{"id":"https://openalex.org/W7160295686","doi":"https://doi.org/10.48550/arxiv.2605.01549","title":"Hugging Carbon: Quantifying the Training Carbon Emissions of AI Models at Scale","display_name":"Hugging Carbon: Quantifying the Training Carbon Emissions of AI Models at Scale","publication_year":2026,"publication_date":"2026-05-02","ids":{"openalex":"https://openalex.org/W7160295686","doi":"https://doi.org/10.48550/arxiv.2605.01549"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.01549","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.01549","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.01549","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135411429","display_name":"Xinlei Wang","orcid":"https://orcid.org/0009-0009-8461-6454"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xinlei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135346791","display_name":"Ruibo Ming","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ming, Ruibo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135295221","display_name":"Jing Qiu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qiu, Jing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135370666","display_name":"Junhua Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Junhua","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135348511","display_name":"Jinjin Gu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gu, Jinjin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12238","display_name":"Green IT and Sustainability","score":0.1395999938249588,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12238","display_name":"Green IT and Sustainability","score":0.1395999938249588,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.0820000022649765,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.04619999974966049,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.7128000259399414},{"id":"https://openalex.org/keywords/sustainability","display_name":"Sustainability","score":0.6572999954223633},{"id":"https://openalex.org/keywords/greenhouse-gas","display_name":"Greenhouse gas","score":0.6075999736785889},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.5914000272750854},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5697000026702881},{"id":"https://openalex.org/keywords/carbon-fibers","display_name":"Carbon fibers","score":0.47440001368522644},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.43650001287460327},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.3952000141143799}],"concepts":[{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.7128000259399414},{"id":"https://openalex.org/C66204764","wikidata":"https://www.wikidata.org/wiki/Q219416","display_name":"Sustainability","level":2,"score":0.6572999954223633},{"id":"https://openalex.org/C47737302","wikidata":"https://www.wikidata.org/wiki/Q167336","display_name":"Greenhouse gas","level":2,"score":0.6075999736785889},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.5914000272750854},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5697000026702881},{"id":"https://openalex.org/C140205800","wikidata":"https://www.wikidata.org/wiki/Q5860","display_name":"Carbon fibers","level":3,"score":0.47440001368522644},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.43650001287460327},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.43209999799728394},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.4009000062942505},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.3952000141143799},{"id":"https://openalex.org/C134560507","wikidata":"https://www.wikidata.org/wiki/Q753291","display_name":"Environmental economics","level":1,"score":0.37959998846054077},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.3644999861717224},{"id":"https://openalex.org/C76758496","wikidata":"https://www.wikidata.org/wiki/Q191118","display_name":"Tonne","level":2,"score":0.35910001397132874},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.34769999980926514},{"id":"https://openalex.org/C2993678126","wikidata":"https://www.wikidata.org/wiki/Q167751","display_name":"Carbon flux","level":3,"score":0.3441999852657318},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.33880001306533813},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.3165999948978424},{"id":"https://openalex.org/C2780898871","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Performance metric","level":2,"score":0.314300000667572},{"id":"https://openalex.org/C2781420532","wikidata":"https://www.wikidata.org/wiki/Q140182","display_name":"Carbon accounting","level":3,"score":0.3138999938964844},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.2989000082015991},{"id":"https://openalex.org/C133199616","wikidata":"https://www.wikidata.org/wiki/Q25386885","display_name":"Empirical modelling","level":2,"score":0.2750000059604645},{"id":"https://openalex.org/C107826830","wikidata":"https://www.wikidata.org/wiki/Q929380","display_name":"Environmental resource management","level":1,"score":0.2689000070095062},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.265500009059906},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.26260000467300415},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2619999945163727},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.25690001249313354},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2540000081062317}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.01549","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.01549","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.01549","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.01549","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.5226486325263977,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"scaling-law":[1],"era":[2],"has":[3,170],"transformed":[4],"artificial":[5],"intelligence":[6],"(AI)":[7],"from":[8,194],"research":[9],"into":[10],"a":[11,87,120,146,185],"global":[12],"industry,":[13],"but":[14],"its":[15,73],"rapid":[16],"growth":[17],"also":[18],"raises":[19],"concerns":[20],"over":[21,167],"energy":[22],"usage,":[23],"carbon":[24,38,94,140,179,200],"emissions,":[25,107],"and":[26,62,89,110,197,208],"environmental":[27],"sustainability.":[28],"Unlike":[29],"traditional":[30],"sectors,":[31],"the":[32,47,58,69,81,150,161,204],"AI":[33,79,138,205],"industry":[34],"still":[35],"lacks":[36],"systematic":[37],"accounting":[39],"methods":[40],"that":[41,131,159],"support":[42],"large-scale":[43,88],"estimates":[44],"without":[45],"reproducing":[46],"original":[48],"training":[49,99,139,160,192],"process.":[50],"This":[51],"leaves":[52],"open":[53],"questions":[54],"about":[55],"how":[56,63],"large":[57,64],"problem":[59],"is":[60],"today":[61],"it":[65],"might":[66],"be":[67],"in":[68,76,173,203],"near":[70],"future.":[71],"Given":[72],"central":[74],"role":[75],"hosting":[77],"open-source":[78,103,164],"models,":[80],"Hugging":[82],"Face":[83],"(HF)":[84],"platform":[85],"provides":[86,184],"publicly":[90],"accessible":[91],"corpus":[92],"for":[93,190],"accounting.":[95],"We":[96,135],"estimate":[97],"aggregate":[98],"emissions":[100,143,193],"of":[101,153,178],"HF":[102],"models":[104,165],"using":[105],"available":[106,211],"energy,":[108],"compute,":[109],"model":[111,154],"metadata.":[112],"To":[113],"address":[114],"uneven":[115],"disclosure":[116],"quality,":[117],"we":[118],"introduce":[119,137],"tiered":[121],"approach":[122],"to":[123,148],"handle":[124],"incomplete":[125,195],"metadata,":[126],"supported":[127],"by":[128],"empirical":[129],"regressions":[130],"assess":[132,149],"estimation":[133],"reliability.":[134],"further":[136],"intensity":[141],"(ATCI,":[142],"per":[144],"compute),":[145],"metric":[147,176],"sustainability":[151],"efficiency":[152],"training.":[155],"Our":[156],"results":[157],"show":[158],"most":[162],"popular":[163],"(with":[166],"5,000":[168],"downloads)":[169],"already":[171],"resulted":[172],"approximately":[174],"$6.0\\times10^4$":[175],"tons":[177],"emissions.":[180],"Overall,":[181],"this":[182],"paper":[183],"scalable,":[186],"empirically":[187],"grounded":[188],"framework":[189],"estimating":[191],"disclosures":[196],"informing":[198],"future":[199],"reporting":[201],"standards":[202],"industry.":[206],"Data":[207],"code":[209],"are":[210],"at":[212],"https://github.com/insait-institute/HuggingCarbon.":[213]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-06T00:00:00"}