[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126554-en":3,"doc-seo-126554-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126554,13056712833777,"Logic","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Nonparametric approaches for analyzing carbon emission: from statistical and machine learning perspectives","Linear regression models, especially the extended STIRPAT model, are widely used to analyze carbon emissions, yet their parametric assumptions may be inadequate because emissions are driven by complex interactions among multiple factors. This study investigates nonparametric statistics and machine learning methods, including kernel regression, random forest, and neural networks. Experiments use panel data from ten Chinese cities (2005–2019), where neural networks achieve the strongest fitting and prediction performance. An additional case on Wuhu illustrates practical modeling and supports quantitative characterization and carbon-reduction policy discussion.","arXiv :2303 . 14900v1 [ stat .AP] 27 Mar 2023  \nNonparametric approaches for analyzing carbon emission: from statistical and machine learning perspectives  \nYiming Ma, Hang Liu and Shanyong Wang  \nAbstract Linear regression models, especially the extended STIRPAT model, are routinely-applied for analyzing carbon emissions data. However, since the relationship between carbon emissions and the influencing factors is complex, fitting a simple parametric model may not be an ideal solution. This paper investigated various nonparametric approaches in statistics and machine learning (ML) for modeling carbon emissions data, including kernel regression, random forest and neural network. We selected data from ten Chinese cities from 2005 to 2019 for modeling studies. We found that neural network had the best performance in both fitting and prediction accuracy, which implies its capability of expressing the complex relationships between carbon emissions and the influencing factors. This study provides a new means for quantitative modeling of carbon emissions research that helps to understand how to characterize urban carbon emissions and to propose policy recommendations for“carbon reduction”. In addition, we used the carbon emissions data of Wuhu city asan example to illustrate how to use this new approach.  \n1 Introduction  \nClimate change due to carbon emissions is causing unprecedented impacts and challenges to human society and the natural environment. Carbon emissions are greenhouse gas emissions produced in various fields and activities, mainly including  \nYiming Ma  \nDepartment of Statistics and Finance, School of Management, University of Science and Technology [of China e-mail:](of China e-mail: mayiming@mail.ustc.edu.cn)[ mayiming@mail.ustc.edu.cn](of China e-mail: mayiming@mail.ustc.edu.cn)  \nHang Liu  \nInternational Institute of Finance, School of Management, University of Science and Technology [of China e-mail:](of China e-mail: hliu01@ustc.edu.cn)[ hliu01@ustc.edu.cn](of China e-mail: hliu01@ustc.edu.cn)  \nShanyong Wang at Department of Business Administration, School of Management, University of  \nScience [and Technology of China e-mail:](and Technology of China e-mail: wsy1988@ustc.edu.cn)[ wsy1988@ustc.edu.cn](and Technology of China e-mail: wsy1988@ustc.edu.cn)  \n2 Yiming Ma, Hang Liu and Shanyong Wang  \ncarbon dioxide, methane, nitrous oxide, etc. China’s carbon emissions in 2019 were about 2,777 million tons, accounting for 27% of the world’s total, making it the world’s top carbon emitter[1] . On September 22, 2020, President of China Xi Jinping announced at the 75th session of the United Nations General Assembly that “China will increase its autonomous national contribution to CO2 emissions will strive to peak by 2030 and work towards achieving carbon neutrality by 2060.” China has implemented a series of strategies, measures and actions to address climate change and participate in global climate governance.  \nThere have been numerous studies on carbon emissions for China at the overall, regional and provincial levels [2-3] . Most of their analysis is based on the STIRPAT model and/or its extended version[4-5] . STIRPAT is an important model for the study of environmental impacts, decomposing them into the products of population size, wealth per capita and technology. In this article, we consider population, affluence, energy intensity, and industrial structure as factors influencing carbon emissions.  \nIn previous studies, population and affluence are the two factors that most directly affect carbon emissions. Dietz et al. [6] believe that population and carbon emissions are proportional within a certain range, but there is a lag between policy interventionson population, and we cannot change the status of carbon emissions quickly by controlling population. In addition, they argue that carbon emissions increase and then remain constant or even decrease as the level of affluence increases. This is because being very affluent mea","cbCaijEbb7cVptCM","https://ap.wps.com/l/cbCaijEbb7cVptCM","pdf",688079,3,1,16,"English","en",105,"# Introduction\n# Nonparametric modeling approaches\n## Kernel regression\n## Random forest\n## Neural network\n# Empirical study on Chinese cities (2005–2019)\n# Case illustration: Wuhu city\n# Conclusion and policy implications","[{\"question\":\"Why are traditional parametric regression models not ideal for carbon emission analysis?\",\"answer\":\"Carbon emissions depend on complex relationships and interactions among influencing factors, so simple parametric models may fit poorly and offer limited explanatory power.\"},{\"question\":\"Which nonparametric approaches are compared in the study?\",\"answer\":\"The study compares kernel regression, random forest, and neural networks, alongside linear regression/STIRPAT-based modeling for reference.\"},{\"question\":\"What data and evaluation results does the paper report?\",\"answer\":\"Using panel data from ten Chinese cities from 2005 to 2019, the paper finds neural networks deliver the best fitting and prediction accuracy.\"}]","Nonparametric approaches for analyzing carbon emission: from statistical and machine learning perspectives | 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are traditional parametric regression models not ideal for carbon emission analysis?","Question",{"text":76,"@type":77},"Carbon emissions depend on complex relationships and interactions among influencing factors, so simple parametric models may fit poorly and offer limited explanatory power.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which nonparametric approaches are compared in the study?",{"text":81,"@type":77},"The study compares kernel regression, random forest, and neural networks, alongside linear regression/STIRPAT-based modeling for reference.",{"name":83,"@type":74,"acceptedAnswer":84},"What data and evaluation results does the paper report?",{"text":85,"@type":77},"Using panel data from ten Chinese cities from 2005 to 2019, the paper finds neural networks deliver the best fitting and prediction 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