The accountability imperative for quantifiying the uncertainty of emission forecasts : evidence from Mexico

Daniel Puig (1) , Oswaldo Morales-Nápoles (2, 3, 3, 4) , Fatemeh Bakhtiari , Gissela Landa (5)

1 UNEP - United Nations Environmental Programme
2 TNO - The Netherlands Organisation for Applied Scientific Research
3 TU Delft - Delft University of Technology
4 Department of Civil Engineering and Geosciences [Delft]
5 OFCE - Observatoire français des conjonctures économiques (Sciences Po)

Governmental climate change mitigation targets are typically developed with the aid of forecasts of greenhouse-gas emissions. The robustness and credibility of such forecasts depends, among other issues, on the extent to which forecasting approaches can reflect prevailing uncertainties. We apply a transparent and replicable method to quantify the uncertainty associated with projections of gross domestic product growth rates for Mexico, a key driver of greenhouse-gas emissions in the country. We use those projections to produce probabilistic forecasts of greenhouse-gas emissions for Mexico. We contrast our probabilistic forecasts with Mexico’s governmental deterministic forecasts. We show that, because they fail to reflect such key uncertainty, deterministic forecasts are ill-suited for use in target-setting processes. We argue that (i) guidelines should be agreed upon, to ensure that governmental forecasts meet certain minimum transparency and quality standards, and (ii) governments should be held accountable for the appropriateness of the forecasting approach applied to prepare governmental forecasts, especially when those forecasts are used to derive climate change mitigation targets.

Mots clés: Uncertainty; Projections; Structured expert judgment; Accountability; Emission-reduction targets; Gross domestic product growth rates

Domaines: Economies et finances

Daniel Puig, Oswaldo Morales-Nápoles, Fatemeh Bakhtiari, Gissela Landa. The accountability imperative for quantifiying the uncertainty of emission forecasts : evidence from Mexico. 2017. ⟨hal-03389325⟩