoke impacts negatively on human health where close to 15,000 lives are lost annually in Kenya and implications are severe among women and girls, whose household energy use revolves around biomass [12, 13]. With the prevailing energy consumption trends, more people are likely to die annually due to respiratory infections and chronic obstructive pulmonary disease [14].

Most of the energy studies analyze households’ energy preference and consumption behavior as conjoined components of the energy use behavior. This study exemplifies that the decision to acquire a certain energy source and the proportions consumed may be affected by a set of different factors. Therefore, there is need to simultaneously analyze the factors affecting energy preference and consumption intensity at the household level.

Materials and methods

Description of the study area and data acquisition

The study was carried out in Kenya. Based on the research gap identified, the study utilized a cross-sectional household dataset that was acquired through the National Energy Survey, 2009. The data comprised comprehensive representative and reliable household energy use patterns. Sampling was initiated by deriving a sampling frame from the National Sample Survey and Evaluation Programme comprised of 6,371,370 households (Table 1). For the eight former administrative provinces including; Nairobi, Coast, Central, Eastern, Western, North Eastern, Nyanza and the Rift Valley. The sampling frame comprised of 1800 clusters, each with a 100 households. Out of the 1800 clusters, 540 were in the urban areas while 1260 in the rural areas (Table 1). Subsquently, a 20% sub-sample of the clusters was selected and comprised of 108 and 252 clusters for rural and urban areas respectively. Further, using the proportionate random sampling technique a sample of 3663 households was derived. It is worthy noting that following the promulgation of the Constitution of Kenya, 2010 the country adopted the devolved system of government and future energy studies are expected to explore the dynamics in the established 47 counties. However, the data is still useful as it represents the regional aspect of energy preference and consumption intensity at the household level.
Table 1

Distribution of clusters and households in national sample as per the Kenya National Bureau of Statistics (KNBS)

Province

NASSEP IV clusters

Household strength

Rural

Urban

Total

Rural

Urban

Total

Central

162

48

210

651,157

273,388

924,545

Coast

111

66

177

234,598

292,829

527,427

Eastern

237

58

295

766,893

190,755

957,648

Nairobi

0

108

108

0

649,426

49,426

North Eastern

45

20

65

118,077

29,929

148,006

Nyanza

228

84

312

669,813

298,201

968,014

Rift Valley

342

100

442

1,114,773

380,208

1,494,981

Western

135

56

191

537,094

164,229

701,323

Total

1260

540

1800

4,092,405

2,278,965

6,371,370

Data analyses

The consumption intensity on various energy sources was identified as the dependent variable for this study which was computed household's expenditure on various energy sources and used as a proxy for the intensity or the level of consumption. Consumption intensity is expressed as the ratio of the expenses on a given energy source to total expenses for all the other energy sources in a household. Therefore, the dependent variable was expressed as a continuous proportionate variable comprised of zeros or positive (0 and +n …) values. Zero observations arise from the households that do not consume a certain energy source. According to [15], the presence of zero observations in the dependent variable poses difficulties when analyzing micro-data. Therefore, there was need to consider an appropriate estimation model. The independent variables were identified as socio-economic characteristics pertinent to energy consumption behavior at the household level.

The diagnostic and specification tests

Due to the limited nature of the dependent variable, the study explored on diagnostic and specification tests which aid in the selection of the most appropriate model. The preliminary tests act as a precaution for inconsistent parameter estimates arising from non-normality, heteroscedasticity, and choice of the wrong model [16, 17]. Lagrange Multiplier (LM) test for homoscedasticity [15] and Conditional Moment (CM) based tests [16] for normality were conducted to ascertain whether Tobit model was the appropriate model for the underlying study.

According to Table 2, the LM test values were found to be below the relevant critical value which is an indication of heteroskedasticity hence, rejection of the Tobit model as a suitable tool for analysis. Equally, the CM test showed non-normality distribution led to the rejection of the Tobit model.
Table 2

Lagrange Multiplier (LM) and Conditional Moment (CM) test values

Tobit model

The Lagrange Multiplier test value

Conditional Moment test value

Electricity

720.76 (40) [0.000]

29.196 (40) [0.000]

Liquefied Petroleum Gas

754.18 (40) [0.000]

11.603 (40) [0.000]

Kerosene

176.46 (40) [0.000]

188.26 (40) [0.000]

Charcoal

576.68 (40) [0.000]

117.39 (40) [0.000]

Wood fuel

1003.7 (40) [0.000]

323.89 (40) [0.000]

Following validity tests’ fail for the Tobit model, specification tests were carried out to affirm the suitability of double-hurdle models as an appropriate technique for analysis. Therefore, the efficacy of the Tobit model was tested against that of the double-hurdle model using the likelihood ratio test (often referred to as the superiority test or Tobit test statistic) as defined by [18]. The Tobit test statistic was computed as shown in Eq. (i)
$$ \mathrm{LR}=-2\times {x}_{y^2}\left(\mathrm{lnLDH}-\mathrm{lnLT}\right)\sim {x}^{2_{\mathrm{k}}} $$
(i)
where
  • LR = Tobit test statistic

  • lnLDH = the log-likelihood estimation for the double-hurdle model

  • lnLT = the log-likelihood estimates for the Tobit model

  • \( {x}^{2_k} \) = chi-squared distribution with k degrees of freedom, k represents the number of variables in the participation equation i.e. the number of coefficients that are assumed to be zero under the restricted model. Therefore, can also be indicated as Tobit test = 2 × (llProbit + lltrncreg − llTobit) or (− 2 × (Double-hurdle − Tobit).

For this test, the null hypothesis was that there is no significant difference between the double-hurdle model and Tobit model, which would imply that the Tobit model fits the data better. Rejection of the null hypothesis would imply that the double-hurdle model fits the data better [18].

The likelihood ratio (LR) values of the two models were estimated, and the Tobit test values for each equation were compared against the critical values for the chi-square distribution with the specified degrees of freedom (Table 3).
Table 3

Likelihood ratio tests for Tobit model versus double-hurdle model

Double-hurdle vs. Tobit models for

Test type

Tobittest value

Decision

Electricity

LR

111.14731 (40) [0.000]

Reject H0

LPG

LR

662.2952 (40) [0.000]

Reject H0

Kerosene

LR

1016.704 (40) [0.000]

Reject H0

Charcoal

LR

2053.368 (40) [0.000]

Reject H0

Wood fuel

LR

692.0104 (40) [0.000]

Reject H0

Material residue

LR

823.84511 (33) [0.000]

Reject H0

H0: Tobit; H1: double-hurdle

LR likelihood-ration, H0 null hypothesis

Results indicate that LR test values were above the critical value indicating that the test statistic Γ = exceeds the critical value of the χ2 distribution. This qualifies the rejection of the Tobit model and adoption of the double-hurdle model. This implies that zero observations could have been as a result of either non-participation or participation but non-consumption [19]. Therefore, the double-hurdle model was considered appropriate in explaining households’ consumption preference and consumption intensity.

Cragg’s double-hurdle model specification and empirical framework

As aforementioned, Cragg’s double-hurdle postulates that households must pass two separate hurdles before they are observed with a positive level of consumption [20]. The first hurdle corresponds to factors affecting preference for a certain energy source and the second to the level of consumption. The unique feature of the double-hurdle model is that factors affecting the energy preference and consumptions are allowed to differ.

As modified from [19, 21] frameworks, the double-hurdle equations are specified as follows:
  1. (i)
    Participation decision
    $$ {\displaystyle \begin{array}{l}{y_{il}}^{\ast }={w}_ia+{u}_i\\ {}d=\left\{{}_{0\kern0.24em \mathrm{otherwise}}^{1\;\mathrm{if}\;{y_{il}}^{\ast}\kern0.84em >0}\right.\end{array}} $$
    (ii)
     
  2. (ii)
    Consumption decision
    $$ {\displaystyle \begin{array}{l}{y_{i2}}^{\ast }={x}_i\beta +{v}_i\\ {}{y}_i={x}_i\beta +{u}_i\;\mathrm{If}\;{y^{\ast}}_{il}>0\;\mathrm{and}\ {y^{\ast}}_{i2}>0\\ {}{y}_i=0\kern0.98em \mathrm{Otherwise}\end{array}} $$
    (iii)
     

Equation (ii) represents the dependent variable yil as the latent variable representing household’s choice for a particular energy source. wi is a vector of explanatory variables explaining the choice. wi is a set of individual characteristics explaining the choice; ui is the disturbance term randomly distributed as uiN(0, 1). d is an unobserved latent variable; yil is a binary indicator equaling one if household i consumes the particular energy item under consideration and zero otherwise [19, 21].

In Eq. (iii), the dependent variable (y*i2) indicate energy share by household i from a particular energy source. x1 is a vector of variables explaining the consumption decision. vi is the error term distributed as viN(0, σ2). yi is the observed household consumption intensity on a particular energy source. yi2 is a latent endogenous variable representing households’ consumption level. A positive level of consumption yi is the dependent variable (household energy consumption intensity on various energy sources) which is positive if the household chooses a particular energy source (yil > 0) and also consumes the energy (yi2 > 0). a and β in Eqs. 1.1 and 1.2 are linear parameters exhibiting the effect on the participation and consumption decisions respectively [19, 21].

The double-hurdle model estimation

Double-hurdle maximum likelihood estimation is as shown in Eq. (iv).
$$ \sum \limits_0\;{LL}_{\mathrm{Double}\hbox{-} \mathrm{Hurdle}}=\sum \limits_0\ln \left[1-\phi \left({w}_ia\right)\phi \left(\frac{x_i\beta }{\sigma}\right)\right]+\sum \limits_{+}\ln \left[\phi \left(w\kern0.24em a\right)\frac{1}{\sigma_1}\phi \left(\frac{y_{i-}{x}_i\beta }{\sigma}\right)\right] $$
(iv)

The first term in Eq. (iv) corresponds to the contribution of all the observations with an observed zero [22]. It indicates that the zero observations are coming not only from the participation decision but also from the level of consumption decision. The second term in the equation accounts for the contribution of all the observations with non-zero consumption intensity [23]. Using the maximum likelihood estimation, three marginal effects derived include probability of participation and consumption intensity (unconditional and conditional) to properly estimate the effects of various factors on the dependent variable [23, 24] as illustrated in Eqs. (v) and (vi).

Marginal effects for probability of participation

p(yi > 0| x): The probability of a positive value of yi for the values of the explanatory variables, x showing marginal effects for the probability of participation or acquiring a certain energy source [23].

Marginal effects for unconditional expectation

$$ E\left[y|{x}_i\right]=p\left({y}_i>0|x\right)E\left({y}_i|{y}_i>0,x\right) $$
(v)

Refers to the overall effect on the dependent variable, that is, the expected value of yi for the values of the explanatory variables, x also known as the unconditional expectation of yiE [yi|x] [23].

Unconditional marginal effects refer to the total effect on the level of consumption whereby all households under the study are included in the model. Therefore, a positive value for the marginal effect would suggest an increase in energy consumption across all households. Unconditional marginal test helps gain an understanding of the overall impact of an explanatory variable when for instance the participation effect and consumption effect show different signs.

Marginal effects for conditional expectation

E(yi| yi > 0, x): The conditional expectation that is the expected value of yi for values of the explanatory variables, x, condition of y > 0 showing the intensity of consumption conditional on participation [23].

The specific estimated equation is shown in Eq. (vi):
$$ {Y}_i=\upalpha +{\beta}_1\ \left(\mathrm{Age}\right)+{\beta}_2\ \left(\mathrm{Education}\right)+{\beta}_3\ \left(\mathrm{Location}\right)+{\beta}_4\ \left(\mathrm{Gender}\right)+{\beta}_5\ \left(\mathrm{Dwelling}\_\mathrm{unit}\right)+{\beta}_6\ \left(\mathrm{Household}\_\mathrm{income}\right)+{\beta}_7\ \left(\mathrm{Marital}\_\mathrm{status}\right)+{\beta}_8\ \left(\mathrm{Decision}\_\mathrm{maker}\right)+\upvarepsilon $$
(vi)

Energy sources considered for double-hurdle estimation include electricity, liquefied petroleum gas, kerosene, charcoal, and wood fuel.

Results and discussion

Characterizing household socio-economic patterns and energy source utilization

Majority of the households were situated in rural areas (66.04%) as compared to urban (33.96% households). It was established that wood fuel is a dominant (91.06%) source of energy for rural households while LPG (70.48%) and electricity (67.53%) are dominant energy sources for urban households (Table 4).
Table 4

Household socio-economic characteristics and energy utilized (%)

Variables

National (sample)

Fuel wood

Charcoal

Kerosene

LPG

Electricity

Location

 Urban = 0

33.96

8.94

40.5

28.25

70.48

67.53

 Rural = 1

66.04

91.06

59.5

71.75

29.52

32.47

Gender of the household head

 Male = 0

65.79

29.43

32.71

33.65

34.07

36.52

 Female = 1

34.21

70.57

67.29

66.35

65.93

63.48

Decision maker on energy use

 Household head = 1

46.10

46.04

43.59

45.69

47.31

46.32

 Spouse = 2

47.90

46.04

49.88

48.26

47.59

47.77

 Child = 3

6.00

7.93

6.52

6.05

5.1

5.91

Household dwelling unit

 Permanent = 1

50.40

18.85

57.39

44.56

88.14

82.02

 Semi-permanent = 2

37.40

31.15

34.07

42.51

9.24

14.25

 Temporary = 3

12.00

50

8.54

12.93

2.62

3.72

Household head: average monthly income (KSh)

 Below 2500 = 1

4.40

4.49

2.72

4.75

0.69

0.57

 2501–5000 = 2

12.60

11.86

8.68

14.16

0.83

3.08

 5001–10,000 = 3

21.80

16.35

20.65

24.59

6.21

10.93

 10,001–15,000 = 4

17.50

16.42

18.96

18.54

11.45

15.87

 15,001–20,000 = 5

15.00

13.25

16.94

15.6

14.48

17.73

 20,001–50,000 = 6

20.70

29.68

23.09

17.44

37.79

33.52

 50,001–100,000 = 7

6.30

3.61

6.95

4.31

21.66

14.09

 Above 100,000

1.80

1.35

2.01

0.62

6.9

4.21

Household head: education level

 No formal education = 1

6.90

10.71

4.13

7.05

0.55

1.94

 Primary school = 2

29.10

36.93

26.79

32.97

4.41

11.01

 Secondary school = 3

31.80

32.21

34.54

34.06

21.79

27.77

 Vocational/diploma = 4

21.20

16.44

23.42

19.32

36.69

33.36

 Bachelor’s degree = 5

8.90

2.95

9.1

5.54

29.1

20.73

 Postgraduate = 6

2.10

0.76

2.02

1.06

7.45

5.18

Household head age in years

 Below 30 years = 1

21.50

13.59

20.37

21.07

21.1

23.24

 31–35 years = 2

18.80

13.76

18.4

18.5

23.86

22.43

 36–40 years = 3

16.80

16.54

17.97

17.24

17.38

17.25

 41–45 years = 4

12.30

13.16

12.95

12.38

12.14

13.04

 46–50 years = 5

11.60

14.85

11.59

11.8

10.48

9.8

 51–60 years = 6

12.70

18.57

13.28

12.65

10.9

10.12

 Above 60 = 7

6.10

9.53

5.44

6.36

4.14

4.13

Household head: marital status

 Single = 1

12.80

12.8

10.61

11.25

17.66

16.84

 Married = 2

77.90

77.86

81.37

78.66

77.79

77.89

 Widowed = 3

8.60

8.6

7.23

9.34

3.72

4.37

 Divorced = 4

0.50

0.8

0.75

0.83

0.89

0.74

n = 3663

Notably, majority of the household heads were dominantly 45 years of age (69%) and female spouses were equally involved in decision-making regarding energy consumption. In terms of education, it was observed that majority of household heads had acquired formal secondary education (31.8%). Households with heads without formal education were found to significantly consume more wood energy (over 80%) while households with higher sources of income were observed to rely more on LPG and electricity sources of energy.

Factors affecting the probability, conditional and unconditional energy consumption intensity among households

Determining the dependent variable

This study defines the dependent variable as a proportion of the household’s consumption intensity on a specific energy source. Preliminary results show that the dependent variable comprises of positive proportionate values and zero observations (Table 5).
Table 5

Dependent variable summary statistics for positive consumption

Dependent variable

Proportion of households with positive consumption intensity

Consumption intensity

Minimum

Maximum

Wood fuel

0.323417

0.01

1

Charcoal

0.581878

0.0002

1

Kerosene

0.798308

0.006

1

Liquefied Petroleum Gas

0.198144

0.01

1

Electricity

0.337336

0.006

1

n = 3663

The concept underlying this study is that of single and multiple energy use among households, which gives room for in-depth analysis on factors influencing consumption intensity for clean and non-clean energy sources. The results indicate that both positive and zero consumption levels for various energy sources were recorded among households. According to Table 5, 79%, 58%, 32%, 33%, and 19% of households consumed kerosene, charcoal, wood fuel, electricity, and liquefied petroleum gas respectively. Further, the maximum consumption intensity recorded as one indicates that various households used a single source of energy.

The probability and conditional and unconditional marginal effects of households’ socio-economic factors on energy preference and consumption intensity

The log-likelihood parameters are used to estimate the marginal effects which explain how various factors affect the probability for participation, conditional and unconditional consumption for various energy sources. The discussions focus on the significant results identified across the three estimations, whereby significant and positive observations signify an increase in energy consumption based on the reference category while negative observations indicate a decrease in consumption.

Electricity

Results indicate that the location of a household in an urban area does not affect the probability of electricity use. However, the conditional and unconditional marginal effects indicate that households in urban areas consumed higher proportions on electricity as compared to households in rural areas (Table 6).
Table 6

Probability and conditional and unconditional discrete marginal effects for household’s energy consumption intensity

Variables

First hurdle

Second hurdle

Probability

Conditional

Unconditional

Electricity

 Location

  Urban

0.2209

0.0067***

0.0076***

 Gender

  Female

− 0.001***

− 0.0223***

− 0.0255*

 Decision maker on energy consumption

  Spouse

− 0.0210***

− 0.0081***

− 0.0092***

  Child

− 0.0280***

0.0113***

0.0128

 Dwelling unit

  Semi-permanent

− 0.1608***

− 0.0234***

− 0.0269**

  Temporary

− 0.1575***

− 0.0283***

− 0.0327

 Average monthly income (KSh)

  2501–5000

0.0550***

0.0989981***

0.1169991

  5001–10,000

0.1013**

0.0788541***

0.0941023*

  10,001–15,000

0.1634*

0.0674013***

0.0808989*

  15,001–20,000

0.2028

0.0539039***

0.0651523*

  20,001–50,000

0.2422

0.0885763***

0.1052032

  50,001–100,000

0.33225*

0.093369***

0.1106408

  100,001 and above

0.2535

0.2025687**

0.02295856**

 Education level

  Primary school

0.0445***

− 0.0212***

− 0.0245**

  Secondary school

0.0977*

− 0.0212***

− 0.0245**

  Vocational/diploma

0.1667

− 0.0051***

− 0.0058

  Bachelor’s degree

0.2285

0.0421***

0.0474**

  Postgraduate level

0.0455***

0.0744***

0.2367*

 Age of HHH (years)

  31–35

0.01250***

− 0.0362***

− 0.04158**

  36–40

− 0.0108***

− 0.0310***

− 0.0355**

  41–45

− 0.0096***

− 0.0110***

− 0.0126**

  46–50

− 0.0418***

− 0.0050***

− 0.0057***

  51–60

− 0.0147***

0.0028***

0.0031***

  61 and above

0.0203***

− 0.0200***

− 0.0228**

 Marital status

  Married

− 0.0192***

− 0.0781***

− 0.0876***

  Widowed

0.0008***

− 0.0837***

− 0.0940***

  Divorced

− 0.0462***

− 0.0323***

− 0.0356**

LPG

 Location

  Urban

0.2209

− 0.0286

0.0005***

 Gender

  Female

− 0.0206***

− 0.0295

− 0.0005***

 Decision maker on energy consumption

  Spouse

− 0.0206***

0.0121***

0.0132**

  Child

− 0.0282***

0.0147***

0.0389**

 Dwelling unit

  Semi-permanent

− 0.1608***

− 0.0092***

0.0121**

  Temporary

− 0.1575***

0.0240***

0.0160

 Average monthly income (KSh)

  2501–5000

0.0418***

0.0068***

0.0078***

  5001–10,000

0.0392**

0.1030***

0.1129

  10,001–15,000

0.0399

0.1086***

0.1189

  15,001–20,000

0.0408

0.0617***

0.0687*

  20,001–50,000

0.0412

0.0576***

0.0643*

  50,001–100,000

0.0510

0.0345***

0.038**

  100, 001 and above

0.0793*

0.0422***

0.0121**

 Education level

  Primary school

0.0309***

0.0238

0.0262**

  Secondary school

0.0309**

0.0271

0.0298**

  Vocational/diploma

0.0334

0.0173

0.0191

  Bachelor’s degree

0.0431

0.0403

0.0441**

  Postgraduate level

0.1347***

0.1969*

0.2071**

 Age of HHH (years)

  31–35

0.0192***

0.0444***

0.0476**

  36–40

0.0206***

− 0.0269***

− 0.0296**

  41–45

0.0223***

− 0.0260***

− 0.0286**

  46–50

0.0232***

− 0.0001***

− 0.001***

  51–60

0.0240**

− 0.0332***

− 0.0367**

  61 and above

0.0341***

− 0.0135***

− 0.0148**

 Marital status

  Married

0.0200***

− 0.058***

− 0.0633***

  Widowed

0.0312***

− 0.0640***

− 0.0691*

  Divorced

0.0817***

− 0.0631***

− 0.0680*

Kerosene

 Location

  Urban

0.1090

− 0.0616***

− 0.0827319*

 Gender

  Female

0.0213704***

− 0.0246***

− 0.0330867**

 Decision maker on energy consumption

  Spouse

− 0.0158

0.0080***

0.010**

  Child

− 0.0577

− 0.0055***

− 0.0073

 Dwelling unit

  Semi-permanent

− 0.0990

0.0262***

0.0359**

  Temporary

− 0.0795

0.0841**

0.1138

 Average monthly income (KSh)

  2501–5000

− 0.0516***

0.015***

− 0.0206**

  5001–10,000

− 0.0004***

− 0.0433***

− 0.0599**

  10,001–15,000

0.0349***

− 0.0688***

− 0.0948**

  15,001–20,000

0.0628***

− 0.0946***

0.0687*

  20,001–50,000

0.1263***

− 0.1228***

0.0643*

  50,001–100,000

0.3028**

− 0.2729***

0.0389**

  100,001 and above

0.3746

− 0.1229***

0.0121**

 Education level

  Primary school

0.0378***

0.0039***

0.0262**

  Secondary school

0.1182*

− 0.0182***

0.029**

  Vocational/diploma

0.1816

− 0.0189***

0.0191

  Bachelor’s degree

0.2439

− 0.0603***

0.0441**

  Postgraduate level

0.1038***

− 0.1349***

0.2071

 Age of HHH (years)

  31–35

0.0158***

− 0.0435***

− 0.0585***

  36–40

− 0.0154***

− 0.0451***

− 0.0606**

  41–45

− 0.035***

− 0.0326***

− 0.0439**

  46–50

− 0.0312***

− 0.0330***

− 0.0444**

  51–60

− 0.00***

− 0.0556***

− 0.0748**

  61 and above

0.0261***

− 0.0720***

− 0.0968**

 Marital status

  Married

− 0.0260***

− 0.0387***

− 0.0520**

   Widowed

− 0.0025***

− 0.0318***

− 0.0427**

  Divorced

− 0.0962***

− 0.0400***

− 0.0265**

Charcoal

 Location

  Urban

0.1135*

− 0.0249***

− 0.0292**

 Gender

  Female

0.0427*

− 0.0176***

− 0.0203**

 Decision maker on energy consumption

  Spouse

0.02073**

0.0248***

0.0291**

  Child

0.0609***

0.0038***

0.0045

 Dwelling unit

  Semi-permanent

− 0.0753***

0.0138***

0.0161**

  Temporary

− 0.1262***

0.0215***

0.0251

 Average monthly income (KSh)

  2501–5000

0.0016***

0.0048***

0.0053***

  5001–10,000

0.1003**

− 0.0222***

− 0.0249**

  10,001–15,000

0.1300**

− 0.0713***

− 0.0814**

  15,001–20,000

0.1601*

− 0.0744***

− 0.0851***

  20,001–50,000

0.1425**

− 0.0968***

− 0.1118***

  50,001–100,000

0.1486**

− 0.1786***

− 0.2151***

  100,001 and above

0.1911*

− 0.2432***

− 0.2855***

 Education level

  Primary school

0.1773

− 0.0394***

− 0.0451**

  Secondary school

0.1948

− 0.0391***

− 0.0451**

  Vocational/diploma

0.13022*

− 0.0677***

− 0.0784***

  Bachelor’s degree

0.0364***

− 0.091***

− 0.1069***

  Postgraduate level

0.04518**

− 0.1342***

− 0.1602***

 Age of HHH (years)

  31–35

− 0.0112***

0.0333***

0.0383**

  36–40

0.0416***

− 0.0004***

− 0.0005

  41–45

0.0399***

− 0.0073***

− 0.0085***

  46–50

0.0354***

− 0.0244***

− 0.0288**

  51–60

0.0699***

− 0.0333***

− 0.0008**

  61 and above

0.0180***

− 0.0507***

− 0.0604**

 Marital status

  Married

0.1221*

− 0.0069***

− 0.0081***

  Widowed

0.0953**

− 0.0276***

− 0.0325**

  Divorced

0.0232***

− 0.0225***

− 0.0265**

Wood fuel

 Location

  Urban

− 0.2889**

− 0.0249***

− 0.0117**

 Gender

  Female

0.0502**

0.0173***

0.0242*

 Decision maker on energy consumption

  Spouse

− 0.0352***

0.0248***

0.0104**

  Child

0.03543**

0.0038***

− 0.0569**

 Dwelling unit

  Semi-permanent

0.127308*

0.0138072***

0.078***

  Temporary

0.0294456***

0.0215289***

0.1110***

 Average monthly income (KSh)

  2501–5000

− 0.0015***

0.004***

0.0386**

  5001–10,000

0.0303***

− 0.0222***

0.026**

  10,001–15,000

0.0515***

− 0.0713***

0.0485**

  15,001–20,000

0.0526**

− 0.0744***

0.0172**

  20,001–50,000

0.0486***

− 0.0968***

0.0516**

  50,001–100,000

0.0627***

− 0.178***

− 0.030**

  100,001 and above

− 0.0761***

− 0.2432***

− 0.1220

 Education level

  Primary school

− 0.0244***

− 0.0394***

− 0.1460***

  Secondary school

− 0.0342***

− 0.0394***

− 0.1593***

  Vocational/diploma

− 0.0733***

− 0.0677***

− 0.1696**

  Bachelor’s degree

− 0.173***

− 0.0914***

− 0.033***

  Postgraduate level

− 0.1367***

− 0.1342***

− 0.0129**

 Age of HHH (years)

  31–35

0.0032***

0.0333***

− 0.0087***

  36–40

0.0518***

− 0.0004***

− 0.000***

  41–45

0.0798**

− 0.0073***

0.0076**

  46–50

0.1106**

− 0.0244***

0.010**

  51–60

0.1384*

− 0.0331***

− 0.0141**

  61 and above

0.1573*

− 0.0507***

0.0158**

 Marital status

  Married

0.0938**

− 0.0069***

− 0.0065***

  Widowed

0.0796*

− 0.0276***

− 0.020**

  Divorced

0.1241*

− 0.0225***

0.1667

***p value < 0.01, **p value < 0.05, *p value < 0.10 Excluded reference categories: rural, male household head, permanent, below 2500, no formal education, below 30 years, and single

In terms of gender, preference for electricity as a source of energy among female-headed households was found to be lower as compared to male-headed households. Similarly, consumption intensity based on conditional and unconditional level indicated a decrease among female-headed households. It is further observed that semi-permanent and temporary characteristics of the households’ dwelling unit have a negative probability on participation, conditional and unconditional level of energy consumption. This indicates that there are lower chances and level of consuming electricity among households with temporary and semi-permanent units as compared to households dwelling in permanent units.

Household heads with primary and secondary level education recorded a higher probability of electricity consumption. However, the negative effect on the conditional and unconditional level of consumption implies that lower levels of education negatively affect electricity consumption. Conversely, the marginal effects for household heads with postgraduate degree indicate an increase in electricity consumption intensity.

Liquefied petroleum gas

Results indicate that households in urban areas were more likely to consume higher proportions of liquefied petroleum gas as compared to those in rural areas (Table 6).

In addition, it was observed that female-headed households are less likely to consume LPG as a clean energy source compared to the male-headed households (Table 6). The marginal effects would further implore that female-headed households consumed lower proportions of LPG as compared to their male counterparts. In terms of decision-making on acquiring and utilizing LPG as a source of energy, it was observed that it would be less likely to acquire LPG if the decision maker is a spouse or a child. However, consumption level increases when female spouses and children are the main household decision makers. Results further indicated that there exists a low probability of consuming LPG among households dwelling in semi-permanent housing structures. Households with an average monthly income of KSh 100,001 recorded a higher probability of acquiring LPG and similarly, consume higher proportions of LPG.

Kerosene

The marginal effects estimates for kerosene indicate diverse variations on various household socio-economic factors. Households located in urban areas appear to consume lower proportions of kerosene as compared to their rural counterparts (Table 6).

Further, households headed by males were more likely to consume higher proportions of kerosene as compared to those headed by females. Notably, households with an average monthly income of KSh 100,000 recorded lower consumption intensity on kerosene as compared to households in the lower income brackets. The age of the household head was also found to be a significant factor affecting kerosene consumption intensity among households. The probability of using kerosene for households heads aged 60 years and above was high but recorded lower consumption intensity.

Charcoal

Households located in urban areas recorded a higher probability of consuming charcoal as an energy source. However, proportions consumed were lower as compared to households in rural areas. Similarly, female-headed households were more likely to use charcoal but in lower proportions as compared to male-headed households (Table 6).

When the decision maker on energy consumption is the spouse, there is a higher preference for charcoal and an increase in consumption. Further, results indicate that increase in household monthly income increases the chances of using charcoal as an energy source but negatively affects the level of consumption. This is a true replica of the notion that well-off households prefer and consume clean and efficient energy sources when compared to poor households. The level of education was also identified as a critical factor for understanding energy use dynamics in Kenya. Results are consistent for households headed by persons possessing vocational, bachelors, and postgraduate studies. This implies that educated household heads are more aware of the health risks associated with charcoal and so they end up consuming lower proportions of charcoal.

Wood fuel

For the wood fuel, results indicate that urban households were less likely to use wood fuel and consumption intensity was consistently low. This implies that the majority of households in rural areas consumed higher proportions of wood fuel compared to their urban counterparts (Table 6).

On the other hand, female-headed households were more likely to acquire wood fuel as well as consume it in higher proportions as compared to male-headed households. Households dwelling in semi-permanent and temporary units consumed lower proportions of firewood compared to those in permanent households. Households in the upper-income level (over KSh 100,000) showed a consistent pattern across the model estimates. This indicates that households in the highest income level were less likely to use wood fuel and consume lower proportions of wood fuel. In reference to non-formal education, household heads with higher levels of education are less likely to consume wood fuel hence. Married household heads are likely to higher proportions of wood fuel in reference to a single household head.

Conclusions and policy implication

The incumbent study sought to examine factors that affect energy preference and consumption intensity for various energy sources by utilizing a nationally representative energy micro-level dataset. It can be concluded that the use of the double-hurdle model vividly justifies the notion that households must pass two separate hurdles before a positive level of consumption is observed. The first hurdle corresponds to factors affecting preference for various energy sources and the second on the level of consumption. Results indicate that households’ energy consumption is skewed towards non-clean energy sources. The urban or rural location was observed as a major factor in determining household preference and consumption intensity. It was further observed that households in rural areas consume higher proportions of non-clean energy sources compared to urban households. In addition, household heads with a higher level of education tend to consume higher proportions of clean energy such as electricity, liquefied petroleum gas, and transitional fuel such as kerosene which is mainly used as a substitute. It can further be concluded that an increase in a household’s income translated to an increase in proportions of clean energy consumed and lower proportions of kerosene, charcoal and wood fuel. On the gender perspective, it was observed that electricity consumption decreased among female-headed households as compared to male headed households.

These findings are essential for deriving specific policies that can enhance consumption intensity of clean energy sources. In this regard, promotion of clean energy use should target households in rural areas, households with lower education levels, elderly household heads, and households living in semi-permanent and temporary dwelling units as well as those in the lower income segments. There is a need to encourage liquefied petroleum gas consumption especially among the urban poor and rural households by reducing the upfront cost of acquiring liquefied petroleum gas cylinders. Similarly, energy access programs should integrate the aspect of sensitizing the households on the utilization of clean energy which focuses on health, productive gains, and address misconceptions on various clean energy sources. This strategy is important especially for illiterate households whose preferences and consumption decision are based on ignorant opinions.

Abbreviations

AGECC: 

Advisory Group on Energy and Climate Change

CM: 

Conditional Moment

ERC: 

Energy Regulatory Commission

ICEBSS: 

International Conference on Entrepreneurship, Business, and Social Sciences

IEA: 

Institute of Economic Affairs

IEA: 

Intelligent Energy Europe

IEA: 

International Energy Agency

KIPPRA: 

Kenya Institute for Public Policy Research and Analysis

KNBS: 

Kenya National Bureau of Statistics

LM: 

Lagrange Multiplier

LPG: 

Liquefied Petroleum Gas

LR: 

Likelihood ratio

NASSEP: 

National Sample Survey and Evaluation Programme

OECD: 

Organization for Economic Co-operation and Development

PIEA: 

Petroleum Institute of East Africa

REP: 

Rural Electrification Programme

SDG’s: 

Sustainable Development Goals

SSA: 

Sub-Saharan Africa

WHO: 

World Health Organization

Declarations

Acknowledgements

The lead author is grateful to the Kenya Institute for Public Policy Research and Analysis for providing the data and necessary support in conceptualizing the study. Moreover, the authors are also indebted to the reviewers for their exceptional suggestions that greatly contributed to improving the article.

Funding

Not applicable

Availability of data and materials

The dataset is available from the data owner upon a reasonable request. The authors do not have the permission to share or re-distribute these data.

Authors’ contributions

CK conceptualized the study, conducted analysis, and drafted the manuscript. JG and OM contributed to the evaluation and elaboration of the manuscript. All authors revised and approved the final manuscript.

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors’ Affiliations

(1)
Kenya Institute for Public Policy Research and Analysis (KIPPRA), P.O. Box 56445-00200, Nairobi, Kenya
(2)
The National Treasury and Planning, P.O. Box 30007-00200, Harambee Avenue, Nairobi, Kenya
(3)
Ecole Doctorale: GAIA – Biodiversité, CIRAD and Montpellier SupAgro, Agriculture, Alimentation, Environnement, Terre, Eau, Montpellier, France
(4)
Department of Agroforestry and Rural Development, University of Kabianga, P.O Box 2030-20200, Kericho, Kenya

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Copyright

© The Author(s). 2019

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