Less than 2 °C warming by 2100 unlikely

Abstract

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.

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Figure 1: Carbon intensity, expressed in tonnes of CO2 per US$10,000 in 2010 Purchasing Power Parity for USA, China, India, and Nigeria.
Figure 2: Out-of-sample predictive validation of model for world CO2 emissions.
Figure 3: Probabilistic forecast to 2100, with IPCC RCP scenarios.
Figure 4: Probabilistic CO2 emissions forecasts for leading countries and regions, with Paris climate agreement targets.

References

  1. 1

    IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2014).

  2. 2

    World Population Prospects: The 2015 Revision (United Nations, Department of Economic and Social Affairs, Population Division, 2015).

  3. 3

    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).

    CAS  Article  Google Scholar 

  4. 4

    Gerland, P. et al. World population stabilization unlikely this century. Science 346, 234–237 (2014).

    CAS  Article  Google Scholar 

  5. 5

    van Vuuren, D. P. et al. The representative concentration pathways: an overview. Climatic Change 109, 5–31 (2011).

    Google Scholar 

  6. 6

    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).

    Article  Google Scholar 

  7. 7

    Wise, M. et al. Implications of limiting CO2 concentrations for land use and energy. Science 324, 1183–1186 (2009).

    CAS  Article  Google Scholar 

  8. 8

    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).

    Google Scholar 

  9. 9

    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).

    Article  Google Scholar 

  10. 10

    World Population Prospects: The 2012 Revision (United Nations, Department of Economic and Social Affairs, Population Division New York, 2013).

  11. 11

    Raftery, A. E., Alkema, L. & Gerland, P. Bayesian population projections for the United Nations. Stat. Sci. 29, 58–68 (2014).

    Article  Google Scholar 

  12. 12

    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).

    Google Scholar 

  13. 13

    Kaya, O. Impacts of Carbon Dioxide Emissions on GWP: Interpretation of Proposed Scenarios (IPCC/Response Strategies Working Group, 1989).

    Google Scholar 

  14. 14

    Ehrlich, P. R. & Holden, J. P. Impact of population growth. Science 171, 1212–1217 (1971).

    CAS  Article  Google Scholar 

  15. 15

    IPCC Climate Change 2014: Synthesis Report (eds Core Writing Team, Pachauri, R. K. & Meyer, L. A.) (IPCC, 2015).

  16. 16

    Lucas, R. E. Some macroeconomics for the 21st century. J. Econ. Perspect. 14, 159–168 (2000).

    Article  Google Scholar 

  17. 17

    Nelson, C. R. & Plosser, C. Trends and random walks in macroeconomic time series: some evidence and implications. J. Monetary Econ. 10, 139–162 (1982).

    Article  Google Scholar 

  18. 18

    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).

    Article  Google Scholar 

  19. 19

    Carson, Richard T. The environmental Kuznets curve: seeking empirical regularity and theoretical structure. Rev. Environ. Econ. Policy 4, 3–23 (2010).

    Article  Google Scholar 

  20. 20

    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).

    Article  Google Scholar 

  21. 21

    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).

    Google Scholar 

  22. 22

    Cuaresma, J. C. Income projections for climate change research: a framework based on human capital dynamics. Glob. Environ. Change 42, 226–236 (2015).

    Article  Google Scholar 

  23. 23

    Submitted Intended Nationally Determined Contributions (Center for Climate and Energy Solutions, 2015); https://www.c2es.org/international/2015-agreement/indcs

  24. 24

    Rogelj, J. et al. Paris agreement climate proposals need a boost to keep warming well below 2 °C. Nature 534, 631–639 (2016).

    CAS  Article  Google Scholar 

  25. 25

    Webster, M. et al. Uncertainty analysis of climate change and policy response. Climatic Change 61, 295–320 (2003).

    Article  Google Scholar 

  26. 26

    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).

    Article  Google Scholar 

  27. 27

    Monier, E. et al. A framework for modeling uncertainty in regional climate change. Climatic Change 131, 51–66 (2015).

    Article  Google Scholar 

  28. 28

    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).

  29. 29

    The Maddison Project (GGDC, 2013); http://www.ggdc.net/maddison/maddison-project/home.htm

  30. 30

    GDP Implicit Price Deflator in United States (OECD, accessed 14 February 2016); https://research.stlouisfed.org/fred2/series/USAGDPDEFAISMEI

  31. 31

    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).

    Google Scholar 

  32. 32

    Gelman, A. et al. Bayesian Data Analysis 3rd edn (Chapman and Hall, 2013).

    Google Scholar 

  33. 33

    Perron, P. The great crash, the oil price shock, and the unit root hypothesis. Econometrica 57, 1361–1401 (1989).

    Article  Google Scholar 

  34. 34

    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).

    Google Scholar 

  35. 35

    Plummer, M., Best, N., Cowles, K. & Vines, K. CODA: convergence diagnosis and output analysis for MCMC. R News 6, 7–11 (2006).

    Google Scholar 

  36. 36

    R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2014); http://www.R-project.org

  37. 37

    McGlade, C. & Ekins, P. The geographical distribution of fossil fuels unused when limiting global warming to 2 °C. Nature 517, 187–190 (2015).

    CAS  Article  Google Scholar 

  38. 38

    IPCC Climate Change 2014: Mitigation of Climate Change (eds Edenhofer, O. et al.) Annex II.2 (Cambridge Univ. Press, 2014).

  39. 39

    Allen, M. R. et al. Warming caused by cumulative carbon emissions towards the trillionth tonne. Nature 458, 1163–1166 (2009).

    CAS  Article  Google Scholar 

  40. 40

    IPCC Climate Change 2013: The Physical Science Basis Ch. 12 (Cambridge Univ. Press, 2014).

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Acknowledgements

This work was supported by NIH grants R01 HD054511 and R01 HD070936.

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Contributions

The first four authors wrote the manuscript and developed the statistical model. A.E.R. and D.M.W.F. designed the study. A.Z. and P.L. compiled and analysed data and wrote computer code.

Corresponding author

Correspondence to Adrian E. Raftery.

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The authors declare no competing financial interests.

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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

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