{"id":"https://openalex.org/W7138464525","doi":"https://doi.org/10.3390/systems14030319","title":"Analysis of Influencing Factors and Prediction of Provincial Energy Poverty in China Based on Explainable Deep Learning","display_name":"Analysis of Influencing Factors and Prediction of Provincial Energy Poverty in China Based on Explainable Deep Learning","publication_year":2026,"publication_date":"2026-03-17","ids":{"openalex":"https://openalex.org/W7138464525","doi":"https://doi.org/10.3390/systems14030319"},"language":"en","primary_location":{"id":"doi:10.3390/systems14030319","is_oa":true,"landing_page_url":"https://doi.org/10.3390/systems14030319","pdf_url":null,"source":{"id":"https://openalex.org/S4210219410","display_name":"Systems","issn_l":"2079-8954","issn":["2079-8954"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.3390/systems14030319","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Zihao Fan","orcid":"https://orcid.org/0009-0006-0583-7151"},"institutions":[{"id":"https://openalex.org/I179026463","display_name":"Beijing Technology and Business University","ror":"https://ror.org/013e0zm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I179026463"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zihao Fan","raw_affiliation_strings":["School of Economics, Beijing Technology and Business University, Beijing 102488, China"],"raw_orcid":"https://orcid.org/0009-0006-0583-7151","affiliations":[{"raw_affiliation_string":"School of Economics, Beijing Technology and Business University, Beijing 102488, China","institution_ids":["https://openalex.org/I179026463"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123838585","display_name":"Pengying Fan","orcid":null},"institutions":[{"id":"https://openalex.org/I179026463","display_name":"Beijing Technology and Business University","ror":"https://ror.org/013e0zm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I179026463"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pengying Fan","raw_affiliation_strings":["School of Economics, Beijing Technology and Business University, Beijing 102488, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Economics, Beijing Technology and Business University, Beijing 102488, China","institution_ids":["https://openalex.org/I179026463"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5123960171","display_name":"Yile Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I179026463","display_name":"Beijing Technology and Business University","ror":"https://ror.org/013e0zm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I179026463"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yile Wang","raw_affiliation_strings":["School of Economics, Beijing Technology and Business University, Beijing 102488, China"],"raw_orcid":"https://orcid.org/0009-0008-3195-5631","affiliations":[{"raw_affiliation_string":"School of Economics, Beijing Technology and Business University, Beijing 102488, China","institution_ids":["https://openalex.org/I179026463"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I179026463"],"apc_list":{"value":1600,"currency":"CHF","value_usd":1732},"apc_paid":{"value":1600,"currency":"CHF","value_usd":1732},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.31453774,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":"3","first_page":"319","last_page":"319"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11336","display_name":"Energy and Environment Impacts","score":0.5460000038146973,"subfield":{"id":"https://openalex.org/subfields/2310","display_name":"Pollution"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11336","display_name":"Energy and Environment Impacts","score":0.5460000038146973,"subfield":{"id":"https://openalex.org/subfields/2310","display_name":"Pollution"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10438","display_name":"Energy, Environment, Economic Growth","score":0.16050000488758087,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10435","display_name":"Environmental Impact and Sustainability","score":0.07569999992847443,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/poverty","display_name":"Poverty","score":0.5989999771118164},{"id":"https://openalex.org/keywords/per-capita","display_name":"Per capita","score":0.49470001459121704},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4902999997138977},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.43549999594688416},{"id":"https://openalex.org/keywords/energy-poverty","display_name":"Energy poverty","score":0.4205000102519989},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.412200003862381},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4011000096797943},{"id":"https://openalex.org/keywords/china","display_name":"China","score":0.3986000120639801},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.3871999979019165}],"concepts":[{"id":"https://openalex.org/C189326681","wikidata":"https://www.wikidata.org/wiki/Q10294","display_name":"Poverty","level":2,"score":0.5989999771118164},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5712000131607056},{"id":"https://openalex.org/C127598652","wikidata":"https://www.wikidata.org/wiki/Q558635","display_name":"Per capita","level":3,"score":0.49470001459121704},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4902999997138977},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4869000017642975},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.47200000286102295},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45010000467300415},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.43549999594688416},{"id":"https://openalex.org/C2777416314","wikidata":"https://www.wikidata.org/wiki/Q1341244","display_name":"Energy poverty","level":4,"score":0.4205000102519989},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.412200003862381},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4011000096797943},{"id":"https://openalex.org/C191935318","wikidata":"https://www.wikidata.org/wiki/Q148","display_name":"China","level":2,"score":0.3986000120639801},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.3871999979019165},{"id":"https://openalex.org/C552854447","wikidata":"https://www.wikidata.org/wiki/Q131201","display_name":"Sustainable development","level":2,"score":0.3822999894618988},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.36880001425743103},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.36320000886917114},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3434999883174896},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.34130001068115234},{"id":"https://openalex.org/C129963666","wikidata":"https://www.wikidata.org/wiki/Q17105857","display_name":"Small area estimation","level":3,"score":0.3319999873638153},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.33169999718666077},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.3102000057697296},{"id":"https://openalex.org/C131046424","wikidata":"https://www.wikidata.org/wiki/Q1805337","display_name":"Energy policy","level":3,"score":0.3095000088214874},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.3086000084877014},{"id":"https://openalex.org/C2777382242","wikidata":"https://www.wikidata.org/wiki/Q6017816","display_name":"Index (typography)","level":2,"score":0.3061999976634979},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.2971000075340271},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.29170000553131104},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2766000032424927},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.272599995136261},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.27239999175071716},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2671000063419342},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2565000057220459}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/systems14030319","is_oa":true,"landing_page_url":"https://doi.org/10.3390/systems14030319","pdf_url":null,"source":{"id":"https://openalex.org/S4210219410","display_name":"Systems","issn_l":"2079-8954","issn":["2079-8954"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Systems","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:f4d6f934e300430ab4b984f3e1bffe38","is_oa":true,"landing_page_url":"https://doaj.org/article/f4d6f934e300430ab4b984f3e1bffe38","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Systems, Vol 14, Iss 3, p 319 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/systems14030319","is_oa":true,"landing_page_url":"https://doi.org/10.3390/systems14030319","pdf_url":null,"source":{"id":"https://openalex.org/S4210219410","display_name":"Systems","issn_l":"2079-8954","issn":["2079-8954"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Systems","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/1","display_name":"No poverty","score":0.5429084897041321}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":56,"referenced_works":["https://openalex.org/W1901616594","https://openalex.org/W2010808248","https://openalex.org/W2072462334","https://openalex.org/W2078199941","https://openalex.org/W2560136348","https://openalex.org/W2787928697","https://openalex.org/W2891503716","https://openalex.org/W2892289649","https://openalex.org/W2922705140","https://openalex.org/W2945976633","https://openalex.org/W2969134041","https://openalex.org/W2987450187","https://openalex.org/W2999615587","https://openalex.org/W3126988156","https://openalex.org/W4211261155","https://openalex.org/W4281851323","https://openalex.org/W4286696742","https://openalex.org/W4308071520","https://openalex.org/W4309779729","https://openalex.org/W4363647641","https://openalex.org/W4367312374","https://openalex.org/W4376148914","https://openalex.org/W4385806565","https://openalex.org/W4390769038","https://openalex.org/W4391055565","https://openalex.org/W4391134486","https://openalex.org/W4392919573","https://openalex.org/W4393965340","https://openalex.org/W4396973366","https://openalex.org/W4398787479","https://openalex.org/W4399494446","https://openalex.org/W4399547269","https://openalex.org/W4400912187","https://openalex.org/W4401076218","https://openalex.org/W4404192526","https://openalex.org/W4404771429","https://openalex.org/W4408070952","https://openalex.org/W4410077178","https://openalex.org/W4410200458","https://openalex.org/W4411345541","https://openalex.org/W4412479275","https://openalex.org/W4412944325","https://openalex.org/W4412944429","https://openalex.org/W4413120019","https://openalex.org/W4413120915","https://openalex.org/W4413795606","https://openalex.org/W4413974662","https://openalex.org/W4414064381","https://openalex.org/W4414338679","https://openalex.org/W4415166883","https://openalex.org/W4416448426","https://openalex.org/W4416726299","https://openalex.org/W4417198614","https://openalex.org/W7119997432","https://openalex.org/W7124955747","https://openalex.org/W7125472167"],"related_works":[],"abstract_inverted_index":{"Energy":[0,35],"poverty":[1,174,185],"remains":[2],"an":[3,168],"important":[4],"challenge":[5],"for":[6,55,80,183],"sustainable":[7],"development":[8],"in":[9,121,159],"China,":[10],"with":[11,64,145,154],"pronounced":[12],"regional":[13,147],"disparities":[14],"and":[15,22,45,93,117,135,170,177,187],"evolving":[16],"temporal":[17],"dynamics":[18],"that":[19,33,109,128],"require":[20],"accurate":[21,169],"interpretable":[23,171],"prediction":[24,123,143,175],"tools.":[25],"This":[26],"study":[27],"develops":[28],"a":[29,61,71,179],"provincial":[30,53,172],"panel-based":[31],"framework":[32,166],"combines":[34],"Poverty":[36],"Index":[37],"(EPI)":[38],"construction,":[39],"SSA-LSTM":[40,111],"prediction,":[41],"SHAP-based":[42,125],"model":[43,112],"interpretation,":[44],"two-way":[46],"fixed":[47],"effects":[48],"(TWFE)":[49],"regression":[50,95],"analysis.":[51,162],"Using":[52],"data":[54],"China":[56],"(2003\u20132022),":[57],"we":[58],"first":[59],"construct":[60],"composite":[62],"EPI":[63,81],"the":[65,110,160,164],"entropy":[66],"weight":[67],"method,":[68],"then":[69],"apply":[70],"Sparrow":[72],"Search":[73],"Algorithm":[74],"(SSA)":[75],"to":[76,86,90,98,142],"optimize":[77],"LSTM":[78],"hyperparameters":[79],"forecasting.":[82],"SHAP":[83,161],"is":[84,96],"used":[85,97],"interpret":[87],"feature":[88],"contributions":[89],"model-predicted":[91],"EPI,":[92],"TWFE":[94,149],"provide":[99],"complementary":[100],"panel-data":[101],"evidence":[102],"on":[103],"factor\u2013EPI":[104],"associations.":[105],"The":[106],"results":[107,150],"show":[108],"outperforms":[113],"benchmark":[114],"machine":[115],"learning":[116,119],"deep":[118],"models":[120],"out-of-sample":[122],"performance.":[124],"interpretation":[126],"indicates":[127],"variables":[129],"such":[130],"as":[131],"GDP,":[132],"energy":[133,173,184],"intensity,":[134],"power":[136],"generation":[137],"per":[138],"capita":[139],"contribute":[140],"strongly":[141],"variation,":[144],"notable":[146],"heterogeneity.":[148],"are":[151],"broadly":[152],"consistent":[153],"several":[155],"key":[156],"patterns":[157],"identified":[158],"Overall,":[163],"proposed":[165],"provides":[167],"approach":[176],"offers":[178],"useful":[180],"empirical":[181],"reference":[182],"monitoring":[186],"policy":[188],"discussion.":[189]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2026-03-18T00:00:00"}