Forecasting of Carbon Emission in China Based on Gradient Boosting Decision Tree Optimized by Modified Whale Optimization Algorithm

As the global temperature continues to rise, people have become increasingly concerned about global climate change. In order to help China to effectively develop a carbon peak target completion plan, this paper proposes a carbon emission prediction model based on the improved whale algorithm-optimiz...

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Autores principales:	Xiwen Cui, Shaojun E, Dongxiao Niu, Bosong Chen, Jiaqi Feng
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	gradient lifting tree whale optimization algorithm carbon emissions carbon peak Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/73185cca81e74e8f803553cbcbe24024
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spelling	oai:doaj.org-article:73185cca81e74e8f803553cbcbe240242021-11-11T19:50:51ZForecasting of Carbon Emission in China Based on Gradient Boosting Decision Tree Optimized by Modified Whale Optimization Algorithm10.3390/su1321123022071-1050https://doaj.org/article/73185cca81e74e8f803553cbcbe240242021-11-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/21/12302https://doaj.org/toc/2071-1050As the global temperature continues to rise, people have become increasingly concerned about global climate change. In order to help China to effectively develop a carbon peak target completion plan, this paper proposes a carbon emission prediction model based on the improved whale algorithm-optimized gradient boosting decision tree, which combines four optimization methods and significantly improves the prediction accuracy. This paper uses historical data to verify the superiority of the gradient boosting tree prediction model optimized by the improved whale algorithm. In addition, this study also predicted the carbon emission values of China from 2020 to 2035 and compared them with the target values, concluding that China can accomplish the relevant target values, which suggests that this research has practical implications for China’s future carbon emission reduction policies.Xiwen CuiShaojun EDongxiao NiuBosong ChenJiaqi FengMDPI AGarticlegradient lifting treewhale optimization algorithmcarbon emissionscarbon peakEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 12302, p 12302 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	gradient lifting tree whale optimization algorithm carbon emissions carbon peak Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	gradient lifting tree whale optimization algorithm carbon emissions carbon peak Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Xiwen Cui Shaojun E Dongxiao Niu Bosong Chen Jiaqi Feng Forecasting of Carbon Emission in China Based on Gradient Boosting Decision Tree Optimized by Modified Whale Optimization Algorithm
description	As the global temperature continues to rise, people have become increasingly concerned about global climate change. In order to help China to effectively develop a carbon peak target completion plan, this paper proposes a carbon emission prediction model based on the improved whale algorithm-optimized gradient boosting decision tree, which combines four optimization methods and significantly improves the prediction accuracy. This paper uses historical data to verify the superiority of the gradient boosting tree prediction model optimized by the improved whale algorithm. In addition, this study also predicted the carbon emission values of China from 2020 to 2035 and compared them with the target values, concluding that China can accomplish the relevant target values, which suggests that this research has practical implications for China’s future carbon emission reduction policies.
format	article
author	Xiwen Cui Shaojun E Dongxiao Niu Bosong Chen Jiaqi Feng
author_facet	Xiwen Cui Shaojun E Dongxiao Niu Bosong Chen Jiaqi Feng
author_sort	Xiwen Cui
title	Forecasting of Carbon Emission in China Based on Gradient Boosting Decision Tree Optimized by Modified Whale Optimization Algorithm
title_short	Forecasting of Carbon Emission in China Based on Gradient Boosting Decision Tree Optimized by Modified Whale Optimization Algorithm
title_full	Forecasting of Carbon Emission in China Based on Gradient Boosting Decision Tree Optimized by Modified Whale Optimization Algorithm
title_fullStr	Forecasting of Carbon Emission in China Based on Gradient Boosting Decision Tree Optimized by Modified Whale Optimization Algorithm
title_full_unstemmed	Forecasting of Carbon Emission in China Based on Gradient Boosting Decision Tree Optimized by Modified Whale Optimization Algorithm
title_sort	forecasting of carbon emission in china based on gradient boosting decision tree optimized by modified whale optimization algorithm
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/73185cca81e74e8f803553cbcbe24024
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_version_	1718431414888693760

Forecasting of Carbon Emission in China Based on Gradient Boosting Decision Tree Optimized by Modified Whale Optimization Algorithm

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