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Optimizing the Location of Biomass Energy Facilities by Integrating Multi-Criteria Analysis (MCA) and Geographical Information Systems (GIS)

1
ARC Centre for Forest Value, Discipline of ICT, College of Science and Engineering, University of Tasmania, Hobart, TAS 7001, Australia
2
Forest Industries Research Centre, University of the Sunshine Coast, Locked Bag 4, Maroochydore DC, Queensland 4558, Australia
3
Private Forests Tasmania, 30 Patrick Street, Hobart, TAS 7000, Australia
*
Author to whom correspondence should be addressed.
Forests 2018, 9(10), 585; https://doi.org/10.3390/f9100585
Received: 4 September 2018 / Revised: 12 September 2018 / Accepted: 18 September 2018 / Published: 20 September 2018
(This article belongs to the Special Issue Supply Chain Optimization for Biomass and Biofuels)
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Abstract

Internationally forest biomass is considered to be a valuable renewable energy feedstock. However, utilization of forest harvesting residues is challenging because they are highly varied, generally of low quality and usually widely distributed across timber harvesting sites. Factors related to the collection, processing and transport impose constraints on the economic viability of residue utilization operations and impact their supply from dispersed feedstock locations. To optimize decision-making about suitable locations for biomass energy plants intending to use forest residues, it is essential to factor in these supply chain considerations. This study conducted in Tasmania, Australia presents an investigation into the integration of Multi-criteria analysis (MCA) and Geographical Information systems (GIS) to identify optimal locations for prospective biomass power plants. The amount of forest harvesting biomass residues was estimated based on a non-industrial private native resource model in Tasmania (NIPNF). The integration of MCA and a GIS model, including a supply chain cost analysis, allowed the identification and analysis of optimal candidate locations that balanced economic, environmental, and social criteria within the biomass supply. The study results confirm that resource availability, land use and supply chain cost data can be integrated and mapped using GIS to facilitate the determination of different sustainable criteria weightings, and to ultimately generate optimal candidate locations for biomass energy plants. It is anticipated that this paper will make a contribution to current scientific knowledge by presenting innovative approaches for the sustainable utilization of forest harvest residues as a resource for the generation of bioenergy in Tasmania. View Full-Text
Keywords: biomass supply chain optimization; facility location; multi-criteria analysis; analytic hierarchy process; GIS; Tasmania; Australia biomass supply chain optimization; facility location; multi-criteria analysis; analytic hierarchy process; GIS; Tasmania; Australia
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Woo, H.; Acuna, M.; Moroni, M.; Taskhiri, M.S.; Turner, P. Optimizing the Location of Biomass Energy Facilities by Integrating Multi-Criteria Analysis (MCA) and Geographical Information Systems (GIS). Forests 2018, 9, 585.

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