Estimation of the Pareto tail index from extreme order statistics is an important problem in many settings. The upper tail of the distribution, where data are sparse, is typically fitted with a model, such as the Pareto model, from which quantities such as probabilities associated with extreme...

This paper uses machine learning to improve the prediction of corporate emissions so that financial regulators and investors can make better decisions about climate transition risk. The need for predictions arises because only a subset of global companies report emissions. The novelty is to use...

We propose a model based on statistical learning techniques to predict unreported corporate greenhouse gas emissions, which generates considerably better results than existing approaches. The model uses one linear and one non-linear learners only, which reduces its complexity to the minimum...

The demographic transition is a phenomenon affecting many industrialized societies. These economies are experiencing a decline in mortality alongside low fertility rates - a situation that puts social security systems under severe pressure. To implement appropriate reform measures, adequate...

Nowadays, many countries adopt an active agenda to mitigate the impact of greenhouse gas emissions by moving towards less polluting energy generation technologies. The environmental costs, directly or indirectly generated to achieve such a challenging objective, remain however largely...