Abstract
We conduct a systematic and interdisciplinary review of empirical literature assessing evidence on induced innovation in energy and related technologies. We explore links between demand-drivers (both market-wide and targeted); indicators of innovation (principally, patents); and outcomes (cost reduction, efficiency, and multi-sector/macro consequences). We build on existing reviews in different fields and assess over 200 papers containing original data analysis. Papers linking drivers to patents, and indicators of cumulative capacity to cost reductions (experience curves), dominate the literature. The former does not directly link patents to outcomes; the latter does not directly test for the causal impact of on cost reductions. Diverse other literatures provide additional evidence concerning the links between deployment, innovation activities, and outcomes. We derive three main conclusions. (a) Demand-pull forces enhance patenting; econometric studies find positive impacts in industry, electricity and transport sectors in all but a few specific cases. This applies to all drivers—general energy prices, carbon prices, and targeted interventions that build markets. (b) Technology costs decline with cumulative investment for almost every technology studied across all time periods, when controlled for other factors. Numerous lines of evidence point to dominant causality from at-scale deployment (prior to self-sustaining diffusion) to cost reduction in this relationship. (c) Overall innovation is cumulative, multi-faceted, and self-reinforcing in its direction (path-dependent). We conclude with brief observations on implications for modelling and policy. In interpreting these results, we suggest distinguishing the economics of active deployment, from more passive diffusion processes, and draw the following implications. There is a role for policy diversity and experimentation, with evaluation of potential gains from innovation in the broadest sense. Consequently, endogenising innovation in large-scale models is important for deriving policy-relevant conclusions. Finally, seeking to relate quantitative economic evaluation to the qualitative socio-technical transitions literatures could be a fruitful area for future research.
Highlights
The last few decades have seen a huge growth of literature around the economics of technological innovation from diverse perspectives
The literature is strongly suggestive of higher learning rates in smaller, more modular and relatively less complex technologies, with indications of higher learning rates in earlier stages of deployment, implying declining learning rates as technologies become more established and mature—though this remains to be seen in some technologies, including solar PV
The relationship between them is complex, including∗∗, the feedback loop illustrated in figure 3, as technology improvements should enhance diffusion
Summary
Michael Grubb[1 ], Paul Drummond[1 ], Alexandra Poncia[1], Will McDowall[1 ], David Popp[2,3], ACCEPTED FOR PUBLICATION Sascha Samadi[4 ], Cristina Penasco[5 ], Kenneth T Gillingham3,6 , Sjak Smulders[7 ], Matthieu Glachant[8 ], Gavin Hassall[9], Emi Mizuno[10], Edward S Rubin[11 ], Antoine Dechezlepretre[12 ] and Giulia Pavan[13]
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