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The diffusion of clean technologies: a review with suggestions for future diffusion analysis

Abstract This article gives an overview of the literature on clean technology diffusion, followed by suggestions for future analysis. Findings from diffusion analysis are presented in the form of 10 stylised facts, helping the reader to see the forest for the trees. The overall conclusion is that the diffusion of clean technology (same as the diffusion of normal innovations) is governed by endogenous mechanism (epidemic learning and learning economies) and by exogenous mechanisms. Policy is important for clean technology diffusion but other factors are important too: the characteristics of the clean technology, absorptive capacities of potential adopters and the age structure of capital. It is often overlooked that companies have a choice: they can choose between an end-of-pipe solution, a process change (adaptation) and a change of process (substitution). This means that the diffusion and evolution of one clean technology will be at the expense of the diffusion of another clean technology, something overlooked in studies on clean technology diffusion. Further research is needed on the influence of public policy on clean technology choice, expectations (about learning economies and prices), adjustment costs, network externalities and complementary innovations on clean technology adoption choices.
- University of Milan Italy
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).184 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 1% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
