The recently published Intergovernmental Panel on Climate Change (IPCC) projections to 2100 give likely ranges of global temperature increase in four scenarios for population, economic growth and carbon use1. However, these projections are not based on a fully statistical approach. Here we use a country-specific version of Kaya’s identity to develop a statistically based probabilistic forecast of CO2 emissions and temperature change to 2100. Using data for 1960–2010, including the UN’s probabilistic population projections for all countries2,3,4, we develop a joint Bayesian hierarchical model for Gross Domestic Product (GDP) per capita and carbon intensity. We find that the 90% interval for cumulative CO2 emissions includes the IPCC’s two middle scenarios but not the extreme ones. The likely range of global temperature increase is 2.0–4.9 °C, with median 3.2 °C and a 5% (1%) chance that it will be less than 2 °C (1.5 °C). Population growth is not a major contributing factor. Our model is not a ‘business as usual’ scenario, but rather is based on data which already show the effect of emission mitigation policies. Achieving the goal of less than 1.5 °C warming will require carbon intensity to decline much faster than in the recent past.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Summer compound heatwaves over China: projected changes at different global warming levels and related physical processes
Climate Dynamics Open Access 07 November 2023
Nature Open Access 18 October 2023
Nature Communications Open Access 15 April 2023
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Rent or buy this article
Prices vary by article type
Prices may be subject to local taxes which are calculated during checkout
IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2014).
World Population Prospects: The 2015 Revision (United Nations, Department of Economic and Social Affairs, Population Division, 2015).
Raftery, A. E., Li, N., Ševčíková, H., Gerland, P. & Heilig, G. K. Bayesian probabilistic population projections for all countries. Proc. Natl Acad. Sci. USA 109, 13915–13921 (2012).
Gerland, P. et al. World population stabilization unlikely this century. Science 346, 234–237 (2014).
van Vuuren, D. P. et al. The representative concentration pathways: an overview. Climatic Change 109, 5–31 (2011).
van Vuuren, D. P. et al. Stabilizing greenhouse gas concentrations at low levels: an assessment of reduction strategies and costs. Climatic Change 81, 119–159 (2007).
Wise, M. et al. Implications of limiting CO2 concentrations for land use and energy. Science 324, 1183–1186 (2009).
Hijioka, Y., Matsuoka, Y., Nishimoto, H., Masui, T. & Kainuma, M. Global GHG emission scenarios under GHG concentration stabilization targets. J. Glob. Environ. Eng. 13, 97–108 (2008).
Riahi, K., Grübler, A. & Nakicenovic, N. Scenarios of long-term socio-economic and environmental development under climate stabilization. Technol. Forecast. Soc. Change 74, 887–935 (2007).
World Population Prospects: The 2012 Revision (United Nations, Department of Economic and Social Affairs, Population Division New York, 2013).
Raftery, A. E., Alkema, L. & Gerland, P. Bayesian population projections for the United Nations. Stat. Sci. 29, 58–68 (2014).
Moss, R. H. & Schneider, S. H. in Cross-Cutting Issues in the IPCC Third Assessment Report (eds Pachauri, R. & Taniguchi, T.) (IPCC, Cambridge Univ. Press, 2000).
Kaya, O. Impacts of Carbon Dioxide Emissions on GWP: Interpretation of Proposed Scenarios (IPCC/Response Strategies Working Group, 1989).
Ehrlich, P. R. & Holden, J. P. Impact of population growth. Science 171, 1212–1217 (1971).
IPCC Climate Change 2014: Synthesis Report (eds Core Writing Team, Pachauri, R. K. & Meyer, L. A.) (IPCC, 2015).
Lucas, R. E. Some macroeconomics for the 21st century. J. Econ. Perspect. 14, 159–168 (2000).
Nelson, C. R. & Plosser, C. Trends and random walks in macroeconomic time series: some evidence and implications. J. Monetary Econ. 10, 139–162 (1982).
Morley, J. C., Nelson, C. R. & Zivot, E. Why are the Beveridge-Nelson and unobserved-components decompositions of GDP different? Rev. Econ. Stat. 85, 235–243 (2003).
Carson, Richard T. The environmental Kuznets curve: seeking empirical regularity and theoretical structure. Rev. Environ. Econ. Policy 4, 3–23 (2010).
Dellink, R., Chateau, J., Lanzi, E. & Magné, B. Long-term economic growth projections in the shared socioeconomic pathways. Glob. Environ. Change 42, 200–214 (2015).
Leimbach, M., Kriegler, E., Roming, N. & Schwanitz, J. Future growth patterns of world regions—a GDP scenario approach. Glob. Environ. Change 24, 215–225 (2015).
Cuaresma, J. C. Income projections for climate change research: a framework based on human capital dynamics. Glob. Environ. Change 42, 226–236 (2015).
Submitted Intended Nationally Determined Contributions (Center for Climate and Energy Solutions, 2015); https://www.c2es.org/international/2015-agreement/indcs
Rogelj, J. et al. Paris agreement climate proposals need a boost to keep warming well below 2 °C. Nature 534, 631–639 (2016).
Webster, M. et al. Uncertainty analysis of climate change and policy response. Climatic Change 61, 295–320 (2003).
Sokolov, A. P. et al. Probabilistic forecast for twenty-first-century climate based on uncertainties in emissions (without policy) and climate parameters. J. Clim. 22, 5175–5204 (2009).
Monier, E. et al. A framework for modeling uncertainty in regional climate change. Climatic Change 131, 51–66 (2015).
Gillingham, K. et al. Modeling Uncertainty in Climate Change: A Multi-Model Comparison Report 290 (MIT Joint Program on the Science and Policy of Global Change, 2015).
The Maddison Project (GGDC, 2013); http://www.ggdc.net/maddison/maddison-project/home.htm
GDP Implicit Price Deflator in United States (OECD, accessed 14 February 2016); https://research.stlouisfed.org/fred2/series/USAGDPDEFAISMEI
Boden, T. A., Marland, G. & Andres, R. J. Global, Regional, and National Fossil-Fuel CO2 Emissions (Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, US Department of Energy, 2013).
Gelman, A. et al. Bayesian Data Analysis 3rd edn (Chapman and Hall, 2013).
Perron, P. The great crash, the oil price shock, and the unit root hypothesis. Econometrica 57, 1361–1401 (1989).
Plummer, M. JAGS: a program for analysis of Bayesian graphical models using Gibbs sampling. Proc. 3rd Int. Workshop Distributed Statistical Computing Vol. 124, 125 (Technische Universität Wien, 2003).
Plummer, M., Best, N., Cowles, K. & Vines, K. CODA: convergence diagnosis and output analysis for MCMC. R News 6, 7–11 (2006).
R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2014); http://www.R-project.org
McGlade, C. & Ekins, P. The geographical distribution of fossil fuels unused when limiting global warming to 2 °C. Nature 517, 187–190 (2015).
IPCC Climate Change 2014: Mitigation of Climate Change (eds Edenhofer, O. et al.) Annex II.2 (Cambridge Univ. Press, 2014).
Allen, M. R. et al. Warming caused by cumulative carbon emissions towards the trillionth tonne. Nature 458, 1163–1166 (2009).
IPCC Climate Change 2013: The Physical Science Basis Ch. 12 (Cambridge Univ. Press, 2014).
This work was supported by NIH grants R01 HD054511 and R01 HD070936.
The authors declare no competing financial interests.
About this article
Cite this article
Raftery, A., Zimmer, A., Frierson, D. et al. Less than 2 °C warming by 2100 unlikely. Nature Clim Change 7, 637–641 (2017). https://doi.org/10.1038/nclimate3352
This article is cited by
Nature Climate Change (2023)
Nature Climate Change (2023)
Nature Communications (2023)
Endogenous Hormones Improve Lodging Tolerance of Maize (Zea mays L.) by Regulating Stalk Structure Under Elevated Temperature
Journal of Plant Growth Regulation (2023)