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When fuelwood is harvested at a rate exceeding natural growth and inefficient conversion technologies are used, negative environmental and socio-economic impacts, such as fuelwood shortages, natural forests degradation and net GHG emissions arise. In this study, we argue that analyzing fuelwood supply/demand spatial patterns require multiscale approaches to effectively bridge the gap between national results with local situations.


Greenhouse gas release from land use change (the socalled ?carbon debt?) has been identified as a potentially significant contributor to the environmental profile of biofuels. The time required for biofuels to overcome this carbon debt duetolandusechangeandbeginprovidingcumulativegreenhouse gas benefits is referred to as the ?payback period? and has been estimated to be 100-1000 years depending on the specific ecosystem involved in the land use change event. Two mechanisms for land use change exist: ?direct?


The different versions of the CLUE model (CLUE, CLUE-CR, CLUE-s, Dyna-CLUE and CLUE-Scanner) are among the most frequently used land use models globally. Applications range from small regions to entire continents. The CLUE model is a flexible, generic land use modeling framework which allows scale and context specific specification for regional applications.

Verburg, P.H.

The actual land use consequences of crop consumption are not very well reflected in existing life cycle inventories. The state of the art is that such inventories typically include data from crop production in the country in which the crop is produced, and consequently the inventories do not necessarily consider the land ultimately affected in the systems being studied.

Kl?verpris Jesper

Ground-based data on crop production in the USA is provided through surveys conducted by the National Agricultural Statistics Service (NASS) and the Census of Agriculture (AgCensus). Statistics from these surveys are widely used in economic analyses, policy design, and for other purposes. However, missing data in the surveys presents limitations for research that requires comprehensive data for spatial analyses.We created comprehensive county-level databases for nine major crops of the USA for a 16-yr period, by filling the gaps in existing data reported by NASS and AgCensus.

Erandathie ,Lokupitiya

The Energy-Economy-Environment Modelling Laboratory E3MLab operating within the Institute of Communication and Computer Systems of the National Technical University of Athens (ICCS/NTUA), Department of Electrical and Computer Engineering, is a laboratory that specializes in the construction and use of large scale computerised models covering the areas of Energy, the Economy and the Environment. Such models are used to make projections and analyse complex issues requiring system-wide consideration. Special emphasis is given to policy analysis and support.

Two of the most widely used land-cover data sets for the United States are the National Land-Cover Data (NLCD) at 30-m resolution and the Global Land- Cover Characteristics (GLCC) at 1-km nominal resolution. Both data sets were produced around 1992 and expected to provide similar land-cover information. This study investigated the spatial distribution of NLCD within major GLCC classes at 1-km unit over a total of 11 agricultural-related eco-regions across the continental United States.

Pei-Yu Chen

Increasing energy use, climate change, and carbon dioxide (CO2) emissions from fossil fuels make switching to lowcarbon fuels a high priority. Biofuels are a potential lowcarbon energy source, but whether biofuels offer carbon savings depends on how they are produced. Converting rainforests, peatlands, savannas, or grasslands to produce food-based biofuels in Brazil, Southeast Asia, and the United States creates a ?biofuel carbon debt? by releasing 17 to 420 times more CO2 than the annual greenhouse gas (GHG) reductions these biofuels provide by displacing fossil fuels.

Fargione, Joseph

For several years the Idaho National Laboratory (INL) has been developing a Decision Support System for Agriculture (DSS4Ag) which determines the economically optimum recipe of various fertilizers to apply at each site in a field to produce a crop, based on the existing soil fertility at each site, as well as historic production information and current prices of fertilizers and the forecast market price of the crop at harvest.

Hoskinson Reed L.

This paper describes a methodology to explore the (future) spatial distribution of biofuel crops in Europe. Two main types of biofuel crops are distinguished: biofuel crops used for the production of biodiesel or bioethanol, and second-generation biofuel crops. A multiscale, multi-model approach is used in which biofuel crops are allocated over the period 2000?2030. The area of biofuel crops at the national level is determined by a macroeconomic model. A spatially explicit land use model is used to allocate the biofuel crops within the countries.


IBSAL is a dynamic simulation model of the connections existing between feedstock producers, biorefinery locations and the requisite storage and distribution systems. The model is primarily focused on the front end of the biofuels supply chain at the local level. The local data sources that are inputs include field area, dry matter, production equipment, soil and biomass moisture, weather conditions, transportation networks and associated costs. The model was developed at Oak Ridge National Laboratory.
This model can be downloaded from

Shahab Sokhansanj

The POLYSYS model uses policy variables along with land use and crop production data to provide a regional econometric model of biofuels feedstock production and pricing. The model was developed at the University of Tennessee and Oak Ridge National Laboratory and is used extensively at the University of Tennessee's Agricultural Policy Analysis Center.

GTM is an econometric model that estimates regional and national land use for timber production and associated greenhouse gas sequestration. It can be adapted for biomass models that include wood chip or wood biomass as part of the biofuels feedstock supply chain.

The model is a vehicle fuel-cycle model for transportation systems. The model provides a set of outcomes that would involve feedstock production, biorefinery production, storage and consumer demand as the complete fuel-cycle. The data is internal to the model, but might be adaptive to different biofuels specifications. This model was developed by the Energy Systems Division at Argonne National Laboratory.

Michael Wang
Funded from the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Bioenergy Technologies Office.

HyDRA is a GIS visualization tool that can be adapted for examination of biofuels infrastructure across the supply chain. HyDRA was developed at NREL for the geographic representation of the hydrogen fuel supply chain. Required data would be spatial data on crop production, storage and biorefinery locations, the transportation network for finished biofuels, blending facilities and consumer demand locations.

PNNL and the University of Maryland's Joint Global Change Research Institute is the home and primary development institution for the Global Change Assessment Model (GCAM - formerly MiniCAM), an integrated assessment tool for exploring consequences and responses to global change. GCAM is a dynamic-recursive model with technology-rich representations of the economy, energy sector, land use and water linked to a climate model that can be used to explore climate change mitigation policies including carbon taxes, carbon trading, regulations and accelerated deployment of energy technology.

Phoenix replaces the Second Generation Model (SGM) that was formerly used, and is a policy analysis tool that operates at regional and national scales using economic input-output data for agriculture, transportation land use, energy consumption, and various policy variables. Resulting outputs are consumer demand for biofuels and regional agricultural land use devoted to biomass production. This model was developed at Pacific Northwest National Laboratory.