Greenhouse Gas Emissions Forecasts in Countries of the European Union by Means of a Multifactor Algorithm

A novel multifactor algorithm is developed with the aim of estimating GHG emissions in the EU countries and forecasting different future scenarios. This is created starting from (1) GDP, (2) population and (3) renewable energy share (RES). The determination coefficient (R<sup<2</sup<) of...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Antonio Marotta [verfasserIn] César Porras-Amores [verfasserIn] Antonio Rodríguez Sánchez [verfasserIn] Paola Villoria Sáez [verfasserIn] Gabriele Masera [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	European Union sustainable development greenhouse gas emissions carbon neutrality gross domestic product renewable energy

Übergeordnetes Werk:	In: Applied Sciences - MDPI AG, 2012, 13(2023), 14, p 8520
Übergeordnetes Werk:	volume:13 ; year:2023 ; number:14, p 8520

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/app13148520

Katalog-ID:	DOAJ093949197

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callnumber-first	T - Technology
author	Antonio Marotta
spellingShingle	Antonio Marotta misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc European Union misc sustainable development misc greenhouse gas emissions misc carbon neutrality misc gross domestic product misc renewable energy misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry Greenhouse Gas Emissions Forecasts in Countries of the European Union by Means of a Multifactor Algorithm
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topic_title	TA1-2040 QH301-705.5 QC1-999 QD1-999 Greenhouse Gas Emissions Forecasts in Countries of the European Union by Means of a Multifactor Algorithm European Union sustainable development greenhouse gas emissions carbon neutrality gross domestic product renewable energy
topic	misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc European Union misc sustainable development misc greenhouse gas emissions misc carbon neutrality misc gross domestic product misc renewable energy misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry
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title	Greenhouse Gas Emissions Forecasts in Countries of the European Union by Means of a Multifactor Algorithm
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title_sort	greenhouse gas emissions forecasts in countries of the european union by means of a multifactor algorithm
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title_auth	Greenhouse Gas Emissions Forecasts in Countries of the European Union by Means of a Multifactor Algorithm
abstract	A novel multifactor algorithm is developed with the aim of estimating GHG emissions in the EU countries and forecasting different future scenarios. This is created starting from (1) GDP, (2) population and (3) renewable energy share (RES). The determination coefficient (R<sup<2</sup<) of the multiple regression adopted reaches a value of 0.96; thus, only 4% of the GHG variation cannot be explained by the combination of the three variables. Germany is removed from the model after analysing the statistical outliers, as it presents an unusual behaviour within the European context. Also, France, Italy and Ireland are removed in the forecast analysis since they are characterised by corrected weighting values above the threshold value of the algorithm (0.156). The results show that GHG emissions decrease 14% in a low-growth-rate scenario, increase 24% in an average-growth scenario and increase 104% in a high-growth-rate scenario. Countries that improve the most are the ones that are currently underdeveloped in RES and are expected to decrease their population in the future (Croatia, Latvia, Cyprus and Greece). Other countries currently well positioned but with expected population growth (Sweden, Luxemburg and Denmark) or with expected intense GDP growth (Estonia and Malta) may lack decarbonisation levers. Therefore, policy makers should introduce additional subsidy schemes and tax exemptions in both developed and less developed countries to meet EU decarbonisation targets.
abstractGer	A novel multifactor algorithm is developed with the aim of estimating GHG emissions in the EU countries and forecasting different future scenarios. This is created starting from (1) GDP, (2) population and (3) renewable energy share (RES). The determination coefficient (R<sup<2</sup<) of the multiple regression adopted reaches a value of 0.96; thus, only 4% of the GHG variation cannot be explained by the combination of the three variables. Germany is removed from the model after analysing the statistical outliers, as it presents an unusual behaviour within the European context. Also, France, Italy and Ireland are removed in the forecast analysis since they are characterised by corrected weighting values above the threshold value of the algorithm (0.156). The results show that GHG emissions decrease 14% in a low-growth-rate scenario, increase 24% in an average-growth scenario and increase 104% in a high-growth-rate scenario. Countries that improve the most are the ones that are currently underdeveloped in RES and are expected to decrease their population in the future (Croatia, Latvia, Cyprus and Greece). Other countries currently well positioned but with expected population growth (Sweden, Luxemburg and Denmark) or with expected intense GDP growth (Estonia and Malta) may lack decarbonisation levers. Therefore, policy makers should introduce additional subsidy schemes and tax exemptions in both developed and less developed countries to meet EU decarbonisation targets.
abstract_unstemmed	A novel multifactor algorithm is developed with the aim of estimating GHG emissions in the EU countries and forecasting different future scenarios. This is created starting from (1) GDP, (2) population and (3) renewable energy share (RES). The determination coefficient (R<sup<2</sup<) of the multiple regression adopted reaches a value of 0.96; thus, only 4% of the GHG variation cannot be explained by the combination of the three variables. Germany is removed from the model after analysing the statistical outliers, as it presents an unusual behaviour within the European context. Also, France, Italy and Ireland are removed in the forecast analysis since they are characterised by corrected weighting values above the threshold value of the algorithm (0.156). The results show that GHG emissions decrease 14% in a low-growth-rate scenario, increase 24% in an average-growth scenario and increase 104% in a high-growth-rate scenario. Countries that improve the most are the ones that are currently underdeveloped in RES and are expected to decrease their population in the future (Croatia, Latvia, Cyprus and Greece). Other countries currently well positioned but with expected population growth (Sweden, Luxemburg and Denmark) or with expected intense GDP growth (Estonia and Malta) may lack decarbonisation levers. Therefore, policy makers should introduce additional subsidy schemes and tax exemptions in both developed and less developed countries to meet EU decarbonisation targets.
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title_short	Greenhouse Gas Emissions Forecasts in Countries of the European Union by Means of a Multifactor Algorithm
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Other countries currently well positioned but with expected population growth (Sweden, Luxemburg and Denmark) or with expected intense GDP growth (Estonia and Malta) may lack decarbonisation levers. 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