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Explanatory analysis of the key factors in an energy sustainability index

Massimiliano Agovino, Antonio Garofalo (), Antonio Angelo Romano () and Giuseppe Scandurra ()
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Antonio Garofalo: University of Naples “Parthenope”
Antonio Angelo Romano: University of Naples “Parthenope”
Giuseppe Scandurra: University of Naples “Parthenope”

Quality & Quantity: International Journal of Methodology, 2018, vol. 52, issue 6, No 11, 2597-2632

Abstract: Abstract The need to reduce the emissions of greenhouse gases in energy generation has led to international and national bodies recommending and encouraging a series of policies and measures to boost so-called green energy generation and achieve sustainability in the energy sector. In order to assess the energy sustainability path of countries worldwide, we first propose a dynamic analysis of an energy sustainability index (ESI) based on the methodology of composite indicators built on a large dataset of 136 heterogeneous countries and 38 variables observed in 3 years (2000, 2005 and 2011). Second, to isolate the influence of socio-demographic and economic characteristics on the energy sustainability of countries, we perform quantile regression with clustered data. Our findings demonstrate that major changes are required in developing countries, where development is pursued mainly through the use of energy sources based on fossil fuels. To ensure access to sustainable energy, policy makers need to consider and improve the key factors according to the ESI’s quantiles. In particular, human development variables (i.e., life expectancy and the percentage of completed secondary schooling attained in the population) and the variables related to civil rights and institutions (e.g., percentage of women in national parliaments) are crucial in promoting energy sustainability. By contrast, high unemployment rates, high congestion costs and the continued use of highly polluting energy sources have the effect of reducing energy sustainability.

Keywords: Composite indicators; Quantile regression; Energy sustainability; Principal component analysis (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s11135-017-0679-0

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