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Explanatory analysis of the key factors in an energy sustainability index

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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.

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Notes

  1. Furthermore, composite indices have been criticized because, in a way, they re-introduce unidimensionality. For the pros and cons of using CIs see, e.g., Nardo et al. (2005) and OECD (2008).

  2. Exergy is a thermodynamic property that links the first and second principles of thermodynamics, besides connecting a system under study with the environment where it belongs.

  3. PCA is usually employed in an explanatory data analysis framework as well as for developing predictive models and a wide range of applications concerned with both economic, social and environmental aspects.

  4. Variance inflation factors (VIF) measure how much the variance of the estimated regression coefficients are inflated as compared to when the predictor variables are not linearly related. It describes how much multicollinearity (correlation between predictors) exists in a regression analysis. Multicollinearity is problematic because it can increase the variance of the regression coefficients, making them unstable and difficult to interpret. We use the following guidelines to interpret the VIF: VIF = 1, Not correlated; 1 < VIF < 5, Moderately correlated; VIF > 5–10, Highly correlated.

  5. HDI is a development indicator developed by the Pakistani economist Mahbubul Haq, based on the ideas of Amartya Sen. It has been used alongside GDP by the United Nations since 1993 to assess the quality of life in member countries. The HDI takes account of three elements in particular: the level of health, educational level, and per capita GDP.

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Authors and Affiliations

  1. Department of Economic and Legal Studies, University of Naples “Parthenope”, Naples, Italy

    Massimiliano Agovino & Antonio Garofalo

  2. Department of Management and Quantitative Studies, University of Naples “Parthenope”, Naples, Italy

    Antonio Angelo Romano & Giuseppe Scandurra

Authors
  1. Massimiliano Agovino
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  2. Antonio Garofalo
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  3. Antonio Angelo Romano
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  4. Giuseppe Scandurra
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Correspondence to Massimiliano Agovino.

Appendices

Appendix A

See Table 7.

Table 7 Proportion of the total unit variance of the indicator which is explained by the factor (squared factor loadings scaled to unity sum)
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Appendix B

See Table 8.

Table 8 Energy sustainability index
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Agovino, M., Garofalo, A., Romano, A.A. et al. Explanatory analysis of the key factors in an energy sustainability index. Qual Quant 52, 2597–2632 (2018). https://doi.org/10.1007/s11135-017-0679-0

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