[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127566-en":3,"doc-seo-127566-105":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},127566,687207020761,"Patrick","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Quantifying the impact of energy consumption sources on GHG emissions in major economies - A machine learning approach","Quantifying how energy consumption sources shape greenhouse gas (GHG) emissions for the United States, China, and the European Union, this study uses 1965–2021 energy and emissions data from Our World in Data. Gradient Boosting identifies the dominant contributing energy sources, while an Artificial Neural Network estimates the magnitude of their effects on GHG outcomes. Results indicate tailored mitigation priorities: coal reduction in the USA, nuclear displacement benefits in China, and coal-focused strategies in the EU, with limited oil impact.","Energy Strategy Reviews 49 (2023) 101159  \nContents lists available at ScienceDirect Energy Strategy Reviews  \njournal [homepage:](homepage: www.elsevier.com/locate/esr)[ www.elsevier.com/locate/esr](homepage: www.elsevier.com/locate/esr)  \n| Quantifying the impact of energy consumption sources on GHG emissions in major economies: A machine learning approach\u003Cbr>Mutaz AlShafeeya, *, Omar Rashdan b\u003Cbr>a Institute of Data Analytics and Information Systems, Corvinus University of Budapest, Budapest, F˝ov´am t´er 13-15, H-1093, Hungary b Faculty of Pharmacy, Middle East University, Amman, Airport Rd., 11831, Jordan |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Handling Editor: Mark Howells |  | This article aims to quantify the impact of different energy consumption sources on greenhouse gas (GHG) emissions for three major economies: the United States of America (USA), China, and the European Union (EU). To achieve this, energy consumption and GHG emissions data were obtained from “Our World in Data” for the period 1965–2021. Then, two machine learning techniques were utilized. Gradient Boosting (GB) was used to identify the major energy consumption sources contributing to GHG. While Artificial Neural Network (ANN) was used to quantify the effects of these major energy consumption sources on GHG emissions. The findings have significant implications for policymakers, as they suggest that effective strategies to reduce GHG emissions must be tailored based on the energy utilization sources of each country. Specifically, for the USA it was found that reducing coal consumption could be the most effective strategy to reduce GHG emissions, as increasing coal consumption by 25% would result in a 13% increase in GHG emissions. In contrast, increasing nuclear consumption by 25% in China would result in an 11% decrease in GHG emissions due to the displacement of fossil fuel-based energy sources. Increasing wind energy consumption by 25% in China would result in a 3% decrease in GHG emissions. In the EU, the study found that increasing oil consumption has a minor effect on GHG emissions while increasing coal consumption by 25% would result in an 11% increase in GHG emissions, highlighting the importance of reducing coal consumption. This study’s originality lies in the use of machine learning techniques to identify the key energy consumption sources driving GHG emissions in the three major economies, as well as its specific recommendations for reducing emissions. |\n| Keywords:\u003Cbr>Greenhouse gas emissions Energy consumption sources Machine learning\u003Cbr>United States China European Union |  |  |\n\nCredit author statement  \nMutaz AlShafeey: Conceptualization, Methodology, Software, Data curation, Writing – original draft. Omar Rashdan: Methodology, Investigation, Validation, Writing-Reviewing and Editing.  \n1. Introduction  \nGreenhouse gases (GHG) are well known for their harmful impactson our planet [1]; Carbon dioxide contributes to rising global temperatures, altered precipitation patterns, and sea level rise. Methane exacerbates climate change and ground-level ozone formation. While nitrous oxide has a high warming potential and poses risks to ecosystems and human health. The growing rates of GHG emissions have imposed substantial risks to human life and the overall environment [2]. Starting from the first industrial revolution, a gradual, but significant increase in  \naverage annual temperature that is associated with extreme weather events and severe temperatures was observed. This change has directly impacted agriculture as well as some other sectors [3–5]. The issue was exacerbated as the effects of climate change have become more serious with an average increase in global temperature between 0.5 and 1 ◦ Cover the past decade [5]. One of the reasons for this temperature rise is the high levels of GHG emissions associated with manufacturing and economic activities [3,6]. The United States (US), China, and the E","cbCaisdOsJ6IujRy","https://ap.wps.com/l/cbCaisdOsJ6IujRy","pdf",8492555,2,1,11,"English","en",105,"# Introduction\n## Greenhouse gases and climate risk\n## Role of energy consumption in GHG emissions\n# Data and methods\n## Gradient Boosting for key source identification\n## Artificial Neural Network for effect quantification\n# Results and country-specific implications\n## United States strategy assessment\n## China strategy assessment\n## European Union strategy assessment\n# Conclusions and policy recommendations","[{\"question\":\"What question does the study address for the USA, China, and the EU?\",\"answer\":\"It quantifies how different energy consumption sources affect greenhouse gas emissions in the United States, China, and the European Union.\"},{\"question\":\"How do the authors identify key energy sources driving GHG emissions?\",\"answer\":\"Gradient Boosting is used to identify the major energy consumption sources contributing to GHG emissions.\"},{\"question\":\"What mitigation implication does the study give for the three economies?\",\"answer\":\"Policy strategies should be tailored by country: reducing coal is most effective for the USA, nuclear-related displacement yields benefits for China, and coal reduction is crucial for the EU.\"}]","Quantifying the impact of energy consumption sources on GHG emissions in major economies - 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