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Energy in Economic Growth: Is Faster Growth Greener?

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  • Gregor Semieniuk

    (Department of Economics, SOAS University of London, UK)

Abstract

An influential theoretical hypothesis holds that if aggregate productivity growth accelerates, then so does the decline in energy intensity. Whether faster growth is greener in this sense is crucial for modeling future growth and climate change mitigation, but empirical evidence is lacking. This paper characterizes the global, long-run historical relationship between changes in energy intensity and labor productivity growth rates. Basing estimates on an unbalanced panel of 180 countries for the period 1950-2014 and the world as a whole, it captures a significantly larger historical window than previous studies. The paper finds a stylized fact whereby the rate at which energy intensity changes is constant or even increases as labor productivity accelerates. Faster growth is not greener. This provides important new information for calibrating integrated assessment models, many of which make a green growth assumption in near term projections.

Suggested Citation

  • Gregor Semieniuk, 2018. "Energy in Economic Growth: Is Faster Growth Greener?," Working Papers 208, Department of Economics, SOAS University of London, UK.
    • Handle: RePEc:soa:wpaper:208
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    Citations

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    Cited by:

    1. C. Seri & A. de Juan Fernandez, 2021. "The relationship between economic growth and environment. Testing the EKC hypothesis for Latin American countries," Papers 2105.11405, arXiv.org.
    2. C. Seri & A. de Juan Fernández, 2023. "CO2 emissions and income growth in Latin America: long-term patterns and determinants," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(5), pages 4491-4524, May.
    3. Deleidi, Matteo & Mazzucato, Mariana & Semieniuk, Gregor, 2020. "Neither crowding in nor out: Public direct investment mobilising private investment into renewable electricity projects," Energy Policy, Elsevier, vol. 140(C).
    4. Lance Taylor & Özlem Ömer, 2019. "Race to the Bottom: Low Productivity, Market Power, and Lagging Wages," International Journal of Political Economy, Taylor & Francis Journals, vol. 48(1), pages 1-20, January.
    5. Richters, Oliver & Siemoneit, Andreas, 2019. "Growth imperatives: Substantiating a contested concept," Structural Change and Economic Dynamics, Elsevier, vol. 51(C), pages 126-137.
    6. Gregor Semieniuk & Isabella M. Weber, 2019. "Inequality in Energy Consumption: Statistical Equilibrium or a Question of Accounting Conventions?," Working Papers 228, Department of Economics, SOAS University of London, UK.
    7. Hannesson, Rögnvaldur, 2018. "CO2 intensity and GDP per capita," Discussion Papers 2018/16, Norwegian School of Economics, Department of Business and Management Science.
    8. Savona, Maria & Ciarli, Tommaso, 2019. "Structural Changes and Sustainability. A Selected Review of the Empirical Evidence," Ecological Economics, Elsevier, vol. 159(C), pages 244-260.
    9. Oliver Richters & Andreas Siemoneit, 2018. "The contested concept of growth imperatives: Technology and the fear of stagnation," Working Papers V-414-18, University of Oldenburg, Department of Economics, revised Nov 2018.

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    More about this item

    Keywords

    energy intensity; labor productivity; decoupling; green growth; stylized fact;
    All these keywords.

    JEL classification:

    • O44 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Environment and Growth
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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