Nota di Lavoro, Fondazione Eni Enrico Mattei 13.2006
In most cases, empirical analyses of environmental innovations based on firm-level data relied on survey data for one point in time. These surveys, especially designed for the analysis of environmental innovations, are useful because they allow for the inclusion of many explanatory variables such as different policy instruments or the influence of stake-holders and pressure groups. On the other hand, it is not possible to address the dynamic character of the environmental innovation process. This paper uses two German panel data bases, the establishment panel of the Institute for Employment Research (IAB) and the Mannheim Innovation Panel (MIP) of the Centre for European Economic Research (ZEW), to explore the determinants of environmental innovations. These data bases were not specifically collected to analyze environmental issues, but they contain questions that allow the identification of environmental innovations. We use discrete choice models for each of the data bases to analyze hypotheses derived from the theoretical (environmental) innovation literature. The econometric estimations show that the improvement of the technological capabilities (knowledge capital) by R&D or further education measures triggers environmental innovations this result is confirmed by both data bases and both methods to measure environmental innovation. The hypothesis that Innovation breeds innovation is confirmed by the analysis of the MIP data. General and environmental innovative firms in the past are more likely to innovate in the present. Environmental regulation, environmental management tools and general organizational changes and improvements trigger environmental innovation, a result that has also been postulated by the famous Porter-hypothesis. Environmental management tools especially help to detect cost-savings (specifically material and energy savings). Following our econometric results, cost-savings are an important driving force of environmental innovation.
Environmental innovation Panel data analysis Discrete choice models