Impact of different bioenergy crops on area allocation and cellulosic ethanol feedstock mix

Although a cellulosic ethanol mandate for 2022 is in place, significant political, economic, and agronomic uncertainty exists surrounding the attainability of the mandate. This paper evaluates the effects of bioenergy crop yield and cost uncertainty on land allocation and the feedstock mix for cellulosic ethanol in the United States. The county-level model focuses on corn, soybeans, and wheat as the field crops and corn stover, wheat straw, switchgrass, and miscanthus as the biomass feedstocks. The economic model allocates land optimally among the alternative crops given a binding cellulosic biofuel mandate. The model is calibrated to 2022 in terms of yield, crop demand, and baseline prices. The bioenergy and commodity prices resulting from a mandate are endogenous to the model. The scenarios simulated differ in terms of bioenergy crop types (switchgrass and miscanthus), bioenergy crop yields, bioenergy production cost, and the cellulosic biofuel mandate ranging from 15 to 60 billion gallons. Our results indicate that the largest proportion of agricultural land dedicated to either switchgrass or miscanthus is found in the Southern Plains and the Southeast. Almost no bioenergy crops are grown in the Midwest across all scenarios. The 15 and 30 billion liter mandates in the high production cost scenarios for switchgrass and in all miscanthus scenarios are covered to 95\% by agricultural residues. Changes in the prices for the three commodities are negligible for low cellulosic ethanol mandates because most of the mandate is met with agricultural residues. The amount of bioenergy crops brought into production at the highest imposed mandate result in price increases ranging from 5% for corn and soybeans to almost 14% for wheat

Co-firing in Coal Power Plants and its Impact on Biomass Feedstock Availability

Several states have a renewable portfolio standard (RPS) and allow for biomass co-firing to meet the RPS requirements. In addition, a federal renewable fuel standard (RFS) mandates an increase in cellulosic ethanol production over the next decade. This paper quantifies the effects on local biomass supply and demand of different co-firing policies imposed on 398 existing coal-fired power plants. Our model indicates which counties are most likely to be able to sustain cellulosic ethanol plants in addition to co-firing electric utilities. The simulation incorporates the county-level biomass market of corn stover, wheat straw, switchgrass, and forest residues as well as endogenous crop prices. Our scenarios indicate that there is sufficient feedstock availability in Southern Minnesota, Iowa, and Central Illinois. Significant supply shortages are observed in Eastern Ohio, Western Pennsylvania, and the tri-state area of Illinois, Indiana, and Kentucky which are characterized by a high density of coal-fired power plants with high energy output

Impact of agronomic uncertainty in biomass production and endogenous commodity prices on cellulosic biofuel feedstock composition

This study evaluates the effect of agronomic uncertainty on bioenergy crop production as well as endogenous commodity and biomass prices on the feedstock composition of cellulosic biofuels under a binding mandate in the United States. The county-level simulation model focuses on both field crops (corn, soybean, and wheat) and biomass feedstocks (corn stover, wheat straw, switchgrass, and Miscanthus). In addition, pasture serves as a potential area for bioenergy crop production. The economic model is calibrated to 2022 in terms of yield, crop demand, and baseline prices and allocates land optimally among the alternative crops given the binding cellulosic biofuel mandate. The simulation scenarios differ in terms of bioenergy crop type (switchgrass and Miscanthus) and yield, biomass production inputs, and pasture availability. The cellulosic biofuel mandates range from 15 to 60 billion L. The results indicate that the 15 and 30 billion L mandates in the high production input scenarios for switchgrass and Miscanthus are covered entirely by agricultural residues. With the exception of the low production input for Miscanthus scenario, the share of agricultural residues is always over 50% for all other scenarios including the 60 billion L mandate. The largest proportion of agricultural land dedicated to either switchgrass or Miscanthus is found in the southern Plains and the southeast. Almost no bioenergy crops are grown in the Midwest across all scenarios. Changes in the prices for the three commodities are negligible for cellulosic ethanol mandates because most of the mandate is met with agricultural residues. The lessons learned are that (1) the share of agricultural residue in the feedstock mix is higher than previously estimated and (2) for a given mandate, the feedstock composition is relatively stable with the exception of one scenario

The effects of uncertainty under a cap-and-trade policy on afforestation in the United States

To combat climate change, cap-and-trade policies have been proposed and implemented in countries around the world. The stochastic carbon price that results from a cap-and-trade policy makes investment decisions in carbon mitigating and sequestering practices more complex. This letter illustrates the consequence of uncertainty by analyzing forest carbon offset credits under a potential cap-and-trade policy in the United States. The effects of uncertainty on afforestation, carbon sequestration, cropland allocation, and commodity prices using a real option framework are assessed. When compared with deterministic models, less land gets converted from cropland to forestry over the projection period of 40 years because landowners find it optimal to wait before changing land-use to gain more information about the carbon price evolution. The simulation shows that most afforestation occurs in the south and the northeast with almost no conversion in the Corn Belt. The lesson for policy makers is that under carbon price uncertainty, lower afforestation and carbon sequestration takes place. To foster afforestation, mechanisms are necessary to reduce uncertainty at the expense of higher commodity prices

The impact of forest offset credits under a stochastic carbon price on agriculture using a rational expectations and real options framework

With climate change becoming an increasingly pressing issue and a world population expecting to reach seven billion people in 2011, policies to mitigate greenhouse gas emissions are likely to be enacted domestically as well as internationally. The possible interference of those policies with commodity supply, and hence food security, are the subject of this dissertation. In 2009, a bill to reduce U.S. greenhouse gas emissions passed the House of Representatives but did not pass in the Senate. The bill would have established an emission trading system to reduce emissions from the energy, industrial, and transportation sectors. The bill also included an amendment which would have allowed the agricultural sector to provide the market with carbon offset credits to lower compliance costs for capped sectors and to compensate farmers for an expected increase in energy prices. Soon after the announcement of the offset provisions, concerns of higher commodity prices surfaced because the amendment allowed for credits from afforestation activities on cropland. This dissertation quantifies the effects of those offsets in terms of commodity prices, land allocation, landowner\u27s welfare, and carbon sequestration. The basic model involves a landowner whose plot of land can be in either of two regimes: agriculture or forestry. Revenues in both regimes are uncertain due to price and yield fluctuations while in agriculture and allowance price volatility while in forestry. The sunk cost associated with switching as well as the uncertainty motivates the use of a real option switching model. It might be optimal for a landowner to delay afforestation in order to gain more information about the future carbon price or agricultural revenue. Furthermore, the investment in planting a forest is difficult to reverse. Besides the high costs of forest clearing, the legislation requires a plot of land to be in forestry for several years in order to earn carbon credits. In our model, the landowner observes each period\u27s net revenue in both activities and forms expectations about the future evolution of prices and then decides whether switching to a different regime is optimal or not. A key aspect of our model is the presence of competitive markets. Real option models usually assume an exogenous stochastic process. In our case, revenues are influenced by the switching of landowners from one regime to the other and thus, are endogenous. The model is calibrated to the contiguous United States and includes nine crops plus pasture while in agriculture. For forestry, we impose the type of trees to be planted and show when and where land conversion between agriculture and forests occurs under domestic forestry offsets. The analysis is done at the county level in the United States to take spatial heterogeneity and biophysical constraints such as sequestration rates and yields into account. The value of the wood is included in our analysis but is assumed to be non-stochastic which facilitates the computational analysis. We show that in the presence of uncertainty, significantly less land gets converted from cropland to forestry over the projection period of 40 years. Pasture area is reduced because of low opportunity costs and because it serves as a land pool in the case of cropland expansion in counties which do not switch to forestry but increase crop area because of higher prices. In general, switching from agriculture to forestry starts occurring after a period of 25 years and leads to rising commodity prices thereafter. Ultimately, net revenue from agriculture and forestry start rising with the allowance price. Also, almost no afforestation takes place in the Corn Belt. From a policy perspective, less afforestation leads to smaller welfare effects for farmers than previously estimated and to a higher carbon price because domestic offsets are not supplied in quantities that allows for a significant allowance price reduction

State and federal fuel taxes: The road ahead for U.S. infrastructure funding

Taxes on gasoline and diesel are the primary sources of transportation funding at the state and federal level. Due to inflation and improved fuel efficiency, these taxes are increasingly inadequate to maintain the transportation system. In most states and at the federal level, the real fuel tax rates decrease because they are fixed at a cents-per-gallon amount rather than indexed to inflation. In this paper, we provide a forecast on state and federal tax revenue based on different fuel taxation policies such as indexing to inflation, imposing a sales tax on gasoline and diesel, or using a mileage fee on vehicles. We compare how those taxation policies perform compared to the policies states use currently under different macroeconomic conditions relating to the price of oil, economic growth, and vehicle miles traveled. The baselines projections indicate that between 2015 and 2040, fuel tax revenue will decrease 42.9–50.5% in states that do neither index taxes to inflation nor impose a sales tax. Revenue will decrease 10.3–33.4% that currently impose a sales tax but do not index to inflation. The decrease for states that index to inflation is 3.4–16%. For all states, the median increase in revenue in 2040 compared to 2015 is 62% from switching to a mileage fee. Indexing fuel taxes to inflation in addition to imposing a states' sales tax increases revenue significantly but suffers from a continuous decline in the long-run due to increased fuel efficiency. Our results indicate that although a mileage fee is politically and technologically difficult to achieve, it avoids a declining tax revenue in the long-run

Towards an Integrated Global Agricultural Greenhouse Gas Model: Greenhouse Gases from Agriculture Simulation Model (GreenAgSiM)

The Greenhouse Gases from Agriculture Simulation Model (GreenAgSiM) presented in this paper aims to quantify emissions from agricultural activity on a global scale. The model takes emissions into account that are directly attributable to agricultural production, such as enteric fermentation (methane), manure management (methane and nitrous oxide), and agricultural soil management (nitrous oxide). Furthermore, carbon stock differences from land-use change (carbon dioxide) induced by agriculture are included in the model. The model will provide policy makers with information about the greenhouse gas implications of policy changes

Implications of a US Carbon Tax on Agricultural Markets and GHG Emissions from Land-use Change

Rising concerns about climate change have led to the introduction of carbon policies around the globe. In January 2019, the Energy Innovation and Carbon Dividend (EICD) Act of 2019 was introduced to the House of Representatives.1 The act proposes a carbon tax of $15/ton of carbon dioxide equivalent (t-1 CO2-e) starting in calendar year 2019, and covers entities such as refineries, coal mines, and natural gas producers. Adjusted for inflation, the tax increases$10 each year and is subject to adjustments given the under- or over-achievement of annual emission reduction targets. The tax ceases if greenhouse gas (GHG) emissions are at or below 10% of the 2016 GHG emissions

Towards and Integrated Agricultural Greenhouse Gas Model: Greenhouse Gases from Agriculture Simulation Model (GreenAgSiM)

The present model aims to quantify greenhouse gas (GHG) emissions from changes in agricultural activity on a global scale. It is based on the FAPRI Agricultural Outlook Model, which is used to project changes in agricultural activity in approximately 35 countries and regions covering 13 crops (grains, oilseeds, rice, cotton, sugar) and two major livestock categories (cattle and swine). The FAPRI model is used to project the impact of policy changes on agriculture, and then the GreenAgSiM model is used to calculate the impact of these changes on GHG emissions. The GreenAgSiM model can be used to evaluate the GHG implications of a change in agricultural, bio-energy and environmental policies in the US and elsewhere. It follows closely the guidelines for GHG inventories established by the Intergovernmental Panel on Climate Change (IPCC)

Where should we apply biochar?

The heating of biomass under low-oxygen conditions generates three co-products, bio-oil, biogas, and biochar. Bio-oil can be stabilized and used as fuel oil or be further refined for various applications and biogas can be used as an energy source during the low-oxygen heating process. Biochar can be used to sequester carbon in soil and has the potential to increase crop yields when it is used to improve yield-limiting soil properties. Complex bio-physical interactions have made it challenging to answer the question of where biochar should be applied for the maximum agronomic and economic benefits. We address this challenge by developing an extensive informatics workflow for processing and analyzing crop yield response data as well as a large spatial-scale modeling platform. We use a probabilistic graphical model to study the relationships between soil and biochar variables and predict the probability and magnitude of crop yield response to biochar application. Our results show an average increase in crop yields ranging from 4.7% to 6.4% depending on the biochar feedstock and application rate. Expected yield increases of at least 6.1% and 8.8% are necessary to cover 25% and 10% of US cropland with biochar. We find that biochar application to crop area with an expected yield increase of at least 5.3%–5.9% would result in carbon sequestration offsetting 0.57%–0.67% of US greenhouse gas emissions. Applying biochar to corn area is the most profitable from a revenue perspective when compared to soybeans and wheat because additional revenues accrued by farmers are not enough to cover the costs of biochar applications in many regions of the United States