Leveraging on circular economy technologies for recombinant dynamics: do localised knowledge and digital complementarities matter?

ABSTRACT

This paper investigates the relationship between local knowledge bases and recombinant dynamics in circular economy (CE) technologies. We focus on the role of accumulated green and digital complementary capabilities and posit that they are positively associated to the regional ability to absorb and integrate new technological opportunities in CE-based recombinations. The empirical analysis, conducted on a dataset of European NUTS2 regions over the period 1985–2015, suggests that both green and digital complementary localised capabilities represent crucial leverage for regional recombinant activities around CE technologies.

KEYWORDS:

• Circular economy
• twin transition
• regional recombination capabilities
• localised knowledge
• digital technologies

JEL:

• O33
• R11

ACKNOWLEDGEMENTS

We thank the editor and the anonymous referees for their helpful suggestions. An earlier version of this work was presented at the 6th Geography of Innovation Conference 2022, Università Bocconi, Milan, 4-7 July 2022; XLIII Annual Scientific Conference, 2022 and The Italian Association of Regional Sciences, Milan, 5-7 September, 2022. We are grateful to the participants of these events for their useful comments. Fabrizio Fusillo acknowledges the funding of the Italian Ministry of University and Research, within the context of the PON project ‘Economia circolare ed innovazione nel settore automotive: progettazione, design e ricettività delle nuove soluzioni tecnologiche’ (contract number: 31-G-14616-2).

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author(s).

Notes

1. Patent applications beyond 2015 are excluded because of the known drop in recorded applications due to the time required to complete the patent application process.

2. https://ec.europa.eu/eurostat/web/circular-economy/indicators/monitoring-framework.

3. NUTS-2 regions, being larger than NUTS-3 regions, may include a number of smaller administrative units with possible heterogeneous characteristics. Despite this limitation, two main reasons motivate our choice of considering NUTS-2 as the appropriate geographical level. Firstly, we rely on the extensive economic geography literature investigating local recombination dynamics and technological capabilities of NUTS-2 regions. The second reason is grounded on data and methodological constraints. As discussed in the previous section, CE, as well as its codified technological development, is a relatively recent construct with highly heterogeneous efforts both in time and across European regions. This is confirmed by the still relatively low number of patents by NUTS-2 regions recombining CE knowledge. Measuring CE patent dynamics at the NUTS-3 levels, thus, would lead to the observation of an excessive number of regions with zero (or a few) CE citing patents in each period of our sample, thereby hindering the proper measurement of the mechanisms at stake.

4. Existing literature made several attempts to estimate the patent depreciation rate with inconclusive evidence (Pakes & Schankerman, 1979; Schankerman, 1998). In this work, we set the obsolescence rate at 15%, which is the most frequent value employed in the literature (see, among others, Hall et al., 2005; Keller, 2002; McGahan & Silverman, 2006; Nesta, 2008).

5. GPD and population data are extracted from Eurostat.

6. For the sake of consistency between technological classification, IPC codes are converted into CPC codes by exploiting the concordance tables available at https://www.cooperativepatentclassification.org/cpcConcordances.

7. In order to check the robustness of our findings to a different estimation procedure, we use the dynamic approach of a generalised method of moments (GMM) model and implemented the GMM estimator as proposed by Arellano and Bond (1991). In particular, we employ a GMM system estimator (Arellano & Bover, 1995; Blundell & Bond, 1998) which instruments the level variables with lagged first differenced terms. The results of the GMM system estimator, available upon request from the authors, are qualitatively robust and confirm our main findings.

8. Results table report at the bottom the mean value of the VIF tests performed across all specifications. The mean value and the individual VIF values of all the variables below 10 – the upper bound generally indicated by the relevant literature – allow us to exclude critical multicollinearity issues.

9. The former encompasses (a) environmental management and (b) water-related adaptation technologies; the latter includes (c) climate change mitigation technologies (CCMT) related to energy generation, transmission or distribution, (d) capture, storage, sequestration or disposal of greenhouse gases, (e) CCMT related to transportation, (f) CCMT related to buildings, (g) CCMT related to wastewater treatment or waste management and (h) CCMT in the production or processing of goods.