Abstract
While the term ‘innovation ecosystem’ is often utilized, the concept is rarely quantified. Oak Ridge National Lab conducted a ground-breaking application of natural language processing, link analysis and other computational techniques to transform text and numerical data into metrics on clean energy innovation activity and geography for the U.S. Department of Energy. The project demonstrates that a machine-assisted methodology gives the user a replicable method to rapidly identify, quantify and characterize clean energy innovation ecosystems. EPSA advanced a novel definition for clean energy innovation ecosystem as the overlap of five Ecosystem Components: 1) nascent clean energy indicators, 2) investors, 3) enabling environment, 4) networking assets and 5) large companies. The tool was created with the flexibility to allow the user to choose the weights of each of the five ecosystem components and the subcomponents. This flexibility allows the user to visualize different subsets of data as well as the composite IE rank. In an independent parallel effort, a DOE analyst in EPSA developed a short list of 22 top US clean energy innovation ecosystems; the Ecosystem Discovery tool was able to identify over 90% of the analyst-reported ecosystems. Full validation and calibration remain outstanding tasks. The tool and the underlying datasets have the potential to address a number of important policy questions. The initial broad list of U.S. clean energy innovation ecosystems, with geographic area, technology focus, and list and types of involved organizations can help describe regional technology activities and capabilities. The implementation of knowledge discovery techniques also revealed both the potential and limitations of an automatic machine extraction methodology to gather ecosystem component data. The project demonstrates that a machine-assisted methodology gives the user a replicable method to rapidly identify, quantify, and characterize clean energy innovation ecosystems.
| Original language | English |
|---|---|
| Pages (from-to) | 64-75 |
| Number of pages | 12 |
| Journal | Electricity Journal |
| Volume | 29 |
| Issue number | 8 |
| DOIs | |
| State | Published - Oct 1 2016 |
Funding
Investors and financing mechanisms are key to the growth and commercialization of nascent clean energy assets. Many early stage finance companies have a geographical bias, investing locally, because their work tends to be very hands-on. Access to finance is particularly important in the clean energy space where the amount of funding needed as well as the time horizons to commercialization are typically larger than other sectors, such as IT or biotech. The funding types and levels could vary based on the size of the nascent clean energy indicators and could range from grants such as those offered by Small Business Innovation Research (SBIR) and the Advanced Research Projects Agency − Energy (ARPA-E) programs, to venture capital to crowdsourced funds. In general, these will be more risk-tolerant investing mechanisms. The authors thank Eric Hsieh (U.S. DOE), Hugh Chen (U.S. DOE), Hugh Ho (U.S. DOE), John Jennings (U.S. DOE), Jeff Dowd (U.S. DOE), and Jeff Alexander (RTI) for their review and input. This work was financed by the Office of Program and Innovation Policy Analysis of the Energy Policy and System Analysis Office (EPSA-52) of the U.S. Department of Energy .
| Funders | Funder number |
|---|---|
| U.S. Department of Energy | |
| Advanced Research Projects Agency - Energy | |
| Small Business Innovation Research |
Keywords
- Clean energy innovation ecosystems
- Mapping
- Natural language processing
