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Consumption-based and territory-based carbon emissions intensity: Determinants and forecasting using club convergence across countries

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  • Bhattacharya, Mita
  • Inekwe, John N.
  • Sadorsky, Perry

Abstract

Understanding the dynamics of carbon emissions across time and space is important when formulating energy policies to minimise climate changes in the future. If per capita carbon emissions (or carbon intensity) converge over time, then any negotiation of multilateral agreements will be easier than if convergence is absent. It is also possible for club convergence to exist where countries within a club converge but countries between clubs do not converge. We examine the convergence of consumption-based and territory-based carbon emissions intensity across 70 countries. We find two convergent clubs for consumption-based emissions and three convergent clubs for territory-based emissions. Increases in each of total factor productivity, renewable energy consumption and urbanisation increase the odds of belonging to a low carbon emissions intensity club. An increase in industry value added reduces the odds of belonging to a low carbon intensity club. Increases in total factor productivity can be obtained through effective macroeconomic policy, while increasing renewable energy consumption may require structural changes in economies focused on transitioning to a low carbon economy. Under a business as usual forecasting scenario the number of consumption-based carbon emissions intensity clubs increases between 2014 and 2030 making it more difficult to negotiate multilateral agreements on climate change in the future.

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  • Bhattacharya, Mita & Inekwe, John N. & Sadorsky, Perry, 2020. "Consumption-based and territory-based carbon emissions intensity: Determinants and forecasting using club convergence across countries," Energy Economics, Elsevier, vol. 86(C).
  • Handle: RePEc:eee:eneeco:v:86:y:2020:i:c:s0140988319304293
    DOI: 10.1016/j.eneco.2019.104632
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    More about this item

    Keywords

    Carbon emissions; Club convergence; Renewable energy; Total factor productivity;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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