Computation of uncertainty for atmospheric emission projections from key pollutant sources in Spain

Emission projections are important for environmental policy, both to evaluate the effectiveness of abatement strategies and to determine legislation compliance in the future. Moreover, including uncertainty is an essential added value for decision makers. In this work, projection values and their associated uncertainty are computed for pollutant emissions corresponding to the most significant activities from the national atmospheric emission inventory in Spain. Till now, projections had been calculated under three main scenarios: “without measures” (WoM), “with measures” (WM) and “with additional measures” (WAM). For the first one, regression techniques had been applied, which are inadequate for time-dependent data. For the other scenarios, values had been computed taking into account expected activity growth, as well as policies and measures. However, only point forecasts had been computed. In this work statistical methodology has been applied for: a) Inclusion of projection intervals for future time points, where the width of the intervals is a measure of uncertainty. b) For the WoM scenario, ARIMA models are applied to model the dynamics of the processes. c) In the WM scenario, bootstrap is applied as an additional non-parametric tool, which does not rely on distributional assumptions and is thus more general. The advantages of using ARIMA models for the WoM scenario including uncertainty are shown. Moreover, presenting the WM scenario allows observing if projected emission values fall within the intervals, thus showing if the measures to be taken to reach the scenario imply a significant improvement. Results also show how bootstrap techniques incorporate stochastic modelling to produce forecast intervals for the WM scenario.