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Modeling Innovation Patterns in Solar Technology By Sameer Rao Complete darkness. The year is 2030 and the the efficiency at which a solar cell converts sunlight United States power grid is no better than it was in into usable energy, reported in recognized test labs 2020. However, there are now Perovskite solar pan- and compared them to the values reported in sciels on the roof. In a flash, the energy stored from entific literature.1 By comparing the PCE values in the panels kicks in and the day is saved. Perovskites test labs to those documented by literature, Kittner are a new generation of solar panels which have and his team could confirm the accuracy of the PCE garnered much attention for their high efficiency. values and utilize them to construct innovation rate Understanding what drives innovation for solar models called “learning curves.” Dr. Kittner explains technologies is essential to determining techno- that learning curves are graphic models that inlogical improvement in time to halt climate change. vestigate the root causes of a phenomenon – solar Noah Kittner, an assistant professor in UNC-Chapel technology in this case.1 Learning curves illustrate Hill’s Department of Environmental Sciences and the why: what is the impetus behind solar efficiency acclaimed researcher for his work in sustainabil- innovation? Dr. Kittner and his team investigated ity, has recently pub- three factors that affect PCE learning curves - time lished research on the since entering the industry, number of publications innovation of emerg- on that solar technology, and the number of pating solar technolo- ents on the technology.2 gies, where he and his To put their ideas to the test, Dr. Kittner and his team study learning team first needed to confirm the values reported by curves to understand the test labs. His team first scraped a database with if perovskites demon- keywords on the technologies such as “perovskites” Dr. Noah Kittner strate typical innovation or “dye-sensitized” to find the proper articles. They patterns found in solar technology.2 then used text data mining to locate PCE values Dr. Kittner and his team embarked on their re- within literature, searching mainly the abstract secsearch with the intention of comparing Perovskite tions of the literature.2 After compiling a list of the to two other solar technologies: dye-sensitized so- PCE values, Dr. Kittner and his team compared the lar cells and quantum dot solar values reported in sciencells. Dye-sensitized solar cells tific literature to those re“Understanding what and quantum dot solar cells ported by test labs. They drives innovation for solar found that the PCE values are two well-established solar technologies which have long technologies is essential to were nearly identical.2 been the subjects of clean enKittner and his determining whether we can ergy research. Perovskite solar team now had accucells, on the other hand, have improve our technology in rate enough dates and only been in studies since 2009. PCE values to construct time to halt climate change. “ The team analyzed power cona learning curve. They version efficiency (PCE) values, modeled two equations
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