Author
Listed:
- Tiago Bastos
(DEGEIT, Departamento de Economia, Gestão, Engenharia Industrial e Turismo, Universidade de Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
Laboratório Associado de Sistemas Inteligentes (LASI), Instituto de Engenharia Eletrónica e Informática de Aveiro (IEETA), Universidade de Aveiro, 3810-193 Aveiro, Portugal)
- Leonor Teixeira
(DEGEIT, Departamento de Economia, Gestão, Engenharia Industrial e Turismo, Universidade de Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
Laboratório Associado de Sistemas Inteligentes (LASI), Instituto de Engenharia Eletrónica e Informática de Aveiro (IEETA), Universidade de Aveiro, 3810-193 Aveiro, Portugal)
- Leonel J. R. Nunes
(DEGEIT, Departamento de Economia, Gestão, Engenharia Industrial e Turismo, Universidade de Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
ESCE, Escola Superior de Ciências Empresariais, Instituto Politécnico de Viana do Castelo, Rua da Escola Industrial e Comercial de Nun’ Alvares, 4900-347 Viana do Castelo, Portugal
PROMETHEUS, Unidade de Investigação em Materiais, Energia, Ambiente para a Sustentabilidade, Instituto Politécnico de Viana do Castelo, Rua da Escola Industrial e Comercial de Nun’Alvares, 4900-347 Viana do Castelo, Portugal
CISAS, Instituto Politécnico de Viana do Castelo, Rua da Escola Industrial e Comercial de Nun’Alvares, 4900-347 Viana do Castelo, Portugal)
Abstract
Background : Rural fires, worsened by climate factors such as drought, biomass buildup, and ignition sources, threaten sustainability. Recovering residual biomass (RB) presents a promising way to lower fire risk by reducing fuel loads and generating renewable energy; however, logistical costs in the RB supply chain—due to poor stakeholder coordination—limit its feasibility. App-based models can help solve these issues by improving information sharing, but their economic viability remains largely unexplored. This study suggests that such models work well when large amounts of biomass are involved and moisture content is low. Still, they might need external incentives for widespread use and fire risk reduction. Methods : The study modeled recovery scenarios by comparing costs (harvesting, retrieval, transport, and pre-processing) with biomass market value, using expert inputs and sensitivity analysis on variables like fuel prices and wages. Results : The economic feasibility is possible for large volumes (e.g., 10-ton loads) with low moisture (<30%), allowing transportation distances up to 459 km; however, small-scale or high-moisture situations often are not viable without support. Conclusions : App-based models need external support, like subsidies, to overcome owner and RB challenges, ensuring effective fire mitigation and sustainability benefits.
Suggested Citation
Tiago Bastos & Leonor Teixeira & Leonel J. R. Nunes, 2025.
"App-Based Logistics for Residual Biomass Recovery: Economic Feasibility in Fire Risk Mitigation,"
Logistics, MDPI, vol. 9(3), pages 1-17, September.
Handle:
RePEc:gam:jlogis:v:9:y:2025:i:3:p:127-:d:1744334
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