Sensitivity of projected long-term CO2 emissions across the Shared Socioeconomic Pathways (original) (raw)

References

  1. Riahi, K. et al. The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob. Environ. Change http://dx.doi.org/10.1016/j.gloenvcha.2016.05.009 (2016).
  2. Borgonovo, E. Sensitivity analysis with finite changes: an application to modified EOQ models. Eur. J. Oper. Res. 200, 127–138 (2010).
    Article Google Scholar
  3. Stern, D. I., Pezzey, J. C. V. & Lambie, N. R. Where in the world is it cheapest to cut carbon emissions? Aust. J. Agric. Resour. Econ. 56, 315–331 (2012).
    Article Google Scholar
  4. Blanford, G. J., Rose, S. K. & Tavoni, M. Baseline projections of energy and emissions in Asia. Energy Econ. 34 (suppl. 3), S284–S292 (2012).
    Article Google Scholar
  5. IPCC Climate Change 2014: Mitigation of Climate Change (ed. Edenhofer, O.) (Cambridge Univ. Press, 2014).
  6. IPCC Special Report on Emissions Scenarios (eds Nakićenović, N. & Swart, R.) (Cambridge Univ. Press, 2000).
  7. Moss, R. H. et al. The next generation of scenarios for climate change research and assessment. Nature 463, 747–756 (2010).
    Article CAS Google Scholar
  8. van Vuuren, D. P. et al. A new scenario framework for climate change research: scenario matrix architecture. Climatic Change 122, 373–386 (2013).
    Article Google Scholar
  9. O’Neill, B. C. et al. A new scenario framework for climate change research: the concept of shared socioeconomic pathways. Climatic Change 122, 387–400 (2013).
    Article Google Scholar
  10. Kriegler, E. et al. A new scenario framework for climate change research: the concept of shared climate policy assumptions. Climatic Change 122, 401–414 (2014).
    Article Google Scholar
  11. O’Neill, B. C. et al. The roads ahead: narratives for shared socioeconomic pathways describing world futures in the 21st century. Glob. Environ. Change http://dx.doi.org/10.1016/j.gloenvcha.2015.01.004 (2015).
  12. Nordhaus, W. D. A Question of Balance: Economic Modeling of Global Warming (Yale Univ. Press, 2008).
    Google Scholar
  13. van Vuuren, D. P., de Vries, B., Beusen, A. & Heuberger, P. S. C. Conditional probabilistic estimates of 21st century greenhouse gas emissions based on the storylines of the IPCC-SRES scenarios. Glob. Environ. Change 18, 635–654 (2008).
    Article Google Scholar
  14. Kriegler, E. et al. Will economic growth and fossil fuel scarcity help or hinder climate stabilization? Climatic Change 136, 7–22 (2016).
    Article Google Scholar
  15. Gillingham, K. et al. Modeling Uncertainty in Climate Change: A Multi-Model Comparison Tech. Rep. (National Bureau of Economic Research, 2015); http://www.nber.org/papers/w21637
  16. Anderson, B., Borgonovo, E., Galeotti, M. & Roson, R. Uncertainty in climate change modeling: can global sensitivity analysis be of help? Risk Anal. 34, 271–293 (2014).
    Article Google Scholar
  17. Butler, M. P., Reed, P. M., Fisher-Vanden, K., Keller, K. & Wagener, T. Identifying parametric controls and dependencies in integrated assessment models using global sensitivity analysis. Environ. Model. Software 59, 10–29 (2014).
    Article Google Scholar
  18. Bosetti, V. et al. Sensitivity to energy technology costs: a multi-model comparison analysis. Energy Policy 80, 244–263 (2015).
    Article Google Scholar
  19. van Vuuren, D. P. et al. Energy, land-use and greenhouse gas emissions trajectories under a green growth paradigm. Glob. Environ. Change http://dx.doi.org/10.1016/j.gloenvcha.2016.05.008 (2016).
  20. Fricko, O. et al. The marker quantification of the Shared Socioeconomic Pathway 2: a middle-of-the-road scenario for the 21st century. Glob. Environ. Change http://dx.doi.org/10.1016/j.gloenvcha.2016.06.004 (2016).
  21. Fujimori, S. et al. SSP3: AIM implementation of shared socioeconomic pathways. Glob. Environ. Change http://dx.doi.org/10.1016/j.gloenvcha.2016.06.009 (2016).
  22. Matthews, H. D., Gillett, N. P., Stott, P. A. & Zickfeld, K. The proportionality of global warming to cumulative carbon emissions. Nature 459, 829–832 (2009).
    Article CAS Google Scholar
  23. Allen, M. R. et al. Warming caused by cumulative carbon emissions towards the trillionth tonne. Nature 458, 1163–1166 (2009).
    Article CAS Google Scholar
  24. Capros, P. et al. GEM-E3 model documentation JRC Scientific and Policy Reports 26034 (Publications Office of the European Union, 2013); ftp://s-jrcsvqpx102p.jrc.es/pub/EURdoc/EURdoc/JRC83177.pdf
  25. Stehfest, E. et al. Integrated Assessment of Global Environmental Change with IMAGE 3.0. Model Description and Policy Applications (PBL Netherlands Environmental Assessment Agency, 2014).
    Google Scholar
  26. Waisman, H., Guivarch, C., Grazi, F. & Hourcade, J. C. The Imaclim-R model: infrastructures, technical inertia and the costs of low carbon futures under imperfect foresight. Climatic Change 114, 101–120 (2012).
    Article Google Scholar
  27. Anandarajah, G., Pye, S., Usher, W., Kesicki, F. & Mcglade, C. TIAM-UCL Global Model Documentation (UK Energy Research Centre, 2011); http://discovery.ucl.ac.uk/1413198
  28. Emmerling, J. et al. The WITCH 2016 Model-Documentation and Implementation of the Shared Socioeconomic Pathways Nota di Lavoro 42.2016 (Fondazione Eni Enrico Mattei, 2016); http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2800970
  29. IPCC Climate Change 2014: Mitigation of Climate Change (eds Edenhofer, O. et al.) (Cambridge Univ. Press, 2015).
  30. Kriegler, E. et al. Diagnostic indicators for integrated assessment models of climate policy. Technol. Forecast. Soc. Change 90, 45–61 (2015).
    Article Google Scholar
  31. Lempert, R. Blindside: How to Anticipate Forcing Events and Wild Cards in Global Politics (Brookings Institution Press, 2007).
    Google Scholar
  32. Drouet, L., Bosetti, V. & Tavoni, M. Selection of climate policies under the uncertainties in the Fifth Assessment Report of the IPCC. Nat. Clim. Change 5, 937–940 (2015).
    Article Google Scholar
  33. Wilson, C., Grubler, A., Gallagher, K. S. & Nemet, G. F. Marginalization of end-use technologies in energy innovation for climate protection. Nat. Clim. Change 2, 780–788 (2012).
    Article Google Scholar
  34. Bauer, N. et al. Shared socio-economic pathways of the energy sector—quantifying the narratives. Glob. Environ. Change http://dx.doi.org/10.1016/j.gloenvcha.2016.07.006 (2016).
  35. KC, S. & Lutz, W. The human core of the shared socioeconomic pathways: population scenarios by age, sex and level of education for all countries to 2100. Glob. Environ. Change http://dx.doi.org/10.1016/j.gloenvcha.2014.06.004 (2014).
  36. Dellink, R., Chateau, J., Lanzi, E. & Magné, B. Long-term economic growth projections in the Shared Socioeconomic Pathways. Glob. Environ. Change http://dx.doi.org/10.1016/j.gloenvcha.2015.06.004 (2015).
  37. Ang, B. LMDI decomposition approach: a guide for implementation. Energy Policy 86, 233–238 (2015).
    Google Scholar
  38. Borgonovo, E. A methodology for determining interactions in probabilistic safety assessment models by varying one parameter at a time. Risk Anal. 30, 385–399 (2010).
    Article Google Scholar
  39. Krey, V. et al. Message-globiom 1.0 documentation Tech. Rep. (International Institute for Applied Systems Analysis, 2016); http://data.ene.iiasa.ac.at/message-globiom

Download references