Large Cities as the Cradle of Sustainable Energy Innovation

Abstract

Large cities have empirically confirmed to act as the cradle of innovation. We explore whether this is also true for sustainable energy technology. We pose the question to what extent large cities act as concentrations of sustainable energy inventions and market introduction, and to what extent agglomeration and network factors are involved and large cities offer specific advantages. Our empirical outcomes tend to be mixed. In the past years, large cities have remained clusters of sustainable energy inventions, however, spread over a larger number of (single) cities. With regard to market introduction, large cities tend to be slightly more successful than smaller cities, however, this is not true for early market introduction. The weak and somewhat ambiguous relationships with large cities may be connected with the typical location of some sustainable energy sources, namely, as fixed natural assets in sparsely populated areas, like windy seashore and hills, strong coastal water currents, extended woodland, etc., favouring research in nearby small university towns. At the same time, the abundant knowledge (diversity) in large cities may enhance inventions with larger risk-taking in newness, specialization and global markets, and concomitantly, delay and longer time to market.

Appendices

Appendix 1: Measurement and OLS Model Indicators after Transformation (n Clusters = 103)

Indicator	Measured as	Min	Mean	Max
Dependent variable
Invention performance	Citations per inventor 2008–2011	−2.3	−0.68	2.6
Independent variables
Agglomeration
Cluster size (log)	Number of patents	−0.35	1.7	6.3
Size adjacent clusters (log)	Patents outside main cluster within 0–200 km from this cluster	−2.3	2.6	8.6
Specialization (log)	Sustainable energy patent share in all patents	−9.5	−6.4	−2.0
Corporate R&D	Corporate patent share in all sustainable energy patents	Nil	0.87	1.0
Knowledge networks
Inbound flow	Assignee-inventor links per inventor, e.g. from MNC remote lab toward headquarter in cluster	Nil	0.61	6.7
Outbound flow	Inventor-assignee links per inventor, e.g. from MNC remote lab in cluster toward headquarter elsewhere	Nil	0.52	2.2
Simple degree centrality	Co-invention network, total number of connections to different (unique) clusters	−2.3	1.7	3.6
Weighted degree centrality	Co-invention network, number of connections to other clusters per inventor	−2.3	−1.1	0.90
Past invention performance	Citations per inventor 2004–2007	−2.3	−0.20	2.5

1. All data are drawn from scientometric sources

Appendix 2: Measurement and Descriptive Results of Selected Sample in Rough-Set Analysis (n = 37)

Variables	Attributes’ share
Condition attributes (‘independent’ variables)
Strategic choice
Energy technology	Solar: 35.1%; wind: 18.9%; other (biofuels, fuel cells, combination, etc.): 27.0%; automotive: 18.9%
Value creation	Core (fundamentals) of energy technology: 67.6% Additional application of technology: 32.4%
Strategy archetype	First mover: 35.1% Otherwise (follower/customer intimate): 64.9%
Diversification/focus	Diversification: 27.0%; focus: 73.0%
Competence
Market/business experience	Business experience: 56.7%; no business experience: 43.3%
Technical/practical competence	PhD: 70.3%; only master: 29.7%
Interaction in entrepreneurial ecosystems
Developing networks	Multiple: 54.1%; otherwise (no/one-sided): 45.9%
Accessing investment capital	No: 54.0%; yes: 46.0%
Countries’ profile in innovation	Finland, Denmark, Sweden (innovation leaders): 43.2% Norway (innovation follower): 18.9% Netherlands (innovation follower): 37.8%
Decision attribute (‘dependent’ variable)
Development in bringing inventions to market	Positive: 59.5%; problematic: 40.5%

1. Source: Adapted from Nejabat and van Geenhuizen (2019)