{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T10:36:52Z","timestamp":1773916612420,"version":"3.50.1"},"reference-count":58,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T00:00:00Z","timestamp":1773705600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Social Science Fund of China","award":["22BJY259"],"award-info":[{"award-number":["22BJY259"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>Energy poverty remains an important challenge for sustainable development in China, with pronounced regional disparities and evolving temporal dynamics that require accurate and interpretable prediction tools. This study develops a provincial panel-based framework that combines Energy Poverty Index (EPI) construction, SSA-LSTM prediction, SHAP-based model interpretation, and two-way fixed effects (TWFE) regression analysis. Using provincial data for China (2003\u20132022), we first construct a composite EPI with the entropy weight method, then apply a Sparrow Search Algorithm (SSA) to optimize LSTM hyperparameters for EPI forecasting. SHAP is used to interpret feature contributions to model-predicted EPI, and TWFE regression is used to provide complementary panel-data evidence on factor\u2013EPI associations. The results show that the SSA-LSTM model outperforms benchmark machine learning and deep learning models in out-of-sample prediction performance. SHAP-based interpretation indicates that variables such as GDP, energy intensity, and power generation per capita contribute strongly to prediction variation, with notable regional heterogeneity. TWFE results are broadly consistent with several key patterns identified in the SHAP analysis. Overall, the proposed framework provides an accurate and interpretable provincial energy poverty prediction approach and offers a useful empirical reference for energy poverty monitoring and policy discussion.<\/jats:p>","DOI":"10.3390\/systems14030319","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T16:20:14Z","timestamp":1773764414000},"page":"319","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Analysis of Influencing Factors and Prediction of Provincial Energy Poverty in China Based on Explainable Deep Learning"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-0583-7151","authenticated-orcid":false,"given":"Zihao","family":"Fan","sequence":"first","affiliation":[{"name":"School of Economics, Beijing Technology and Business University, Beijing 102488, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengying","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Economics, Beijing Technology and Business University, Beijing 102488, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3195-5631","authenticated-orcid":false,"given":"Yile","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Economics, Beijing Technology and Business University, Beijing 102488, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"127487","DOI":"10.1016\/j.energy.2023.127487","article-title":"The perspective of energy poverty and 1st energy crisis of green transition","volume":"275","author":"Hussain","year":"2023","journal-title":"Energy"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Halkos, G.E., and Aslanidis, P.-S.C. (2023). Addressing Multidimensional Energy Poverty Implications on Achieving Sustainable Development. Energies, 16.","DOI":"10.3390\/en16093805"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Sun, X., Zheng, X., Li, S., Zhang, J., and Shi, H. (2025). The Impact of Rural Energy Poverty on Primary Health Services Efficiency: The Case of China. Systems, 13.","DOI":"10.3390\/systems13080675"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"110396","DOI":"10.1016\/j.est.2023.110396","article-title":"Energy poverty in the face of stringent environmental policies: An analysis of mitigating role of energy storage in China","volume":"81","author":"Haroon","year":"2024","journal-title":"J. Energy Storage"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"125973","DOI":"10.1016\/j.energy.2022.125973","article-title":"Do the photovoltaic poverty alleviation programs alleviate local energy poverty?\u2014Empirical evidence of 9 counties in rural China","volume":"263","author":"Zhao","year":"2023","journal-title":"Energy"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"141821","DOI":"10.1016\/j.jclepro.2024.141821","article-title":"Can smart energy alleviate energy poverty in China?\u2014Empirical evidence using synthetic control methods","volume":"449","author":"Tang","year":"2024","journal-title":"J. Clean. Prod."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"112209","DOI":"10.1016\/j.rser.2022.112209","article-title":"Regional cooperation for mitigating energy poverty in Sub-Saharan Africa: A context-based approach through the tripartite lenses of access, sufficiency, and mobility","volume":"159","author":"Monyei","year":"2022","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"9411326","DOI":"10.1155\/2024\/9411326","article-title":"Predictive Modeling of Energy Poverty with Machine Learning Ensembles: Strategic Insights from Socioeconomic Determinants for Effective Policy Implementation","volume":"2024","author":"Gawusu","year":"2024","journal-title":"Int. J. Energy Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"116178","DOI":"10.1016\/j.rser.2025.116178","article-title":"Quantifying gender in energy poverty: A critical review of data, methodologies and contextual constraints","volume":"225","author":"Zhang","year":"2026","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Jessel, S., Sawyer, S., and Hern\u00e1ndez, D. (2019). Energy, poverty, and health in climate change: A comprehensive review of an emerging literature. Front. Public Health, 7.","DOI":"10.3389\/fpubh.2019.00357"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1016\/j.esd.2022.11.005","article-title":"A multidimensional energy poverty measurement in China\u2014Based on the entropy method","volume":"71","author":"Liang","year":"2022","journal-title":"Energy Sustain. Dev."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"103420","DOI":"10.1016\/j.erss.2024.103420","article-title":"Revisiting energy poverty measurement for the European Union","volume":"109","author":"Kashour","year":"2024","journal-title":"Energy Res. Soc. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"112234","DOI":"10.1016\/j.enbuild.2022.112234","article-title":"Energy poverty: The paradox between low income and increasing household energy consumption in Brazil","volume":"268","author":"Leder","year":"2022","journal-title":"Energy Build."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"120034","DOI":"10.1016\/j.renene.2024.120034","article-title":"Does China\u2019s outward foreign direct investment alleviate energy poverty in host countries? Evidence from countries along the Belt and Road Initiative","volume":"223","author":"Zhou","year":"2024","journal-title":"Renew. Energy"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wu, W., Li, M., Li, J., and Wang, Y. (2025). Exploring the role of renewable energy expansion in mitigating energy poverty in China. Environ. Dev. Sustain., 1\u201319.","DOI":"10.1007\/s10668-025-07077-4"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1007\/s10260-025-00808-x","article-title":"Application of small area estimation using big data sources to estimate electricity consumption per capita households (case study: Sub-district level in Central Java, Indonesia)","volume":"34","author":"Odriansyah","year":"2025","journal-title":"Stat. Methods Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"103263","DOI":"10.1016\/j.apgeog.2024.103263","article-title":"The Russia-Ukraine war, energy poverty, and social conflict: An analysis based on global liquified natural gas maritime shipping","volume":"166","author":"Zhang","year":"2024","journal-title":"Appl. Geogr."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.rser.2011.07.150","article-title":"Measuring energy poverty: Focusing on what matters","volume":"16","author":"Nussbaumer","year":"2012","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"102756","DOI":"10.1016\/j.scs.2021.102756","article-title":"Energy poverty indicators: A systematic literature review and comprehensive analysis of integrity","volume":"67","author":"Streimikiene","year":"2021","journal-title":"Sustain. Cities Soc."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"104315","DOI":"10.1016\/j.erss.2025.104315","article-title":"Assessing the effects of low-carbon transition on energy poverty: Empirical evidence from selected EU member states","volume":"127","author":"Tutar","year":"2025","journal-title":"Energy Res. Soc. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1016\/j.rser.2015.03.041","article-title":"Energy poverty in China: An index based comprehensive evaluation","volume":"47","author":"Wang","year":"2015","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"109135","DOI":"10.1016\/j.eneco.2026.109135","article-title":"Can photovoltaic poverty alleviation reduce multidimensional energy poverty? Evidence from 1928 counties in China","volume":"154","author":"Zhou","year":"2026","journal-title":"Energy Econ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"115023","DOI":"10.1109\/ACCESS.2019.2933915","article-title":"Relay-Assisted D2D Underlay Cellular Network Analysis Using Stochastic Geometry: Overview and Future Directions","volume":"7","author":"Amodu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"108560","DOI":"10.1016\/j.eneco.2025.108560","article-title":"Will alleviating energy poverty enhance social trust in China? An approach based on dual machine learning modeling","volume":"147","author":"Liu","year":"2025","journal-title":"Energy Econ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"138502","DOI":"10.1016\/j.energy.2025.138502","article-title":"Determinants of energy poverty among Chinese households: Risk prediction model using machine learning algorithms","volume":"337","author":"Lu","year":"2025","journal-title":"Energy"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1126\/science.aaa8415","article-title":"Machine learning: Trends, perspectives, and prospects","volume":"349","author":"Jordan","year":"2015","journal-title":"Science"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"52138","DOI":"10.1109\/ACCESS.2018.2870052","article-title":"Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)","volume":"6","author":"Adadi","year":"2018","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","article-title":"From Local Explanations to Global Understanding with Explainable AI for Trees","volume":"2","author":"Lundberg","year":"2020","journal-title":"Nat. Mach. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Li, Z., and Ke, Z. (2025). Cross-modal augmentation for low-resource language understanding and generation. Proceedings of the 1st Workshop on Multimodal Augmented Generation via Multimodal Retrieval (MAGMaR 2025), Vienna, Austria, 31 July 2025, Association for Computational Linguistics.","DOI":"10.18653\/v1\/2025.magmar-1.9"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Li, Z., and Ke, Z. (2025). Domain meets typology: Predicting verb-final order from universal dependencies for financial and blockchain NLP. Proceedings of the 7th Workshop on Research in Computational Linguistic Typology and Multilingual NLP, Vienna, Austria, 1 August 2025, Association for Computational Linguistics.","DOI":"10.18653\/v1\/2025.sigtyp-1.15"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","article-title":"Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead","volume":"1","author":"Rudin","year":"2019","journal-title":"Nat. Mach. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1111\/ecog.02881","article-title":"Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure","volume":"40","author":"Roberts","year":"2017","journal-title":"Ecography"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1146\/annurev-economics-080217-053433","article-title":"Machine Learning Methods That Economists Should Know About","volume":"11","author":"Athey","year":"2019","journal-title":"Annu. Rev. Econ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1016\/j.energy.2018.09.056","article-title":"Artificial neural networks and linear regression prediction models for social housing allocation: Fuel Poverty Potential Risk Index","volume":"164","year":"2018","journal-title":"Energy"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"108027","DOI":"10.1016\/j.eneco.2024.108027","article-title":"The interaction of income inequality and energy poverty on global carbon emissions: A dynamic panel data approach","volume":"140","author":"Wang","year":"2024","journal-title":"Energy Econ."},{"key":"ref_36","first-page":"100841","article-title":"Energy poverty and environmental degradation in South Asian countries with the role of poverty, globalization and institutional quality: Static panel data approach","volume":"27","author":"Khan","year":"2025","journal-title":"Environ. Sustain. Indic."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1007\/s10668-024-05015-4","article-title":"Spatial analysis and predictive modeling of energy poverty: Insights for policy implementation","volume":"28","author":"Gawusu","year":"2026","journal-title":"Environ. Dev. Sustain."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/j.eap.2026.01.032","article-title":"Potential for energy poverty reduction by error decomposition with machine learning","volume":"90","author":"Koppolu","year":"2026","journal-title":"Econ. Anal. Policy"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1038\/s41560-018-0095-2","article-title":"Challenges and gaps for energy planning models in the developing-world context","volume":"3","author":"Debnath","year":"2018","journal-title":"Nat. Energy"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3575","DOI":"10.1002\/ese3.1541","article-title":"Electricity theft detection for energy optimization using deep learning models","volume":"11","author":"Pamir","year":"2023","journal-title":"Energy Sci. Eng."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"133904","DOI":"10.1016\/j.energy.2024.133904","article-title":"Forecasting energy poverty using different machine learning techniques for Missouri","volume":"313","author":"Balkissoon","year":"2024","journal-title":"Energy"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Qi, H., Xue, Q., Shi, Y., Qi, X., Yang, J., Zheng, J., and Ren, L. (2025). Leveraging Explainable AI to Decode Energy Poverty in China: Implications for SDGs and National Policy. Sustainability, 17.","DOI":"10.3390\/su172411080"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"101225","DOI":"10.1016\/j.sftr.2025.101225","article-title":"Revisiting China\u2019s energy poverty: Machine learning insights into key predictors and nonlinear relationships","volume":"10","author":"Li","year":"2025","journal-title":"Sustain. Futures"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Guo, L., Yu, F., Sui, C., and Yang, M. (2026). Country-Level Vulnerability in Maritime Bulk Commodity Supply Chains: An Integrated Framework for Identification, Monitoring, and Extrapolation. Systems, 14.","DOI":"10.3390\/systems14020120"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Kwilinski, A., Lyulyov, O., and Pimonenko, T. (2024). Energy Poverty and Democratic Values: A European Perspective. Energies, 17.","DOI":"10.3390\/en17122837"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"35775","DOI":"10.1038\/s41598-025-11793-2","article-title":"Application of the adaptive sparrow search algorithm in medical supply engineering","volume":"15","author":"Li","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"7307","DOI":"10.1038\/s41598-025-91939-4","article-title":"Integrating evolutionary algorithms and enhanced-YOLOv8+ for comprehensive apple ripeness prediction","volume":"15","author":"Li","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"107890","DOI":"10.1016\/j.frl.2025.107890","article-title":"Early warning of cryptocurrency reversal risks via multi-source data","volume":"85","author":"Ke","year":"2025","journal-title":"Financ. Res. Lett."},{"key":"ref_49","first-page":"45","article-title":"Long Short-Term Memory","volume":"Volume 385","author":"Graves","year":"2012","journal-title":"Supervised Sequence Labelling with Recurrent Neural Networks"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Chen, X., Hu, G., and Wen, H. (2025). Investigating the Factors Influencing Household Financial Vulnerability in China: An Exploration Based on the Shapley Additive Explanations Approach. Sustainability, 17.","DOI":"10.3390\/su17125523"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s11205-023-03129-2","article-title":"Multidimensional Energy Poverty in China: Measurement and Spatio-Temporal Disparities Characteristics","volume":"168","author":"Wang","year":"2023","journal-title":"Soc. Indic. Res."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Singh, N.K., and Nagahara, M. (2024). LightGBM-, SHAP-, and Correlation-Matrix-Heatmap-Based Approaches for Analyzing Household Energy Data: Towards Electricity Self-Sufficient Houses. Energies, 17.","DOI":"10.20944\/preprints202407.1769.v1"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Madan, S., Lentzen, M., Brandt, J., Rueckert, D., Hofmann-Apitius, M., and Fr\u00f6hlich, H. (2024). Transformer models in biomedicine. BMC Med. Inform. Decis. Mak., 24.","DOI":"10.1186\/s12911-024-02600-5"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1016\/S0731-7085(99)00272-1","article-title":"Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research","volume":"22","author":"Beresford","year":"2000","journal-title":"J. Pharm. Biomed. Anal."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"114302","DOI":"10.1016\/j.asoc.2025.114302","article-title":"A stable technical feature with GRU-CNN-GA fusion","volume":"187","author":"Zong","year":"2026","journal-title":"Appl. Soft Comput."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"9119","DOI":"10.1016\/j.egyr.2022.07.033","article-title":"Optimal allocation of regional water resources based on simulated annealing particle swarm optimization algorithm","volume":"8","author":"Wang","year":"2022","journal-title":"Energy Rep."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Abualigah, L. (2024). 1\u2014Particle swarm optimization algorithm: Review and applications. Metaheuristic Optimization Algorithms, Morgan Kaufmann.","DOI":"10.1016\/B978-0-443-13925-3.00019-4"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"145645","DOI":"10.1016\/j.jclepro.2025.145645","article-title":"Water erosion risk assessment and predictive modelling for cultural heritage under climate change: A case study of the Great Wall in the Yellow River Basin, China","volume":"510","author":"Wang","year":"2025","journal-title":"J. Clean. Prod."}],"container-title":["Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-8954\/14\/3\/319\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T05:18:58Z","timestamp":1773897538000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-8954\/14\/3\/319"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,17]]},"references-count":58,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["systems14030319"],"URL":"https:\/\/doi.org\/10.3390\/systems14030319","relation":{},"ISSN":["2079-8954"],"issn-type":[{"value":"2079-8954","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,17]]}}}