Empirical assessment of carbon emissions in Guangdong Province within the framework of carbon peaking and carbon neutrality: a lasso-TPE-BP neural network approach.

Chen, Ruihan; Ye, Minhua; Li, Zhi; Ma, Zebin; Yang, Derong; Li, Sheng

Chen, Ruihan; Ye, Minhua; Li, Zhi; Ma, Zebin; Yang, Derong; Li, Sheng.

Afiliação

Chen R; School of Mathematics and Computer, Guangdong Ocean University, Zhanjiang, China.
Ye M; College of Ocean Engineering and Energy, Guangdong Ocean University, Zhanjiang, China.
Li Z; School of Mathematics and Computer, Guangdong Ocean University, Zhanjiang, China.
Ma Z; School of Mathematics and Computer, Guangdong Ocean University, Zhanjiang, China.
Yang D; School of Mathematics and Computer, Guangdong Ocean University, Zhanjiang, China.
Li S; School of Mathematics and Computer, Guangdong Ocean University, Zhanjiang, China. lish_ls@gdou.edu.cn.

Environ Sci Pollut Res Int ; 30(58): 121647-121665, 2023 Dec.

Article em En | MEDLINE | ID: mdl-37953421

Assuntos

Carbono; Gases de Efeito Estufa; Carbono/análise; Gases de Efeito Estufa/análise; Condições Sociais; Redes Neurais de Computação; Desenvolvimento Econômico; China; Dióxido de Carbono/análise

Palavras-chave

BP neural network; Carbon emissions; Carbon neutrality; Carbon peaking; Lasso regression; Scenario setting; Tree-structured Parzen estimator

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Carbono / Gases de Efeito Estufa País como assunto: Asia Idioma: En Ano de publicação: 2023 Tipo de documento: Article

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