Understanding Households’ Choice of Cooking
Fuels: Evidence from Urban Households
in Pakistan
Dil Bahadur Rahut, Akhter Ali, Khondoker Abdul Mottaleb,
and Jeetendra Prakash Aryal∗

Households in developing countries predominantly rely on solid fuel for
cooking, which is injurious to both the environment and human health. 这
provision of clean energy for cooking, 所以, is essential for safeguarding
the environment and human health, primarily of women and children in
developing countries. Using the 2014–2015 Pakistan Social and Living
Standards Measurement Survey and robust econometric methods, this study
analyzes different types of energy used for cooking among urban households
in Pakistan. The study shows that although urban households in Pakistan
mostly use gas for cooking, the use of solid fuels, particularly among poor
and relatively less educated households, is pervasive. The econometric findings
confirm that households with a higher level of education and wealthy families
mainly use clean energy, such as gas, and are less likely to use dirty solid fuels,
such as cake dung and crop residue for cooking. Considering the expansion
of middle-class households and anticipating their demand for clean fuel for
cooking, this study suggests ensuring an adequate supply of clean sources of
energy to meet future demand as well as augmenting the affordability and
awareness among households who are still dependent on solid fuels.

关键词: 选择, cooking fuels, 教育, 气体, 巴基斯坦, solid fuels, wealth
JEL codes: D10, I31, Q40

我. 介绍

全球范围, 关于 1.1 billion people do not have access to electricity, 关于 2.8
billion people lack access to clean cooking fuel, 和 2.5 billion people use solid
biomass for cooking purposes (国际能源署 2017). The hazardous
impacts of indoor air pollution from the use of fuelwood and other solid fuels have

∗Dil Bahadur Rahut (corresponding author): International Maize and Wheat Improvement Center (CIMMYT),
El Batan, 墨西哥. 电子邮件: d.rahut@cgiar.org, dilbhutan@gmail.com; Akhter Ali: CIMMYT, Islamabad, 巴基斯坦.
电子邮件: akhter.ali@cgiar.org; Khondoker Abdul Mottaleb: CIMMYT, El Batan, 墨西哥. 电子邮件: k.mottaleb
@cgiar.org; Jeetendra Prakash Aryal: CIMMYT, El Batan, 墨西哥. 电子邮件: jeetenaryal@gmail.com. We would like
to thank the managing editor and the anonymous referees for helpful comments and suggestions. ADB recognizes
“China” as the People’s Republic of China and “Hong Kong” as Hong Kong, 中国. The usual ADB disclaimer
applies.

Asian Development Review, 卷. 37, 不. 1, PP. 185–212
https://doi.org/10.1162/adev_a_00146

© 2020 Asian Development Bank and
Asian Development Bank Institute.
在知识共享下发布
归因 3.0 国际的 (抄送 3.0) 执照.

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186 Asian Development Review

been widely documented (Lelieveld, Haines, and Pozzer 2018; Nie, Sousa-Poza,
and Xue 2016; Rahut, Ali, and Behera 2016; 世界卫生组织 2018;
Ezzati and Kammen 2002). 而且, substantial use of fuelwood for cooking
has been significantly adding to global carbon emissions (Akpalu, Dasmani, 和
Aglobitse 2011; Duflo, 绿玉, and Hanna 2008). Given the negative impacts
of using solid fuel on both human health and the environment, it is essential
to ensure the provision of clean fuels at a reasonable price to safeguard the
环境, secure better health, and contribute to sustainable development.
Sustainable development is clearly interconnected with the quality of household
energy consumption (AGECC 2010). Lack of access to affordable, 干净的, 和
reliable energy sources is the leading cause of excessive use of solid fuels in many
developing countries, particularly in South Asia and sub-Saharan Africa (Behera
等人. 2015; Rahut et al. 2014; Rahut, Mottaleb, Ali, and Aryal 2017; Rahut, Behera,
Ali, and Marenya 2017; Rahut, Behera, and Ali 2016b, 2017; Mottaleb, Rahut, 和
Ali 2017).

The energy ladder hypothesis (Leach 1975, 1992) explains the relationship
between income or wealth and the types of energy used. The energy ladder ranks
fuel according to quality, ease of use, and price, from solid fuels such as wood
and coal at the bottom, liquid fuels such as gas and kerosene in the middle, 和
finally, electricity at the top (Leach 1992, 1975). The use of wood, cow dung, 和
crop residue is common among poor households in South Asia, while more affluent
households use electricity, 气体, and liquid petroleum gas (LPG) (Behera et al. 2015).
The energy ladder hypothesis assumes that in response to an increase in income,
families will move away from biomass and other solid fuels to more efficient fuels
such as LPG, 气体, and electricity. 然而, studies in Mexico and other places have
found that, due to energy stacking or the use of multiple energy sources, 活力
transition is a complex phenomenon (van der Kroon, Brouwer, and van Beukering
2013; Masera, Saatkamp, and Kammen 2000).

The ladder-within-a-ladder energy hypothesis explicates the transition in
electricity use from only lighting to include other uses such as cooking (Rahut,
Mottaleb, Ali, and Aryal 2017; Rahut, Behera, Ali, and Marenya 2017) 因此
can elucidate the more complex behavior in energy transition than in the simple
energy ladder hypothesis, which shows the link between energy choice and income
(Leach 1975, 1992). The central engine influencing the movement up the energy
ladder is theorized to be the level of household income and relative fuel prices
(巴恩斯, Krutilla, and Hyde 2010; Leach 1992; Barnes and Floor 1999; Rahut et al.
2014). Together with the expenditure on energy, the category of energy employed
varies with income (Rao and Reddy 2007), with a shift to contemporary clean fuels
(Daioglou, van Ruijven, and van Vuuren 2012), 尤其, the use of electricity
increases with income (丘陵 1994). Poor households are inclined to use solid fuels
for domestic purposes, which are harmful to the environment and human health
(Rehfuess, Mehta, and Prüss-Üstün 2006; 布鲁斯, Perez-Padilla, and Albalak 2000;

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Understanding Households’ Choice of Cooking Fuels in Pakistan 187

Holdren et al. 2000), but when their income rises, households usually, 但不是
always, shift to cleaner fuels (Masera, Saatkamp, and Kammen 2000; Nansaior et al.
2011).

The energy ladder and the factors influencing a household’s decision to
switch to cleaner fuels when household income increases has been documented
in many developing countries (Özcan, Gülay, and Üçdo˘gruk 2013; Karimu 2015;
Rahut, Behera, Ali, and Marenya 2017; Mottaleb, Rahut, and Ali 2017; Hou
等人. 2017; Rahut, Ali, and Mottaleb 2017). 而且, family demographic
特征, 习惯, and gender of the household head play important roles in a
household’s choice of fuel for cooking. 因此, ascertaining the relative significance
of the above factors that affect a household’s fuel choice for cooking is critical for
policy making in the context of Pakistan.

With a population of 197 million and an annual gross domestic product
(GDP) per capita of $1,547 在 2017, Pakistan is one of the most urbanized countries in South Asia, 在哪里 38.3% of the total population resides in urban areas (World Bank 2019). 重要的, it is projected that by 2050, the population of Pakistan will be between 276 million and 344 百万 (depending on the assumption of the fertility rate), 其中 52.2% will be residing in urban areas (UN DESA 2015). With this background, it is useful to understand the fuel choice behavior of urban households, which is an underresearched subject in Pakistan. The commonly available fuel choices among urban households are gas, LPG, fuelwood, and animal dung. 然而, a small number of urban households also use kerosene oil and electricity as the primary source of cooking fuel. Planned urban areas mostly use gas, whereas slum areas mostly use fuelwood and gas. Most urban households use gas, which is connected through pipes, and a small number use LPG. The dataset used for this study does not distinguish between gas and LPG. Irrespective of whether it is gas or LPG, both are assumed to be clean sources of energy from the user’s perspective. This study contributes to the existing research in several ways. 第一的, it uses a large, nationally representative dataset from Pakistan from over 13,965 households in urban areas. The data represent all four major provinces of Pakistan—Balochistan, Khyber Pakhtunkhwa, Punjab, and Sindh. 第二, 据我们所知, this is the first attempt to study the patterns and determinants of cooking fuel use of urban households in Pakistan. 第三, ordered probit econometrics are used to analyze the factors determining the household’s choice of fuel. 第四, the study uses different indicators of wealth and education to confirm the effect of these factors on fuel choice. 最后, as the dataset encompasses a large number of variables, the study enabled us to conduct several robustness tests on the role of household education at different levels and wealth on fuel choice behavior. The rest of the paper is organized as follows. Section II provides a review of relevant literature, while section III contains a background of Pakistan with a focus on energy use, population growth, and level of income growth between 1990 and l D o w n o a d e d f r o m h t t p : / / 直接的 . 米特 . / e d u a d e v / 文章 – p d l f / / / / 3 7 1 1 8 5 1 8 4 6 7 9 5 a d e v _ a _ 0 0 1 4 6 压力 / . 来宾来访 0 8 九月 2 0 2 3 188 Asian Development Review 2014. Section IV explains the sampling methods, 数据, and methodology applied in the study. Section V presents the findings of the study with some discussions of the results. Section VI concludes the study and includes some policy implications and recommendations. 二. Literature Review In the past, a number of studies have focused on household fuel use behavior. Some studies in South Asia have found that household income or poverty, wealth, family size and composition, and gender influence the households’ choice of energy (Yasmin and Grundmann 2019; Nasir, Murtaza, and Colbeck 2015; Aryal et al. 2019; Mottaleb, Rahut, and Ali 2017; Rao and Reddy 2007; Khandker, 巴恩斯, and Samad 2012). Since clean fuels are more convenient to use, households obtain more utility from their use compared to dirty fuels, and thus they are more willing to pay for clean fuel. 因此, households with more wealth are more likely to shift to clean fuel. As income increases, households in India tend to change to clean sources of fuel instead of using solid fuel such as fuelwood, dung cake, and crop residue (Rao and Reddy 2007). The demand for energy increases with an increase in total per capita consumption in India (Pachauri et al. 2004). Education plays an important role in the use of clean energy for cooking purposes in mainly two ways: (我) human capital increases earnings, which leads to increases in purchasing power and the value of time; 和 (二) education increases awareness, which in turn affects preferences. Households whose head of the family has a higher level of education are more reliant on clean and efficient fuel (Rao and Reddy 2007). The number of educated female members between 10 和 50 years old is positively associated with the choice of clean fuel (Pandey and Chaubal 2011). The education of household heads and their spouses positively influences the use of modern and clean fuel, as these provide notable savings of time which could be used for leisure and other productive employment (Reddy and Srinivas 2009). The level of education is positively related with the use of clean and modern fuel and negatively associated with the use of solid fuel (Rahut, Behera, and Ali 2017; Rahut, Ali, and Mottaleb 2017; Mottaleb, Rahut, and Ali 2017; Heltberg 2004). A study in Bolivia found that the education and income of female household members led to a reduction in the use of fuelwood and other solid fuels (以色列 2002) because education increases awareness about the benefits and costs of using clean fuels. 在发展中国家, female members are mostly involved in fuelwood collection and cooking (Heltberg, Arndt, and Sekhar 2000), hence when females take the decision-making role in the household, the inclination to use clean and modern fuel increases. 反过来, a global study found that financial reward drives the collection of fuelwood rather than the cultural norm that women collect fuelwood (库克, Köhlin, and Hyde 2008). Family size also matters. With more family members, the labor available for collecting fuelwood increases and energy l D o w n o a d e d f r o m h t t p : / / 直接的 . 米特 . / e d u a d e v / 文章 – p d l f / / / / 3 7 1 1 8 5 1 8 4 6 7 9 5 a d e v _ a _ 0 0 1 4 6 压力 / . 来宾来访 0 8 九月 2 0 2 3 Understanding Households’ Choice of Cooking Fuels in Pakistan 189 needs also increase, such that a negative relationship was noted between household size and use of clean cooking fuel (Pandey and Chaubal 2011). Against the backdrop of limited studies on household fuel use behavior in urban Pakistan, and the adverse health impact from the use of solid fuel, this study empirically explores the factors that actually drive the choice of cooking fuel among urban households in Pakistan. increasing urbanization, 三、. Background Pakistan is a developing South Asian nation surrounded by the Arabian Sea in the south, Afghanistan and Iran in the west, the People’s Republic of China in the north, and India in the east. The country is divided into four major provinces: Balochistan, Khyber Pakhtunkhwa, Punjab, and Sindh. GDP at current prices grew sixfold from $40 十亿 1990 到 $244.4 十亿 2014. During the same period, GDP per capita has increased from $371.6 到
$1,317 (桌子 1). The population of Pakistan increased from 107.7 百万 1990 到 185.5 百万 2014 with an annual growth rate of over 2.1%. Data show the gradual urbanization of Pakistan: the urban population gradually increased from 30.6% 在 1990 到 38.3% 在 2014, while the rural population declined from 69.4% 到 61.7%. During the last 2.5 几十年, energy use (kilograms of oil equivalent per capita) increased by only 22%, while electric power consumption (kilowatt- hours per capita) increased by about 70%. The percentage of urban population with access to electricity increased significantly from 94.4% 在 1995 到 100% 在 2014, while the percentage of the rural population with access to electricity increased from 54.4% 到 95.6%. The percentage of the population with access to clean cooking fuels and technologies rose from 23.8% 在 2000 到 44.8% 在 2014. Harnessing existing renewable energy sources is of paramount importance as Pakistan faces a severe shortage of energy (Chaudhry, Raza, and Hayat 2009). Limited fossil fuel resources and a poor economy, which limits the purchase of fossil fuels in large quantities from abroad, are the leading causes of undersupply and the lack of access to clean sources of energy for Pakistani households. Biomass is a primary source of energy, and it provides 36% of the total energy supply (Asif 2009). 因此, it is essential for Pakistan to stimulate investment in readily available home-grown sources of clean energy such as solar, hydropower, and wind. 在 2008, the gap between supply and demand of electricity in Pakistan stood at 4,500 megawatts (Asif 2009). Pakistan produces much less than its actual potential in the hydropower sector, having the potential for more than 42 gigawatts, out of which only 6.5 gigawatts have been utilized. The annual radiant flux (力量) received by a surface per unit area in Pakistan is 1,900 到 2,200 kilowatt-hours per square meter, making it one of the highest solar insolation in the world, which can be exploited to generate electricity and supply to off-grid households in Pakistan. l 从http下载 : / / 直接的 . 米特 . / e d u a d e v / 文章 – p d l f / / / / 3 7 1 1 8 5 1 8 4 6 7 9 5 a d e v _ a _ 0 0 1 4 6 压力 / . 来宾来访 0 8 九月 2 0 2 3 190 Asian Development Review Indicator 1990 1995 2000 2005 2010 2014 桌子 1. Electricity Access Rate Economy GDP (current $ 百万)
GDP per capita (current $)
GDP growth (annual %)

40.0
371.6
4.5

60.6
493.7
5.0

74.0
533.9
4.3

109.5
711.5
7.7

177.4
1,040.1
1.6

244.4
1,317.0
4.7

Population

Total population (百万)
Urban population (% of total)
Rural population (% of total)
Population growth (annual %)
Urban population growth (annual %)
Rural population growth (annual %)

107.7
30.6
69.4
2.9
3.7
2.6

122.8
31.8
68.2
2.5
3.3
2.2

138.5
33.2
66.8
2.3
3.1
1.9

153.9
34.7
65.3
2.1
3.0
1.5

170.6
36.6
63.4
2.1
3.2
1.5

185.5
38.3
61.7
2.1
3.3
1.4

Energy use per capita (kilograms of oil

398.4

435.9

462.5

497.1

498.5

484.4

Energy use

equivalent)

Electric power consumption
(kilowatt-hours per capita)

277.4

357.9

372.4

463.1

465.2

471.0

Access to electricity

Access to electricity (% of population)
Access to electricity, 城市的 (% of urban

58.7
……

67.1
94.4

75.2
95.5

83.9
96.4

人口)

Access to electricity, rural (% of rural

43.3

54.4

65.1

74.3

人口)

Access to clean fuels and equipment for

……

……

23.8

31.5

cooking (% of population)

91.0
98.6

86.6

38.9

97.5
100.0

95.6

44.8

GDP = gross domestic product.
来源: 联合国. http://data.un.org; World Bank. https://data.worldbank.org/indicator (both accessed June
2018).

Besides the generation of electricity, solar energy has numerous direct applications
like water heaters and stoves (Mirza, Maroto-Valer, and Ahmad 2003).

Wind power is also an important source of clean and renewable energy.
Pakistan has enormous potential to harness wind energy: the coastline in the
south stretches more than 1,000 kilometers, and some mountainous regions in the
north are gifted with an excellent source of wind energy, which can be harnessed
to generate electricity. A study in Pakistan found that enormous potential for
harnessing wind energy exists, but considerable efforts are required to successfully
utilize this cheap, 干净的, and renewable energy source (Mirza et al. 2007).

桌子 2 provides summary statistics on energy production and consumption
by major sources during the last 2.5 几十年. Total kerosene consumption declined
从 1,132 thousand metric tons (公吨) 在 1990 到 182 thousand MT in 2014, 尽管
the consumption of fuelwood increased from 20,701 thousand cubic meters (立方米) 在
1990 到 29,053 thousand m3 in 2014, which is entirely consumed by households.
The consumption of charcoal does not show any particular trend, but the total

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Understanding Households’ Choice of Cooking Fuels in Pakistan 191

桌子 2. Energy and Fuels in Pakistan

1990

1995

2000

2005

2010

2014

Other kerosene (thousand metric tons)

Production
Consumption by households
Final consumption

411
1,117
1,132

463
585
603

286
461
494

209
129
236

122
85
162

182
103
182

Production
Consumption by households
Final consumption

21,043
20,701
20,701

22,683
22,023
22,023

30,880
30,550
30,550

26,500
26,116
26,116

29,660
29,226
29,226

29,533
29,053
29,053

Fuelwood (thousand cubic meters)

Production
Consumption by households
Final consumption

Charcoal (thousand cubic meters)

57
57
57

110
110
110

55
55
55

64
64
64

72
72
72

Liquefied petroleum gas (thousand cubic meters)

Production
Consumption by households
Final consumption

127
95
127

128
128
171

206
179
239

558
385
578

432
259
465

80
80
80

469
245
562

Gross production

Combustible fuels
Hydro
核
风

Net production

Imports
Own use by electricity, heat,

and CHP plants

Electricity (million kilowatt-hours)

37,660
20,442
16,925
293

53,555
30,186
22,858
511

65,760
46,064
19,288
399

36,577

51,811

63,120

1,083

1,744

2,640

93,629
60,283
30,862
2,484

89,963
146
3,666

94,383
59,152
31,811
3,420

92,144
269
2,239

105,305
68,390
31,428
5,090
397
101,504
428
3,801

Losses

7,808

12,191

17,553

22,506

15,315

18,333

CHP = combined heat and power.
来源: 联合国. http://data.un.org (六月访问 2018).

charcoal consumption has increased from 57 thousand MT to 80 thousand MT,
and households consume 100% 其中. The production and consumption of gas have
grown considerably during the last 30 年. Gross electricity production increased
2.8 times from 37,660 million kilowatt-hours (千瓦时) 在 1990 到 105,305 百万
kWh in 2014. The share of hydropower in electricity generation has gradually
declined and currently stands at 30%.

一般来说,

是
in developing countries like Pakistan,
completely controlled and managed by the government. This means that prices of
fuel are not derived from the interaction of supply and demand but are fixed by
the government. A study in Pakistan found that fuel consumption is highly price
inelastic (Burney and Akhtar 1990). 因此, we do not see much variation in fuel
prices across the region. 此外, the datasets used for our current research do

the fuel market

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192 Asian Development Review

桌子 3. Fuel Prices

2014

2015

2016

2017

2018

来源

Fuelwood (PRs/kg)
Kerosene (PRs/liter)
Electricity (PRs/unit)
LPG (PRs/kg)

32
57
15
130

35
56
16
133

34
48
16
125

38
66
17
118

37
85.3
18
112

Different sources
OGRA
NEPRA and other sources
OGRA

kg = kilogram, LPG = liquid petroleum gas, NEPRA = National Electric Power Regulatory Authority,
OGRA = Oil and Gas Regulatory Authority, PRs = Pakistan rupees.
来源: National Electric Power Regulatory Authority; Oil and Gas Regulatory Authority.

not have information on prices of fuel; 因此, we do not use the price variable in the
模型. As fuelwood is not sold in an established market, it is difficult to determine
its price (库克, Köhlin, and Hyde 2008).

Secondary data in Table 3 show that the price of fuelwood was around
37 Pakistan rupees (PRs) per kilogram (kg) 在 2018, and during the last 5 年
(之间 2014 和 2018), it ranged from PRs32 to PRs38 with a steady increasing
趋势. The price of kerosene has increased sharply over the years to PRs85 in 2018.
Kerosene use declines with increases in income and education of households and
household heads (Rahut, Behera, and Ali 2016a; Rahut et al. 2014). The use of
kerosene in Pakistan has been declining over the years. 例如, 在 1986, daily
use of kerosene was 19 thousand barrels but has declined to 13 thousand barrels in
1996 和 3.5 thousand barrels in 2012 (IndexMundi 2018). This is mainly due to
the availability of LPG and an increase in the price of kerosene. 在过去, 最多
urban households did not have access to gas. But over the years, access to gas has
been increasing, hence replacing the use of kerosene oil in cities. The unit price of
electricity was PRs18 in 2018 and has remained relatively constant during the last 5
年. The price of LPG has marginally declined over the years. A kilogram of LPG
was about PRs112 in 2018.

In urban areas, 86% of fuelwood are purchased, whereas in rural areas, 仅有的
31% are purchased while the rest are mostly collected by women and children
(世界卫生组织 2005). 因此, there is a little scope to discuss the
role of women and children in collecting fuelwood in urban areas of Pakistan,
and the datasets do not provide information on fuelwood collection (World Health
组织 2005).

The energy crisis continues in Pakistan, and the availability of energy both
for lighting and cooking is a serious issue. Although the majority of the urban
人口, especially in major cities, use gas for cooking, the availability of gas
always remains an issue. 因此, people in urban and semi-urban areas turn to
alternative energy sources like fuelwood and even animal dung and crop residue.
因此, this study explores the determinants of energy sources for cooking in urban
areas of Pakistan using a large nationally representative dataset and provides some
important policy implications.

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Understanding Households’ Choice of Cooking Fuels in Pakistan 193

桌子 4. Sample Distribution

Fixed for the Survey
2014–2015

Covered during the Survey
2014–2015

Urban

Rural

全部的

Urban

Rural

全部的

Primary sampling units

Balochistana
Khyber Pakhtunkhwa
Punjab
Sindh
全部的

140
104
594
376
1,214

670
777
1,860
907
4,214

810
881
2,454
1,283
5,428

137
104
594
375
1,210

591
764
1,860
901
4,116

728
868
2,454
1,276
5,326

Secondary sampling units—Households

Balochistana
Khyber Pakhtunkhwa
Punjab
Sindh
全部的

1,680
1,248
7,128
4,512
14,568

10,720
12,432
29,760
14,512
67,424

12,400
13,680
36,888
19,024
81,992

1,568
1,184
6,814
4,399
13,965

9,248
11,898
29,188
14,336
64,670

10,816
13,082
36,002
18,735
78,635

aIncludes Islamabad and used as a base category.
来源: Pakistan Social and Living Standards Measurement Survey.

IV. Sampling, 数据, and Methodology

A.

Sampling and Data

This study uses the 2014–2015 Pakistan Social and Living Standards
Measurement Survey to analyze the sources of energy for cooking fuel and their
determinants. The current study is of great value as it is based on a nationally
representative survey collected from all the four major provinces of Pakistan—
Balochistan, Khyber Pakhtunkhwa, Punjab, and Sindh. The Pakistan Bureau of
Statistics designed the sampling frame for both urban and rural areas. Every city
and town is split into enumeration blocks comprising of 200–250 households. 为了
rural areas, an updated list of villages (called mouzas or dehs) or their parts (blocks)
based on the 2011 house listings is taken as a sampling frame. A two-stage stratified
sampling methodology was used to sample households for the survey. Enumeration
blocks were designated as primary sampling units (PSUs) for urban and rural
域. PSUs were sampled using the proportion-to-size (PPS) 方法. 这
number of households in the enumeration blocks was taken as a measure of size
for both rural and urban areas.

Households listed in the sampled PSUs were taken as the secondary
sampling units (SSUs), 以及关于 12 households from each urban sample SSU
和 16 households from the sample PSUs were chosen with equal probability
using a systematic random sampling method. 因此 5,428 sample blocks (PSUs)
comprising 81,992 households (SSUs) were selected for the survey (桌子 4).
Among the households selected for the survey, 67,424 were from rural areas and
14,568 were from urban areas of Pakistan.

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194 Asian Development Review

Owing to challenging circumstances in the Panjgur district of Balochistan,
it was not included in the final survey. 此外, 7 PSUs from Sindh, 13 PSUs
from Khyber Pakhtunkhwa, 和 82 PSUs from Balochistan were excluded from the
final survey due to the security situation. From the selected 81,992 households, 仅有的
78,635 households (13,965 urban households and 64,670 rural households) 是
actually covered during the survey. As the number of households using kerosene
oil and electricity were very small, we could not use this data for the analysis.
因此, we dropped 16 households who were using kerosene oil and two households
who were using electricity, 这样 13,947 sampled households were used for the
学习. We did not encounter the problem of missing observations, which could have
been for three reasons. 第一的, the variables used in the analysis were mostly from
the household roster and categorical variables, which are easy for the enumerators
捕捉. 第二, the enumerators might have been trained well to capture all
the information. 第三, the survey instrument was only 13 页面, capturing mostly
simple and categorical information, which are easy to capture and review to follow
up for missing variables.

乙.

Econometric Model Specifications: Ordered Probit

An ordered probit model is employed when the dependent variable has more
than two outcomes and an ordinal scale. In this study, the explained variable is
a family’s ranking based on the types of fuel used for cooking, 那是, 一个家庭
that uses dung cake and crop residue as cooking fuels were graded at the lowest
level followed by fuelwood; households that use gas were ranked at the highest
等级. The model is estimated using the maximum likelihood method as it cannot be
consistently estimated using ordinary least squares due to the ordered nature of the
explanatory variable.

Suppose the underlying association to be considered is

y∗ = X (西德:3)β + εi
where y∗ is the exact but unobserved explained variable, X (西德:3) is the vector of
explanatory variables, and β is the vector of regression coefficients, 我们
wish to approximate. While we cannot observe y∗, we instead can only observe
the following response categories:
⎧

0

1

⎪⎪⎪⎪⎪⎪⎨
⎪⎪⎪⎪⎪⎪⎩

if y∗ ≤ 0,
如果 0 < y∗ ≤ μ1 if μ1 < y∗ ≤ μ2 2 ... N if μN−1 < y∗ l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 7 1 1 8 5 1 8 4 6 7 9 5 a d e v _ a _ 0 0 1 4 6 p d . / f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 Understanding Households’ Choice of Cooking Fuels in Pakistan 195 Then the ordered probit method uses the observations on y, which is a form of censored data on y∗, to fit the parameter vector β. The variable y∗ is a dependent variable which takes the value of 1, 2, and 3 depending on the ranking, which is described as y∗ = 1, if a household uses dung and crop residue as fuel for cooking; y∗ = 2, if a household uses fuelwood as fuel for cooking; and y∗ = 3, if a household uses gas as fuel for cooking. In the equation, X (cid:3) is a vector of explanatory variables that include the following: (i) Demographic (a) Age of household head in years (b) Square of the age of household head in years to take nonlinearity into account (coefficient of age squared is multiplied by 1,000 to present the results in four decimal places) (c) A gender or sex dummy that assumes a value of 1 if the household head is a female and 0 otherwise (d) Household size measured by the number of family members (in addition, in an alternate specification of the model, we used number of children [<=15 years], number of adults [>15 years and <65 years], number of adult males and females [>15 years and <65 years], and number of elderly members [>=65 years])

(二) 教育

(A) Below primary: a dummy that assumes a value of 1 if the household head
is educated up to less than the primary level (年级 5
和 =grade 8 和 =grade 10 和 =grade 12 but not completed university) 或者 0 否则
(F) University completed: a dummy that assumes a value of 1 如果
household head has completed university-level education or 0 否则
Illiterate dummy that assumes a value of 1 if the household head has not
attended school or 0 否则 (base category)

(G)

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196 Asian Development Review

(H)

In an alternate specification of the model, we used the average and
maximum years of schooling of adults, adult males, and adult females;
education of elderly members; and years of schooling of the household
head and spouse. Households who do not have members in these groups
(adults, adult males, adult females) were treated as missing observations
instead of given a value of 0. 因此, the sample size in this alternate
specification is lower.

(三、) 财富

(A) We used PCA to compute a household assets index based on durable
assets (flat iron, fan, sewing machine, 收音机, clock, 电视, 视频
compact disc player, refrigerator, air cooler, air conditioner, 电脑,
bicycle, motorcycle, car, truck, washing machine, microwave oven, 和
发电机) owned by the household.
In an alternative specification, we used roof and wall materials of the
house and type of toilet as dependent variables.
• Cement and tin roof: a dummy that assumes a value of 1 if the roof of

(乙)

a house is made of cement, concrete, or tin, 或者 0 否则

• Toilet type: a dummy that assumes a value of 1 if no toilet or 0
否则; a dummy that assumes a value of 1 if there is a pit toilet or
0 否则; and a dummy that assumes a value of 1 if there is a flush
toilet or 0 否则

(C) Agricultural land owned by the family (hectares)
(d) Value of nonagricultural land owned by the family (million PRs)
(e) Livestock assets: tropical livestock unit, which is based on weights of

0.7 for cattle, 0.2 for goats and sheep, 和 0.01 for poultry

(F) Distance from household to high school (之内 30 minutes from home)
(G) Location

• A dummy that assumes a value of 1 if the household is located in

Khyber Pakhtunkhwa or 0 否则

• A dummy that assumes a value of 1 if the household is located in

Punjab or 0 否则

• A dummy that assumes a value of 1 if the household is located in

Sindh or 0 否则

• A dummy that assumes a value of 1 if the household is located in

either Balochistan or Islamabad or 0 否则 (base category)

Note that in reality, rural households, 尤其, use more than one source
of energy for cooking. 例如, mostly in the dry season, households in rural
areas use biomass such as leaves and branches for cooking, whereas in the wet
and rainy season, they mostly rely on fuelwood and a kerosene stove or cooker. 在
一般的, in urban areas, the major sources of cooking fuel are electricity, LPG or

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Understanding Households’ Choice of Cooking Fuels in Pakistan 197

气体, and fuelwood. Due to the existence of fuel stacking, especially with cooking
fuel in rural areas in developing countries (Masera, Saatkamp, and Kammen 2000;
van der Kroon, Brouwer, and van Beukering 2013), it would be interesting to
examine the choice of multiple energy sources for cooking. Another study in India
found that fuel stacking has decreased in lighting over time (Cheng and Urpelainen
2014). Fuel stacking is comparatively lower in urban areas. 很遗憾, 这
survey only captured information on major fuels used for cooking and essentially
failed to capture the fuel-stacking behavior and the reliance on multiple sources of
活力. 因此, this study cannot analyze a multivariate choice model that considers
households’ fuel-stacking behavior.

V. Findings and Discussions

A.

Descriptive Findings

桌子 5 provides descriptive statistics of the variables used in the empirical
分析. The mean age of the household head was 45 年, with little variation
across different groups using gas, fuelwood, 粪, and crop residue. 信息
about the household head’s gender shows that females headed about 7% of the urban
households. The average urban household size was about six, with variations across
households that use different types of fuel. Very few family members were older
比 65 years in the household, 那是, 仅有的 0.2 一般. The number of children
in a household was 2.6 一般, and the number of adults in the household was
3.6, with an almost equal proportion of adult males and females.

The average years of schooling of the urban household head was about 6.8,
and it was highest for households using gas (7.7 年), while it was 4.5 years for
households using fuelwood and 4 years for those using dung cake and crop residue.
The overall education level of the spouse of the household head was about 4.2 年,
和 5.2 years for households using gas, 1.8 for households using fuelwood, 和
1.7 for those using dung cake and crop residue. For urban households, 平均值
education levels of both male and female adults was about 6.6 年, 与
average level of education of adult males (7.7 年) slightly higher than females
(5.4 年). The highest level of school attained by an adult member of the family
was approximately 9.8 years on average. The maximum education of adults was
higher for households using gas (10.9 年) than households using fuelwood (7.2
年) and dung cake and crop residue (6 年), and it was higher for males
compared to females. The maximum education of the oldest members is higher
for those households using gas (6.4 年) 相比 3.5 years for households
using fuelwood and 2.8 years for households using cow dung and crop residue. 这
average years of schooling of adult males was higher than for adult females for all
age categories, and the differences were statistically significant (see Appendix).

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198 Asian Development Review

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200 Asian Development Review

In urban areas, 关于 30% of the household heads did not go to school, 4%
attended some primary school, 15% completed primary education, 11% completed
middle school, 17% completed secondary school, 9% completed senior secondary
学校, 和 14% completed university. 关于 50% of the households using dung
cake and crop residue did not attend school, 47% of the households using fuelwood
for cooking did not go to school, 但仅 24% of the households using gas did not
attend school.

Using PCA, we constructed a wealth index for the urban households and
found that the wealth index was much higher for households using gas than
households using fuelwood, dung cake, and crop residue for cooking. The average
agricultural land owned (hectares) and livestock assets (total livestock unit) 是
higher for households using dung cake and crop residue compared to other
households using gas, while the value of owned nonagricultural land was higher for
households using gas than households using fuelwood, dung cake, and crop residue
for cooking.

Only about 1% of the households did not have a toilet, 5% of households
had a pit latrine, 和 94% had a latrine with a flush. 关于 98% of households with
flush toilets use gas for cooking compared to 83% for those using fuelwood and
82% for those using dung cake and crop residue. 关于 91% of the households had
a brick-walled house, 和 38% had a cement and metal roof. Descriptive statistics
show that households with brick walls and cement and metal roofs are more likely
to use gas for cooking.

关于 98% of the households were less than 30 minutes from a high school,
and for all the surveyed households, the distance to a store was less than 30 minutes.
The provincewide distribution of the sample indicates that 51% of the respondents
were from Punjab, 8% from Khyber Pakhtunkhwa, 32% from Sindh, 和 9% 从
Balochistan.

乙.

Econometric Findings

1.

Results from the Ordered Probit Model Estimation

We estimated the ordered probit econometric model to study the factors that
determine a household’s choice of cooking fuel. We organized and ordered the
households into different categories based on the fuel used for cooking, with dung
and crop residue at the base of the energy ladder followed by fuelwood and then gas
at the top. 桌子 6 summarizes the marginal effects of the results obtained from the
ordered probit model.

The results show that households with older heads are more likely to adopt
gas and unlikely to use fuelwood, 粪, and crop residue as sources of fuel for
cooking. The elderly need more convenient sources of fuel as they do not have
the strength to travel longer distances to collect fuelwood. Unlike other studies, 这

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Understanding Households’ Choice of Cooking Fuels in Pakistan 201

桌子 6. Determinants of Choice of Energy Sources for Cooking: Ordered Probit
模型 (marginal effects)

Dung and
Crop Residue

Fuelwood

Gas

Demographic

Age of household head

Age squared (multiplied by 1,000)A

Gender of the head (女性)乙,C

Household size

Human capital and education

Below primaryb,d

Primary completedb,d

Middle school completedb,d

Secondary school completed b,d

Senior secondary school completedb,d

University completedb,d

Wealth and assets
Assets index

Agricultural land owned (hectares)

Value of nonagricultural land owned

(million Pakistan rupees)

Livestock assets

Access to facilities

Distance to high school (之内 30 minutes)乙,e

Location: province

Khyber Pakhtunkhwab,F

Punjabb,F

Sindhb,F

Number of observations
Wald chi-squared test (18)
Prob > chi-squared
Pseudo R-squared
Log pseudolikelihood

−0.0006**
(0.0003)
0.0043
(0.0026)
−0.0020
(0.0022)
0.0010***
(0.0003)

−0.0023
(0.0024)
−0.0070***
(0.0019)
−0.0097***
(0.0025)
−0.0109***
(0.0026)
−0.0089***
(0.0027)
−0.0139***
(0.0035)

−0.0052***
(0.0010)
0.0001
(0.0001)

−0.0020***
(0.0007)
0.0015***
(0.0005)

0.0049***
(0.0018)
−0.0342*
(0.0188)
0.0166
(0.0178)

−0.0043***
(0.0016)
0.0299*
(0.0163)
−0.0146
(0.0157)
0.0068*** −0.0078***
(0.0019)

(0.0022)

−0.0170
(0.0181)
−0.0541***
(0.0104)
−0.0813***
(0.0119)
−0.0891***
(0.0127)
−0.0745***
(0.0166)
−0.1240***
(0.0168)

−0.0363***
(0.0029)
0.0006
(0.0004)

0.0193
(0.0204)
0.0611***
(0.0115)
0.0910***
(0.0132)
0.1000***
(0.0138)
0.0834***
(0.0184)
0.1380***
(0.0183)

0.0416***
(0.0027)
−0.0006
(0.0005)

0.0156***
(0.0045)

−0.0136***
(0.0040)
0.0107*** −0.0123***
(0.0023)

(0.0026)

−0.0174*
(0.0092)

−0.0914**
(0.0393)

0.1090**
(0.0479)

−0.0129***
(0.0044)
−0.0219**
(0.0095)

−0.0236***
(0.0066)
13,947
825
0.000
0.149
−8,263

−0.1190**
(0.0469)
−0.1430***
(0.0547)

0.1320***
(0.0501)
0.1650***
(0.0631)

−0.1840***
(0.0492)

0.2070***
(0.0537)

Continued.

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202 Asian Development Review

桌子 6. Continued.

Notes: *** = 1% level of significance, ** = 5% level of significance, 和 * = 10% level of significance. Robust
standard errors in parentheses.
aCoefficient multiplied by 1,000 to represent results within four digits after the decimal.
bDummy variables
cExcluded category: male household head
dExcluded category: household head without any formal education
eExcluded category: households more than 30 minutes away from high schools
fExcluded category: Balochistan
来源: 作者的计算.

current study does not show a significant influence of a female head in choosing
clean energy. The number of members of a household is found to be positive
and significantly associated with a household’s use of dung and crop residue and
fuelwood at the 1% level of significance, while it is negative and significant for a
household’s use of gas at the 1% 等级. Households with larger families are likely
to have an abundant supply of labor to collect fuelwood and prepare dung cake, 和
the amount of energy needed for large households is quite high so it may be too
expensive to purchase and use gas.

The probability of using gas increases with the level of education, 尽管
the likelihood of using dirty fuel decreases progressively as the level of education
增加. This finding once again endorses the distinctive role of human capital in
the household’s decision to select clean fuel versus dirty fuel for cooking. The role
of education in adopting clean energy emerges as one of the critical variables in
this study and several previous studies (Rahut, Behera, and Ali 2016a and 2016b;
Ekholm et al. 2010; Heltberg 2004; Hosier and Dowd 1987; Hou et al. 2017). 这
importance of education in clean energy use arises from the fact that the opportunity
cost for educated people of collecting fuelwood and making dung cake is high.
而且, education brings about awareness of the adverse health impacts of using
dirty fuel.

The coefficient of the household wealth index is positive and significant (在
这 1% 等级) for gas, while it is negative and significant (在 1% 等级) 为了
use of dung, crop residue, and fuelwood. A positive link with gas and a negative
relationship with dirty fuels signify that richer households are more likely to use
cleaner fuels such as gas and are less likely to use solid fuels such as dung, crop
residue, and fuelwood. Wealth and income of households determine the household
capacity to pay for modern fuel, which is expensive, while fuelwood, crop residue,
and dung could be collected by using surplus labor for free. The positive association
of income and wealth on the adoption and use of clean energy is clearly pointed out
by a number of previous studies (Bisu, Kuhe, and Iortyer 2016; 黄 2015; Joshi
and Bohara 2017; Karimu 2015; Kwakwa, Wiafe, and Alhassan 2013; Makonese,
Ifegbesan, and Rampedi 2017; Ouedraogo 2006; Pachauri and Jiang 2008; Pandey
and Chaubal 2011; Rao and Reddy 2007; Reddy and Srinivas 2009; Rahut, Behera,

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Understanding Households’ Choice of Cooking Fuels in Pakistan 203

and Ali 2016a). The agricultural land owned does not have an impact on the
choice of cooking energy used, while the value of nonagricultural landed owned
is positively associated with the use of gas and negatively related to the use of
粪, crop residue, and fuelwood. Livestock assets are positively and significantly
associated with dung, crop residue, and fuelwood, while negatively related to gas,
which is obvious as those households with more livestock are likely to produce dung
and use dung cake.

The coefficient of the dummy for the distance from home to a high school
(a dummy value of 1 if a high school is within 30 minutes from the household;
0 否则) is positive and significant (在 1% 等级) for the use of gas as
cooking fuel, while it is negative and significant (在 1% 等级) for the use
of solid fuels such as dung, crop residue, and fuelwood. The distance to a high
学校 (可达性) has a divergent influence on a household’s choice of clean
and dirty fuels: households with better access are more likely to use clean fuels,
while households with poor access to facilities are more likely to use dirty fuels.
The closeness to the market and facilities means easy access and an adequate supply
of clean energy, which is critical for the household to use clean fuels. This finding
supports results from other studies such as those by Rahut et al. (2014); Rahut,
Mottaleb, and Ali (2017); Karimu (2015); and Mensah, Marbuah, and Amoah
(2016).

To control for spatial heterogeneity, we included a provincial dummy variable
and found that, compared to urban households in the province of Balochistan,
households in Khyber Pakhtunkhwa, Punjab, and Sindh are more likely to use gas
for cooking and less likely to use dung, crop residue, and fuelwood.

2.

Robustness Tests

桌子 7 shows the results of the ordered probit model, which was reestimated
by keeping other independent and dependent items the same and replacing a few key
variables such as education and wealth.1 Following recent literature which suggests
that research on energy switching behaviors should go beyond income (van der
Kroon, Brouwer, and van Beukering 2013), we have used different specifications
of education in this section to explain the relationship between education and
household energy choice.

In specification 1 of Table 7, the level of education of household heads
was replaced by the maximum years of schooling of the adult members in the
家庭. The coefficient of this variable is positive and significant (在 1%
等级) for households using gas, while it is negative and significant (在 1%
等级) for households using solid fuels (fuelwood, dung cake, and crop residue).
In specification 2, the level of schooling of the head of the family was substituted

1A complete and detailed model is available upon request.

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204 Asian Development Review

桌子 7. Factors Influencing Choice of Energy Sources for Cooking:
Ordered Probit Model for Robustness Checks (marginal effects)

Dung and
Crop Residue

Specification 1

Fuelwood

Gas

Maximum education of adults

−0.0015***
(0.0004)

−0.0106***
(0.0013)

0.0121***
(0.0015)

Specification 2

Maximum education of adult males

Maximum education of adult females

−0.0005**
(0.0002)
−0.0015***
(0.0004)

−0.0038***
(0.0012)
−0.0111***
(0.0010)

0.0043***
(0.0014)
0.0126***
(0.0011)

Mean education of adults

Mean education of adult males

Mean education of adult females

Specification 3

−0.0024***
(0.0006)

−0.0181***
(0.0018)

0.0206***
(0.0021)

Specification 4

−0.0005**
(0.0002)
−0.0019***
(0.0004)

−0.0040***
(0.0013)
−0.0146***
(0.0012)

0.0045***
(0.0015)
0.0164***
(0.0014)

Specification 5

Years of schooling of household head

−0.0012***
(0.0003)

−0.0085***
(0.0012)

0.0098***
(0.0014)

Specification 6

Mean education of spouse

−0.0018***
(0.0004)

−0.0140***
(0.0013)

0.0158***
(0.0015)

Maximum education of elderly

−0.0007**
(0.0003)

−0.0058***
(0.0022)

0.0065***
(0.0024)

Specification 7

Specification 8

Mean education of elderly

−0.0007**
(0.0003)

−0.0060***
(0.0023)

0.0067***
(0.0025)

Education of oldest family member

−0.0012***
(0.0003)

−0.0081***
(0.0010)

0.0092***
(0.0012)

Specification 9

Years of schooling = 1a,乙

Years of schooling = 2a,乙

Years of schooling = 3a,乙

Specification 10

0.0199
(0.0233)
−0.0052
(0.0037)
−0.0006
(0.0039)

0.1020
(0.0875)
−0.0415
(0.0324)
−0.0044
(0.0278)

−0.1220
(0.1100)
0.0467
(0.0359)
0.0050
(0.0317)

Continued.

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Understanding Households’ Choice of Cooking Fuels in Pakistan 205

桌子 7. Continued.

Years of schooling = 4a,乙

Years of schooling = 5a,乙

Years of schooling = 6a,乙

Years of schooling = 7a,乙

Years of schooling = 8a,乙

Years of schooling = 9a,乙

Years of schooling = 10a,乙

Years of schooling = 11a,乙

Years of schooling = 12a,乙

Years of schooling = 13a,乙

Years of schooling = 14a,乙

Years of schooling = 15a,乙

Years of schooling = 16a,乙

Cement and metal roofa,C

No toileta,d

Pit toilet a,d

Brick-walled housea,e

Dung and
Crop Residue
−0.0031
(0.0032)
−0.0065***
(0.0018)
−0.0088***
(0.0027)
−0.0054*
(0.0029)
−0.0097***
(0.0025)
−0.0076***
(0.0029)
−0.0107***
(0.0026)
−0.0093***
(0.0028)
−0.0002
(0.0058)
−0.0137***
(0.0032)
−0.0114***
(0.0035)
−0.0110***
(0.0033)
−0.0156***
(0.0037)

Fuelwood
−0.0234
(0.0252)
−0.0516***
(0.0106)
−0.0778***
(0.0227)
−0.0428*
(0.0244)
−0.0831***
(0.0115)
−0.0646***
(0.0241)
−0.0887***
(0.0127)
−0.0791***
(0.0173)
−0.0011
(0.0408)
−0.1360***
(0.0171)
−0.1130**
(0.0448)
−0.0999***
(0.0208)
−0.2260***
(0.0223)

Gas

0.0265
(0.0283)
0.0581***
(0.0117)
0.0866***
(0.0246)
0.0481*
(0.0270)
0.0928***
(0.0127)
0.0721***
(0.0265)
0.0994***
(0.0138)
0.0884***
(0.0191)
0.0012
(0.0465)
0.1500***
(0.0178)
0.1240***
(0.0472)
0.111***
(0.0231)
0.2420***
(0.0227)

Specification 11
−0.0283***
(0.0052)

Specification 12
0.0814***
(0.0142)
0.0667***
(0.0157)

Specification 13
−0.0666***
(0.0122)

Specification 14

−0.1890***
(0.0228)

0.2170***
(0.0237)

0.2200*** −0.3020***
(0.0241)
0.2050*** −0.2720***
(0.0211)

(0.0310)

(0.0310)

−0.2140***
(0.0224)

0.2810***
(0.0274)

−0.0175*
(0.0097)

0.0153*
(0.0086)
0.0109*** −0.0125***
(0.0024)
0.0005
(0.0027)

(0.0027)
−0.0006
(0.0030)

Continued.

Number of elderly (>65 years)

Number of children (<15 years) Adult members (15–65 years) 0.0022* (0.0011) 0.0016*** (0.0004) 0.0001 (0.0004) l D o w n o a d e d f r o m h t t p : >65 年)

0.0016***
(0.0004)
0.00004
(0.0005)
0.0001
(0.0006)
0.0022*
(0.0012)

0.0109*** −0.0125***
(0.0024)
0.0003
(0.0033)
0.0009
(0.0043)
0.0153*
(0.0087)

(0.0027)
−0.0003
(0.0038)
−0.0010
(0.0050)
−0.0175*
(0.0098)

Notes: ***1% level of significance, **5% level of significance, 和 *10% level of significance.
Robust standard errors in parentheses. 表中 7, we reported only those variables that were
replaced; the rest of the variables can be provided upon request.
aDummy variables
bExcluded category: household head without any formal education
cExcluded category: households with nonmetal and cement roofs
dExcluded category: households with flush toilets
eExcluded category: households with mud, 木头, and bamboo walls
来源: 作者的计算.

by the maximum years of schooling of the adult female and the adult male member
of the household separately. Both variables are positive and significant (在 1%
等级) for households using gas, while it is negative and significant (在 1% 等级)
for those using solid fuels.

In specification 3, the level of education of the household heads was replaced
by the average years of schooling of the adult member of the household. 这
variable’s coefficient is positive and significant (在 1% 等级) for households
using gas, while it is negative and significant (在 1% 等级) for those using
dirty fuels. In specification 4, the level of education of the household heads was
substituted by the average years of schooling of the adult female and male members
of the family. Both variables are positive and significant (在 1% 等级) 为了
households using gas, while it was found to be negative and significant (在 1%
等级) for households using dirty fuels.

In specification 5, the educational attainment of the head of the family was
substituted by the head’s number of years of schooling. This variable’s coefficient
is positive and significant (在 1% 等级) for households using gas, while it was
found to be negative and significant (在 1% 等级) for those using solid fuels.
In specification 6, the educational attainment of the head of the household was
substituted by the years of schooling attained by the spouse of the head of the
家庭. This variable’s coefficient is positive and significant (在 1% 等级) 为了
households using gas, while it was found to be negative and significant (在 1%
等级) for households using solid fuels.

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Understanding Households’ Choice of Cooking Fuels in Pakistan 207

In specification 7, the level of education of the household heads was replaced
by the maximum years of schooling of the elderly household members. 这
coefficient of this variable is positive and significant (在 1% 等级) for households
using gas, while it was found to be negative and significant (在 1% 等级) 为了
those using solid fuels. In specification 8, the level of education of household heads
was replaced by the mean years of schooling of the elderly household members,
and its coefficient is positive and significant (在 1% 等级) for households using
gas and negative and significant (在 1% 等级) for those using solid fuels. 在
specification 9, the level of education of household heads was replaced by the years
of schooling of the oldest members of the household. The coefficient of this variable
is positive and significant (在 1% 等级) for households using gas, while it is
negative and significant (在 1% 等级) for households using solid fuels.

In specification 10, we converted every year of schooling into a dummy and
reestimated the ordered probit model. To confirm the importance of education, 这
coefficient of the dummy for every year of schooling of the household heads after
年 5 is positive and significant (在 1% 等级) for households using gas, while it
was found to be negative and significant at the 1% level for those using solid fuels.
Wealth of the family is a crucial factor that influences the family’s decision
to select fuel for domestic purposes. In specifications 11, 12, 和 13, the model was
reestimated separately by replacing the assets index with a dummy for roof materials
(metal and cement roof equal to 1, 否则 0); a dummy for the quality of the wall
of the house (house with a brick wall equal to 1, 否则 0); and a dummy for
toilet types (no latrine equal to 1, pit latrine equal to 1, and flush toilet equal to 0).
We found that wealthy households are more likely to choose gas for cooking and
that poor households depend on solid fuels. These findings on the positive impact
of wealth on the choice of clean fuel is consistent with the theory and also with the
results obtained in Table 1. In specifications 14 和 15, we disaggregated household
size into number of children, number of adult members (male and female), 和
number of elderly. We found that the number of children and elderly was positively
associated with the use of gas and fuelwood and negatively associated with the use
of dung and crop residue.

六、. Conclusion and Policy Implications

Using the latest nationally representative dataset, the 2014–2015 Pakistan
Social and Living Standards Measurement Survey, this study investigates the factors
contributing to a family’s choice of cooking fuels. The narrative inquiry illustrates
那, even today, a significant proportion of the population in urban areas of Pakistan
rely on solid fuels for cooking, which is injurious to the environment and human
健康. It also shows that human capital, economic status of households, 性别,
and location influence the fuel used by households for cooking. 更具体地说,
households with educated heads and wealthier households prefer to use clean energy

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208 Asian Development Review

sources such as gas for cooking, while poorer households tend to depend on dung,
crop residue, and fuelwood.

Ordered probit models illustrate that financial and human capital are the key
drivers of the household’s cooking fuel choice in urban Pakistan. 不同的
models with various measures of education (years of schooling of the head and
配偶, level of education of the head, average years of schooling of adult males and
女性, and education of the adult male and female by age categories) and wealth
(types of roofing, wall, and toilets) were estimated and the results were found to
be extremely robust, signifying the importance of education and wealth on energy
use behavior in urban Pakistan. Even the sensitivity analysis model with different
sample sizes showed the consistency of the findings. Families with low levels of
educational attainment are more likely to rely on solid fuels for cooking because
they often do not have adequate knowledge about the adverse implications of using
solid fuels. 而且, their earnings are too low to be able to pay the cost of gas.
Low-income families or families with low levels of financial and physical capital are
reliant on traditional and solid, dirty fuels for cooking, while families with higher
levels of human, physical, and financial capital are reliant on clean sources of fuel
such as gas for cooking.

The results of the current study provide crucial inputs for household energy
policies in Pakistan. Human capital is vital for enhancing knowledge about the
health cost of using solid fuels and for improving family earnings by providing
the capacity to diversify employment opportunities in lucrative sectors. 因此,
regular campaigns to create awareness of the negative impacts of dirty energy on
human health and the environment, better provision of education, and enhancing
opportunities to increase family income are the key catalysts for encouraging the
use of clean fuel by households. 因此, policies should aim at enhancing human
capital by investing in education on the one hand and generating opportunities for
livelihood diversification on the other.

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