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

    Summarize the paper's key **research findings** in the style of a policy brief. Your response should:
    - Focus on the major empirical or model-based results
    - Present findings that are policy-relevant and supported by the study
    - Use concise, non-technical language with essential technical terms as needed
    - Avoid quoting directly or summarizing minor details

    Write a short, clear paragraph appropriate for the “Scientific Research Findings” section of a policy brief.

    Scientific Research Paper:
    Analysis
https://doi.org/10.1038/s41560-019-0429-8
1Institute for Resources, Environment and Sustainability,  University of British Columbia, Vancouver, British Columbia, Canada. 2International Institute
for Applied Systems Analysis, Laxenburg, Austria. 3Stockholm Environment Institute, United States Centre, Somerville, MA, USA. 4Forest Resources
Management, Faculty of Forestry,  University of British Columbia, Vancouver, British Columbia, Canada. *e-mail: abhishek.kar@alumni.ubc.ca
C
lean cooking energy transitions away from solid fuels present
a major public policy challenge for the Global South1,2. About
2.9 billion people lack access to clean cooking fuels such as
gas and electricity and burn solid fuels in open fires and other simple
devices3. Exposure to household air pollution from solid fuel com-
bustion is associated with 1.6 million avoidable deaths each year4 and
results in a global welfare loss of roughly US$1.5 trillion5. Worldwide,
~30% of wood fuels burnt for cooking are not sustainably harvested.
This contributes to forest degradation and global emissions of
1.0–1.2 GtCO2e6. Women often engage in solid fuel collection, thereby
limiting their economic and educational opportunities7,8.
To tackle these negative externalities, India recently launched the
Pradhan Mantri Ujjwala Yojana (hereafter, PMUY) to promote lique-
fied petroleum gas (LPG)1,9. The programme provides poor women
with a one-time subsidy and an optional loan to cover the initial up-
front cost1,10 (see Supplementary Figs. 1–4 in Supplementary Note 1
for details on PMUY, and Supplementary Table 1 in Supplementary
Note 2 for details of enrolment costs). PMUY’s deployment in low-
income, rural settings is unmatched in terms of scale and pace in
the history of modern cooking energy access. Seventy million poor
households11 took advantage of PMUY to purchase LPG stove kits
within 35 months since its launch. PMUY has garnered interna-
tional attention12,13 and enquiries from other countries hoping to
replicate elements of the programme14.
However, there has been no objective evaluation of the extent
to which PMUY has actually induced a transition away from solid
fuels15. Assessments of LPG use, beyond just acquisition, are critical,
as the health, social, economic and environmental benefits of a tran-
sition2 are conditional on the extent to which solid fuels are replaced
by LPG16. Typically, a clean cooking transition starts with the pur-
chase of an LPG starter kit and partial fuel stacking. For a successful
transition, this needs to be followed by sustained and growing LPG
use17, and eventually, complete solid fuel displacement by LPG18,19.
At a national level, aggregate state-level consumption data
(A. Jindal, personal communication) for PMUY beneficiaries (as of
November 2018) show that about half of the total beneficiaries from
30 states have been enrolled for at least 1 year. Of the PMUY benefi-
ciaries who have completed at least 1 year, 28% purchased 5 or more
cylinders, while 24% did not return for even a single refill purchase
in their first year (see Supplementary Note 1 for background infor-
mation on India’s LPG market and state-level patterns of use).
Notably, LPG is a commercial fuel and sold in cylinders (14.2 kg
or 5 kg). All beneficiaries buy the first (installation) cylinder with
the stove kit and have to purchase refill cylinders for continued use20.
Hereafter, cylinder implies 14.2 kg and total LPG purchase includes
installation cylinder unless mentioned otherwise. Typically, 10–12
cylinders are required annually to use LPG as an exclusive cooking
fuel for an average-sized family20. PMUY beneficiaries do not get
incentives for refill purchases, beyond a subsidy that is available to
all LPG consumers.
For the purposes of this study, we view transitions as a two-
step process—acquisition of an LPG kit and sustained use through
the regular purchase of LPG19. The core premise of PMUY1 is that
‘deprived’ women aspire to use LPG21 but cannot afford the up-
front capital cost22. In other words, the growth in the number of
PMUY consumers should ideally be additive (that is, in addition to
any business-as-usual (BAU) growth that would have happened in
the absence of PMUY). However, it is reported that ‘many well-to-
do [sic] households—who could otherwise afford LPG’ benefitted
from PMUY23. If PMUY targets consumers who could and would
have purchased LPG without financial incentives, ‘subsidy target-
ing’ emerges as an important line of inquiry. Hence, from a policy
evaluation perspective, to understand the impact of PMUY, assess-
ing the growth in the number of customers after accounting for the
BAU trend is required to assess the actual ‘purchase’ transition.
There is also an assumption in the PMUY promotions24,25 that
once the poor are unburdened with the capital outlay for stoves, they
will be able to buy enough LPG to ‘lead a smoke-free, less polluted,
convenient and healthy life’26. Past studies17–19 suggest, however,
that purchases by low-income rural households do not necessarily
Using sales data to assess cooking gas adoption
and the impact of India’s Ujjwala programme in
rural Karnataka
Abhishek Kar   1*, Shonali Pachauri   2, Rob Bailis   3 and Hisham Zerriffi   4
More than 70 million poor women in India have received liquefied petroleum gas (LPG) stoves within the first 35 months under
a government programme, Pradhan Mantri Ujjwala Yojana (PMUY). Here, we analyse multi-year LPG sales data from a district
in Karnataka to assess enrolment and consumption trends for both PMUY beneficiaries and general (non-PMUY) rural consum-
ers. We find rapid growth in enrolments of LPG consumers, but this is not matched by an increase in LPG sales, suggesting that
LPG access has not induced a full transition away from the use of polluting solid fuels. The number of LPG refills among PMUY
beneficiaries is less than half that of rural general consumers. We also find no observable increase in LPG consumption among
general rural consumers with years of experience. These results suggest that mid-course policy revisions to encourage regular
LPG use are needed for both PMUY and general rural consumers.
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result in sustained use23,27,28. The gap between consumption (refill
purchases) and consumer growth rates are important indicators of
the impact of PMUY’s one-time capital cost incentive on promoting
regular usage (repeat refill purchases).
India, like many developing countries, has a large urban–rural
disparity in LPG use. Urban populations have greater access to
LPG distributors, lower access to solid fuels, higher incomes and
higher opportunity costs of labour1,18,28,29. Urban women also have
higher income and education levels. These indicators are tradition-
ally associated with greater decision-making autonomy and lower
gender disparity30. Hence, comparing rural PMUY beneficiary con-
sumption levels against urban or national averages may be mislead-
ing, especially in the short term, and LPG consumption by general
consumers in rural areas may provide a better benchmark.
The literature on technology adoption, in general, suggests that
increasing familiarity with technology can help reduce people’s
attachment to earlier technologies due to greater comfort, expertise
and confidence that come with experience31,32. A recent study sug-
gests that LPG consumption in general households is strongly cor-
related with experience, with a median difference of two cylinders
between consumers after their first year and consumers after their
tenth year23. This remains to be assessed for PMUY beneficiaries.
Past literature also suggests that household demographics, fuel
prices, socio-economics and climatic conditions influence the con-
sumption of clean cooking fuels33–35. A better understanding of the
quantitative impact of LPG price and seasonality on LPG purchases
is, however, largely absent in the literature, as most studies are cross-
sectional in nature36. To design better promotional campaigns and
optimize pricing policies, understanding the relative effect of price
and seasonality can be informative.
Literature hitherto published on PMUY15,20,23,28,37,38 has primarily
focused on national-level LPG connections, average consumption
or in-depth surveys of PMUY beneficiaries from rural areas based
on self-reported consumption data. These prior works are either too
general (national-level and average consumption figures) or need to
be complemented with other data sources on consumption. Here, we
use an original dataset of LPG sales transactions records (>200,000)
for more than 25,000 rural (PMUY and general) consumers from
Koppal district in Karnataka state to address these knowledge gaps
(see Methods) to assess the efficacy of PMUY in promoting a cook-
ing energy transition, and the determinants of adoption, which can
inform potential mid-course corrections, and similar future initia-
tives. The dataset used contains details for all LPG sales transactions
in Koppal district (see Supplementary Note 3 and Supplementary
Table 2 for a socio-economic profile of the region) between January
2016 and December 2018 for three distributors of a public sector
LPG marketing company (Indian Oil) and data from earlier than
2016 for distributors 1 and 2 (D1 and D2; see Methods). While
the results presented here are specific to the conditions within the
Koppal region, they address key questions around LPG use and
point to implementation issues that may have broader implications.
They also demonstrate the value of large consumer purchase data
analysis to support transparent programme evaluation.
PMUY and LPG consumer growth
PMUY was launched in Koppal in June 2017. Within 16 months,
PMUY beneficiaries in this region exceeded the number of general
rural consumers. By the end of the available data window (December
2018), there were approximately 15,000 PMUY customers and
12,500 general customers in the database. The median monthly
growth rate in PMUY customers was approximately six times
that of the general customers over the same time period (Fig. 1)
and twice that of the general customers in the pre-PMUY period.
However, that rapid increase in PMUY customers is also associ-
ated with a drop in the growth of general customers. The median
value for the distribution of month-to-month general consumer
growth rate decreased from 2.4% to 0.9% after PMUY was rolled
out (Fig. 1). Overall, the compounded monthly enrolment growth
rate among general consumers dropped by half from 3.2% to 1.5%
during this time period. Under a BAU trend, with 3.2% monthly
growth, the number of (general) consumers ought to have increased
from 9,629 consumers (July 2017) to 16,423 (December 2018).
Instead, the general consumer numbers reached only 12,364. The
current number of actual consumers (27,431) would probably not
have been reached until April 2020. Thus, after adjusting for the
BAU trend, we estimate that PMUY has been able to fast-track LPG
consumer enrolments by 16 months, but at the same time there was
a drop in general customers from the expected number.
We consider two explanations for the decline in growth of gen-
eral consumers. First, some PMUY-eligible consumers would have
become consumers regardless but are now enrolled as PMUY con-
sumers and are, essentially, ‘free-riders’ on the programme. If the
lower rate of growth of general consumers is due to a diversion of
potential general consumers to PMUY beneficiaries, then PMUY’s
absolute addition of 15,067 consumers should be adjusted for the
loss in general consumers to examine PMUY’s effective contribu-
tion to uptake of LPG in rural households. This adjustment suggests
that the effective impact of PMUY in expanding the consumer base
is only 73% of the absolute number of enrolled PMUY consumers.
A second explanation emerges from interviews with distributors
and executives from oil marketing companies (OMCs)—the three
public sector companies responsible for household LPG distribu-
tion and marketing. These informants suggest that people ineligible
for PMUY may have adopted a ‘wait-and-watch’ approach, hoping
that rules would change, making them eligible in the future. Indeed,
the eligibility has already been expanded two times in the last 18
months (see Supplementary Note 1). A combination of both factors
may be at play. Hence, PMUY enrolment numbers are not strictly
additive in nature.
We also find the rapid expansion in LPG users attributable to
enrolment in PMUY has not resulted in a comparable increase in
LPG sales (Fig. 2). There were only ~50 daily refill sales recorded
for the 15,000 PMUY customers in December 2018. By con-
trast, the 12,000 general consumers purchase ~150 refills daily
(see Supplementary Fig. 5). This is not surprising, as the poor-
est households were specifically targeted by the programme and
their rapid inclusion into the consumer pool has reduced average
consumption levels. Thus, while PMUY has been successful in
promoting LPG enrolment among the rural poor1,20, their enrol-
ment has led to an overall decline (see Supplementary Fig. 6 in
Supplementary Note 4) in consumption among households with
an LPG connection.
LPG use in PMUY beneficiaries versus general consumers
We compare normalized and adjusted monthly refill sales for PMUY
and general consumers (see Methods). Figure 3 shows monthly
sales from January 2016 to December 2018 (equally split between
the pre-PMUY and post-PMUY period) normalized by the number
of registered consumers.
In recent months, PMUY refill sales have fluctuated around
100 cylinders per 1,000 consumers (suggesting rare use), while the
monthly refill sales for general consumers is around 400 cylinders
per 1,000 consumers. This suggests that average general consumers
use LPG as a secondary or primary cooking fuel. Considering that
the national (urban + rural) monthly average is about 600 cylinders
per 1,000 consumers, there is considerable scope to encourage more
regular use among general consumers in rural areas.
To avoid capturing any effect of experience, we also compare
LPG consumption trends of both general and PMUY customers for
just their first year as customers (Fig. 4). The mean consumption
rate for PMUY and general customers is 2.3 and 4.7, respectively
(this includes refill purchases as in Fig. 3 plus the initial cylinder).
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The gap between PMUY and general consumers widens moving
across the purchase spectrum (at 5th and 95th percentiles, the gap
increases from 1 to 5 cylinders). Notably, 35% of PMUY consumers
purchased no refills in their first year. Only 7% of PMUY consumers
have purchased 4 or more cylinders, which is the median purchase
level for general consumers.
LPG use over time and the effect of experience
An increase in LPG consumption with experience would conform
to patterns of technology adoption in other sectors, but there are
few studies that directly measure this for cooking fuels. One recent
Indian study does find an increase in LPG consumption with expe-
rience, based on self-reported data23. As PMUY consumers in this
region have not yet completed two years since enrolment, we can-
not assess their use over time. However, we do have up to five years
of data for general customers, and for these users we do not find
a discernible increase in LPG cylinder purchases over this interval
(Fig. 5). The median refill rates and distribution of refills remain
nearly constant over this period. There is a slight decrease in the
time between refills as consumers consume more, but not enough
to result in an increase in a number of cylinders purchased per year
(see Supplementary Fig. 7 in Supplementary Note 6).
The largely unchanging distribution of annual consump-
tion for general consumers masks the varying patterns of annual
consumption when individual consumers are tracked (Fig. 6).
Counter-intuitively, only a minority of consumers increased their
consumption (number of LPG cylinders purchased annually) in
their second year of use. This suggests that policy interventions
to encourage regular use should also target general consumers.
Roughly 75% of consumers have purchases that stay the same or
fluctuate by 1–2 cylinder a year (see Supplementary Figs. 8 and 9).
Notably, the steady refill rate is despite the fact that macro-eco-
nomic conditions have improved in the region during this period.
The net price of LPG was mostly stable (see Supplementary Fig. 10
in Supplementary Note 7) while both the Indian39 and Karnataka
state40 economies experienced >5% gross domestic product growth
in the 2013–2018 period.
While we are not able to directly assess multi-year consump-
tion trends for PMUY customers, the data on general customers
1
10
100
(Pre-national PMUY)
General
consumer
(Pre-local PMUY)
General
consumer
(Post-local PMUY)
General
consumer
PMUY
consumer
Consumer group category
Month-to-month growth in number of LPG consumers (%)
Fig. 1 | Monthly LPG consumer growth. Monthly PMUY and general consumer enrolment growth rates from 2016 (pre-national PMUY) to December 2018
(post-local PMUY) in Koppal. A logarithmic y axis is used to capture month-to-month growth rates in consumer enrolment in the form of box plots. The
first three box plots (from left to right) refer to monthly average growth rates for general (non-PMUY) consumers before PMUY was launched nationally,
before PMUY was launched in Koppal and after PMUY was launched in Koppal, respectively. The last box plot presents the growth rate for PMUY
consumers after the Koppal launch in June 2017. The range of the box represents the interquartile range (IQR) of the distribution, the horizontal line inside
the box is the median value and the Tukey whiskers are extended to the farthest points of the distribution that are not outliers (no more than 1.5 × IQR from
the edge of the box). The outliers are denoted by dots.
0
50
100
150
0
5,000
10,000
15,000
Jun. 2017
Dec. 2017
Jun. 2018
Dec. 2018
Purchase month year
Total daily refill sales (n) to PMUY consumers
Total PMUY registered consumers (n)
Daily refill sales
Cumulative registered consumers
Smoothed daily refill sales
Fig. 2 | PMUY consumer growth and daily refill sales. Cumulative daily
PMUY consumer enrolment (green line) and daily refill sales across all
three distributors (red dots) between July 2017 and December 2018. The
trend in daily refill sales data is depicted by the orange line (smoothed trend
line). PMUY consumer enrolment began in June 2017, but refill purchase
began in August 2017.
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are indicative of what the trends may look like over time for PMUY.
Given the very low base year consumption level that PMUY con-
sumers are starting from, it is unclear what impact experience
will have on increasing their refill rates substantially in the near
future. If they follow the pattern of the general consumers, however,
they will probably remain at low levels of consumption absent addi-
tional interventions.
Notably, the first year median consumption levels have been
declining (Fig. 5). Those who enrolled as LPG consumers earlier
(for example, in 2014) used more LPG to cook during their first year
as consumers than later enrollees (for example, 2017). This may
mean that older consumers were either more motivated or wealthier
than later adopters.
Determinants of LPG consumption
We ran several linear regression models to determine what factors
best explain monthly normalized (per 1,000 registered consumers)
LPG sales by distributor and what drives the observed fluctuations
in sales patterns (see Supplementary Note 8 and Supplementary
Fig. 11). We find that a standard deviation (s.d.) increase in distribu-
tor (up-front) price reduces normalized monthly sales by 0.27 s.d.
We also see a comparable effect of seasonal factors in explaining the
variation in refill sales. A shift from ‘no cropping’ season to cropping
or harvest season increases monthly refill sales by 0.22 or 0.26 s.d.
respectively, everything else remaining equal. Refill rates in summer,
when agricultural activity is limited, are ~10% lower than rates dur-
ing cropping and harvest seasons when people are busy with agricul-
tural work. During the cropping season, people are busy preparing
fields, planting and weeding. They place a high value on their time
and meals tend to be quick, which is conducive to cooking with LPG.
Later, during the harvest season, people have cash in hand, making
LPG purchases easier33. In contrast, during the summer, people have
more time and less income. The climate is hot and dry, so people
have easy access to dry biomass as well as crop residues from after the
LPG as rare/irregular fuel
LPG as secondary fuel
LPG as primary fuel
LPG as exclusive fuel
0
200
400
600
800
1,000
Jan. 2016
Jul. 2016
Jan. 2017
Jul. 2017
Jan. 2018
Jul. 2018
Dec. 2018
Sales month year
Refills per 1,000 registered consumers
Rural (General), Koppal
Urban (General + PMUY) + Rural (General + PMUY), India
Rural (PMUY), Koppal
Fig. 3 | Monthly refill sales trends. Normalized monthly refill sales to
PMUY and general consumers from 2016 to 2018 in rural Koppal. To
estimate monthly sales per 1,000 consumers, refill numbers per household
are multiplied by 1,000 (consumers) and then divided by 12 (months).
As installation cylinders purchased during enrolment typically take one
month to consume even if LPG is used exclusively for cooking, the effective
number of registered consumers is considered using a lag of one month.
Exclusive, primary, secondary and rare use are defined as 10–12, 5–9, 2–4
and 0–1 cylinders purchased in one year by an average rural household,
respectively20 (see Supplementary Note 5 and Supplementary Table 3 for
the derivation of the categories). All-India (General + PMUY) pre-PMUY
average purchase was 7.3 cylinders per household in 2015–201615 (blue
dashed line).
Ujjwala
PMUY = 5,848 consumers; general = 7,585 consumers
LPG cylinders purchased in first year
General
1
2
3
4
5
6
7
8
9
10
11
12
Consumer category
Fig. 4 | LPG consumption in the first customer year. Histogram plot of the distribution of general (n = 7,585) and PMUY (n = 5,848) consumers by
the amount of LPG consumption (including installation cylinder) in their first year as customers. Only consumers with at least one year of customer
experience as of 31 December 2018 and whose full purchase history since first (installation) cylinder purchase is known are considered. The red, orange,
green and blue areas represent rare, secondary, primary and exclusive LPG usage, respectively. The red square and circle represent the median and mean
values of the distribution, respectively. The black square, circle, triangle and diamond indicate the 5th, 25th, 75th and 95th quartile of the distributions,
respectively. All LPG consumers have to purchase one cylinder in the first year during enrolment, while subsequent refill cylinder purchase varies.
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harvest period. During the cropping season, which coincides with
the monsoon, wood is wet and crop residues have been consumed34.
Notably, there are three active distributors in Koppal and the regres-
sion results indicate that customers procuring from one distributor
purchase fewer cylinders than those procuring LPG from the other
two, other predictors remaining constant. On the basis of several
interviews, we found that the worst performing distributor (D3) pro-
vides home delivery to all villages in its coverage area, while the other
two do not, which appears counter-intuitive as home delivery should
facilitate purchases23,28. Whether the comparatively higher normalized
monthly sales for D1 and D2 is due to greater sales acumen of the
distributors or better socio-economic/behavioural perspectives of
the consumers under the catchment area of these distributors cannot
be determined, given the limitations of the data.
Discussion
We should note the limitations of the study. We rely on mul-
tiple datasets. Some are national in scope but have limited detail.
6
6
6.1
6.6
6.4
6
6
6
6
6
4.7
4.6
4.8
4
4
4
6.4
5.9
6.2
6.4
6
6
6
6
4.6
4.2
4
4
2015 enrolment (n = 1,498)
2016 enrolment (n = 3,073)
2013 enrolment (n = 87)
2014 enrolment (n = 680)
1
2
3
4
5
1
2
3
4
5
0
2
4
6
8
10
12
0
2
4
6
8
10
12
Years of LPG ownership/experience
Total LPG cylinders purchased annually
Experience (years)
1
2
3
4
5
Fig. 5 | LPG consumption distribution as a function of experience. Box plot of annual LPG cylinder sales by years of experience. Only those who enrolled
before 2017 (consumers for at least two years as on 31 December 2018) and whose full purchase history since first (installation) cylinder purchase is
known are considered. Only when consumers have completed an entire year, that year’s purchase data are considered. The mean value is indicated by a
black dot. The mean and median values are printed above and below the box plots respectively. The range of the box represents the IQR of the distribution,
the horizontal line inside the box is the median value and the Tukey whiskers are extended to the farthest points of the distribution that are not outliers (no
more than 1.5 × IQR from the edge of the box). The outliers are denoted by dots.
16%
7%
7%
7%
22%
5%
6%
13%
18%
17%
7%
3%
4%
28%
3%
4%
11%
24%
13%
8%
4%
3%
21%
6%
8%
15%
23%
16%
6%
3%
2%
26%
4%
5%
13%
25%
2015 enrolment (n =  1,498 )
2016 enrolment (n =  3,073 )
2013 enrolment (n = 87 )
2014 enrolment (n =  680 )
<−3
−3
−2
−1
0
1
2
3
>3
<−3
−3
−2
−1
0
1
2
3
>3
0%
10%
20%
30%
0%
10%
20%
30%
Relative change in annual LPG cylinder purchase
Percentage of consumers
Relative change in LPG purchase
Positive
No Change
Negative
In year 2 compared to year 1
Fig. 6 | Relative change in LPG consumption with experience. Relative change in annual LPG consumption in the second year compared to the first year
by general consumers based on year of enrolment. Only those who enrolled before 2017 (consumers for at least two years as of 31 December 2018)
and whose full purchase history since first (installation) cylinder purchase is known are considered. Purchase data are considered only when consumers
completed an entire year as customers.
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Another is more detailed, covering 25,000 LPG users, but lacks
socio-demographic details and represents only a single district.
The findings drawn from the last dataset, which include com-
parisons of refill rates between PMUY and general consumers as
well as seasonal variations in consumption, though instructive,
should not be generalized beyond the study area. In addition, we
are looking only at trends after the initial 18 months of the pro-
gramme. Energy transitions take time and it would be premature
to pass judgement on PMUY at this stage37, although it is a good
time to take stock and suggest course corrections. Despite these
limitations, there are a number of key implications of this work for
policy and research.
First, overcoming the initial capital cost barrier for PMUY
beneficiaries in our study site has clearly not resulted in the use
of LPG by all consumers. This concern extends to the overall
programme as 24% of PMUY beneficiaries nationally purchased
no refills during the first year (A. Jindal, personal communica-
tion). On the basis of their auditors’ recommendations, the OMCs
are securing more than 3 billion rupees in preparation for loan
defaults. This has a high opportunity cost in a developing country
such as India41. These results raise questions regarding whether
PMUY should have been more selective in its targeting of price
discounts and loans for enrolment as taxpayers are now subsidiz-
ing LPG connections for households who are not willing or able
to buy refills. These households are therefore likely to default on
their loans and remain exclusive biomass users for longer and at
a higher risk from exposure to household air pollution than those
that use LPG even for a portion of their cooking. However, from
a health perspective, individual household gains will come only
with widespread adoption as household reductions in pollution
concentrations and exposure are dependent on both in-house
and community-level emissions reductions42. Despite issues in
targeting and refills, PMUY has clearly encouraged an unprec-
edented number of households along the path to clean cooking.
It, therefore, marks a significant departure from a market-based
BAU approach and addresses the up-front cost burden that has
prevented the poorest households from beginning the clean cook-
ing transition process in the past at a scale to potentially have
community-level effects.
Second, the low refill rates (and a large fraction of non-returning
customers) suggest that more effective incentives are needed for
PMUY beneficiaries to become frequent LPG users and benefit
from lower household air pollution exposure. One option would be
to offer vouchers with seasonal discounts during summer months
when demand drops. Other options might include behaviour change
communications and strategies18 to ‘nudge’43 poorer consumers to
substitute solid fuels with LPG more often. Such policies could also
be beneficial in raising usage rates for non-PMUY customers. Our
data indicate that the general customers, while consuming more
LPG per year than PMUY customers, do have low refill rates com-
pared to the levels needed to attain the goals of a cooking energy
transition. With a median refill rate of four cylinders per year for
general consumers, the government’s over-arching goal of creating
‘smokeless’ villages cannot be attained by creating new incentives
just for PMUY consumers.
Finally, the use of this novel dataset provides new insights
into the clean cooking energy transition, which would have been
difficult to reliably obtain through standard tools such as self-
reported surveys and would have been prohibitively expensive
to achieve via thermal sensors at this scale44. Datasets consisting
of distributor-level transactions are invaluable sources of infor-
mation that, properly anonymized, should be made accessible to
researchers and policy analysts. This not only promotes trans-
parency but could facilitate objective programme evaluation and
potential design changes to this programme to improve targeting
and effectiveness.
Methods
Novel dataset of electronic LPG sales records. For this paper, we capitalize on
the OMCs’ robust information technology platform, which electronically stores
enrolment and sales records for all registered LPG consumers. We use this source
to extract two datasets at different resolutions.
For the first, we use a national database of LPG purchase by PMUY
beneficiaries aggregated at the state level. These data were made available by a
senior official from the Ministry of Petroleum and Natural Gas, Government of
India. It contains data on 30 million PMUY beneficiaries, aggregated by state,
who have completed at least 1 year as LPG consumers (based on their date of
enrolment). For regression analysis to explain the state-wise variation in LPG
consumption, refer to Supplementary Note 9 and Supplementary Fig. 12. For
further details of the regression analyses and validity checks of the underlying
assumptions for linear models, see Supplementary Code 1 hosted on Figshare45.
The second dataset comprises sub-district level data gathered from three
Indian Oil Corporation Ltd (IOCL) LPG distributors in Koppal district of
Karnataka state. These were conveniently located near another ongoing project
site. The three distributors D1, D2 and D3 serve 25,000 domestic consumers
from around 120 villages across four taluks. While the data gathered include
both commercial and domestic consumers, we analysed only data for domestic
consumers in this research.
This second dataset comprises micro-records for individual customers (for full
analysis, see Supplementary Code 2 hosted on Figshare46) based on our merging of
two separate databases from within the IOCL data management system platforms
(Spandan/Indsoft). These were accessed from the distributor’s terminals with
special permission. Spandan includes a log of every LPG cylinder delivery and
critical information such as date of purchase, type of purchase (installation or refill
cylinder), equipment category (single cylinder/double cylinder), distributor price,
consumer name and consumer ID (a unique 16-digit identity number). Indosoft
contains the consumer’s home address, enrolment date, consumer category
(PMUY or general) and so on, which can be linked to the first database by the
unique consumer ID. Rural Koppal is primarily a dryland region, where rain-fed
agriculture is practised. We compare relevant key socio-economic indicators of
rural Koppal with rural India to examine how closely the study area resembles the
rest of rural India (see Supplementary Table 2), without making any judgement on
whether data from rural Koppal can in any way be treated as representative of the
rest of rural India.
Description of sub-district database. Out of 30,303 (domestic, commercial and
institutional) consumers in the dataset, we identify 28,075 domestic consumers
(of which 15,071 are PMUY beneficiaries) and a corresponding 208,694 LPG
cylinder sales records across three distributors as of 31 December 2018. For D1 and
D3, we have data since they started operations in December 2013 and November
2015, respectively. For D2, who started operations in 2012, data are available only
since January 2016. We have used the cumulative number of total consumers as
of 1 January 2016 and the number of new enrolments as a consumer on that day
to estimate the total number of consumers as of 31 December 2015 for D2 (the
baseline). Hence, the full records of LPG consumers in the study area are available
from January 2016 to December 2018. As some of the customers with D3 were
transferred from other locations when they started, we have some consumers
whose ‘full view’ purchase history (all transactions from the date of enrolment as
LPG consumer up to 31 December 2018) is unknown. We have full records for all
PMUY consumers and 9,695 general (non-PMUY) consumers.
These 36 months of data are equally split into pre-PMUY (January 2016–June
2017) and post-PMUY (July 2017–December 2018) periods. This is because
PMUY was launched in Karnataka state on 17 June 2017 (D2 and D3 started
enrolment in July 2017), while PMUY’s national launch was on 1 May 2016 in
Uttar Pradesh state.
The individual cylinder sales transactions are formally known as ‘delivery’ logs
in the IOCL system as LPG cylinders are supposed to be home-delivered. However,
as is the case with many rural distributors, only D3 undertakes home delivery (in a
partial manner) despite it being ‘highly recommended’ by OMCs. We subsequently
refer to these ‘delivery’ transactions as purchase or sales transactions as we
discuss the results from the consumers’ or distributors’ perspective, respectively.
Information on domestic cylinder subsidies, which vary on a monthly basis and
by the distributor, were verified manually using the OMC guidelines issued every
month. D1 and D3 also charge a Re 10–15 premium (as their outlets are farther
from the OMC bottling plant, so additional transportation cost is incurred), and
their consumers get an additional subsidy premium of an equivalent amount,
which keeps the net price uniform for all consumers, irrespective of the distributor
that they are associated with. We combine this information with the datasets to get
a complete picture of individual purchase transactions, including information on
who purchased, when at what net (and distributor) price and the number of days
between transactions.
On the basis of the date of the transaction, we also broadly designate an
individual transaction using two different seasonal classifications—climatic and
agricultural. The standard seasonal classification used by India’s meteorological
department is pre-monsoon (March–May), monsoon (southwest monsoon)
(June–September), autumn (post monsoon) (October–December) and winter
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(January, February)47. For Koppal, we have slightly modified this as summer
(March–May), monsoon (June–August), autumn (September–November) and
winter (December–February) based on local temperature and rainfall trends.
We also consider three local agricultural seasons—cropping (June–September),
harvest (October–January) and no agricultural activity (February–May)
based on interviews with local stakeholders. A vast majority of the rural
population is directly or indirectly dependent on agriculture or allied activity
for their livelihoods.
Data cleaning and grouping. All data processing, analysis and plot generation
were carried out in the R platform48. Details of the code and the packages used
are provided in Supplementary Codes 1 (National—state-level variance) and 2
(Sub-district—Koppal) hosted on Figshare45,46. The raw data collected in MS Excel
format from the distributor terminals were first processed by the field staff of a
partner organization—SAMUHA—in accordance with the Institutional Review
Board guidelines of the University of British Columbia. In addition to deleting
names, and other personal identifiers (different from the unique 16-digit number
provided by OMCs), the first 10 characters of the address were scrubbed. The
anonymized dataset was then transmitted online to the research team. We used a
series of codes in R to join datasets and format them for further analysis.
We primarily use two types of datasets created for this study from the available
data. First, delivery transactions of the domestic consumers in the form of a
data frame, which were then summarized into daily and monthly sales records
both by distributor and in total (see Supplementary Code 2 hosted on Figshare46
for details). Second, we generate a customer database by summarizing all of the
purchase transactions related to an individual consumer to get each consumer’s
individual records. This includes their enrolment date, and annual purchase
records of LPG cylinders, where the annual cycle begins from the date of first
delivery of the installation cylinder for each individual consumer. Once the
customer database was created, we grouped these consumer records by category
(PMUY and general), equipment (single or double), by years of experience as a
consumer (2 indicating the consumer is in the second year of LPG usage) and
by year of enrolment. For all these groups, we also sub-grouped the data by the
distributor. In addition, we manually collected data on the subsidy amount that
is credited to a consumer’s bank account by each distributor for every month. We
applied those data to the transaction database to calculate the up-front (distributor
price) and the net (up-front price minus subsidy) price.
We also categorized the data into four categories of users to indicate the
position/role of LPG in the cooking fuel mix of each consumer: an occasional
(rare) use of LPG, secondary use of LPG, the primary use of LPG, and exclusive
use of LPG as cooking fuel. This is estimated on the basis of how much useful
cooking energy is needed for a typical family of five members, and therefore how
much LPG is needed to cater to the needs of individual consumers translated into
monthly cylinder purchases, on average (see Supplementary Note 5).
As the Spandan platform contains data for both domestic and commercial
consumers, for our study, we used three filters to select individual transactions
for domestic consumers after consultation with OMC officials and accounting for
typos (detailed explanation is available within the R code notes in Supplementary
Code 2 hosted on Figshare46), an issue also raised by India’s federal auditor49. The
data were thus also cleaned to adjust for these errors in the following manner. First,
we checked if there was a replication of sales transaction records with identical
consumer, delivery date and type of refill (installation/refill sales). We rejected
110 transactions out of a total 214,037 transactions. Second, to select domestic
consumers out of the total list, which included commercial and institutional
subsidized consumers (schools), we followed a two-step verification process
by focusing on two critical columns—equipment and scheme associated with
a transaction. We applied a filter for the type of ‘equipment’—14.2 kg cylinder,
which is used only by domestic consumers. While the Government of India has
introduced 5 kg cylinders in recent months14, there were no consumers in the
available data window who purchased these small-size cylinders. Next, we applied
a filter for four unique scheme codes (‘DNSC’, ‘DSC’, ‘DSCBANK’, ‘DSCDBTL’)
that are currently or were previously associated with domestic consumers. Even
if the codes changed for a domestic consumer’s records due to policy shifts (DSC
to DNSC when the Direct Benefit Transfer scheme was launched), or errors, if
they were domestic, we included them. Generally, the base equipment code (14.2
indicates 14.2 kg LPG) and domestic codes (starting with D) are less error-prone as
they are linked to payment. Entries linked to payment are processed more carefully
by the operators. We shortlisted 28,414 unique domestic consumers in this process
from the selected transactions. Third, we identified that the ‘category’ column was
critical for analysis purposes. When the ‘category’ for consumers was missing, we
could not distinguish PMUY or general consumer groups for further analysis.
Hence, we dropped a further 339 consumers with ‘null’ category. Fourth, we
removed all consumer transactions if the consumer status was either ‘cancelled’ or
‘suspended’, generally for failure to provide documentation or because it was found
fraudulent during an inspection on a later date.
Once clean ‘domestic’ transactions were identified, we grouped the purchase
by individual consumers and further filtered the data to eliminate consumers who
had purchased more than 12 cylinders in a year. Field interviews indicate that the
likelihood of a domestic consumer in the study area paying for a non-subsidized
LPG cylinder for household purpose is very low. Thus, higher (>12) usage by
domestic consumers could indicate diversion for commercial purpose. We also
excluded ‘discontinued’ consumers from the analysis (that is, those who ceased to
be LPG consumers).
For analysis, which requires data since the first purchase (that is, installation
cylinder purchase during enrolment) to track the effect of experience, we selected
only consumers whose enrolment date was either available or could be reasonably
estimated, if these enrolment dates were within our data window. For example, we
do not have transaction data for distributor D2 before January 2016, although it
was operating before this. In this case, we selected only those consumers of D2 who
enrolled on or after 1 January 2016.
Data analysis. PMUY and LPG consumer growth in low-income rural settings. We
calculated the compounded monthly growth rate and month-on-month growth
rates in consumer numbers for both general and PMUY consumers. If there
were differences in growth rates of general consumers in the pre and post-PMUY
period, the pre-PMUY growth was considered as the BAU rate to estimate when
the LPG customer base would have expanded to current levels without programme
implementation. The difference between this and the actual numbers of enrolled
customers was then taken to be a measure of the impact of the programme. We
also considered multiple pre-PMUY periods: before national launch, and before
Koppal launch of PMUY. We also plotted the daily total refill sales and daily
cumulative consumer number for July 2017 to December 2018 to visually assess the
gap between enrolment and consumption. The smoothened daily refill sales trend
line is created with a smoothing function using gam in the R platform. It depicts
a polynomial spline with 30 degrees of freedom and a span of 0.3 using the ‘bs’
function in the ‘splines’ package48 (see Supplementary Code 2 hosted on Figshare46
for details).
LPG use in PMUY beneficiaries versus general consumers. We compared the LPG
demand of PMUY consumers with general consumers in two ways. First, we
normalized the monthly refill sales by dividing the refills sold in a month by the
total number of registered general or PMUY consumers. This shows seasonal
variances in demand for LPG, as well as how new enrolments and monthly
(distributor and net) price changes impact the average refill trends. For registered
consumers, we use the concept of ‘effective’ registered consumers for a given
month, which involved using a one-month ‘lag’ adjustment. As rural consumers
who enrol in a given month buy an installation (first) cylinder with the stove kit,
they would not be able to fully consume their installation cylinder, return the
cylinder and then purchase a refill within the same enrolment month, irrespective
of the season or price. As these new enrollees would not possibly buy refills in the
same month that they enrol, even as exclusive LPG users, including them in the
denominator (number of consumers) does not provide an accurate picture of the
actual demand for LPG refills. Hence, we consider the previous month’s consumer
number as the denominator. This is particularly important as there are some
months of very high consumer enrolments.
Second, we also compared the population distribution of annual LPG cylinder
purchases of PMUY and general consumers. When examining the distribution of
individual consumer’s purchases, we used all available (including pre-2016) data
to capture the first year of purchase data of pre-PMUY consumers. As no PMUY
customers have completed a second full year since enrolment, we compared first
year mean and median purchases to first year purchases of general consumers to
avoid capturing the effect of experience. We also compared the distributions for
these two categories of consumers across key percentiles (5th, 25th, 50th, 75th,
95th) to better describe how much one distribution would need to shift to match
the other one50.
LPG use increase over time and effect of experience. We tested for an experience
effect by comparing the annual LPG purchase for all consumers who are in their
nth year of use over the years 1, 2,…, n (n > 2). As there are no PMUY consumers
who have completed two or more complete years, this analysis is limited to only
general consumers. We examined both changes for a cohort over time as well as
the initial range of LPG purchases in the first year of each cohort. In addition, we
also examined whether there are any differences in use over time by comparing
the median number of days between two consecutive LPG cylinder purchases over
time. This last indicator has a finer resolution (365 days versus 12 cylinders), which
enabled us to detect the marginal effects of experience if any. Box plots were used
to display the comparison of the distribution of consumption by experience.
Determinants of LPG consumption. We used linear regression models to explore
the effect of an increase in a number of consumers, prices (distributor price, bank
subsidy and net price) and season on LPG cylinder refill purchases, in addition to
the distributor and the number of consumers. As price changes on a monthly basis,
we used aggregated normalized monthly refill sales as the response variable.
We converted the two seasonal categorical variables and the distributor
details as factors using the base ‘is.factor’ function in R48. We then ran stepwise
regression using packages ‘olsrr’51 (based on P value) and ‘MASS’52 (based on the
Akaike information criterion) for auto-selection of models. Forward selection is
a strategy that starts with no predictors in the model, iteratively adds the most
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contributive predictors and stops adding predictor variables when no further
improvements are statistically significant53. We also perform backward selection as
well as simultaneous forward–backward selection51,52. The output models were also
subjected to rigorous testing to see whether the five key underlying assumptions of
a linear regression model held true. We check for a linear relationship, multivariate
normality, homoscedasticity, statistically insignificant auto-correlation and low
multicollinearity (for details, see Supplementary Note 8 and Supplementary Code
2 hosted on Figshare46). We also reported scaled coefficients (‘beta’) to avoid
dismissing an effect as ‘small’ when it is just the units of measure that are small,
especially when we have very different units such as a number of consumers and
price included in the model.
In addition, we have also tested whether there is an effect of a loan deferment
scheme for PMUY consumers that incentivizes LPG purchase (Supplementary
Note 10).
Limitations. Unlike surveys, which routinely collect demographic and
socioeconomic data at the individual and household level, this dataset has limited
information on these aspects. Moreover, while the implicit assumption is that
purchase by domestic consumers equals consumption for residential cooking
needs, this may not always hold true. LPG for domestic consumers is subsidized
(for the first 12 cylinders in a year), but may sometimes be diverted to commercial
consumers who avoid paying for non-subsidized commercial cylinders54. While
reforms preceding the launch of PMUY have reduced this ‘black-marketing’, field
observation suggests that subsidized cylinders are still used in roadside restaurants
and tea shops in villages. Moreover, the purchase of a cylinder does not necessarily
imply that the last purchased cylinder has been entirely consumed within the data
window for consumption analysis.
Reporting Summary. Further information on research design is available in the
Nature Research Reporting Summary linked to this article.
Data availability
The authors do not have ownership over the LPG sales data downloaded from the
Indian Oil Corporation system. Researchers who seek access to the raw data to
replicate this study or to perform other analyses should contact the Ministry of
Petroleum and Natural Gas and/or IOCL.
Code availability
All the code and plots that were generated for this research, irrespective of whether
they were used in the paper, are available via Figshare at https://doi.org/10.6084/
m9.figshare.7961069 (Supplementary Code 1) and https://doi.org/10.6084/
m9.figshare.7961066 (Supplementary Code 2). For state-wise data analysis for
PMUY consumer use, refer to Supplementary Code 145. For Koppal sub-district
LPG sales data-based analysis, refer to Supplementary Code 246.
Received: 9 January 2019; Accepted: 5 June 2019;
Published online: 15 July 2019
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Acknowledgements
This article was developed under Assistance Agreement No. 83542102 awarded by the
US Environmental Protection Agency (EPA) to R.B. (sub-award to H.Z.). The EPA has
not formally reviewed it. The views expressed in this document are solely those of the
authors and do not necessarily reflect those of the Agency. EPA does not endorse any
products or commercial services mentioned in this publication. Part of the research
was developed by A.K. during his time in the Young Scientists Summer Program at the
International Institute for Applied Systems Analysis, Laxenburg, Austria, with financial
support from the German National Member Organization. A.K. and H.Z. acknowledge
support from the Wall Solutions Initiative provided by the Peter Wall Institute for
Advanced Studies, the ‘Collaborative Research and Training Experience- Atmospheric
Aerosol Program’ (CREATE-AAP) at the University of British Columbia and the Clean
Cooking Alliance (United Nations Foundation). Access to data and interviews with
officials from the Ministry of Petroleum and Natural Gas, Government of India, IOCL,
Hindustan Petroleum Corporation Ltd and Bharat Petroleum Corporation Ltd was
indispensable to this study. The authors are also indebted to the staff of the partner non-
governmental organization SAMUHA for assistance with data collection. The authors
also thank M. Brauer and S. Mehta for their feedback on the early drafts of the paper.
Author contributions
A.K., S.P. and H.Z. designed the study. A.K. performed the data cleaning and coding.
A.K., S.P., R.B. and H.Z. contributed to the writing of the paper.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information is available for this paper at https://doi.org/10.1038/
s41560-019-0429-8.
Reprints and permissions information is available at www.nature.com/reprints.
Correspondence and requests for materials should be addressed to A.K.
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© The Author(s), under exclusive licence to Springer Nature Limited 2019
Nature Energy | VOL 4 | SEPTEMBER 2019 | 806–814 | www.nature.com/natureenergy
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Corresponding author(s):
Abhishek Kar
Last updated by author(s): Jun 4, 2019
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Data collection
The sub-district level 'delivery' (purchase, as most homes did not get home delivery) transactions for 25,000 consumers were
downloaded from the three distributor terminals of the 'Centralized Ind-Soft' system of the Indian Oil Corporation Ltd. with special
permission. The national level data for 30 million consumers at state-wise aggregated level was provided by the Ministry of Petroleum
and Natural Gas, Govt. of India via email.
Data analysis
We used open source R platform and open source R packages as detailed in the code (Supplementary Codes 1 &2 ). For the maps, we use
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For the purpose of full transparency, we are making available all the codes written and the plots that were generated for this database, irrespective of whether it
was used in the paper in Supplementary Codes 1,and 2. The data that support the findings of this study are available from [MoPNG and IOCL] but restrictions apply
to the availability of these data, which were used under special permission from Government of India for the current study, and so are not publicly available.
Researchers who seek the raw data to replicate this study or to do other analyses should contact MoPNG and/or IOCL. [Note to reviewers: The data and all codes
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https://cp.sync.com/dl/61f786390/ujsuvkq2-xsrjzsi7-72a6gzbx-d5az72jq
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Study description
There are two datasets- one national level and one sub-district level. The data analysis is primarily quantitative, however, the authors
have included in the discussion section some inputs received from stakeholders.
Research sample
There are two sets of research samples to analyze the PMUY program. First, for the national level data, all consumers who completed
one year as the project (PMUY) beneficiary. Second, for the sub-district level data, we chose three distributors of IOCL for convenience
purpose (detailed in Methods), and then included all domestic consumers (both PMUY and general) under their catchment area. All these
consumers resided in rural areas. The PMUY beneficiaries were all women and the general (non-PMUY) consumers were a mix of male
and female.
Sampling strategy
The distributors were selected based on convenience sampling. But there was no other sampling strategy as we included 100% of all
domestic consumers.
Data collection
The sub-district data was collected on monthly/ bi-monthly basis  by field staff from the partner NGO- SAMUHA- from the IOCL
centralized data management system (distributor terminal) in .csv format. The data was then manually cleaned by the IT dept. of
SAMUHA as per standard written protocol to erase all names and other personal identifying information such as bank account/ ID card
numbers. In case of address, the first 10 characters are removed. Only the sixteen digit unique IOCL consumer code has been retained as
primary key for join previous batches of data with future batches. The cleaned database with only IOCL consumer ID were then emailed
to the first author for analysis as per University's Institutional Review Board guidelines.
Timing
The data collection from the IOCL software (distributor terminal) happened in batches throughout 2017 and 2019 (up to January second
week). So, we have all purchase transaction data till 10 October 2018. The MoPNG data on national level state-wise PMUY refills was
shared with us via email on 4th December 2018.
Data exclusions
For the national level data, all consumers who completed one year as the project (PMUY) beneficiary were included. We collected data
from distributor, then sub-selected all consumers who are domestic households (use 14.2 kg cylinder and have bank account linked for
subsidy transfer).  We made some assumptions in the exclusion process (clearly noted in the R codes) primarily because the database is
prone to manual data entry error (see, Indian federal auditor- CAG report discussed and referenced in the Methods). Also, for some
analysis, we only considered consumer data for the first year, to avoid experience effect, if any.
Non-participation
Not applicable
Randomization
For the national level data, all consumers who completed one year as the project (PMUY) beneficiary were included. For the sub-district
level data, we chose three distributors of IOCL for convenience purpose (detailed in Methods), and then included all domestic consumers
under their catchment area.
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    Scientific Research Findings:

Answer: We estimate that an average rural family needs to purchase five 14.2 kg cylinders annually to meet half of their cooking needs. We find that just 7% of PMUY beneficiaries in Koppal district in Karnataka, India, purchased five or more cylinders annually, suggesting that the beneficiaries seldom use LPG. The general (nonPMUY) consumers in this region use on average two times more LPG cylinders than PMUY beneficiaries. Yet, only 45% of general consumers use five or more cylinders per year, even after several years of experience with LPG. We also find that LPG consumers are sensitive to price and seasonality: LPG cylinder refill rates are lower in the summer when agricultural activity is limited and cash is scarce. These findings suggest the need for additional measures to promote regular LPG use for all rural populations. Although the findings come from a single district in Southern India, they may also apply to other areas with similar socio-economic conditions. A more expansive evaluation of PMUY would help inform the design of targeted incentives to transform infrequent users to regular users.