In this paper, we adopt an evolutionary model to describe the coevolution of technological transition and pollution in a country, where the choice of technology does not only give firms access to cleaner (but more expensive) or dirtier (cheaper and illegal) forms of production, but also access to social groups and information. Firms’ activity may be harmful to the environment and, due to the existence of ambient pollution charges, economic activity is affected by the level of pollution in the country. Our analysis describes how the evolution of the transition to clean technology and pollution generates a rich set of possible equilibria, which include stable pure strategies (where all firms choose the same technology) and inner equilibria (where both technologies could be adopted in the long run). We also observe more complex behavior and coexistence of different attractors as well as highlight the importance of initial conditions and uncover how the regulator may face possible pollution traps.

Agricultural activities have imposed significant impacts on water resources, leading to hypoxic zones and harmful algal blooms all over the world. Government agencies, nongovernmental organizations, and individuals have been making various efforts to reduce this non-point source pollution. Among those efforts, even the more cost-effective examples of performance-based environmental payment programs generally have low participation rates. We investigate the effects of externalities in farmers’ decisions on neighboring farms, incorporating both a knowledge spillover effect and a positive environmental outcome externality of farmers’ best-management practice (BMP) adoption decisions. Our focus is on how these effects may influence the outcome of performance-based payment programs and how policy makers might recognize these effects in the design of cost-effective policies to promote program participation and BMP adoption. Rather than imposing an assumption of profit-maximization or forward-looking behavior, we allow outcomes to emerge from interactions among neighboring farmers. We recommend cost-effective policies across communities depending on their composition. It is more cost-effective to target communities with fewer innovators and/or target the programs towards the least-innovative individuals.