Induced innovation in energy technologies and systems: a review of evidence and potential implications for CO₂ mitigation

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.

Comparing expert elicitation and model-based probabilistic technology cost forecasts for the energy transition

“…The intuition behind Wright's law model is that expending effort on a technology generally leads to updates and improvements. This pattern is observed across many technologies (37).…”

“…These include the fact that they do not capture explicitly the various complex, interconnected innovation processes that ultimately result in technology cost (39,40). These model-based methods also rely on empirical relationships that capture correlations without implying causation (37,41). For example, they are known to suffer from omitted variable bias since exogenous technological change from spillovers takes place alongside deployment, and learning by doing takes place alongside the passing of time.…”

Modelling the economic effects of COVID-19 and possible green recovery plans: a post-Keynesian approach

“…Further policy action is clearly needed; our results should be interpreted as showing the benefits of bringing some of this action forward to a time of economic stagnation, rather than replacing other potential policy. Furthermore, because of learning effects, aggressive policy action now could reduce future costs, for example by reducing prices for renewables or electric vehicles (Grubb et al, 2021).…”

The Green New Deal and the Future of Carbon Pricing

“…Weak carbon prices, which occurred with the collapse of allowance values for several years in the EU ETS, diminished clean patenting activity (Bel & Joseph, 2018). Studies of other carbon pricing systems confirm that carbon prices encourage innovation, although primarily incrementally rather than through disruptive innovation (Grubb et al., 2021).…”