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Factors of Renewable Energy Consumption in the European Countries—The Bayesian Averaging Classical Estimates Approach

Authors: Atif Maqbool Khan; Jacek Kwiatkowski; Magdalena Osińska; Marcin Błażejowski;

Factors of Renewable Energy Consumption in the European Countries—The Bayesian Averaging Classical Estimates Approach

Abstract

The paper aims to identify the most likely factors that determine the demand for energy consumption from renewable sources (renewable energy consumption—REC) in European countries. Although in Europe, a high environmental awareness is omnipresent, countries differ in scope and share of REC due to historical energetic policies and dependencies, investments into renewable and traditional energetic sectors, R&D development, structural changes required by energetic policy change, and many other factors. The study refers to a set of macroeconomic, institutional, and social factors affecting energetic renewable policy and REC in selected European countries in two points of time: i.e., before and after the Paris Agreement. The Bayesian Average Classical Estimates (BACE) is applied to indicate the most likely factors affecting REC in 2015 and 2018. The comparison of the results reveals that the Gross Domestic Product (GDP) level, nuclear and hydro energy consumption were the determinants significant in both analyzed years. Furthermore, it became clear that in 2015, the REC depended strongly on the energy consumption structure, while in 2018, the foreign direct investment and trade openness played their role in increasing renewable energy consumption. The direction of changes is gradual and positive. It complies with the Sustainable Development Goals (SDGs).

Keywords

institutional and social factors, Technology, economic, energy from renewable sources; economic; institutional and social factors; Bayesian Average Classical Estimates (BACE); Paris Agreement, T, energy from renewable sources, economics, Paris Agreement, Bayesian Average Classical Estimates (BACE)

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    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).
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    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.
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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).
BIP!Citations provided by BIP!
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.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
8
Top 10%
Top 10%
Top 10%
Green
gold