1,720,966 research outputs found
Spatial Pricing Patterns of Cellulosic Biomass under Oligopsony – A Multi-agent Simulation Model
Full text linkAgribusiness, Industrial Organization, Research Methods/ Statistical Methods, Resource /Energy Economics and Policy,
Intentional and Evolutionary Visions of U.S. Antitrust Law
No full textThis teaching module shows how U.S. antitrust laws are affected by 'intentional' and 'evolutionary' visions of socio-economic market processes. The extent of government's role is studied to review whether the U.S. antitrust laws should be strengthened, reformed or repealed. The focus of Chicago (and Austrian) school is discussed: 'antitrust laws should promote and enhance the trio of -- consumer welfare, competition process, and dynamic (productive and allocative) efficiency.’The role of U.S. antitrust laws in a globalized world and the need for universal law are mentioned. The economic reasons used to rule in U.S. antitrust enforcement since the 1890s are provided in a tabular format for easy use in the classrooms. Teaching tips are provided on how to use the material in classroom discussion and assessments.
Spatial Pricing Patterns of Cellulosic Biomass under Oligopsony – A Multi-agent Simulation Model
Full text linkThe economic prospects of cellulosic biomass for biofuel production
Full text linkAlternative fuels for transportation have become the focus of intense policy debate and legislative action due to volatile oil prices, an unstable political environment in many major oil producing regions, increasing global demand, dwindling reserves of low-cost oil, and concerns over global warming. A major potential source of alternative fuels is biofuels produced from cellulosic biomass, which have a number of potential benefits. Recognizing these potential advantages, the Energy Independence and Security Act of 2007 has mandated 21 billion gallons of cellulosic/advanced biofuels per year by 2022. The United States needs 220-300 million tons of cellulosic biomass per year from the major sources such as agricultural residues, forestry and mill residues, herbaceous resources, and waste materials (supported by Biomass Crop Assistance Program) to meet these biofuel targets. My research addresses three key major questions concerning cellulosic biomass supply. The first paper analyzes cellulosic biomass availability in the United States and Canada. The estimated supply curves show that, at a price of 50 per metric ton or lower. The second paper evaluates the farmers' perspective in growing new energy crops, such as switchgrass and miscanthus, in prime cropland, in pasture areas, or on marginal lands. My analysis evaluates how the farmers' returns from energy crops compare with those from other field crops and other agricultural land uses. The results suggest that perennial energy crops yielding at least 10 tons per acre annually will be competitive with a traditional corn-soybean rotation if crude oil prices are high(ranging from 178 per barrel over 2010-2019). If crude oil prices are low, then energy crops will not be competitive with existing crops, and additional subsidy support would be required. Among the states in the eastern half of US, the states of Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee, and Virginia are found to be economically more suitable to cultivate perennial energy crops. The third paper estimates the optimal feedstock composition of annual and perennial feedstocks from a biorefinery's perspective. The objective function of the optimization model is to minimize the cumulative costs covering harvesting, transport, storage, and GHG costs, of biomass procurement over a biorefinery's productive period of 20 years subject to various constraints on land availability, feedstock availability, processing capacity, contracting needs and storage. The results suggest that the economic tradeoff is between higher production costs for dedicated energy crops and higher collection and transport costs for agricultural residues; the delivered costs of biomass drives the results. These tradeoffs are reflected in optimal spatial planting pattern as preferred by the biorefinery: energy crops are grown in fields closer to the biorefinery and agricultural residues can be sourced from fields farther away from the biorefinery. The optimization model also provides useful insights into the price premiums paid for annual and perennial feedstocks. For the parameters used in the case study, the energy crop price premium ranges from 8 per ton for fields located within a 10 mile radius. For agricultural residues, the price premiums range from 16 per ton within a 10-20 mile radius.Thesis (Ph. D.)--Michigan State University. Agricultural, Food, and Resource Economics, 2011Includes bibliographical reference
Choice of optimum feedstock portfolio for a cellulosic ethanol plant – A dynamic linear programming solution
Full text linkWhen the lignocellulosic biofuels industry reaches maturity and many types of biomass sources become economically viable, management of multiple feedstock supplies – that vary in their yields, density (tons per unit area), harvest window, storage and seasonal costs, storage losses, transport distance to the production plant – will become increasingly important for the success of individual enterprises. The manager’s feedstock procurement problem is modeled as a multi-period sequence problem to account for dynamic management over time. The case is illustrated with a hypothetical 53 million annual US gallon cellulosic ethanol plant located in south west Kansas that requires approximately 700,000 metric dry tons of biomass. The problem is framed over 40 quarters (10 years), where the production manager minimizes cumulative costs by choosing the land acreage that has to be contracted with for corn stover collection, or dedicated energy production and the amount of biomass stored for off-season. The sensitivity of feedstock costs to changes in yield patterns, harvesting and transport costs, seasonal costs and the extent of area available for feedstock procurement are studied. The outputs of the model include expected feedstock cost and optimal mix of feedstocks used by the cellulosic ethanol plant every year. The problem is coded and solved using GAMS software. The analysis demonstrates how the feedstock choice affects the resulting raw material cost for cellulosic ethanol production, and how the optimal combination varies with two types of feedstocks (annual and perennial)
Choice of optimum feedstock portfolio for a cellulosic ethanol plant – A dynamic linear programming solution
Full text linkWhen the lignocellulosic biofuels industry reaches maturity and many types of biomass sources become economically viable, management of multiple feedstock supplies – that vary in their yields, density (tons per unit area), harvest window, storage and seasonal costs, storage losses, transport distance to the production plant – will become increasingly important for the success of individual enterprises. The manager’s feedstock procurement problem is modeled as a multi-period sequence problem to account for dynamic management over time. The case is illustrated with a hypothetical 53 million annual US gallon cellulosic ethanol plant located in south west Kansas that requires approximately 700,000 metric dry tons of biomass. The problem is framed over 40 quarters (10 years), where the production manager minimizes cumulative costs by choosing the land acreage that has to be contracted with for corn stover collection, or dedicated energy production and the amount of biomass stored for off-season. The sensitivity of feedstock costs to changes in yield patterns, harvesting and transport costs, seasonal costs and the extent of area available for feedstock procurement are studied. The outputs of the model include expected feedstock cost and optimal mix of feedstocks used by the cellulosic ethanol plant every year. The problem is coded and solved using GAMS software. The analysis demonstrates how the feedstock choice affects the resulting raw material cost for cellulosic ethanol production, and how the optimal combination varies with two types of feedstocks (annual and perennial).Cellulosic ethanol, feedstock, switchgrass, miscanthus, corn stover, optimization, biofuels, biomass, energy, renewable, Agribusiness,
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Testing for Speculative Behavior in US Corn Ethanol Investments
Full text linkCrude oil price speculation during 2000s could have increased installed capacity in corn ethanol plants beyond what was warranted by the market factors. We use Muth’s commodity pricing model and Flood and Garber’s tests to test for speculative investment in US corn ethanol industry. The ethanol price expectations are derived using a system of supply-demand-inventory describing US ethanol markets under rational expectations (perfect foresight). These price expectations can help differentiate the installed capacity into two: capacity supported by the market fundamentals and the probable capacity that is installed based on speculation. Econometric estimation procedures and functional form approximations are discussed.ethanol, speculation, commodity prices, investment, Agricultural Finance, Financial Economics, Q14, Q41, D8, L71,
Optimal biomass-harvesting model for biobutanol biorefineries
Full text linkThe Energy Independence and Security Act (EISA) 2007 mandate the use of 21 billion gallons of advanced biofuels including 16 billion gallons of cellulosic biofuels by the year 2022. While much previous advanced biofuel related research has focused on cellulosic ethanol, advanced drop-in-biofuels such as biobutanol and renewable diesel are gaining significant attention because of their attractive combustion properties, compatibility with existing vehicle fleet, fuel distribution, and retailing infrastructure. While corn ethanol production has increased fast enough to keep up with the mandates, production of cellulosic and advanced biofuels has been well below the targets despite significant government support. A number of pilot and demonstration scale advanced biofuel facilities have been set up, but commercial scale facilities are yet to become operational. Scaling up this new biofuel sector poses significant economic and logistical challenges for regional planners and biofuel entrepreneurs in terms of feedstock supply assurance, supply chain development, bioefinery establishment, and setting up transport, storage and distribution infrastructure.
Economies of scale in processing mean that, future cellulosic biorefineries are expected to be large-scale facilities using multiple sources of feedstocks. Assuring a reliable supply of feedstock in adequate quantity and appropriate quality at reasonable cost and low environmental impacts is a key factor driving emergence of a sustainable bioenergy sector.
Assuming that a biorefinery is set up in a region that has more than adequate biomass potential, biorefinery managers then face the problem of contracting with producers for the actual supply quantities of feedstock over the expected operational life time of the biorefinery. These supply contracts specify the quantities of different feedstocks (e.g. agricultural crops, perennial grasses, woody biomass), the timing of the deliveries, and the geographical location of production. In other words, through these supply contracts, the biorefinery managers essentially have an opportunity to design the biomass harvest-shed both temporally and spatially. Considerations in determining the optimal mix of these supply contracts include: (i) lowering procurement costs (harvest, baling, transport, storage, and seasonal costs), (ii) maximizing fuel yields and minimizing conversion costs, (iii) reducing in greenhouse gas (GHG) emissions to qualify as a cellulosic biofuel under the federal renewable fuels standard or similar regulations, and possibly for tradable GHG credits, and (iv) meeting contracting constraints to assure supply, for example while annual crop producers may be willing to supply under annual contracts, perennial grass producers may demand longer term contracts with varying quantities matching the temporal yield patterns. In addition to the above criteria used by biorefinery managers, regional planners may impose additional constraints related to protection of ecosystem services, habitat protection, water resources, traffic patterns, and congestion.
In this article, we develop a multi-period optimization model aimed determining the optimal mix of woody biomass, annual crops and perennial grasses for a biorefinery, taking into account the necessary contract terms, feedstock costs, transport costs, GHG emissions and other environmental impacts, production capacity constraints etc. The decision variables of the optimization model are the acreages of various feedstocks (woody biomass,
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annual crops and perennial grasses) that are contracted for harvesting during each month of a 25 year planning horizon. While the model is structured to be applicable to a generic biorefinery regardless of location, we parameterize the model using information for a hypothetical biorefinery located in the Midwest, producing biobutanol. Two versions of the model are developed, one optimizing the private costs faced by the biorefinery manager, and a second version taking the perspective of a regional planner with additional optimization criteria and social constraints. Mathematical programming software GAMS and solver program MINOS are used to code and solve the formulated optimization programs.
A growing body of literature has previously addressed issues surrounding the supply of biomass feedstock for biofuel production (e.g. Epplin et al., 2007; Mapemba et al., 2007; Mapemba et al., 2008; Sokhansanj et al., 2009; Khanna et al., 2010; Kang et al., 2010). While drawing on previous research, the models developed in this article have several novel features. (i) Existing studies treat the available biomass quantities in the region as exogenously given and then try to minimize procurement costs. In comparison, this model treats biomass acreage to be harvested as an endogenous decision variable subject to overall biomass availability constraints. (ii) Unlike most existing studies, in this model transport costs are endogenously determined as a function of harvesting decisions. (iii) The temporal yield patterns of energy crops are modeled explicitly unlike many other studies which use steady state average yields. (iv) GHG emissions are also endogenously determined based on feedstock sourcing decisions. (v) The legal, institutional, and ecosystem sustainability constraints that are necessary from a regional planning perspective are also incorporated. (vi) While almost all previous studies model cellulosic ethanol biorefineries, this model is specifically aimed at biobutanol biorefineries.
As a result, these models provide better insights into the realities of biomass procurement, especially for the emerging drop-in advanced biofuel production
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