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2017 | OriginalPaper | Chapter

Global Land Use Impacts of U.S. Ethanol: Revised Analysis Using GDyn-BIO Framework

Authors : Alla A. Golub, Thomas W. Hertel, Steven K. Rose

Published in: Handbook of Bioenergy Economics and Policy: Volume II

Publisher: Springer New York

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Abstract

This paper describes dynamic extension of the comparative static computable general equilibrium (CGE) GTAP-BIO model—framework employed in assessments of biofuel policies. In the dynamic extension, called GDyn-BIO, several structural components of the static model, including food demand responses to higher incomes and intensification options in land-based sectors and food processing, were revised to better capture changes in derived demand for land under pressure of growing population and per capita incomes. The impact of 15-billion gallon biofuel mandate on land use, analyzed with the GDyn-BIO model, evolves significantly over time. In particular, net global cropland brought into production due to the mandate declines over time, which is in sharp contrast to the results of static analysis where policy impacts are pictured as fixed for the next 30 years. Despite the fact that land use change impacts of this policy are transitory, environmental impacts and the global warming implications of such policies should not be underestimated. The policy causes earlier conversion of forest and pasture lands to cropland, resulting in earlier GHG emissions and lost carbon sequestration that contribute to global warming.

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Appendix
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Footnotes
1
CDE functional form was first proposed by Hanoch (1975). It is called “constant difference” because for consumption goods i, j and k the difference between Allen partial elasticity of substitution between commodities i and k and Allen partial elasticity of substitution between commodities i and j is constant. CDE lies midway between the nonhomothetic constant elasticity of substitution and the fully flexible functional forms (Hertel 1997).
 
2
We increase elasticity of substitution in value added from 0.2, magnitude usually used in applications with the standard GTAP model, to 1.
 
3
In each region of the model, there is a single national production function for each commodity. AEZs enter as inputs into this production function. Thus, in the model land use change results are AEZ and region specific, while changes in land using sector output are obtained at regional level. See Hertel et al. (2009) for further discussion of this modeling approach.
 
4
See Golub and Hertel (2012) for detailed discussion of the endogenous productivity adjustments.
 
5
We emphasize that this is an assumption. Using TEM, Tyner et al. (2010) estimated productivity of marginal land relative to productivity of current cropland. In principal, the productivity of forest hectares relative to productivity of current agricultural land can be estimated using TEM model and approach similar to one described in Tyner et al. (2010). However, such estimation is beyond the scope of this study.
 
6
The step of projection is one year.
 
7
We use FAPRI-CARD control case production levels available at http://​www.​noticeandcomment​.​com/​3-Fapri-Card-Control-Case-Results-fn-53809.​aspx. Historical volumes of US corn ethanol production were higher during 2009–2011 and lower in 2012 and 2013 than in FAPRI-CARD control case. Historical volumes are reported by Renewable Fuels Association http://​www.​ethanolrfa.​org/​pages/​statistics#B.
 
8
Khanna and Crago (2012) compare this metric across studies devoted to estimation of land use change impacts of expanded production of biofuels. For earlier studies employing GTAP-BIO, Khanna and Crago (2012) report ILUC in 30-90 hectares/million liters range for expanded production of US corn ethanol. In this chapter, we start from static version of GTAP-BIO and find that ILUC impact of expanded production of US corn ethanol is 0.18 ha/1000 gallons. 0.18 ha/1000 gallons equivalent to 48 hectares/million litters. This is within the range reported in Khanna and Crago (2012) and close to the results in Tyner et al. (2010) included in Khanna and Crago (2012) survey.
 
9
The weighted average is calculated as sum over time of additional (to baseline) cropland divided by sum over time of additional ethanol forced into market.
 
10
Small changes in the net cropland requirement may be due to general equilibrium effects. For example, compare to two levels CET, with three levels CET changes in the composition will leave more land in forests and less land in pasture. This, in turn, will affect relative prices of crops, livestock and timber, which in turn, may affect demand for crops, cropland area employed in production, and finally, net cropland requirement.
 
11
The calculation assumes that ethanol yield (gallons per tonne of corn) does not change over time.
 
12
Depreciation (fixed rate in this analysis) and investment determine capital stock in each period in each region. Investments are driven by disparities in rates of return to capital across regions. Over time, investors gradually reallocate capital across regions to equalize rates of return in the long run. When the hypothetical economy achieves steady state, capital does not change and investment is only sufficient to cover depreciation. See Ianchovichina and McDougall (2001) for details.
 
13
Average yield, however, is not fixed due to changes in yields on extensive margin (land conversion from one crop to another and conversion of marginal lands to cropland); these cumulative 2004–2030 changes in yields are small in this experiment.
 
14
In the model, under the standard closure, the market price is an endogenous variable and output tax/subsidy is exogenous. To measure GE elasticity, market price is “swapped” with output tax such that the tax variable become endogenous and price become exogenous and available for shock.
 
15
Hertel et al. (2010a) attempts to overcome this “fixed” impact by conducting post simulation adjustment to reflect corn yield growth in US between 2001 and 2007.
 
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Metadata
Title
Global Land Use Impacts of U.S. Ethanol: Revised Analysis Using GDyn-BIO Framework
Authors
Alla A. Golub
Thomas W. Hertel
Steven K. Rose
Copyright Year
2017
Publisher
Springer New York
DOI
https://doi.org/10.1007/978-1-4939-6906-7_8