Abstract
Sustainable bioenergy production is essential for mitigating greenhouse gas emissions and reducing dependence on fossil fuels. The logistics of managing dispersed and low-value biomass from forestry, agricultural, and municipal solid waste value chains pose significant challenges, including high transportation costs, seasonal availability, and storage limitations. This systematic literature review examines the critical operations, including collection, transportation, and preprocessing, necessary to optimize bioenergy supply chains. A central contribution of this paper is an analysis of integrating biomass value chains through collaborative models that leverage shared infrastructure and adaptive logistics to enhance cost efficiency and resource utilization. It also identifies critical gaps in optimization models, particularly the lack of comprehensive multi-biomass value chain integration frameworks and limited consideration of uncertainties in logistics planning. The analysis highlights that while mixed integer linear programming models dominate, they often overlook cross-chain synergies and logistics. By examining 112 articles, we show that integrating forestry, agricultural, and municipal solid waste value chains through shared infrastructure and collaborative planning can significantly reduce transportation costs, enhance supply stability, and improve resource utilization in bioenergy systems.
| Original language | English |
|---|---|
| Article number | 101105 |
| Journal | Energy Conversion and Management: X |
| Volume | 27 |
| DOIs | |
| Publication status | Published - Jul 2025 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 2 Zero Hunger
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SDG 7 Affordable and Clean Energy
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SDG 11 Sustainable Cities and Communities
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SDG 12 Responsible Consumption and Production
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SDG 13 Climate Action
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SDG 15 Life on Land
!!!Keywords
- Biomass integration
- Multi-feedstock systems
- Preprocessing
- Supply chain optimization
- Sustainability logistics
- Transportation
- Waste-to-energy modeling
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