[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126185-en":3,"doc-seo-126185-105":31,"detail-sidebar-cat-0-en-105":93},{"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},126185,549768072016,"River Wang","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Exploring N₂O Emissions at World Level - Advanced Econometric and Machine Learning Approaches in the ESG Context","The paper examines nitrous oxide (N₂O) emissions from an Environmental, Social, and Governance (ESG) standpoint using econometric and machine learning methods to reveal global trends and policy implications. Results indicate a dominant influence of ESG factors, with interdependencies among economic growth, resource productivity, and environmental policy. Econometric results highlight forest degradation, energy intensity, and income inequality as key determinants requiring policy focus, while machine learning improves prediction when drivers and country trends are identifiable. Panel methods and clustering separate countries by ESG performance and emission trajectories.","Munich Personal RePEc Archive  \nExploring NO Emissions at World Level: Advanced Econometric and Machine Learning Approaches in the ESG Context  \nDrago, Carlo and Arnone, Massimo and Leogrande, Angelo  \nUnicusano University, University of Catania, Independent Researcher  \n18 March 2025  \nOnline at [https://mpra. ub. uni-muenchen. de/124006/](https://mpra. ub. uni-muenchen. de/124006/)  \n[MPRA Paper No. 124006](MPRA Paper No. 124006) , [posted 18 Mar 2025 07:30 UTC](posted 18 Mar 2025 07:30 UTC)  \nExploring N₂O Emissions at World Level: Advanced Econometric and Machine Learning  \nApproaches in the ESG Context  \nCarlo Drago  \nUnicusano University, Rome, Italy  \n[carlo.drago@unicusano.it](carlo.drago@unicusano.it)  \nMassimo Arnone  \nUniversity of Catania, Catania, Italy  \n[massimo.arnone@unict.it](massimo.arnone@unict.it)  \nAngelo Leogrande  \nIndependent Researcher, Bari, Italy  \n[angelo.economics@gmail.com](angelo.economics@gmail.com)  \nAbstract  \nThe paper examines nitrous oxide (N₂O) emissions from an Environmental, Social, and Governance (ESG) standpoint with a combination of econometric and machine learning specifications to uncover global trends and policy implications. Results show the overwhelming effect of ESG factors onemissions, with intricate interdependencies between economic growth, resource productivity, and environmental policy. Econometric specifications identify forest degradation, energy intensity, and income inequality as the most significant determinants of N₂O emissions, which are in need of policy attention. Machine learning enhances predictive power insofar as emission drivers and countryspecific trends are identifiable. Through the integration of panel data techniques and state-of-the-art clustering algorithms, the paper generates a highly differentiated picture of emission trends, separating country groups by ESG performance. The findings of the study are that while developed nations have better energy efficiency and environmental governance, they remain significant contributors to N₂O emissions due to intensive industry and agriculture. Meanwhile, developing economies with energy intensity have structural impediments to emissions mitigation. The paper also identifies the contribution of regulatory quality in emission abatement in that the quality of governance is found to be linked with better environmental performance. ESG-based finance instruments, such as green bonds and impact investing, also promote sustainable economic transition. The findings have the further implications of additional arguments for mainstreaming sustainability in economic planning, developing ESG frameworks to underpin climate targets.  \nKeywords: Nitrous Oxide Emissions, ESG Models, Econometric Analysis, Machine Learning, Sustainability Policy  \nJEL Codes: Q53, Q54, C23, C45, G32  \n1. Introduction  \nThe study of nitrous oxide (N₂O) emissions in ESG models on a global level is a new frontier of research, characterized by increasing interest in innovative methodology for understanding the complexity of the interaction of the environmental, economic, and social determinants. While the literature on the emissions of greenhouse gases has been led by CO₂ and methane (CH₄), N₂O is relatively uncharted territory despite its high global warming potential and long-term contribution to climate change. This research innovation is located at the nexus of environmental economics, sustainable finance, and predictive analytics, and takes an innovative methodological inspiration from the nexus of econometric techniques and machine learning with the aim of obtaining an improved understanding of the determinants of N₂O emissions and their interaction with ESG models. The literature gap is evident on several fronts. First, much of the literature on analysis of the emissions of N₂O has been focused on sectoral analyses, the most prominent of which is agriculture and soil management practice, with little macroeconomic consideration of the mitigation ","cbCairGRMysPfpEu","https://ap.wps.com/l/cbCairGRMysPfpEu","pdf",1511259,12,1,43,"English","en",105,"# Introduction\n## Research innovation and literature gap\n## Data and methodological approach\n# Abstract\n# Keywords","[{\"question\":\"How does the paper study N₂O emissions in the ESG context?\",\"answer\":\"It combines econometric specifications with machine learning approaches to model and analyze how ESG-related factors relate to global N₂O emission trends.\"},{\"question\":\"Which variables does the econometric analysis identify as most significant for N₂O emissions?\",\"answer\":\"Forest degradation, energy intensity, and income inequality are reported as the most significant determinants, indicating priority areas for policy attention.\"},{\"question\":\"How do machine learning methods contribute beyond traditional econometrics?\",\"answer\":\"Machine learning is used to improve predictive power and to uncover latent structures by combining clustering algorithms with regression and related techniques to separate country groups by ESG performance.\"}]","Exploring N₂O Emissions at World Level - Advanced Econometric and Machine Learning Approaches in the ESG Context | PDF",1785903683,108,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"exploring-no-emissions-at-world-level-advanced-econometric-and-machine-learning-approaches-in-the-esg-context","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/exploring-no-emissions-at-world-level-advanced-econometric-and-machine-learning-approaches-in-the-esg-context/126185/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"How does the paper study N₂O emissions in the ESG context?","Question",{"text":77,"@type":78},"It combines econometric specifications with machine learning approaches to model and analyze how ESG-related factors relate to global N₂O emission trends.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Which variables does the econometric analysis identify as most significant for N₂O emissions?",{"text":82,"@type":78},"Forest degradation, energy intensity, and income inequality are reported as the most significant determinants, indicating priority areas for policy attention.",{"name":84,"@type":75,"acceptedAnswer":85},"How do machine learning methods contribute beyond traditional econometrics?",{"text":86,"@type":78},"Machine learning is used to improve predictive power and to uncover latent structures by combining clustering algorithms with regression and related techniques to separate country groups by ESG performance.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,112,117,122,125,130,133,137],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},5,"Comic",60,"comic",{"id":113,"doc_module":4,"doc_module_name":47,"category_name":114,"show_sort_weight":115,"slug":116},6,"Technology",50,"technology",{"id":118,"doc_module":4,"doc_module_name":47,"category_name":119,"show_sort_weight":120,"slug":121},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":123,"slug":124},30,"research-report",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":127,"show_sort_weight":128,"slug":129},9,"Religion & Spirituality",20,"religion-spirituality",{"id":128,"doc_module":4,"doc_module_name":47,"category_name":131,"show_sort_weight":128,"slug":132},"World Cup","world-cup",{"id":134,"doc_module":4,"doc_module_name":47,"category_name":135,"show_sort_weight":134,"slug":136},10,"Lifestyle","lifestyle",{"id":138,"doc_module":4,"doc_module_name":47,"category_name":139,"show_sort_weight":108,"slug":140},19,"General","general"]