![Sample our Geography journals, sign in here to start your FREE access for 14 days]({"type":"image" , "src" : "/sda/5937/TOC-Subject-Sample-2021-Geography.jpg"})
Abstract
On the basis of a unique firm-level dataset from the German manufacturing sector, this paper empirically examines the determinants of environmental and non-environmental product and process innovations. The micro-econometric analysis with multivariate probit models points to an extremely high relevance of R&D activities for all technological innovation types, as well as to the relevance of a few market pull factors. The estimation results additionally reveal the importance of organisational measures for environmental product and process innovations. In this regard, not only certified environmental management systems, but also specific process- and product-related environmental organisational measures and even general organisational measures, such as the certification of a quality management system according to ISO 9001, play a crucial role. The estimation results therefore suggest the encouragement of firm-internal R&D activities and organisational measures by (environmental) policy in order to stimulate environmental technological innovations, which are able to both limit the environmental burden and contribute to the technological modernisation of the economy.
Notes
1. An exception in this regard is the study of Horbach Citation(2008), which also includes both environmental and non-environmental technological innovations. However, the corresponding data do not allow the important disentanglement of (environmental and non-environmental) product and process innovations.
2. This hypothesis is empirically confirmed in the study of Horbach Citation(2008).
3. For the discussion of technology push factors, particularly in comparison to market pull factors, see also Horbach Citation(2008); Rennings and Rammer Citation(2009); Belin, Horbach, and Oltra Citation(2011); Rave, Goetzke, and Larch Citation(2011); Horbach, Rammer, and Rennings Citation(2012).
4. The estimates of the parameters for the sector dummy variables are not reported for reasons of brevity, although they were included in all econometric models.
5. Both Tables report whether a parameter of the explanatory variables is different from zero at the 1%, 5% or even 10% significance level. However, the following discussion of the estimation results focuses on corresponding correlations at the 1% or 5% significance level, since a rejection of the underlying null hypothesis that the parameter is zero at the 10% significance level seems to be insufficient for robust conclusions, particularly if the relatively small sample sizes are considered.
6. The only exception is the insignificant correlation between environmental process and non-environmental product innovations in the stochastic components.
7. The corresponding correlation of ‘life cycle’ with environmental product innovations is only different from zero at the 10% significance level, which seems to be too high to draw robust conclusions from this result, as discussed above.
8. The corresponding correlations with environmental product innovations (for ‘increased competition’) or environmental process innovations (for ‘quality important’) are not significant, although the respective parameter estimates are (in line with the corresponding estimation results in also positive.
9. The corresponding estimation results are not reported for reasons of brevity, but are available upon request.
10. This variable is based on evaluations of ‘Creditreform’, the largest German credit rating agency.
11. In line with, for example, Horbach Citation(2008) and Horbach, Rammer, and Rennings Citation(2012), the skill structure in a firm can be considered as an additional technology push factor.
12. The corresponding estimation results additionally reveal a weak correlation of ‘EMS’ with non-environmental process innovations, however, only at the 10% significance level.