Question:
You are given the full text of a scientific research paper.

    Summarize the study’s **policy implications**—how the research findings can inform policy or decision-making. Your response should:
    - Interpret what the results suggest for governments, regulators, or institutions
    - Highlight potential actions, strategies, or decisions informed by the study
    - Use policy-appropriate language without speculation beyond the study’s conclusions
    - Be clear, practical, and informative

    Write a short paragraph for the “Policy Implications” section of a policy brief.

    Scientific Research Paper:
    Articles
https://doi.org/10.1038/s41560-020-0596-7
1Council on Energy, Environment and Water, New Delhi, India. 2Department of Environmental Health Sciences, Columbia Mailman School of Public Health,
New York, NY, USA. ✉e-mail: sunil.mani@ceew.in; saurabhtripathi.in@gmail.com
I
ndia is in the midst of the largest household energy transition
to date, with about one hundred million households acquiring a
liquified petroleum gas (LPG) connection since 20161. In 2011,
three-fifths of Indian households—primarily in rural areas—relied
on solid fuels to meet their daily cooking and heating needs2, result-
ing in substantial negative impacts on health3, the economy4 and the
environment5. The Government of India’s flagship cooking energy
programme—Pradhan Mantri Ujjwala Yojana (PMUY)—has
sought to alleviate the public health burden of household air pollu-
tion by providing a subsidy and loan for the upfront cost of adopting
an LPG connection, which typically includes a double-burner gas
stove, a 14.2-kilogram gas cylinder, a gas regulator and a hose pipe.
Although over 80 million households have benefited from PMUY6,
steep recurring expenses and the poor availability of LPG in rural
areas continue to hinder the extent to which households use LPG
for all cooking needs7,8.
Following the connection-oriented approach of PMUY, most
evaluations of the programme have discussed enrolment rates
using government-provided data9–11. Few studies have examined
LPG use after adoption, and most of these have focused on average
cylinder refill rates10,12–14. Government records indicate that about
one-quarter of PMUY beneficiaries purchased five or more refills
each year, suggesting the use of LPG as their primary cooking fuel,
while another one-quarter did not purchase any refills in the first
year, suggesting sparing use10,14. Recently, researchers used refill
records in Karnataka to show that the average annual LPG consump-
tion for PMUY customers was 2.3 cylinders (32.7 kg per year), com-
pared with 4.7 cylinders (66.7 kg per year) for general (non-PMUY)
customers15. According to a government audit, PMUY customers
averaged 2.98 cylinder refills per year (42.3 kg per year) and general
customers averaged 6.73 cylinder refills per year (95.6 kg per year)
across India between March 2018 and March 20191.
Adoption of a clean cooking fuel like LPG is a necessary first step
in ensuring smoke-free kitchens and reducing the drudgery of bio-
mass collection. However, both sustained use of cleaner fuels16,17 and
cessation of solid fuel use are required18 to achieve the stated health
outcomes of PMUY. Many households simultaneously use multiple
fuels—termed fuel stacking—rather than moving step-by-step
along an energy ladder towards cleaner fuels19.
Using panel data collected from the rural areas of six major
energy access-deprived states in India in 2014–2015 and 2018, we
assess the determinants of upward shifts in LPG use—from use as a
minority fuel for cooking only, to the primary fuel and then as the
exclusive cooking fuel. By examining dynamic fuel stacking instead
of LPG consumption, this study identifies factors that help house-
holds to transition away from solid fuel use and towards cleaner
fuels. We report a number of important findings. First, PMUY ben-
eficiaries have considerably lower odds of primary and exclusive use
of LPG than general customers. Second, the village-level penetra-
tion of LPG as a primary fuel and the age of the LPG connection are
both positively associated with sustained use. Third, we find that
easy access to free-of-cost biomass is a major deterrent to the exclu-
sive use of LPG. Finally, households depending on agriculture and
labour as the primary source of income have much lower odds of
increased use of LPG.
Review of the literature on household cooking fuel choice
The body of research characterizing the determinants of household
cooking fuel choice has been summarized elsewhere20–22. We outline
a few econometric approaches that have explained the determinants
of cooking fuel use in India. Previous work—while employing rig-
orous analytical approaches—has been limited, primarily owing to
two main gaps.
First, studies seeking to understand the drivers for adoption and
use of clean cooking fuels, as well as enablers of the reduction and
cessation of solid fuel use, are primarily based on cross-sectional
data. Such studies are inherently unable to observe household-level
transitions. Many studies of cooking fuel choice in India have also
been geographically limited, with relatively limited sample sizes23,24.
Other studies have employed nationally representative micro-
economic survey data that benefit from large sample sizes, such
as those from the National Sample Survey (NSS)25–27, the India
Human Development Survey28–30 and the National Family Health
Survey30–33. All three of these surveys have multiple waves, enabling
The drivers of sustained use of liquified petroleum
gas in India
Sunil Mani   1 ✉, Abhishek Jain1, Saurabh Tripathi   1 ✉ and Carlos F. Gould   2
Ninety-five per cent of Indian households now have access to liquified petroleum gas (LPG), with 80 million acquiring it under
the Pradhan Mantri Ujjwala Yojana (PMUY) since 2016. Still, having a connection is not enough to eliminate household air pol-
lution. Studying panel data from rural households in six major states from 2014–2015 and 2018, we assess the determinants of
cooking energy transition from solid fuels to LPG. We find that PMUY beneficiaries have much lower odds of using LPG as the
primary or exclusive fuel compared with general customers, irrespective of their economic status. Village-level penetration of
LPG as a primary fuel and the years of LPG use positively influence its sustained use, while ease of access to freely available
biomass and reliance on uncertain and irregular income sources hinder LPG use. The findings highlight the need to interlace
cooking fuel policies with rural development, to enable a complete transition towards cleaner cooking fuels.
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researchers to leverage multiple rounds of data34–36; although, only
the India Human Development Survey has a panel structure.
Two recent studies using multiple NSS rounds are notable. The
first study separately analyses the 50th round (1993–1994), the
61st round, (2004–2005) and the 66th round (2009–2010) to assess
the determinants of multiple cooking fuel use, and, separately, the
exclusive use of clean cooking fuels37. The authors find a strong
association between increasing household expenditures and clean
cooking fuel use. However, this study neither models the data in
a panel structure, nor models household-level fuel switching. The
second study creates a synthetic panel of 100,000 households using
the 55th NSS round (1999–2000) and the 68th round (2011–2012),
by matching households on the basis of state, urban/rural and
caste38. The authors model the extent to which LPG has offset fire-
wood use to estimate the change in carbon dioxide emissions due
to increasing LPG adoption and use. This study does not primar-
ily study the determinants of fuel switching or fuel stacking. Taken
together, these studies have contributed to our understanding of the
differences between LPG adopters and non-adopters, but have not
assessed household-level cooking fuel transitions.
Second, many previous analyses have focused only on the house-
hold’s primary cooking fuel as the variable of interest. Current evi-
dence suggests that the full health benefits of clean cooking fuel
use are only obtained when used nearly exclusively16,18. Yet, many
surveys have collected data only on households’ primary cooking
fuel; although this may be changing with recent efforts to incor-
porate multiple fuel use into surveys, worldwide39. Previous studies
have assessed cooking fuel choice using several different outcomes:
ownership of a clean cooking fuel; clean cooking fuel as the primary
cooking fuel; and, in some cases, ordered fuel choice25,40.
Two additional studies employing panel multinomial logit
models using multiple rounds of survey data are noteworthy. The
first uses three rounds of surveys to assess the socio-economic
factors that determine cooking fuel choice in urban households
in Ethiopia41. The authors find that household expenditures, fuel
prices and education are key determinants of fuel stacking behav-
iour. The second study uses three waves of panel data in Tanzania to
model fuel choice for cooking and lighting42. This study takes sev-
eral approaches to assessing fuel stacking and incorporates impor-
tant contextual covariates, such as fuel prices and local luminosity
data. However, neither study directly models households switching
fuels, and both include kerosene as a clean cooking fuel, despite
evidence of substantial health risk from kerosene combustion due to
its polluting emissions43. Still, efforts to formally model fuel stacking
in India are limited28.
Research design
Our analysis is based on two waves of household surveys carried out
in six historically energy-poor states in India—Bihar, Jharkhand,
Madhya Pradesh, Odisha, Uttar Pradesh and West Bengal—collec-
tively representing 40% of the Indian population (Supplementary
Table 1). The first wave covered 8,563 households and was collected
between November 2014 and February 2015. The second wave cov-
ered 9,072 households—7,317 of which were also surveyed in wave 1
—and was gathered between March and June 2018. The survey
design is described in full in Supplementary Note 1.
To simultaneously study increased LPG use and displacement of
solid fuel use, we specify a categorical outcome variable: LPG as a
minority cooking fuel (secondary to solid fuels), LPG as a primary
cooking fuel (with solid fuels retained as secondary options) and
LPG as the exclusive cooking fuel.
We use two approaches to model the determinants of increased
LPG use. In the first approach, we use a generalized-ordered logis-
tic regression to study the cross-section of LPG-owning households
in wave 2. We consider 4,102 households that reported LPG use
in wave 2, but that may or may not have used LPG in wave 1. We
only include households that were surveyed in both waves despite
analysing the cross-section of wave 2, owing to the inclusion of
certain lagged (wave 1) covariates. We identify covariates from the
literature, including socio-economic and demographic contextual
factors and household experience with cooking fuels (discussed in
full in Methods).
In the second approach, we use a panel-ordered logistic regres-
sion to assess the household-level determinants of increasing the
share of LPG in a household’s cooking fuel mix. We consider 1,411
households that reported LPG use during both survey waves (dis-
tribution by category in Supplementary Table 2). While the focus of
this study is understanding the factors for upward movement, we
include movements up, down and across to account for all variation
in household-level changes between the two survey waves.
We also undertake subsample analyses to assess whether the
explanatory factors influence households differently depending on
their starting point of LPG use (minority users versus primary users
in wave 1) (Supplementary Table 3).
Increases in LPG use between 2015 and 2018
The overall sample (n = 17,635) witnessed a dramatic increase in
LPG adoption between 2015 and 2018—from 22% LPG users in
wave 1 to 58% in wave 2. PMUY has been an important driver of
this shift: 43% of new LPG owners in wave 2 were enroled through
PMUY. However, a lower proportion of PMUY beneficiaries were
exclusive LPG users compared to general customers (Fig. 1).
In addition to a greater number of households having LPG in
wave 2, we also observe an upward progression of LPG-use category.
Among the panel subset, exclusive use was the smallest category in
wave 1, but the largest in wave 2 (Fig. 2). Still, there was some down-
ward movement from primary to minority use, and from exclusive
use to primary or minority use.
Determinants of LPG use and fuel stacking
Cross-sectional generalized-ordered logistic regressions show that
households acquiring their LPG connections through PMUY have
significantly lower odds of being primary users compared to gen-
eral customers (odds ratio (OR), 0.64; 95% confidence interval
(CI), 0.52–0.78) (Table 1). The odds drop further still for exclu-
sive use (OR, 0.56; 95% CI, 0.45–0.71). The lower odds of PMUY
households using more LPG is neither a factor of their relatively
poor socio-economic status, nor of the infancy of their connection,
37%
(N = 1,519)
32%
(N = 1,306)
31%
(N = 1,277)
0
25
50
75
100
All LPG users
(N = 4,102)
Percentage of households
56%
(N = 687)
30%
(N = 369)
14%
(N = 170)
29%
(N = 832)
33%
(N = 937)
39%
(N = 1,107)
PMUY
households
(N = 1,226)
General
households
(N = 2,876)
Exclusive use
Primary use
Minority use
Fig. 1 | Distribution of LPG-use categories among LPG households in 2018.
Percentages refer to the fraction of households in each LPG-use category
in wave 2 (2018). Sample sizes are included in parentheses. Black lines
indicate standard errors. Using a two-sample test of proportions (which
uses the t-statistic), we find that the difference between the proportions of
exclusive users of LPG among PMUY households and general customers is
statistically significant.
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as both of these factors are controlled for in the model. Rather, we
suggest that households that made a decision to spend the upfront
cost and effort to procure an LPG connection are probably more
willing and more financially prepared to use LPG for the majority
of their cooking needs than those who have been provided with a
free connection.
We also observe village-level popularity of LPG as a primary fuel
influencing LPG use. The fraction of households in a participant’s
village using LPG as their primary cooking fuel is significantly posi-
tively associated with that household being a primary or exclusive
LPG user (Table 1). Our panel model corroborates this finding.
Households in a village where the percentage of primary LPG users
increased by one percentage point had 1.046 (95% CI, 1.041–1.052)
times higher odds of moving to primary or exclusive LPG use than
households in villages where there was no change in percentage of
primary LPG users (Table 2). The positive effect of village-level pen-
etration of LPG as a primary cooking fuel suggests a possible peer
effect of LPG use and the importance of community norms in influ-
encing LPG use. Although we account for geographic heterogeneity
at the state level, higher percentages of primary users of LPG may
also be related to village-level variation in the cost and availability of
biomass, infrastructure quality or LPG availability.
As hypothesized, we find that households relying on agriculture
or on labour as their primary source of income had lower odds of
using LPG as their primary and exclusive fuel compared to house-
holds getting their primary income from salaried employment
(Table 1). Agriculture-dependent households may have lower odds
due to the unpredictability and irregularity of cash flow, which may
make the large, ‘lumpy‘ LPG refill payments a challenge. Indeed,
households where the primary income changed to agriculture or
labour between wave 1 and wave 2 had lower odds of increasing
their LPG-use category than those whose occupation stayed the
same or changed away from agriculture or labour (Table 2).
Years of owning LPG was positively associated with higher use
category in both models. This effect of time may be because house-
holds get used to the convenience of LPG-based cooking, have more
time to adjust their cooking habits and practices to LPG and/or have
more time to align their household expenditures and cash flows
with the recurring expense of LPG refills.
Counterintuitively, we did not find involvement of women
in household decisions to purchase durable goods to be a reli-
able predictor of household use of LPG after adoption. Further
research on the involvement of women in cooking energy-related
decision-making and its impact on fuel choice is warranted.
Distance travelled to procure LPG was not significantly associ-
ated with higher odds of using LPG for primary or exclusive use.
However, households that travelled one kilometre less to procure
LPG in wave 2 than in wave 1 had somewhat higher odds (OR, 1.04;
95% CI, 1.01–1.08) of moving up from minority use than those for
whom distance did not change (Supplementary Table 3).
As hypothesized, households that owned cattle had considerably
lower odds of being primary and exclusive users of LPG, probably
because of easy access to dung cakes (Table 1). The magnitude of
the association was higher for exclusive use compared to primary
use, implying that the push towards complete cessation of solid fuel
use is likely to face strong resistance from households that have easy
access to biomass. Furthermore, households that acquired cattle
between the two waves had lower odds of shifting to primary and
exclusive use than other households (Table 2).
We find that households that collected firewood daily or a few
times a week (a proxy for easy availability of biomass) in wave 1 had
lower odds of exclusive use of LPG in wave 2 than those who col-
lected firewood less frequently (Table 1). While easier availability of
free-of-cost firewood impedes exclusive use of LPG, we did not find
it hindering use of LPG as a primary fuel. Surprisingly, the effect of
weekly expenditure on traditional biomass for cooking in wave 1
on the odds of increased use of LPG in wave 2 was not statistically
significant (Table 1). This finding is contrary to our hypothesis and
warrants further research.
Respondents’ perceptions that LPG affected their health less
than traditional cook stoves in wave 1 was not associated with LPG
use in wave 2 (Supplementary Table 4). However, their percep-
tions in wave 2 were positively associated with LPG use in wave 2
(Supplementary Table 5). It is possible that knowledge of the health
benefits of LPG is a consequence of LPG use, as opposed to a driver
of its use, suggesting that health-related awareness in isolation may
not be sufficient to nudge sustained use of LPG.
Limitations and future work
Before concluding, we note some limitations of our research. We
rely on primary survey data for our analysis, including self-reported
information on quantitative variables, which could be affected by
typical limitations and biases of survey-based reporting. However, on
the basis of a couple of robustness checks for our dependent variable
(Supplementary Table 6–9), we can still claim that our self-reported
variable is reasonable for this analysis. While approaches such as
stove-use monitors and in-field fuel measurements can potentially
lead to even more precise estimates, these measurement strategies
are costly and can be prone to the Hawthorne effect, where partici-
pants behave differently to usual because they are being observed44.
In addition, we focused on fuel stacking, but only pertaining to
cooking. Future studies may investigate the end uses for each stove
or fuel in the household. At times, solid fuels are also used for other
heating applications—space heating, water heating or food prepara-
tion of animals—that are not easily replicated by LPG or other clean
cooking fuels. Elsewhere, such non-cooking end uses—or even
preparation of specific dishes—have been motivators for sustained
solid fuel use, even after long-term LPG adoption and use45,46. The
specific nature of household solid fuel uses can potentially inform
locally targeted interventions to facilitate complete transitions to
clean household energy.
Recommendations and conclusions
India has witnessed a revolution in clean cooking energy adop-
tion between 2016 and 2019 owing to the Pradhan Mantri Ujjwala
Yojana programme. With 95% of households now having access to
LPG, the policy focus in India is shifting from initial adoption to
the sustained use of LPG to reduce air pollution exposure. Using
cross-sectional and panel analyses, we examine and identify factors
Minority use
N = 570
Primary use
N = 546
Exclusive use
N = 295
Minority use
N = 371
Primary use
N = 457
Exclusive use
N = 583
2015
2018
Fig. 2 | Household-level shifts in fuel stacking from 2015 to 2018 in the
panel subset. The nodes on the left of the alluvial diagram refer to LPG-use
category status in wave 1, and those on the right of the chart refer to
category status in wave 2. Sample sizes in each category in each wave are
indicated below the labels. The links flowing from the left nodes to the right
nodes indicate upward, lateral and downward movement of households in
each LPG-use category from wave 1 to wave 2. We discuss specific patterns
of downward movement in the Methods.
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between and within households that drive sustained use of LPG and
result in the complete cessation of solid fuel use.
Our results show that PMUY beneficiaries have systematically
lower odds of using LPG for primary and exclusive cooking needs
than general customers, even after controlling for socio-economic dif-
ferences. Without targeted support for their energy transition, mean-
ingful reductions in household air pollution among the majority of
80 million PMUY households will probably remain out of reach. A
common notion worth assessing in future quantitative and qualitative
studies is that the decision and effort to procure an LPG connection
by general customers meant their greater preparedness (behaviourally
and financially) to use LPG as their primary fuel for cooking.
Given that village-level penetration of LPG as the primary fuel
has a strong positive association with household-level LPG use, we
suggest that policies with community-level targeting could effec-
tively increase LPG use. Thanks to the age of the LPG connection
having a positive influence on its use, we may witness increasing use
of LPG among recent adopters over time. However, targeted policies
to further improve affordability of LPG refills for poor households
can accelerate the transition.
Table 1 | Explaining LPG use in wave 2 (cross-section model)
Odds of primary use as opposed to minority use
Odds of exclusive use as opposed to primary use
LPG-use category: dependent
variable
OR (s.e.m.)
P > z
95% CI
OR (s.e.m.)
P > z
95% CI
Whether household is a PMUY
beneficiary
0.641 (0.066)
<0.001***
0.523
0.784
0.564 (0.068)
<0.001***
0.446
0.714
Percentage of households in a
village using LPG as their primary
cooking fuel
1.043 (0.002)
<0.001***
1.039
1.048
1.027 (0.002)
<0.001***
1.023
1.031
Weekly expenditure on biomass
(wave 1) (ln + 1)
1.012 (0.016)
0.467
0.981
1.044
0.982 (0.017)
0.270
0.950
1.015
Whether firewood is collected
multiple times a week (wave 1)
0.895 (0.085)
0.245
0.743
1.079
0.788 (0.081)
0.021**
0.643
0.965
Whether women are involved in the
household decision-making
1.135 (0.099)
0.149
0.956
1.348
0.873 (0.082)
0.146
0.727
1.048
Number of years that the household
has had LPG (ln)
1.260 (0.088)
0.001***
1.099
1.445
1.226 (0.089)
0.005***
1.064
1.412
One-way distance to procure LPG
cylinders (km)
1.010 (0.007)
0.155
0.996
1.023
1.000 (0.008)
0.984
0.985
1.015
Whether the household owns cattle
0.447 (0.039)
<0.001***
0.377
0.530
0.423 (0.036)
<0.001***
0.358
0.499
Whether the household owns land
0.838 (0.084)
0.079*
0.687
1.021
0.835 (0.086)
0.080*
0.683
1.022
Economic status index
1.147 (0.030)
<0.001***
1.089
1.208
1.171 (0.030)
<0.001***
1.113
1.232
Household size (ln + 1)
0.604 (0.067)
<0.001***
0.487
0.750
0.484 (0.053)
<0.001***
0.390
0.600
Education of the household head (by category; base category is 12th standard and above)
 No education
0.742 (0.086)
0.010**
0.592
0.931
0.742 (0.090)
0.014**
0.585
0.941
 Up to 5th standard
0.919 (0.103)
0.449
0.737
1.145
0.755 (0.086)
0.013**
0.602
0.941
 Between 5th and 10th standard
1.019 (0.127)
0.882
0.797
1.302
1.005 (0.121)
0.959
0.785
1.259
Caste of the household head (by category; base category is OBCs and general caste together)
 Scheduled caste
0.984 (0.099)
0.877
0.808
1.200
0.948 (0.104)
0.568
0.757
1.165
 Scheduled tribe
0.936 (0.165)
0.707
0.662
1.323
0.964 (0.184)
0.810
0.658
1.388
Primary source of income of the household (by category; base category is salaried occupation)
 Agriculture on own land or
leased land
0.595 (0.094)
0.001***
0.437
0.810
0.619 (0.086)
<0.001***
0.469
0.806
 Casual agricultural or daily-wage
labour
0.496 (0.082)
<0.001***
0.358
0.686
0.610 (0.092)
0.001***
0.449
0.809
 Own business
0.850 (0.159)
0.385
0.588
1.227
0.905 (0.144)
0.500
0.658
1.226
 Others
0.989 (0.296)
0.971
0.551
1.777
0.920 (0.246)
0.729
0.540
1.538
Number of households (n)
4,102
State fixed effects
Yes
log-likelihood
−3,603.996
Probability > χ2
<0.0010
Pseudo R2
0.1981
Results from the cross-section generalized-ordered logistic regression model are shown here. The response variable is the LPG-use category in wave 2. The model explains determinants of households being
primary users of LPG (compared to minority users), and of being exclusive users of LPG (compared to primary users) in wave 2. There are 4,102 households, all of which were surveyed in both waves, and
were using LPG in wave 2. Standard errors of the mean (s.e.m.) are in parentheses after the OR values. ***P < 0.01, **P < 0.05, *P < 0.10.
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Ownership of cattle and a high frequency of firewood collection—
both indicators of easy access to free-of-cost biomass—considerably
lower the odds of exclusive LPG use. Creating opportunities for
households to sell the biomass for commercial purposes—such as
alternative transport or industrial fuel—could create an opportunity
cost for biomass to facilitate greater use of modern cooking fuels.
Reduction in the distance travelled to procure LPG increases a
minority user household’s odds of moving to primary or exclusive
use of the fuel. While the government has taken steps to improve
fuel accessibility in recent years, further efforts are required to
increase the density of distribution outlets in rural areas. To reduce
the distance travelled in remote areas, the government and oil mar-
keting companies could pilot locally tailored business models, such
as extending LPG distribution to village-level entrepreneurs and
local cooperatives.
Finally, the underlying economic transitions of India’s rural
economy cannot be ignored. Households relying on agriculture and
labour as the primary source of income need livelihood support that
enables predictable and regular cash flow to facilitate sustained use
of clean cooking fuels. Convergence across government schemes on
rural livelihoods and employment guarantees with clean cooking
fuel promotion could become an important driver of the transition
away from polluting solid fuels.
Our findings underscore that there is no ‘silver bullet’ that will
yield exclusive clean cooking fuel use in rural India. PMUY has
resulted in a tremendous national transition towards LPG, and we
need multipronged approaches to accelerate its sustained use over
time. Only by going beyond cooking fuel policies, and interlacing
them with overall rural development priorities, can India move
forward in enabling a complete transition towards clean cooking
fuels for all.
Methods
Response variable. We report here on the specifications of the response,
explanatory and control variables. Data collection in wave 1 (refs. 47,48) and
wave 2 (refs. 49,50) of ACCESS have been described previously, and in further
depth in Supplementary Note 1. The survey questionnaire used is available in
Supplementary Note 2.
We define three mutually exclusive categories of LPG use to assess factors that
determined progression towards exclusive use of LPG amongst rural households
in India between 2014 and 2018. These categories are used as the response
variable in the panel and cross-sectional analysis: LPG as a minority cooking fuel
(secondary to solid fuels), LPG as a primary cooking fuel (with solid fuels retained
as secondary options) and LPG as the exclusive cooking fuel.
These categories are based on self-reported responses. Households are
classified as exclusive users if they report LPG use but do not use any other fuel
source for cooking. Primary users are those that state that LPG is their ‘primary
cooking fuel’, but also report using other cooking fuels. Minority users are the
remaining households, which use LPG but report some other fuel as their primary
cooking fuel.
Elsewhere, continuous variables have been used to model LPG consumption
after adoption. While outcomes such as ‘refills per year’ or ‘kilograms of LPG
consumed per year’ capture overall LPG use, they are limited because they do
not directly account for solid fuel use. Intuitively, increases in LPG use have been
shown to yield decreases in solid fuel use; however, the extent to which there is
perfect displacement is unknown. Furthermore, continued solid fuel use may not
be a function of needing to meet household energy demands exclusively, but is
probably also about preferences and meeting household end uses, such as cooking
specific dishes or non-cooking energy demands such as space or water heating.
Therefore, we use the three categories outlined above to capture dynamic shifts in
cooking fuel stacking patterns. As a first-degree check, we assessed our dependent
variable among 1,411 panel households by observing the changes in the amount
of firewood (kg per person per month), dung cakes (pieces per person per month)
and LPG (kg per person per year) used for cooking within households by each LPG
category shift. This fuel displacement analysis is reported in Supplementary
Table 6, and clearly shows that all the upward (downward) movements in LPG
category among panel households also come up with reduction (increase) in
traditional biomass consumption.
To further ensure robustness of our choice of dependent variable, we assessed
the deviation between self-reported primary fuel and the primary fuel based on
useful energy analysis (considering quantity of fuel and stove efficiencies). We
found the self-reported variable to be reasonable for our analysis (Supplementary
Tables 7–9).
We noticed some downward movement across the categories between the two
waves. There were few systematic differences between households that regressed
and those that made lateral or upward movement. Among those that moved
down from exclusive use, the proportion that owned cattle increased from 45% to
58%, while cattle ownership declined for all other households in the panel subset.
Furthermore, those that moved down from exclusive and primary use had a
Table 2 | Explaining household-level change in LPG use between wave 1 and wave 2
Dependent variable: LPG-use category
OR (s.e.m.)
P > z
95% CI
Percentage of households in a village using LPG as their primary cooking fuel 1.046 (0.003)
<0.001***
1.041
1.052
Women are involved in the household decision-making
0.784 (0.086)
0.027**
0.633
0.972
Number of years that the household has had LPG (ln + 1)
1.161 (0.066)
0.008***
1.040
1.297
One-way distance to procure LPG cylinders (km)
1.000 (0.008)
0.970
0.985
1.015
Household owns cattle
0.275 (0.031)
<0.001***
0.221
0.344
Household owns land
1.070 (0.148)
0.623
0.816
1.403
Economic status index
1.204 (0.032)
<0.001***
1.143
1.269
Household size (ln + 1)
0.510 (0.065)
<0.001***
0.398
0.655
Primary source of income of the household
 Agriculture on own land or leased land
0.662 (0.093)
0.003***
0.502
0.872
 Casual agricultural or daily-wage labour
0.643 (0.100)
0.005***
0.474
0.872
 Salaried occupation
1.192 (0.190)
0.271
0.872
1.629
Number of observations
2,821
Number of households
1,411
State fixed effects
Yes
log-likelihood
−2,515.184
Wald χ2
577.569
Probability > χ2
<0.0010
The table shows results from the panel-ordered logistic regression model. The response variable is the LPG-use category in both waves of survey. The model explains household-level determinants of the
movement to higher LPG-use categories from wave 1 to wave 2. It includes 1,411 households, all which used LPG in both waves. The s.e.m. are in parentheses after the OR values. ***P < 0.01, **P < 0.05, *P < 0.10.
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sharper drop in their asset index score than those that did not regress. Last, among
those that regressed, the proportion relying on agriculture for primary income
increased, whereas that statistic decreased for the households that remained in the
same category or moved up a category between the two waves.
Explanatory variables. Here, we outline the specification of explanatory variables
included in our models, as well as discuss their inclusion in previous studies.
The variables were chosen on the basis of the existing literature on cooking
energy adoption and use. Supplementary Table 10 (cross-section subset) and
Supplementary Table 11 (panel subset) contain descriptive summaries of all
explanatory variables, along with the hypothesized direction of association between
the covariate and LPG-use category.
We include an indicator variable for whether a household is a PMUY
beneficiary. Elsewhere, PMUY beneficiaries have been shown to utilize LPG after
adoption differently from general consumers; often purchasing fewer refills relative
to their peers15. Some PMUY beneficiaries may be financially unable to support
regular cooking with LPG, and without the programme may not have purchased an
LPG connection. In addition, it is possible that PMUY beneficiaries have different
perceptions, attitudes or abilities compared to general customers, although this
remains unexplored. We found that PMUY households were generally from the
same rural villages as general customers (Supplementary Fig. 1), indicating no
geographical bias in sampling. In the cross-section subset, 45% of minority users,
26% of primary users and 13% of exclusive users of LPG were PMUY households.
In this subset, we found 134 households that identified as PMUY beneficiaries
in wave 2, while also having reported LPG use in wave 1. Since PMUY only
started after wave 1 of the survey, we force-coded these households as general
(non-PMUY) customers to maintain internal consistency. We note the specific
timing of data collection in both wave 1 and wave 2 in relation to when PMUY
enroled the beneficiaries in each state in Supplementary Table 12.
We include the percentage of households in a village using LPG as their
primary cooking fuel as a potential measure of local popularity of LPG, which, as
a community norm, could influence household use of LPG. In addition to the peer
effect, we also expect this covariate to capture unobserved village-level variations,
such as local road infrastructure and availability of LPG. Empirical evidence
regarding the associations between LPG penetration in a community or community
norms is otherwise limited, but there is some positive evidence for increased
probability of LPG ownership with higher LPG community penetration51. There are
ongoing trials testing the effects of peer effect and community norms52,53.
We include the natural log of total weekly expenditure on biomass procurement
in wave 1. We expect that households that spent a higher amount on biomass in
wave 1, might have higher odds of increased use of LPG in wave 2 because they
would find LPG to be relatively cheaper54. We find that households that are primary
or exclusive users of LPG in wave 2 had a higher mean spending on biomass in
wave 1 than minority users of LPG. Of the households in the cross-section subset,
57% did not spend any money on procuring biomass in wave 1, which made the
natural log calculations impossible. Therefore, before converting the values into
their logarithmic form they were increased by one. In the cross-section analysis, we
use the lagged (wave 1) value of this covariate instead of the wave 2 value because
we expect it to be endogenous with LPG-use category. For the same reason, we do
not consider this covariate for the panel analysis.
To capture historical availability of biomass, we also include whether firewood
was collected multiple times a week in wave 1 as a binary variable—where ‘1’ is
assigned to households that collected firewood daily or a few times in a week,
and ‘0’ is assigned for any other frequency. High frequency may indicate ease of
firewood collection, and the resistance a household is likely to face in transitioning
to the exclusive use of LPG. As above, we use the lagged (wave 1) value of this
covariate instead of the wave 2 value in the cross-section analysis because we
expect it to be endogenous with LPG-use category. Similarly, we do not consider
this covariate for the panel analysis.
The involvement of women in household decision-making may be an important
determinant of LPG use. We asked respondents who made the decision to purchase
durable goods; responses were categorized as (1) a woman was involved in
household decision-making (woman alone or joint with male household head), or
(2) a woman is not involved in household decision-making. In the panel subset,
there is a net increase in the proportion of households where women were involved
in household decision-making between the two waves, from 20 to 28%. Women are
often the primary cooks and, as such, may understand the benefits of transitioning
to modern fuels, such as LPG, better. If women are involved in household
decision-making, then their opinions can influence cooking fuel choice. A previous
study showed that households where women were involved in decision-making
had higher odds of owning LPG among the first wave of ACCESS55. Elsewhere, it
has been relatively uncommon to directly model women’s decision-making power
in studies of cooking fuel choice20,56. However, in some cases households headed by
a woman have been more likely to have a clean cooking fuel25,57, and in others less
likely, compared with households headed by a man58,59.
We include the natural log of the number of years that a household has had
LPG to capture shifts in attitudes or in abilities for using cooking gas efficiently
due to increased familiarity with the fuel over time. Increased ability is often cited
as an important aspect of behavioural change52,60. This covariate required changes
similar to those in the biomass expenditure covariate. While studying data for
all households that were surveyed in both the waves, we came across internal
inconsistencies in this covariate. First, for households that had an LPG connection
in both waves, the reported age in wave 1 and wave 2 should have differed by no
more than 3.5 years—the time between the two waves of surveys. However, the
difference was off for most of the households. Therefore, we assumed the age
reported in wave 1 to be true and added 3.5 to that to arrive at the estimated age
in wave 2. Second, for households that did not use LPG in wave 1 but did so in
wave 2, the age of connection—as reported in wave 2—could not be higher than
3.5 years. However, for 22% of the households that reported a higher value, age of
connection was capped at 3.5.
We asked participants whether they considered LPG to be better than
traditional cook stoves from the viewpoint of impact on health, coding responses
into a binary variable where ‘1’ is assigned to households that found LPG better,
and ‘0’ to those that thought LPG was not better. Positive perceptions of LPG and
an understanding of the negative health impacts of traditional solid fuel cooking
practices may indicate a household valuing the benefits of LPG adoption and use.
Elsewhere, such positive perceptions have been observed among LPG owners. We
use this covariate twice in the cross-section subset—first, the response in wave 1
to assess the causal impact on LPG category in wave 2, and second, the response in
wave 2 to confirm if perception is endogenous with use. This covariate is only used
in the alternate cross-section specifications (Supplementary Tables 4 and 5).
To account for the difficulty associated with accessing LPG cylinders in rural
areas, we include the self-reported one-way distance covered by households
to procure LPG cylinders (in kilometres). Fuel accessibility is a well-defined
constraint to LPG adoption and use8. There is increasing empirical evidence for
direct associations between fuel accessibility and use51,61,62 and interventions testing
potential solutions52. In the cross-section subset, we find that 43% of households
received doorstep delivery of LPG cylinders, and while 22% of households
travelled up to 3 km (one-way) to procure LPG, 24% travelled more than 5 km.
We also find that the proportion of households receiving doorstep delivery has
increased unevenly across categories of LPG use between the two waves—from
12% to 29% for minority users, but from 24% to 57% for exclusive users—even
as the distance travelled to procure LPG for those who do not receive doorstep
delivery has declined evenly across all three categories. We acknowledge the
potential for measurement error, since not all households may accurately report
the distance in kilometres. Although we cannot quantify the extent of this error in
the cross-section subset, we expect a degree of self-correction in the panel subset,
where respondents might have committed a similar random error in both waves,
balancing out the error in the observations.
To capture easier availability of free-of-cost biomass, we include two binary
variables—ownership of cattle and ownership of land. In the cross-section, 68%
own land and 57% own cattle. Literature suggests that these may be significant
predictors of continued biomass use24. We account for access to land because many
households cooking with firewood or crop residue obtain it from their own land63.
Control variables. We use a set of variables to control for socio-economic factors
that are likely to impact regular use of LPG by rural households, and include
state-level fixed effects to account for variation in unspecified state-level factors
that could affect household consumption of LPG48,64. Supplementary Table 10
(cross-section subset) and Supplementary Table 11 (panel subset) also contain
descriptive summaries of all control variables, along with the hypothesized
direction of association between the covariate and LPG-use category.
We utilize an economic status index as a measure of a household’s relative
wealth and economic status, based on the Filmer and Pritchett65 approach. Such
indices are commonly used across studies in regions where fixed incomes are
uncommon65–67. Overall measures of wealth and income, including asset indices,
have been predominately positively associated with ownership and use of clean
cooking fuels57,68,69. The list of variables included in the economic status index
can be found in Supplementary Note 3 and summarized in both survey waves in
Supplementary Table 13.
The level of education of the household head is used as a measure of general
awareness in the household and the ability to make informed decisions on clean
cooking energy. Higher levels of education of the household head have often been
positively associated with adoption of clean cooking energy27,70,71. We use four
categories of education: (1) no education, (2) up to 5th standard, (3) between
5th and 10th standard and (4) 12th standard and above (the base category). A
significantly lower proportion of households where the household head has not
received a formal education are exclusive users of LPG than those where the
household head has studied beyond the 5th standard.
The caste of the respondent is used as a categorical control variable to account
for the impact of longstanding social hierarchies on access to LPG connections
and refills. Caste is a social stratification system that has been negatively associated
with clean cooking energy adoption66. The sample has households belonging to
four caste categories: (a) scheduled caste (SC), (b) scheduled tribe (ST), (c) other
backward classes (OBC) and (d) general. We assess the likelihood of sustained use
of LPG for SCs and STs, against the combined base category of OBCs and general,
because SCs and STs are historically considered to be the more systematically
disadvantaged communities.
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The primary source of income of the household is used as a categorical control
variable for the varying impact of different cash-flow structures on a household’s
ability to pay for the recurring cost of cooking gas. We use five categories:
(1) agriculture on own or leased land, (2) labour (agricultural or daily wage),
(3) salaried employment, (4) own business and (5) others, with salaried
employment as the base category. ‘Other’ sources of primary income mostly
included cattle rearing, employment in religious institutions, driving and pension.
A larger proportion of households with salaried employment and business as
primary sources of income are using LPG as an exclusive fuel, while a larger
proportion of those reliant on agriculture and labour are using LPG as a minority
fuel. Dependence on agricultural or daily-wage labour has been negatively
associated with clean cooking energy access, which is probably attributable to lower
socio-economic status or inconsistent cash flow21,72. In other cases, the primary
source of income has not been statistically significantly associated with fuel choice
or is absent from the analyses20. In the panel model, we change this covariate by
including binary variables for the three most theoretically relevant primary sources
of income from an affordability (cash flow) perspective on LPG uptake: agriculture
on own or leased land, labour (agricultural or daily wage) and salaried employment.
The natural log of household size is included to account for its potential
impact on LPG use. In previous studies, household size has been both positively23
and negatively40,68 associated with clean cooking energy use. In some cases, larger
households may seek faster or more cooking options (a positive association with
cleaner cooking), while in others it is possible that larger households require the
substantially greater capacity of traditional stoves to handle large quantities of food,
and are less likely to see the benefit of a possibly limited cleaner cook stove, or have
more available labour for fuelwood collection (negative association). There are
other studies that have found no association between household size and cooking
fuel choice73. This covariate required changes similar to those in the biomass
expenditure covariate.
Tests of model assumptions, multicollinearity and robustness. The
generalized-ordered logistic regression enables us to relax the parallel lines
assumptions of ordered logistic regressions because we do not see a theoretical
justification to impose the parallel lines restriction. However, we also use an
autofit script in Stata that uses a Wald test to determine which covariates
have different beta coefficients by outcome category. Then, the autofit script
relaxes the parallel lines assumption for these covariates only. Supplementary
Table 14 shares the results from the Wald tests for parallel lines applied to
each covariate. Supplementary Table 15 shows results from the regression no
autofit script and parallel lines assumption imposed (Supplementary Table 15).
While general directions and magnitudes of the associations are similar to our
main findings presented in Table 1, we note that our main results with relaxed
assumptions show meaningful variation in the coefficients by outcome for some
variables, such as household size, education of the household head and primary
source of income.
We also tested for the assumption of multicollinearity after finalizing variables
by estimating the variance inflationary factor for each covariate. We found
acceptably low levels of multicollinearity in our model (Supplementary Tables 16
and 17). To account for potential residual spatial autocorrelation at the village level
(beyond what we have captured using state-level fixed effects in the cross-sectional
model and other village-level covariates), we carried out an additional
generalized-ordered logistic regression clustering standard errors at the village level
(Supplementary Table 18). Coefficients are similar to those presented in Table 1.
Reporting Summary. Further information on research design is available in the
Nature Research Reporting Summary linked to this article.
Data availability
The data that support the findings of this study (both of panel and cross-sectional
analysis) are made available through Figshare at https://doi.org/10.6084/
m9.figshare.9810170.
Code availability
The Stata.do files that format, clean and analyse the merged and appended datasets
are available through Figshare at https://doi.org/10.6084/m9.figshare.9810167,
while the R scripts that produce the figures are available at https://doi.org/10.6084/
m9.figshare.11838963. Information on unique identifiers between the datasets is
available in the Stata.do file.
Received: 16 August 2019; Accepted: 28 February 2020;
Published online: 27 April 2020
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Acknowledgements
The Council on Energy, Environment and Water supported time spent by S.M., A.J. and
S.T. on this research. The data collection was supported by the Shakti Sustainable Energy
Foundation and the National University of Singapore. C.F.G. is supported by the United
States National Institute of Environmental Health Sciences grant no. T32 ES007322.
Author contributions
A.J. conceptualized the study and led the design of the work. A.J., S.T. and S.M.
contributed to the collection of data. S.M. and A.J. led the data analysis, with input from
all team members. S.T. led the interpretation of results and the writing of the manuscript,
with input from all team members. C.F.G. contributed to writing the manuscript, led
reviewing of the literature and designed the figures. All authors discussed the results and
commented on the manuscript. Please direct any comments or requests for data used in
the figures or analysis to S.M. (sunil.mani@ceew.in).
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information is available for this paper at https://doi.org/10.1038/
s41560-020-0596-7.
Correspondence and requests for materials should be addressed to S.M. or S.T.
Reprints and permissions information is available at www.nature.com/reprints.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
© The Author(s), under exclusive licence to Springer Nature Limited 2020
Nature Energy | VOL 5 | June 2020 | 450–457 | www.nature.com/natureenergy
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The study design, which is primarily quantitative, comprised of cross-sectional statistical analysis of the latest wave of survey data, and
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The sample for this article is rural households from six states in India - Bihar, Jharkhand, Madhya Pradesh, Odisha, Uttar Pradesh, and
West Bengal. These states were selected because they contain 51% of India's rural population and are historically some of the most
energy-access deprived states in the country. In the first wave, we surveyed 714 villages in 51 districts across the six states, reaching out
to 8,563 households. In the second wave, we doubled the sample in Odisha (which was earlier 504 households), and surveyed in 756
villages across 54 districts, and reached out to 9,072 households. In each surveyed village, we surveyed 12 households at random. Over
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Owing to budgetary constraints, in the first wave, we sampled one district from each administrative division of each state, except for in
West Bengal, which only has three large divisions, where we sampled two districts in each division. In the second wave, we sampled two
districts in each division of West Bengal as well as Odisha, thereby doubling the sample in Odisha. This was done in order to improve
representation of the sample in Odisha. Using Census 2011 data, each district was chosen with a probability proportional to its
population relative to the division population. Once the district is selected, we split the total population of each district into two groups
of villages such that one comprises primarily large villages and other comprises primarily small, but the population in each group is more
or less equal. Although the number of households in each group is the same, the group with large villages has fewer villages. Seven
villages were then sampled from each group with probability proportional to population. This approach ensured that the sampling is self-
weighted within a district, yet guaranteed both small and large villages are well represented in the sample. In each village, we surveyed
12 households at random.
Data collection
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Morsel Research and Development) interviewed the head of the household (if not available, an adult member) of each sampled
household using a questionnaire that was prepared by the authors and their collaborators and tested in the field pilots. The second wave
of data was collected using the app-based SurveyCTO software by the enumerators. This allowed for real-time monitoring of data quality
and enumerator bias, which were used to commission re-surveys. About 5 per cent of the sample was resurveyed in each wave. Each of
the enumerators were trained and evaluated by the researchers in both waves. The researchers were not present at the time of the
survey and hence did not interfere with the process of data collection. The data was converted into .csv files by the survey company and
shared with the researchers for analysis a few weeks post-data collection.
Timing
The first wave of data was collected from November 2014 to February 2015. The second wave was collected from March to June 2018.
Data exclusions
For the analysis contained in this article, two subsets were used. In the first, i.e. cross-section subset (4,102 households), only those that
used LPG in Wave 2, yet may or may not have used LPG in Wave 1 were considered. Further, we included only those that were surveyed
in both waves because of the inclusion of certain lagged (Wave 1) covariates. In the second panel subset (1,411 households), only those
that used LPG in both waves of survey were included, in order to determine factors that determine upward mobility in LPG-use.
Non-participation
In the second wave of the survey, we were able to retain about 85% of the households that participated in the first wave. Drop outs were
primarily owing to enumerators finding the particular household's door locked at the time of visit, or owing to them not finding the
particular household available at the address provided (perhaps because the family has shifted to a different location). In these cases, the
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    Policy Implications:

Answer:
• Although LPG use has increased since 2014–15, the lower odds of PMUY households using LPG as the primary or exclusive cooking fuel imply that further targeted interventions are needed to facilitate a complete and sustained energy transition.
• In areas with low LPG uptake, government schemes on rural livelihoods and employment guarantees should converge with LPG promotion to enable predictable cash flows (to pay for LPG refills) among rural households.
• Village-level targeting of LPG promotion could be an efficient approach in laggard areas to promote sustained use through community-level awareness campaigns and improved LPG availability.
• Promoting the use of biomass in alternative value chains — thereby creating an opportunity cost for free-of-cost biomass — will be critical to the cessation of solid fuel use in rural households.