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(Green) Knowledge spillovers and regional environmental support: do they matter for the entry of new green tech-based firms?

Abstract

This paper studies the role played by the features of the local knowledge base and the regional environmental support in the entry of new green tech-based firms. We compute a set of specific knowledge indicators which capture the recombination of technologies that make up green technologies in French départements (NUTS3 regions) for the period 2003–2013. By using a rich and unique dataset based on firm-level microdata and patents information, we find that the main determinants of the entry of new green tech-based firms are: (i) the combination of high levels of internal coherence with sufficient diversification into unrelated technological categories other than green and (ii) the regional efforts to reduce the environmental impact of economic activities. Indeed, by applying spatial econometrics we found that the impact of knowledge spillovers mainly accrues within the département of entry. These results suggest that more attention should be paid to the context-specific recombination of technologies constituting green technologies and that environmental regulations may have, an indirect, but important impact when it comes to promote green technological entry in regions.

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Notes

  1. NUTS (Nomenclature of Territorial Units for Statistics). The European Commission divides its territory according to the classification established by Eurostat. It is based on national administrative units.

  2. Due to the data unavailability, in the REGPAT database, on the marketability of the patented objects and their economic value, we use the terms of invention and innovation interchangeably.

  3. Alternative definitions for the entry of new green tech-based firms (i.e., at least one green patent, more than 75% or 100% of green patents over the period observed, without considering the year of their first green patent application within the first 5 years of existence, etc.) have been applied, but results do not significantly vary. Still, results are available upon request.

  4. Each patent can be assigned to several technological classes. For example, let us assume for simplicity that we have two patents. The patent EPO48213 includes four technology classes (at 3-digit level): H02 (Generation and distribution of electric power), B05 (Spraying or atomising in general), Y04 (Information or communication technologies having an impact on other technology areas) and Y02 (Technologies for mitigation or adaptation against climate change). The patent EPO5579 includes three technology classes C08 (Organic macromolecular compounds), B60 (Vehicles in general) and B29 (Working of plastics). When we compute the variety and green variety indicators the co-occurrences change. In fact, when we calculate green unrelated variety (UKV_GREEN) the co-occurrence of the EPO5579 patent is not considered because it is not assigned to a green technological class. Similarly, for the calculation of green related variety (RKV_GREEN), we consider only the co-occurrences within the macro-field Y. This means that we only account for the co-occurrence of Y02 with Y04 in EPO48213 patent. Co-occurrences within the nongreen technological macro-fields (e.g., B60 and B29) are not considered.

  5. To elaborate the variable VOTE_ECO it is assumed that a given election is only relevant for a given 5-year period if the year of the election is lagged with at least 3-years from the start of a 5-year period (Santoalha and Boschma 2019). According to that, results in the first round of the French national elections of 2002 are associated with the period 2003–2009; results in 2007 are associated with the period 2010–2013; and results in 2012 are associated with the period 2014–2015.

  6. The data comes from a survey whose aim is to assess the environmental protection expenditure in the industry. Expenditures concern investments (equipment entirely dedicated to environmental protection, purchases of more efficient equipment in terms of the environment, etc.), current expenditures to protect environment (operating and maintenance costs dedicated to environmental protection), as well as the cost of feasibility studies (return on investment, evaluation of regulatory impact, audits to obtain an environmental certification) (See https://www.insee.fr/fr/metadonnees/source/serie/s1232, for more details). The data are available at NUTS 2 level so, to compute the NUTS 3 corresponding variables, we weight the NUTS 2 variables by the NUTS 3 share of regional GDP.

  7. The focus on the entry of new green tech-based firms implies that in a year t and a département i the mean maximum number of entries would be equal to 3. The nature of this data implies that by using our dichotomic variable we can, to a great extent, explain the capacity of the region to incentivise the successful green technological entry, as Corradini (2019) also suggested.

  8. Other spatial weighting definitions, such as 5-nearest neighbours or an inverse distance-based matrix, were considered. Even so, the best fit of the model is obtained when we rely on a row-standardised contiguity weighting matrix, an approach that has already been used in previous contributions for the case of French metropolitan départements. See, for instance, Elhorst and Fréret (2009).

  9. More details on the test procedure to find out the most appropriate model can be found in Sect. 5.1.

  10. See Soete and Patel (1985) for similar approaches. Alternative rates of obsolescence have been applied with no significant changes from the final calculations.

  11. See Saviotti (1988), Frenken and Nuvolari (2004) and Stirling (2007) for further details on the definition and properties of this index.

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Acknowledgements

The authors would like to acknowledge S. Bourdin for providing access to essential data for the realisation of this paper, S. Wu for her suggestions regarding the construction of the knowledge variety indicators and J.M. Arauzo-Carod, R. Boschma, M. Dejardin, S. Hazir and the participants at the CREM, LÉO and LEREPS Seminars, the 68th AFSE Annual Meeting, the 59th ERSA Congress, the 1st ECO-SOS Workshop, and the INFER 2020 Annual Conference for their fruitful comments and suggestions. The comments of the anonymous reviewers received on earlier versions of the paper are particularly acknowledged. Any errors are, of course, our own.

Funding

This paper was partially funded by the project SETTEEC (Système Entrepreneurial et Territorial pour la Transition Energétique et l’Economie Circulaire) funded by Normandy Region Programme Recherche d’Intérêt Normand (RIN), by the Centre de Recherche en Économie et Manegement (CREM) and by the FEDER/Ministerio de Ciencia, Innovación y Universidades – Agencia Estatal de Investigación [grant number ECO2017-88888-P].

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Appendix: The implementation of knowledge spillovers

Appendix: The implementation of knowledge spillovers

This section builds on Quatraro (2010) and Colombelli and Quatraro (2019).

A. Knowledge stock

To measure the local knowledge stock (STOCK_ALL) based on patent applications, we calculate the accumulated stock of past patent applications applying the permanent inventory method as follows:

$$STOCK\_ALL_{it} = h_{it} + \left( {1 - \delta } \right)STOCK_{it - 1}$$
(2)

where \({h}_{it}\) is the flow of patent applications, \(\delta\) is the rate of obsolescence of 10%,Footnote 10\(i\) is the region and \(t\) is the time period. This measure has also been calculated for both non-environmental (STOCK_NGREEN) and green technologies (STOCK_GREEN).

B. Knowledge variety

Our measure of knowledge variety is based on the information entropy index by following a multidimensional approach proposed by (Colombelli and Quatraro 2019).Footnote 11

Let us consider a pair of events (\({X}_{l},{Y}_{j}\)) and the probability of their co-occurrence \({p}_{lj}\). A two-dimensional total variety (KV) measure can be expressed as follows:

$${\text{KV}} = H\left( {X,Y} \right) = \mathop \sum \limits_{l} \mathop \sum \limits_{j} p_{lj} {\text{log}}_{2} \left( {\frac{1}{{p_{lj} }}} \right)$$
(3)

where \({p}_{lj}\) is the probability that two technological classes l and j co-occur within the same patent. The measure of multidimensional entropy focuses on the variety of co-occurrences or pairs of technological classes in patent applications and provides an index of how much the creation of new knowledge is focused on a narrower set of possible combinations.

The total index can be decomposed into “within” and “between” parts, whenever the events under study can be aggregated into smaller number of subsets. Within-group entropy measures the average degree of variety within the subsets (at 3-digit level); between-group entropy focuses on the subsets and measures the average degree of variety across them (at 1-digit level).

Let the technologies l and j belong to the subsets g and z of the classification scheme, respectively. If one allows \(l\in {S}_{g}\) and \(j\in {S}_{z}\), it is possible to write:

$$P_{gz} = \mathop \sum \limits_{{l \in S_{g} }} \mathop \sum \limits_{{j \in S_{z} }} p_{lj}$$
(4)

which is the probability of observing the pair \(lj\) in the subsets g and z, while the intra-subsets variety can be measured as follows:

$$H_{gz} = \mathop \sum \limits_{{l \in S_{g} }} \mathop \sum \limits_{{j \in S_{z} }} \frac{{p_{lj} }}{{p_{gz} }}{\text{log}}_{2} \left( {\frac{1}{{p_{lj} /P_{gz} }}} \right)$$
(5)

The weighted within-group entropy or related variety (RKV) can therefore be written as follows:

$${\text{RKV}} = \mathop \sum \limits_{g = 1}^{G} \mathop \sum \limits_{z = 1}^{Z} P_{gz}$$
(6)

The between-group or unrelated variety (UKV) can be calculated using the following equation:

$${\text{UKV}} \equiv H_{Q} = \mathop \sum \limits_{g = 1}^{G} \mathop \sum \limits_{z = 1}^{Z} P_{gz} {\text{log}}_{2} \left( {\frac{1}{{P_{gz} }}} \right)$$
(7)

According to the decomposition theorem, the total entropy H(X,Y) can be rewritten as follows:

$${\text{KV}} = H_{Q} + \mathop \sum \limits_{g = 1}^{G} \mathop \sum \limits_{z = 1}^{Z} P_{gz} H_{gz}$$
(8)

The first term on the right-hand side of Eq. (8) is the between-entropy and the second term is the weighted within entropy.

Indeed, as we argued in Sect. 3, we adapt the entropy index to capture the recombination of technological classes associated with green technologies.

C. Knowledge coherence

The degree of complementarity among the technological classes composing the local patent’s portfolio has been calculated in different steps for the 96 metropolitan départements of France (Nesta and Saviotti 2006; Nesta 2008; Quatraro 2010).

Following Teece et al. (1994), the weighted average relatedness (\({\mathrm{WAR}}_{l}\)) is defined as the degree to which technology \(l\) is related to all other technologies \(j\ne l\) in the regions’ patent portfolio, weighted by patent count \({P}_{jt}\).

$${\text{WAR}}_{lt} = \frac{{\mathop \sum \nolimits_{j \ne l} \tau_{lj} P_{jt} }}{{\mathop \sum \nolimits_{j \ne l} P_{jt} }}$$
(9)

Finally, the coherence of the region’s knowledge base at time \(t\) is defined as the weighted average of the \({\mathrm{WAR}}_{lt}\) measure:

$${\text{COH}}_{t} = \mathop \sum \limits_{l} {\text{WAR}}_{lt} \times \frac{{P_{lt} }}{{\mathop \sum \nolimits_{l} P_{lt} }}$$
(10)

The technological relatedness measure \({\tau }_{lj}\) indicates that the utilisation of technology \(l\) also implies the use of technology \(j\), in order to perform specific functions that are not reducible to their independent use.

To set the \(\tau\) parameter first, we built a relatedness matrix as follows (Nesta, 2008). Let the technological universe consist of \(k\) patent applications. Let\({P}_{jk}=1\), if the patent \(k\) is assigned to technology \(j\) [\(j=1,\dots , n\)] and 0 otherwise. The total number of patents assigned to technology \(j\) is \({O}_{j}={\sum }_{k}{P}_{jk}\). Similarly, the total number of patents assigned to technology \(m\) is \({O}_{m}={\sum }_{m}{P}_{mk}\). Since two technologies may be present within the same patent, \({O}_{j}\cap {O}_{m}\ne \varnothing\), the number of observed co-occurrences of technologies \(j\) and \(m\) is\({J}_{jm}={\sum }_{k}{P}_{jk}{P}_{mk}\). By applying this relationship to all the possible pairs, we obtain a square matrix \(\Omega (n \times n)\) where the generic cell is the observed number of co-occurrences:

$$\Omega = \left( {\begin{array}{*{20}c} {J_{11} } & \ldots & {J_{n1} } \\ \vdots & \ddots & \vdots \\ {J_{1n} } & \ldots & {J_{nn} } \\ \end{array} } \right)$$
(11)

We can assume that the number \({x}_{jm}\) of patents assigned to both the \(j\) and \(m\) technologies is a hypergeometric random mean and variance variable:

$$\mu_{jm} = E\left( {X_{jm} = x} \right) = \frac{{O_{j} O_{m} }}{K}$$
(12)
$$\sigma_{jm}^{2} = \mu_{jm} \left( {\frac{{K - O_{j} }}{K}} \right)\left( {\frac{{K - O_{m} }}{K - 1}} \right)$$
(13)

If the observed number of co-occurrences \({J}_{jm}\) is larger than the expected number of random co-occurrences \({\mu }_{jm}\), then the two technologies are closely related: the fact that the two technologies occur together in the number of patents \({x}_{jm}\) is not random. Thus, the measure of relatedness is given by the difference between the observed and the expected number of co-occurrences, weighted by their standard deviation:

$$\tau_{jm} = \frac{{J_{jm} - { }\mu_{jm} }}{{\sigma_{jm} }}$$
(14)

It should be noted that this measure of relatedness has lower and upper bounds: \({\tau }_{jm} \in (-\infty , +\infty )\). Moreover, the index shows a similar distribution to a t-student distribution; so, if \({\tau }_{jm}\in (-1.96, +1.96)\), one can assume the null hypothesis of non-relatedness of the technologies. Therefore, the technological relatedness matrix \(\Omega\) can be considered a weighting scheme to evaluate the technological portfolio of regions.

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Coll-Martínez, E., Kedjar, M. & Renou-Maissant, P. (Green) Knowledge spillovers and regional environmental support: do they matter for the entry of new green tech-based firms?. Ann Reg Sci (2022). https://doi.org/10.1007/s00168-022-01111-3

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  • DOI: https://doi.org/10.1007/s00168-022-01111-3

JEL Classification

  • L26
  • M13
  • R11
  • O33