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Average distance from depot to biorefinery as a function of biorefinery size (corn stover, Census Division 3)
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... and resulting distances will be different de- pending on whether we assume that areas around a dry mill specialize in producing biofuel or that only a fraction of the surrounding production will be captured by the biorefinery. Figure 3 displays the relationship between density and distance implied by the above formulas. This relationship is applied to all regions and feedstocks. ...
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This document provides a summary of the key outcomes of the MARCAN project
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... We are not aware of additional bottom-up estimates, so it is not included in GCAM. (including corn for ethanol) are assumed to have shorter transport distances than crops used for other purposes as we assume that biorefineries are sited close to the croplands which supply them [52]. Therefore, the assumed transport distance between the average corn production site and USA corn ethanol refinery is shorter than the assumed transport distance between the average international crude oil production site and the average USA oil refinery, but the transport modes used are different. ...
... Increased domestic demand for corn in the USA leads to reduced corn exports, which reduces demand for international shipping energy associated with these exports. Since we assume that biorefineries for ethanol production are closer to croplands [52], average transport distances for dried distiller's grains with solubles (DDGS), a coproduct of corn ethanol production consumed by the feed sector, are also assumed to be shorter than those for competing feed sources. This results in a net decline in animal feed tonne-km. ...
Comprehensive study of the environmental impacts associated with demand for an energy resource or carrier in any one sector requires a full consideration of the direct and indirect impacts on the rest of the regional and global energy system. Biofuels are especially complex since they have feedbacks to both the energy system and to regional and global crop markets. In this study, we present a strategy for dynamically including the upstream energy and transportation links to the Global Change Analysis Model. We incorporate the following inter-sectoral linkages: energy inputs to crop production, energy inputs to fossil resource production, and freight transport requirements of energy and agricultural commodities. We assess the implications of explicitly including these links by measuring the global impacts of increased corn ethanol demand in the United States with and without these links included. Although the net global impact of the upstream links on energy and emissions are relatively modest in the scenarios analyzed, the inclusion of these links illustrates interesting trade-offs in energy and transportation demand among fossil fuel and agriculture sectors. We find that the increment in agricultural energy driven by the additional biofuel production associated with the corn ethanol shock is higher than the decrease of energy associated with the displaced fossil fuel consumption. However, this effect is compensated by the reduction in freight transportation requirements of energy. These sectoral interactions suggest that this level of modeling detail could be important in evaluating future analytical questions.
We explore the role of biofuels in mitigating the negative impacts of oil supply shocks on fuel markets under a range of oil price trajectories and biofuel blending mandate levels. Using a partial equilibrium model of US biofuels production and petroleum fuels trade, we discuss the adjustments in light‐duty vehicle fuel mix, fuel prices, and renewable identification number (RIN) prices following each shock as well as the distribution of shock costs across market participants. Ethanol is used as both a complement (blend component in E10) and a substitute (in E15 and E85 blends) to gasoline. Results show that, during oil supply shocks, the role of ethanol as a substitute dominates and allows some mitigation of the shock. As US petroleum imports decrease with growing US oil production, the net economic welfare effect of sudden oil price changes and the energy security role of biofuels becomes less clear than it has been in the past. Although fuel consumers lose when oil price increases due to an external shock, domestic fuel producers gain. In some cases, depending on import share and supply and demand elasticities, we show that the gain to producers could more than offset consumer losses. However, in most cases evaluated here, sudden oil‐price increases remain costly. © 2018 Society of Chemical Industry and John Wiley & Sons, Ltd
This analysis estimates the cost of selected oil and biomass supply shocks for producers and consumers in the light-duty vehicle fuel market under various supply chain configurations using a mathematical programing model, BioTrans. The supply chain configurations differ by whether they include selected flexibility levers: multi-feedstock biorefineries; advanced biomass logistics; and the ability to adjust ethanol content of low-ethanol fuel blends, from E10 to E15 or E05. The simulated scenarios explore market responses to supply shocks including substitution between gasoline and ethanol, substitution between different sources of ethanol supply, biorefinery capacity additions or idling, and price adjustments. Welfare effects for the various market participants represented in BioTrans are summarized into a net shock cost measure. As oil accounts for a larger fraction of fuel by volume, its supply shocks are costlier than biomass supply shocks. Corn availability and the high cost of adding biorefinery capacity limit increases in ethanol use during gasoline price spikes. During shocks that imply sudden decreases in the price of gasoline, the renewable fuel standard (RFS) biofuel blending mandate limits the extent to which flexibility can be exercised to reduce ethanol use. The selected flexibility levers are most useful in response to cellulosic biomass supply shocks.
This paper discusses a static multiregional linear programming model of energy supply from western Canada. This model has been constructed as part of an effort to measure production labor requirements, regional wealth accrual, and economic resource rents associated with different energy resource development scenarios. Demands and prices are exogenous to the model and supplies determined to maximize the increment to gross domestic product generated by energy resource production. The optimal solution yields a pattern of production and economic rent distribution which would be obtained if actual prices were specified exogenously and energy markets functioned in a perfectly competitive manner to meet demands. Thus the most economic supply alternatives are utilized most intensively, low-cost producers rewarded most heavily, and highest rents awarded to production capacities and reserves in particularly short supply. Two major advantages of approach are (1) the most economic patterns of production and transport of energy products can be determined independently of the externally imposed restrictions characterizing Canadian energy product markets, allowing inefficiencies to be pinpointed and quantified, and (2) it allows the price of each energy product to have a direct impact on the shadow prices of constraints on energy production and consumption, in turn allowing empirical estimation of changes in natural resource rents as energy product prices change.
