[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118620-en":3,"doc-seo-118620-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118620,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","ExioML: Eco-economic dataset for Machine Learning in Global Sectoral Sustainability","The Environmental Extended Multi-Regional Input-Output (EE-MRIO) framework serves as the core approach in ecological economics for estimating environmental impacts of economic activities. ExioML introduces a machine learning benchmark dataset for sustainability analysis, designed to reduce barriers and strengthen collaboration between machine learning and ecological economics. A greenhouse gas emission regression task evaluates sectoral sustainability and demonstrates practical dataset usability. Experiments compare shallow baseline models and deep learning models using diverse factor accounting features, with results showing low mean squared errors from deep and ensemble models. ExioML aims to support broad ML applications and inform climate actions and sustainable investment decisions.","arXiv :2406 .09046v2 [ cs .LG] 6 Jul 2024  \nExioML: Eco-economic dataset for Machine Learning in Global Sectoral Sustainability  \nYanming Guo 1 , Charles Guan2 , Jin Ma 1  \n1 School of Electrical & Computer Engineering, University of Sydney.  \n2 Rich Data Co.  \nContributing authors: [yguo0337@uni.sydney.edu.au](yguo0337@uni.sydney.edu.au) ;  \n[charles.guan@richdataco.com](charles.guan@richdataco.com) ; [j.ma@sydney.edu.au](j.ma@sydney.edu.au) ;  \nAbstract  \nThe Environmental Extended Multi-Regional Input-Output analysis is the predominant framework in Ecological Economics for assessing the environmental impact of economic activities. This paper introduces ExioML, the first Machine Learning benchmark dataset designed for sustainability analysis, aimed at lowering barriers and fostering collaboration between Machine Learning and Ecological Economics research. A crucial greenhouse gas emission regression task was conducted to evaluate sectoral sustainability and demonstrate the usability of the dataset. We compared the performance of traditional shallow models with deep learning models, utilizing a diverse Factor Accounting table and incorporating various categorical and numerical features. Our findings reveal that ExioML, with its high usability, enables deep and ensemble models to achieve low mean square errors, establishing a baseline for future Machine Learning research. Through ExioML, we aim to build a foundational dataset supporting various Machine Learning applications and promote climate actions and sustainable investment decisions.  \nKeywords: Environmental Extended Multi-regional Input-Output, Machine Learning, Sustainability Development, Global Trading Network  \n1 Background & Summary  \nThe increase in Greenhouse Gas (GHG) emissions due to fossil-fuel-driven economic development has precipitated a global warming crisis. This concern led to establishing the Paris Agreement in 2015, aiming to limit long-term temperature rise to no more  \n1  \nthan 2 ◦ C above pre-industrial levels [1] . Concurrently, the Sustainable Development Goals (SDGs), specifically Goal 13 and Goal 8, were proposed to emphasize taking climate action to reduce emissions while maintaining economic growth. To address such climate-trade dilemma, researchers from various disciplines strive to balance climate action with economic growth and human well-being [2, 3] . Recently, the Machine Learning (ML) technique [4] has emerged as a significant tool for accurate prediction to assist climate change decision-making [5] . Specifically, ML algorithms have been explored in aiding nearly real-time global weather forecasting [6], land monitoring via satellite imagery [7, 8], and the prediction of disturbances in electric grids [9] .  \nThe predominant Ecological Economics (EE) research framework, the Environmentally Extended Multiregional Input-Output (EE-MRIO) analysis, effectively models global economic interactions of sectors within a network structure [10–14] . As EEMRIO describes the environmental footprint for global economic activities, it has become the fundamental framework for EE research, illustrated in Figure 1. EEMRIO supports various studies such as Structure Decomposition Analysis (SDA) and Index Decomposition Analysis (IDA) to identify changes by decomposing key drivers [15–17]; monitoring embodied emissions with resource transfer and sustainability evaluation of supply chain in global trade [12, 18–23] . Recently, ML algorithms have been applied with EE-MRIO for several applications, such as accurately identifying ecological hotspots and inefficiencies within the global supply chain to optimise logistic paths for decreased carbon emissions while considering cost-effectiveness [24] . ML algorithms are naturally suitable for learning multi-dimensional patterns and are utilised for better sectoral sustainability assessment considering environmental, economic and social impacts [25, 26] . Additionally, the ML algorithms extract the patterns from the ","cbCaisK8k21Oif8B","https://ap.wps.com/l/cbCaisK8k21Oif8B","pdf",5750484,1,19,"English","en",105,"# Background & Summary\n## Climate and sustainability motivation\n## EE-MRIO framework and research applications\n## Need for benchmarks and data openness\n# ExioML contribution","[{\"question\":\"What problem does ExioML address in ecological economics and machine learning?\",\"answer\":\"ExioML addresses the lack of public benchmark datasets and the resulting inability to fairly compare, reproduce, or evaluate EE-ML models across sectors and regions.\"},{\"question\":\"How is ExioML evaluated in the paper?\",\"answer\":\"The paper evaluates ExioML through a greenhouse gas emission regression task to assess sectoral sustainability and demonstrate dataset usability.\"},{\"question\":\"What modeling approaches are compared using ExioML?\",\"answer\":\"The study compares traditional shallow models with deep learning models, including deep and ensemble approaches, using a Factor Accounting table plus categorical and numerical features.\"}]","ExioML: Eco-economic dataset for Machine Learning in Global Sectoral Sustainability | 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