The optimal timing of clean technology adoption: A stochastic cost–benefit analysis

This paper develops a quantitative framework to determine the optimal timing for transitioning to clean technologies, which is crucial for sustainable development and climate action. We propose a stochastic model using optimal stopping theory, analyzing the dynamic cost advantages of clean versus conventional technologies. The model derives explicit timing solutions adaptable to market trends and user-specific factors. To illustrate the model’s practical application, we apply it to an empirical case study focused on the adoption of electric vehicles (EVs). Our results indicate that users with higher usage intensity or greater anticipated improvements in cost advantages related to future running costs tend to adopt EVs earlier. In contrast, factors such as increased volatility in cost advantages—often affected by fluctuating energy prices—or unpredictable negative jumps in initial EV costs can delay adoption decisions. This finding highlights the role of stable energy markets, potentially supported by policies like renewable energy investments, grid stabilization, and price guarantees, in promoting EV adoption. Additionally, our results underscore the importance of technological advancements in accelerating cost reductions. Policies that establish financial incentives to reduce initial EV costs can significantly lower adoption barriers, encouraging broader and earlier EV uptake, particularly among high-mileage users.