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The effect of education on determinants of climate change risks

Abstract

Increased educational attainment is a sustainable development priority and has been posited to have benefits for other social and environmental issues, including climate change. However, links between education and climate change risks can involve both synergies and trade-offs, and the balance of these effects remains ambiguous. Increases in educational attainment could lead to faster economic growth and therefore higher emissions, more climate change and higher risks. At the same time, improved attainment would be associated with faster fertility decline in many countries, slower population growth and therefore lower emissions, and would also be likely to reduce vulnerability to climate impacts. We employ a multiregion, multisector model of the world economy, driven with country-specific projections of future population by level of education, to test the net effect of education on emissions and on the Human Development Index (HDI), an indicator that correlates with adaptive capacity to climate impacts. We find that improved educational attainment is associated with a modest net increase in emissions but substantial improvement in the HDI values in developing country regions. Avoiding stalled progress in educational attainment and achieving gains at least consistent with historical trends is especially important in reducing future vulnerability.

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Fig. 1: Population size for three population–education projections based on the SSP2 scenario.
Fig. 2: Percentage of the population living in households by the education level of the household head, in 2000 and in 2050, for three education projections.
Fig. 3: Total carbon emissions from energy use in the SSP2 scenario for three education variants.
Fig. 4: Proportional differences in 2100 in emissions, population, per capita emissions and per capita GDP.
Fig. 5: HDI for all three SSP2 variants over time.

Data availability

The samples of census datasets analysed during the current study are publicly available from IPUMS International at https://international.ipums.org/international/. The national sample household survey data analysed for this study are publicly available for Brazil (https://www.ibge.gov.br/estatisticas/sociais/habitacao/9050-pesquisa-de-orcamentos-familiares.html?=&t=downloads), China (https://opendata.pku.edu.cn/dataverse/CFPS?language=en), India (Human Development Survey, https://www.icpsr.umich.edu/icpsrweb/DSDR/studies/36151), Mexico (http://en.www.inegi.org.mx/programas/enigh/tradicional/2005/), South Africa (https://www.datafirst.uct.ac.za/dataportal/index.php/catalog/316) and Uganda (http://microdata.worldbank.org/index.php/catalog/2059), after registering and submitting requests. The national survey data for some countries are available but restrictions apply to the availability of these data, which were used under licence for the current study and so are not publicly available. These include India (National Sample Survey 2004–2005 and 2011–2012, http://www.icssrdataservice.in/datarepository/index.php/) and Indonesia (https://microdata.bps.go.id/mikrodata/index.php/catalog/SUSENAS). While these original full datasets have restrictions on availability, tables of derived results from the original datasets can be provided upon request.

Code availability

The code for the version of the iPETS model used to produce economic and emissions projections for this analysis is available upon request. It will eventually be publicly available at ipetsmodel.com.

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Acknowledgements

We thank the Asian Demographic Research Institute at Shanghai University for hosting research stays for B.C.O. during which parts of this work were carried out. A substantial amount of the work for this study was completed while B.C.O., L.J., E.K.L. and X.R. were at the National Center for Atmospheric Research, Boulder, CO.

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Contributions

B.C.O. led, and L.J., S.KC and S.P. contributed to, the design of the study. B.C.O. coordinated the paper and led the writing, with contributions from L.J., S.KC, S.P. and E.K.L. L.J., R.F., S.P. and E.K.L. led the analysis of household survey data, with contributions from T.Z. and W.Z. S.KC carried out the population–education projections. L.J. carried out the household projections. X.R. carried out the iPETS model projections, with contributions from B.C.O. B.C.O., L.J., S.KC, S.P., E.K.L. and X.R. interpreted results.

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Correspondence to Brian C. O’Neill.

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Supplementary Information

Supplementary Methods 1–3, Tables 1–4, Figs. 1–4 and references.

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O’Neill, B.C., Jiang, L., KC, S. et al. The effect of education on determinants of climate change risks. Nat Sustain 3, 520–528 (2020). https://doi.org/10.1038/s41893-020-0512-y

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