Using probabilistic analysis to improve greenhouse gas baseline forecasts in developing country contexts: the case of Chile

Raul O’ Ryan, Carlos Benavides, Manuel Díaz, Juan Pablo San Martín and Javier Mallea
Authors registered in the RePEc Author Service: Raul O'Ryan GAllardo ()

Climate Policy, 2019, vol. 19, issue 3, 299-314

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.

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://hdl.handle.net/10.1080/14693062.2018.1503153 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tcpoxx:v:19:y:2019:i:3:p:299-314

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tcpo20

DOI: 10.1080/14693062.2018.1503153

Access Statistics for this article

Climate Policy is currently edited by Professor Michael Grubb

More articles in Climate Policy from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().