Green technology adoption under uncertainty, increasing returns, and complex adaptive dynamics
We consider firms’ choices between a clean technology that benefits, and a dirty technology that harms, the environment. Green firms are more suited to the clean technology and brown firms are more suited to the dirty technology. We use a model derived from complexity theory that takes account of true uncertainty and increasing returns to technology adoption. We examine theoretically, the properties of the long-run equilibrium, and provide simulated time paths of technology adoption, using plausible dynamics. The long-run outcome is an ‘emergent property’ of the system, and is unpredictable despite there being no external technological or preference shocks. We describe the role of taxes and subsidies in facilitating adoption of the clean technology; the conflict between optimal Pigouvian taxes and adoption of clean technologies; the optimal temporal profile of subsidies; and the desirability of an international fund to provide technology assistance to poorer countries. • We study green vs brown technology choice, allowing for complexity, increasing returns, and firm heterogeneity. • Small accidents of history have large effects; and long-run outcome is an ‘emergent property’ of the system. • The long-run outcome is unpredictable although there is no fundamental uncertainty. • Pigouvian taxes and long-run green technology adoption might be in conflict. • Optimal temporal pattern of subsidies is decreasing. • Stochastic dynamics result in bifurcations and critical role for public policy.
Authors
- Paolo Zeppini (ORCID: https://orcid.org/0000-0002-1630-9462)
- Sanjit Dhami (ORCID: https://orcid.org/0000-0002-3880-7454)
Institutions
- Centre National de la Recherche Scientifique (FR)
- University of Leicester (GB)
- Université Côte d'Azur (FR)
- Groupe de Recherche en Droit, Économie, Gestion (FR)
- University of Bath (GB)
Publication Details
- Journal
- Journal of Economic Behavior & Organization
- Published
- 2025-03-08
- DOI
- https://doi.org/10.1016/j.jebo.2025.106953
- Citations
- 2
- Primary Topic
- Innovation Diffusion and Forecasting
- Type
- article
- Field-Weighted Citation Impact
- 4.21