AGLOBAL GENERAL EQUILIBRIUM ANALYSIS OF BIOFUEL MANDATES AND GREENHOUSE GAS EMISSIONS

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1 American Journal of Agricultural Economics Advance Access published January 28, 2011 Land Use and Climate Change:A Global Perspective on Mitigation Options (Carol A. Jones, ERS USDA, Organizer) AGLOBAL GENERAL EQUILIBRIUM ANALYSIS OF BIOFUEL MANDATES AND GREENHOUSE GAS EMISSIONS JAYSON BECKMAN, CAROL ADAIRE JONES, AND RONALD SANDS In the last decade, global biofuel production has grown rapidly as a result of oil price increases and government policies promoting the development of biofuels. Though reducing greenhouse gas (GHG) emissions is one of the stated policy goals (along with increasing energy security and promoting rural development), the GHG implications of biofuels have come under intense scrutiny. Particular attention has been focused on GHG impacts of the direct and indirect land use change (LUC) associated with feedstock production for the commercially viable, first-generation biofuels. Global biofuel production currently represents about 3% of total transportation fuels, with the vast majority of production concentrated in the United States, Brazil, and the European Union. The primary sources are maize feedstock ethanol in the United States, sugarcane feedstock ethanol in Brazil, and oilseed feedstock biodiesel in the European Union. All three regions have mandatory requirements to increase production or consumption of biofuels over the next five to seven years, as well as other policies in place to promote production. The conventional wisdom has been that substituting ethanol for gasoline will reduce GHG emissions modestly with maize feedstock and substantially with sugarcane or second-generation cellulosic feedstocks and Jayson Beckman, Carol Adaire Jones and Ronald Sands are economists with the Economic Research Service, USDA. We thank William Coyle for contributing his compilations of global biofuels data and information on energy mandates, as well as his insights into biofuels markets.this article was presented in an invited-paper session at the 2010 annual meeting of the Allied Social Science Associations in Atlanta, GA. The articles in these sessions are not subjected to the journal s standard refereeing process. The views expressed are those of the authors and should not be attributed to the Economic Research Service or USDA. that substituting biodiesel for diesel fuel will reduce GHG emissions modestly with oilseed feedstocks, more with waste vegetable oil, and substantially with palm oil (Fehrenbach and Reinhardt 2006). Searchinger and coauthors (2008) challenged the conventional wisdom, arguing that with a complete accounting for GHG emissions from direct and indirect land use change (LUC), LUC emissions greatly outweigh combustion-related emission reductions for many crop-based feedstocks. Subsequently Hertel et al. (2010) illustrated the sensitivity of LUC projections to various assumptions including the price elasticity of crop yields and the substitutability of ethanol coproducts as animal feed and produced a central estimate of LUC emissions that was substantially lower. In this paper, we explore the impacts of a broader range of global, medium-term mandates for first-generation biofuels on agricultural production, LUC, and GHG emissions on the global scale. Previous research on biofuel mandates has highlighted the substantial impacts of the U.S. and EU mandates (either singly or in tandem) on the global agricultural economy, including prices, production, trade, and land use (Banse et al. 2008; Valin, Dimaranan, and Bouet 2009; Hertel, Tyner, and Birur 2010; Hertel et al. 2010). In addition to the United States and the European Union, we also include mandates for Brazil, which has had an aggressive biofuel development policy for decades, as well as for various other countries in Latin America and Asia that have established legal mandates for production/consumption blending targets. We employ a global computable general equilibrium (CGE) model to simulate the regional impacts of mandates on economic patterns of production, consumption, trade, land use, and GHG emissions. The model Amer. J. Agr. Econ. 1 8; doi: /ajae/aaq086 Published by Oxford University Press on behalf of the Agricultural and Applied Economics Association 2011

2 2 Amer. J. Agr. Econ. includes an explicit representation of heterogeneous land productivity, which allows us to take into account differences across regions in land and other factor productivity, as well as in competitive advantage for agricultural trade. Methods CGE Model To capture feedback effects across production sectors and countries, we use a global CGE model, the Global Trade Analysis Project on biofuels (GTAP-Bio; Taheripour et al. 2007), which incorporates biofuels and biofuel coproducts into the energy-environmental version of the GTAP, the GTAP-E model (Beckman, Hertel, andtyner 2009). GTAP-Bio has been used to analyze the global economic and environmental implications of biofuels by Hertel, Tyner, and Birur (2010); Taheripour et al. (2010); and Hertel et al. (2010). In the GTAP-Bio model, the three biofuel commodities (biodiesel, coarse-grains ethanol, and sugarcane ethanol) are specified as substitutes in the production and consumer sectors for fossil fuel based transportation fuels (included in the oil products sector). Biofuel coproducts, distillers dried grains with solubles (DDGS) and vegetable oil cake, are included as separate commodities that serve as feed substitutes for livestock. The GHG accounting in the model captures CO 2 emissions from energy use by the energy sector (i.e., by the coal, oil, natural gas, and petroleum products sectors) and CO 2 emissions (or carbon sequestration) from LUC in the agricultural and forestry sectors. Capturing non-co 2 GHG emissions is beyond the scope of this analysis. The features of the land competition model are critical to the model s capacity for simulating the direct and indirect LUC from increased production of biofuel feedstocks, highlighted in the analysis in Searchinger et al. (2008). (In their terminology, direct LUC refers to the conversion of land from forestry or pastures to produce the additional feedstocks; indirect LUC refers to land conversion, perhaps in other countries, to produce crops displaced by additional feedstock production.) To capture heterogeneous land quality, GTAP-Bio includes a detailed land use module (GTAP Agro-Ecological Zone [AEZ]), in which land use is disaggregated into eighteen AEZs that share common climate, precipitation, and moisture conditions (Hertel, Tyner, and Birur 2008). Alternative agriculture and forestry land uses then compete for lands of heterogeneous quality. Land use competition is modeled in the AEZ module with a nested constant elasticity of transformation (CET) function. By imposing homothetic separability on the revenue function, the land allocation decision is split into two sequential stages. In the first stage, the landowner decides on land cover,i.e.,whether a given parcel of land will be in crops, forestry, or pasture; in the second stage, cropland is allocated across different uses. GTAP is a comparative static model, forecasting changes from one equilibrium to another. To estimate the CO 2 implications from the forecast LUC, the model includes emission and sequestration factors for landcover transitions among forestland, pastureland, and cropland (Hertel et al. 2010). Shifting from cropland to pasture or forest or from pasture to forest will increase carbon sequestration (i.e., reduce CO 2 emissions). To compare net CO 2 impacts from a onetime LUC against the future stream of annual flows of reduced fossil fuel emissions enabled by the LUC, we follow Searchinger et al. (2008) and Hertel et al. (2010) in annualizing the estimated LUC emissions using the thirty-year time horizon and zero discount rate. GTAP-Bio is constructed with version 6 of the GTAP database, which has a 2001 base year. We conduct an historical updating simulation to shift the base year to In the simulation, we apply shocks to the structure of the bioeconomy, including the price of petroleum, the ethanol additive requirement in the United States, and U.S. and EU biofuels policies, as well as to demand and supply factors, including population, factor endowments, and technology productivity parameters. Benchmarking predictions against actual data on price and quantity changes in the agriculture sector indicates that the simulation tracks actual outcomes reasonably well. (For more details, see Beckman 2008, pp ) Further, to reflect the rapidly expanding global biofuel production, we update the new 2007 database with 2007 levels of production, consumption, and trade amounts for three biofuel commodities: ethanol from coarse-grain feedstock, ethanol from sugarcane feedstock, and biodiesel from oilseed feedstock. Policy Scenarios Scenario 1 includes medium-term (five to seven years into the future) biofuel mandates for the

3 J. Beckman et al. A Global General Equilibrium Analysis of Biofuel Mandates and Greenhouse Gas Emissions 3 United States, the European Union, and Brazil. Scenario 2 covers medium-term mandates (as opposed to target goals) that have been legislatively set for all world regions, including the United States, the European Union, and Brazil. For the United States, we set the target at the maximum quantity mandated for conventional biofuels, which is to be attained in 2015 (and carried through 2022). For other countries, the mandates are specified as a share of transportation fuel usage; therefore, the shocks are modeled as consumption shares of road transportation fuels (with some specific to gasoline or diesel, others for total road fuels.) The eighteen regional aggregations used in this study, including separate regions for the United States, the twenty-seven country European Union, Brazil, China, Canada, and India, allow us to capture the variation in future mandates across countries and regions. For simplicity in reporting, we aggregate all nonmandate countries into a single rest of world (ROW) category. Results Table 1 reports the main results changes in CO 2 emissions for the two biofuel policy scenarios. We focus on the direction and relative magnitudes of the results. A positive (negative) number in table 1 represents an increase (decrease) in emissions relative to 2007 baseline levels. For LUC, a reduction in CO 2 emissions represents an increase in the sequestration of carbon for example, from shifting cropland to forestland. The first three columns report emissions from the onetime LUC relative to the 2007 baseline land use.the fourth column reports annualized LUC emissions, which we compare against the annual fossil fuel emissions (column 5). In Scenario 1, as global biofuel production increases by about 80% (16 billion gallons) from the baseline level of 20 billion gallons, the model projects that LUC CO 2 emissions will increase and fossil fuel CO 2 emissions will decrease. The pattern observed for mandate regions drives the world pattern: the three mandate regions consistently generate higher LUC emissions and lower fossil fuel emissions. Based on the thirty-year averaging method we employ to annualize emissions from onetime LUCs, the net global effect is a reduction in annualized global CO 2 emissions. In aggregate, the spillover effects to nonmandate regions on both fossil fuel and LUC emissions reinforce the respective fossil fuel and LUC patterns in mandate regions. However, across individual regions, the directions of the effects for each type of emissions vary, which contributes to the much smaller size of the effects for the set of nonmandate regions. As biofuels are substituted for gasoline or diesel in mandate regions, the decline in demand for oil products results in lower global oil prices, which tends to increase consumption of fossil fuel based products in regions without mandates. Indeed, in various individual regions without mandates, fossil fuel emissions increase. Induced price changes in the agricultural and forestry sectors have more complex feedback effects. Prices are higher for crops, particularly for feedstock crops, but also for other crops, forestry, and livestock, which is a stimulus for increasing production. At the same time, higher crop prices translate to higher feed costs for livestock, which is a damper on demand in that sector. At the individual region level, pastureland emissions consistently increase in both mandate and nonmandate regions as land is converted out of livestock production. In contrast, the pattern of forestland emissions varies across regions; further, across the set of nonmandate regions, forestland emissions decrease (i.e., additional carbon is sequestered), as forestland increases on net. The relative shares of net LUC from pasture into forestland versus cropland are influenced by regional variations in productivity by land use and in market opportunities both domestically and through trade. In the aggregate, the set of mandate regions have higher total emissions from LUC out of both forest and pasture into cropland; however, the set of nonmandate regions have higher LUC emissions from net conversion out of only pastureland on net, they are sequestering additional carbon in forestlands. In other words, when GHG accounting for LUC is limited to emissions from land converted to cropland, only part of the global market-mediated LUC impacts associated with increased allocation of feedstock crops to biofuels is captured, which results in overstating the change in total LUC emissions in nonmandate countries, and consequently in the global GHG accounting for LUC. In Scenario 2, as global biofuel production increases by another 3 billion gallons (about one-fifth the production increase in Scenario 1), global fossil fuel emissions are projected to decline further by about two-fifths of

4 Table 1. CO2 Emissions, by Region, for Biofuel Mandate Scenarios (Mt CO 2 ) Scenario 1: U.S.-EU-Brazil Biofuel Mandates Scenario 2: Global Biofuel Mandates Total Emissions from LUC Annual Emissions Total Emissions from LUC Annual Emissions Source of emission: Forest Pasture Total LUC Fossil-fuel Net Forest Pasture Total LUC Fossil-fuel Net Tier 1 subtotal United States EU Brazil Tier 2 subtotal Canada India Latin Am EEX S. Asia EEX Rest of Asia ROW Subtotal: with mandates , , Subtotal: without mandates Global total , Notes: Units = million tons CO 2 (Mt CO 2 ). Annualized land-use emissions are a thirty-year average of total land-use emissions. Tier 1 mandates introduced in Scenario 1: U.S. 15B gal, ethanol, 1B gal biodiesel; EU27, 5.75% total biofuels blend; Brazil, 9.7B gal ethanol, 5% biodiesel. Tier 2 mandates introduced in Scenario 2: Canada, 5% ethanol, 2% biodiesel; India, 10% ethanol; LAEEX, 2% ethanol, 2% biodiesel (Argentina, Colombia only); SAsiaEEX, 2% ethanol, 1% biodiesel (Indonesia, Vietnam only); RoAsia, 3.5% ethanol, 1% biodiesel (Philippines only). Sources: Coyle 2007, and personal communication. Definition of regions: Latin Am EEX (Argentina, Colombia, Mexico, Venezuela); SAsiaEEX (Indonesia, Malaysia, Vietnam, Rest of Southeast Asia); Rest of Asia (Bangladesh, Sri Lanka, Philippines, Singapore, Thailand, Rest of East Asia, Rest of South Asia); ROW (all other countries in GTAP Database6. For the list, see = 6.0) 4 Amer. J. Agr. Econ.

5 J. Beckman et al. A Global General Equilibrium Analysis of Biofuel Mandates and Greenhouse Gas Emissions 5 the Scenario 1 decrease, and global LUC emissions are projected to increase by about twofifths of the Scenario 1 increase. The net effect, using the thirty-year annualized LUC emission accounting, is that total global CO 2 emissions (accounting for both fossil fuel usage and LUC in both mandate and nonmandate regions) further decrease relative to Scenario 1. Interestingly, for Scenario 2, both the fossil fuel and LUC nonmandate region spillover effects in the aggregate reinforce the GHG gains of the policy. Though fossil fuel based emissions increase for some individual regions, we do not observe leakage for the set of nonmandate regions as a whole, consistent with the Scenario 1 pattern. (The projected reduction in CO 2 emissions is 0.06% from baseline in nonmandate countries, compared with 0.56% in mandate countries.) Further, the decline in LUC emissions in aggregate across nonmandate regions moderates the LUC impacts in mandate countries, which reverses the Scenario 1 pattern. The difference in LUC patterns between the two scenarios appears to be due substantially to differences in the composition of the set of nonmandate regions between the two scenarios. In Scenario 1, emissions increase in Tier 2 regions (in the aggregate) but decrease in ROW (which does not have mandates in either scenario); and emissions decrease in ROW in Scenario 2 as well. As the set of mandate regions expand to include Tier 2 as well as Tier 1, the increase in LUC emissions is associated with a change in their composition. Because on the global scale, the additional emissions in Scenario 2 come predominantly from conversion of forestland, the share of global LUC emissions from pasture falls from 75% in Scenario 1 to 55% in Scenario 2. Individual mandate regions consistently have a greater share of additional LUC emissions originating from forest as opposed to pastureland conversions, with the exception of Brazil,Argentina, and Colombia (of the Latin American Energy Exporting Countries [EEX]). However, on net, nonmandate regions sequester carbon in forests while releasing CO 2 from pastureland. So, again with Scenario 2, we find that if the GHG accounting focuses on only emissions from conversion of lands to croplands, LUC emissions in nonmandate regions and consequently globally will be overstated. To better understand the patterns of LUC emissions, it is helpful to look at the underlying market impacts. Table 2 reports Scenario 2 changes (relative to the 2007 baseline) in outputs for biofuels and oil products, crop sectors, livestock, and forestry, as well as in land use conversions. It is apparent that the mandates are projected to generate very large increases in production in many of the regions, particularly for biodiesel, which raises questions of their feasibility in the time frame. As global biofuel production increases, global production of feedstock crops (coarse grains, sugarcane, oilseeds) increases, and production of other grains (paddy rice and wheat), forestry, and livestock decreases. The effects in regions with mandates are driving the global patterns; the spillover effects to the rest of the world reinforce the impacts observed in mandate regions for some commodities and moderate them for others. The increase in demand for feedstock crops is sufficiently large to generate large price increases in many regions, inducing additional production in ROW to supplement the additional production in the mandate regions. The spillover effect is particularly strong for oilseeds, where close to 30% of the increase in production occurs in nonmandate regions. Livestock decreases in all regions mandate and ROW (though there is some diversity within ROW) due to the higher costs of feed and land. The scale of land use requirements to grow coarse grains to satisfy demand for biofuel feedstock, as well as livestock feed, is moderated by the decline in the livestock sector, as well as by substitution of the biofuel by-products (DDGS and vegetable oil cake) for coarse grains and oilseeds in livestock feed. In contrast, some of the production of other grains and of forestry products that is displaced from mandate countries to accommodate feedstock production is shifted to ROW, which increases production relative to the baseline. Simulated quantities of LUC (in million hectares) for Scenario 2 are reported at the bottom of table 2. Of the 8 million ha increase in global cropland acres, on net, one-quarter comes from forestry and three-quarters from pastureland. Of the 7 million ha converted out of pastureland, most is converted to cropland, but 1 million ha alternatively is converted to forestland. From table 1, we can see that this global net increase in forestland is associated with the sequestration of 462 million tons (Mt) of CO 2 : if this land use conversion to forestry were not taken into account in the GHG accounting, LUC emissions would have been

6 Table 2. Changes in Output for Related Sectors and Changes in Land Use Scenario 2: Global Biofuel Mandates (changes relative to 2007 baseline) % Change in Output % Change in Land Use Coarse Other Oil Coarse-grain Sugarcane Bio Region Grains Grains Oilseeds Sugarcane Livestock Forestry Prdcts Ethanol Ethanol diesel Forestland Pastureland Cropland Tier United States EU Brazil Tier Canada India LatinAmEEX S. Asia EEX Rest of Asia ROW: Subtotal: with mandates (Tier 1 + 2) Subtotal: without mandates (ROW) Global total Land-use change (million ha) Tier Tier ROW Global total Notes: *Indicates essentially no production in that region. 6 Amer. J. Agr. Econ.

7 J. Beckman et al. A Global General Equilibrium Analysis of Biofuel Mandates and Greenhouse Gas Emissions 7 calculated as one-third higher (1,775 Mt rather than 1,313 Mt). Conclusion Global CGE model simulations, combined with our assumption of a thirty-year time horizon for LUC GHG accounting, project that current global biofuel mandates in the United States, European Union, Brazil, and several other Latin American and Asian countries will yield relatively small reductions in global CO 2 emissions in the medium term. Our global CO 2 accounting captures all categories of market-mediated feedback effects of the biofuel mandates, including direct and price-change-induced feedback effects to all segments of the agriculture, forestry, and fossil fuel sectors and to all regions. Consequently, it is broader in scope than studies implementing life-cycle analysis procedures, which include emissions from conversion of land on the global scale but only conversion to crop production (for feedstock crops and for other crops displaced by additional feedstock production) and include transportation fuel emissions in mandate countries only. We find that, on net, by including all categories of market feedback effects, projections of the total reduction in global CO 2 emissions are greater than the projected reduction in emissions for the more narrow life-cycle accounting. Consistent with LUC results for other studies, as feedstock crop production increases in mandate regions, competition for land results in declining livestock and forest products output, releasing pasture and forestland for conversion to crop production (and releasing CO 2 ). (Also nonfeedstock crop production declines, allowing for shifts of cropland to produce feedstocks.) The spillover effects in nonmandate regions from the induced price increases (but also cost increases for livestock due to increasing feed costs) include declining livestock production, which releases land for increasing crop production. In contrast to mandate regions, the price-change-induced spillover effects also include increasing forest products production. Livestock production falls sufficiently to provide land for conversion both to crop production and to forestry production, with the latter resulting in net carbon sequestration. The projected increase in global CO 2 emissions from all direct and pricechange-induced classes of LUC is lower than if the GHG accounting is limited to emissions from conversion to cropland. The projected decrease in global CO 2 emissions from fossil fuel use across all regions is slightly greater than the decrease in projected emissions in mandate regions. The results in this paper should be interpreted as qualitative and illustrative for several reasons. Previous studies have shown that the economic impacts of biofuel mandates are sensitive to a number of factors about which there is substantial uncertainty, even in the mediumrun: energy prices, the degree of substitutability in livestock feed between biofuel coproducts (DDGS and vegetable oil cake) and coarse grains, and the rates of technological change in agricultural production and in biofuel production. Further, the GHG accounting includes only CO 2 from LUC and from fossil fuel emissions, and the comparisons of levels of CO 2 emissions from fossil fuels and LUC are sensitive to the particular approach to annualization. An important next step in the modeling would be to include non-co 2 GHG emissions from agriculture. An important extension of the policy scenarios would be to consider incentives to reduce GHG emissions. References Banse, M., H. van Meijl, A. Tabeau, and G. Woltjer Will EU Biofuel Policies Affect Global Agricultural Markets? European Review of Agricultural Economics 35: Beckman, J. F Energy Policy Analysis in a Global Context: Applications to Biofuels, Livestock and Feed. Ph.D dissertation, Department of Agricultural Economics, Purdue University, West Lafayette, IN. Beckman, J. F., T. W. Hertel, and W. E. Tyner Why Previous Estimates of the Cost of Climate Mitigation are Likely Too Low. GTAP Working Paper No iadb.org/intal/intalcdi/pe/ 2009/03764.pdf (accessed September 6, 2010). Coyle, W The Future of Biofuels: A Global Perspective. Amber Waves 5(5): Fehrenbach, H., and Reinhardt, G Greenhouse Gas Balances of Biofuels. Institute for Energy and Environmental Research, Heidelberg Germany. PowerPoint presentation at conference Sustainable Energy Challenges and Opportunities, Bonn, October

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