Making green growth a reality: Reconciling sobriety with stakeholders' satisfaction.

The notion of sobriety is considered a key variable in various energy transition scenarios. Often associated with a form of punitive ecology, it is, nevertheless, possible to make it a component that supports green growth, by linking it to the concept of "satisfaction". In this work, we have invented a way to achieve both "digital", "economic", and "ecological" sobriety, while ensuring the satisfaction of the end user. Directly correlated to the production of goods or services, the satisfaction function is built on the well-documented marginal utility function, which measures the need (or not) to consume further resources to satisfy the economic agents. Hence, it is justified and exists because it stands for the expectations of end users and makes sure the latter is met. This product itself is a function of the allocation of a set of resources, mapped using activity-based costing tools (ABC method). In this work, we focus on an AI proof-of-concept and demonstrate that it is possible to reach numerical sobriety by controlling the size of the training dataset while ensuring roughly the same model performance. In general, we show that it is possible to preserve the efficiency of AI processes while significantly minimizing the need for resources. In this sense, after establishing an analytical model, we suggest reducing the amount of data required to train the machine learning (ML) models, while guaranteeing zero change in terms of performance (say their accuracy). We show that it affects the energy consumed, and, thereby, the associated cost (i.e., economic and ecological) and the associated CO2eq emission. We thus confirm the existence of a "triangle of sobriety". It is defined as a virtual circle governed by a digital-economic-ecological sovereignty. We also propose that if AI production processes have a potential for sobriety, all identical activities have the same characteristics, thus opening the path to green growth.