Sensitivities of heat-wave mortality projections: Moving towards stochastic model assumptions.

This paper analyses the probabilistic future behaviour of heat-waves (HWs) in the city of Madrid in the twenty-first century, using maximum daily temperatures from twenty-one climate circulation models under two representative concentration pathways (RCP 8.5 & RCP 4.5). HWs are modelled considering three factors: number per annum, duration and intensity, characterised by three stochastic processes: Poisson, Gamma and truncated Gaussian, respectively. Potential correlations between these processes are also considered. The probabilistic temperature behaviour is combined with an epidemiological model with stochastic mortality risk following a generalized extreme value distribution (gev). The objective of this study is to obtain probability distributions of mortality and risk measures such as the mean value of the 5% of worst cases in the 21st century, in particular from 2025 to 2100. Estimates from stochastic models for characterising HWs and epidemiological impacts on human health can vary from one climate model to another, so relying on a single climate model can be problematic. For this reason, the calculations are carried out for 21 models and the average of the results is obtained. A sensitivity adaptation analysis is also performed. Under RCP 8.5 for 2100 for Madrid city a mean excess of 3.6 °C over the 38 °C temperature threshold is expected as the average of all models, with an expected attributable mortality of 1614 people, but these figures may be substantially exceeded in some cases if the highest-risk cases occur.

Valuing uncertain cash flows from investments that enhance energy efficiency.

There is a broad consensus that investments to enhance energy efficiency quickly pay for themselves in lower energy bills and spared emission allowances. However, investments that at first glance seem worthwhile usually are not undertaken. One of the plausible, non-excluding explanations is the numerous uncertainties that these investments face. This paper deals with the optimal time to invest in an energy efficiency enhancement at a facility already in place that consumes huge amounts of a fossil fuel (coal) and operates under carbon constraints. We follow the Real Options approach. Our model comprises three sources of uncertainty following different stochastic processes which allows for application in a broad range of settings. We assess the investment option by means of a three-dimensional binomial lattice. We compute the trigger investment cost, i.e., the threshold level below which immediate investment would be optimal. We analyze the major drivers of this decision thus aiming at the most promising policies in this regard.