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Biogas Cook Stoves for Healthy and Sustainable Diets? A Case Study in Southern India

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Alternative cook stoves that replace solid fuels with cleaner energy sources, such as biogas, are gaining popularity in low-income settings across Asia, Africa, and South America. Published research on these technologies focuses on their potential to reduce indoor air pollution and improve respiratory health. Effects on other cooking-related aspects, such as diets and women's time management, are less understood. In this study, in southern India, we investigate if using biogas cook stoves alters household diets and women's time management. We compare treatment households who are supplied with a biogas cook stove with comparison households who do not have access to these stoves, while controlling for several socio-economic factors. We find that diets of treatment households are more diverse than diets of comparison households. In addition, women from treatment households spend on average 40 min less cooking and 70 min less collecting firewood per day than women in comparison households. This study illustrates that alongside known benefits for respiratory health, using alternative cook stoves may benefit household diets and free up women's time. To inform development investments and ensure these co-benefits, we argue that multiple dimensions of sustainability should be considered in evaluating the impact of alternative cook stoves.
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September 2015 | Volume 2 | Article 281
ORIGINAL RESEARCH
published: 16 September 2015
doi: 10.3389/fnut.2015.00028
Frontiers in Nutrition | www.frontiersin.org
Edited by:
Luca Fernando Ruini,
Barilla, Italy
Reviewed by:
Eda Bozkir,
Kirklareli University, Turkey
Penelope Anne Field,
University of Otago, New Zealand
*Correspondence:
Tal Lee Anderman,
Environmental Defense Fund,
123Mission Street, San Francisco,
CA 94105, USA
tal.anderman@gmail.com
Specialty section:
This article was submitted to Nutrition
and Environmental Sustainability, a
section of the journal
Frontiers in Nutrition
Received: 09June2015
Accepted: 27August2015
Published: 16September2015
Citation:
AndermanTL, DeFriesRS, WoodSA,
RemansR, AhujaR and UllaSE
(2015) Biogas cook stoves for healthy
and sustainable diets? A case study
in Southern India.
Front. Nutr. 2:28.
doi: 10.3389/fnut.2015.00028
Biogas cook stoves for healthy and
sustainable diets? A case study in
Southern India
Tal Lee Anderman1* , Ruth S. DeFries
2 , Stephen A. Wood
2,3 , Roseline Remans
3,4 ,
Richie Ahuja1 and Shujayath E. Ulla5
1 Environmental Defense Fund, San Francisco, CA, USA, 2 Department of Ecology, Evolution, and Environmental Biology,
Columbia University, New York, NY, USA, 3 Agriculture and Food Security Center, The Earth Institute, Columbia University,
New York, NY, USA, 4 Bioversity International, Addis Ababa, Ethiopia, 5 Department of Social Work, St. Joseph’s College,
Bangalore, India
Alternative cook stoves that replace solid fuels with cleaner energy sources, such as
biogas, are gaining popularity in low-income settings across Asia, Africa, and South
America. Published research on these technologies focuses on their potential to reduce
indoor air pollution and improve respiratory health. Effects on other cooking-related
aspects, such as diets and women’s time management, are less understood. In this
study, in southern India, we investigate if using biogas cook stoves alters household
diets and women’s time management. We compare treatment households who are
supplied with a biogas cook stove with comparison households who do not have access
to these stoves, while controlling for several socio-economic factors. We nd that diets
of treatment households are more diverse than diets of comparison households. In
addition, women from treatment households spend on average 40min less cooking and
70min less collecting rewood per day than women in comparison households. This
study illustrates that alongside known benets for respiratory health, using alternative
cook stoves may benet household diets and free up women’s time. To inform develop-
ment investments and ensure these co-benets, we argue that multiple dimensions of
sustainability should be considered in evaluating the impact of alternative cook stoves.
Keywords: alternative cook stove, biogas cook stove, diet diversity, nutrition, time allocation, time savings, India
Introduction
Alternative cook stoves are becoming increasingly popular in low-income settings across Asia,
Africa, and South America (1). ese stoves replace solid fuels, such as wood, animal dung, and crop
residue, with cleaner energy sources such as biogas. Yet an estimated 2.6 billion people still cook with
solid fuels, with more than 95% of them in Asia and Sub-Saharan Africa (1). Household air pollution
from openly combusting solid fuels indoors has been cited as the third largest risk of premature
mortality, responsible for more than 108 million disability-adjusted life-years (25). Reducing usage
of solid fuels through, for example, biogas cook stoves is therefore crucial to avoid the more than 4.3
million deaths attributed to household indoor air pollution annually (2, 3, 6).
Serious health consequences are an important reason to address the challenge of transitioning
households away from openly combusting solid fuels. Yet many economic, technological, and cul-
tural obstacles can deter households from making these changes including, for example, a lack of
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aordable alternative energy options and cultural preferences for
traditional fuel sources (4, 79).
In recent years, there has been a surge in eorts to promote
alternative cook stoves (10, 11). ese eorts include replacing
traditional solid cooking fuels with cleaner energy sources,
such as liquid petroleum gas (LPG) and biogas. Other options
include rocket stoves, which improve heat-use eciency thereby
reducing the amount of fuel required, and gasier stoves, which
more thoroughly combust particulate matter to reduce net emis-
sions (12). Organizations such as the Global Alliance for Clean
Cookstoves, which has almost 400 partners in 36 countries, sup-
port the nancing and deployment of these technologies.
e main body of existing literature focuses on the eect of
alternative cook stoves on respiratory health. Evidence shows
that using alternative cook stoves signicantly reduces indoor
air pollution, including concentrations of particulate matter and
carbon monoxide (1317). A plethora of studies illustrate the link
between reductions in indoor air pollution and improved respira-
tory health (1824). Additional research provides insights on best
practices for the distribution and promotion of alternative cook
stoves by carefully considering cultural, physical, and economic
barriers to adoption (4, 7, 8, 2529).
Taking together the existing literature, there is a clear knowl-
edge gap in how changing cooking technologies inuences other
aspects related to cooking, such as dietary patterns and women’s
time management. In nutrition science, diet diversity is well
established as a key component of a quality diet (3034). Studies
have shown that higher individual diet diversity relates to higher
micronutrient intake for children (3538), adolescents (39), and
adults (30, 31, 40). Household diet diversity is used as an indicator
for household access to a variety of foods (41).
We hypothesize that changing the type of cook stove a
household uses could trigger shis in the diversity of foods they
consume (42). For example, because cooks have more control
over temperature and cooking time with an alternative stove,
they might choose to add dierent items to their meals that were
previously too time-consuming or posed too high a spoilage risk
because of uncontrollable stove temperatures. Similarly, families
may have avoided preparing foods with longer cooking times,
such as certain legumes, to save fuel wood, or to avoid creating
indoor air pollution. Changing cooking technologies may also
shi household expenditures and time management, allowing
greater access to a diversity of foods.
Similar to the eects on diet diversity, little research has
investigated how households manage their time depending on
the cooking technology they use (43, 44). We hypothesize that
because alternative cook stoves require less or no wood, the use of
these stoves reduces the amount of time that households, particu-
larly women, need to spend on rewood collection. In addition,
the time required to cook could also be reduced because of better
heat control. We hypothesize that such time-savings can free up
time for other activities, such as income generation or relaxation.
To test these two hypothesesthe eect of alternative cook
stoves on diet diversity and womens time managementwe use
a case study of biogas cook stoves in southern India.
Biogas cook stoves are fueled through anaerobic digestion of
dung, which takes place in an anaerobic digester belowground
(45). One product of this digestion (methane) is piped to the
house to fuel a cook stove, while the remaining content (known as
slurry) ows out of the digester and can be used as fertilizer (45).
India is an important case study because roughly 70% of the
population lives in rural communities and more than 75% of those
rural households (over 750 million people) rely on traditional
solid fuels for cooking (3, 11, 46, 47). In these regions, household
air pollution is responsible for over 550,000 premature deaths of
poor women and children each year (6, 10, 48, 49). e country
has employed a variety of strategies to address this signicant
public health risk, including subsidizing biogas cook stoves (10,
11). At present, there are many biogas cook stove development
projects operating throughout India (10, 11). However, to our
knowledge, this is the rst study that looks into the eect of these
biogas cook stoves on diet diversity and time allocation.
Materials and Methods
Ethics Statement
is study has been reviewed and approved by the Chair of the
Columbia University Institutional Review Board-Morningside
(IRB Protocol #AAAK9905). Following IRB-approved directions
for this study, each head of household that participated provided
voluntary informed consent, either in writing or through authori-
zation of a portrait photograph taken of them by an enumerator.
Study Area and Sampling Design
We analyzed household-level data for members of the Agricultural
Development and Training Society (ADATS) in the state of
Karnataka in southern India. ADATS is a membership-based
organization of smallholder households that pool capital to address
nancial needs. Operating out of the town of Bagepalli, 100km
north of Bangalore, ADATS has about 30,000 participating families
in over 1,000 villages in ve panchayats (Indian self-governments
at the village level) in the Kolar district of Karnataka (Figure1)
(50, 51). ADATS’ work spans multiple sectors, including adult
literacy, alternative energy development, agriculture, child educa-
tion, public health, legal aid, and mitigation of climate change.
We surveyed 199 households in 15 ADATS member vil-
lages (Figure2; Table S1 in Supplementary Material) from the
Bagepalli panchayat in the Kolar district of Karnataka (Figure1)
(5153). e Bagepalli panchayat skirts the southern border of
the Rayalaseema desert. e terrain is semi-arid and drought
prone, with rainfall averaging 560mm/year (54). e majority
of families in the region are labor workers, while a few tend their
own agricultural plots.
Data from households using biogas cook stoves (the “treat-
ment” group) were collected for families in 10 villages participat-
ing in the Bagepalli clean development mechanism (CDM) Biogas
Programme. Half of the households in each selected village were
randomly chosen for surveying using a random number generator.
e Programme is an ADATS project launched in 2006 that built
5,500 biogas cook stove units in 128 villages. All ADATS member
households in villages that could run a biogas cook stovemean-
ing that they had a cow, yard space, and the ability to dig a deep
hole for the anaerobic digesterwere given the option to obtain a
stove. All stove materials were provided for free by ADATS.
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A second set of ve villages in the Bagepalli panchayat that
did not have biogas cook stoves (but were still part of ADATS)
were also surveyed to provide reference data (the comparison
group). Due to disparities between the number of treatment and
comparison households, all eligible households in comparison
villages were surveyed. Comparison villages were chosen based
on (1) their having been unable to dig the hole needed to construct
the digester for the biogas cook stoves because of large subsurface
rocks, (2) their physical proximity to the 10 randomly selected
treatment villages, (3) their participation in ADATS, therefore
receiving other wide-ranging benets of membership, and (4)
their having households that owned a cow and had adequate yard
space and would have thus been able to run a stove when the
Biogas Programme was launched in 2006, and when surveys were
enumerated in 2013.
All households without a biogas cook stove (the comparison
group) used a wood-burning stove inside the house to cook.
Some households used multiple cook stoves. Five comparison
households used a kerosene stove. No other alternative cook
stoves were used in the comparison or treatment groups. Two
treatment households used a rewood stove alongside their
biogas unit. Whether a household owned a biogas, rewood, and/
or kerosene stove is included in each of our models.
Data Collection and Indicators
Data for this study (summarized in Ta b l e1; available in
Supplementary Material) were gathered from January to March
2013. A survey (Supplementary Material) was administered to 199
households, with the female household head acting as the main
respondent. Of the 10 villages with biogas cook stoves, 141 house-
holds were surveyed. Fiy-eight households were surveyed from
FIGURE 1 | Map depicting (A) the study area in the state of Karnataka, India; (B) in the district of Kolar; (C) in the Bagepalli panchayat. Created in
ArcGIS using external data (5053).
the ve comparison villages. We used several metrics to assess the
relationship between ownership of a biogas cook stove and co-
benets for household diet diversity and time savings (Tab le1).
To generate a diet diversity score, which measures the variety
of food groups consumed over a given time period, we asked the
female household head to report on the household’s diet using a
semi-quantitative food frequency survey. is included report-
ing the frequency (times per day, week, month, or year) that the
household consumed 36 locally available food items (TablesS2
and S3 in Supplementary Material) (41, 55).
ese foods were categorized into 10 food groups following
the women’s diet diversity score as outlined by the Food and
Nutrition Technical Assistance III Project (FANTA) and the
Food and Agriculture Organization of the United Nations (FAO)
(Table S2 in Supplementary Material): starchy staples, beans and
peas, nuts and seeds, dairy, esh foods, eggs, Vitamin A-rich
dark green leafy vegetables, other Vitamin A-rich fruits and
vegetables, other vegetables, and other fruit (55). Each food item
received a value based on how oen it was reportedly consumed
(0=never; 1=once a week; 7=once a day, and all other values
scaled accordingly). ese values were then summed across food
groups for each household. By applying cut-os in one-point
increments, we established diet diversity scores that reect the
number of food groupsranging from 0 to 10that households
consumed on a daily (Day Diet Diversity Score) and weekly
(Week Diet Diversity Score) basis.
e reported frequency of consuming the food items catego-
rized into food groups for the diet diversity score were also used
to generate a food variety score. e food variety score gives equal
weight to the consumption of each of the 36 food items by summing
the consumption frequencies of all items, rather than dividing the
FIGURE 2 | Map illustrating the distribution of the study’s 10 villages with biogas cook stoves and ve villages used as comparisons. Created in
ArcGIS using external data (5053).
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items into groups (41). e food variety score ranges from 0 to 36
food items consumed on a daily (Day Food Variety Score) and
weekly (Week Food Variety Score) basis. e food variety score is
also a proxy for diet quality (37). It complements the diet diversity
score by oering insight into the number of dierent food items
accessible, thus also providing a measure of household resilience.
Because it has greater variation than the diet diversity score, it is
more sensitive to dierences between households.
e daily diet diversity score was also used to categorize diets
according to a threshold suggested by FAO and FANTA as the
womens minimum diet diversity score (55). is yes/no indicator,
in comparison to the range of values in the diet diversity and food
variety scores, reects if households have reached a minimum
diet diversity threshold by consuming at least 5 out of 10 food
groups on a daily basis (55). FAO and FANTA established the
womens minimum diet diversity score to reect the nding that
women consuming foods from ve or more of the 10 food groups
in the diet diversity score have a greater likelihood of meeting
their micronutrient needs than women consuming foods from
fewer food groups (55, 56).
Our second set of response variables quantied how much time
female heads of households spent cooking, doing housework,
doing paid labor work, collecting rewood, and relaxing during
a workday. By having the female household head (respondent)
map out her daily activities via a 24-h recall, we calculated the
time she spent each day on these ve activities. Each activity was
analyzed independently.
A third set of data, including demographic, cultural, and socio-
economic variables, was collected and used to account for other
potentially confounding factors in our analysis. A household’s
total size and its’ dependency ratio indicated household demo-
graphics. Household size was reported as the aggregate number
of people dwelling on one property. e dependency ratio divided
the number of household members between 0 and 14 and over
65years of age by the number between 15 and 64years of age (57).
A larger value indicates higher dependency within the household.
Cultural characteristics were represented by binary variables for a
household’s religion (Hindu or Muslim) and caste. A range of the
14 castes, including Muslims, present in our study were grouped
into upper and lower castes in consultation with local authorities.
Socio-economic variables were represented by a household’s
duration of membership in ADATS, distance to a major market,
and wealth. e distance on navigable roads between each village
and the area’s major market was calculated using Google Maps
TABLE 1 | Synthesis of study variable characteristics, including variables for household dietary patterns, women’s time allocations, and a set of
potentially confounding factors.
Variable Description Range Mean Sample size
(treatment/
comparison)
All Treatment Comparison
Household dietary patterns
Daily diet diversity score Summed score on daily basis using 10 food groups 0–10 6.1
(1.0)
6.3
(1.0)
5.6
(0.9)
(141/58)
Weekly diet diversity
score
Summed score on weekly basis using 10 food groups 0–10 9.2
(0.9)
9.3
(0.8)
8.9
(1.0)
(141/58)
Daily food variety score Summed score on daily basis using 36 food items 0–36 9.1
(2.4)
9.8
(2.4)
7.6
(1.6)
(141/58)
Weekly food variety
score
Summed score on weekly basis using 36 food items 0–36 20.8
(4.7)
22.1
(4.3)
17.7
(4.3)
(141/58)
Minimum diet diversity If household consumes greater than ve food groups
daily
0 or 1 1.0
(0.2)
1.0
(0.1)
0.9
(0.2)
(141/58)
Women’s time allocations
Cooking Hours/day cooking and preparing food/snacks 0–7 2.9
(1.2)
2.7
(1.2)
3.4
(0.9)
(141/58)
Housework Hours/day doing housework or yard work 0–10 2.4
(1.3)
2.4
(1.4)
2.5
(0.8)
(141/58)
Labor work Hours/day engaged in paid labor work 0–10 6.5
(3.5)
6.2
(3.7)
7.2
(2.8)
(141/58)
Collecting rewood Hours/day spent collecting rewood 0–4 0.4
(0.8)
0.0
(0.1)
1.2
(0.9)
(141/58)
Relaxing Hours/day spent relaxing 0–5 1.3
(0.8)
1.3
(0.9)
1.1
(0.7)
(141/58)
Potentially confounding factors
Kerosene Household uses kerosene stove 0 or 1 n/a 0.0
(0.0)
0.1
(0.3)
(141/58)
Additional rewood
stove
Treatment household uses rewood and biogas stove 0 or 1 n/a 0.0
(0.1)
0.0
(0.0)
(141/58)
Caste Lower caste
Upper caste
n/a 49%
51%
39%
61%
67%
33%
(105/57)
Religion Hindu n/a 89% 86% 97% (141/58)
Muslim 11% 14% 3%
Years membership
in ADATS
# years household a member of ADATS 3–34 13.3
(8.6)
12.1
(7.6)
15.6
(9.9)
(105/57)
Asset index Wealth indicator (PCA of 20 assets) 2.97.4 0.2
(2.0)
0.9
(2.0)
1.0
(1.2)
(102/55)
Distance to major
market
Kilometer from village to major market 7.8–37.8 22.0
(9.5)
21.6
(11.0)
23.2
(3.9)
(141/58)
Household size Number of people living on same property 1–15 5.0
(2.4)
5.0
(2.5)
4.9
(2.0)
(141/58)
Dependency ratio # Dependents/ # Providers 0–2 0.4
(0.4)
0.3
(0.4)
0.4
(0.4)
(136/57)
Table includes variable description, range, mean (all, treatment, and comparison households), and sample size. SDs are reported in parentheses for mean values.
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(58). Wealth was measured using an asset index, which aggregates
household stocks with dierent units (e.g., livestock, landholdings,
and household appliances) to generate a wealth ranking between
households in a study population (5961). Many development
economists now advocate the use of household welfare measures
based on assets to assess poverty, given fewer biases related to
respondent recall errors, survey seasonality, and measurement
error (60). We collected data on household reported ownership
of 20 asset indicators (Table S4 in Supplementary Material) (59).
We then used a principal component analysis (PCA) to generate
weights for each of the 20 assets. e rst principal component in
the analysis is the linear combination of the assets that explains
the greatest sample variance in the data. e index itself is a sum-
mation of asset ownership, weighted by each asset’s contribution
to the explanation of total variance relative to the rst principal
component (61). e asset index serves as a comparative measure
of poverty; each household’s poverty ranking is relative to the
study population used in the PCA (60). Research on the relative
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merit of dierent asset indices, including a metric of structured
income, shows that the PCA-based asset index used here oers
the best indication of the relative socio-economic position of
each household, and therefore of local wealth distributions and
orderings, in a study population (59, 60).
Aer initial surveys, a basic qualitative module was performed
to enhance our understanding of community perceptions on
household diet diversity, women’s allocations of time, and any
other changes respondents experienced in association with
owning a biogas cook stove (6267). e informal focus group
discussions were conducted in March 2013 in two villages with
biogas cook stoves that were randomly selected from the 10
treatment villages. rough this random selection, the focus
groups were held with the female household heads of surveyed
households in one village in Region 1 and one village in Region 5
(see Figure2). Because of the informal nature and structure of the
focus groups, these discussions were only used to gain feedback
on the preliminary trends and results found in the quantitative
analysis, and to give back to the community by informing them
about the expected outcomes of the research.
Analysis
All statistical analyses were conducted in STATA (version 13) (68).
Separate mixed models with xed eects were run for each of the
response variables. e 15 villages surveyed were grouped into
ve regions based on the geographic distribution of the villages,
with two to three biogas cook stove and one comparison village
per region (Figure2). Individual villages were also applied as a
grouping factor. However, the limitations in sample size per vil-
lage made it dicult to interpret results from this grouping, which
was therefore not used in the nal analysis. Because comparison
villages were intentionally selected based on requirements, such
as geographic location and an inability to construct biogas cook
stoves, a mixed model with xed eects grouped by region was
applied to account for possible non-random undocumented
socio-economic or spatial village-level grouping eects. To fur-
ther account for potential non-random eects, we applied robust
clustering of standard error at the village level. is lowered the
residuals in each of our models, measured through root mean
square error (RMSE).
roughout our analysis, continuous predictor variables were
standardized by two standard deviations (SDs), binary predictor
variables were centered, and response variables were le unstand-
ardized (69). is standardization procedure was used to ensure
that variables were expressed in common units so that correlation
coecients within each model could be compared. e data were
tested for outliers using Cook’s Distance. Cook’s Distance was cal-
culated for each model and, using the standard cut-o value of 1
(70), did not identify any outliers. Collinearity between variables
was systematically checked using variance ination factors (VIF);
results indicated that there were no instances of collinearity as
outlined in Results.
To add robustness to testing the assumption that our study
population was analogous despite natural variations in household
characteristics, we also ran the analysis on a subset of the popula-
tion selected using propensity score matching (PSM). PSM is one
approach to tackle the possibility of selection bias in choosing
a treatment population. It addresses this issue by identifying a
subset of the comparison group that is similar to the treatment
group in all relevant pretreatment characteristics. Within this
population subset, then, dierences in outcomes between the
comparison and treatment groups can be more easily attributed
to the treatment itself (71, 72). We used a probit regression model
to calculate the propensity score for each household, which
estimates the probability of a household participating in the
treatment given its observed covariates (72, 73). We included the
following equally weighted covariates to calculate the propensity
score: asset index, caste, religion, household size, dependency
ratio, and duration of membership in ADATS (72, 73). When
applying this structure to our data, the balancing property was
satised. We then identied the population with a region of
common support across all covariates by removing households
with a propensity score smaller than the minimum or larger than
the maximum of the opposite group (71, 72). is selected 19
households (13 comparison and 6 treatment households) that fell
outside the region of common support and were thus removed
for the supplementary analysis, bringing the population subset to
138 households out of the original 157. All models were run on
this population subset as described in Section “Results.
Results
Description of Household Characteristics
e 199 households in 15 villages analyzed in this study present a
range of socio-economic, cultural, and geographic characteristics
(Tabl e 1; Table S1 in Supplementary Material).
Hypothesis 1: households with biogas cook
stoves have a more diverse diet as compared to
households without the stoves
To test our rst hypothesis, we assess the relationship between
household diet diversity and ownership of a biogas cook stove
using a set of mixed models with xed eects, which allows us
to control for various socio-economic variables (Table2). We
consistently nd a signicant relationship between diet diversity
and cook stove technology, independent of the type of diver-
sity metric used (daily or weekly diet diversity or food variety
score)households that use a biogas cook stove have a more
diverse diet than households that use a rewood stove.
One particular food item or food group does not explain the
dierences observed in diet diversity between treatment and
comparison households. Instead, the dierence is related to the
treatment households’ higher average consumption of several
food groups. Starchy staples, beans and peas, Vitamin A fruits
and vegetables, and other vegetables are the most frequently
consumed food groups in both populations (Figure3; Table S3
in Supplementary Material). However, treatment households
consume the following food groups signicantly more frequently
on a daily basis: starchy staples, nuts and seeds, dairy, esh foods,
Vitamin A fruits and vegetables, and other fruits (Figure3; Table
S3 in Supplementary Material). In the context of the local situa-
tion, where nuts and seeds, esh foods, green leafy vegetables, and
eggs are among the least consumed items in general (Table S3 in
Supplementary Material), this higher consumption of a variety
FIGURE 3 | The percentage of households consuming each of the 10
food groups in the diet diversity score on a daily basis in the
treatment and comparison populations, respectively. Signicant
differences in the consumption of food groups between the two populations
represented by asterisks, based on analyses from mixed models with xed
effects.
FIGURE 4 | The amount of time female household heads spent
cooking, doing housework, doing labor work, collecting rewood,
and relaxing per day in the treatment and comparison populations,
respectively. Values presented as mean hours spent per day on each
activity. Signicant differences in respondent time allocations between the
two populations represented by asterisks, based on analyses from mixed
models with xed effects (see Table3).
TABLE 2 | Mixed model with xed effects grouped by region for the daily and weekly diet diversity score and food variety score, and the minimum diet
diversity score. Biogas cook stove ownership reported as a binary variable with comparison households (0) and treatment households (1).
Variables (1)
Day diet
diversity score
(2)
Week diet
diversity score
(3)
Day food
variety score
(4)
Week food
variety score
(5)
Minimum
diet diversity
Firewood_biogas 0.424***
(0.098)
0.361*
(0.152)
1.523***
(0.247)
3.380***
(0.840)
0.005
(0.022)
Kerosene 0.633***
(0.056)
0.533
(0.375)
1.131*
(0.486)
0.370
(1.179)
0.033
(0.032)
Add_FIrewood 0.204
(0.201)
1.091**
(0.340)
0.589
(0.288)
6.803***
(1.174)
0.015
(0.032)
Asset_index 0.526**
(0.144)
0.313
(0.173)
1.353**
(0.390)
1.147
(1.116)
0.071
(0.048)
Caste 0.268
(0.161)
0.073
(0.121)
0.502
(0.240)
0.009
(0.620)
0.008
(0.043)
Religion 0.126
(0.459)
0.209
(0.205)
0.593
(0.588)
1.727*
(0.741)
0.018
(0.015)
Dist_market 0.274
(0.149)
0.285
(0.346)
1.031**
(0.301)
0.622
(1.176)
0.150**
(0.040)
HH_size 0.245
(0.169)
0.059
(0.095)
0.571
(0.438)
0.264
(0.697)
0.023
(0.026)
Age_ADATS 0.228
(0.206)
0.013
(0.200)
0.470
(0.355)
0.026
(1.260)
0.058
(0.041)
Observations 157 157 157 157 157
Number of region 5 5 5 5 5
Adjusted R-squared 0.159 0.079 0.416 0.274 0.046
RMSE 0.904 0.804 2.115 3.942 0.170
Robust standard errors (SEs) are in parentheses.
***p<0.001, **p<0.01, *p<0.05.
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of food groups by households with a biogas cook stove can add
important nutritional diversity to the diet.
For the minimum diet diversity score, there was not a signicant
dierence between the proportion of treatment and comparison
households meeting the diet diversity cut-o of consuming ve
food groups daily. We do, however, observe that a household’s
distance to a major market is a signicant determining factor in
reaching the diversity cut-o; households that were further from
a treatment population. It addresses this issue by identifying a
subset of the comparison group that is similar to the treatment
group in all relevant pretreatment characteristics. Within this
population subset, then, dierences in outcomes between the
comparison and treatment groups can be more easily attributed
to the treatment itself (71, 72). We used a probit regression model
to calculate the propensity score for each household, which
estimates the probability of a household participating in the
treatment given its observed covariates (72, 73). We included the
following equally weighted covariates to calculate the propensity
score: asset index, caste, religion, household size, dependency
ratio, and duration of membership in ADATS (72, 73). When
applying this structure to our data, the balancing property was
satised. We then identied the population with a region of
common support across all covariates by removing households
with a propensity score smaller than the minimum or larger than
the maximum of the opposite group (71, 72). is selected 19
households (13 comparison and 6 treatment households) that fell
outside the region of common support and were thus removed
for the supplementary analysis, bringing the population subset to
138 households out of the original 157. All models were run on
this population subset as described in Section “Results.
Results
Description of Household Characteristics
e 199 households in 15 villages analyzed in this study present a
range of socio-economic, cultural, and geographic characteristics
(Tabl e 1; Table S1 in Supplementary Material).
Hypothesis 1: households with biogas cook
stoves have a more diverse diet as compared to
households without the stoves
To test our rst hypothesis, we assess the relationship between
household diet diversity and ownership of a biogas cook stove
using a set of mixed models with xed eects, which allows us
to control for various socio-economic variables (Table2). We
consistently nd a signicant relationship between diet diversity
and cook stove technology, independent of the type of diver-
sity metric used (daily or weekly diet diversity or food variety
score)households that use a biogas cook stove have a more
diverse diet than households that use a rewood stove.
One particular food item or food group does not explain the
dierences observed in diet diversity between treatment and
comparison households. Instead, the dierence is related to the
treatment households’ higher average consumption of several
food groups. Starchy staples, beans and peas, Vitamin A fruits
and vegetables, and other vegetables are the most frequently
consumed food groups in both populations (Figure3; Table S3
in Supplementary Material). However, treatment households
consume the following food groups signicantly more frequently
on a daily basis: starchy staples, nuts and seeds, dairy, esh foods,
Vitamin A fruits and vegetables, and other fruits (Figure3; Table
S3 in Supplementary Material). In the context of the local situa-
tion, where nuts and seeds, esh foods, green leafy vegetables, and
eggs are among the least consumed items in general (Table S3 in
Supplementary Material), this higher consumption of a variety
FIGURE 3 | The percentage of households consuming each of the 10
food groups in the diet diversity score on a daily basis in the
treatment and comparison populations, respectively. Signicant
differences in the consumption of food groups between the two populations
represented by asterisks, based on analyses from mixed models with xed
effects.
TABLE 3 | Mixed model with xed effects grouped by region for female household heads’ allocations of time for ve activities: cooking, housework, labor
work, collecting rewood, and relaxing. Biogas cook stove ownership reported as a binary variable with comparison households (0) and treatment households (1).
Variables (1)
Resp_cooking
(2)
Resp_housework
(3)
Resp_labor_work
(4)
Resp_rewood
(5)
Resp_relaxing
Firewood_biogas -0.743**
(0.218)
0.037
(0.209)
0.229
(0.415)
1.178***
(0.150)
0.219
(0.175)
Kerosene 0.328
(0.178)
0.768**
(0.188)
0.455
(0.619)
0.668
(0.381)
0.512**
(0.126)
Add_rewood 0.276
(0.223)
1.230***
(0.222)
2.267***
(0.447)
0.269
(0.185)
1.088***
(0.218)
Asset_index 0.267
(0.225)
0.102
(0.309)
1.286*
(0.458)
0.125
(0.068)
0.198
(0.125)
Caste 0.258
(0.165)
0.139
(0.212)
0.000
(0.411)
0.027
(0.044)
0.082
(0.113)
Religion 0.181
(0.473)
0.751
(0.515)
0.772
(1.006)
0.020
(0.086)
0.161
(0.286)
HH_size 0.151
(0.266)
0.311
(0.211)
0.401
(0.665)
0.006
(0.047)
0.329
(0.156)
Dependency_ratio 0.071
(0.204)
0.122
(0.180)
0.899
(0.704)
0.021
(0.041)
0.289
(0.144)
Observations 152 152 152 152 152
Number of region 5 5 5 5 5
Adjusted R-squared 0.061 0.004 0.015 0.549 0.055
RMSE 1.027 1.310 3.346 0.500 0.800
Robust standard errors (SEs) are in parentheses.
***p<0.001, **p<0.01, *p<0.05.
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a major market less frequently consumed ve or more of the 10
food groups each day (Table 2). is nding is consistent with
other recent studies (74).
e analysis did not experience problems with collinearity
(all VIF values <1.23). Furthermore, all models were run on
the data subset identied as comparable through PSM, and
results were consistent with the full sample analysis (Table S5 in
Supplementary Material).
Hypothesis 2: women in households with biogas
cook stoves have different time allocations than
women without the stoves
As with the rst hypothesis, we test the relationship between
a female household head’s allocations of time and ownership
of a biogas cook stove using mixed models with xed eects
(Figure4; Ta b l e 3). None of the models experienced problems
with collinearity (all VIF values <1.22). e models were also run
on the data subset identied as comparable through PSM, and
results were consistent with the full sample analysis (Table S6 in
Supplementary Material).
Results clearly show that women in households with a biogas
cook stove spend signicantly less time cooking and collecting
rewood than women in comparison households (Tab l e 3; Table
S6 in Supplementary Material). No signicant eects of owning
a biogas cook stove were found on time spent doing housework,
labor work, or relaxing.
Respondents from the comparison group reported spending,
on average, about 0.7 more hours (~40min) cooking and 1.2
more hours (~70min) collecting rewood per day than respond-
ents from the treatment group (Tables1 and 3). is implies that
women with biogas cook stoves save, on average, roughly 2h per
day just by owning these stoves. is presents an opportunity
cost: while there is no guarantee of how they use the freed time,
respondents with a biogas cook stove have the opportunity to re-
allocate their freed time toward other activities, such as income
generation, while respondents from comparison households do
not have this opportunity.
Figure4 and Table1 further illustrate that women’s time allo-
cations vary signicantly between the treatment and comparison
populations. Women with biogas cook stoves spend on average
more time relaxing and less time cooking, collecting rewood,
doing housework, and doing labor work than women from
comparison households. However, our current ndings do not
provide in-depth insights or clear patterns on how women in
treatment households use their extra time.
Insights from focus group discussions
Focus group discussions provide further insight on the relation-
ships observed in the quantitative models. First, women conrmed
that households with a biogas cook stove have more diverse diets.
e cooking eciency and consumer friendly nature of the stoves
were mentioned as major reasons behind this trend. Because the
fuel source is now self-sucient, burning without the constant
need to maintain an adequate ame, women are able to multi-task
while cooking. is allows them both to add more food items
to a single dish and to diversify the type and quantity of dishes
included in a meal.
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Furthermore, women in the focus groups communicated
a variety of other positive outcomes from owning a biogas
cook stove. Specically, they indicated that risks of collecting
rewoodincluding thorns, snakes, and harassment from
menwere considerably reduced because of the biogas cook
stoves. ey also reported that indoor rescaused, for example,
by a sari catching re from the wood-burning stovehappened
far less frequently with the new technology. Finally, they noted
that having households in the region that used biogas cook stoves
decreased total wood use in the area, leaving more available for
families continuing to use wood-burning stoves.
Discussion
Our quantitative and qualitative results indicate that households
with access to a biogas cook stove have more diverse diets than
households without the stoves (Figure3; Ta b l e 2; TablesS3 and
S5 in Supplementary Material). Results also indicate that women
with a biogas cook stove spend less time cooking and collecting
rewood (Figure4; Tab l e 3; Table S6 in Supplementary Material).
is study therefore provides evidence of two distinct co-benets
of biogas cook stove interventions in the Bagepalli panchayat.
Several mechanisms may explain why diets are more diverse
among households that own a biogas cook stove. Women described
how using a biogas cook stove allowed them to multi-task while
cooking, which in turn facilitated their ability to prepare a larger
variety of items for a given meal. Women also described a range
of further benets from owning a biogas cook stove, including
re safety inside the house and decreased risks from collecting
rewood.
Additional factors that may have contributed to the higher
diet diversity among biogas cook stove users include the ability
to adjust cooking temperatures and the elimination of indoor air
pollution. With a wood-burning stove, it is dicult to control
or predict stovetop temperatures. As a result, cooking more
heat-sensitive dishes such as meat or sh can be challenging, and
women using traditional fuel sources may have chosen to avoid
these dishes, given the consequent spoilage risk involved. With
a biogas cook stove, dishes with longer cooking times, such as
rice could be cooked in a structured timeframe, thus facilitating
the ease of cooking these other items. Given the diculties and
time involved in collecting rewood, dishes with longer cooking
times may have also been cooked less frequently prior to owning
a biogas cook stove in an eort to conserve wood.
Furthermore, several studies have illustrated that cooking
with rewood produces larger amounts of particulate matter
and carbon monoxide than alternative cook stoves (1316). In
line with this existing evidence (13, 18, 19, 2124, 75), women in
our study oen reported concern about the impact of indoor air
pollution on the health of their families. For households without
a biogas cook stove, the health risk related to extensive cooking
might limit their food choices. is information is purely anec-
dotal, as our study did not collect data specically to examine this
possible explanation. Further studies can provide more insights
on these household decisions and behaviors.
It is important to note that the diet diversity of a household is
mainly interpreted as an indicator of food access and can have little
bearing on the nutrient intake of individuals in the household,
where intra-household distribution of food plays an important
role (34). Investigating eects on individual diet diversity will
shed further light on the nutritional impact of biogas cook stoves.
Respondents with biogas cook stoves also spent signicantly
less time cooking and collecting rewoodapproximately 2h
per daythan respondents relying on rewood stoves (Figure4;
Tables1 and 3; Table S6 in Supplementary Material). is indi-
cates an opportunity cost to women relying on wood-burning
stoves. Female household heads with biogas stoves have the
option to put their additional time toward, for example, income
generation and relaxation. e value of these activities cannot be
underestimated, especially given the strains of the desert climate,
and that local employment oen includes intense agricultural
labor. Collectively, time saved by cooking on a biogas rather
than wood stove becomes a signicant additional benet of this
technological intervention.
Households in the treatment and comparison groups varied
in certain socio-economic and cultural characteristics, such as
caste and assets. PSM sought to account for these dierences,
by running the models on a subset of the study population that
were equally likely to have received the treatment eect accord-
ing to a collection of equally weighted variables (72). Results run
on this data subset were robust with those using the full sample
population.
Currently, there is a dearth of studies evaluating outcomes,
such as diet diversity and time management, when considering
the impact of alternative cook stoves (76, 77), and multiple reports
have called for further research (76, 7880). We hope that the
promising results described in this paper will encourage other
research and development initiatives to expand their set of impact
indicators when studying the use of improved cook stoves. is will
facilitate a more comprehensive understanding of the benets and
disadvantages of biogas and other alternative cook stove projects.
Conclusion
In this study, we sought to evaluate two co-benets of a biogas
cook stove project in southern India. Results indicate that own-
ing a biogas cook stove has a signicant positive correlation with
household diet diversity (31, 8183). Female heads of households
with a biogas cook stove also reported spending approximately
two less hours cooking and collecting rewood per day. ese
women have the option of putting their freed time toward other
activities, such as income generation and relaxation, presenting
an opportunity cost to families continuing to rely on solid fuel
for cooking.
Currently, low- and middle-income countries continue to
extensively use solid fuels for energy intensive activities, such as
cooking. Given today’s severe global health burden from openly
combusting solid fuels, it is critical to explore alternative energy
sources and their implications for families transitioning to new
types of cook stoves (2, 4, 5, 12). While results from this analysis
may be situation specic, the positive relationships observed
underscore the importance of quantifying environmental, social,
and economic advantages and disadvantages of household-level
technological interventions, in order to better align incentives
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Author Contributions
TA, RD, and RA conceived and designed the experiments; TA and
SU performed the experiments; TA, RD, SW, and RR analyzed the
data; all authors contributed to the writing of this paper.
Acknowledgments
Environmental Defense Fund provided the funding for this
study. e authors would like to thank the Agricultural
Development and Training Society (ADATS) and its members
for their cooperation and constant assistance in conducting
this research. e ADATS panchayat leaders as well as direc-
tors Ram and Mario Esteves deserve our heartfelt thanks for
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like to thank Steven Hamburg, K. Kritee, Filip Tetaert, Tara
Sriram, Mukhaem Pasha, and Abid Pasha for comments on this
manuscript.
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Conict of Interest Statement: e authors declare that the research was con-
ducted in the absence of any commercial or nancial relationships that could be
construed as a potential conict of interest.
Copyright © 2015 Anderman, DeFries, Wood, Remans, Ahuja and Ulla. is is an
open-access article distributed under the terms of the Creative Commons Attribution
License (CC BY). e use, distribution or reproduction in other forums is permitted,
provided the original author(s) or licensor are credited and that the original publica-
tion in this journal is cited, in accordance with accepted academic practice. No use,
distribution or reproduction is permitted which does not comply with these terms.
... A report by the World Food Programme states that high fuel costs and inadequate access to energy requires households to employ coping strategies including trading food rations for cooking fuel and skipping or undercooking food [34]. Fuelwood scarcity also contributes to households preparing food with low nutritional value requiring less cooking time and not sufficiently boiling water to eliminate impurities [35]. ...
... LPG use can have a neutral or cooling effect on climate when accounting for the reduction in emissions of CO 2 [24,76], and reductions in localized deforestation [69,77]. Studies have found that LPG can save the primary cook, typically women, an average of 40-45 min of cooking time per day [27,28], and prevent the need to gather firewood, which can take up to an hour or more per day in some cases [35]. However, despite the potential time savings and health benefits of using LPG, many primary cooks still prefer to use biomass as their primary cooking fuel. ...
... Given that household energy makes up a significant portion of monthly household expenditure (up to 20%) in Mukuru kwa Reuben [57], freely available firewood may have been viewed as a desirable option to cut a significant proportion of household expenditures. As LPG has been shown to provide households more diverse diets than those cooking with biomass in some instances [35], minimizing the costs of LPG refills (e.g. via fuel subsidies) may help households maintain a more balanced diet that can protect against malnourishment [34]. ...
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... Although improved biomass cookstove interventions have achieved important reductions in emissions and indoor concentrations, HAP concentrations remained several fold higher than the WHO recommended guidelines as shown in literature reviews Pope et al. 2017Pope et al. , 2021. Thus, intervention efforts are shifting toward stoves that use cleaner fuels such as liquefied petroleum gas (LPG) (Albalak et al. 2001;Anderman et al. 2015;Begum et al. 2009;Checkley et al. 2021;Clasen et al. 2020;Dutta et al. 2012Dutta et al. , 2018Naeher et al. 2000;Nie et al. 2016;Pollard et al. 2018;Sukhsohale et al. 2013). However, many previous HAP studies involving LPG stoves lack personal exposure measurements (Bruce et al. 2000;Pope et al. 2017), which could help reduce bias from measurement error when quantifying the relationship between HAP and health risks. ...
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... Eight-three percent of surveyed households obtain wood from forests for cooking and have not yet adopted alternatives such as LPG ( Figure S1). A large potential exists in this landscape to reduce forest dependence, improve indoor air quality, and achieve other benefits from alternative energy sources (Anderman et al., 2015;Ranjan, 2019;Singh et al., 2017). Durable housing would reduce dependence on forests for construction for 66% of households ( Figure S2). ...
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... We find that LPG use has a positive association with dietary diversity during summer, also found in the full model. This finding aligns with another study from India where alternative cook stoves (biogas) were found to be associated with diverse household diets (Anderman et al 2015). However, LPG is expensive and inconvenient for the households (require refilling), in addition to high dependence on imports and sensitivity to market fluctuations (Hameed 2020). ...
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... Furthermore, the use of wood and charcoal for cooking is a major driver of deforestation and GHG emissions (Carvalho et al., 2019). Evidence shows that using alternative cook stoves significantly reduces indoor air pollution, and numerous studies demonstrate the link between reductions in household air pollution and improved respiratory health (Anderman et al., 2015). Tumwesige et al. (2017) monitored real-time PM2.5 and CO concentrations in 35 households in Cameroon and Uganda where biogas and firewood (or charcoal) were used and found that fully switching to biogas for cooking reduces both CO and PM2.5 concentrations to below WHO recommended limits. ...
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... 1. Enhancement in efficient productivity [17,18] 2. Improvement of medical conditions of user [19][20][21] 3. Decrease in costs of conventional energy sources [22][23][24][25][26] 4. Reduced expenditures for chemical fertilizer [27][28][29][30] 5. Increased opportunity for (small scale) animal husbandry [31,32] 6. Enhancement of agricultural yields [33,34] 7. Increase in income of family members [12,35] . Anaerobic digestion process for sustainable development ture and address a wider scientific audience [42]. ...
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