SMEs and barriers to Eco-innovation in the EU: exploring different firm profiles

Abstract

Eco-innovation is an explicit aim of major EU policy strategies. Many environmental policies de facto require firms to eco-innovate to comply with policy requirements, while the overlap between policy-driven and market-driven eco-innovation strategies is increasingly important for many firms. Barriers to eco-innovation can then emerge as a critical factor in either preventing or stimulating EU strategies, policy implementation, and the green strategies of firms. In this paper we focus on EU-27 SMEs. We single out and explore different firm profiles, considering eco-innovation barriers and engagement. Our analysis is based on a particularly suitable dataset: the Eurobarometer survey on “Attitudes of European entrepreneurs towards eco-innovation”. We identify six clusters of SMEs. These clusters include firms facing either ‘Revealed barriers’ or ‘Deterring barriers’, ‘Cost deterred’ firms, ‘Market deterred’ firms, ‘Non eco-innovators’, and ‘Green champions’. The clusters display substantial differences in terms of eco-innovation adoption. We show that our taxonomy has little overlap with sector classifications. This diversity should be taken into account for successful environmental and innovation policies.

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Fig. 1
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Notes

  1. 1.

    An oft-quoted definition of eco-innovation is “The production, assimilation or exploitation of a product, production process, service or management or business method that is novel to the organisation (developing or adopting it) and which results, throughout its life-cycle, in a reduction of environmental risks, pollution and other negative impacts of resources use (including energy use) compared to relevant alternatives” (UNU-MERIT et al. 2008). See also Europe Innova (2008) and CML et al. (2008).

  2. 2.

    SMEs represent the core of the EU27 private sector, employing 66.7% of the workforce in non-financial business sectors in the year 2008. (Source: Eurostat.)

  3. 3.

    Before the inclusion of the questions on environmental innovations (i.e. before the 2008 version), the harmonized CIS questionnaire included a specific section on innovation barriers.

  4. 4.

    The latter industries belong to the Environmental Goods and Services Sector (EGSS) according to the Eurostat classification; see Eurostat (2009) and http://ec.europa.eu/eurostat/statistics-explained/index.php/Environmental_goods_and_services_sector. Due to their direct involvement in environmental activities, firms in these industries are supposed to adopt a wider range of innovative solutions to environmental problems than other industries.

  5. 5.

    In the year 2011, the sectors considered here accounted for about 30% of total CO2 emissions in EU27 countries (Eurostat, Air emissions accounts by industry and households - NACE Rev. 2), while the industries excluded from our analysis only accounted for 5% of total CO2 emissions.

  6. 6.

    After controlling for country and industry characteristics, the firms belonging to our sectors of interest but excluded from the analysis do not differ, on average, from our sample firms in terms of size (employees and turnover), turnover growth, product eco-innovation outcome, cost and market barriers. Yet, they have slightly lower eco-innovation investment, process eco-innovation outcome, and knowledge barriers.

  7. 7.

    Hair et al. (2009) suggest 0.6 as an acceptable threshold for the Cronbach’s alpha.

  8. 8.

    Scores are expressed by means of a 4-point likert scale concerning the relevance of each barrier. The scale is as follows: 1 not at all serious; 2 not serious; 3 somewhat serious; 4 very serious.

  9. 9.

    When selecting the number of retained components, the general rule of thumb (Hair et al. 2009) is to keep the principal components for which the eigenvalue is greater than one. In our case, the first four eigenvalues 5.9, 0.99, 0.95 and 0.8, suggest the presence one-to-three components. However, another rule of the thumb suggests that the variance explained by the retained components should be at least 60%. In this respect, our principal component analysis is rather poor.

  10. 10.

    The factor loadings are smaller than 0.3 for many items, which means that the variance of the items accounted for by the factors is smaller than 10 %.

  11. 11.

    Item-to-total correlation always exceeds 0.6. Hair et al. (2009) suggest 0.5 as the minimum threshold to test the validity of the proposed grouping.

  12. 12.

    0: 0 %; 1: less than 10 %; 2: between 10 % and 29 %; 3: between 30 % and 49 %; 4: more than 50 %. Note that this variable does not refer to absolute spending in eco-innovation investment (which was not available in the dataset) but rather to the relative orientation of innovation investment towards green innovation.

  13. 13.

    In the survey, business executives were asked to rate the stringency of their country’s environmental regulations on a 7-point Likert scale (where 0 stands for ‘very lax’ and 7 for ‘among the world’s most stringent’).

  14. 14.

    Similarity is measured by means of the squared Euclidean distance.

  15. 15.

    The six-cluster solution has the minimum level of pseudo T-squared statistics (16.57 compared to 22.68 for the five-cluster solution and 54.21 for the seven-cluster solution) and the maximum level of Je(2)/Je(1) statistics (0.97 compared to 0.9 for the five-cluster solution and 0.8 for the seven-cluster solution). The Calinski-Harabasz pseudo F does not suggest any other specific clustering solution.

  16. 16.

    As an additional robustness check, we also perform the various steps of the cluster analysis by using, instead of our measures of perceived barriers, the principal components extracted from the principal component analysis described in Section 3.1. Despite the low explained variance observed in the principal component analysis, the results show a substantial overlap between our chosen clustering and the one based on principal components (about 74% of firms originally assigned to the correct cluster).

  17. 17.

    A potential action line, in this respect, might be to implement ‘soft’ policies (e.g. benchmarking, sharing of best practices) to enhance the understanding of the beneficial effects of eco-innovation (e.g. Antonietti and Marzucchi, 2014).

  18. 18.

    This could be especially true for those ‘Green champions’ start-ups which, through the adoption of novel and diverse behaviors and technologies, may help overcome environment-related lock-ins (e.g. Van den Bergh 2007).

  19. 19.

    This means that all the pairs not reported in Table 1 display pairwise significant differences. As we have found significant differences in clustering and external variables for the vast majority of pairs, our clustering can be deemed to be robust.

  20. 20.

    While clustering variables should differ across clusters almost by construction, the extent to which non-clustering variables theoretically related to clustering variables differ across clusters represents a way to check the validity of the clustering analysis.

  21. 21.

    As mentioned above, the variable used here to capture the level of engagement in eco-innovation reflects the intensity of investments in eco-innovative activities rather than their volume. The availability (and use) of the latter variable would possibly have yielded an even stronger match with the eco-innovation output captured by the propensity to introduce eco-innovations. The variable used here, instead, would possibly have led to a stronger match with the output variables, had the latter been captured by the relative number of eco-innovations adopted over the total number of innovations introduced by the firm.

  22. 22.

    We also look at differences in size (Table 11): the only information about firm size available in the survey refers to the distinction between small firms (10–49 employees) and medium firms (50–250 employees). This is quite a rough distinction, especially if we consider the fact that firm size distribution is substantially skewed, making the size class ‘50-250 employees’ extremely heterogeneous. Nevertheless, the evidence suggests that medium-sized firms tend to be over-represented in the ‘Green champions’ cluster, while small firms are relatively more concentrated in the ‘Deterring barriers’ and ‘Market deterred’ clusters. Size seems to be linked to success in engaging in eco-innovation, as small firms appear to be more exposed to the risk of being hindered by major, especially market-related, barriers.

  23. 23.

    Unfortunately, we cannot directly account for sector-specific regulation stringency, which may affect the (lack of) incentives to engage in eco-innovations.

  24. 24.

    Differences are slightly smaller when we look at the emission intensity of sectors (p-value of the χ2 test equal to 0.01).

  25. 25.

    Results are robust when, instead of the latter, we use dummies for country-level regulation stringency. For the sake of brevity, we do not report these results, which are available on request.

  26. 26.

    The tests report only weakly significant values for joint significance of R&D intensity dummies in predicting the probability of belonging to the ‘Deterring barriers’ cluster. Similarly, the test of the joint significance of emission intensity dummies is weakly significant in predicting the probability of belonging to the ‘Market deterred’ cluster.

  27. 27.

    This result seems to contradict partly the Pearson tests reported in Table 4. It should be noted, however, that: i) we are now controlling for country-specific characteristics, and ii) we now consider the role of sector specificities ‘cluster by cluster’. The results of the Pearson tests reported in Table 4 might actually be ascribed to strong sectoral components in one or more clusters (e.g. ‘Green champions’) and/or to country-specific concentrations of firms in specific sectors, thus making the Pearson tests partly misleading.

  28. 28.

    Due to the limited amount of information available in the survey, which results in the absence of a credible instrumental variable for eco-innovation investment intensity, we have not been able to correct for endogeneity.

  29. 29.

    The number of observations in these regressions is slightly smaller than the number of observations in our clustering, since information on eco-innovation adoption is missing for a limited number of firms.

  30. 30.

    The significance of the difference between each cluster and the ‘Non eco-innovators’ cluster, i.e. the reference category, is directly tested by looking at the significance of the cluster dummies.

  31. 31.

    This difference in performance is positive and significant for ‘Product EI’ and ‘Product or process EI’, it is positive (but insignificant) for ‘Process EI’ and it is negative (but insignificant) for ‘Product and process EI’. The latter result is in line with the one discussed in section 4.2, which explains that for ‘Product and process EI’ the performance of the two clusters is different and in favour of the ‘Green champions’ cluster.

  32. 32.

    The difference in favour of firms in the ‘Deterring barriers’ cluster, compared to firms in the ‘Non eco-innovators’ cluster, is statistically significant for ‘Product EI’ and ‘Product or process EI’ and insignificant (but still positive) for the other two adoption measures.

  33. 33.

    Actually, inter-firm diversity, especially in terms of pollution abatement costs, is a pillar of environmental policy theory, above all in relation to the advantages of Pigouvian taxation over command-and-control instruments and the workings of emission trading systems (see Baumol and Oates 1988).

  34. 34.

    Differently from large firms in oligopolistic industries, which actually have the ability to influence policies.

  35. 35.

    Many studies analysing the drivers of eco-innovation (e.g. Horbach 2008; Horbach et al. 2012; Horbach et al. 2013; Demirel and Kesidou, 2011) use self-reported qualitative measures of regulatory stringency but, to date, no investigation has evaluated the role played by more objective measures of regulatory stringency.

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Acknowledgments

We are grateful to the participants of the EU-SPRI Conference (University of Manchester, June 2014) and the 2014 Conference of the Italian Association of Environmental and Resource Economists (University of Milan, February 2014) for providing feedback on preliminary versions of the manuscript. We also thank three anonymous referees for their valuable suggestions. Alberto Marzucchi and Roberto Zoboli acknowledge the financial support from the project PRIN-MIUR 2010–2011 “Climate changes in the Mediterranean area: scenarios, mitigation policies and technological innovation” (2010S2LHSE). Giovanni Marin and Roberto Zoboli acknowledge the financial support from the 7th Framework Progamme project EMInInn “Environmental Macro Indicator of Innovation” (283002). The usual disclaimer applies.

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Appendix

Appendix

Box 1 Relevant questions in the Eurobarometer survey

Q6. Over the last 5 years, what share of innovation investments in your company were related to eco-innovation, i.e. implementing new or substantially improved solutions resulting in more efficient use in material, energy and water? (More than 50 %; Between 30 % and 49 %; Between 10 % and 29 %; Less than 10 %; None; No innovative activities).
D5. During the past 24 months have you introduced the following eco-innovation (yes/no):
a. a new or significantly improved eco-innovative product or service to the market
b. a new or significantly improved eco-innovative production process or method
1Q7. I will list you some barriers that could represent an obstacle to accelerated eco-innovation uptake and development for a company. Please tell me for each of them if you consider them a very serious, somewhat serious, not serious or not at all serious barrier in case of your company? (4 Very serious; 3 Somewhat serious; 2 Not serious; 1 Not at all serious)
a. Lack of funds within the enterprise
b. Lack of external financing
c. Uncertain return on investment or too long payback period for eco-innovation
d. Lack of qualified personnel and technological capabilities within the enterprise
e. Limited access to external information and knowledge, including lack of well developed technology support services
f. Lack of suitable business partners
g. Lack of collaboration with research institutes and universities
h. Uncertain demand from the market
i. Reducing material use is not a innovation priority
j. Reducing energy use is not a innovation priority
k. Technical and technological lock-ins in economy (e.g. old technical infrastructures)
l. Market dominated by established enterprises
m. Existing regulations and structures not providing incentives to eco-innovate
2n. Insufficient access to existing subsidies and fiscal incentives
Table 7 Factor loadings (>.3) of the principal component analysis (see Box 1 for coding of variables)
Table 8 Correlation between summated scales, principal components and single items (see Box 1 for coding of variables)
Table 9 EU27 countries by perceived environmental regulation
Table 10 Sectors by R&D intensity (CIS2008) and emission intensity (NAMEA 2008–2010)
Table 11 Clusters with respect to size class
Table 12 Ratio between the country-level share of the cluster and the EU average share of the same cluster (>1: higher share in the country)

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Marin, G., Marzucchi, A. & Zoboli, R. SMEs and barriers to Eco-innovation in the EU: exploring different firm profiles. J Evol Econ 25, 671–705 (2015). https://doi.org/10.1007/s00191-015-0407-7

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Keywords

  • Eco-innovation
  • Barriers to innovation
  • SMEs
  • Green strategy
  • JEL
  • O33
  • Q55