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Factors Influencing Carbon Emission and Low-Carbon Development Levels in Shandong Province: Method Analysis Based on Improved Random Forest Partial Least Squares Structural Equation Model and Entropy Weight Method

文献类型：期刊论文

作者Zhu, Yingjie1,4; Guo, Yinghui1; Chen, Yongfa1; Ma, Jiageng1,2; Zhang, Dan3
刊名SUSTAINABILITY
出版日期2024-10-01
卷号16期号:19页码:21
关键词carbon emission influencing factors RF-PLS-SEM EWM low-carbon development level
DOI10.3390/su16198488
产权排序3
英文摘要Comprehensively clarifying the influencing factors of carbon emissions is crucial to realizing carbon emission reduction targets in China. To address this issue, this paper develops a four-level carbon emission influencing factor system from six perspectives: population, economy, energy, water resources, main pollutants, and afforestation. To analyze how these factors affect carbon emissions, we propose an improved partial least squares structural equation model (PLS-SEM) based on a random forest (RF), named RF-PLS-SEM. In addition, the entropy weight method (EWM) is employed to evaluate the low-carbon development level according to the results of the RF-PLS-SEM. This paper takes Shandong Province as an example for empirical analysis. The results demonstrate that the improved model significantly improves accuracy from 0.8141 to 0.9220. Moreover, water resources and afforestation have relatively small impacts on carbon emissions. Primary and tertiary industries are negative influencing factors that inhibit the growth of carbon emissions, whereas total energy consumption, the volume of wastewater discharged and of common industrial solid waste are positive and direct influencing factors, and population density is indirect. In particular, this paper explores the important role of fisheries in reducing carbon emissions and discusses the relationship between population aging and carbon emissions. In terms of the level of low-carbon development, the assessment system of carbon emission is constructed from four dimensions, namely, population, economy, energy, and main pollutants, showing weak, basic, and sustainable stages of low-carbon development during the 1997-2012, 2013-2020, and 2021-2022 periods, respectively.
WOS关键词CO2 EMISSION ; QUALITY ; CITIES ; CITY
资助项目National Natural Science Foundation of China ; Jilin Provincial Department of Science and Technology[20230101232JC] ; Project Grant for Teaching Cases of Graduate Students in Jilin Province[JJKH20230100YJG] ; [41701054]
WOS研究方向Science & Technology - Other Topics ; Environmental Sciences & Ecology
语种英语
WOS记录号WOS:001333014800001
出版者MDPI
资助机构National Natural Science Foundation of China ; Jilin Provincial Department of Science and Technology ; Project Grant for Teaching Cases of Graduate Students in Jilin Province
源URL[http://ir.igsnrr.ac.cn/handle/311030/210043]  
专题生态系统网络观测与模拟院重点实验室_外文论文
通讯作者Zhu, Yingjie; Zhang, Dan
作者单位1.Changchun Univ, Sch Math & Stat, Changchun 130022, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China
3.Changchun Univ, Sch Landscape Architecture, Changchun 130022, Peoples R China
4.Changchun Univ, Key Lab Intelligent Rehabil & Barrier Free Disable, Minist Educ, Changchun 130022, Peoples R China
推荐引用方式
GB/T 7714		 Zhu, Yingjie,Guo, Yinghui,Chen, Yongfa,et al. Factors Influencing Carbon Emission and Low-Carbon Development Levels in Shandong Province: Method Analysis Based on Improved Random Forest Partial Least Squares Structural Equation Model and Entropy Weight Method[J]. SUSTAINABILITY,2024,16(19):21.
APA Zhu, Yingjie,Guo, Yinghui,Chen, Yongfa,Ma, Jiageng,&Zhang, Dan.(2024).Factors Influencing Carbon Emission and Low-Carbon Development Levels in Shandong Province: Method Analysis Based on Improved Random Forest Partial Least Squares Structural Equation Model and Entropy Weight Method.SUSTAINABILITY,16(19),21.
MLA Zhu, Yingjie,et al."Factors Influencing Carbon Emission and Low-Carbon Development Levels in Shandong Province: Method Analysis Based on Improved Random Forest Partial Least Squares Structural Equation Model and Entropy Weight Method".SUSTAINABILITY 16.19(2024):21.

入库方式： OAI收割

来源：地理科学与资源研究所

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