Modeling technological deployment and renewal: monotonic vs. oscillating industrial dynamics

The deployment of a technology typically follows an S-shaped curve, characterized by an initial phase of exponential growth, followed by a saturation phase where deployment slows and stabilizes at a maximum level. While existing literature has primarily focused on modeling and theorizing this growth pattern—particularly the early exponential phase—less attention has been paid to the long-term dynamics of sustaining a technological stock after its deployment peak. This gap is critical for incrementally evolving technologies without technical disruption, especially in the context of long-term industrial sustainability. In this study, we propose a model combining an S-curve deployment trajectory with a lifetime distribution of technological equipment, enabling us to simulate both the initial deployment and the subsequent renewal phases. Our key finding is that the characteristics of the deployment phase significantly influence the renewal dynamics. Specifically, when deployment is fast relative to equipment lifespan, production trajectories exhibit overshoot and oscillations—contrary to the smoother dynamics observed with slower deployment. Case studies, such as nuclear reactor deployment, illustrate these phenomena, revealing production overshoots exceeding 200%. We also present case studies on smartphones, passenger cars, consumer goods, photovoltaic panels, and wind turbines. These endogenous production cycles raise concerns about the post-deployment viability of industries, as observed in the nuclear sector. More broadly, our findings highlight the importance of anticipating long-term maintenance challenges for rapidly deployed technologies, a consideration that is particularly relevant in the context of the energy transition. This model provides a foundation for future work on the systemic implications of technology deployment and renewal in low-carbon transitions.