[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121627-en":3,"doc-seo-121627-105":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},121627,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","A machine learning approach to analyse and predict the electric cars scenario: The Italian case","The automotive market is undergoing deep transformation driven by tighter pollutant-emission rules and growing consumer awareness of air quality, accelerating the shift toward sustainable mobility. Electric cars are viewed as the most credible alternative to internal combustion vehicles due to their low polluting potential and strong expected growth. This study proposes machine learning methods to estimate factors affecting the spread of the electric-car fleet in Italy, using new public-repository data and a designed survey across national, regional, and provincial geography.","PLOS ONE  \nOPEN ACCESS  \nCitation: Miconi F, Dimitri GM (2023) A machine learning approach to analyse and predict the electric cars scenario: The Italian case. PLoS ONE 18(1): e0279040 . [https://doi.org/10.1371/journal](https://doi.org/10.1371/journal). pone.0279040  \nEditor: Gen Li, Nanjing Forestry University, CHINA  \nReceived: August 3, 2022  \nAccepted: November 28, 2022  \nPublished: January 20, 2023  \nCopyright: © 2023 Miconi, Dimitri. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nData Availability Statement: The data and script are available on GitHub ([https://github.com/](https://github.com/)[ ](https://github.com/)[GiovannaMariaDimitri/A-machine-learning-](GiovannaMariaDimitri/A-machine-learning-)[approach-to-analyse-and-predict-the-electric-cars-](approach-to-analyse-and-predict-the-electric-cars-)[scenario-the-Italian-case](scenario-the-Italian-case)) .  \nFunding: The Dipartimento di Ingegneria dell’Informazione e Scienze Matematiche (DIISM) of the Universit´a di Siena and the “FONDO DI ATENEO PER IL SUPPORTO ALLA PUBBLICAZIONE IN Open Access” of the Universit´a di Siena provided support for this study in the form of open-access publication fees.  \nRESEARCH ARTICLE  \nA machine learning approach to analyse and predict the electric cars scenario: The Italian case  \nFederico Miconi☯, Giovanna Maria Dimitri *☯  \nDipartimento di Ingegneria dell’Informazione e Scienze Matematiche (DIISM), Universit´a di Siena, Siena, Italy  \n☯ These authors contributed equally to this work.  \n* giovanna.dimitri@unisi. it  \nAbstract  \nThe automotive market is experiencing, in recent years, a period of deep transformation. Increasingly stricter rules on pollutant emissions and greater awareness of air quality by consumers are pushing the transport sector towards sustainable mobility. In this historical context, electric cars have been considered the most valid alternative to traditional internal combustion engine cars, thanks to their low polluting potential, with high growth prospects in the coming years. This growth is an important element for companies operating in the electricity sector, since the spread of electric cars is necessarily accompanied by an increasing need of electric charging points, which may impact the electricity distribution network. In this work we proposed a novel application of machine learning methods for the estimation of factors which could impact the distribution of the circulating fleet of electric cars in Italy. We first collected a new dataset from public repository to evaluate the most relevant features impacting the electric cars market. The collected datasets are completely new, and were collected starting from the identification of the main variables that were potentially responsible for the spread of electric cars. Subsequently we distributed a novel designed survey to further investigate such factors on a population sample. Using machine learning models, we could disentangle potentially new interesting information concerning the Italian scenario. We analysed it, in fact, according to different geographical Italian dimensions (national, regional and provincial) and with the final identification of those potential factors that could play a fundamental role in the success and distribution of electric cars mobility. Code and data are available at: [https://github.com/GiovannaMariaDimitri/A-machine-learning-approach-to-analyse](https://github.com/GiovannaMariaDimitri/A-machine-learning-approach-to-analyse)and-predict-the-electric-cars-scenario-the-Italian-case.  \n1. Introduction  \nIn the latest years, great attention has been devoted to the problem of climate change and an effort has been put towards the decrease of gas emissions in the atmosphere, especially due to the still excessive presence of internal combustion en","cbCaibQ7xil9BfEZ","https://ap.wps.com/l/cbCaibQ7xil9BfEZ","pdf",3703506,1,26,"English","en",105,"# Introduction\n## Context: climate change and decarbonization\n## Motivation: electric vehicles and adoption barriers\n## Related work and key factors","[{\"question\":\"Why is the electric cars market important in Italy’s electricity sector?\",\"answer\":\"The spread of electric cars increases the demand for electric charging points, which can affect the electricity distribution network. This creates a need to understand the factors influencing EV diffusion.\"},{\"question\":\"What data sources are used to analyze the Italian electric cars scenario?\",\"answer\":\"The study collects a new dataset from public repositories based on variables potentially responsible for EV spread and adds insights from a newly designed survey on a population sample.\"},{\"question\":\"How do the researchers evaluate factors across geography in Italy?\",\"answer\":\"They analyze the scenario using different geographical dimensions—national, regional, and provincial—to identify potential factors that could play a fundamental role in EV success and distribution.\"}]","A machine learning approach to analyse and predict the electric cars scenario: The Italian case | PDF",1785805808,66,{"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},"a-machine-learning-approach-to-analyse-and-predict-the-electric-cars-scenario-the-italian-case","",{"@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/a-machine-learning-approach-to-analyse-and-predict-the-electric-cars-scenario-the-italian-case/121627/",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","2026-08-04",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},"Why is the electric cars market important in Italy’s electricity sector?","Question",{"text":75,"@type":76},"The spread of electric cars increases the demand for electric charging points, which can affect the electricity distribution network. This creates a need to understand the factors influencing EV diffusion.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data sources are used to analyze the Italian electric cars scenario?",{"text":80,"@type":76},"The study collects a new dataset from public repositories based on variables potentially responsible for EV spread and adds insights from a newly designed survey on a population sample.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the researchers evaluate factors across geography in Italy?",{"text":84,"@type":76},"They analyze the scenario using different geographical dimensions—national, regional, and provincial—to identify potential factors that could play a fundamental role in EV success and distribution.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]