Global modeling of wood biomass supply costs in G4M and GLOBIOM

Fulvio Di Fulvio, of the Ecosystems Services and Management Program, aims to map global forest resources and calculate the costs of supplying wood biomass for both material and energy uses.

Fulvio Di Fulvio

Fulvio Di Fulvio

Introduction

There is increasing competition for wood biomass between material and energy uses, and this is expected to increase in future, due to the limited availability of forest resources. Currently, the supply costs for wood biomass from forests and plantations in different countries are not well known. At the same time, it is difficult to compare information from the existing studies that are based on single case studies and a harmonized approach to modeling wood production and delivery costs is missing. This research project set out to fill this knowledge gap, and to provide relevant information needed for a global assessment of land-use change and climate impacts within the Global Biosphere Management Model (GLOBIOM) and the Global Forest Model (G4M). The first part of this project focused on creating a general framework for harmonizing forest production costs in different economies. I now aim to refine the general structure by using specific models and parameters for adapting the framework to the specific countries and forest conditions.

Methods

The project integrates models from the literature for assessing forest production rates, global economic indicators for adaptation of costing and information from experts for validation. Relevant wood supply systems are selected according to specific forestry features. For each of the systems, production rates were obtained by using models from the literature applied to the characteristics of geographically explicit production and delivery sites. Since costs are strongly influenced by demographic borders, the costs for operating each of the production systems were collected from experts and databases in the reference countries and adapted to each of the countries by using global economic indicators. Specific indicators are used for transferring the costs for machinery, labor and fuels, as each of them affected in different ways by the country borders. Information on the unitary costs and operative characteristics for each of the systems was collected by collaborating with experts in the different regions, in order to iteratively validate and refine the current modeling.

Expected results

The project aims to map forestry production systems globally, while accounting for the unitary costs for each of the systems in different forest conditions and countries. By integrating the potential amounts of harvestable biomass in future scenarios from G4M and GLOBIOM with the unitary costs from the current modeling, it will be possible to obtain cost supply curves for different wood biomass assortments. The cost supply curves will be used as an instrument for comparing delivered amounts, costs, and prices and addressing future changes in the allocation of land for forests and plantations. In addition to the costs, efficiencies will be expressed also in terms of labor demand, energy demand and CO2 emissions. Currently I am modeling selected forest production systems at the European scale and early results are available for the supply of roundwood for material use and logging residues for energy.

Note

Fulvio Di Fulvio is an Italian citizen, who was previously studying at the Swedish University of Agricultural Sciences, and is a Kempe Postdoctoral Scholar (Aug 2014 – Aug 2016).


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Last edited: 02 March 2016

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Davis KF, Yu K, Herrero M, Havlik P, Carr JA, & D’Odorico P (2015). Historical trade-offs of livestock’s environmental impacts. Environmental Research Letters 10 (12): p. 125013. DOI:10.1088/1748-9326/10/12/125013.

Wilson C & Grubler A (2015). Historical Characteristics and Scenario Analysis of Technological Change in the Energy System. In: Technology and Innovation for Sustainable Development. Eds. Vos, R. & Alarcon, D., pp. 45-80 Norwich, UK: Bloomsbury Academic. ISBN 978-1-4725-8079-5 DOI:10.5040/9781472580795.ch-003.

Duarte R, Feng K, Hubacek K, Sanchez-Choliz J, Sarasa C, & Sun L (2015). Modeling the carbon consequences of pro-environmental consumer behavior. Applied Energy 184: 1207-1216. DOI:10.1016/j.apenergy.2015.09.101.

Mochizuki J, Vitoontus S, Wickramarachchi B, Hochrainer-Stigler S, Williges K, Mechler R, & Sovann R (2015). Operationalizing iterative risk management under limited information: fiscal and economic risks due to natural disasters in Cambodia. International Journal of Disaster Risk Science 6 (4): 321-334. DOI:10.1007/s13753-015-0069-y.

Prell C, Sun L, Feng K, & Myroniuk T W (2015). Inequalities in Global Trade: A Cross-Country Comparison of Trade Network Position, Economic Wealth, Pollution and Mortality. PLoS ONE 10 (12): e0144453. DOI:10.1371/journal.pone.0144453.

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