Does green innovation crowd out other innovation of firms? - based on the extended CDM model and unconditional quantile regressions

Richard Tol and Yi Yiang
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Yi Yiang: Department of Economics, University of Sussex, B91 NSL Falmer, United Kingdom
Authors registered in the RePEc Author Service: Yi Jiang

Working Paper Series from Department of Economics, University of Sussex Business School

Abstract: In the era of sustainability, firms grapple with the decision of how much to invest in green innovation and how it influences their economic trajectory. This study employs the Crepon, Duguet, and Mairesse (CDM) framework to examine the conversion of R&D funds into patents and their impact on productivity, effectively addressing endogeneity by utilizing predicted dependent variables at each stage to exclude unobservable factors. Extending the classical CDM model, this study contrasts green and non-green innovations' economic effects. The results show non-green patents predominantly drive productivity gains, while green patents have a limited impact in non-heavy polluting firms. However, in high-pollution and manufacturing sectors, both innovation types equally enhance productivity. Using unconditional quantile regression, I found green innovation's productivity impact follows an inverse U-shape, unlike the U-shaped pattern of non-green innovation. Significantly, in the 50th to 80th productivity percentiles of manufacturing and high-pollution firms, green innovation not only contributes to environmental sustainability but also outperforms non-green innovation economically.

Keywords: Green innovation; Crowding-out effects; Productivity; CDM framework; Quantile regression; Recentered influence function (search for similar items in EconPapers)
JEL-codes: D24 O31 Q55 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cse, nep-ene, nep-env, nep-ino, nep-sbm and nep-tid
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Working Paper: Does green innovation crowd out other innovation of firms? Based on the extended CDM model and unconditional quantile regressions (2024)
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