Minimizing $CO_{2}$ Emissions Through Predictive Maintenance: A Stochastic Modeling Approach

Industrial CO₂ emissions are influenced by both deterministic factors, such as energy consumption and operational processes, and stochastic variables, including equipment degradation and unforeseen system failures. In this study, we model CO₂ emissions as a function of system degradation, where the cumulative emissions up to time t are represented by a stochastic integral of this function. This approach enables the prediction of emission trajectories under uncertain system conditions, capturing the inherent randomness in operational dynamics. The primary objective of this paper is to determine an optimal maintenance policy that minimizes both maintenance costs and cumulative CO₂ emissions. To achieve this, we develop a mathematical framework that integrates stochastic degradation modeling with maintenance optimization techniques. The proposed model is validated through a simulation study, demonstrating its effectiveness in balancing economic and environmental objectives. The results offer valuable insights into sustainable industrial operations by providing strategies for proactive maintenance that mitigate emissions while ensuring cost efficiency.

Keywords: Predictive maintenance, deteriorating systems, carbon emissions.