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Fertility transition powered by women’s access to electricity and modern cooking fuels


Access to electricity and modern cooking fuels, especially for women, leads to time savings in the home, improved health and better access to information. These factors increase women’s well-being and enhance their ability to make reproductive choices, which is empirically expressed by falling birth rates. This study provides an international analysis of the relationship between access to modern energy and fertility, based on panel data synthesized from 155 Demographic and Health Surveys over 26 years. Controlling for other determinants, we find that access to electricity and modern cooking fuels, along with education, negatively affects fertility. Energy and education effects are complementary and strongest in regions with initially high fertility rates. Expanded access to modern energy and education would accelerate the demographic transition. Therefore, the energy demand and carbon emissions needed to achieve the Sustainable Development Goal of energy access while ensuring gender equality and climate action would be lower in the long term than currently assumed.

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Fig. 1: Links between modern energy and fertility.
Fig. 2: Trends in access to electricity, modern cooking fuels and fertility in 44 countries.
Fig. 3: Marginal effects of access to modern energy on fertility.
Fig. 4: Standardized coefficients of different factors on fertility in three models.
Fig. 5: Predicted fertility decline from 2015 to 2040 in 25 sub-Saharan African countries under three scenarios of access to electricity and female education.

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Data availability

The DHS data used to produce the main panel data are publicly available and free of charge at, but access to the data requires permission from the DHS Program. The country-level data were downloaded from the World Bank Open Data repository and are publicly available at and free of charge. The conflict data from the Uppsala Conflict Data Program are publicly available at and free of charge. The processed data are available on the git repository of this Article: Source data are provided with this paper.

Code availability

The preprocessing and the analysis were carried out in R and Rmarkdown and are fully reproducible. All the code is available on the git repository of this Article: The following R packages were central to the analysis: tidyverse63, plm64, knitr65, wbstats66, rdhs67 and DHS.rates68.


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R.H. acknowledges funding from the EPICC (East Africa Peru India Climate Capacities) project, which is part of the International Climate Initiative (IKI). The German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU) supports this initiative on the basis of a decision adopted by the German parliament. The authors are grateful to S. Pachauri and N. Milojevic-Dupont for useful feedback on the manuscript.

Author information

Authors and Affiliations



H.W. conceived the project; C.B. and R.H. designed methods; C.B. performed data preprocessing; C.B. performed statistical analyses; C.B., R.H., P.-P.P. and H.W. discussed methods and interpreted results; C.B. wrote the initial manuscript; C.B., R.H., P.-P.P. and H.W. wrote the final manuscript. P.-P.P. and C.B. prepared figures.

Corresponding author

Correspondence to Helga Weisz.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Sustainability thanks Marina Fischer-Kowalski, Subhrendu Pattanayak and Julia Steinberger for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Marginal effects of access to modern energy on fertility by average number of schooling years.

Panel a, b) represent the effect on fertility of a one unit change in, respectively, access to electricity and access to modern cooking fuels depending on the region’s baseline level of schooling year in the first year available in our data. The solid line corresponds to the marginal effect and the dotted lines delimitate the 95% confidence intervals. Panel a, b) correspond to model M8 and M9 (Supplementary Table 7).

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–6, Methods 1–7, Notes 1 and 2, Tables 1–27 and Discussion.

Source data

Source Data Fig. 2

Excel file with five sheets corresponding to the data frames necessary to produce the five panels of Fig. 2.

Source Data Fig. 3

Excel file with four sheets corresponding to the data frames necessary to produce the four panels of Fig. 3. For the maps (panel c and the inset), the data provided do not include the geometries that allow to plot the map but contain all the values of marginal effects for each country/region shown on the maps.

Source Data Fig. 4

Excel file with the data frame with effect size, standard errors and significance levels allowing to produce Fig. 4.

Source Data Fig. 5

Excel file with two sheets corresponding to the data frames necessary to produce the two panels of Fig. 5.

Source Data Extended Data Fig. 1

Excel file with two sheets corresponding to the data frames necessary to produce the two panels of Extended Fig. 1.

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Belmin, C., Hoffmann, R., Pichler, PP. et al. Fertility transition powered by women’s access to electricity and modern cooking fuels. Nat Sustain 5, 245–253 (2022).

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