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The Impact of Environmentally Friendly Innovations on Value Added

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Abstract

While recent literature has focused on explaining the determinants of green innovations, it is not well understood how such innovations affect performance. To analyse the relationship between green innovation and performance, new industry-level panel data were exploited: these include 12 OECD countries, the whole manufacturing sector and a period of 30 years. The results show that green inventions are U-shape related to performance. However, the turning point is quite high and hence only relevant for a few industries. This indicates that—given the current level of green promotion—market incentives alone are not sufficient to allow the green invention activities of industries to rise considerably. To verify these results and to get a better understanding of the mechanisms in the green market, we finally made several interviews with multinational firms that have a good understanding of what happens on the global market of green innovation.

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Notes

  1. In our case such a policy measure should be available on the industry level, a period of 30 years, 12 OECD countries and, as the effects may differ by type of policy, it should represent the whole policy environment (e.g. push and pull policies). As we do not have such a measure, we control only for country specific policy shocks by including country-time fixed effects (see Sect. 4).

  2. Please notice that our conceptual framework refers to the firm level and our empirical investigation is based on more aggregated industry data (see, e.g., Aghion et al. (2005) for a similar practice).

  3. Only very few green inventions were patented before 1980 (see Fig. 2). The invention activities increased in the 90s and only in the last decade we see a considerable rise in green invention activities relative to other inventions.

  4. For an overview of the effectiveness of policies see Popp et al. (2010).

  5. We may also have used the inventor’s address instead. However, there may be a risk of distorting the analysis, especially for smaller countries, because the inventor may not live in the country where the invention occurs. Conversely, by using the applicant’s address the analysis may be biased by patent applications from multinationals for which the country of residence of the applicant possibly differs from the country where the invention occurred. In order to investigate if there are considerable differences, we took both the inventor’s information and the applicant’s information for Germany. In fact, we did not see any significant differences between the analysis based on the inventor’s and applicant’s address for that country.

  6. The European classification system (ECLA) is an extension of the IPC with about twice as many classification codes.

  7. Lybbert and Zolas (2012), suggest new methods for constructing concordances. In comparing different concordance, they confirmed that on a relatively coarse level (e.g., 2 digit), the Schmoch et al. (2003) concordance enable a useful empirical and policy analysis.

  8. Since the OECD Indicator of Environmental Technologies (see OECD 2012) is based on the patent classification, each patent is classified at the same time (a) as green or non-green and (b) is assigned to a certain industry class. This allows us to identify for each industry class the total number of green and non-green patents.

  9. The initial value of the invention stock was set at \( Green\_stock_{1980}/(\delta +g)\), where \(g\) is the pre-1980 growth in invention stock. In line with Aghion et al. (2012) we assumed \(g\) to be 15 %. However, as the number of green inventions in 1980 was very limited (see Fig. 2), the impact of \(g\) was small. To test the robustness of our results, we reduced the influence of the initial stock by increasing the lag between the estimation period and the initial stock (see Table 4 for alternative estimates).

  10. The robustness of our results with respect to alternative model specifications (e.g., no quadratic terms) is tested in alternative estimates (see Table 7).

  11. As can be seen in Fig. 2, the number of green inventions steadily increased over time. Accordingly, average green invention stocks also increased from one time window to the next. In line with this trend also the turning points slightly increased over time (turning point 1983–2009: 3,051; turning point 1989–2000: 3,116; turning point 1994–2000: 3,289; turning point 1999–2009: 3,813). As both trends go in the same direction, the turning points are, however, of low relevance. In each time window less than 1.5 % of the observations exceed these levels. Accordingly, we decided to refrain from interpreting the turning points and instead focus on the marginal effects of additional Green stock up to 1,400 only (in each time window less than 5 % of the observations exceed this level).

  12. Our main estimates presented in column (1) of Table 3 are based on 146 groups. To check for outliers, we excluded all groups with an average clean or dirty invention stock greater THAN or equal to the top 1 % of the groups. All in all, we thus dropped two groups that account for 1.6 % of the observations.

  13. Overall, we made interviews with employees of three different globally active firms operating in the chemical industry, power and automation technology areas and power generation and transportation markets, respectively. To get a representative picture, the head of the R&D department as well as employees responsible for specific environmental technologies were interviewed. The employees were explicitly asked to express not only their own view but also the view of the whole industry they belong to.

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Acknowledgments

We thank the Editor and two anonymous reviewers of this journal for their valuable comments and suggestions. Moreover, we benefited from discussions with the participants at the Patent Statistics for Decision Makers Conference 2012 in Paris, at the IIOC 2012 in Arlington, at the research seminar in Innsbruck (April 2012), and at the 37th IAEE International Conference 2014 in New York City. We gratefully acknowledge financial support from the MTEC Foundation in Switzerland.

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Authors and Affiliations

  1. European Patent Office, 1030, Vienna, Austria

    Christian Soltmann

  2. KOF Swiss Economic Institute, ETH Zurich, 8092, Zurich, Switzerland

    Tobias Stucki & Martin Woerter

Authors
  1. Christian Soltmann
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  2. Tobias Stucki
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  3. Martin Woerter
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Corresponding author

Correspondence to Tobias Stucki.

Appendix

Appendix

See Appendix Tables 5, 6, 7, 8, 9, 10.

Table 5 Correlation matrix (based on model (1) of Table 3; 2,936 observations)
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Table 6 Descriptive statistics (based on model (1) of Table 3; 2,936 observations)
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Table 7 Estimate for alternative model specifications (fixed-effects regressions for time window 1981–2009)
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Table 8 Estimate of the production function based on flows of inventions (fixed-effects regression for time window 1981–2009)
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Table 9 Alternative estimates of model (1) of Table 3 (fixed-effects regressions for time window 1981–2009)
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Table 10 Estimates with alternative depreciation rates (fixed-effects regressions for time window 1981–2009)
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Soltmann, C., Stucki, T. & Woerter, M. The Impact of Environmentally Friendly Innovations on Value Added. Environ Resource Econ 62, 457–479 (2015). https://doi.org/10.1007/s10640-014-9824-6

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