The EcoIndex metric, reviewed from the perspective of Data Science techniques

EcoIndex has been proposed to evaluate the absolute environmental performance of a given URL using a score ranging from 0 to 100 (higher is better). In this article, we revisit the calculation method of the EcoIndex metric through low-cost Machine Learning (ML) approaches. Our research aims to extend the initial idea of analytical computation, i.e., a relation (equation) between three variables, in the direction of algorithmic Machine Learning (ML) computations, allowing to treat large numbers of data, which is not the case with the current computation. For a URL, our new calculation methods mimic the initial metric and return an environmental performance score but make fewer assumptions than the initial method. We develop several ML ways, either using learning techniques (Locality Sensitive Hashing, K Nearest Neighbor) or matrix computation constitutes the paper’s first contribution. We use standard methods to keep the solutions simple and understood by the public. The second contribution corresponds to a discussion on our implementations, available on a GitHub repository. As major findings or trends of our study, we also discuss the limits of the past and new approaches in a search for new metrics regarding the environmental performance of HTTP requests admissible by the most significant number of people. Our work refers to the uses of digital technology. Therefore, explaining the environmental footprint measures with few words seems important if we want to move towards greater digital sobriety. Otherwise, we run the risk of not being followed by civil society.