Suchergebnisse

Answer -- Creating a technology -- Scientific origins -- US technology push -- Building a market -- Japanese niche markets -- German demand pull -- Making it cheap -- Chinese entrepreneurs -- Local learning -- Doing it again -- Solar as a model to follow -- Applying the model -- Accelerating innovation.

Moving non-incremental innovations from the pilot scale to full commercial scale raises questions about the need and implementation of public support. Heuristics from the literature put policy makers in a dilemma between addressing a market failure and acknowledging a government failure: incentives for private investments in large scale demonstrations are weak (the valley of death) but the track record of governance in large demonstration projects is poor (the technology pork barrel). We reassess these arguments in the literature, particularly as to how they apply to sup- porting demonstration projects for decarbonizing industry. Conditions for the valley of death exist with: low appropriability, large chunky investments, unproven reliability, and uncertain future markets. We build a data set of 511 demonstration projects in nine technology areas and code characteristics for each project, including timing, motivations, and scale. We argue that the literature and the results from the case studies have five main implications for policy makers in making decisions about demonstration support. Policy makers should consider: 1) prioritizing learning, 2) iterative upscaling, 3) private sector engagement, 4) broad knowledge dissemination, and 5) making demand pull robust.

Expert elicitations are now frequently used to characterize uncertain future technology outcomes. However, their usefulness is limited, in part because: estimates across studies are not easily comparable; choices in survey design and expert selection may bias results; and overconfidence is a persistent problem. We provide quantitative evidence of how these choices affect experts' estimates. We standardize data from 16 elicitations, involving 169 experts, on the 2030 costs of five energy technologies: nuclear, biofuels, bioelectricity, solar, and carbon capture. We estimate determinants of experts' confidence using survey design, expert characteristics, and public R&D investment levels on which the elicited values are conditional. Our central finding is that when experts respond to elicitations in person (vs. online or mail) they ascribe lower confidence (larger uncertainty) to their estimates, but more optimistic assessments of best‐case (10th percentile) outcomes. The effects of expert affiliation and country of residence vary by technology, but in general: academics and public‐sector experts express lower confidence than private‐sector experts; and E.U. experts are more confident than U.S. experts. Finally, extending previous technology‐specific work, higher R&D spending increases experts' uncertainty rather than resolves it. We discuss ways in which these findings should be seriously considered in interpreting the results of existing elicitations and in designing new ones.