Energy as the new frontier: Dynamic panel data analysis revealing energy's transformative role in economic growth and technological progress

Marinko Skare, Ilhan Ozturk, Małgorzata Porada-Rochoń and Sasa Stjepanovic

Technological Forecasting and Social Change, 2024, vol. 200, issue C

Abstract: This research presents a novel exploration of the Cobb–Douglas production model by integrating energy as a critical input alongside labor and capital, a relatively underexplored dimension in the context of real GDP analysis. Through a comprehensive study of over 5500 observations across 126 countries, we employed diverse, state-of-the-art econometric techniques to reveal that energy plays a significant and consistently positive role in driving economic growth, alongside the traditional factors of labor and capital. This finding was consistently robust across various model specifications in both static and dynamic frameworks (heterogeneous production function). Incorporating energy as a key variable in the Cobb–Douglas framework marks a substantial contribution to the model's application to contemporary global energy consumption and sustainability challenges. This study not only reinforces the traditional understanding of economic growth drivers but also underscores the pivotal role of energy, offering new insights for policy formulation aimed at sustainable development. Our research enriches the discourse in the field of technological forecasting and social change, providing empirical evidence that highlights the integral role of energy in economic production processes and its broader implications in shaping future economic trajectories.

Keywords: Technological progress; Dynamic panel data analysis; Energy-efficiency and economic growth; Sustainable development; Augmented Cobb-Douglas function; Forecasting economic trends (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:200:y:2024:i:c:s0040162523008600

DOI: 10.1016/j.techfore.2023.123175

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