[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125752-en":3,"doc-seo-125752-105":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},125752,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Cleantech and policy framework in Europe - A machine learning approach","The paper addresses how Europe can advance a sustainable, clean energy transition through the joint role of cleantech firms, policy frameworks, and data-driven analytics. It proposes a machine-learning approach to identify cleantech firms from mission statements while addressing limitations of prior methods. It then compiles a unique dataset of national policies aligned with European Green Deal topics and tests, via country-level regression, how regulatory frameworks shape the emergence and growth of innovator versus ecosystem firms.","Please cite this article as:  \nCroce A, Toschi L., Ughetto E, Zanni S. (2024), \"Cleantech and policy framework in Europe: A machine learning approach\", Energy policy, 186, 114006, ISSN 0301-4215, [https://doi.org/10.1016/j.enpol.2024.114006](https://doi.org/10.1016/j.enpol.2024.114006)  \nCleantech and policy framework in Europe: A machine learning approach  \nAnnalisa Croce – Politecnico di Milano  \nLaura Toschi – Università degli Studi di Bologna  \nElisa Ughetto – Politecnico di Torino  \nSara Zanni – Università degli Studi di Bologna  \nAbstract  \nThe pursuit of a sustainable and clean energy future has emerged as a paramount global imperative of the 21st century. Achieving this transition is a multifaceted and complex endeavor that requires a harmonious interplay of factors: effective policy frameworks, cleantech firms, and the transformative power of data science. By focusing on the European context, this paper advances the field in several directions. First, it explores the use of machine learning techniques to identify cleantech firms by analyzing their mission statements and addressing the weaknesses of the existing methods. Second, it collects a unique and comprehensive dataset of national-level policies addressing the different topics covered by the European Green Deal. Third, in a regression analysis at country level, it examines the interplay between the national regulatory framework and the birth and growth of the cleantech landscape, by distinguishing between innovators (firms which develop the cleantech) and ecosystem firms (which adopt the cleantech) . Our results indicate that the introduction of policies favors by itself the birth of cleantech innovator companies and their growth in the country. An increasing number of policies has a regulatory effect in the cleantech ecosystem limiting the number of newborn companies while favoring their growth.  \nKeywords: Cleantech, Policy, Machine Learning, European Green Deal, Europe  \n1. Introduction  \nThe pursuit of a sustainable and clean energy future has emerged as a paramount global imperative in the 21st century. In response to escalating concerns about climate change, resource depletion, and environmental degradation (IPCC, 2014), nations around the world are initiating various actions to support the implementation of a sustainable transition (Hiatt et al., 2015). The transition to clean energy sources, characterized by a reduced carbon footprint and increased reliance on renewable technologies, is a prerequisite for meeting these challenges. However, achieving this transition is not just a matter of technological innovation, but a multi-  \nfaceted and complex endeavor that requires a harmonious interplay offactors. In this paper, we focus in particular on policy frameworks, cleantech firms, and the transformative power of data science.  \nThe European Green Deal (EGD) was published in December 2019, in response to the declaration of a climate emergency. It is designed to define a broad strategy to address the challenges of climate change and sustainable development, limiting the trade-offs of environmental degradation, and their interlinkages (European Commission, 2019) . Funded by a third of the €1.8 trillion investment in the NextGenerationEU Recovery Plan, the initiatives included in the package aim to reduce the continent’s greenhouse gases emissions by 55% by 2030 and achieve carbon neutrality by 2050. By combining several elements (e.g. skills and competences, public and efficient transportation, healthy and affordable food, energy efficient buildings, clean energy, fresh air, clean water, healthy soil and biodiversity), the EGD aims to support the competitiveness of the EU economy, while ensuring both a decoupling between economic growth and resource depletion, and that no one is left behind, i.e. a just and sustainable transition (Mura et al., 2023) .  \nNational cleantech policies are an integral part of the EGD, as they help policymakers in EU member states ","cbCaiuUq8tYpSSvt","https://ap.wps.com/l/cbCaiuUq8tYpSSvt","pdf",821900,1,45,"English","en",105,"# Abstract\n# Introduction\n## European Green Deal and national cleantech policies\n## Identifying cleantech firms and evaluating policy impact\n## Machine learning approach and country-level analysis","[{\"question\":\"How does the study identify cleantech firms?\",\"answer\":\"It uses machine learning techniques to classify cleantech firms by analyzing their mission statements, improving over weaknesses in existing identification methods.\"},{\"question\":\"What dataset is used to analyze European cleantech policy?\",\"answer\":\"The paper builds a unique and comprehensive dataset of national-level policies covering topics addressed by the European Green Deal.\"},{\"question\":\"What relationship does the paper find between policy and cleantech ecosystem development?\",\"answer\":\"Policies by themselves support the birth and growth of cleantech innovator firms; as the number of policies increases, a regulatory effect limits newborn ecosystem firms while favoring their growth.\"}]","Cleantech and policy framework in Europe - A machine learning approach | PDF",1785901021,113,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"cleantech-and-policy-framework-in-europe-a-machine-learning-approach","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/cleantech-and-policy-framework-in-europe-a-machine-learning-approach/125752/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does the study identify cleantech firms?","Question",{"text":75,"@type":76},"It uses machine learning techniques to classify cleantech firms by analyzing their mission statements, improving over weaknesses in existing identification methods.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What dataset is used to analyze European cleantech policy?",{"text":80,"@type":76},"The paper builds a unique and comprehensive dataset of national-level policies covering topics addressed by the European Green Deal.",{"name":82,"@type":73,"acceptedAnswer":83},"What relationship does the paper find between policy and cleantech ecosystem development?",{"text":84,"@type":76},"Policies by themselves support the birth and growth of cleantech innovator firms; 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