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Renewable Energy Innovations in Europe: A Dynamic Panel Data Approach

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  • Nadia Ayari

    (Facultad de Ciencias Económicas y Empresariales, Universidad de Navarra)

  • Szabolcs Blazsek

    (Facultad de Ciencias Económicas y Empresariales, Universidad de Navarra)

  • Pedro Mendi

    (Facultad de Ciencias Económicas y Empresariales, Universidad de Navarra)

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 dataset of European firms of 19 different countries, with some patenting activity in areas related with renewable energies during the 1987-2007 period. The results that we obtain confirm our a prioris on the determinants of the rapid development of renewable energy R&D intensity during the last decades. Additionally, we find evidence that renewable patent intensity has significant dynamic impact on the stock market value of firms.

Suggested Citation

  • Nadia Ayari & Szabolcs Blazsek & Pedro Mendi, 2009. "Renewable Energy Innovations in Europe: A Dynamic Panel Data Approach," Faculty Working Papers 11/09, School of Economics and Business Administration, University of Navarra.
  • Handle: RePEc:una:unccee:wp1109
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    Cited by:

    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. 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.
    3. 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).
    4. 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).
    5. 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.
    6. 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).
    7. 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.
    8. 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).
    9. 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.

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    More about this item

    Keywords

    R&D intensity; Stock market value; Patents; Count data; Dynamic panel data;
    All these keywords.

    JEL classification:

    • 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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