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Erschienen in:

2010 | OriginalPaper | Buchkapitel

13. A Simple Framework for Regulation of Biofuels

verfasst von : Deepak Rajagopal, Gal Hochman, David Zilberman

Erschienen in: Handbook of Bioenergy Economics and Policy

Verlag: Springer New York

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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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Vorheriges Kapitel Could Bioenergy Be Used to Harvest the Greenhouse: An Economic Investigation of Bioenergy and Climate Change?

Nächstes Kapitel Market and Social Welfare Effects of the Renewable Fuels Standard

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/

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

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

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.

A detailed description of the technique of LCA can be found in Rajagopal and Zilberman (2008a).

gCO₂e/MJ - grams of carbon dioxide equivalent emissions per megajoule of energy.

In all these scenarios, ILUC emissions are constant and take a value equal to one-third that estimated by Searchinger et al. (2008). This value was chosen because we expect their estimate of ILUC to be more than three times larger compared to ours. For more details see Hochman et al. (2008).

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.

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).

Nitrous oxide is a potent GHG with a global warming potential (GWP) 170 to 290 times that of carbon dioxide.

The Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) Model. Available online at http://www.transportation.anl.gov/modeling_simulation/GREET/

Biofuel Energy System Simulator (BESS) Model. Available online at http://www.bess.unl.edu/

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Hochman G, Rajagopal D and Zilberman D (2008) “Regulation of GHG emissions from biofuel blended energy”, Farm Foundation conference on Transition to a Bioecomy, Environmental and Rural Development Impacts, St. Louis, Missouri, October 15th–16th.

Keeney R and Hertel TW (2008) The Indirect Land Use Impacts of US Biofuel Policies: The Importance of Acreage, Yield, and Bilateral Trade Responses, GTAP Working Paper (forthcoming), Center for Global Trade Analysis, Purdue University, West Lafayette.

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Rajagopal D and Zilberman D (2008a) Prices, Policies and Environmental Life Cycle Analysis of Energy, Farm Foundation conference on Lifecycle Footprint of Biofuels, Washington DC, January.

Rajagopal D and Zilberman D (2008b) “Environmental, Economic and Policy Aspects of Biofuels”, Foundations and Trends in Microeconomics,Vol. 4, No. 5.

Searchinger T, Heimlich R, Houghton RA et al. (2008) “Use of U.S. Croplands for Biofuels Increases Greenhouse Gases Through Emissions from Land Use Change,” Science 319, 5867: 1238–1240.CrossRef

Tilman D, Hill J, and Lehman C (2006) “Carbon-Negative Biofuels from Low-Input High Diversity Grassland Biomass,” Science 314, 5805:1598–1600.CrossRef

Thompson W, Meyer S and Westhoff P (2008) Potential for Uncertainty about Indirect Effects of Ethanol and Land Use in the Case of Brazil, Working paper, Food and Agricultural Policy Research Institute, University of Missouri.

Titel: A Simple Framework for Regulation of Biofuels
verfasst von: Deepak Rajagopal
Gal Hochman
David Zilberman
Verlag: Springer New York
Buch: Handbook of Bioenergy Economics and Policy
Print ISBN: 978-1-4419-0368-6

Electronic ISBN: 978-1-4419-0369-3

Copyright-Jahr: 2010
DOI: https://doi.org/10.1007/978-1-4419-0369-3_13

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