Switching to Clean(er) Technologies in a Stochastic Environment

This paper develops a theoretical model analyzing the optimal timing of switching from fossil-fuel-based energy to cleaner technologies in a stochastic environment. The economy consists of two interacting sectors: a backstop-production sector (e.g., solar panels), which uses both fossil fuels and backstop energy, and a consumption sector that initially relies exclusively on fossil fuels but can adopt a hybrid (cleaner) technology by incurring a fixed, irreversible investment cost. Both pollution accumulation and backstop accumulation are assumed to be stochastic. Our results indicate that the optimal timing for switching is significantly influenced by technological parameters, particularly the dependence on fossil fuels in post-switch production and the extent of technological gains in backstop manufacturing. Specifically, reducing fossil-fuel reliance and improving backstop technology both accelerate the adoption of cleaner technologies. We also find that uncertainty can either accelerate or delay adoption, depending on technological progress and intertemporal substitution preferences. These findings underscore the importance of policies that decrease fossil fuel dependence while fostering innovation in renewable energy technologies.