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Author (up) O' Ryan, R.; Benavides, C.; Diaz, M.; San Martin, J.P.; Mallea, J. doi  openurl
  Title Using probabilistic analysis to improve greenhouse gas baseline forecasts in developing country contexts: the case of Chile Type Journal Article
  Year 2019 Publication Climate Policy Abbreviated Journal Clim. Policy  
  Volume 19 Issue 3 Pages 299-314  
  Keywords Energy systems modelling; uncertainty; climate change policy; probabilistic analysis; emission baselines; nationally determined contributions  
  Abstract In this paper, initial steps are presented toward characterizing, quantifying, incorporating and communicating uncertainty applying a probabilistic analysis to countrywide emission baseline forecasts, using Chile as a case study. Most GHG emission forecasts used by regulators are based on bottom-up deterministic approaches. Uncertainty is usually incorporated through sensitivity analysis and/or use of different scenarios. However, much of the available information on uncertainty is not systematically included. The deterministic approach also gives a wide range of variation in values without a clear sense of probability of the expected emissions, making it difficult to establish both the mitigation contributions and the subsequent policy prescriptions for the future. To improve on this practice, we have systematically included uncertainty into a bottom-up approach, incorporating it in key variables that affect expected GHG emissions, using readily available information, and establishing expected baseline emissions trajectories rather than scenarios. The resulting emission trajectories make explicit the probability percentiles, reflecting uncertainties as well as possible using readily available information in a manner that is relevant to the decision making process. Additionally, for the case of Chile, contradictory deterministic results are eliminated, and it is shown that, whereas under a deterministic approach Chile's mitigation ambition does not seem high, the probabilistic approach suggests this is not necessarily the case. It is concluded that using a probabilistic approach allows a better characterization of uncertainty using existing data and modelling capacities that are usually weak in developing country contexts. Key policy insights Probabilistic analysis allows incorporating uncertainty systematically into key variables for baseline greenhouse gas emission scenario projections. By using probabilistic analysis, the policymaker can be better informed as to future emission trajectories. Probabilistic analysis can be done with readily available data and expertise, using the usual models preferred by policymakers, even in developing country contexts.  
  Address [O' Ryan, Raul] Univ Adolfo Ibanez, Fac Engn & Sci, EARTH Ctr, Santiago, Chile, Email: mdiaz@centroenergia.cl  
  Corporate Author Thesis  
  Publisher Taylor & Francis Ltd Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1469-3062 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000455949300003 Approved no  
  Call Number UAI @ eduardo.moreno @ Serial 1164  
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