Keeping track of cleantech development using innovation clusters and member’s website data: Evidence from leading energy clusters in Germany
The main research question addressed in this work is how energy clusters can be evaluated and what general conclusions can be drawn out of their activities. Traditional innovation cluster analysis approaches chiefly rely on surveys, interviews, open publications, and patents—lack of using updated ac...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Mahendra Singh [verfasserIn] Denilton Luiz Darold [verfasserIn] Marian Klobasa [verfasserIn] Andrea Zielinski [verfasserIn] Rainer Frietsch [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
In: Energy Reports - Elsevier, 2016, 10(2023), Seite 756-767 |
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| Übergeordnetes Werk: |
volume:10 ; year:2023 ; pages:756-767 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.egyr.2023.07.026 |
|---|
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10.1016/j.egyr.2023.07.026 doi (DE-627)DOAJ098765639 (DE-599)DOAJ34200dc7493f4746980c80aaf3e2d9a9 DE-627 ger DE-627 rakwb eng TK1-9971 Mahendra Singh verfasserin aut Keeping track of cleantech development using innovation clusters and member’s website data: Evidence from leading energy clusters in Germany 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The main research question addressed in this work is how energy clusters can be evaluated and what general conclusions can be drawn out of their activities. Traditional innovation cluster analysis approaches chiefly rely on surveys, interviews, open publications, and patents—lack of using updated activities of innovation clusters. Therefore, preceding cluster analysis methodologies always lack of providing up-to-date information. In this sense, analyzing energy cluster activities is an obvious interest for policymakers, investors, companies, etc. Moreover, such assessment help to track the development of new technologies, participation of different actors in an innovation ecosystem, and emerging topics in the energy sector. This work presents the research outcomes on the leading energy-innovation clusters in Germany. To this end, this paper exploits the publicly available website data from the clusters and member’s web-pages to investigate their geographical distribution, key focus areas, cluster, and member activities. In the course of the project, a web-scraping tool has been developed to crawl the clusters and member’s websites and scrape their text data. The tool performs systematic and guided web-scraping for searching a keyword presence on a particular web-page. In addition to this, data from commercially available company databases are used to complement the missing information from the website data. A total of 44 energy clusters along with 4524 members are taken into account in this study. The proposed methodology has shown that unstructured web-data is a valuable source for analyzing the clusters and their member’s innovation activities. Results have also indicated that there is a strong correlation (r=0.85) between Research and Development (R&D) expenditure and cluster count in individual federal states. The overall results have indicated that the majority of energy clusters are very specialized in certain topics, nevertheless, topics such as hydrogen, carbon, and bioenergy are getting notable attention from various stakeholders. Simultaneously, various cross-sectoral topics are also emerging due to the coupling between different sectors. Findings could help policymakers and federal innovation agencies to understand the ongoing progress in cleantech innovation activities. From the methodological point of the view, it provides an underlying ground to access the impact of cluster policies. Innovation Cleantech Energy cluster Web-scraping Cluster member Cluster policy Electrical engineering. Electronics. Nuclear engineering Denilton Luiz Darold verfasserin aut Marian Klobasa verfasserin aut Andrea Zielinski verfasserin aut Rainer Frietsch verfasserin aut In Energy Reports Elsevier, 2016 10(2023), Seite 756-767 (DE-627)820689033 (DE-600)2814795-9 23524847 nnns volume:10 year:2023 pages:756-767 https://doi.org/10.1016/j.egyr.2023.07.026 kostenfrei https://doaj.org/article/34200dc7493f4746980c80aaf3e2d9a9 kostenfrei http://www.sciencedirect.com/science/article/pii/S235248472301106X kostenfrei https://doaj.org/toc/2352-4847 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 10 2023 756-767 |
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10.1016/j.egyr.2023.07.026 doi (DE-627)DOAJ098765639 (DE-599)DOAJ34200dc7493f4746980c80aaf3e2d9a9 DE-627 ger DE-627 rakwb eng TK1-9971 Mahendra Singh verfasserin aut Keeping track of cleantech development using innovation clusters and member’s website data: Evidence from leading energy clusters in Germany 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The main research question addressed in this work is how energy clusters can be evaluated and what general conclusions can be drawn out of their activities. Traditional innovation cluster analysis approaches chiefly rely on surveys, interviews, open publications, and patents—lack of using updated activities of innovation clusters. Therefore, preceding cluster analysis methodologies always lack of providing up-to-date information. In this sense, analyzing energy cluster activities is an obvious interest for policymakers, investors, companies, etc. Moreover, such assessment help to track the development of new technologies, participation of different actors in an innovation ecosystem, and emerging topics in the energy sector. This work presents the research outcomes on the leading energy-innovation clusters in Germany. To this end, this paper exploits the publicly available website data from the clusters and member’s web-pages to investigate their geographical distribution, key focus areas, cluster, and member activities. In the course of the project, a web-scraping tool has been developed to crawl the clusters and member’s websites and scrape their text data. The tool performs systematic and guided web-scraping for searching a keyword presence on a particular web-page. In addition to this, data from commercially available company databases are used to complement the missing information from the website data. A total of 44 energy clusters along with 4524 members are taken into account in this study. The proposed methodology has shown that unstructured web-data is a valuable source for analyzing the clusters and their member’s innovation activities. Results have also indicated that there is a strong correlation (r=0.85) between Research and Development (R&D) expenditure and cluster count in individual federal states. The overall results have indicated that the majority of energy clusters are very specialized in certain topics, nevertheless, topics such as hydrogen, carbon, and bioenergy are getting notable attention from various stakeholders. Simultaneously, various cross-sectoral topics are also emerging due to the coupling between different sectors. Findings could help policymakers and federal innovation agencies to understand the ongoing progress in cleantech innovation activities. From the methodological point of the view, it provides an underlying ground to access the impact of cluster policies. Innovation Cleantech Energy cluster Web-scraping Cluster member Cluster policy Electrical engineering. Electronics. Nuclear engineering Denilton Luiz Darold verfasserin aut Marian Klobasa verfasserin aut Andrea Zielinski verfasserin aut Rainer Frietsch verfasserin aut In Energy Reports Elsevier, 2016 10(2023), Seite 756-767 (DE-627)820689033 (DE-600)2814795-9 23524847 nnns volume:10 year:2023 pages:756-767 https://doi.org/10.1016/j.egyr.2023.07.026 kostenfrei https://doaj.org/article/34200dc7493f4746980c80aaf3e2d9a9 kostenfrei http://www.sciencedirect.com/science/article/pii/S235248472301106X kostenfrei https://doaj.org/toc/2352-4847 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 10 2023 756-767 |
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10.1016/j.egyr.2023.07.026 doi (DE-627)DOAJ098765639 (DE-599)DOAJ34200dc7493f4746980c80aaf3e2d9a9 DE-627 ger DE-627 rakwb eng TK1-9971 Mahendra Singh verfasserin aut Keeping track of cleantech development using innovation clusters and member’s website data: Evidence from leading energy clusters in Germany 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The main research question addressed in this work is how energy clusters can be evaluated and what general conclusions can be drawn out of their activities. Traditional innovation cluster analysis approaches chiefly rely on surveys, interviews, open publications, and patents—lack of using updated activities of innovation clusters. Therefore, preceding cluster analysis methodologies always lack of providing up-to-date information. In this sense, analyzing energy cluster activities is an obvious interest for policymakers, investors, companies, etc. Moreover, such assessment help to track the development of new technologies, participation of different actors in an innovation ecosystem, and emerging topics in the energy sector. This work presents the research outcomes on the leading energy-innovation clusters in Germany. To this end, this paper exploits the publicly available website data from the clusters and member’s web-pages to investigate their geographical distribution, key focus areas, cluster, and member activities. In the course of the project, a web-scraping tool has been developed to crawl the clusters and member’s websites and scrape their text data. The tool performs systematic and guided web-scraping for searching a keyword presence on a particular web-page. In addition to this, data from commercially available company databases are used to complement the missing information from the website data. A total of 44 energy clusters along with 4524 members are taken into account in this study. The proposed methodology has shown that unstructured web-data is a valuable source for analyzing the clusters and their member’s innovation activities. Results have also indicated that there is a strong correlation (r=0.85) between Research and Development (R&D) expenditure and cluster count in individual federal states. The overall results have indicated that the majority of energy clusters are very specialized in certain topics, nevertheless, topics such as hydrogen, carbon, and bioenergy are getting notable attention from various stakeholders. Simultaneously, various cross-sectoral topics are also emerging due to the coupling between different sectors. Findings could help policymakers and federal innovation agencies to understand the ongoing progress in cleantech innovation activities. From the methodological point of the view, it provides an underlying ground to access the impact of cluster policies. Innovation Cleantech Energy cluster Web-scraping Cluster member Cluster policy Electrical engineering. Electronics. Nuclear engineering Denilton Luiz Darold verfasserin aut Marian Klobasa verfasserin aut Andrea Zielinski verfasserin aut Rainer Frietsch verfasserin aut In Energy Reports Elsevier, 2016 10(2023), Seite 756-767 (DE-627)820689033 (DE-600)2814795-9 23524847 nnns volume:10 year:2023 pages:756-767 https://doi.org/10.1016/j.egyr.2023.07.026 kostenfrei https://doaj.org/article/34200dc7493f4746980c80aaf3e2d9a9 kostenfrei http://www.sciencedirect.com/science/article/pii/S235248472301106X kostenfrei https://doaj.org/toc/2352-4847 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 10 2023 756-767 |
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10.1016/j.egyr.2023.07.026 doi (DE-627)DOAJ098765639 (DE-599)DOAJ34200dc7493f4746980c80aaf3e2d9a9 DE-627 ger DE-627 rakwb eng TK1-9971 Mahendra Singh verfasserin aut Keeping track of cleantech development using innovation clusters and member’s website data: Evidence from leading energy clusters in Germany 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The main research question addressed in this work is how energy clusters can be evaluated and what general conclusions can be drawn out of their activities. Traditional innovation cluster analysis approaches chiefly rely on surveys, interviews, open publications, and patents—lack of using updated activities of innovation clusters. Therefore, preceding cluster analysis methodologies always lack of providing up-to-date information. In this sense, analyzing energy cluster activities is an obvious interest for policymakers, investors, companies, etc. Moreover, such assessment help to track the development of new technologies, participation of different actors in an innovation ecosystem, and emerging topics in the energy sector. This work presents the research outcomes on the leading energy-innovation clusters in Germany. To this end, this paper exploits the publicly available website data from the clusters and member’s web-pages to investigate their geographical distribution, key focus areas, cluster, and member activities. In the course of the project, a web-scraping tool has been developed to crawl the clusters and member’s websites and scrape their text data. The tool performs systematic and guided web-scraping for searching a keyword presence on a particular web-page. In addition to this, data from commercially available company databases are used to complement the missing information from the website data. A total of 44 energy clusters along with 4524 members are taken into account in this study. The proposed methodology has shown that unstructured web-data is a valuable source for analyzing the clusters and their member’s innovation activities. Results have also indicated that there is a strong correlation (r=0.85) between Research and Development (R&D) expenditure and cluster count in individual federal states. The overall results have indicated that the majority of energy clusters are very specialized in certain topics, nevertheless, topics such as hydrogen, carbon, and bioenergy are getting notable attention from various stakeholders. Simultaneously, various cross-sectoral topics are also emerging due to the coupling between different sectors. Findings could help policymakers and federal innovation agencies to understand the ongoing progress in cleantech innovation activities. From the methodological point of the view, it provides an underlying ground to access the impact of cluster policies. Innovation Cleantech Energy cluster Web-scraping Cluster member Cluster policy Electrical engineering. Electronics. Nuclear engineering Denilton Luiz Darold verfasserin aut Marian Klobasa verfasserin aut Andrea Zielinski verfasserin aut Rainer Frietsch verfasserin aut In Energy Reports Elsevier, 2016 10(2023), Seite 756-767 (DE-627)820689033 (DE-600)2814795-9 23524847 nnns volume:10 year:2023 pages:756-767 https://doi.org/10.1016/j.egyr.2023.07.026 kostenfrei https://doaj.org/article/34200dc7493f4746980c80aaf3e2d9a9 kostenfrei http://www.sciencedirect.com/science/article/pii/S235248472301106X kostenfrei https://doaj.org/toc/2352-4847 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 10 2023 756-767 |
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keeping track of cleantech development using innovation clusters and member’s website data: evidence from leading energy clusters in germany |
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Keeping track of cleantech development using innovation clusters and member’s website data: Evidence from leading energy clusters in Germany |
| abstract |
The main research question addressed in this work is how energy clusters can be evaluated and what general conclusions can be drawn out of their activities. Traditional innovation cluster analysis approaches chiefly rely on surveys, interviews, open publications, and patents—lack of using updated activities of innovation clusters. Therefore, preceding cluster analysis methodologies always lack of providing up-to-date information. In this sense, analyzing energy cluster activities is an obvious interest for policymakers, investors, companies, etc. Moreover, such assessment help to track the development of new technologies, participation of different actors in an innovation ecosystem, and emerging topics in the energy sector. This work presents the research outcomes on the leading energy-innovation clusters in Germany. To this end, this paper exploits the publicly available website data from the clusters and member’s web-pages to investigate their geographical distribution, key focus areas, cluster, and member activities. In the course of the project, a web-scraping tool has been developed to crawl the clusters and member’s websites and scrape their text data. The tool performs systematic and guided web-scraping for searching a keyword presence on a particular web-page. In addition to this, data from commercially available company databases are used to complement the missing information from the website data. A total of 44 energy clusters along with 4524 members are taken into account in this study. The proposed methodology has shown that unstructured web-data is a valuable source for analyzing the clusters and their member’s innovation activities. Results have also indicated that there is a strong correlation (r=0.85) between Research and Development (R&D) expenditure and cluster count in individual federal states. The overall results have indicated that the majority of energy clusters are very specialized in certain topics, nevertheless, topics such as hydrogen, carbon, and bioenergy are getting notable attention from various stakeholders. Simultaneously, various cross-sectoral topics are also emerging due to the coupling between different sectors. Findings could help policymakers and federal innovation agencies to understand the ongoing progress in cleantech innovation activities. From the methodological point of the view, it provides an underlying ground to access the impact of cluster policies. |
| abstractGer |
The main research question addressed in this work is how energy clusters can be evaluated and what general conclusions can be drawn out of their activities. Traditional innovation cluster analysis approaches chiefly rely on surveys, interviews, open publications, and patents—lack of using updated activities of innovation clusters. Therefore, preceding cluster analysis methodologies always lack of providing up-to-date information. In this sense, analyzing energy cluster activities is an obvious interest for policymakers, investors, companies, etc. Moreover, such assessment help to track the development of new technologies, participation of different actors in an innovation ecosystem, and emerging topics in the energy sector. This work presents the research outcomes on the leading energy-innovation clusters in Germany. To this end, this paper exploits the publicly available website data from the clusters and member’s web-pages to investigate their geographical distribution, key focus areas, cluster, and member activities. In the course of the project, a web-scraping tool has been developed to crawl the clusters and member’s websites and scrape their text data. The tool performs systematic and guided web-scraping for searching a keyword presence on a particular web-page. In addition to this, data from commercially available company databases are used to complement the missing information from the website data. A total of 44 energy clusters along with 4524 members are taken into account in this study. The proposed methodology has shown that unstructured web-data is a valuable source for analyzing the clusters and their member’s innovation activities. Results have also indicated that there is a strong correlation (r=0.85) between Research and Development (R&D) expenditure and cluster count in individual federal states. The overall results have indicated that the majority of energy clusters are very specialized in certain topics, nevertheless, topics such as hydrogen, carbon, and bioenergy are getting notable attention from various stakeholders. Simultaneously, various cross-sectoral topics are also emerging due to the coupling between different sectors. Findings could help policymakers and federal innovation agencies to understand the ongoing progress in cleantech innovation activities. From the methodological point of the view, it provides an underlying ground to access the impact of cluster policies. |
| abstract_unstemmed |
The main research question addressed in this work is how energy clusters can be evaluated and what general conclusions can be drawn out of their activities. Traditional innovation cluster analysis approaches chiefly rely on surveys, interviews, open publications, and patents—lack of using updated activities of innovation clusters. Therefore, preceding cluster analysis methodologies always lack of providing up-to-date information. In this sense, analyzing energy cluster activities is an obvious interest for policymakers, investors, companies, etc. Moreover, such assessment help to track the development of new technologies, participation of different actors in an innovation ecosystem, and emerging topics in the energy sector. This work presents the research outcomes on the leading energy-innovation clusters in Germany. To this end, this paper exploits the publicly available website data from the clusters and member’s web-pages to investigate their geographical distribution, key focus areas, cluster, and member activities. In the course of the project, a web-scraping tool has been developed to crawl the clusters and member’s websites and scrape their text data. The tool performs systematic and guided web-scraping for searching a keyword presence on a particular web-page. In addition to this, data from commercially available company databases are used to complement the missing information from the website data. A total of 44 energy clusters along with 4524 members are taken into account in this study. The proposed methodology has shown that unstructured web-data is a valuable source for analyzing the clusters and their member’s innovation activities. Results have also indicated that there is a strong correlation (r=0.85) between Research and Development (R&D) expenditure and cluster count in individual federal states. The overall results have indicated that the majority of energy clusters are very specialized in certain topics, nevertheless, topics such as hydrogen, carbon, and bioenergy are getting notable attention from various stakeholders. Simultaneously, various cross-sectoral topics are also emerging due to the coupling between different sectors. Findings could help policymakers and federal innovation agencies to understand the ongoing progress in cleantech innovation activities. From the methodological point of the view, it provides an underlying ground to access the impact of cluster policies. |
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Keeping track of cleantech development using innovation clusters and member’s website data: Evidence from leading energy clusters in Germany |
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