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Econometrics of Anthropogenic Emissions, Green Energy-Based Innovations, and Energy Intensity across OECD Countries

Author

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  • Samuel Asumadu Sarkodie

    (Nord University Business School (HHN), Post Box 1490, 8049 Bodø, Norway)

  • Ahdi Noomen Ajmi

    (Department of Business Administration, College of Science and Humanities in Slayel, Prince Sattam bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia
    ESC de Tunis, Manouba University, Manouba 2010, Tunisia)

  • Festus Fatai Adedoyin

    (Department of Computing and Informatics, Bournemouth University, Poole BH12 5BB, UK)

  • Phebe Asantewaa Owusu

    (Nord University Business School (HHN), Post Box 1490, 8049 Bodø, Norway)

Abstract

The increasing global attention on climate change underscores the importance of alternative energy technologies with emission reduction effects. However, there are several caveats of economic productivity and environmental sustainability tradeoffs that require empirical consideration—owing to long-term effects on climate change. Here, we examine the relationship between emissions, green energy-based innovations, and energy research and development across energy-intensive OECD countries while accounting for industrial structure dynamics. We utilize several novel time series and panel estimation techniques including time-varying causality, defactored instrumental variable-based homogeneous, and heterogeneous slope dynamics that control for unobserved common factors. Our empirical assessment emphasizes the significance of energy research and development in expanding green energy innovations while reducing long-term emissions. Conversely, continual dependence on obsolete energy research and development may worsen environmental sustainability. However, the inclusion of green energy technologies offset environmental pollution without compromising economic productivity. Besides, the mitigation effect of energy research and development is channeled through a decline in energy intensity and technological advancement. We show that green energy-based innovations and energy research and development play a critical role in achieving environmental sustainability in OECD countries.

Suggested Citation

  • Samuel Asumadu Sarkodie & Ahdi Noomen Ajmi & Festus Fatai Adedoyin & Phebe Asantewaa Owusu, 2021. "Econometrics of Anthropogenic Emissions, Green Energy-Based Innovations, and Energy Intensity across OECD Countries," Sustainability, MDPI, vol. 13(8), pages 1-18, April.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:8:p:4118-:d:531678
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    References listed on IDEAS

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    1. Stephen H. Schneider & Lawrence H. Goulder, 1997. "Achieving low-cost emissions targets," Nature, Nature, vol. 389(6646), pages 13-14, September.
    2. Wurlod, Jules-Daniel & Noailly, Joëlle, 2018. "The impact of green innovation on energy intensity: An empirical analysis for 14 industrial sectors in OECD countries," Energy Economics, Elsevier, vol. 71(C), pages 47-61.
    3. Shuping Shi & Peter C. B. Phillips & Stan Hurn, 2018. "Change Detection and the Causal Impact of the Yield Curve," Journal of Time Series Analysis, Wiley Blackwell, vol. 39(6), pages 966-987, November.
    4. Alexander Chudik & M. Hashem Pesaran, 2013. "Large Panel Data Models with Cross-Sectional Dependence: A Survey," CESifo Working Paper Series 4371, CESifo.
    5. John C. Driscoll & Aart C. Kraay, 1998. "Consistent Covariance Matrix Estimation With Spatially Dependent Panel Data," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 549-560, November.
    6. Peter C. B. Phillips & Shuping Shi & Jun Yu, 2015. "Testing For Multiple Bubbles: Limit Theory Of Real‐Time Detectors," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 56, pages 1079-1134, November.
    7. Sebastian Kripfganz & Vasilis Sarafidis, 2021. "Instrumental-variable estimation of large-T panel-data models with common factors," Stata Journal, StataCorp LP, vol. 21(3), pages 659-686, September.
    8. Ana Sofía Aron & Oswaldo Molina, 2019. "Green Innovation in Natural Resource Industries: The case of Local Suppliers in the Peruvian Mining Industry," Working Papers 145, Peruvian Economic Association.
    9. Shahbaz, Muhammad & Raghutla, Chandrashekar & Song, Malin & Zameer, Hashim & Jiao, Zhilun, 2020. "Public-private partnerships investment in energy as new determinant of CO2 emissions: The role of technological innovations in China," Energy Economics, Elsevier, vol. 86(C).
    10. Toda, Hiro Y. & Yamamoto, Taku, 1995. "Statistical inference in vector autoregressions with possibly integrated processes," Journal of Econometrics, Elsevier, vol. 66(1-2), pages 225-250.
    11. Chakraborty, Saptorshee Kanto & Mazzanti, Massimiliano, 2020. "Energy intensity and green energy innovation: Checking heterogeneous country effects in the OECD," Structural Change and Economic Dynamics, Elsevier, vol. 52(C), pages 328-343.
    12. Jordaan, Sarah M. & Romo-Rabago, Elizabeth & McLeary, Romaine & Reidy, Luke & Nazari, Jamal & Herremans, Irene M., 2017. "The role of energy technology innovation in reducing greenhouse gas emissions: A case study of Canada," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 1397-1409.
    13. Kong, Dongmin & Yang, Xiandong & Xu, Jian, 2020. "Energy price and cost induced innovation: Evidence from China," Energy, Elsevier, vol. 192(C).
    14. Zhang, Jian & Zhang, Wei & Song, Qi & Li, Xin & Ye, Xuanting & Liu, Yu & Xue, Yawei, 2020. "Can energy saving policies drive firm innovation behaviors? - Evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 154(C).
    15. Westerlund, Joakim & Urbain, Jean-Pierre, 2015. "Cross-sectional averages versus principal components," Journal of Econometrics, Elsevier, vol. 185(2), pages 372-377.
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    3. Adrian Ioan Felea & Ioan Felea & Calin Radu Hoble, 2023. "Multicriteria Quantification of the Compatibility of the Targets from Romania’s Relevant Strategies with the European Green Deal," Sustainability, MDPI, vol. 15(18), pages 1-14, September.
    4. Li, Yaya & Cobbinah, Joana & Abban, Olivier Joseph & Veglianti, Eleonora, 2023. "Does green manufacturing technology innovation decrease energy intensity for sustainable development?," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 1010-1025.
    5. Wei Liu & Youfa Sun & Serhat Yüksel & Hasan Dinçer, 2021. "Consensus-based multidimensional due diligence of fintech-enhanced green energy investment projects," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-31, December.

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