Current Methods for Life-Cycle Analyses of Low-Carbon Transportation Fuels in the United States (2022) / Chapter Skim
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4 Key Considerations: Direct and Indirect Effects, Uncertainty, Variability, and Scale of Production
4 Key Considerations: Direct and Indirect Effects, Uncertainty, Variability, and Scale of Production
Pages 49-73
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From page 49... ...
In other definitions, focal activities are those associated with a particular process, a supply chain, or an economic sector. LCA studies differ substantially in how they choose focal activities and define direct and indirect emissions.
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Entity "Indirect emissions are those that result from an organization's activities, but are actually emitted from sources owned by other entities." GHG Protocol Entity "Direct GHG emissions are emissions from sources that are owned or controlled by the reporting entity. Indirect GHG emissions are emissions that are a consequence of the activities of the reporting entity, but occur at sources owned or controlled by another entity.
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From page 51... ...
triggered by crop-based biofuels…" On the other hand, according to these authors emissions from producing feedstock and conversion to biofuel is direct: "Direct releases of GHG also occur during the culti vation and industrial processing of maize ethanol. Estimates of these, not including ILUC." Figure 4-1 provides examples of how the boundaries between direct and indirect emissions differ across a selection of corn ethanol LCA studies and standards.
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For example, the GHG Protocol states "Indirect GHG emissions are emissions that are a consequence of the activities of the reporting entity." Some LCA studies of biofuels estimate fuel supply chain emissions using attributional LCA (ALCA) and refer to these as "direct emissions" but add consequential estimates of induced LUC as "indirect emissions." Table 4-2 shows the relationship between these concepts.
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TABLE 4-3 Examples of Factors Often Referred to as "Direct" or "Indirect" in the Transportation Fuels Literature Direct/Indirect Biofuel Examples Electric Vehicle Examples Potentially referred to Emissions from corn and soybeans in supply chain of N/A as direct effects an ethanol or biodiesel plant (Plevin et al., 2014a) Potentially referred to Land use emissions and sequestration effects due to Induced emissions due to changes in as indirect effects changes in demand for a given feedstock: demand for electricity: – changes in agricultural biomass, – shift in dispatch towards marginal – changes in forgone sequestration, resources to accommodate EVs – changes in soil organic carbon, – adjustments in generation capacity – changes of forest or grassland expansion planning (Taheripour et al., 2017)
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From page 54... ...
These are typically based on the source of emissions, the entities that generate the emissions, or, in the case of some approaches that mix ALCA and CLCA, the ALCA system boundary used in the analysis. Some ALCA studies use the ALCA system boundary to define focal activities but also add other factors beyond the ALCA system boundary, such as CLCA estimates of induced land use change due to market-mediated responses, and label all activities captured by the ALCA system boundary as direct effects.
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Nevertheless, valuation of tradeoffs among far distant futures influenced by planetary processes such as climate change, that the current generation will not even experience, present substantial challenges to any such methodologies. Stochastic modeling approaches such as Monte Carlo simulation or other Monte Carlo–type propagation techniques have been widely used in LCA to characterize uncertainty in model outputs given un
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. Most LCA studies apply Monte Carlo simulation for parameter uncertainty (Bamber et al., 2020)
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Parameter uncertainty is commonly considered in LCA studies. Scenario and model uncertainty are considered in some studies, but it is rare for LCA studies to explicitly consider all sources of uncertainty.
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Conclusion 4-3: Explicitly considering parametric, scenario, and model uncertainty can help to represent the degree of confidence in model results. Recommendation 4-3: LCA studies used to inform policy should explicitly consider parameter uncertainty, scenario uncertainty, and model uncertainty.
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Conclusion 4-5: LCA studies can produce different estimates depending on regional scope or as sumptions. Recommendation 4-6: LCA studies used to inform transportation fuel policy should be explicit about the feedstock and regions to which the study applies and to the extent possible should explic itly report the sensitivity of the results to variation in these assumptions.
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Conclusion 4-6: ALCA studies may produce substantially different results depending on modeling choices about how emissions are assigned to co-products. Recommendation 4-7: ALCA studies used to inform fuel policy should justify the approach used to handle co-products, and as necessary report sensitivity of results to variation in approaches to assigning emissions to co-products.
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Recommendation 4-8: LCA studies used to inform transportation policy regarding processes that do not yet exist at scale should explicitly report sensitivity of findings to uncertainty, in order to produce bounding estimates. Land Use Change Over the past 15 years various efforts have been made to assess the magnitudes of GHG emissions induced by changes in land use and land cover due to biofuel production and policy.
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Variation Due to the Choice of Amortization Time Period The choice of amortization time horizon directly affects the size of ILUC values. While the Intergovernmental Panel on Climate Change (IPCC)
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In general, other factors being equal, one may expect variation among ILUC values for biofuels produced from different feedstocks. Differences in yield per unit of land across feedstocks, variations in energy content per ton of alternative feedstocks, differences in fuel production technologies, and differences in properties of animal feed by-products of biofuel pathways are important factors, among others, that explain differences in ILUC values associated with biofuels produced from crops.
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Variations Due to Changes in the Implemented Emissions Account Framework To calculate ILUC values, after evaluating LUCs for a given biofuel pathway, one needs some emissions parameters and assumptions to convert changes in land areas to GHG emissions. These coefficients measure sinks and sources of GHG gas emissions due to changes in land use and land cover items including above-ground live biomass, below ground live biomass, dead organic matter, soil organic matter, harvest wood during land conversion, non-CO2 emissions due to land conversion, and forgone carbon sequestration associated with changes in land cover.
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using two different modeling styles (a simple reduced form model and a comprehensive computable general equilibrium model) and applying a Monte Carlo approach to assess sensitivity of ILUC values with respect to models' assumptions and parameters.
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Recommendation 4-9: Modelers should conduct sensitivity analysis to understand implications of variation. Recommendation 4-10: To effectively inform policy making, LCA studies should document re sults for a range of input values.
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To address this, LCAs may be coupled with techno-economic analyses that explicitly consider how maintaining future options to vary regulatory policy may change incentives for market actors. Both the emissions associated with fuels and the economic returns of fuel production may change in uncertain ways on timescales substantially shorter than capital project investment cycles, which often cover decades.
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Increased use of food and feed crops for fuel production can affect crop, food, and cropland prices; the mix and area of crop production can affect air and water quality and biodiversity; while diversion of electricity to transportation will affect other ratepayers, grid stability, the sources of generation, and logistics, such as for emergency evacuations. Fuel LCA for GHG emissions is an incomplete window into these impacts, and a fuel policy based on CIs will affect these concerns.
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2016. "A Protocol for the Global Sensitivity Analysis of Impact Assessment Models in Life Cycle Assessment." Risk Analysis 36(2)
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The International Journal of Life Cycle Assessment 25(2)
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International Journal of Life Cycle Assessment 7:237–243. Kløverpris, J
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2015. A framework for modelling indirect land use changes in life cycle assessment.
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2015. How to conduct a proper sensitivity analysis in life cycle assessment: Taking into account correlations within LCI data and interactions within the LCA calculation model.
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