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Renewable energy innovations in Europe: a dynamic panel data approach

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  • Nadia Ayari
  • Szabolcs Blazsek
  • Pedro Mendi

Abstract

We investigate the determinants of renewable energy R&D intensity and the impact of renewable energy innovations on firm performance, using several dynamic panel data models. We estimate these models using a large data set of European firms from 19 different countries, with some patenting activity in areas related to renewable energies during the 1987 to 2007 period. Our results confirm our priors on the determinants of the rapid development of renewable energy R&D intensity during the past decades. Additionally, we find evidence that renewable patent intensity has a significant dynamic impact on the stock market value of firms.

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  • Nadia Ayari & Szabolcs Blazsek & Pedro Mendi, 2012. "Renewable energy innovations in Europe: a dynamic panel data approach," Applied Economics, Taylor & Francis Journals, vol. 44(24), pages 3135-3147, August.
    • Handle: RePEc:taf:applec:v:44:y:2012:i:24:p:3135-3147
    DOI: 10.1080/00036846.2011.570720
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    1. Nadia Ayari & Szabolcs Blazsek & Pedro Mendi, 2012. "Renewable energy innovations in Europe: a dynamic panel data approach," Applied Economics, Taylor & Francis Journals, vol. 44(24), pages 3135-3147, August.
    2. Bongsuk Sung & Myung-Bae Yeom & Hong-Gi Kim, 2017. "Eco-Efficiency of Government Policy and Exports in the Bioenergy Technology Market," Sustainability, MDPI, vol. 9(9), pages 1-18, September.
    3. Bruns, Stephan B. & Kalthaus, Martin, 2020. "Flexibility in the selection of patent counts: Implications for p-hacking and evidence-based policymaking," Research Policy, Elsevier, vol. 49(1).
    4. Mai Miyamoto & Kenji Takeuchi, 2018. "Explaining Trade Flows in Renewable Energy Products: The Role of Technological Development," Discussion Papers 1819, Graduate School of Economics, Kobe University.
    5. Modhurima Dey Amin & Syed Badruddoza & Jill J. McCluskey, 2021. "Does conventional energy pricing induce innovation in renewable energy? New evidence from a nonlinear approach," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 43(2), pages 659-679, June.
    6. Wang, Qiang & Li, Shuyu & Pisarenko, Zhanna, 2020. "Heterogeneous effects of energy efficiency, oil price, environmental pressure, R&D investment, and policy on renewable energy -- evidence from the G20 countries," Energy, Elsevier, vol. 209(C).
    7. Zastempowski, Maciej, 2023. "Analysis and modeling of innovation factors to replace fossil fuels with renewable energy sources - Evidence from European Union enterprises," Renewable and Sustainable Energy Reviews, Elsevier, vol. 178(C).
    8. Su, Hsin-Ning & Moaniba, Igam M., 2017. "Does innovation respond to climate change? Empirical evidence from patents and greenhouse gas emissions," Technological Forecasting and Social Change, Elsevier, vol. 122(C), pages 49-62.
    9. Kruse, Juergen, 2016. "Innovation in Green Energy Technologies and the Economic Performance of Firms," EWI Working Papers 2016-2, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).

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    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies

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