Innovation Without Magic Bullets: Stock Pollution and R&D Sequences - Timo Goeschl; Grischa Perino (University of Heidelberg, Department of Economics, 2006)
We study the optimal R&D trajectory in a setting where new technologies are never perfect backstops in the sense that there is no perfectly clean technology that eventually solves the pollution problem once and for all. New technologies have stings attached, i.e. each emits a specific stock pollutant. Damages are convex in individual pollution stocks but additive across stocks, creating gains from diversification. The research and pollution policies are tightly linked in such a setting. We derive the optimal pollution path and R&D program. Pollution stocks overshoot and in the long run all available technologies produce. Research is sequential and the optimal portfolio of technologies is finite.
Keywords: Q55; Q53; O32; C61; 330; horizontal innovation; stock pollution; backstop technology; multistage optimal control; pollution thresholds; overshooting; Innovation; Forschung; Umweltverträglichkeit; Umweltbelastung; Umweltschutz; Dynamische Optimierung; Theorie