Climatic Change

, Volume 142, Issue 3–4, pp 521–529

Using population projections in climate change analysis

Article

Abstract

The two leading sources of long-range population projections, the United Nations (UN) and the International Institute for Applied Systems Analysis (IIASA), currently disagree on the most likely end-of-the-century world population by over two billion people. Because climate change policy models are influenced by population uncertainty, this poses an underappreciated problem for analysts. Furthermore, long-range population projections have not been predictably stable over time and climate change policy models have not consistently used one set of population projections. This only increases the difficulty of comparing research results. Comparing the UN and IIASA population projections, the UN’s probabilistic population projections should be used with caution as they tend to understate the uncertainty in long-range population forecasts. Currently, the IIASA scenario projections are better suited to long-range climate change policy analysis. As a final recommendation, a simple demographic sub-model is proposed for use in cost-benefit climate change integrated assessment models that performs better than current alternatives.

Supplementary material

10584_2017_1968_MOESM1_ESM.docx (187 kb)
ESM 1(DOCX 186 kb)

References

  1. Abel GJ, Sander N (2014) Quantifying global international migration flows. Science 80(343):1520–1522. doi:10.1126/science.1248676 CrossRefGoogle Scholar
  2. Ackerman F, Munitz C (2016) A critique of climate damage modeling: carbon fertilization, adaptation, and the limits of FUND. Energy Res Soc Sci 12:62–67. doi:10.1016/j.erss.2015.11.008 CrossRefGoogle Scholar
  3. Basten S, Yip P, Chui E (2013) Remeasuring ageing in Hong Kong SAR; or “keeping the demographic window open”. J Popul Res 30:249–264. doi:10.1007/s12546-013-9113-1 CrossRefGoogle Scholar
  4. Billari FC, Graziani R, Melilli E (2014) Stochastic population forecasting based on combinations of expert evaluations within the Bayesian paradigm. Demography 51:1933–1954. doi:10.1007/s13524-014-0318-5 CrossRefGoogle Scholar
  5. Bongaarts J (2016) Slow down population growth. Nature 530:409–412. doi:10.1038/530409a CrossRefGoogle Scholar
  6. Bongaarts J, Bulatao RA (2000) Beyond six billion: forecasting the world’s population. National Academies Press, Washington, DCGoogle Scholar
  7. Booth H (2006) Demographic forecasting: 1980 to 2005 in review. Int J Forecast 22:547–581CrossRefGoogle Scholar
  8. Bradshaw CJA, Brook BW (2014) Human population reduction is not a quick fix for environmental problems. Proc Natl Acad Sci. doi:10.1073/pnas.1410465111 Google Scholar
  9. Clarke L, Jiang K, Akimoto K et al (2014) Assessing transformation pathways. In: Edenhofer O, Pichs-Madruga R, Sokona Y et al (eds) Climate change 2014: mitigation of climate change. Contribution of working group III to the fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, pp 413–510Google Scholar
  10. Cohen JE (1995) How many people can the earth support? WW Norton & Company, New YorkGoogle Scholar
  11. Flato G, Marotzke J, Abiodun B et al (2013) Evaluation of climate models. In: Stocker TF, Qin D, Plattner G-K et al (eds) Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, pp 741–866Google Scholar
  12. Gaffin SR, O’Neill BC (1997) Population and global warming with and without CO2 targets. Popul Environ 18:389–413CrossRefGoogle Scholar
  13. Gerland P, Raftery AE, Ševčíková H et al (2014) World population stabilization unlikely this century. Science 234:234–237. doi:10.1126/science.1257469 CrossRefGoogle Scholar
  14. Hamilton C, Turton H (2002) Determinants of emissions growth in OECD countries. Energy Policy 30:63–71. doi:10.1016/S0301-4215(01)00060-X CrossRefGoogle Scholar
  15. Hunter LM, O’Neill BC (2014) Enhancing engagement between the population, environment, and climate research communities: the shared socio-economic pathway process. Popul Environ 35:231–242. doi:10.1007/s11111-014-0202-7 CrossRefGoogle Scholar
  16. IPCC (2014a) Summary for policymakers. In: Edenhofer O, Pichs-Madruga R, Sokona Y, et al. (eds) Climate change 2014: mitigation of climate change. Contribution of working group III to the fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, CambridgeGoogle Scholar
  17. IPCC (2014b) Climate change 2014: synthesis report. Contribution of working groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change. Geneva, SwitzerlandGoogle Scholar
  18. Jones DW (1991) How urbanization affects energy-use in developing countries. Energy Policy 19:621–630. doi:10.1016/0301-4215(91)90094-5 CrossRefGoogle Scholar
  19. KC S, Lutz W (2014a) Demographic scenarios by age, sex and education corresponding to the SSP narratives. Popul Environ 35:243–260. doi:10.1007/s11111-014-0205-4 CrossRefGoogle Scholar
  20. KC S, Lutz W (2014b) The human core of the shared socioeconomic pathways: population scenarios by age, sex and level of education for all countries to 2100. Glob Environ Chang. doi:10.1016/j.gloenvcha.2014.06.004 Google Scholar
  21. Krey V, Masera O, Blanford G et al (2014) Annex II: Metrics & Methodology. In: Edenhofer O, Pichs-Madruga R, Sokona Y (eds) Climate change 2014: mitigation of climate change. Contribution of working group III to the fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, pp 1281–1328Google Scholar
  22. Kriegler E, O’Neill BC, Hallegatte S et al (2012) The need for and use of socio-economic scenarios for climate change analysis: a new approach based on shared socio-economic pathways. Glob Environ Chang 22:807–822. doi:10.1016/j.gloenvcha.2012.05.005 CrossRefGoogle Scholar
  23. Lee RD (1998) Probabilistic approaches to population forecasting. In: Lutz W, Vaupel JW, Ahlburg DA (eds) Frontiers of population forecasting: supplement to population and development review. Population Council, New York, pp 156–190Google Scholar
  24. Liddle B (2013) Urban density and climate change: a STIRPAT analysis using city-level data. J Transp Geogr 28:22–29. doi:10.1016/j.jtrangeo.2012.10.010 CrossRefGoogle Scholar
  25. Liddle B (2014) Impact of population, age structure, and urbanization on carbon emissions/energy consumption: evidence from macro-level, cross-country analyses. Popul Environ 35:286–304. doi:10.1007/s11111-013-0198-4 CrossRefGoogle Scholar
  26. Lutz W, KC S (2010) Dimensions of global population projections: what do we know about future population trends and structures? Philos Trans R Soc Lond Ser B Biol Sci 365:2779–2791CrossRefGoogle Scholar
  27. Lutz W, Sanderson W, Scherbov S (2001) The end of world population growth. Nature 412:543–545CrossRefGoogle Scholar
  28. Lutz W, Butz WP, KC S et al (2014a) Population growth: peak probability. Science 346(80):561. doi:10.1126/science.346.6209.561-a CrossRefGoogle Scholar
  29. Lutz W, Butz WP, KC S (2014b) World population and human capital in the twenty-first century. Oxford University Press, OxfordCrossRefGoogle Scholar
  30. Morgan MG (2014) Use (and abuse) of expert elicitation in support of decision making for public policy. Proc Natl Acad Sci 111:7176–7184. doi:10.1073/pnas.1319946111 CrossRefGoogle Scholar
  31. Mouratiadou I, Biewald A, Pehl M et al (2016) The impact of climate change mitigation on water demand for energy and food: an integrated analysis based on the shared socioeconomic pathways. Environ Sci Pol 64:48–58. doi:10.1016/j.envsci.2016.06.007 CrossRefGoogle Scholar
  32. Nordhaus WD (1992a) The “DICE” model: background and structure of a Dynamic Integrated Climate-Economy model of the economics of global warming. Cowles Foundation discussion paper no. 1009Google Scholar
  33. Nordhaus WD (1992b) Optimal greenhouse-gas reductions and tax policy in the “DICE” modelGoogle Scholar
  34. Nordhaus WD (1994) Managing the global commons: the economics of climate change. MIT, CambridgeGoogle Scholar
  35. Nordhaus WD (2007) Accompanying notes and documentation on development of DICE-2007 model: notes on DICE-2007.delta.v8 as of June 7, 2007Google Scholar
  36. Nordhaus WD (2008) A question of balance: weighing the options on global warming policies. Yale University Press, New HavenGoogle Scholar
  37. Nordhaus WD, Boyer J (2000) Warming the world: economic models of global warming. MIT, CambridgeGoogle Scholar
  38. Nordhaus WD, Sztorc P (2013) DICE 2013: introduction and user’s manualGoogle Scholar
  39. O’Neill BC, Dalton M, Fuchs R et al (2010) Global demographic trends and future carbon emissions. Proc Natl Acad Sci U S A 107:17521–17526. doi:10.1073/pnas.1004581107 CrossRefGoogle Scholar
  40. O’Neill BC, Kriegler E, Riahi K et al (2014) A new scenario framework for climate change research: the concept of shared socioeconomic pathways. Clim Chang 122:387–400. doi:10.1007/s10584-013-0905-2 CrossRefGoogle Scholar
  41. Popp A, Dietrich JP, Lotze-Campen H et al (2011) The economic potential of bioenergy for climate change mitigation with special attention given to implications for the land system. Environ Res Lett 6:34017. doi:10.1088/1748-9326/6/3/034017 CrossRefGoogle Scholar
  42. Riahi K, van Vuuren DP, Kriegler E et al (2017) The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob Environ Chang 42:153–168. doi:10.1016/j.gloenvcha.2016.05.009 CrossRefGoogle Scholar
  43. Romaniuk A (2010) Population forecasting: epistemological considerations. Genus 66:91–108Google Scholar
  44. Sanderson WC, Scherbov S (2010) Remeasuring aging. Science 329(80):1287–1288CrossRefGoogle Scholar
  45. Smith K (2008) The population problem. Nat Reports Clim Chang:72–74. doi:10.1038/climate.2008.44
  46. Smith K (2011) We are seven billion. Nat Clim Chang 1:331–335. doi:10.1038/nclimate1235 CrossRefGoogle Scholar
  47. United Nations (2015) World population prospects: the 2015 revision. http://esa.un.org/unpd/wpp/
  48. van Vuuren DP, Edmonds J, Kainuma M et al (2011) The representative concentration pathways: an overview. Clim Chang 109:5–31. doi:10.1007/s10584-011-0148-z CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  1. 1.Stony Brook UniversityNew YorkUSA

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