A percolation model of eco-innovation diffusion: The relationship between diffusion, learning economies and subsidies

Abstract

An obstacle to the widespread adoption of environmentally friendly energy technologies such as stationary and mobile fuel cells is their high upfront costs. While much lower prices seem to be attainable in the future due to learning curve cost reductions that increase rapidly with the scale of diffusion of the technology, there is a chicken and egg problem, even when some consumers may be willing to pay more for green technologies. Drawing on recent percolation models of diffusion by Solomon et al. [7], Frenken et al. [8] and Höhnisch et al.[9], we develop a network model of new technology diffusion that combines contagion among consumers with heterogeneity of agent characteristics. Agents adopt when the price falls below their random reservation price drawn from a lognormal distribution, but only when one of their neighbors has already adopted. Combining with a learning curve for the price as a function of the cumulative number of adopters, this may lead to delayed adoption for a certain range of initial conditions. Using agent-based simulations we explore when a limited subsidy policy can trigger diffusion that would otherwise not happen. The introduction of a subsidy policy seems to be highly effective for a given high initial price level only for learning economies in a certain range. Outside this range, the diffusion of a new technology either never takes off despite the subsidies, or the subsidies are unnecessary. Perhaps not coincidentally, this range seems to correspond to the values observed for many successful innovations.JEL Codes: C61, H23, O32, O33

When the collective acts on its components: economic crisis autocatalytic percolation

“…As opposed to physical systems, where the interactions are usually individual↔individual in social and economic systems, the individual→collective and collective→individual effects are significant , Hohnisch et al 2008, Cantono and Silverberg 2009.…”

“…In particular, as opposed to physical systems where the dynamics are usually individual↔individual, in social and economic systems there are significant collective→individual and individual→collective effects, such as government interventions, the general state of the economy, the 'mood of the 3 market' and mass media. Such effects have been shown in the past to lead to self-organized percolation criticality , different stages of technological development along the product lifecycle (Frenken et al 2008), long tails at the early stages of innovation diffusion (Hohnisch et al 2008) and dependence of demand-pull policies on the learning curve (Cantono and Silverberg 2009).…”

“…for N b = 1. Equation (2) has been used in the past to analyze the dependence of demand-pull policies on the learning curve (Cantono and Silverberg 2009). In our current theoretical formulation, the individual↔collective interaction exponent α expresses the capacity of the system to react and absorb the implications of the bankruptcy of its individual elements.…”

Peer-to-peer and mass communication effect on opinion shifts

“…Therefore, if individuals with less than two revolutionary neighbors join the revolution, we obtain a phase shift and reach total revolution. This is because in this case, the revolution grows first by joining of individuals from Z k for k=2, 3,4 and when these dynamics reach a steady state, new members from Z 1 or Z 0 join the revolution, and 'rekindle' its growth.…”

“…Previous works in the field (for example [3], [4]) include only some of the effects described above, but not a full combination of them.…”