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We propose a causal analysis framework to increase understanding of land-use change (LUC) and the reliability of LUC models. This health-sciences-inspired framework can be applied to determine probable causes of LUC in the context of bioenergy. Calculations of net greenhouse gas (GHG) emissions for LUC associated with biofuel production are critical in determining whether a fuel qualifies as a biofuel or advanced biofuel category under regional (EU), national (US, UK), and state (California) regulations. Biofuel policymakers and scientists continue to discuss to what extent presumed indirect land-use change (ILUC) estimates should be included in GHG accounting for biofuel pathways. Current estimates of ILUC for bioenergy rely largely on economic simulation models that focus on causal pathways involving global commodity trade and use coarse land-cover data with simple land classification systems. This paper challenges the application of such models to estimate global areas of LUC in the absence of causal analysis. The proposed causal analysis framework begins with a definition of the change that has occurred and proceeds to a strength-of-evidence approach that includes plausibility of relationship, completeness of causal pathway, spatial co-occurrence, time order, analogous agents, simulation model results, and quantitative agent–response relationships. We discuss how LUC may be allocated among probable causes for policy purposes and how the application of the framework has the potential to increase the validity of LUC models and resolve controversies about ILUC, such as deforestation, and biofuels.

Phone
Publication Year
Email
efroymsonra@ornl.gov
DOI
http://dx.doi.org/10.1016/j.landusepol.2016.09.009
Contact Person
R. A. Efroymson
Contact Organization
Oak Ridge National Laboratory
Bioenergy Category
Author
Efroymson RA , Kline KL , Angelsen A , Verburg PH , Dale VH , Langeveld JWA , McBride A
Funded from the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Bioenergy Technologies Office.

For analyzing sustainability of algal biofuels, we identify 16 environmental indicators that fall into six categories: soil quality, water quality and quantity, air quality, greenhouse gas emissions, biodiversity, and productivity. Indicators are selected to be practical, widely applicable, predictable in response, anticipatory of future changes, independent of scale, and responsive to management. Major differences between algae and terrestrial plant feedstocks, as well as their supply chains for biofuel, are highlighted, for they influence the choice of appropriate sustainability indicators. Algae strain selection characteristics do not generally affect which indicators are selected. The use of water instead of soil as the growth medium for algae determines the higher priority of water- over soil-related indicators. The proposed set of environmental indicators provides an initial checklist for measures of algal biofuel sustainability but may need to be modified for particular contexts depending on data availability, goals of stakeholders, and financial constraints. Use of these indicators entails defining sustainability goals and targets in relation to stakeholder values in a particular context and can lead to improved management practices.

Phone
Publication Year
Email
efroymsonra@ornl.gov
Contact Person
R. A. Efroymson
Contact Organization
Oak Ridge National Laboratory
Bioenergy Category
Author
R. A. Efroymson
Funded from the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Bioenergy Technologies Office.

EXECUTIVE SUMMARY: Life cycle assessment (LCA) is a powerful tool that may be used to quantify the environmental impacts of products and services. It includes all processes, from cradle-to-grave, along the supply chain of the product. When analysing energy systems, greenhouse gas (GHG) emissions (primarily CO2, CH4 and N2O) are the impact of primary concern. In using LCA to determine the climate change mitigation benefits of bioenergy, the life cycle emissions of the bioenergy system are compared with the emissions for a reference energy system. The selection of reference energy system can strongly affect the outcome.
 
When reviewing the literature one finds large ranges of GHG emissions per unit of energy from LCA studies of similar bioenergy systems. The differences occur for a multitude of reasons including differences in technologies, system boundaries, and reference systems. Some studies may be incomplete in that the bioenergy system and reference system provide different services. Others may omit some sources of emissions (e.g. land use change).
 
This paper discusses key criteria for comprehensive LCAs based on IEA Bioenergy Task 38 case studies. LCAs of the GHG balance of four different bioenergy systems and their counterpart reference system are highlighted using the case study examples.
 
The first example investigates heat production from woody biomass and grasses. This study shows that the emissions saved for the same type of service can vary due to the source of the biomass. The bioenergy systems studied reduce GHG emissions by 75-85% as compared to the counterpart reference systems.
 
In the second example, electricity is produced from woody biomass using two different technologies with different efficiencies. Depending on the technology, the biomass must
be transported different distances. The example illustrates the importance of the efficiency of the system and the small impact of soil organic carbon (SOC) decline in comparison
with emissions saved. Since the bioenergy systems include carbon sequestration, they reduce GHG emissions by 108-128% as compared to the counterpart reference systems.
 
A biogas plant providing combined heat and power is analysed in the third example, which illustrates the importance of finding a beneficial use for the heat produced, and of controlling fugitive emissions. In the optimal configuration of closed storage and maximised use of heat, the biogas system reduces emissions by 71% as compared to the counterpart reference system. This reduction decreases to 44% when the heat is not fully used and to only 27% if fugitive emissions are not controlled.
 
In the final example the bioenergy system provides biodiesel for transport. This example demonstrates the importance of the use of co-products, as the same bioenergy chain produces very different emissions savings per kilometre depending on whether the co-product is used as a material or combusted for energy. Compared to the reference system, the bioenergy system reduce GHG emissions by 18% and 42% when the co-products are used for energy or materials respectively.
 
Similar to the case studies presented here, published studies find that GHG mitigation is greater where biomass is used for heat and electricity applications rather than for liquid transport fuels. Overall, the emissions savings from bioenergy systems tend to be similar to that of other renewable energy sources.

Despite a rapid worldwide expansion of the biofuel industry, there is a lack of consensus within the scientific community about the potential of biofuels to reduce reliance on petroleum and decrease greenhouse gas (GHG) emissions. Although life cycle assessment provides a means to quantify these potential benefits and environmental impacts, existing methods limit direct comparison within and between different biofuel systems because of inconsistencies in performance metrics, system boundaries, and underlying parameter values. There is a critical need for standardized life-cycle methods, metrics, and tools to evaluate biofuel systems based on performance of feedstock production and biofuel conversion at regional or national scales, as well as for estimating the net GHG mitigation of an individual biofuel production system to accommodate impending GHG-intensity regulations and GHG emissions trading. Predicting the performance of emerging biofuel systems (e.g., switchgrass cellulosic ethanol) poses additional challenges for life cycle assessment due to lack of commercial-scale feedstock production and conversion systems. Continued political support for the biofuel industry will be influenced by public perceptions of the contributions of biofuel systems towards mitigation of GHG emissions and reducing dependence on petroleum for transportation fuels. Standardization of key performance metrics such as GHG emissions mitigation and net energy yield are essential to help inform both public perceptions and public policy.

Recent legislative mandates have been enacted at state and federal levels with the purpose of reducing life cycle greenhouse gas (GHG) emissions from transportation fuels. This legislation encourages the substitution of fossil fuels with ‘low-carbon’ fuels. The burden is put on regulatory agencies to determine the GHG-intensity of various fuels, and those agencies naturally look to science for guidance. Even though much progress has been made in determining the direct life cycle emissions from the production of biofuels, the science underpinning the estimation of potentially signifi cant emissions from indirect land use change (ILUC) is in its infancy. As legislation requires inclusion of ILUC emissions in the biofuel life cycle, regulators are in a quandary over accurate implementation. In this article, we review these circumstances and offer some suggestions for how to proceed with the science of indirect effects and regulation in the face of uncertain science. Besides investigating indirect deforestation and grassland conversion alone, a more comprehensive assessment of the total GHG emissions implications of substituting biofuels for petroleum needs to be completed before indirect effects can be accurately determined. This review fi nds that indirect emissions from livestock and military security are particularly important, and deserve further research. © 2009 Society of Chemical Industry and John Wiley & Sons, Ltd

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