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Energy consumption as an indicator of energy efficiency and emissions in the European Union: A GMM based quantile regression approach

Abstract Energy efficiency can boost economic growth and reduce greenhouse gas (GHG) emissions. However, the global rate of energy efficiency improvement is slowing – a trend that has implications for consumers, businesses and the environment as the adverse climate change and pollution impacts deepen. The emissions in the EU have decreased by 17% in the past decade while member countries such as Germany, United Kingdom and Poland were among the largest emitters of CO2. This paper examines the effect of energy efficiency, proxied by final energy consumption, on CO2 emissions in the EU using a generalized methods of moments based on a quantile regression approach. We use the Panel Quantile ARDL for inference at various quantiles and support the ARDL estimations by the 2SLS estimations for robustness checks. We use the final energy consumption data as an indicator of energy efficiency as included in the energy efficiency indicators database of the International Energy Agency and emissions for the time period spanning 1980–2018. The results indicate that energy efficiency has reduced emissions by 8.6% on average in all the quantiles. Improving efficiency in petroleum consumption will enable the EU to reduce emissions as well as improve energy security by reducing imports as per the aims of the European Green Deal and the Efficiency First Principle. We also recommend that energy efficiency policies should account for possible rebound effects, which can limit their effectiveness.
- University of Wollongong Australia
- University of Tasmania Australia
- Copenhagen Business School Denmark
- University of Wollongong Australia
- University of Tasmania Australia
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).34 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 1%
