![Sample our Environment & Agriculture journals, sign in here to start your access, latest two full volumes FREE to you for 14 days]({"type":"image" , "src" : "/sda/5934/TOC-Subject-Sample-2021-Environment-Agriculture.jpg"})
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.
Acknowledgements
The authors acknowledge the financial support of the MAPS Programme for this research. Funding for the MAPS Programme was provided by The Children's Investment Fund Foundation and the Swiss Agency for Development and Cooperation. We are also grateful to support from the Industrial Engineering Department of the Universidad de Chile and Project Fondef 2-24. We thank two anonymous reviewers and the editor for helpful comments.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes
1 In this paper, GHG emissions are interpreted as CO2eq emissions.
2 Sources of uncertainty include variability, inherent randomness, statistical variation and subjective judgement.
3 We are very grateful to an anonymous referee for pointing out this framework to us.
4 In a personal communication with the analyst of the Finance Ministry who participated in Chile’s NDC, she reported that the commitment finally chosen was not as strict as it might have been because probabilities for each scenario were not known.
5 (Kwakkel, Walker, & Marchau, Citation2010) using a framework developed by (Walker et al., Citation2013) refer to epistemic uncertainties when they are due to lack of knowledge about the phenomena, or inherent variability when it is inherent in their nature respectively.
6 For the energy sector see (Eyre & Baruah, Citation2015).
7 We refer to these four as sectors rather than subsectors, and use subsectors to refer to sources of emissions within each sector.
9 The authors participated in the process of commissioning these reports and integrating them into the final model. The underlying reports used to develop the models for each sector were reviewed and the assumptions on uncertainty flushed out.
10 Of course, copper production affects long term GDP but this impact works through investments, so each year these variables can be assumed to be uncorrelated. A Pearson correlation test of 0.1 between GDP and copper production growth rates between 1960 and 2016 confirms this assumption. See (Ye et al., Citation2017) for an application with correlations among variables.
11 (Puig et al., Citation2017) follow an alternative approach incorporating uncertainty based on expert judgement elicitation for GDP growth rates for different scenarios.
12 Uncertainty is not considered in this parameter. This is in-line with current practice that uses specific IPCC emission factors.
13 These percentile paths are referred to as ‘trajectories’ to differentiate them from the deterministic ‘paths’.
14 Of course, the fact that the high and low deterministic scenarios are similar to the 95th and 5th trajectory percentiles is only a coincidence and depends on the distributions. They could be very different in other cases.
15 Given the process followed to incorporate uncertainty and the relatively normally distributed emission curves obtained, emissions from the 50th trajectory can be expected to be similar to the ones in the deterministic medium scenario.