تاثیر رشد سوخت های زیستی بر کشاورزی : چرا طیف وسیعی از برآوردها گسترش یافته است؟

The rapid expansion of biofuel production has generated considerable interest within the body of empirical economic literature that has sought to understand the impact of biofuel growth on the global food economy. While the consensus within the literature is that biofuel emergence is likely to have some effect on future world agricultural market, there is a considerable range in the estimated size of the impact. Despite the importance of this topic to policy makers, there has been no study that has tried to reconcile the differences among various outlook studies. This paper undertakes an in-depth review of some key outlook studies which quantify the impacts of biofuels on agricultural commodities, and which are based on either general-equilibrium (GE) or partial-equilibrium (PE) modeling approaches. We attempt to reconcile the systematic differences in the estimated impacts of biofuel production growth on the prospective prices and production of three major feedstock commodities, maize, sugar cane, and oilseeds across these studies. Despite the fact that all models predict positive impacts on prices and production, there are large differences among the studies. Our findings point to a number of key assumptions and structural differences that seem to jointly drive the variations we observe, across these studies. The differences among the PE models are mainly due to differences in the design of scenarios, the presence or absence of biofuel trade, and the structural way in which agricultural and energy market linkages are modeled. The differences among the GE models are likely to be driven by model assumptions on agricultural land supply, the inclusion of the byproducts, and assumptions on crude oil prices and the elasticity of substitution between petroleum and biofuels.

The world has seen rapid growth in biofuel production in recent years. Global biofuel production has tripled from 18 billion liters in 2000 to over 62 billion liters in 2007, 90% of which was concentrated in the US, Brazil, and the EU (Coyle, 2007 and OECD, 2008). Global ethanol production – dominated in growth by the US and Brazil – reached 52 billion liters in 2007 and the production of biodiesel – centered mostly within the EU – increased more than 10-fold during the same period, to more than 10 billion liters (OECD, 2008). Correspondingly, the use of major feedstock crops for biofuel production has increased dramatically. The International Grain Council reported an overall growth in the use of cereals for ethanol production by 32% in 2007/2008 and by 41% in the US from the previous year (International Grain Council data cited in von Braun (2008)). The global use of maize for ethanol grew especially rapidly from 2004 to 2007 and used 70% of the increase in global maize production (Mitchell, 2008). Biodiesel production in 2007 accounted for 7% of the global vegetable oil supplies, and one-third of the increase in consumption from 2004 to 2007 was due to biodiesel (Mitchell, 2008). Among the largest biofuel producers, the US used 25% of its maize production for biofuels in 2007 (USDA, 2007); Brazil used 50% of its sugar cane for biofuels; and the EU used 68% of its vegetable oil production, primarily rapeseeds, for biofuels (World Bank, 2008). The potential impact of the emergence of biofuels on food commodity prices and production has generated considerable interest in the empirical economic literature. A great deal of research has been undertaken to understand the implications for agricultural markets – both at the country-specific and international level. Generally-speaking, there are two groups of studies: backward-looking ones and forward-looking ones. The first group estimates the degree to which biofuel demand has influenced the recent food and commodity price trends based on historical data. Estimates vary widely. For instance, the USDA (2008a) believes that biofuels only accounted for 3% of the retail food price increase. In contrast, others have suggested that more than 70% of the rise in food prices was due to biofuels (Mitchell, 2008). Lipsky (2008) estimates that biofuels account for 70% of the maize price increase and 40% of the soybean price increase. Unfortunately, these ex post estimates are difficult, if not impossible to compare. The estimates differ widely due to the fact that authors examined different time periods, used data from different price series (export, import, wholesale, and retail) and focused their attention on different types of food products ( Mitchell, 2008). For example, the estimate by USDA (2008a), which is low in comparable terms, is in part because the authors only considered the impact of maize prices, directly and indirectly, on retail prices ( Mitchell, 2008). This paper focuses on the second group of studies, the forward-looking ones, which generate medium- and long-term predictions of the impacts of biofuel expansion on commodity market, using equilibrium modeling techniques. For example, US-focused studies mostly have looked at the implications of energy policy (e.g., Energy Independence and Security Act or EISA) on food and feed prices (e.g., FAPRI, 2008); EU-focused studies have frequently examined the implications of EU-directives and impact on world prices and production (e.g., Banse et al., 2008); Outside of the US and EU, other studies have sought to predict the impact on prices in the developing world (e.g., OECD-FAO, 2008), malnutrition (e.g., Rosegrant et al., 2008) and implications for poverty (e.g., Yang et al., 2009). While the consensus within the literature is that biofuel growth is likely to have at least some impact on future commodity prices, there is a considerable range in the estimates. Some studies claim strong linkages (e.g., Qiu et al., 2009). Others suggest that the linkages between biofuels and commodity prices are relatively weak (e.g., Banse et al., 2008). Studies that project the impact of future biofuel production on agricultural prices provide important guidelines for setting long-term agricultural, food security, and energy policies, as well as development agenda. Therefore, when predictions vary so much, policy makers face uncertainty about which ones to depend on. Despite the importance of this topic to policy makers, there have been few studies that have tried to reconcile the differences among these outlook studies, except Golub and Hertel, 2011 and Dumortier et al., 2011 and JRC (2011), which indicate that the land use change and carbon emission impacts of biofuels policies are extremely sensitive to model assumptions. The study aims to put the range of numbers regarding the impact of biofuel production on agricultural market in the literature into perspective and provide a guide to the range of assumptions and modeling techniques necessary to draw policy conclusions. This paper reviews the results of a number of the key medium- and long-term forward-looking partial and general equilibrium models. Above all, we are interested in understanding why the predictions about the future effects of biofuels vary widely among the studies. Our study focuses on a subset of the studies—in particular, on the prices and production of three biofuel feedstock crops, maize, sugar cane and oilseeds. To reach this goal we have two specific objectives. First, we will describe the range of projections from a group of papers that are focused on forecasting prices and production of the three key biofuel feedstock crops globally as well as in different parts of the world. Second, we seek to explain the differences in the projections by examining the differences in underlying assumptions and model structures. To meet these objectives the rest of the paper is organized as follows. In Section “Issues to consider when trying to make the studies comparable” we review a number of issues that need to be considered when trying to produce a set of studies that can be compared. In Section “Identifying differences in projected impact of biofuel growth” we compare the studies and identify the variations in their results with respect to the impact of biofuel emergence on food prices and production. In Section “Explaining the differences” we examine, in detail, the underlying assumptions and structure of the analytical approaches used in the studies and draw implications of these factors on model outcomes. Finally, in Section “Conclusions” we highlight key findings of the study and suggest future research directions.

Biofuels production and distribution are extremely complex processes involving markets for land, crops, livestock, energy and food (Golub and Hertel, 2011). The predicted impact of increased biofuels production depends on the model assumptions about the economic structure and parameters governing each of these processes (Golub and Hertel, 2011). This study undertakes an indepth review of some key outlook studies which quantify the impacts of biofuels on agriculture, and which are based on either general- equilibrium (GE) or partial-equilibrium (PE) modeling approaches. We attempt to reconcile the systematic differences in the estimated impacts of biofuel production growth on the prospective prices and production of three major feedstock commodities, maize, sugar cane, and oilseeds across these studies, with the range of assumptions and model characteristics that are embedded within them. Overall, all outlook studies reviewed indicate that biofuel growth will lead to higher prices and production levels for the three primary feedstock crops of 1st generation biofuels by 2015. In other words, all modeling efforts believe that—to a greater or lesser extent—biofuel development is likely to remain an important driving force in world agricultural markets over the medium term. Since most of these basic results are driven by comparing the baseline of ‘no-biofuels’ with an alternative scenario which allows the policy-driven emergence of biofuels, the impacts of ambitious policy objectives in major biofuels-producing countries are shown to be significant and uniformly consistent, as to the direction of the simulated effects. Despite the fact that all models predict positive impacts on prices and production, there are large differences among the studies. Our findings point to a number of key assumptions and structural differences in the modeling approach that seem to jointly drive the variation we observe, across these studies – some being related to particular assumptions or behavioral specifications built into individual models, and some being more systematic across broader classes of models, such as the partial (PE) or generalequilibrium (GE) models. First, the scenario design – and the underlying assumptions of biofuel policy, market penetration etc. – appears to be an important factor for the PE models and has likely contributed to relatively high price impact of biofuels in the WEMAC projections. Second, the presence or absence of biofuel trade, and the structural way in which agriculture and energy market linkages are modeled, are likely to account for some of the differences we see between models within the PE class, such as the OECD and IFPRI model projections for sugar and vegetable oil. Third, relaxing restrictions on total agricultural land supply may be the driving force for LEITAP’s relatively low estimate of price impacts, relative to the other GE-based projections. Fourth, accounting for the possible contribution of DDG by-products to animal feed is the distinguishing difference between the GTAPbased Purdue I and Purdue II models – and also extends to models within the PE family that account for those effects such as the FAPRI and OECD-FAO models and those that do not, such as the IMPACT model. Fifth, the high degree of substitutability between petroleum and biofuels (especially when combined with assumptions on future crude oil prices) is the distinguishing feature of the GF study and contributes towards its relatively high predictions on price and production impacts, relative to other GE models. The assumption—whether true or false—relates closely to what is envisioned in terms of future technology adoption within the transportation sector. Policy and economic factors will weigh in heavily on determining which types of the flexible-fuel vehicles will become widely available and also the types of fuel sold at filling stations. For example, if policies are slow to encourage the adoption of flexible-fuel vehicles or impose ‘blending walls’ such as those which exist in some regions of the US, then the degree to which biofuels are substitutable with gasoline and diesel may be limited.8 Furthermore, if biofuels are sold at or below their energy values with respect to gasoline and diesel, there will be enough consumer demand to encourage the building of E-85 or other alternative fuel pumps. Because these differences in assumptions make a significant difference, policy makers should take into account the underlying assumption-based and structural differences among models when using model-generated outcomes to evaluate economic and environmental impacts, and to guide decision-making. Based on our findings, we have identified a number of urgent knowledge gaps and uncertainties that need to be addressed by future research. First, there is a need to learn more about key model parameters such as the elasticity of substitution between oil-based fuels and biofuels because of their importance in driving GE model results. Knowledge on these parameters is extremely limited, so far, especially with regard to how they might evolve over time, given that their values are often set based on calibration to a relatively short series of historical data or by expert judgment. Second, better predictions of future crude oil prices are needed for both PE- and GE-based studies, ideally, in the same way that IPCC harmonizes the assumptions underlying quantitative assessments of future climate change impacts, and examines the model- based sensitivity and other key sources of uncertainty that are embodied in the wide range of scenario results. Third, the future expandability of agricultural land supply and the contribution that by-products of biofuel production can make to livestock feed balances are likely to be crucial factors that determine the price impacts of model-based projections, and should be carefully studied. Fourth, the on-going research that is being undertaken by various groups in projecting long-term biofuel impacts on agriculture can benefit greatly from more coordinated modeling efforts, so as to improve the sharing of knowledge and generate a better understanding of the key factors that may offset or aggravate aggravate the effects that biofuels can have on market dynamics, environmental quality and, ultimately, human welfare. The lesson for policymakers is that results from economic models depend heavily on assumptions, and because we are trying to predict long-run human behavior, legitimate differences can be present in those assumptions (Dumortier et al., 2011).