Ever since the publication of “The Origin of Species by Means of Natural Selection” by Charles Darwin in 1859, many theories have been put forward regarding the reasons as to why animals and humans are different. Whilst there was an initial tendency to focus on the differences apparent in the main anatomic characteristics of the human body, in more recent time research has concentrated on differences that are evident both in the anatomy and in that of the workings of the human brain. While in the past, the essence of the human being was placed on the pelvic structure, the creation of tools or even the intermaxillary bone, the introduction of new technology and cerebral imaging is enabling us to glean previously unthinkable information regarding the evolution of specific structures and parts of the brain. All these discoveries can be associated with new theories based on a better understanding of the workings of the human mind. Hence, as a result of an enormous joint effort, a comprehensible description of the cause of the origin of mankind is emerging. This allows “love”, once considered as merely a simple emotion, to be at the very core of explaining the evolutionary characteristics of the human being.

Short-term and mid-term projections of energy consumption and carbon emissions raise significant concern about the availability of the necessary energy resources to meet the growing demand and about the impact of emissions on global change. Different macroeconomic models address this issue through global variables, such as gross domestic product, production of goods and services, total population and natural resources extraction. However, the relations among these variables are neither linear nor simple. In an attempt to base said relations on a “bottom-up” perspective, the individual behavior of representative agents of economy, in terms of energy consumption and related carbon emissions, was studied, with particular emphasis on their investment in human capital. It was found that a higher investment in human capital (e.g., education, research) was translated into a better distribution of consumption, with a higher level of energy efficiency and a slight improvement in carbon emissions intensity.

In this article, we present a new family of estimators for the regression parameter β in the Additive Hazards Model which represents a gain in robustness not only against outliers but also against unspecific contamination schemes. They are consistent and asymptotically normal and furthermore, and they have a nonzero breakdown point. In Survival Analysis, the Additive Hazards Model proposes a hazard function of the form , where ？is a common nonparametric baseline hazard function and z is a vector of independent variables. For this model, the seminal work of Lin and Ying (1994) develops an estimator for the regression parameter β which is asymptotically normal and highly efficient. However, a potential drawback of that classical estimator is that it is very sensitive to outliers. In an attempt to gain robustness, álvarez and Ferrarrio (2013) introduced a family of estimators for β which were still highly efficient and asymptotically normal, but they also had bounded influence functions. Those estimators, which are developed using classical Counting Processes methodology, still retain the drawback of having a zero breakdown point.