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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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-12-01
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| Series: | Sustainable Futures |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666188825005490 |
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| Summary: | 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. |
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| ISSN: | 2666-1888 |