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Forestry biomass residue supply chains in Tasmania : developing a digital tool and enhancing modelling to improve data accuracy, location mapping and impact assessments for future bioenergy

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Woo, H ORCID: 0000-0002-7649-0953 2020 , 'Forestry biomass residue supply chains in Tasmania : developing a digital tool and enhancing modelling to improve data accuracy, location mapping and impact assessments for future bioenergy', PhD thesis, University of Tasmania.

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Abstract

This research investigates forestry biomass residue supply chains in Tasmania with the aim of contributing new knowledge, tools and techniques to support decision-making on future bioenergy investments. This research develops and tests a prototype digital tool that improves the accuracy of residue estimations from harvesting operations. The research also enhances the modelling of optimised location mapping and socio-economic impact assessments for future bioenergy plants in Tasmania. The results generated by this research directly enhance the quality of information that can be used in decision-making about the feasibility of future bioenergy plants using forestry biomass residues. It is anticipated that the outputs of this research will contribute to supporting the forest industry and Governments to better balance sustainable practices alongside socio-economic priorities in Tasmania into the future.
Internationally the use of forestry biomass residues (including un-merchantable trees, small-diameter trees, tops, limbs and chunks) produced by commercial harvesting operations continues to grow. These biomass residues are already being used to produce bioenergy and bio-based forest products (including engineered wood products) through processes that enhance the value of these materials that have traditionally been perceived as waste. In Australia however, use of forest biomass residues continues to be relatively low, even though large volumes of residues are produced, and numerous governments have funded initiatives to stimulate increased utilisation.
In Tasmania, commercial timber harvesting and land-clearing operations produce approximately 6-8 million dry tons of forestry biomass residues that go uncollected or are removed through controlled burning every year. Treating these biomass residues as a waste product actually poses a number of socio-economic and environmental challenges and costs. However, efforts to develop forestry biomass residue utilisation initiatives have been relatively limited and fragmented along Tasmanian forestry supply chains. Despite some previous research highlighting evidence of the potential for both bioenergy generation and bio-based product development, there are a number of real and/or ‘perceived’ technical, logistical and economic challenges that continue to act as barriers to wide-scale forestry biomass residue utilisation in Tasmania. These challenges include that: (i) Forest residues are highly varied, of low quality and widely distributed across timber harvesting sites in ways that impose residue collection, processing and transportation problems that have implications for the economic viability of residue utilisation operations in any specific location; (ii) Despite the large volumes of residues, continued uncertainty exists about the nature, amount and quality of residues in any particular harvesting operation. Frequently there is also limited knowledge about other factors such as forest site accessibility at different times of the year, weather conditions, availability of pre-processing technology, pre-existing haulage contracting models, as well as total distances to and/or existence of markets for these residues.
The methodological approach used to conduct this investigation involved deploying both quantitative and qualitative techniques through case studies that aimed to combine improved data accuracy on biomass residues with quantitative optimisation modelling and socio-economic impact assessments to enhance decision-making on future bioenergy investments. The research strategy was structured in three phases. Phase one focused on improving the accuracy of estimated biomass availability through the development of FIELD (forest inventory electronic live data) tool capable of interpreting harvester head StanForD data combined with pre-existing allometric equations to determine residue availability. Phase two focused on optimal location mapping for prospective bio-energy facilities by integrating geographic information system (GIS) data on Tasmania with the analytic hierarchy process (AHP) to support multi-criteria decision-making in a selected case study region of Tasmania. In phase three, Meander Valley Council (MVC), one of the identified three biomass potential locations from Phase two, was chosen to explore through scenarios the socio-economic impacts from different sized proposed biomass energy plants for this case.
Analysis of phase one reveals how the FIELD digital tool can produce improved accuracy and near real-time data on the availability, quality and location of harvesting residues at any individual site. The analysis also highlights how FIELD opens up opportunities for value chain mapping. The FIELD software supports the generation of forest value maps that include valuable information for improving forest management and silviculture planning. The value map consists of individual tree DBH that are geo-located in the harvesting site. The use of combined value map and other environmental data such as soil composition, nutrients, and climate, can support analysis of how different site parameters affected tree growing and allow the development of more accurate predictive management plans about optimising tree planting to increase future timber productivity at any individual site. The FIELD tool also pointed towards the possibility of generating Tasmania-wide accurate estimations of forest biomass residues that could be geo-spatially linked into a residues availability database. Unfortunately, due to limitations in access to StanForD data across multiple sites, from multiple industry players, it was not possible to build this residues availability database during this research.
Importantly insights generated from phase one were directly useful for phase two analysis that focused on optimal location mapping for prospective bio-energy facilities. This location optimisation modelling relied on previously prepared residue availability estimations at the State-wide level. More specifically phase two analysis integrated GIS data, and an AHP model with logistics cost analysis to determine the best candidate locations based on optimal biomass logistics supply chain costings. The analysis utilised three main criteria (economic, environmental, and social) as well as a number of sub-criteria established and considered to investigate specific biomass facility locations. From the identified list of optimal locations, one was selected and modelled in detail using a range of scenarios covering different sizes of bio-energy facilities. The case study location selected was in the meander valley council area in northern Tasmania. The same case study location was then carried forward into phase three.
Phase three focused on a range of scenarios analysing socio-economic impact assessments of different sized future bioenergy plants in the meander value case study region. Modelling conducted investigated socio-economic impacts from a prospective co-generation bioenergy plant under 50 MW using data related to biomass energy generation in the case study area (Valley Central Industrial Precinct). The analysis was completed using the jobs and economic development impact model (JEDI) combined with biomass residues estimations based on a range of different distances from the optimal bioenergy facility location. The analysis modelled both the quantity and quality of biomass residue feedstocks suitable to make the most viable bioenergy facility while optimising socio-economic impacts.
The key results of these research investigations conducted across the three phases contribute new knowledge, tools and techniques for supporting decision-making on future bioenergy investments in Tasmania. More specifically, the key results produced can be summarised as follows:
• The FIELD digital tool does improve the accuracy of data on the availability and type of forestry biomass residues and directly answered the first research question of this investigation. The FIELD digital tool also supported the development of forest value maps for individual harvested locations that may be used to improve forest management and silviculture planning, including re-forestation. However, the research also identified several challenges for the wider adoption of the FIELD tool. These include the need for further validation to be conducted through field measurement comparison studies and non-matched allometric equations in study site conditions. Future validation research will further improve the accuracy and utility of the FIELD tool.
• In combining GIS and AHP techniques, it was possible to systematically identify and model optimal locations for future bioenergy plants in Tasmania and to interrogate the impact of different factors on the scenarios modelled including balancing economic and environmental considerations. The case study results confirm resource availability, land use and supply chain cost data can be meaningfully integrated and mapped using GIS to facilitate the determination of different sustainable criteria weightings and to ultimately generate optimal candidate locations for prospective bioenergy energy facilities. These results advance contemporary techniques by presenting innovative approaches for the sustainable utilisation of forestry biomass residues as a resource for the generation of bioenergy in Tasmania.
• In conducting socio-economic impact assessments across different scenarios for future bioenergy facilities in phase three, this research was able to model residue availability and bioenergy generation output as an approach to evaluate potential impacts under a range of conditions. Potential bioenergy residue feedstocks were categorised into viable onsite and offsite sources and quantified in terms of their different bioenergy outputs for different sized bioenergy plants. To complete the evaluation of the potential socio-economic impact of the proposed plant, the analysis was conducted using the JEDI (Jobs and Economic Development Impact model). The results confirm the potential for significant job creation and other socio-economic benefits during both the construction and plant operational periods. Additional socio-economic activity is also expected from the provision of low-cost renewable bioenergy attracting other businesses to establish at VCIP, and from the multiplier effects from related spending in the local economy. These techniques and the explicit articulation of the underlying assumptions used provide a method for studying bioenergy generation options from biomass residues.
It is anticipated that the results of this research investigation will be of direct value to researchers interested in forest supply chains involving biomass residue utilisation. It is also hoped that the results will be of value to the Tasmanian forest industry, their supply chain partners as well as contribute insights to the State government about potential future bioenergy investments.

Item Type: Thesis - PhD
Authors/Creators:Woo, H
Keywords: Forest biomass, Forest residue, biomass energy, precision forestry, value chain optimisation, Forest operation
DOI / ID Number: 10.25959/100.00035326
Copyright Information:

Copyright 2020 the author

Additional Information:

Chapter 2 appears to be, in part, the equivalent of a pre-print version of an article published as: Woo, H., Acuna, M., Cho, S., Park, J., 2019. Assessment techniques in forest biomass along the timber supply chain, Forests, 10(11), 1018. © 2019 by the authors. This is an open access article distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License, (https://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Chapters 3, 4 and 5 appear to be, in part, the equivalent of a pre-print version of an article published as: Woo, H., Acuna, M., Moroni, M., Taskhiri, M. S., Turner, P., 2018. Optimizing the location of biomass energy facilities by integrating multi-criteria analysis (MCA) and geographical information systems (GIS), Forests, 9(10), 585. © 2018 by the authors. This is an open access article distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License, (https://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Chapters 3, 4 and 5 also appear to be, in part, the equivalent of a pre-print version of an article published as: Woo, H., Moroni, M., Park, J., Taskhiri, M. S., Turner, P. 2020. Residues and bioenergy generation: A case study modelling value chain optimization in Tasmania, Energy, 196, 117007

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