Abstract
In the last two decades, there has been a rich debate about the environmental degradation that results from exposure to solid urban waste. Growing public concern with environmental issues has led to the implementation of various strategic plans for waste management in several developed countries, especially in the European Union. In this paper, the relationships were assessed between economic growth, renewable energy extraction and greenhouse gas (GHG) emissions in the waste sector. The Environmental Kuznets Curve hypothesis was analysed for the member states of the European Union, in the presence of electricity generation, landfill and GHG emissions for the period 1995 to 2012. The results revealed that there is no inverted-U-shaped relationship between income and GHG emissions in European Union countries. The renewable fuel extracted from waste contributes to a reduction in GHG, and although the electricity produced also increases emissions somewhat, they would be far greater if the waste-based generation of renewable energy did not take place. The waste sector needs to strengthen its political, economic, institutional and social communication instruments to meet its aims for mitigating the levels of pollutants generated by European economies. To achieve the objectives of the Horizon 2020 programme, currently in force in the countries of the European Union, it will be necessary to increase the share of renewable energy in the energy mix.
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Notes
This refers to the recycling of both organic and non-organic materials (EUROSTAT 2014).
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Acknowledgements
The financial support of the Capes Foundation of the Ministry of Education of Brazil, and of the NECE-UBI, Research Unit in Business Science and Economics, sponsored by the Portuguese Foundation for the Development of Science and Technology, project UID/GES/04630/2013, are gratefully acknowledged.
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Domingos, H.A., De Melo Faria, A.M., Fuinhas, J.A. et al. Renewable energy and greenhouse gas emissions from the waste sectors of European Union member states: a panel data analysis. Environ Sci Pollut Res 24, 18770–18781 (2017). https://doi.org/10.1007/s11356-017-9324-7
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DOI: https://doi.org/10.1007/s11356-017-9324-7