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

    Identify and summarize the **policy problem** that this research addresses. Your response should:
    - Clearly state the societal, environmental, or strategic issue motivating the study
    - Be written in accessible, policy-brief style sentences
    - Focus on the core challenge the research aims to solve
    - Avoid excessive detail or technical jargon unless necessary for clarity

    Write a short paragraph suitable for inclusion under the “Policy Problem” section of a policy brief.

    Scientific Research Paper:
    Analysis
https://doi.org/10.1038/s41560-021-00911-9
1Energy, Climate, and Environment Program, International Institute of Applied Systems Analysis, Laxenburg, Austria. 2Population and Just Societies
Program, International Institute of Applied Systems Analysis, Laxenburg, Austria. 3Climate Analytics, Berlin, Germany. ✉e-mail: pachauri@iiasa.ac.at
E
nergy for cooking is a most fundamental need. Yet today, over
three billion people still cook by burning wood on open fires
and in smoky stoves. The enormous social, public health and
environmental benefits of transitioning to cleaner cooking underpin
the inclusion of a universal access target for this under the United
Nations Sustainable Development Goal 7 (SDG 7)1–3. Even before
the COVID-19 pandemic, data showed that efforts to provide clean
fuels and stoves have been lagging far behind those aimed at extend-
ing electricity access4. A recent report claims that this sluggish prog-
ress in providing clean cooking access is costing the world more
than US$2 trillion each year as a result of health impacts, produc-
tivity losses and environmental degradation5. Mounting evidence
of the impacts of air pollution exposure on COVID-19 mortality
makes universal access to clean cooking services more urgent6,7.
Despite this, emerging evidence suggests that the economic fallout
of the pandemic might pose a further setback to efforts to reach this
goal, as many are forced to climb down the energy ladder8–10.
Previous literature analysing scenarios for achieving univer-
sal access to modern energy services have focused predominantly
on electricity supply to assess the cost-effectiveness of alternative
options to provide connections11–14. There is a paucity of studies
analysing clean cooking scenarios, particularly at a global scale15,16.
Existing studies that focus on cooking access scenarios are limited
in their representation of multiple cooking fuel use (fuel stacking),
population heterogeneity and affordability constraints, which are
critical to understanding whether people will regularly use new
fuels or stoves after they acquire them17–19. The limited existing evi-
dence, preceding the pandemic, suggests that the world is far off the
mark of the SDG 7 goal, with nations in sub-Saharan Africa pro-
jected to not achieve this target even in 20505,20.
In this study we explored clean cooking access until 2050 under
reference scenarios of socioeconomic and demographic change,
ambitious climate mitigation policy scenarios and a slow economic
recovery from the COVID-19 pandemic scenario (see Methods
for scenario details). We applied existing microdata-based cook-
ing choice and demand models that explicitly consider fuel stack-
ing and represent affordability constraints for urban and rural
populations, capturing heterogeneity in household preferences
across the entire income distribution21,22. We find that a slow recov-
ery from the pandemic and fuel price changes because of ambitious
climate mitigation policy could substantially retard progress in
achieving universal clean cooking access if additional policies related
to energy access and poverty alleviation are not simultaneously pur-
sued. Those most at risk of not being able to afford to transition to
clean cooking are low-income households in sub-Saharan Africa
(AFR), developing Asia, and Latin America and the Caribbean
(LAM). A faster transition to clean cooking fuels can attenuate
future growth in cooking energy demand, especially in regions that
currently depend largely on biomass and other solid fuels.
Populations without access to clean cooking
In what follows, we define clean cooking as cooking with modern
fuels such as liquid petroleum gas (LPG), electricity and piped gas,
which when used in modern stoves result in little to no household
pollution. Newer options, for example, bioethanol or solar electric,
might become viable in the future, but were not included in our
analysis as we detected no use of these in the empirical datasets that
we employed. All other fuels, including solid biomass-based fire-
wood or charcoal and coal, are categorized as polluting, because
prevailing stove technologies that use these result in pollution levels
that exceed World Health Organization indoor air quality guide-
lines for household fuel combustion23. We categorize a household
as cooking poor if it depends on polluting cooking fuels, that is, on
fuels other than those we define as clean, for half or more of its cook-
ing energy consumption. In our analysis we compared an especially
constructed COVID-19 recovery scenario (COVID) with three ref-
erence scenarios, namely sustainability (SSP1), middle-of-the-road
(SSP2) and regional rivalry (SSP3), from the Shared Socioeconomic
Pathways (SSP) framework24. We further compared our recov-
ery and reference scenarios with a set of scenarios that impose an
ambitious climate mitigation policy that limits warming to 2 °C by
the end of the century (CP2C) using a previous formulation that
assumes a regionally differentiated carbon price trajectory that rises
gradually over time25 (see Methods).
Access to clean cooking services in energy and
emission scenarios after COVID-19
Shonali Pachauri   1 ✉, Miguel Poblete-Cazenave   1, Arda Aktas2 and Matthew J. Gidden   1,3
Slow progress in expanding clean cooking access is hindering progress on health, gender, equity, climate and air quality goals
globally. Despite a rising population share with clean cooking access, the number of cooking poor remains stagnant. In this
study we explored clean cooking access until 2050 under three reference scenarios, a COVID-19 recovery scenario and ambi-
tious climate mitigation policy scenarios. Our analysis shows that universal access may not be achieved even in 2050. A pro-
tracted recession after the pandemic could leave an additional 470 million people unable to afford clean cooking services in
2030 relative to a reference scenario, with populations in sub-Saharan Africa and Asia the worst affected. Ambitious climate
mitigation needs to be twinned with robust energy access policies to prevent an additional 200 million people being unable to
transition to clean cooking in 2030. Our findings underline the need for immediate acceleration in efforts to make clean cooking
accessible and affordable to all.
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Our findings show that the share of the population with access to
clean cooking rises under all scenarios until 2050, but no scenario
meets the rate of improvement required to achieve the SDG 7 2030
target (Fig. 1 and Supplementary Fig. 1). Even under our most opti-
mistic reference growth scenario SSP1, we found that close to 38%
of the global population could continue to remain cooking poor in
2030. Slower growth and urbanization under the SSP2 and SSP3
reference scenarios could leave an additional 1.2–3.9% of the popu-
lation unable to afford clean cooking in 2030. We found universal
access may not be achieved even in 2050.
Under our COVID recovery scenario, an additional 470 mil-
lion people may remain cooking poor in 2030 as compared with
under SSP3, our most pessimistic reference growth scenario. The
COVID scenario has a persistent impact, as, even though average
income levels are assumed to revert to the reference SSP3 trend
in 2040, income inequality remains higher until the middle of the
century, leaving more families dependent on biomass even in 2050.
Ambitious climate mitigation policy in the absence of additional
targeted support policies could also make transitioning to clean
cooking more difficult for about 200 million people, specifically
Other Paciﬁc Asia
Middle East and North Africa
Centrally Planned Asia and China
Sub-Saharan Africa
South Asia
Latin America and the Caribbean
2010
2020
2030
2040
2050
2010
2020
2030
2040
2050
2010
2020
2030
2040
2050
0
25
50
75
100
0
25
50
75
100
Year
Year
Year
Cooking poor population (%)
Cooking poor population (%)
SSP1
SSP2
SSP3
COVID
b
a
0
50
Cooking poor population (%)
100
Fig. 1 | Cooking poor populations. a, Percentage of cooking poor population in 2030 by MESSAGEix regions under the SSP2 reference scenario. b,
Percentage of cooking poor population until 2050 by scenario in selected MESSAGEix regions. The bars depict shares in reference scenarios and the
crosses above the bars depict shares under the climate mitigation policy scenarios.
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those in Centrally Planned Asia (CPA) and South Asia (SAS), where
fossil fuel demand for cooking is currently high and most house-
holds are unable to afford electric cooking.
In AFR, we observed only very moderate improvements in access
to clean cooking services over time as population growth in this
region outpaces the rate of transition to better stoves and fuels in all
scenarios. In contrast, in CPA, SAS and Other Pacific Asia (PAS), clean
cooking access becomes increasingly affordable, especially under an
optimistic SSP1 reference scenario. Overall, differences on account of
income growth, distribution and urbanization in our reference sce-
narios impact clean cooking access more than shifts in fuel prices as
a result of ambitious climate mitigation policy. However, in regions
of developing and emerging Asia, climate mitigation policy could
increase the cost of clean cooking services. Implementing additional
support policies to make clean cooking affordable will be essential to
achieve both climate goals and SDG 7 simultaneously in these regions.
SSP1
SSP2
SSP3
COVID
REF
CP2C
2010
2020
2030
2040
2050 2010
2020
2030
2040
2050 2010
2020
2030
2040
2050 2010
2020
2030
2040
2050
0
5
10
15
0
5
10
15
Year
Year
Year
Year
Total fuel consumption (EJ)
Total fuel consumption (EJ)
Firewood
Charcoal/Coal
Kerosene
LPG/Natural gas
Electricity
a
SSP3-CP2C
SSP2-CP2C
SSP1-CP2C
SSP3
SSP2
SSP1
COVID
Reference year
0
0.5
1.0
1.5
2.0
Per-capita fuel consumption (GJ)
b
SSP3-CP2C
SSP2-CP2C
SSP1-CP2C
SSP3
SSP2
SSP1
COVID
Reference year
0
1
2
3
Per-capita fuel consumption (GJ)
c
Fig. 2 | Total and average cooking energy demand. a, Total cooking energy demand until 2050 under different scenarios. b, Average cooking energy
demand per capita in the reference year 2010, and in 2030 for biomass-dependent regions (AFR, LAM, PAS and SAS). c, Average cooking energy demand
per capita in the reference year 2010, and in 2030 in other regions (CPA, MEA and the rest of the world).
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Final cooking energy demand
In many developing regions, cooking is still the most energy-intensive
activity in homes. Figure 2 shows future total cooking energy
demand by scenario. We see a decline in biomass use over time in
all reference scenarios with a faster phase out under SSP1 compared
with the other reference scenarios (Fig. 2a). Under the COVID sce-
nario, we observe a much larger share of solid fuels, and higher total
cooking energy demand because of the inefficiency of these fuels.
This large COVID effect could result in an increase in biomass use
until 2030, with a light rebound in 2040, when income levels are
assumed to go back to the reference SSP3 trend. Climate mitigation
policies could attenuate the transition away from polluting stoves by
making oil and gas-based fuels more expensive. The higher prices
could also result in lower average per capita final cooking energy
Firewood
Charcoal/Coal
Kerosene
LPG/Natural gas
Electricity
COVID
SSP1
SSP2
SSP3
REF
CP2C
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
0
0.25
0.50
0.75
1.00
0
0.25
0.50
0.75
1.00
log[Expenditure (US$2010 person–1 yr–1)]
Fuel distribution (%)
Fuel distribution (%)
Sub−Saharan Africa
a
COVID
SSP1
SSP2
SSP3
REF
CP2C
0
0.25
0.50
0.75
1.00
0
0.25
0.50
0.75
1.00
log[Expenditure (US$2010 person–1 yr–1)]
Centrally Planned Asia and China
b
COVID
SSP1
SSP2
SSP3
REF
CP2C
0
0.25
0.50
0.75
1.00
0
0.25
0.50
0.75
1.00
log[Expenditure (US$2010 person–1 yr–1)]
Fuel distribution (%)
Fuel distribution (%)
Fuel distribution (%)
Fuel distribution (%)
Latin America and the Caribbean
c
COVID
SSP1
SSP2
SSP3
REF
CP2C
0
0.25
0.50
0.75
1.00
0
0.25
0.50
0.75
1.00
log[Expenditure (US$2010 person–1 yr–1)]
Fuel distribution (%)
Middle East and North Africa
d
COVID
SSP1
SSP2
SSP3
REF
CP2C
0
0.25
0.50
0.75
1.00
0
0.25
0.50
0.75
1.00
log[Expenditure (US$2010 person–1 yr–1)]
Fuel distribution (%)
Other Pacific Asia
e
COVID
SSP1
SSP2
SSP3
REF
CP2C
0
0.25
0.50
0.75
1.00
0
0.25
0.50
0.75
1.00
log[Expenditure (US$2010 person–1 yr–1)]
Fuel distribution (%)
Fuel distribution (%)
Fuel distribution (%)
Fuel distribution (%)
South Asia
f
Fig. 3 | Total cooking energy mix in 2030 for rural populations. a–f, Distribution of cooking fuels by income and scenario in 2030 for rural households in
different regions, overlaid with the population income distribution: AFR (a), CPA (b), LAM (c), MEA (d), PAS (e) and SAS (f).
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demand under the climate mitigation policy scenarios as compared
with the reference scenarios and a much smaller proportion of gas
in the cooking energy mix.
We find that the rate of change in cooking energy demand across
regions varies. In developed regions, there is little change in the ref-
erence scenarios as there is little transition in fuels over time, and
population and urbanization remain quite stable. In emerging and
developing regions, for example, CPA, income growth and urban-
ization result in shifts from less efficient to more efficient fuels, but
population shifts could mean relatively little change in total cooking
energy demand. On the other hand, in AFR, total cooking energy
demand could increase over time on account of rapid population
growth and a slow transition away from polluting stoves. We also
find demand may rise initially in the region, as populations move
Firewood
Charcoal/Coal
Kerosene
LPG/Natural gas
Electricity
COVID
SSP1
SSP2
SSP3
REF
CP2C
0
0.25
0.50
0.75
1.00
0
0.25
0.50
0.75
1.00
log[Expenditure (US$2010 person–1 yr–1)]
Sub−Saharan Africa
COVID
SSP1
SSP2
SSP3
REF
CP2C
0
0.25
0.50
0.75
1.00
0
0.25
0.50
0.75
1.00
log[Expenditure (US$2010 person–1 yr–1)]
Centrally Planned Asia and China
COVID
SSP1
SSP2
SSP3
REF
CP2C
0
0.25
0.50
0.75
1.00
0
0.25
0.50
0.75
1.00
log[Expenditure (US$2010 person–1 yr–1)]
Fuel distribution (%)
Fuel distribution (%)
Fuel distribution (%)
Fuel distribution (%)
Fuel distribution (%)
Fuel distribution (%)
Fuel distribution (%)
Fuel distribution (%)
Fuel distribution (%)
Fuel distribution (%)
Fuel distribution (%)
Fuel distribution (%)
Latin America and the Caribbean
a
b
c
COVID
SSP1
SSP2
SSP3
REF
CP2C
0
0.25
0.50
0.75
1.00
0
0.25
0.50
0.75
1.00
log[Expenditure (US$2010 person–1 yr–1)]
Middle East and North Africa
COVID
SSP1
SSP2
SSP3
REF
CP2C
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
2.5
5.0
7.5
10.0
12.5
0
0.25
0.50
0.75
1.00
0
0.25
0.50
0.75
1.00
log[Expenditure (US$2010 person–1 yr–1)]
Other Pacific Asia
COVID
SSP1
SSP2
SSP3
REF
CP2C
0
0.25
0.50
0.75
1.00
0
0.25
0.50
0.75
1.00
log[Expenditure (US$2010 person–1 yr–1)]
South Asia
d
e
f
Fig. 4 | Total cooking energy mix in 2030 for urban populations. a–f, Distribution of cooking fuels by income and scenario in 2030 for urban households in
different regions, overlaid with the population income distribution: AFR (a), CPA (b), LAM (c), MEA (d), PAS (e) and SAS (f).
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out of extreme poverty. Cooking energy demand can rise if house-
holds increase cooking frequency, shift diets to eating different
foods or simply cook more food.
Differences in the average per capita cooking energy demand
in the year 2030 by scenario are depicted in Fig. 2b,c. We distin-
guished regions that are more dependent on biomass (that is, AFR,
LAM, PAS and SAS) from other regions of the world that are not.
The differences in average cooking energy demand between the
two are stark. Furthermore, the differences between scenarios are
more pronounced for the non-biomass-dependent regions (Fig.
2c). Under the climate mitigation policy scenarios, we find cook-
ing energy demand could be lower than in the reference scenarios,
as the consumption of gas may decrease substantially, more so in
regions that are non-biomass-dependent to begin with, and there
0
500
1,000
1,500
2,000
0
500
1,000
1,500
2,000
Population spending less that US$5 per capita per day (millions)
Population using solid fuels (millions)
AFR
CPA
LAM
MEA
PAS
SAS
SSP1
SSP1-CP2C
SSP2
SSP2-CP2C
SSP3
SSP3-CP2C
COVID
COVID-CP2C
a
Other Pacific Asia
Middle East and North Africa
Centrally Planned Asia and China
Sub−Saharan Africa
South Asia
Latin America and the Caribbean
SSP1
SSP2
SSP3
COVID
SSP1-CP2C
SSP2-CP2C
SSP3-CP2C
COVID-CP2C
COVID-CP2C
COVID-CP2C
SSP1
SSP2
SSP3
COVID
SSP1-CP2C
SSP2-CP2C
SSP3-CP2C
SSP1
SSP2
SSP3
COVID
SSP1-CP2C
SSP2-CP2C
SSP3-CP2C
−750
−500
−250
0
250
500
−750
−500
−250
0
250
500
Millions of people
Millions of people
Rural, less than US$5 person–1 day–1
Rural, more than US$5 person–1 day–1
Urban, less than US$5 person–1 day–1
Urban, more than US$5 person–1 day–1
b
Fig. 5 | Relationship between income poor and cooking poor populations by region and scenario. a, Cooking poor populations plotted against income
poor in 2030. The diagonal line represents equal numbers of cooking poor and income poor populations. b, Relative changes in cooking poor populations
between 2010 and 2030 by scenario and region.
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could be an increase in electricity consumption instead. In the
COVID scenario, we find cooking demand could be lower than in
the reference scenarios in the biomass-dependent regions, because
of lower income levels (Fig. 2b). In the non-biomass-dependent
regions too, biomass demand could be slightly higher in the COVID
scenario. More biomass use is also likely in the SSP3 reference sce-
nario as compared with the SSP1 reference scenario.
Cooking energy transitions
In line with the literature, we found that rising incomes and urban-
ization drive a transition to cleaner fuels and stoves17,26,27. However,
within and across regions, the nature and pace of this transition
vary vastly depending on several factors beyond income. In Figs. 3
and 4 we present the total cooking energy mix in 2030 for the full
income distribution by region and scenario, separately for rural and
urban populations, respectively. Income is presented in the logarith-
mic scale to better visualize the transitions at lower levels of income.
Clear differences in the pace and nature of the transition in cook-
ing fuels across regions are evident. In rural CPA in the COVID sce-
nario (top left panel of Fig. 3b), we see most households with very
low incomes per capita (approximately less than US$5 per capita per
day). We see a transition from high firewood dependence at lower
income levels to almost equal shares of firewood, gas and electric-
ity at the highest income levels. Households with middle-income
levels could still rely mostly on biomass fuels. By contrast, among
urban households (top left panel of Fig. 4b), income per capita
levels are higher (up to approximately US$17 per capita per day).
These households are likely to depend mostly on gas and electricity.
The pattern varies greatly across other regions, even at comparable
income levels. Nevertheless, overall, we see a strong income effect
on the choice of cooking fuels, with households with higher levels of
income transitioning to either gas or electricity in all regions, except
in the Middle East and North Africa (MEA), a region rich in fos-
sil fuels and poor in biomass. In MEA, higher-income households
could continue to use cheap kerosene, in line with what we observed
in the empirical data (see Methods section).
For lower-income rural households, especially in AFR, SAS and,
to a lesser extent, LAM, we find a large share of total cooking energy
demand could still be biomass-based even in 2050, particularly in
the COVID scenario. In the climate mitigation policy scenarios
too, an increase in fossil fuel prices could increase the dependence
on biomass, with the price effect dominating the income effect in
determining the choice of fuels. Considering regional heterogene-
ity, in AFR and SAS, regions that are most acutely dependent on
biomass today, we find price sensitivity could be higher under the
climate mitigation policy scenarios.
In addition to income, our analysis shows a clear urban–rural
divide in fuel choice, with even richer households in rural areas
likely to continue relying on solid fuels because of their easy access
and the poor accessibility to cleaner alternatives. Indeed, as we can
see from the example of CPA, there are stark differences in the
choice of cooking fuels between urban and rural households even
at the same income levels. We observed this in other regions of the
world as well (Supplementary Fig. 3).
Finally, we find energy prices also affect cooking fuel transitions.
For instance, in SAS, we find that in the climate mitigation policy sce-
narios, rising fossil fuel prices could push LPG out of reach of many.
However, future transitions in this region remain the most uncer-
tain, as there are currently strong policies to expand LPG access to
even rural households in India. The effect of these policies are only
becoming evident now and are not reflected in the data that we used
to estimate the parameters for this region (see Methods section).
Populations most at risk of being cooking poor
Our findings on the transition in cooking fuels discussed above
suggest that access to clean cooking is clearly a poverty issue. This
is further illustrated in Fig. 5. In Fig. 5a we show the relationship
between cooking poor and income poor populations (defined
as those earning less than US$5 per capita per day). We see that
in AFR, and to a lesser extent in LAM and PAS, income poverty
strongly correlates with cooking poverty. In other regions, this is
less so, as the number of poor individuals is much lower than those
dependent on solid fuels. This can be explained by the existence of
natural resources that result in lower prices of liquid fuels in some
regions (for example, MEA), or sustained public policies aimed at
poverty alleviation and increasing access to clean cooking in others
(for example, in CPA and SAS).
In AFR, we find that future income growth may not compensate
for the effect of population growth, so that the number of cooking
poor could increase in all scenarios. What is also evident from Fig.
5b is that AFR could remain largely rural in 2030, and between 94%
(in SSP1) and 98% (in COVID) of rural households could earn less
than US$5 per capita per day and remain cooking poor in 2030. In
SAS too, 78% (in SSP1) to 90% (in COVID) of the rural population
could earn less than US$5 per capita per day and remain cooking
poor in 2030. In other regions of the Global South, we found that
about 15% more people could remain income poor in the COVID
scenario compared with in the SSP3 scenario.
In other regions of Asia, specifically in CPA, but also in PAS,
the number of cooking poor is likely to decline between 2010 and
2030. In these regions, not only do we find that many move out of
poverty, but also rising urbanization makes clean fuels more acces-
sible and affordable. For example, in CPA, in the COVID scenario,
we observe that a little less than half of the rural population could
earn less than US$5 per capita per day and depend on solid fuels,
but the percentage of urban households in this category is likely to
be negligible.
We find outcomes differ by scenario in MEA, LAM and SAS.
In these regions, in the SSP1 and SSP2 scenarios we observe there
could be a reduction in cooking poor, but in the SSP3 and COVID
scenarios we find that the extent of both poverty and cooking pov-
erty could rise. In MEA and SAS this is largely explained by dif-
ferences in urbanization between the SSP1 and SSP2 scenarios as
compared with the SSP3 and COVID scenarios. In LAM, however,
we find even urban households could remain cooking poor, as
income inequality is higher in this region, even in countries that
have a gross domestic product (GDP) per capita comparable to
that in most developed regions. Therefore, we find many people
could remain income poor even in 2030. Especially in the SSP3
and COVID scenarios, we find that 60% of rural and about 50% of
urban populations could earn less than US$5 per capita per day and
depend on solid fuels for cooking.
In general, our findings show that clean cooking access in the
COVID scenario is likely to be lower than under the climate miti-
gation policy scenarios. In SAS, however, the urban poor could be
particularly affected by rising gas prices under the climate mitiga-
tion policy scenarios, with the dependency on solid fuels for this
group almost doubling. However, even in this region, solid fuel
dependency is much higher at 7% in the COVID scenario in 2030
as compared with under the climate mitigation policy scenarios (2%
in SSP3-CP2C).
Discussion and conclusions
Our analysis provides new insights into how access to clean cook-
ing services may change under alternative reference scenarios, a
slow pandemic recovery scenario and climate mitigation policy
scenarios. Our findings show that the SDG 7 target of universal
clean cooking access by 2030 could be out of reach under all the
scenarios we explored. In regions that currently have the highest
access gaps, specifically AFR, SAS, PAS and LAM, universal access
may not be achieved even in 2050. A protracted recession follow-
ing the pandemic could further retard progress towards achieving
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extensive subsidies. But sustaining these subsidies can become a fis-
cal burden. Better targeting of subsidies, through efforts like India’s
Give It Up campaign, and better market segmentation could help37.
In addition, directing green and climate funds and revenues from
carbon pricing to the clean cooking sector can be another way to
increase financing to this sector38.
Despite the challenges in extending clean cooking access, recent
advances in technologies39 and new payment and financing mod-
els40 can help clean cooking services reach even low-income house-
holds. In urban centres, providing piped gas to dense settlements
and introducing smaller LPG cylinder sizes, pay-for-service financ-
ing models, smart metering for gas with electronic payment options
as well as more reliable and affordable electricity can be instrumen-
tal in encouraging a more rapid transition to clean cooking. For
rural regions, awareness-raising and behaviour change campaigns
are also important to ensure that those who gain access use new
stoves regularly41. Our results suggest a need for much greater prior-
itization and coordinated policies to provide access to clean cooking
globally, with efforts targeted at the most disadvantaged, specifically
the poorest regions and populations. This will require considerable
upscaling of investment, capacity and commitment, but can result
in big benefits for planetary and population health and wellbeing.
Methods
Data sources. We used microdata from a large set of nationally representative
household surveys of different countries to estimate energy and cooking
technology choices for regions of the world. We aggregated nations in line with
the 11 regions of the MESSAGEix-GLOBIOM model42. The datasets used to
represent each of these regions are presented in Supplementary Table 1, with a
focus on regions of the world where access to clean cooking is lacking. Regions
where access is not an issue (that is, North America (NAM), Western Europe
(WEU) and Pacific OECD (PAO)) were included in our analysis and modelled
independently, but are presented clubbed together in a single other or rest of the
world region in some results.
None of these surveys report energy consumption for cooking purposes
separately, but instead include data on total consumption or expenditure by fuel.
This was not an issue for the modelling approach we followed for the Global North
countries (as described further in the following subsection), but was relevant
for regions of the Global South. Therefore, we used two different approaches to
separate energy for cooking from energy for other end uses. Both approaches
required us to create profiles of consumers who use the fuel for cooking and those
who do not. Depending on the particulars of the specific national dataset, we then
either used a simple regression approach to back out representative non-cooking
consumption of households depending on a set of characteristics, or we calculated
simple averages differentiated by income categories for urban and rural households
separately. Importantly, the simulation-based estimation approach we followed
largely minimizes the potential biases of such estimation, as we first obtained a
guesstimate of cooking consumption using unbiased simulators derived from the
first- and second-order moments of the empirical distributions, and next matched
the non-biased first moments from the simulated data with the empirical data
while minimizing errors in the second moments.
Models and estimation. We used two different models for different regions of the
world. For the Global South, where energy stacking is more prevalent, we used
a previously developed model of household cooking fuel choices that allows for
multiple fuel use21. For the Global North, where households generally use only
one type of fuel, we used a different more general model of household energy and
appliance choices22.
Both models follow a simulation-based structural econometric approach,
wherein a large set of simulated households are constructed to mimic the
characteristics found in the empirical data. Households choose between cooking
alternatives to maximize utility given budget constraints and the prices they face
in the market. All the simulated households have different characteristics (for
example, income, household size and location, among others), and therefore the
models capture heterogeneity with respect to these characteristics. The models,
although different, work by backing out household preferences for different
cooking fuels and technologies from the choices observed in the empirical data
sources using different simulation-based estimation techniques. Therefore, the
models only include options that appear in the different sources of household data
(Supplementary Table 1), namely, firewood, charcoal and coal (usually lumped
together), kerosene, LPG and natural gas (also usually lumped together), and
electricity. Unfortunately, this implies that preferences for fuels that are lumped
together cannot be distinguished from each other, and therefore are modelled
as a single option. Also, other potential viable alternatives (for example, biogas
this goal. Our findings resonate with recent analysis that suggests
that the aftermath of the pandemic could push half a billion people
back into extreme poverty28. Climate mitigation policies may also
hamper a transition to fossil-based cleaner burning cooking fuels
by making these more expensive and putting them out of reach of
many if not twinned with appropriate additional policies specifi-
cally targeting the cooking poor in AFR, parts of developing Asia
and LAM.
The location and supply of fuels and their affordability are critical
drivers of the trends that we observed. In MEA, where biomass sup-
ply is limited, we found dependence on cheap kerosene could remain
high. In AFR and LAM, where many households are remotely
located, have easy access to biomass resources and limited access to
modern cooking services, we found biomass dependency is likely
to persist. For these populations, the inconveniences and health
impacts of cooking with polluting stoves need to be factored into the
household choice decision. This requires committed and sustained
policies to provide easy access to modern fuels and stoves at afford-
able prices, and information and behaviour change messaging. If
anything, studies in regions that are enforcing such policies now (for
example, India) show that these behaviours can be persistent, and
providing households access to new stoves and cleaner burning fuels
alone may not be enough to promote their regular use29,30.
Our analysis has some limitations that point to important ave-
nues for future research. Our model can capture a wide variety of
heterogeneity in circumstances and population characteristics, but
our results are only as good as the input data used. As our methods
are based on empirical data, we could only include cooking options
that exist in the datasets that we employed. Although newer alter-
natives that might become viable in the future were not explicitly
modelled, our analysis can inform policy of the price points and
income levels at which populations in different regions will be able
to afford new fuels and technologies. Because cooking behaviours
appear to be quite persistent, future research should consider using
panel or pseudo-panel datasets that can better capture longitudinal
shifts. Our analysis could also be further expanded to better assess
how societal changes, such as better education and women’s empow-
erment and labour force participation, relate to cooking energy
choices. We did not include institutional capacity and governance
constraints that might limit the expansion of supply of certain cook-
ing options or the effectiveness of policies in specific contexts. In
addition, our analysis did not assume any climate feedback on bio-
mass availability. Both unsustainable harvesting and future climate
change could reduce the availability of abundant biomass resources
in certain regions. Alternatively, more sustainable land and biomass
management could make supplies more abundant and encourage
new biomass-based clean cooking options31,32.
Insights from our analysis emphasize the increased urgency to
address this issue, which has been left on the back burner for far too
long. A recent report has highlighted the chronic underinvestment
in the sector, particularly in regions where this is needed most33.
Recent estimates suggest that achieving universal access to clean
cooking services by 2030 will require US$4.5–9.8 billion annu-
ally5,33. We estimate an average expenditure gap of US$10.5 billion
annually between 2020 and 2030, comparing the reference SSP3 and
COVID scenarios. This includes the cost of stoves and expenditure
on fuels, and is in line with previous estimates. These estimates are
an order of magnitude lower than the US$2 trillion of estimated
losses incurred each year from a lack of access5. Recent commit-
ments pledged to COVID-19 recovery funds are in the range
US$9–15 trillion34,35. Directing even a fraction of 1% of these funds
to eradicating cooking poverty could help bridge the financing gap
to meet the SDG 7 target.
Countries like Brazil, India and Indonesia have expanded access
to clean cooking services considerably in recent years36. This has
been achieved through strong government commitment and
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where ‘cook’ refers to the particular cooking appliance used by the household and
i takes the value 1 if households use an electric stove and 2 if they use another fuel.
The unknown preference parameters (α and the ϕs and λs) are estimated following
a similar procedure to the one described above, albeit using a different estimation
technique, namely indirect inference44, which is a generalized version of the
method of simulated moments. The empirical fit of the model can be found in the
Supplementary Data file.
Scenario design and future simulations. We considered three different future
reference scenarios following the narratives of the Shared Socioeconomic
Pathways, specifically sustainability (SSP1), middle-of-the-road (SSP2) and
regional rivalry (SSP3). Although the population45 and urbanization46 projections
by country for the SSP scenarios that we used are as described in the references
cited here, a special algorithm was applied to obtain separate income distributions
for urban and rural households. Specifically, a machine learning algorithm47
was applied to a large longitudinal dataset including average household income
and Gini coefficient estimates for urban and rural households of all regions of
the world to obtain associations between these indicators and other SSP drivers.
The algorithm was trained on data from 1988 to 2010, and data from 2011 to
2015 were used for validation. These associations were posteriorly applied to the
corresponding scenarios25,45,46,48 to obtain the future income distributions for urban
and rural households separately in all countries and regions.
The COVID scenario deserves special attention. For its elaboration, the
original GDP trend for the reference SSP3 scenario from 2010 to 2019 was retained
(as this is closer to the actual GDP trends in the past few years). For the years
2020 and 2021, we then used the most recent national GDP growth estimates
from the World Bank49. We assumed convergence in GDP growth trends in the
year 2040, considering a 20-year protracted recovery period from the pandemic
worldwide. The revised GDP estimates were used to adjust the level of average
household income in each region and period. The standard deviation, which is
used to estimate inequality, was also adjusted in line with its association with GDP,
urbanization and year. The Supplementary Data file contains values of all scenario
drivers for the period of analysis.
We also considered ambitious climate mitigation policy scenarios that limit
global warming to below 2 °C by the end of the century. The climate mitigation
policy simulated in this analysis considers regionally differentiated carbon
price trajectories as estimated by McCollum et al.25. Details of all key scenario
drivers and assumptions are presented in the Supplementary Data file. The
microdata-based structural estimation models described above are soft-linked
to the MESSAGEix-GLOBIOM integrated assessment model42, which allows
macroeconomic feedbacks via energy prices to be captured. These reflect changes
in the energy supply mix and land use to meet estimated demands at least cost.
Data availability
Links to the micro datasets that were used in the analysis are included in the
Supplementary Information, when available. Given that some of these datasets are
not publicly available, the data used for the estimation module is only available
from the corresponding author upon reasonable request. The simulated datasets
generated during the current study are also available from the corresponding
author upon reasonable request. All estimated moments, scenario assumptions and
the datasets underlying the plots are available in the Supplementary Data file.
Code availability
The codes used during the current study are available from the corresponding
author upon reasonable request.
Received: 14 December 2020; Accepted: 31 August 2021;
Published online: 28 October 2021
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C,F U (C, F) =

Cα


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efFf
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x1 = ϕ0 +
m
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
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m
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Kjδj
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

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x2 = λ2
λ4 (α −1) + α
λ4
(
ϕ0 + λ1
λ4 + λ3 χ
)
1
p2 + αλ1
λ4
p1
p2
+ α
p2
[
y −ρ
m
∑
j=1
Kjδj +
m
∑
j=1
ϕj
λ4 δj
]
,
where consumption xi is modelled as the sum of the base consumption of electricity
ϕ0 and consumption due to ownership of appliances δjϕj, where δj represents the
ownership of appliance j ∈ m and ϕj is the average electricity consumption of the
appliance. In addition, each household’s consumption is affected by the price of
electricity p1, the price of alternative fuels p2, a vector of household characteristics
χ and the household’s disposable income (that is, total income y minus the
annualized investment cost of electric appliances ρ
m
∑
j=1
Kjδj).
Finally, the actual cooking energy consumption is calculated as:
Ecook =
δcookϕcook
ϕ0 + ∑m
j=1 δjϕj
xi,
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Acknowledgements
M.P.-C. received funding from the European Union’s Horizon 2020 research and
innovation programme under grant agreement no. 821124 (NAVIGATE). This work
was partially funded by the contributions of the National Member Organizations of the
International Institute of Applied Systems Analysis.
Author contributions
S.P. and M.P.-C. conceived the initial framework. S.P. and M.P.-C. designed the research.
M.P.-C., A.A. and M.J.G. prepared the data. M.P.-C. and A.A. performed the modelling,
wrote the codes and carried out the analysis. S.P. and M.P.-C. led the writing of the paper
with all other authors contributing to the writing, revisions and editing.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information The online version contains supplementary material
available at https://doi.org/10.1038/s41560-021-00911-9.
Correspondence and requests for materials should be addressed to Shonali Pachauri.
Peer review information Nature Energy thanks Joshua Rosenthal, Francis Johnson and
the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Reprints and permissions information is available at www.nature.com/reprints.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in
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© The Author(s), under exclusive licence to Springer Nature Limited 2021
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    Policy Problem:

Answer: At the current rate, the Sustainable Development Goal (SDG7) target of universal access to clean cooking services by 2030 is unachievable and may remain unattainable for some countries even by 2050. This can also hinder progress on other SDGs, including those on health, gender, inequality, climate and land. Financial strain following the COVID‑19 pandemic is pushing people further down the energy ladder and deepening inequities. Emerging evidence also suggests that exposure to household air pollution from dirty cooking can exacerbate public health issues. Understanding how access to clean cooking may change under alternative future scenarios is important to inform strategies for achieving health and climate goals. Although there are several climate mitigation scenarios in the literature, it is not clear how the world might develop in the absence of climate policy and how climate change mitigation might interact with clean cooking access goals. As a result, decision makers do not have clear guidance on integrated policy for climate mitigation, development and clean cooking access.