Cargando…
Assessment of carbon emission reduction contribution of Chinese power grid enterprises based on MCS-GA-ELM method
To achieve China’s “double carbon” goal, it is necessary to make quantitative evaluation of the power grid enterprises’ contribution to carbon emission reduction. This paper analyzes the contribution of power grid enterprises to carbon emission reduction from three points: power generation side, pow...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9628424/ https://www.ncbi.nlm.nih.gov/pubmed/36322350 http://dx.doi.org/10.1007/s11356-022-23710-5 |
_version_ | 1784823191059824640 |
---|---|
author | Li, Jinchao Sun, Zihao Lu, Shiqiang |
author_facet | Li, Jinchao Sun, Zihao Lu, Shiqiang |
author_sort | Li, Jinchao |
collection | PubMed |
description | To achieve China’s “double carbon” goal, it is necessary to make quantitative evaluation of the power grid enterprises’ contribution to carbon emission reduction. This paper analyzes the contribution of power grid enterprises to carbon emission reduction from three points: power generation side, power grid side, and user side. Then, PLS-VIP method is used to screen the key influencing factors of carbon emission reduction contribution of power grid enterprises from three aspects: consumption of clean energy emission reduction, reduction of line loss emission reduction, and substitution of electric energy. Based on GA-ELM combined machine learning algorithm, we establish an intelligent evaluation model of power grid enterprises’ carbon emission reduction contribution. Furthermore, according to the distribution law of key influencing factors, this paper uses Monte Carlo simulation method to calculate the contribution of power grid enterprises to carbon emission reduction by scenario, so as to evaluate the contribution of power grid enterprises to carbon emission reduction. Finally, combined with the relevant data of power grid enterprises from 2003 to 2019, this paper makes an empirical study on the completion of carbon emission reduction contribution and the promotion path. GRAPHICAL ABSTRACT: [Image: see text] |
format | Online Article Text |
id | pubmed-9628424 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-96284242022-11-02 Assessment of carbon emission reduction contribution of Chinese power grid enterprises based on MCS-GA-ELM method Li, Jinchao Sun, Zihao Lu, Shiqiang Environ Sci Pollut Res Int Research Article To achieve China’s “double carbon” goal, it is necessary to make quantitative evaluation of the power grid enterprises’ contribution to carbon emission reduction. This paper analyzes the contribution of power grid enterprises to carbon emission reduction from three points: power generation side, power grid side, and user side. Then, PLS-VIP method is used to screen the key influencing factors of carbon emission reduction contribution of power grid enterprises from three aspects: consumption of clean energy emission reduction, reduction of line loss emission reduction, and substitution of electric energy. Based on GA-ELM combined machine learning algorithm, we establish an intelligent evaluation model of power grid enterprises’ carbon emission reduction contribution. Furthermore, according to the distribution law of key influencing factors, this paper uses Monte Carlo simulation method to calculate the contribution of power grid enterprises to carbon emission reduction by scenario, so as to evaluate the contribution of power grid enterprises to carbon emission reduction. Finally, combined with the relevant data of power grid enterprises from 2003 to 2019, this paper makes an empirical study on the completion of carbon emission reduction contribution and the promotion path. GRAPHICAL ABSTRACT: [Image: see text] Springer Berlin Heidelberg 2022-11-02 2023 /pmc/articles/PMC9628424/ /pubmed/36322350 http://dx.doi.org/10.1007/s11356-022-23710-5 Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. 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 Li, Jinchao Sun, Zihao Lu, Shiqiang Assessment of carbon emission reduction contribution of Chinese power grid enterprises based on MCS-GA-ELM method |
title | Assessment of carbon emission reduction contribution of Chinese power grid enterprises based on MCS-GA-ELM method |
title_full | Assessment of carbon emission reduction contribution of Chinese power grid enterprises based on MCS-GA-ELM method |
title_fullStr | Assessment of carbon emission reduction contribution of Chinese power grid enterprises based on MCS-GA-ELM method |
title_full_unstemmed | Assessment of carbon emission reduction contribution of Chinese power grid enterprises based on MCS-GA-ELM method |
title_short | Assessment of carbon emission reduction contribution of Chinese power grid enterprises based on MCS-GA-ELM method |
title_sort | assessment of carbon emission reduction contribution of chinese power grid enterprises based on mcs-ga-elm method |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9628424/ https://www.ncbi.nlm.nih.gov/pubmed/36322350 http://dx.doi.org/10.1007/s11356-022-23710-5 |
work_keys_str_mv | AT lijinchao assessmentofcarbonemissionreductioncontributionofchinesepowergridenterprisesbasedonmcsgaelmmethod AT sunzihao assessmentofcarbonemissionreductioncontributionofchinesepowergridenterprisesbasedonmcsgaelmmethod AT lushiqiang assessmentofcarbonemissionreductioncontributionofchinesepowergridenterprisesbasedonmcsgaelmmethod |