{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T11:11:13Z","timestamp":1773918673885,"version":"3.50.1"},"reference-count":95,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T00:00:00Z","timestamp":1730678400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100018933","name":"Clausthal University of Technology","doi-asserted-by":"publisher","award":["Open Access Publishing Fund"],"award-info":[{"award-number":["Open Access Publishing Fund"]}],"id":[{"id":"10.13039\/501100018933","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Assessing the sustainable development of green hydrogen and assessing its potential environmental impacts using the Life Cycle Assessment is crucial. Challenges in LCA, like missing environmental data, are often addressed using machine learning, such as artificial neural networks. However, to find an ML solution, researchers need to read extensive literature or consult experts. This research demonstrates how customised LLMs, trained with domain-specific papers, can help researchers overcome these challenges. By starting small by consolidating papers focused on the LCA of proton exchange membrane water electrolysis, which produces green hydrogen, and ML applications in LCA. These papers are uploaded to OpenAI to create the LlamaIndex, enabling future queries. Using the LangChain framework, researchers query the customised model (GPT-3.5-turbo), receiving tailored responses. The results demonstrate that customised LLMs can assist researchers in providing suitable ML solutions to address data inaccuracies and gaps. The ability to quickly query an LLM and receive an integrated response across relevant sources presents an improvement over manually retrieving and reading individual papers. This shows that leveraging fine-tuned LLMs can empower researchers to conduct LCAs more efficiently and effectively.<\/jats:p>","DOI":"10.3390\/make6040122","type":"journal-article","created":{"date-parts":[[2024,11,5]],"date-time":"2024-11-05T09:40:54Z","timestamp":1730799654000},"page":"2494-2514","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Advancing Life Cycle Assessment of Sustainable Green Hydrogen Production Using Domain-Specific Fine-Tuning by Large Language Models Augmentation"],"prefix":"10.3390","volume":"6","author":[{"given":"Yajing","family":"Chen","sequence":"first","affiliation":[{"name":"Institute of Mineral and Waste Processing, Recycling and Circular Economy Systems, Clausthal University of Technology, 38678 Clausthal-Zellerfeld, Germany"}]},{"given":"Urs","family":"Liebau","sequence":"additional","affiliation":[{"name":"Center for Digital Green Tech, August-Wilhelm-Scheer Institute, 38678 Clausthal-Zellerfeld, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6820-741X","authenticated-orcid":false,"given":"Shreyas Mysore","family":"Guruprasad","sequence":"additional","affiliation":[{"name":"Center for Digital Green Tech, August-Wilhelm-Scheer Institute, 38678 Clausthal-Zellerfeld, Germany"}]},{"given":"Iaroslav","family":"Trofimenko","sequence":"additional","affiliation":[{"name":"Center for Digital Green Tech, August-Wilhelm-Scheer Institute, 38678 Clausthal-Zellerfeld, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0481-2850","authenticated-orcid":false,"given":"Christine","family":"Minke","sequence":"additional","affiliation":[{"name":"Institute of Mineral and Waste Processing, Recycling and Circular Economy Systems, Clausthal University of Technology, 38678 Clausthal-Zellerfeld, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.ijhydene.2023.08.321","article-title":"Green Hydrogen: A Pathway to a Sustainable Energy Future","volume":"50","author":"Hassan","year":"2024","journal-title":"Int. J. Hydrogen Energy"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"119351","DOI":"10.1016\/j.apenergy.2022.119351","article-title":"Experiments and Microsimulation of High-Pressure Single-Cell PEM Electrolyzer","volume":"321","author":"Dang","year":"2022","journal-title":"Appl. Energy"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e9120032","DOI":"10.26599\/NRE.2022.9120032","article-title":"Status and Perspectives of Key Materials for PEM Electrolyzer","volume":"1","author":"Zhang","year":"2022","journal-title":"Nano Res. Energy"},{"key":"ref_4","first-page":"442","article-title":"Hydrogen Production by PEM Water Electrolysis\u2014A Review","volume":"2","author":"Himabindu","year":"2019","journal-title":"Mater. Sci. Energy Technol."},{"key":"ref_5","unstructured":"(2006). Environmental Management Life Cycle Assessment Principles and Framework (Standard No. BS EN ISO 1404)."},{"key":"ref_6","unstructured":"(2006). Environmental Management Life Cycle Assessment Requirements and Guidelines (Standard No. DIN EN ISO 14044)."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"168969","DOI":"10.1016\/j.scitotenv.2023.168969","article-title":"A Review of Machine Learning Applications in Life Cycle Assessment Studies","volume":"912","author":"Romeiko","year":"2024","journal-title":"Sci. Total Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1162\/daed_a_01909","article-title":"Do Large Language Models Understand Us?","volume":"151","year":"2022","journal-title":"Daedalus"},{"key":"ref_9","unstructured":"(2024, June 04). A Comprehensive Overview of Large Language Models. Available online: https:\/\/ar5iv.labs.arxiv.org\/html\/2307.06435."},{"key":"ref_10","unstructured":"Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., and Askell, A. (2020). Language Models Are Few-Shot Learners. arXiv."},{"key":"ref_11","first-page":"9","article-title":"Language Models Are Unsupervised Multitask Learners","volume":"1","author":"Radford","year":"2019","journal-title":"OpenAI Blog"},{"key":"ref_12","first-page":"27730","article-title":"Training Language Models to Follow Instructions with Human Feedback","volume":"35","author":"Ouyang","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"107062","DOI":"10.1016\/j.resconrec.2023.107062","article-title":"How Can Transformers and Large Language Models like ChatGPT Help LCA Practitioners?","volume":"196","author":"Cornago","year":"2023","journal-title":"Resour. Conserv. Recycl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"142824","DOI":"10.1016\/j.jclepro.2024.142824","article-title":"Large Language Models for Life Cycle Assessments: Opportunities, Challenges, and Risks","volume":"466","author":"Preuss","year":"2024","journal-title":"J. Clean. Prod."},{"key":"ref_15","unstructured":"Namvarpour, M., and Razi, A. (2024). Apprentices to Research Assistants: Advancing Research with Large Language Models. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"121329","DOI":"10.1016\/j.jclepro.2020.121329","article-title":"Impacts of Life Cycle Inventory Databases on Life Cycle Assessments: A Review by Means of a Drivetrain Case Study","volume":"269","author":"Kalverkamp","year":"2020","journal-title":"J. Clean. Prod."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"862","DOI":"10.1016\/j.apenergy.2019.01.001","article-title":"Life Cycle Assessment of Hydrogen from Proton Exchange Membrane Water Electrolysis in Future Energy Systems","volume":"237","author":"Hamacher","year":"2019","journal-title":"Appl. Energy"},{"key":"ref_18","unstructured":"(2024, October 24). Ecoinvent Database. Available online: https:\/\/ecoinvent.org\/."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Pauer, E., Wohner, B., and Tacker, M. (2020). The Influence of Database Selection on Environmental Impact Results. Life Cycle Assessment of Packaging Using GaBi, Ecoinvent 3.6, and the Environmental Footprint Database. Sustainability, 12.","DOI":"10.3390\/su12239948"},{"key":"ref_20","unstructured":"(2024, October 24). GaBi Database & Modelling Principles 2012. Available online: http:\/\/gabi-6-lci-documentation.gabi-software.com\/xml-data\/external_docs\/GaBiModellingPrinciples.pdf."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"110648","DOI":"10.1016\/j.buildenv.2023.110648","article-title":"The Impact of Life Cycle Assessment Database Selection on Embodied Carbon Estimation of Buildings","volume":"243","author":"Teng","year":"2023","journal-title":"Build. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Su, D. (2020). Sustainable Product Development: Tools, Methods and Examples, Springer International Publishing.","DOI":"10.1007\/978-3-030-39149-2"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1007\/BF02978471","article-title":"Comparison of Three Different LCIA Methods: EDIP97, CML2001 and Eco-Indicator 99: Does It Matter Which One You Choose?","volume":"8","author":"Dreyer","year":"2003","journal-title":"Int. J. Life Cycle Assess."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.ecolind.2016.01.056","article-title":"Correlations in Life Cycle Impact Assessment Methods (LCIA) and Indicators for Construction Materials: What Matters?","volume":"67","author":"Lasvaux","year":"2016","journal-title":"Ecol. Indic."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"107575","DOI":"10.1016\/j.eiar.2024.107575","article-title":"Analyzing the Suitability of LCIA Methods to Foster the Most Beneficial Food Loss and Waste Prevention Action in Terms of Environmental Sustainability","volume":"107","year":"2024","journal-title":"Environ. Impact Assess. Rev."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2315","DOI":"10.1007\/s11367-019-01653-3","article-title":"A Taste of the New ReCiPe for Life Cycle Assessment: Consequences of the Updated Impact Assessment Method on Food Product LCAs","volume":"25","author":"Dekker","year":"2020","journal-title":"Int. J. Life Cycle Assess."},{"key":"ref_27","unstructured":"(2024, October 24). Commission Recommendation on the Use of the Environmental Footprint Methods 2021. Available online: https:\/\/environment.ec.europa.eu\/publications\/recommendation-use-environmental-footprint-methods_en."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2300135","DOI":"10.1002\/aesr.202300135","article-title":"Life Cycle Assessment of a 5 MW Polymer Exchange Membrane Water Electrolysis Plant","volume":"5","author":"Peterssen","year":"2024","journal-title":"Adv. Energy Sustain. Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"10143","DOI":"10.1016\/j.ijhydene.2020.06.190","article-title":"Life-Cycle Assessment of Hydrogen Technologies with the Focus on EU Critical Raw Materials and End-of-Life Strategies","volume":"46","author":"Mori","year":"2021","journal-title":"Int. J. Hydrogen Energy"},{"key":"ref_30","unstructured":"(2024, October 24). iPoint Umberto. Available online: https:\/\/www.ifu.com\/umberto\/."},{"key":"ref_31","unstructured":"(2024, October 24). Brightway Developers Brightway LCA Software Framework. Available online: https:\/\/docs.brightway.dev\/en\/latest\/."},{"key":"ref_32","unstructured":"(2024, October 24). European Commission European Platform on LCA\u2014EPLCA\u2014Environmental Footprint. Available online: https:\/\/eplca.jrc.ec.europa.eu\/EnvironmentalFootprint.html."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Rosenbaum, R.K. (2018). Overview of Existing LCIA Methods\u2014Annex to Chapter 10. Life Cycle Assessment, Springer.","DOI":"10.1007\/978-3-319-56475-3_40"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"32313","DOI":"10.1016\/j.ijhydene.2023.05.031","article-title":"Present and Future Cost of Alkaline and PEM Electrolyser Stacks","volume":"48","author":"Krishnan","year":"2023","journal-title":"Int. J. Hydrogen Energy"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.jclepro.2013.07.048","article-title":"Life Cycle Assessment of Hydrogen Production via Electrolysis\u2014A Review","volume":"85","author":"Bhandari","year":"2014","journal-title":"J. Clean. Prod."},{"key":"ref_36","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017, January 4\u20139). Attention Is All You Need. Proceedings of the Advances in Neural Information Processing Systems 30 (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_37","unstructured":"Calzolari, N., B\u00e9chet, F., Blache, P., Choukri, K., Cieri, C., Declerck, T., Goggi, S., Isahara, H., Maegaard, B., and Mariani, J. (2022, January 20\u201325). Attention Understands Semantic Relations. Proceedings of the Thirteenth Language Resources and Evaluation Conference, Marseille, France."},{"key":"ref_38","unstructured":"Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2018). BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding. arXiv."},{"key":"ref_39","unstructured":"Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv."},{"key":"ref_40","unstructured":"OpenAI, Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F.L., Almeida, D., Altenschmidt, J., and Altman, S. (2023). GPT-4 Technical Report. arXiv."},{"key":"ref_41","unstructured":"Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozi\u00e8re, B., Goyal, N., Hambro, E., and Azhar, F. (2023). LLaMA: Open and Efficient Foundation Language Models. arXiv."},{"key":"ref_42","unstructured":"Minaee, S., Mikolov, T., Nikzad, N., Chenaghlu, M., Socher, R., Amatriain, X., and Gao, J. (2024). Large Language Models: A Survey. arXiv."},{"key":"ref_43","unstructured":"Boyko, J., Cohen, J., Fox, N., Veiga, M.H., Li, J.I.-H., Liu, J., Modenesi, B., Rauch, A.H., Reid, K.N., and Tribedi, S. (2023). An Interdisciplinary Outlook on Large Language Models for Scientific Research. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Abu-Jeyyab, M., Alrosan, S., and Alkhawaldeh, I. (2023). Harnessing Large Language Models in Medical Research and Scientific Writing: A Closer Look to The Future: LLMs in Medical Research and Scientific Writing. High Yield Med. Rev., 1.","DOI":"10.59707\/hymrFBYA5348"},{"key":"ref_45","unstructured":"Zhang, Q., Ding, K., Lyv, T., Wang, X., Yin, Q., Zhang, Y., Yu, J., Wang, Y., Li, X., and Xiang, Z. (2024). Scientific Large Language Models: A Survey on Biological & Chemical Domains. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Routray, S.K., Javali, A., Sharmila, K.P., Jha, M.K., Pappa, M., and Singh, M. (2023, January 10\u201312). Large Language Models (LLMs): Hypes and Realities. Proceedings of the 2023 International Conference on Computer Science and Emerging Technologies (CSET), Bangalore, India.","DOI":"10.1109\/CSET58993.2023.10346621"},{"key":"ref_47","unstructured":"Lu, D., Deng, Y., Malof, J.M., and Padilla, W.J. (2024). Can Large Language Models Learn the Physics of Metamaterials? An Empirical Study with ChatGPT. arXiv."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1002\/ev.20556","article-title":"Large Language Model Applications for Evaluation: Opportunities and Ethical Implications","volume":"2023","author":"Head","year":"2023","journal-title":"New Dir. Eval."},{"key":"ref_49","first-page":"e43292","article-title":"Chat GPT and Artificial Intelligence in Medical Writing: Concerns and Ethical Considerations","volume":"15","author":"Doyal","year":"2023","journal-title":"Cureus"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Watkins, R. (2023). Guidance for Researchers and Peer-Reviewers on the Ethical Use of Large Language Models (LLMs) in Scientific Research Workflows. AI Ethics, 1\u20136.","DOI":"10.1007\/s43681-023-00294-5"},{"key":"ref_51","unstructured":"Sahoo, P., Singh, A.K., Saha, S., Jain, V., Mondal, S., and Chadha, A. (2024). A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications. arXiv."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Dhuliawala, S., Komeili, M., Xu, J., Raileanu, R., Li, X., Celikyilmaz, A., and Weston, J. (2023). Chain-of-Verification Reduces Hallucination in Large Language Models. arXiv.","DOI":"10.18653\/v1\/2024.findings-acl.212"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1016\/j.jclepro.2013.03.028","article-title":"Prognostication of Environmental Indices in Potato Production Using Artificial Neural Networks","volume":"52","author":"Khoshnevisan","year":"2013","journal-title":"J. Clean. Prod."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"122321","DOI":"10.1016\/j.apenergy.2023.122321","article-title":"Machine Learning Assisted Techno-Economic and Life Cycle Assessment of Organic Solid Waste Upgrading under Natural Gas","volume":"355","author":"Omidkar","year":"2024","journal-title":"Appl. Energy"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Algren, M., Fisher, W., and Landis, A.E. (2021). Machine Learning in Life Cycle Assessment. Data Science Applied to Sustainability Analysis, Elsevier.","DOI":"10.1016\/B978-0-12-817976-5.00009-7"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"122472","DOI":"10.1016\/j.apenergy.2023.122472","article-title":"Predicting Whole-Life Carbon Emissions for Buildings Using Different Machine Learning Algorithms: A Case Study on Typical Residential Properties in Cornwall, UK","volume":"357","author":"Zheng","year":"2024","journal-title":"Appl. Energy"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.1016\/j.scitotenv.2019.02.004","article-title":"Combined Life Cycle Assessment and Artificial Intelligence for Prediction of Output Energy and Environmental Impacts of Sugarcane Production","volume":"664","author":"Kaab","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"100849","DOI":"10.1016\/j.ijft.2024.100849","article-title":"Prediction of Hydrogen Production in Proton Exchange Membrane Water Electrolysis via Neural Networks","volume":"24","author":"Tawalbeh","year":"2024","journal-title":"Int. J. Thermofluids"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"121152","DOI":"10.1016\/j.jenvman.2024.121152","article-title":"Decision Tree-Based Approach to Extrapolate Life Cycle Inventory Data of Manufacturing Processes","volume":"360","author":"Saad","year":"2024","journal-title":"J. Environ. Manag."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"105343","DOI":"10.1016\/j.envsoft.2022.105343","article-title":"Prediction of Environmental Missing Data Time Series by Support Vector Machine Regression and Correlation Dimension Estimation","volume":"150","author":"Camastra","year":"2022","journal-title":"Environ. Model. Softw."},{"key":"ref_61","unstructured":"(2024, October 24). CML-IA Characterisation Factors. Available online: https:\/\/www.universiteitleiden.nl\/en\/research\/research-output\/science\/cml-ia-characterisation-factors."},{"key":"ref_62","unstructured":"(2024, October 24). United Stated Environmental Protection Agency Tool for Reduction and Assessment of Chemicals and Other Environmental Impacts (TRACI), Available online: https:\/\/www.epa.gov\/chemical-research\/tool-reduction-and-assessment-chemicals-and-other-environmental-impacts-traci."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Gupta, M.R. (2024). ChatGPT-A Generative Pre-Trained Transformer. Int. J. Adv. Res. Sci. Commun. Technol., 590\u2013595.","DOI":"10.48175\/IJARSCT-15087"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"119661","DOI":"10.1016\/j.jclepro.2019.119661","article-title":"Sustainability Assessment and Modeling Based on Supervised Machine Learning Techniques: The Case for Food Consumption","volume":"251","author":"Abdella","year":"2020","journal-title":"J. Clean. Prod."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1016\/j.enbuild.2016.05.054","article-title":"Multi-Objective Optimization of Building Envelope Design for Life Cycle Environmental Performance","volume":"126","author":"Azari","year":"2016","journal-title":"Energy Build."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ecolmodel.2019.02.018","article-title":"Surrogate-Based Multi-Objective Optimization of Management Options for Agricultural Landscapes Using Artificial Neural Networks","volume":"400","author":"Nguyen","year":"2019","journal-title":"Ecol. Model."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"105393","DOI":"10.1016\/j.envint.2019.105393","article-title":"Estimate Ecotoxicity Characterization Factors for Chemicals in Life Cycle Assessment Using Machine Learning Models","volume":"135","author":"Hou","year":"2020","journal-title":"Environ. Int."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"113258","DOI":"10.1016\/j.enconman.2020.113258","article-title":"Slow Pyrolysis as a Platform for Negative Emissions Technology: An Integration of Machine Learning Models, Life Cycle Assessment, and Economic Analysis","volume":"223","author":"Cheng","year":"2020","journal-title":"Energy Convers. Manag."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.scs.2018.09.032","article-title":"Improving Life Cycle-Based Exploration Methods by Coupling Sensitivity Analysis and Metamodels","volume":"44","author":"Duprez","year":"2019","journal-title":"Sustain. Cities Soc."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.ecolind.2014.10.028","article-title":"Extending Life Cycle Assessment Normalization Factors and Use of Machine Learning\u2014A Slovenian Case Study","volume":"50","author":"Slapnik","year":"2015","journal-title":"Ecol. Indic."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Hauschild, M.Z., Rosenbaum, R.K., and Olsen, S.I. (2018). Life Cycle Assessment\u2014Theory and Practice, Springer.","DOI":"10.1007\/978-3-319-56475-3"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Amasyali, K., and El-Gohary, N.M. (2018). A Review of Data-Driven Building Energy Consumption Prediction Studies. Renew. Sustain. Energy Rev., 1192\u20131205.","DOI":"10.1016\/j.rser.2017.04.095"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Yitmen, I., Alizadehsalehi, S., Akiner, L., and Akiner, M.E. (2021). An Adapted Model of Cognitive Digital Twins for Building Lifecycle Management. Appl. Sci., 11.","DOI":"10.3390\/app11094276"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"104440","DOI":"10.1016\/j.autcon.2022.104440","article-title":"Artificial Intelligence and Smart Vision for Building and Construction 4.0: Machine and Deep Learning Methods and Applications","volume":"141","author":"Baduge","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1007\/s00170-021-06882-1","article-title":"Artificial Intelligence in Product Lifecycle Management","volume":"114","author":"Wang","year":"2021","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"117993","DOI":"10.1016\/j.jclepro.2019.117993","article-title":"Artificial Neural Networks to Assess Energy and Environmental Performance of Buildings: An Italian Case Study","volume":"239","author":"Ciulla","year":"2019","journal-title":"J. Clean. Prod."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"117379","DOI":"10.1016\/j.enconman.2023.117379","article-title":"An Integration of Machine Learning Models and Life Cycle Assessment for Lignocellulosic Bioethanol Platforms","volume":"292","author":"Long","year":"2023","journal-title":"Energy Convers. Manag."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"4087","DOI":"10.1016\/j.procs.2022.09.471","article-title":"Machine Learning for Environmental Life Cycle Costing","volume":"207","author":"Markowska","year":"2022","journal-title":"Procedia Comput. Sci."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Koyamparambath, A., Adibi, N., Szablewski, C., Adibi, A.S., and Sonnemann, G. (2022). Implementing Artificial Intelligence Techniques to Predict Environmental Impacts: Case of Construction Products. Sustainability, 6.","DOI":"10.3390\/su14063699"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"105412","DOI":"10.1016\/j.autcon.2024.105412","article-title":"Predictive Models in Machine Learning for Strength and Life Cycle Assessment of Concrete Structures","volume":"162","author":"Dinesh","year":"2024","journal-title":"Autom. Constr."},{"key":"ref_81","first-page":"689","article-title":"Environmental Assessment Coupled with Machine Learning for Circular Economy","volume":"25","author":"Prioux","year":"2023","journal-title":"Clean Technol. Environ. Policy"},{"key":"ref_82","first-page":"e00370","article-title":"Prediction of Greenhouse Gas Emissions Reductions via Machine Learning Algorithms: Toward an Artificial Intelligence-Based Life Cycle Assessment for Automotive Lightweighting","volume":"31","author":"Akhshik","year":"2022","journal-title":"Sustain. Mater. Technol."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1007\/s43995-023-00028-y","article-title":"Machine Learning for Embodied Carbon Life Cycle Assessment of Buildings","volume":"14","author":"Hafdaoui","year":"2023","journal-title":"J. Umm Al-Qura Univ. Eng. Archit."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.envint.2015.05.011","article-title":"Machine Learning for Toxicity Characterization of Organic Chemical Emissions Using USEtox Database: Learning the Structure of the Input Space","volume":"83","author":"Marvuglia","year":"2015","journal-title":"Environ. Int."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"11141","DOI":"10.1021\/acssuschemeng.0c02211","article-title":"Application of Life Cycle Assessment and Machine Learning for High-Throughput Screening of Green Chemical Substitutes","volume":"8","author":"Zhu","year":"2020","journal-title":"ACS Sustain. Chem. Eng."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"122619","DOI":"10.1016\/j.apenergy.2024.122619","article-title":"A Data-Driven Framework for Designing a Renewable Energy Community Based on the Integration of Machine Learning Model with Life Cycle Assessment and Life Cycle Cost Parameters","volume":"358","author":"Elomari","year":"2024","journal-title":"Appl. Energy"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"101545","DOI":"10.1016\/j.rineng.2023.101545","article-title":"Life Cycle Energy and Environmental Impacts in Sugarcane Production: A Case Study of Amirkabir Sugarcane Agro-Industrial Company in Khuzestan Province","volume":"20","author":"Nejad","year":"2023","journal-title":"Results Eng."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1007\/BF02978784","article-title":"Handbook on Life Cycle Assessment\u2014Operational Guide to the ISO Standards","volume":"6","year":"2001","journal-title":"Int. J. Life Cycle Assess"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Guo, G., He, Y., Jin, F., Ma\u0161ek, O., and Huang, Q. (2023). Application of Life Cycle Assessment and Machine Learning for the Production and Environmental Sustainability Assessment of Hydrothermal Bio-Oil. Bioresour. Technol., 379.","DOI":"10.1016\/j.biortech.2023.129027"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Portolani, P., Vitali, A., Cornago, S., Rovelli, D., Brondi, C., Low, J.S.C., Ramakrishna, S., and Ballarino, A. (2022). Machine Learning to Forecast Electricity Hourly LCA Impacts Due to a Dynamic Electricity Technology Mix. Front. Sustain., 3.","DOI":"10.3389\/frsus.2022.1037497"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"106704","DOI":"10.1016\/j.elecom.2020.106704","article-title":"Novel Components in Proton Exchange Membrane (PEM) Water Electrolyzers (PEMWE): Status, Challenges and Future Needs. A Mini Review","volume":"114","author":"Shirvanian","year":"2020","journal-title":"Electrochem. Commun."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1007\/s11367-022-02030-3","article-title":"Advances in Application of Machine Learning to Life Cycle Assessment: A Literature Review","volume":"27","author":"Ghoroghi","year":"2022","journal-title":"Int. J. Life Cycle Assess."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.buildenv.2014.04.025","article-title":"Comparison of Life Cycle Assessment Databases: A Case Study on Building Assessment","volume":"79","author":"Takano","year":"2014","journal-title":"Build. Environ."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"2300449","DOI":"10.1002\/adsu.202300449","article-title":"Electrolyzer and Fuel Cell Recycling for a Circular Hydrogen Economy","volume":"8","author":"Uekert","year":"2024","journal-title":"Adv. Sustain. Syst."},{"key":"ref_95","unstructured":"Barei\u00df, K. (2020). An Enhanced Methodology for Energy System Modeling Including Life-Cycle Analysis: Hydrogen as Power-to-X Element. [Ph.D. Thesis, Technical University of Munich]."}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/6\/4\/122\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:28:18Z","timestamp":1760113698000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/6\/4\/122"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,4]]},"references-count":95,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["make6040122"],"URL":"https:\/\/doi.org\/10.3390\/make6040122","relation":{},"ISSN":["2504-4990"],"issn-type":[{"value":"2504-4990","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,4]]}}}