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Green start-ups and local knowledge spillovers from clean and dirty technologies

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Abstract

There is wide consensus about the importance of green technologies in achieving superior economic and environmental performances. However, the literature on their determinants has neglected the creation of green start-ups as a way of introducing green technologies onto the market. Drawing upon the knowledge spillovers theory of entrepreneurship (KSTE) and on previous literature on the complex and systemic nature of green technologies, we have tested the relevance of local knowledge stocks, distinguishing between clean and dirty stocks, in the creation of green start-ups. Moreover, the effects of the technological composition of local stocks have been investigated, by focusing on both related and unrelated technological variety, as well as on coherence. Consistently with the recent literature, green start-ups are associated with higher levels of variety, thus pointing to the relevance of diverse and heterogeneous knowledge sources, although in related and complementary technological fields.

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Notes

  1. Although interesting, it is beyond the scope of the current work to systematically test for the differences that could arise from the choice of classification. (We selected t) The WIPO IPC green inventory was selected since it is currently a frequently used and well established classification of green technologies. OECD has also developed the OECD Indicator of Environmental Technologies (OECD, 2011), based on the International Patent Classification (IPC), which features seven environmental areas, i.e. (a) general environmental management, (b) energy generation from renewable and non-fossil sources, (c) combustion technologies with mitigation potential, (d) technologies specific to climate change mitigation, (e) technologies with potential or indirect contributions to emission mitigation, (f) emission abatement and fuel efficiency in transportation, and (g) energy efficiency in buildings and lighting. At the same time, the European Patent Office (EPO) is working on completing its own classification system (ECLA) to assign each patent a green tag, depending on the environmental aim of each patent. So far, EPO has allowed technologies to be tagged according to their adaptation or mitigation to climate changes (Y02), in terms of buildings (Y02B), energy (Y02E), transportation (Y02T) and the capturing, storage sequestration and disposal of GHG (Y02C). Costantini et al. (2013b) have recently pointed out the shortcomings of classification methods based on efforts to collect the IPCs that are potentially related to green technologies in one place. Focusing on the biofuel sector, they have shown that the WIPO Green Inventory is likely to overestimate the number of patents that have been assigned due to the fact that IPCs are not specifically designed to identify this narrow and very specific domain. Clinical analyses, based on a keyword search and validations by experts, are likely to yield finer grained classifications. Nonetheless, the WIPO Green Inventory was chosen for this work, due to the wide scope of this analysis, which encompasses many different kinds of green technologies.

  2. The Data are available to the public on the http://startup.registroimprese.it/ web-site. The data used in this paper were updated to May 2015.

  3. However, we do not deny that local markets are not homogenous with respect to size, and that this can introduce some biases in our results. For this reason, as we specify below, we introduced the employment level in the province among the control variables.

  4. The limits of patent statistics as indicators of technological activities are well known. The main drawbacks can be summarized as: their sector-specificity, the existence of non-patentable innovations and the fact that they are not the only protecting tool. Moreover, the propensity to patent tends to vary over time as a function of the cost of patenting, and it is more likely to feature large firms (Pavitt 1985; Griliches 1990). Nevertheless, previous studies highlighted the usefulness of patents as measures of production of new knowledge. Such studies show that patents represent very reliable proxies for knowledge and innovation, compared to analyses that draw upon surveys that have directly investigated the dynamics of process and product innovation (Acs et al. 2002). Apart from the debate on patents as an output rather than an input of innovation activities, empirical analyses have shown that patents and R&D are dominated by a contemporaneous relationship, thus providing further support to the use of patents as a good proxy of technological activities (Hall et al. 1986).

  5. 4-digit technological classes have been used in the calculation.

  6. It should be stressed that in order to compensate for the intrinsic volatility of patenting behavior, each patent application has been made to last five years in order to reduce the noise induced by changes in the technological strategy.

  7. The following Nace Rev. 2 sectors have been classified by ISTAT as ‘dynamic’: CE-Chemicals; CF-Pharmaceuticals; CI-Computers and electronic and optical products; CJ-Electric apparatus; CL-Transport; M – Professional, scientific and technical activities; R – Arts, entertainment, recreation; S – Other service activities.

  8. It is worth noting that we have also checked for the existence of autocorrelation in the dependent variables, by means of Wooldridge’s test for autocorrelation in panel data. In all cases, (we obtained) statistics of between 1.4 and 1.5 were obtained, which do/did not allow us to reject the null hypothesis of no first-order autocorrelation.

  9. Additional regressions have been run separately, in which GT_STOCK and NOGT_STOCK have been included. The results confirm the patterns shown in Table 5.

  10. It is worth recalling that related and unrelated knowledge variety/ are not opposites, but orthogonal in their meaning (Frenken et al. 2007; Castaldi et al. 2015). In principle, a NUTS 3 region can be characterized by both high RKV and UKV. These would be regions that are diversified into unrelated technological categories, while also being diversified into many specific classes in each of these categories. . It is also worth stressing that empirically related and unrelated variety tend to correlate positively (see Table 3; see also Frenken et al. 2007; Quatraro 2010; Boschma et al. 2012; Hartog et al. 2012).

References

  • Acosta, M., Coronado, D., & Flores, E. (2011). University spillovers and new business location in high-technology sectors: Spanish evidence. Small Business Economics, 36(3), 365–376.

    Article  Google Scholar 

  • Acs, Z. J., Anselin, L., & Varga, A. (2002). Patents and innovation counts as measures of regional production of new knowledge. Research Policy, 31, 1069–1085.

    Article  Google Scholar 

  • Acs, Z. J., Braunerhjelm, P., Audretsch, D. B., & Carlsson, B. (2009). The knowledge spillover theory of entrepreneurship. Small Business Economics, 32(1), 15–30.

    Article  Google Scholar 

  • Aghion, P., & Howitt, P. (1992). A model of growth through creative destruction. Econometrica, 60, 323–351.

    Article  Google Scholar 

  • Ambec, S., & Lanoie, P. (2008). Does it pay to be green? A systematic overview. The Academy of Management Perspectives, 22(4), 45–62.

    Article  Google Scholar 

  • Acs, Z. J., & Armington, C. (2006). Entrepreneurship, Geography and American Economic Growth. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Arrow, K. (1962). The economic implication of learning by doing. Review of Economics and Statistics, 80, 155–173.

    Article  Google Scholar 

  • Audretsch, D. B. (1995). Innovation and industry evolution. Cambridge (Mass): MIT Press.

    Google Scholar 

  • Audretsch, D. B., & Fritsch, M. (1994). The geography of firm births in Germany. Regional Studies, 28(4), 359–365.

    Article  Google Scholar 

  • Audretsch, D. B., & Keilbach, M. C. (2007). The localisation of entrepreneurship capital: Evidence from Germany. Papers in Regional Science, 86, 351–365.

    Article  Google Scholar 

  • Audretsch, D. B., & Lehmann, E. E. (2005). Does the knowledge spillover theory of entrepreneurship hold for regions? Research Policy, 34, 1191–1202.

    Article  Google Scholar 

  • Audretsch, D. B., Keilbach, M. C., & Lehmann, E. E. (2006). Entrepreneurship and economic growth. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Audretsch, D. B., Lehmann, E. E., & Hinger, J. (2015). From knowledge to innovation: The role of knowledge spillover entrepreneurship. In C. Antonelli & A. N. Link (Eds.), Routledge handbook of the economics of knowledge. New York: Routledge.

    Google Scholar 

  • Auricchio M., Cantamessa M., Colombelli A., Cullino R., Orame A., Paolucci E., (2014), Business Incubators in Italy, Questioni di Economia e Finanza, No. 216, April 2014, Bank of Italy.

  • Bae, J., & Koo, J. (2008). The nature of local knowledge and firm formation. Industrial and Corporate Change, 18, 1–24.

    Article  Google Scholar 

  • Baptista, R., & Mendonça, J. (2010). Proximity to knowledge sources and the location of knowledge-based start-ups. Annals of Regional Studies, 45, 5–29.

    Article  Google Scholar 

  • Bartik, T. (1985). Business location decisions in the United States: Estimates of the effects of unionization, taxes, and other characteristics of the states. Journal of Business and Economic Statistics, 3(January), 16–22.

    Google Scholar 

  • Bergmann, H., Hundt, C., & Sternberg, R. (2016). What makes student entrepreneurs? On the relevance (and irrelevance) of the university and the regional context for student start-ups. Small Business Economics, 47, 53–76.

    Article  Google Scholar 

  • Bishop, P. (2012). Knowledge, diversity and entrepreneurship: A spatial analysis of new firm formation in great Britain. Entrepreneurship and Regional Development, 24, 641–660.

    Article  Google Scholar 

  • Bjørner, T. B., & Mackenhauer, J. (2013). Spillover from private energy research. Resource and Energy Economics, 35, 171–190.

    Article  Google Scholar 

  • Blalock, G., & Gertler, P. (2004). Learning from exporting revisited in a less developed setting. Journal of Development Economics, 75, 397–416.

    Article  Google Scholar 

  • Bonaccorsi, A., Colombo, M. G., Guerini, M., & Rossi-Lamastra, C. (2013). University specialization and new firm creation across industries. Small Business Economics, 41, 837–863.

    Article  Google Scholar 

  • Bonaccorsi, A., Colombo, M. G., Guerini, M., & Rossi-Lamastra, C. (2014). The impact of local and external university knowledge on the creation of knowledge-intensive firms: Evidence from the Italian case. Small Business Economics, 43(2), 261–287.

    Article  Google Scholar 

  • Boschma, R. A. (2005). Proximity and innovation: A critical assessment. Regional Studies, 39, 61–74.

    Article  Google Scholar 

  • Boschma, R. A. (2011). Regional branching and regional innovation policy. In K. Kourtit, P. Nijkamp, & R. R. Stough (Eds.), Drivers of innovation, entrepreneurship and regional dynamics (pp. 359–368). Berlin: Springer Verlag.

    Chapter  Google Scholar 

  • Boschma, R. A., Minondo, A., & Navarro, M. (2012). Related variety and regional growth in Spain. Papers in Regional Science, 91(2), 241–256.

    Google Scholar 

  • Branstetter, L. (2006). Is foreign direct investment a channel of knowledge spillovers? Evidence from Japan's FDI in the United States. Journal of International Economics, 68(2), 325–344.

    Article  Google Scholar 

  • Brunnermeier, S. B., & Cohen, M. A. (2003). Determinants of environmental innovation in US manufacturing industries. Journal of Environmental Economics and Management, 45, 278–293.

    Article  Google Scholar 

  • Burbidge, J. B., Magee, L., & Robb, A. L. (1988). Alternative transformations to handle extreme values of the dependent variable. Journal of the American Statistical Association, 83, 123–127.

    Article  Google Scholar 

  • Cainelli, G., Mazzanti, M., & Zoboli, R. (2013). Environmental performance, manufacturing sectors and firm growth: Structural factors and dynamic relationships. Environmental Economics and Policy Studies, 15, 367–387.

    Article  Google Scholar 

  • Cainelli, G., D'Amato, A., Mazzanti, M. (2015). Adoption of waste-reducing technology in manufacturing: regional factors and policy issues. Resource and Energy Economics, 39, 53-67.

  • Carlton, D. W. (1983). The location and employment choices of new firms: An econometric model with discrete and continuous endogenous variables. Review of Economics and Statistics, 65(3), 440–449.

    Article  Google Scholar 

  • Carree, M., & Thurik, A. R. (2003). Understanding the role of entrepreneurship for economic growth. In Z. J. Acs & D. B. Audretsch (Eds.), The handbook of entrepreneurship and economic growth (pp. 437–471). Cheltenham: Elgar.

  • Cassia, L., & Colombelli, A. (2008). Do universities knowledge spillovers impact on new firm’s growth? Empirical evidence from UK. The International Entrepreneurship and Management Journal, 4, 453–465.

    Article  Google Scholar 

  • Cassia, L., Colombelli, A., & Paleari, S. (2009). Firms’ growth: Does the innovation system matter? Structural Change and Economic Dynamics, 20, 211–220.

    Article  Google Scholar 

  • Castaldi, C., Frenken, K., & Los, B. (2015). Related variety, unrelated variety and technological breakthroughs. An analysis of US state-level patenting. Regional Studies, 49(5), 767–781.

    Article  Google Scholar 

  • Cohen, B., & Winn, M. I. (2007). Market imperfections, opportunity and sustainable entrepreneurship. Journal of Business Venturing, 22, 29–49.

    Article  Google Scholar 

  • Colombelli, A. (2016). The impact of local knowledge bases on the creation of innovative start-ups in Italy. Small Business Economics, forthcoming. https://doi.org/10.1007/s11187-016-9722-0.

  • Colombelli A., Quatraro F., (2013). The properties of local knowledge bases and entrepreneurship: Evidence from Italian NUTS3 regions. Evolutionary economic geography (PEEG), number 1303, Utecht University, Section of Economic Geography.

  • Colombelli, A., Krafft, J. and Quatraro, F. (2015). Eco-innovation and firm growth: Do green gazelles run faster? Microeconometric evidence from a sample of European firms. WWWforEurope Working Paper n. 88.

  • Colombo, M. G., & Delmastro, M. (2002). How effective are technology incubators? Evidence from Italy. Research Policy, 31, 1103–1122.

    Article  Google Scholar 

  • Costantini, V., Mazzanti, M., & Montini, A. (2013). Environmental performance, innovation and spillovers.Evidence from a regional NAMEA. Ecological Economics, 89, 101–114.

    Article  Google Scholar 

  • Crespi, F., Ghisetti, C., & Quatraro, F. (2015). Environmental and innovation policies for the evolution of green technologies: A survey and a test. Eurasian Business Review, 5, 343–370.

    Article  Google Scholar 

  • De Marchi, V. (2012). Environmental innovation and R&D cooperation: Empirical evidence from Spanish manufacturing firms. Research Policy, 41, 614–623.

    Article  Google Scholar 

  • Dechezlepretre, A., Martin, R., & Mohnen, M. (2013). Knowledge spillovers from clean and dirty technologies: A patent citations analysis. London: Mimeo, London School of Economics.

    Google Scholar 

  • Dechezlepretre, A., Martin, R., & Mohnen, M. (2014). Knowledge spillovers from clean and dirty technologies. CEP discussion paper, Centre for Economic Performance LSE.

  • Dejardin, M., & Fritsch, M. (2011). Entrepreneurial dynamics and regional growth. Small Business Economics, 36(4), 377–382.

    Article  Google Scholar 

  • Del Río, P. (2009). The empirical analysis of the determinants for environmental technological change: A research agenda. Ecological Economics, 68(3), 861–878.

    Article  Google Scholar 

  • Del Río, P., Peñasco, C., & Romero-Jordán, D. (2016). What drives eco-innovators? A critical review of the empirical literature based on econometric methods. Journal of Cleaner Production, 112, 2158–2170.

    Article  Google Scholar 

  • Evans, D. S., & Jovanovic, B. (1989). An Estimated Model of Entrepreneurial Choice under Liquidity Constraints. Journal of Political Economy, 97, 808–827.

    Article  Google Scholar 

  • Feldman, M. (2001). The entrepreneurial event revisited: Firm formation in regional context. Industrial and Corporate Change, 10, 861–891.

    Article  Google Scholar 

  • Florida, R. (1996). Lean and green: the move to environmentally conscious manufacturing. California Management Review, 39(1), 80–105.

    Article  Google Scholar 

  • Frenken, K., van Oort, F. G., & Verburg, T. (2007). Related variety, unrelated variety and regional economic growth. Regional Studies, 41, 685–697.

    Article  Google Scholar 

  • Fritsch, M. (1997). New firms and regional employment change. Small Business Economics, 9(5), 437–448.

    Article  Google Scholar 

  • Fritsch, M., & Schindele, Y. (2011). The contribution of new businesses to regional employment—An empirical analysis. Economic Geography, 87, 153–180.

    Article  Google Scholar 

  • Ghisetti, C., & Quatraro, F. (2013). Beyond the inducement in climate change: Do environmental performances spur environmental technologies? A regional analysis of cross-sectoral differences. Ecological Economics, 96, 99–113.

    Article  Google Scholar 

  • Ghisetti, C., & Quatraro, F. (2017). Green technologies and environmental productivity: A cross-sectoral analysis of direct and indirect effects in Italian regions. Ecological Economics, 132, 1–13.

    Article  Google Scholar 

  • Ghisetti, C., & Rennings, K. (2014). Environmental innovations and profitability: How does it pay to be green? An empirical analysis on the German innovation survey. Journal of Cleaner Production, 75, 106–117.

    Article  Google Scholar 

  • Ghisetti, C., Marzucchi, A., & Montresor, S. (2015). The open eco-innovation mode. An empirical investigation of eleven European countries. Research Policy, 44, 1080–1093.

    Article  Google Scholar 

  • Gilli, M., Mazzanti, M., & Mancinelli, S. (2014). Innovation complementarity and environmental productivity effects: Reality or delusion? Evidence from the EU. Ecological Economics, 103, 56–67.

    Article  Google Scholar 

  • Griliches, Z. (1990). Patent statistics as economic indicators: A survey. Journal of Economic Literature, 28, 1661–1707.

    Google Scholar 

  • Hall, B. H., Griliches, Z., & Hausman, J. A. (1986). Patents and R and D: Is there a lag? International Economic Review, 27, 265–283.

    Article  Google Scholar 

  • Harhoff, D. (1999). Firm formation and regional spillovers. Evidence from Germany. Economics of Innovation and New Technology, 8(1–2), 27–55.

    Article  Google Scholar 

  • Hart, S. (1997). Beyond greening: Strategies for a sustainable world. Harvard Business Review, 75, 66–76.

    Google Scholar 

  • Hartog, M., Boschma, R., & Sotarauta, M. (2012). The impact of related variety on regional employment growth in Finland 1993-2006: High-tech versus medium/lowtech. Industry and Innovation, 19(6), 459–476.

    Article  Google Scholar 

  • Hessels, J., & Van Stel, A. (2011). Entrepreneurship, export orientation, and economic growth. Small Business Economics, 37(2), 255–268.

    Article  Google Scholar 

  • Horbach, J., Oltra, V., Belin, J. (2013). Determinants and specificities of ecoinnovations – An econometric analysis for the French and German Industry based on the Community Innovation Survey. Industry and Innovation, 20(6), 523-543.

  • Johansson, E. (2000). Self-employment and liquidity constraints: evidence from Finland. The Scandinavian Journal of Economics, 102(1), 123–134.

    Article  Google Scholar 

  • Kirchhoff, B. A., Newbert, S. L., Hasan, I., & Armington, C. (2007). The influence of university R&D expenditures on new business formations and employment growth. Entrepreneurship: Theory and Practice, 31(4), 543–559.

    Google Scholar 

  • Lee, S. Y., Florida, R., & Acs, Z. (2004). Creativity and entrepreneurship: A regional analysis of new firm formation. Regional Studies, 38, 879–891.

    Article  Google Scholar 

  • Marin, G., & Mazzanti, M. (2013). The evolution of environmental and labor productivity dynamics. Journal of Evolutionary Economics, 23(2), 357–399.

    Article  Google Scholar 

  • Mazzanti, M., & Zoboli, R. (2009). Environmental efficiency and labour productivity: Trade-off or joint dynamics? Ecological Economics, 68(4), 1182–1194.

    Article  Google Scholar 

  • Meyskens, M., & Carsrud, A. L. (2013). Nascent green-technology ventures: A study assessing the role of partnership diversity in firm success. Small Business Economics, 40, 739–759.

    Article  Google Scholar 

  • Nesta, L. (2008). Knowledge and productivity in the world’s largest manufacturing corporations. Journal of Economic Behavior and Organization, 67, 886–902.

    Article  Google Scholar 

  • Nesta, L., & Saviotti, P. P. (2006). Firm knowledge and market value in biotechnology. Industrial and Corporate Change, 15, 625–652.

    Article  Google Scholar 

  • Oltra, V., & Saint-Jean, M. (2005a). The dynamics of environmental innovations: Three stylised trajectories of clean technology. Economics of Innovation and New Technology, 14, 189–212.

    Article  Google Scholar 

  • Oltra, V., & Saint-Jean, M. (2005b). Environmental innovation and clean technology: An evolutionary framework. International Journal of Sustainable Development, 8, 153–172.

    Article  Google Scholar 

  • Orlitzky, M., Siegel, D. S., & Waldman, D. A. (2011). Strategic corporate responsibility and environmental sustainability. Business and Society, 50(1), 6–27.

    Article  Google Scholar 

  • Pavitt, K. (1985). Patent statistics as indicators of innovative activities: possibilities and problems. Scientometrics, 7, 77–99.

    Article  Google Scholar 

  • Popp, D., & Newell, R. (2012). Where does energy R&D come from? Examining crowding out from energy R&D. Energy Economics, 34(4), 980–991.

    Article  Google Scholar 

  • Porter, M. E., & Van der Linde, C. (1995). Toward a new conception of the environment competitiveness relationship. The Journal of Economic Perspectives, 9(4), 97–118.

    Article  Google Scholar 

  • Quatraro, F. (2010). Knowledge coherence, variety and productivity growth: Manufacturing evidence from Italian regions. Research Policy, 39, 1289–1302.

    Article  Google Scholar 

  • Quatraro, F., & Vivarelli, M. (2015). Drivers of entrepreneurship and post-entry performance of newborn firms in developing countries. World Bank Research Observer, 30, 277–305.

    Article  Google Scholar 

  • Renning, K., & Rammer, C. (2011). The impact of regulation-driven environmental innovation on innovation success and firm performance. Industry and Innovation, 18(3), 253–283.

    Google Scholar 

  • Rennings, K., & Rammer, C. (2009). Increasing energy and resource efficiency through innovation—An explorative analysis using innovation survey data. Czech Journal of Economics and Finance (CJEF), 59, 442–459.

    Google Scholar 

  • Rennings, K., & Rexhäuser, S. (2011). Long-term impacts of environmental policy and eco-innovative activities of firms.International. Journal of Technology Policy and Management, 11(3), 274–290.

    Google Scholar 

  • Reynolds, P., Storey, D. J., & Westhead, P. (1994). Cross-national comparisons of the variation in new firm formation rates. Regional Studies, 28(4), 443–456.

    Article  Google Scholar 

  • Rosenberg, N. (1996). Uncertainty and technological change. In R. Landau, R. Taylor, & G. Wright (Eds.), The mosaic of economic growth. Stanford: Stanford University Press.

    Google Scholar 

  • Stiglitz, J., & Weiss, A. (1981). Credit rationing in markets with imperfect information. The American Economic Review, 71, 393–410.

    Google Scholar 

  • Vivarelli, M. (2013). Is entrepreneurship necessarily good? Microeconomic evidence from developed and developing countries. Industrial and Corporate Change, 22(6), 1453–1495.

    Article  Google Scholar 

  • Wennekers, A. R. M., & Thurik, A. R. (1999). Linking entrepreneurship and economic growth. Small Business Economics, 13, 27–55.

    Article  Google Scholar 

  • Woodward, D., Figueiredo, O., & Guimaraes, P. (2006). Beyond the Silicon Valley: University R&D and high technology location. Journal of Urban Economics, 60(1), 15–32.

    Article  Google Scholar 

  • Zeppini, P., & van den Bergh, J. C. J. M. (2011). Competing recombinant technologies for environmental innovation: Extending Arthur’s model of lock-in. Indudstry and Innovation, 18(3), 317–334.

    Article  Google Scholar 

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Authors and Affiliations

  1. DIGEP, Polytechnic of Turin, Turin, Italy

    Alessandra Colombelli

  2. BRICK, Collegio Carlo Alberto, Moncalieri, Italy

    Alessandra Colombelli & Francesco Quatraro

  3. GREDEG-CNRS, Université Cote d’Azur, Nice, France

    Alessandra Colombelli & Francesco Quatraro

  4. Department of Economics and Statistics Cognetti de Martiis, University of Turin, Turin, Italy

    Francesco Quatraro

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Colombelli, A., Quatraro, F. Green start-ups and local knowledge spillovers from clean and dirty technologies. Small Bus Econ 52, 773–792 (2019). https://doi.org/10.1007/s11187-017-9934-y

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