| Title | Emerging green-tech specializations and clusters - A network analysis on technological innovation at the metropolitan level |
|---|---|
| ID_Doc | 35710 |
| Authors | Marra, A; Antonelli, P; Pozzi, C |
| Title | Emerging green-tech specializations and clusters - A network analysis on technological innovation at the metropolitan level |
| Year | 2017 |
| Published | |
| Abstract | Current models of production and energy use are promoting an effervescent green-technology (green tech) market focused on innovation and improved environmental performance. Most green-tech firms are innovative start-ups or small and medium-sized enterprises (SMEs) characterized by large intangible assets and technological uncertainty, which makes it difficult to identify their research and development (R&D) and innovation paths. In addition, green-tech companies tend to aggregate spatially and, throughout the world, clusters are increasingly seen as a strategy to foster innovative production and R&D activities and stimulate a sustainable energy transition. This paper investigates green-tech companies in San Francisco, New York and London to identify their specialization and underlying technological and/or market complementarities, and emerging aggregates and specific clusters. Based on information from CrunchBase and the web, we propose a network analysis using metadata on technological innovations produced by green-tech companies in these three metropolitan areas. Metadata are keywords and terms that help to describe an item, and in the database they identify the products, services and technologies driving innovation. The main metrics of the networks identified (at both node and network level) are discussed from an economic perspective. The approach helps in the design and implementation of targeted and information-based local policies and facilitates closer relationships between innovative companies, suppliers and clients, venture capitalists, large corporations and research laboratories involved in the green-tech industry. (C) 2016 Elsevier Ltd. All rights reserved. |
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