Abstract
In this chapter, we develop a framework to regulate producers of biofuel-blended fuels using GHG emissions standards, while accounting for heterogeneity and uncertainty. To this end, we categorize the net GHG emissions caused by a regulated site into two parts: direct and indirect emissions. Direct emissions arise both at and away from the final regulated site but are directly attributable to the final output. Indirect emissions, on the other hand, are emissions not attributable to any single regulated entity, but are caused by the interaction of aggregate supply and demand. An example is emissions due to indirect land use change (ILUC). The two parts, i.e., direct and indirect emissions, are computed using the best available data. The sum of the site-specific direct emissions per unit of output and average indirect emissions per unit of output is, then, compared to the emission threshold or standard.
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Notes
- 1.
Low Carbon Fuel Standard (LCFS) for transportation fuels. State of California Executive Order S-01-07 http://gov.ca.gov/index.php?/executive-order/5172/
- 2.
The Regional Greenhouse Gas Initiative (RGGI) comprising 10 states in the US North East. A mandatory market-based program for reducing GHG emissions from electricity sector http://www.rggi.org/home
- 3.
Emission Trading Scheme (ETS) in the European Union. A market-based mechanism or reducing GHG emissions from electricity sector http://ec.europa.eu/environment/climat/emission/index_en.htm
- 4.
Theoretically speaking, this is an indirect effect, which results in agriculture becoming more input intensive and perhaps emission intensive. This can be modeled without much difficulty and incorporated into our framework provided reliable data are available. However, we believe this to be insignificant and second-order effect compared to the magnitude of ILUC emissions and hence do not discuss this further.
- 5.
A detailed description of the technique of LCA can be found in Rajagopal and Zilberman (2008a).
- 6.
gCO2e/MJ - grams of carbon dioxide equivalent emissions per megajoule of energy.
- 7.
- 8.
The idea of using effective input as opposed to applied input is to account for differences in efficiency of in the use of a given quantity of input depending on the technology. For example, natural gas may be used more efficiently by a gas turbine of new vintage compared to that of an old vintage. Another example is from irrigation where a given quantity of water applied to crops may be utilized more effectively with drip irrigation than furrow irrigation.
- 9.
Searchinger et al. (2008), using the FAPRI (partial equilibrium) model, compute that producing 56 billion liters of corn ethanol (requiring 140 million tons of corn at a corn-to-ethanol conversion rate of 2.7 gallons of ethanol per bushel of corn) in the United States would cause global agricultural acreage to expand by 10.8 million hectares. By allocating this acreage across different types of land with differing stocks of carbon, they calculate indirect emission from land-use change as 106.4 gCO2e per MJ of ethanol. However, we find this estimate to be high going by past historical trends (see Hochman et al. 2008). Others have similarly tried to calculate the effect of increase in gasoline prices on induced land-use change (Keeney and Hertel 2008, Thompson et al. 2008).
- 10.
Nitrous oxide is a potent GHG with a global warming potential (GWP) 170 to 290 times that of carbon dioxide.
- 11.
The Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) Model. Available online at http://www.transportation.anl.gov/modeling_simulation/GREET/
- 12.
Biofuel Energy System Simulator (BESS) Model. Available online at http://www.bess.unl.edu/
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Acknowledgments
Funding support for research leading to this publication was provided by the Energy Biosciences Institute and the Farm Foundation.
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Rajagopal, D., Hochman, G., Zilberman, D. (2010). A Simple Framework for Regulation of Biofuels. In: Khanna, M., Scheffran, J., Zilberman, D. (eds) Handbook of Bioenergy Economics and Policy. Natural Resource Management and Policy, vol 33. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-0369-3_13
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