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Identifying contributing factors to China’s declining share of renewable energy consumption: no silver bullet to decarbonisation

Renewable energy consumption (REC) holds the key to sustainable development. Therefore, many studies have considered the role of REC. However, the factors influencing the REC share in total energy usage (SREC) are not well investigated. Especially, the factors of China’s fast-shrinking SREC are unde...

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Autores principales: Sajid, Muhammad Jawad, Khan, Syed Abdul Rehman, Gonzalez, Ernesto D. R. Santibanez
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9126753/
https://www.ncbi.nlm.nih.gov/pubmed/35606592
http://dx.doi.org/10.1007/s11356-022-20972-x
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author Sajid, Muhammad Jawad
Khan, Syed Abdul Rehman
Gonzalez, Ernesto D. R. Santibanez
author_facet Sajid, Muhammad Jawad
Khan, Syed Abdul Rehman
Gonzalez, Ernesto D. R. Santibanez
author_sort Sajid, Muhammad Jawad
collection PubMed
description Renewable energy consumption (REC) holds the key to sustainable development. Therefore, many studies have considered the role of REC. However, the factors influencing the REC share in total energy usage (SREC) are not well investigated. Especially, the factors of China’s fast-shrinking SREC are understudied. This research void on the world’s largest renewable energy producer and consumer, i.e., China’s decreasing SREC, is alarming and requires thorough investigation. Our study intends to fill this gap by analyzing the factors of China’s decreasing SREC. The study uses both the conventional (descriptive and directional correlational analyses) and some unconventional (automatic linear modeling (ALM) and Artificial neural network (ANN) multilayer perceptron (MLP)) approach to investigate the factors of China’s decreasing SREC. The initial hypothesis testing and most reliable model validation were achieved via directional correlational (Pearson and Spearman) and ALM analyses. The ANN MLP (two hidden layers) indicated that the most critical factor is “Combustible renewables and waste,” with a 100% normalized importance. It was followed by “urbanization (64.2%), gross savings (56.1%), and alternative and nuclear energy (38%),” respectively. It is suggested that the Chinese government and private investors prioritize their investments based on factors’ importance ranking. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11356-022-20972-x.
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spelling pubmed-91267532022-05-24 Identifying contributing factors to China’s declining share of renewable energy consumption: no silver bullet to decarbonisation Sajid, Muhammad Jawad Khan, Syed Abdul Rehman Gonzalez, Ernesto D. R. Santibanez Environ Sci Pollut Res Int Research Article Renewable energy consumption (REC) holds the key to sustainable development. Therefore, many studies have considered the role of REC. However, the factors influencing the REC share in total energy usage (SREC) are not well investigated. Especially, the factors of China’s fast-shrinking SREC are understudied. This research void on the world’s largest renewable energy producer and consumer, i.e., China’s decreasing SREC, is alarming and requires thorough investigation. Our study intends to fill this gap by analyzing the factors of China’s decreasing SREC. The study uses both the conventional (descriptive and directional correlational analyses) and some unconventional (automatic linear modeling (ALM) and Artificial neural network (ANN) multilayer perceptron (MLP)) approach to investigate the factors of China’s decreasing SREC. The initial hypothesis testing and most reliable model validation were achieved via directional correlational (Pearson and Spearman) and ALM analyses. The ANN MLP (two hidden layers) indicated that the most critical factor is “Combustible renewables and waste,” with a 100% normalized importance. It was followed by “urbanization (64.2%), gross savings (56.1%), and alternative and nuclear energy (38%),” respectively. It is suggested that the Chinese government and private investors prioritize their investments based on factors’ importance ranking. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11356-022-20972-x. Springer Berlin Heidelberg 2022-05-24 2022 /pmc/articles/PMC9126753/ /pubmed/35606592 http://dx.doi.org/10.1007/s11356-022-20972-x Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Research Article
Sajid, Muhammad Jawad
Khan, Syed Abdul Rehman
Gonzalez, Ernesto D. R. Santibanez
Identifying contributing factors to China’s declining share of renewable energy consumption: no silver bullet to decarbonisation
title Identifying contributing factors to China’s declining share of renewable energy consumption: no silver bullet to decarbonisation
title_full Identifying contributing factors to China’s declining share of renewable energy consumption: no silver bullet to decarbonisation
title_fullStr Identifying contributing factors to China’s declining share of renewable energy consumption: no silver bullet to decarbonisation
title_full_unstemmed Identifying contributing factors to China’s declining share of renewable energy consumption: no silver bullet to decarbonisation
title_short Identifying contributing factors to China’s declining share of renewable energy consumption: no silver bullet to decarbonisation
title_sort identifying contributing factors to china’s declining share of renewable energy consumption: no silver bullet to decarbonisation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9126753/
https://www.ncbi.nlm.nih.gov/pubmed/35606592
http://dx.doi.org/10.1007/s11356-022-20972-x
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