Innovation, Productivity and Environmental Performance of Technology Spillovers Effects: Evidence from European Patents within the Triad

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.


Introduction
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). Empirical literature on environmental issues has deeply explored the complementarity feature between dirty and environmental innovations (Hall et al. 2012;Mancinelli and Mazzanti, 2009;Mohnen and Roller, 2005;Aghion et al. 2016). However, there is a lack of studies focusing only on environmental activities. This is the main motivation of the manuscript.
In this paper, 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 andAldieri, 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.

Theoretical Framework
In order to better specify the relationships and sources of our empirical framework, this section presents a basic theoretical model, which is a set-up of a global economy with multiple sectors and countries. In each country, the production of a sector bases on three environmental targets, such as water pollution abatement, solid waste collection, and different types of green energy (wind, solar and geothermal energy, integrated emissions control, lightning to quote some). Each target combines varieties of technological classes, with physical, human and investments in these technological classes may be assumed to depend on rational agents purposeful decisions (Bretschger et al. 2017). The final output of a sector i, country r, at time t Y t i,r may be taken as the of two different outputs from two different production techniques: green ( Y Nt i,r ) and not ( Y gt i,r ), and written as: Hence in order to determine the short run impacts of innovation on the green and total technology we may easily derive: From the previous model we can identify two main research hypotheses:

[H1] : The integration process between environmental technology fields is relevant in the computation of Spillover components of firms
[H2] : The effect of Spillovers stemmed from diversified environmental technology fields on firms' productivity is positive.

Data
We use three sources of data. First, we use information from OECD, REGPAT database, February 2016 (Note 1), as in Aldieri and Vinci (2017). Second, we match the name of the same firms to applicant's name from European Commission (2013), as in Aldieri (2013). The third source of data is the World Input Output Database (WIOD), which is made up of four different accounts (World Tables, National Tables, Socio Economic Accounts and Environmental Accounts). For purposes of this paper, we use the Environmental Accounts providing CO2 emissions variable by country and by year.
In Table 1, we report those patents with IPC code belonging to the groups selected by the OECD or the World Intellectual Property Organization (WIPO), as in Marin and Lotti (2016). Moreover, in order to identify the environmental performance of technology spillovers, we estimate also another model with ratio between productivity and CO2 (SCO2) as dependent variable (Repetto, 1990) and regressors like in (13). In Table 2, we show the summary statistics of our sample. In order to handle both firms' unobserved heterogeneity and the weak exogeneity of the explanatory variables, we estimate equation (13) using a one-stage generalized method of moments (GMM) (Note 2) estimator, as in Aldieri and Cincera (2009).
In Table 3 and Table 4, we present the empirical estimates for the GMM-SYS estimator. In particular, we show the effects of specialized activities spillovers (SPEC) and diversified technology fields spillovers (DIV) on firms' productivity in Table 3 and environmental performance effects of spillovers in Table 4. We lag environmental spillover components by a year to reflect delayed response and also mitigate contemporaneous feedback effects. Hansen test of over-identifying restrictions, p-value in squared brackets; c: AR(1) and AR(2) are tests for first-and second-order serial correlation; ***, **, coefficient significant at the 1%, 5% level respectively. Country, time and industry dummies included. Endogenous variables are physical capital, labor, R&D capital stock and spillovers. Instruments are lagged values (2-9) of all explanatory variables.  (2) are tests for first-and second-order serial correlation; ***, **, coefficient significant at the 1%, 5% level respectively. Country, time and industry dummies included. Endogenous variables are physical capital, labor, R&D capital stock and spillovers. Instruments are lagged values (2-9) of all explanatory variables.
Country, time, and industry dummies are added in the model to capture the effect of factors that change over time but not over the cross-sectional dimension of the sample. The results of the AR (1) tests is consistent with the assumption of no serial correlation in the residuals in levels and the Hansen tests do not reject the null hypothesis of valid instruments, indicating that the instruments are not correlated with the error term.
In particular, specialized environmental spillovers (SPEC) have a negative impact, while the diversified ones (DIV) have a positive one, by confirming the theoretical predictions: more integrated environmental activities lead to higher diversified spillovers which determine a positive impact both on productivity and environmental efficiency. This finding is extremely important for policy implications. Also the integration process between the environmental technology fields is crucial for a full sustainable achievement of firms and then fiscal incentives to this end are required.

Conclusions
In this paper we investigate the role of spillovers derived from different technological sectors for international firms engaged both in environmental and in non-environmental activities. We can identify a lack of integrated innovation adoption behind environmental productivity performance. 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. In order to deal with the endogeneity of the explanatory variables, a Linear Generalized method of Moments (GMM) is implemented.
The interesting results are relative to causal effects of environmental spillovers on productivity and environmental performance. In particular, specialized environmental spillovers have a negative impact, while the diversified environmental activities have a positive one. This finding is extremely useful for policy implications: more fiscal incentives are necessary to assure the integration between the environmental technology fields for a full sustainable achievement of firms.