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Is firm-level clean or dirty innovation valued more?

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  • Antoine Dechezleprêtre
  • Cal B. Muckley
  • Parvati Neelakantan

Abstract

We examine how Tobin's Q is linked to ‘clean’ and ‘dirty’ innovation and innovation efficiency at the firm level. Clean innovation relates to patented technologies in areas such as renewable energy generation and electric cars, whereas dirty innovation relates to fossil-based energy generation and combustion engines. We use a global patent data set, covering over 15,000 firms across 12 countries. We find strong and robust evidence that the stock market recognizes the value of clean innovation and innovation efficiency and accords higher valuations to those firms that engage in successful clean research and development activities. The results are substantively invariant across innovation measurement, model specifications, estimators adopted, select sub-samples of firms and United States and European patent offices.

Suggested Citation

  • Antoine Dechezleprêtre & Cal B. Muckley & Parvati Neelakantan, 2021. "Is firm-level clean or dirty innovation valued more?," The European Journal of Finance, Taylor & Francis Journals, vol. 27(1-2), pages 31-61, January.
    • Handle: RePEc:taf:eurjfi:v:27:y:2021:i:1-2:p:31-61
    DOI: 10.1080/1351847X.2020.1785520
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    Cited by:

    1. Fischer, Carolyn & Hübler, Michael & Schenker, Oliver, 2021. "More birds than stones – A framework for second-best energy and climate policy adjustments," Journal of Public Economics, Elsevier, vol. 203(C).
    2. Luca Di Simone & Barbara Petracci & Mariacristina Piva, 2022. "Economic Sustainability, Innovation, and the ESG Factors: An Empirical Investigation," Sustainability, MDPI, vol. 14(4), pages 1-16, February.
    3. Jiao, Anqi & Lu, Juntai & Ren, Honglin & Wei, Jia, 2024. "The role of AI capabilities in environmental management: Evidence from USA firms," Energy Economics, Elsevier, vol. 134(C).
    4. Yang, Yong-cong & Nie, Pu-yan, 2022. "Subsidy for clean innovation considered technological spillover," Technological Forecasting and Social Change, Elsevier, vol. 184(C).

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