Net-zero biomass energy sustainable supply chain considering productivity opportunity gap using machine learning
The global transition towards sustainable energy solutions has underscored the need for optimizing biomass power plants to achieve net-zero emissions. This study presents an innovative approach to improving biomass energy sustainability by addressing the Productivity Opportunity Gap (POG) through ma...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Sustainable Futures |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666188825005490 |
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| author | Mohammadmahdi Abbaspour Hamed Fazlollahtabar |
| author_facet | Mohammadmahdi Abbaspour Hamed Fazlollahtabar |
| author_sort | Mohammadmahdi Abbaspour |
| collection | DOAJ |
| description | The global transition towards sustainable energy solutions has underscored the need for optimizing biomass power plants to achieve net-zero emissions. This study presents an innovative approach to improving biomass energy sustainability by addressing the Productivity Opportunity Gap (POG) through machine learning techniques. A Multi-Layer Perceptron (MLP)-based model is employed to evaluate and rank sustainability strategies across environmental, economic, and social dimensions. Expert-driven Likert-scale assessments are transformed using Rough Set Theory (RST) to ensure robustness in decision-making. The results highlight that resource efficiency, policy support, and stakeholder engagement are key drivers of biomass power plant sustainability. The study provides a data-driven framework that enhances decision-making accuracy and supports policymakers and industry stakeholders in optimizing biomass energy production while contributing to global decarbonization goals. |
| format | Article |
| id | doaj-art-e1793246d08b40f0a3043046963efa92 |
| institution | OA Journals |
| issn | 2666-1888 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Sustainable Futures |
| spelling | doaj-art-e1793246d08b40f0a3043046963efa922025-08-20T02:36:28ZengElsevierSustainable Futures2666-18882025-12-011010098510.1016/j.sftr.2025.100985Net-zero biomass energy sustainable supply chain considering productivity opportunity gap using machine learningMohammadmahdi Abbaspour0Hamed Fazlollahtabar1Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, IranDepartment of Industrial Engineering, School of Engineering, Damghan University, Damghan, Iran; Corresponding author.The global transition towards sustainable energy solutions has underscored the need for optimizing biomass power plants to achieve net-zero emissions. This study presents an innovative approach to improving biomass energy sustainability by addressing the Productivity Opportunity Gap (POG) through machine learning techniques. A Multi-Layer Perceptron (MLP)-based model is employed to evaluate and rank sustainability strategies across environmental, economic, and social dimensions. Expert-driven Likert-scale assessments are transformed using Rough Set Theory (RST) to ensure robustness in decision-making. The results highlight that resource efficiency, policy support, and stakeholder engagement are key drivers of biomass power plant sustainability. The study provides a data-driven framework that enhances decision-making accuracy and supports policymakers and industry stakeholders in optimizing biomass energy production while contributing to global decarbonization goals.http://www.sciencedirect.com/science/article/pii/S2666188825005490Biomass power plantsProductivity opportunity gapNet-zero energySustainability |
| spellingShingle | Mohammadmahdi Abbaspour Hamed Fazlollahtabar Net-zero biomass energy sustainable supply chain considering productivity opportunity gap using machine learning Sustainable Futures Biomass power plants Productivity opportunity gap Net-zero energy Sustainability |
| title | Net-zero biomass energy sustainable supply chain considering productivity opportunity gap using machine learning |
| title_full | Net-zero biomass energy sustainable supply chain considering productivity opportunity gap using machine learning |
| title_fullStr | Net-zero biomass energy sustainable supply chain considering productivity opportunity gap using machine learning |
| title_full_unstemmed | Net-zero biomass energy sustainable supply chain considering productivity opportunity gap using machine learning |
| title_short | Net-zero biomass energy sustainable supply chain considering productivity opportunity gap using machine learning |
| title_sort | net zero biomass energy sustainable supply chain considering productivity opportunity gap using machine learning |
| topic | Biomass power plants Productivity opportunity gap Net-zero energy Sustainability |
| url | http://www.sciencedirect.com/science/article/pii/S2666188825005490 |
| work_keys_str_mv | AT mohammadmahdiabbaspour netzerobiomassenergysustainablesupplychainconsideringproductivityopportunitygapusingmachinelearning AT hamedfazlollahtabar netzerobiomassenergysustainablesupplychainconsideringproductivityopportunitygapusingmachinelearning |