Abstract
The aim of this paper is that of investigating whether the integration process between environmental activities is important in the Spillovers flows analysis. For this reason, we explore the role of knowledge externalities for large international firms engaged both in environmental and in non-environmental activities. In particular, we develop a theoretical framework and an empirical analysis of the United States, Japan and Europe based upon a dataset composed of worldwide R&D-intensive firms. In order to deal with the firms’ unobserved heterogeneity and the weak exogeneity of the regressors, we implement the Generalized Method of Moments (GMM) method. The results show a differentiated impact of environmental spillovers on firms’ productivity and green performance, by suggesting some interesting policy implications in terms of actions to favor full sustainability of firms’ production.
Highlights
In order to assure long run sustainability, structural changes in each developed country economy are required (Cainelli et al 2012; De Marchi, 2012; Harbach, 2008; Kemp and Pontoglio, 2011)
We analyze the environmental technology spillovers for large international firms within the Triad, on the basis of proximity computed through European patents data
In order to compute the technological proximity between the firms, we construct an Environmental industry weight matrix, based on the construction of technological vectors for each firm
Summary
In order to assure long run sustainability, structural changes in each developed country economy are required (Cainelli et al 2012; De Marchi, 2012; Harbach, 2008; Kemp and Pontoglio, 2011). There is a lack of studies focusing only on environmental activities. This is the main motivation of the manuscript. We analyze the environmental technology spillovers for large international firms within the Triad, on the basis of proximity computed through European patents data (as in Jaffe, 1986). Since Jaffe’s proximity assumes externalities only occur within the same technology field, we use the Mahalanobis index (Bloom et al, 2013 and Aldieri, 2013), in such a way that we consider the co-location, that is the frequency that patents are taken out in different classes by the same firm (Lychagin et al, 2016). The paper is structured as follows: Section 2 introduces a theoretical framework about firms’ activity; Section 3 describes the data used in the empirical analysis; Section 4 presents the empirical analysis and Section 5 concludes
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
