Increasing demand for crop-based biofuels, in addition to other human drivers of land use, induces direct and indirect land use changes (LUC). Our system dynamics tool is intended to complement existing LUC modeling approaches and to improve the understanding of global LUC drivers and dynamics by allowing examination of global LUC under diverse scenarios and varying model assumptions.
We report on a small subset of such analyses. This model provides insights into the drivers and dynamic interactions of LUC (e.g., dietary choices and biofuel policy) and is not intended to assert improvement in numerical results relative to other works. Demand for food commodities are mostly met in high food and high crop-based biofuel demand scenarios, but cropland must expand substantially.
Meeting roughly 25% of global transportation fuel demand by 2050 with biofuels requires 2 times the land used to meet food demands under a presumed 40% increase in per capita food demand. In comparison, the high food demand scenario requires greater pastureland for meat production, leading to larger overall expansion into forest and grassland. Our results indicate that, in all scenarios, there is a potential for supply shortfalls, and associated upward pressure on prices, of food commodities requiring higher land use intensity (e.g., beef) which biofuels could exacerbate.
Export citation and abstract. Biofuel production has been pursued because of opportunities to contribute to climate change mitigation, among other potential benefits such as securing and diversifying energy supply and providing, economic development opportunities especially in rural areas. Initial biofuel assessments (e.g., Farrell et al ) suggested that biofuels, such as corn ethanol, could help the United States (US) reduce greenhouse gas (GHG) emissions. However, Searchinger et al and Fargione et al highlighted that previous biofuel studies failed to include the effects of global land use change (LUC). These two watershed studies modeled the potential impact of carbon released from soils and above-ground biomass during land clearing activities triggered by increased demand for biofuel. Results from these studies, as well as many other biofuel-induced LUC studies (e.g., see Berndes et al ), hinge on assumptions regarding direct and indirect causal relationships (see section 1 of the supplemental information (SI), available at, for more detail) between drivers of LUC, land availability, biomass yields, population dynamics, dietary choices, relative affluence, and biofuel demand, to name a few. However, the causal relationships that underpin such results are not completely understood and there remains significant disagreement on many fundamental aspects of LUC dynamics.
LUC, as well as those changes attributable to biofuels, has far-reaching implications for many aspects of sustainability i.e., biodiversity and societal impacts (e.g., food security). For example, diversion of land to biofuel crops displaces production that may lead to compensating production of substitute crops elsewhere, affecting regional food crop prices (Chum et al, Dale et al ).
Therefore, a better understanding of the drivers of LUC and high-level influences on system behavior is critical to the responsible and sustainable development of biofuels. Inclusion of LUC impacts in renewable fuels policy is contentious, because it is neither directly measurable nor easily isolated from the myriad of other LUC drivers (Plevin et al ), such as agricultural policies, agricultural product demand changes, and social norms. Biofuel policy analyses typically rely on computer simulations or on extrapolations of historic data to evaluate total LUC. The LUC modeling science lacks consensus with regard to modeling frameworks, boundary conditions, and other fundamental assumptions, which has resulted in highly variable modeling results across a wide range of studies. For example, results of CO 2 emissions from biofuel-induced LUC span an order of magnitude, and subsequent calculations of GHG emissions can even vary in sign (Berndes et al ). Currently, US and European governmental organizations are integrating—or are considering integrating—LUC impacts into their renewable fuel policies (e.g., the US Renewable Fuel Standard (RFS) US EPA ), EU Renewable Energy and Fuel Quality Directive (European Commission ), United Kingdom (UK) Renewable Transport Fuel Obligation (Gallagher, UK Department for Transport ), and California's Low Carbon Fuel Standard (CARB ).
At present, analysts often use agricultural economic models. For example, variations of the Global Trade Analysis Project (GTAP) database and the US EPA's methodologies are commonly being used to estimate GHG LUC for renewable fuel policy purposes (US EPA, Tyner et al, Al-Riffai et al ).
The modeling approach presented in this study is intended to complement existing approaches and to improve the understanding of global LUC drivers and dynamics. Every effort has been made to ensure the model is parsimonious and transparent, both in terms of the underlying data and the feedback effects among drivers.
By using a model with high transparency, ease of use, and dynamic capabilities, our study improves policy-relevant analyses by allowing examination of global LUC under diverse scenarios and varying model assumptions. In this paper, we report on a small subset of such analyses. These are intended as an initial illustration of model functionality, not to assert improvement in numerical results relative to other work. 2. LUC models and approaches. Comparison of models using different methods used to estimate LUC in existing literature (Ackerman, ICF International, CBES ). Specific models may differ from these generalizations. General and partial economic equilibrium models System dynamics Causal descriptive method Deterministic method (simplified) Description Calculates LUC resulting from the cause-and-effect relationships in a market system based on the change resulting from shocking an economic system at equilibrium with an expansion of biofuels.
Uses historic data for calibration, but may incorporate empirically estimated functions. Model parameters are often derived from related literature Calculates LUC using cause-and-effect relationships in a dynamic stock-and-flow framework to assess market responses resulting from increasing demand for biofuels. Uses historic trends as inputs and for calibrating cause-and-effect relationships Calculates LUC using cause-and-effect relationships to assess market responses resulting from increasing demand for biofuels. Uses historic trends as inputs and for calibrating cause-and-effect relationships Generates an 'average' adder for agricultural products considered relevant to the bioenergy sector based on historic land use data. Direct and indirect LUC are sometimes combined but sometimes separate factors Potentially limiting assumptions in existing models (1) LUC is driven primarily by price-related economic forces. (2) Non-price factors are assumed to be exogenous and generally held constant (1) Future market responses (e.g., product substitution, cultivation area of a given crop) to increased demand for biofuels can be extrapolated from historical trends and relationships between economic, agricultural, and social systems. (2) Market responses are driven by relatively independent external assumptions, but internal moderating feedbacks are included (1) Future market responses (e.g., product substitution, cultivation area of a given crop) to increased demand for biofuels can be extrapolated from statistical analysis of historical trends.
(2) Market responses are relatively independent and can be quantified without considering possible correlations (1) Only countries trading evaluated agricultural commodities might be subject to LUC effects. (2) The patterns of global trade of agricultural commodities will remain virtually unchanged in the near future, and therefore the incremental effect of biofuel expansion on land use will always have the same global impact. (3) LUC caused by increased biofuel feedstock production can be directly estimated from the type and share of land used to grow agricultural commodities for export purpose Strengths (1) Captures, in detail, many important market and economic factors that can drive LUC.
(2) Usually based at institutions that offers technical support. (3) In its dynamic form can account for the evolution of demographic variables and resources, and other changes over time (1) Can relatively easily incorporate important LUC drivers other than market and economic factors. (2) Can be transparent and involve stakeholders in running the model and scenario development (1) Can relatively easily incorporate important LUC drivers other than market and economic factors. (2) Can be transparent and involve stakeholders in running the model and scenario development (1) Easy to calculate and transparent Limitations (1) Currently, usually fails to include other important LUC drivers (e.g., political, cultural, demographic, environmental forces) that may not rely on land and commodity prices and elasticities.
(2) Difficult to gain access to or use by non-experts economic modelers (1) Misses dynamic and interlinked LUC driving factors in cause-and-effect chains because of reliance on historical data and relationships to identify LUC drivers. (2) Data and relationships operate at a relatively aggregate low-resolution level. (3) Prices need not be explicitly modeled (1) Misses dynamic and interlinked LUC driving factors in cause-and-effect chains because of reliance on historical data and relationships to identify LUC drivers.
(2) Data and relationships operate at a relatively aggregate low-resolution level. (3) Prices need not be explicitly modeled (1) Generally does not consider land types not used for producing traded agricultural commodities. (2) Omits market feedbacks and policy measures that affect trade patterns. (3) Data and relationships operate at a very aggregate low-resolution level Example papers Hiederer et al , Al-Riffai et al , US EPA , Tyner et al , Wise et al Sheehan Bauen et al , Lywood Kim et al , Fritsche et al , Tipper et al The new model, called BioLUC, is a system dynamics (SD) simulation model (NREL, Bush et al ) that represents key economic and social drivers of global LUC and their interactions over time, enabling exploration of different scenarios with implications for LUC. In particular, BioLUC can explore implications of and assumptions about LUC by analyzing the limits of sustainable biofuels production under varied future conditions regarding inputs, specifically population growth, crop yields, and plant and animal product supply and demand.
BioLUC was created using the STELLA Version 9.1.4 software package (ISEE Systems, Lebanon, NH) using a stock-and-flow structure; it focuses on information feedback processes that underwrite the dynamic movement of key quantities over time. BioLUC generally would fit the causal descriptive category. However, to-date, causal descriptive modeling systems have been spreadsheet-based models that lack dynamic stock-and-flow frameworks. To explain the distinctions we describe SD models in a separate column in table. See section 2 of the SI (available at ) for additional details about SD modeling, its historical context, and its use in projecting the consequences of today's and potential future policies. The BioLUC model recognizes the accomplishments of past LUC modeling efforts and provides a modeling option that may address some of the limitations of current methods evaluating LUC and complement and existing suite of models to improve research insights.
Transparency and ease of use are challenging to achieve when examining issues of biofuel-induced LUC, especially for the many interested stakeholders who are not economic modeling specialists. Enabling this community to access a transparent analytic method can help them work together to understand and analyze underlying LUC drivers; test assumptions about LUC systems against historic data; investigate future conditions; and assess implications of new LUC research results (Sheehan, ).
As tools for this kind of shared exploration, existing models are either too complex, with light model structure documentation, difficult data access, or, alternatively, grossly simplistic (i.e., deterministic method). A SD modeling approach building on the causal descriptive method attempts to fill this analysis space. 3. BioLUC modeling approach. The generalized influence diagram for a generic region of the world in the BioLUC model is presented in figure. The movement of land between usage categories over time is represented in the model using stocks for four different land bases, which flows into or out of those stocks.
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Within the 'crops' land base, land is allocated among multiple competing uses (e.g., food, feed, fuel, and fiber). Note the 'abandoned' category among the land bases: this land category, assumed nonproductive, enables us to explore land abandonment and rehabilitation scenarios. 'Available' land is forest and grassland that is potentially available for productive use as pasture or cropland. Figure 1. Illustrative influence diagram for each geographic regions modeled i.e. US and the rest of the world (ROW). Primary land stocks are represented by boxes, interactions are represented by connecting arrows, and inputs variables are represented by unboxed text. Download figure: Figure also shows how externally defined scenarios for key parameters impact the system: biomass yield and crop land allocation determine production of various crops; and population, per capita demand, and biofuel demand drives 'direct' demands for various crops.
BioLUC represents key feedback processes that drive the allocation of land over time. Examples of these processes include:. Imbalances between production and consumption of various agricultural products motivate changes in the allocation of land among different uses at a regional level. Demand for animal products creates additional demand for crops grown as feed.
Crop and animal product imbalances between production and consumption stimulate adaptive responses in the system to move toward equilibrium. Reallocation of existing crop land among different uses balances the mix of crops produced against the mix of crops required.
Re-distribution of the land bases, for example by converting available land into pastureland or by turning pastureland into crop land, adjusts production to more closely meet demand. Crop or animal product imbalances are further reduced through imports/exports from other regions. An imbalance between demand and supply stimulates multiple feedback mechanisms. For example, holding other things equal, if the combination of population, per capita demand, and biofuel scenarios cause consumption of a particular crop to exceed its production within a region several processes will begin to unfold:. Regional inventories of the crop will begin to decline.
The resulting supply shortfall will constrain consumption to levels lower than those implied by population, per capita demand and biofuel scenarios. The supply shortfall will cause the region to call for imports from outside the region, in the immediate term. Additionally, the supply shortfall will lead to a reallocation of crop land in favor of the crop in question in the longer term. Reallocation of cropland will increase the rate at which land moves from pasture into crops, which in turn will increase pressure to convert land from forest or grassland into pasture. As these processes play out over time, the system will seek to balance itself so that equilibrium between supply and demand for crops within a region is restored.
We initially developed a two-region model that can be used to represent any two regions. See section 2 of the SI (available at ) for more detail on two-region model structure and intra-regional trade. For additional details about model input assumptions including agricultural commodity yields, changes in population, and initial land cover at the start of the model across all scenarios, see data and model calculations in section 3 of the SI (available at ). 4. BioLUC model scenarios.
We explore four scenarios to broadly examine the effects of demand for crop-based biofuels and food on LUC as outlined below and in table. We constructed scenarios to represent an extreme high-intensity agricultural future to adequately highlight model dynamics and test model integrity under high pressures. The details of these scenarios are included in section 4 of the SI (available at ), which describes biofuel yields, conversion yields, food and feed demand, and biofuel demand assumptions for several time steps of each scenario.
BioLUC demand scenarios. The scenarios are modeled from 1990–2050. Input data is annual, but the model runs on a time step of 1/32nd of a year. Higher demand growth based on high-end demand projections (40% increases in per capita food demand by 2050 from 2005) were taken from Tilman et al and modified to be applied to the aforementioned projections from Alexandratos and Bruinsma. Tilman et al does not specify how the demand increase is distributed across individual food categories. We closely approximate the 40% increase by equally applying changes across all commodities through a 45% increase in annual growth of each commodity demanded starting in 2010 relative to the low food demand scenario. The four scenarios, presented in table, are limited in at least two key respects.
First, our examination of yield is constrained. FAO resolution is limited to national-level averages.
In our high demand scenarios, we do not assume higher levels of agricultural intensification (i.e., higher than BAU yields increases) in response to economic forces, nor do we examine reductions in crop yields, as could result from climate change and increases in extreme weather events. Our yield data (from FAOSTAT) are aggregate national averages that can have large internal spatial and temporal variability. A limited scenario analysis examining the impact of higher and lower cellulosic biofuel yield trend assumptions was examined in section 6 of the SI (available at ) and is discussed briefly in the results section, below. Second, we model the high biofuels case essentially as higher land requirements to grow the biofuel feedstock on agricultural land (i.e., not wastes or residues or grown on marginal lands).
We selected these limitations to test extreme land use conditions and to simplify the analyzed scenarios. The suite of feedstocks grown and technologies used is essentially generic: a shift in technology, feedstock, or yield assumption would only alter the aggregate land requirements (i.e., 700 million ha) examined in this analysis. The implications of these limitations will be discussed further in our results.
5. Results and discussion. BioLUC results are not predictions; the model only provides insights into the drivers and dynamic interactions of LUC. Quantities and changes are only provided to facilitate comparisons between our scenarios. Business-as-usual (BAU) projections of LUC for the US and ROW are presented in figure. These results reflect changes in land use in response to global population growth and an overall increase in per capita gross domestic product, which causes diets to shift toward more calories per capita as well as a greater percentage of those calories coming from meat and animal products (e.g., dairy) (Alexandratos and Bruinsma ). Results suggest that the rate of conversion of available land (i.e., forest and grasslands) to cropland increases globally, relative to historic data in the US and the ROW, in order to meet rising food needs. In the ROW the rate of cropland increase remains similar to historic rates of change, but pastureland grows significantly.
Pastureland trends in the ROW reflect the larger relative shift in per capita gross domestic product in developing countries. Land conversion begins to slow circa 2030 because the rate of population growth and per capita food demand growth begins to stabilize. Globally, the most noticeable trend in the ROW is the increase in pastureland area to meet meat and animal product demands as the world's population grows and becomes more affluent. This is consistent with well-documented historic trends that show that increased wealth prompts a shift from diets rich in whole vegetables toward diets that have greater amounts of processed grains and more meat and animal products (Southgate et al ). The type of meat consumed matters: in general, the larger the animal, the greater the ratio of biomass to animal mass. Transitioning to eating more beef (and other larger animals' meat) would require even larger amounts of land than is predicted in the BAU scenario.
Alexandratos and Bruinsma assume that protein consumption will increase in the developing world, but consumption levels will be lower and the commodity mix will be different than has been seen in the developed world (e.g., the US). Our BAU scenario assumes 2050 biofuel energy requirements will be the same as in 2020, and results in an estimate of 80 million ha of land globally needed to be used for energy crops to meet those requirements in 2050.
None of the four scenarios considered in our analysis uses residues and wastes, which would have negligible land use effects (US EPA ). For example, biofuel production from forestry residues, agricultural residues, and wastes could supply about another 5% (9 EJ yr −1) of global transportation fuel if these resources were allocated to cellulosic ethanol production, based on even the most pessimistic technical potential assumptions from Chum et al. Another biofuel-related limitation is that our available land stock (and pastureland and cropland stocks) contains land of a wide variety of qualities. If cellulosic crops use abandoned or less productive lands, the impact on prime agriculture lands may be lessened (US EPA ). Land moves to cropland from pastureland first, and then from available land (i.e., forest and grassland) as shown in the HF scenario in figure. Table lists the per cent change in land use between the HF and other alternative scenarios and the BAU scenario.
In the HF scenario, cropland increases by about 15% globally by the end of the simulation in order to meet growing demand for food products compared to BAU. US cropland expansion to meet rising food requirements occurs mostly at the expense of available forest and grassland but also involves some pastureland. In the ROW, cropland expansion occurs almost exclusively on forest and grassland land. Pastureland in the ROW increases to help supply higher meat requirements. A similar dynamic is not observed in the US because high-land-use intensity meat consumption relative to other meat commodities is projected to decline (Alexandratos and Bruinsma ). These trends are offset in the HF scenario, but the results are a static rather than a growing consumption of high-land-use intensity meat as seen in ROW. Alexandratos and Bruinsma modeled diet trends based on historical data and dietary trajectories of other meat-consuming developed countries.
Cropland expands more significantly in the HB scenario than in the HF scenario to meet the high demand for biofuels and there are different land use tradeoffs. Globally, about 700 million ha of land are required to meet the high biofuel requirements. In the HB scenario (table ), US cropland area increases by about 40% and the ROW cropland area by about 25% by the end of the simulation, compared to BAU. In the HB scenario, US cropland expands onto pastureland, and also available land to pastureland to compensate. The ROW cropland expansion follows this same trend, so compared to the HF scenario, much less available land is actually used. Per cent change in land use from BAU scenario. Figure 2. LUC for two regions across four commodity demand scenarios.
In the BAU and high food demand scenarios, biofuel policies that are in place in as of 2012 are assumed to be met in 2019 by food-based crops. Division in historic and modeled data indicated by dashed vertical line. Download figure: Land use change across all scenarios examined in this study is presented in figure. Even though pastureland increased slightly in the high food demand scenario, it decreased in the HF and HFB scenarios.
These results highlight an underlying dynamic occurring, to some extent across all scenarios of the model. In some years, meat production could fall short, in particular for land-intensive commodities (e.g., beef). Demands for other commodities, such as cereals, are being met at most points in time in non-HFB scenarios. The exception is in the ROW HFB scenario between about 2010 and 2030, when the most rapid shifts are occurring in land use and commodities demanded. The rate of land conversion needed to meet increases in food and biofuel demands between 2010 and 2030 is the highest during this time period. During this time period there are unmet commodity demands (1%–3% of total) occurring every few years as the model seeks to achieve equilibrium under highly stressful conditions. After this time period, non-meat demands were accommodated.
Figure 3. Global change from 1990 to 2050 in cropland, pastureland, and available land (i.e. forest and grassland), in response changes in demand. Division in historic and modeled data indicated by dashed vertical line. Download figure: As described in the methods, the model is equilibrium seeking, responding to demands for commodities. Consumption is constrained not to exceed physically and logistically feasible supply. Within a given region and time period, supply shortfalls of commodity crops lead to constraints in animal product consumption before constraining direct human consumption, so that if humans demanded both more grain and more meat, land would first be used to produce more grain.
Shortfalls in practice imply that people eat less of a commodity or that if possible they shift consumption to a substitute. However, the model does not explicitly capture this potential substitution effects between animal and non-animal products with regard caloric intake. It is assumed that as prices for meat products go up people do not shift their diets toward alternative food commodities in addition to any demand reductions. Supply shortfalls are more common in biofuel scenarios because biofuels' use of land was given priority as their use is policy driven rather than economically driven. Despite higher food demands in the HFB scenario, pastureland growth is much lower than that in the HF scenario, because land is first allocated to biofuel feedstocks.
In practical terms, such shortfalls could be avoided through various means, such as by removing biofuel production requirements, structuring biofuel policies to respond to market conditions, switching diets to lower-land-use intensity meat use, or improving biofuel and food commodity. Section 5 of the SI (available at ) gives an example future in which cellulosic biofuel yields improve or exacerbate shortfalls. An increase or decrease of 0.5% of annual average cellulosic feedstock yield growth from 2020–2050 had enough of an impact to free up or occupy substantial amounts of land. Specifically, in comparison to baseline assumptions and using Alexandratos and Bruinsma , 0.4–0.5 billion people per year would or would not, have wheat demands met, depending on this range of yield growth. A negative trend in annual yield improvement due to climate change (e.g., extreme weather events) would lead to similar if more extreme results (Lobell and Christopher ). While yield levels may not change underlying dynamics, they do have an important role to play in the magnitude of land used by biofuels and other human uses of land. BioLUC results are not predictions, but they may indicate when and why stresses arise in the global agricultural system.
BioLUC is a simple model that focuses on bookkeeping for land stocks, food inventory, and international trade. It tends toward equilibrium conditions that allocate resources to meet demand. The model's main use is to develop insights into the interplay among the myriad of factors impinging on the global land system. Therefore, we examined the dynamics of scenarios, rather than conducting detailed uncertainty analysis around any given scenario. We have already outlined several limitations of our analysis, but one significant limitation is the resolution of global regions.
To address this, the flexible design of the BioLUC modeling framework allows for expansion and contraction of the number of regions as well as the use of alternative data sets (e.g., land cover) (see SI section 6 available at for potential future work with BioLUC). Expanding to additional regions would allow us to improve model resolution and precision of the results, to better evaluate regional land use dynamics, and to compare BioLUC with other LUC modeling frameworks. For example, improving the model resolution should allow for correction of unrealistic land use-related traded disparities between the US and the ROW region that appear in our results. The two-region model does not capture complex inter-regional dynamics.
That is, food and biofuel demands cannot forcibly be met through imports at the expense of the internal demands of the export region, as might be expected in reality, due to different levels of purchasing power across regions. The implications of the two-region simplification are a tendency for the model to internalize land demands regionally.
Modeling more regions would allow us to capture more trade complexity between developing and developed countries. For example, the general equilibrium modeling framework, GTAP, recently aggregated its data sets to 19 regions for modeling of LUC (Tyner et al ). We selected four scenarios to represent extreme biofuel and food consumption conditions, and evaluated them to examine a high-intensity agricultural future. Many other important scenarios are possible, including:. Additional agricultural intensification that might occur in high demand situations. That is, higher prices could lead to investment in higher yielding crops and other intensification technologies, instead of the land expansion that we found herein.
Alternative scenarios that explore the effects of cellulosic-based biofuel production on land that is less likely to compete with food crops, such as on the abandoned land category. Alternative meat consumption scenarios that free up pastureland. For example, can protein and biofuel demands be met at the same time through changes in dietary preferences? BioLUC results are not predictions; the model only provides insights into the dynamic interactions of LUC drivers. Quantities and changes are provided to facilitate comparisons between various scenarios.
The HF and HB scenarios lead to differing levels of cropland expansion and unequal conversion levels of pastureland and available forest and grassland requirements. In our HB scenario by 2050 cropland has expanded by 25% relative to BAU. Cropland expansion occurs mostly at the expanse of pastureland (500 million ha), but also some available forest and grassland to compensate for used pastureland or to be used as new cropland. In our HF scenario, a 15% overall increase in cropland relative to BAU occurred, but cropland expansion onto pastureland is low relative to the HB scenario (i.e., 100 million ha) in part due to meat demand and its higher land requirements relative to other commodities. Pastureland required directly for meat production and indirectly for conversion to cropland leads to greater expansion onto available forest and grassland in the HF scenario. The differences in land converted in the HF and HB scenarios point to alternative approximate reasons for forest and grassland conversion in the HFB scenario. In the HFB scenario the 40% increase in cropland relative to BAU is linked to biofuel and food demand in the BioLUC model.
However, based on trends in the HB scenario, most of the HFB's cropland expansion is needed for biofuels. Based on our model's dynamics and relative to the HB scenario there is a stronger link between food commodities, such as meat, and the conversion of forest and grassland. Based on the comparison of HF and HB scenarios, our results suggest, all else being equal and compared to BAU, that for the HFB scenario by 2050:. About 70% of cropland expansion is linked to higher biofuel demand. 30% of cropland expansion is linked to higher food demand.
The HFB scenario's high pastureland requirements led to a greater expansion into available land that is likely more directly attributed to food than to fuel so roughly 25%–35% of expansion into forest and grassland seen in the HFB scenario is attributable to biofuels. These results have potential implications for GHG emissions because forests are relatively larger carbon sinks (Hoefnagels et al ).
Our scenario analysis shows that, even under the limiting assumptions we assumed about the future, fairly aggressive future biofuel and food demands could mostly be met using a combination of agricultural and other available land. However, across all scenarios demands for the highest land-using meat commodity (e.g., beef) were difficult to meet given diet, population and other assumptions, particularly the assumption that non-meat demands (including biofuels) would be met first. Under the most extreme conditions in the HFB scenario there were some supply shortfalls relative to projected food commodity demand in the 2020–2030 timeframe. These supply shortfalls occurred when the rate of increase in food and biofuel demand was at its peak. Broad conclusions about the drivers and dynamic interactions of LUC using BioLUC allows for an understand and test assumptions about complex systems to more informed decision making and more detailed LUC modeling analysis by other modeling systems.
Our analysis is limited in several keys respects. We did not examine additional land intensifications or advanced biofuel systems using wastes and residues that would reduce land expansion requirements. These systems could mitigate the increases in cropland area, but are not expected to alter the underlying dynamics in the current version of the model because these factors, e.g., land intensifications and using wastes and residues, could change the amount of land required but not the underlying dynamics of how land is used. One exception, which could change the underlying dynamics, is to allow cellulosic fuel feedstocks to grow on the abandoned land category, which is not currently allowed in BioLUC.
BioLUC and SD both often include general simplifications outlined in table primarily related to data and economic relationships, operating at relatively low-resolution level. Of particular importance is the lack of inter-regional dynamics in a two-region system and that price is not explicitly modeled. Model simplifications prevent the study of key dynamics emerging from or the direct result of greater model detail.
With the inclusion of additional regions, trade dynamics in which greater LUC occurred in some areas over others could be explored, beyond the more proportional distribution seen in this analysis. The authors wish to acknowledge the US Department of Energy's Office of Biomass Program which provided the funding for this work. The authors do not have any other potential conflicts of interest. Data sources used in the model are included in the supplemental information, and are cited in the references section. Thank you to Dr Robin Newmark and Nate Blair of the Strategic Energy Analysis Center; Dr Joseph Cleary of the National Bioenergy Center; Dr Helena Chum a Research Fellow; Dr Doug Arent of the Institute for Strategic Energy Analysis; and Bobi Garrett Senior Vice President for Outreach, Planning, and Analysis at the National Renewable Energy Laboratory for providing internal review.
Thanks to Dr Gbadebo Oladosu of Energy and Environmental Sciences at Oak Ridge National Laboratory for external review. Special thanks also to John Sheehan at the Institute on the Environment at the University of Minnesota for reviewing the paper, for helping us define the project scope, and providing feedback on earlier drafts, and to Dana Stright for helping prepare figures for this report. Ethan Warner made major contributions to the writing process, and also made minor contributions to data processing and analysis. Dr Daniel Inman also made major contributions to the writing process and was involved with data processing and analysis as well. Benjamin Kunstman made moderate contributions to both writing and data processing. Dr Brian Bush made major data processing and analysis contributions and made minor contributions to the paper. Laura Vimmerstedt was a minor contributor to the writing and review process.
Steve Peterson was responsible for BioLUC model development and construction, and also contributed in providing project scope and analysis. Jordan Macknick provided minor data processing and writing contributions. Dr Yimin Zhang made minor writing contributions.
In the World Resources Institute (WRI) working paper, “Avoiding Bioenergy Competition for Food Crops and Land,” the authors work off the assumption that land-use decisions are used making an “either-or” approach, i.e., land can either be used to grow food - or biofuels crops. Land can either store carbon -or grow food and fiber. Land can either be devoted to wildlife habitat - or food and fiber production. The ‘either-or’ approach, while straight forward, lacks a basic understanding of the complexities of agricultural and working forest land use, emerging research on the carbon cycle in working lands, and the very real economic pressures on land owners to divert working lands to development. The report, authored by Dr. Searchinger, a Senior Fellow at WRI and scholar at Princeton University, and WRI consultant Ralph Heimlich, leaves no sector of the biofuels economy untouched in their indictment of renewable fuels, as they have concerns about traditional starch based feedstocks (corn starch ethanol, beets, sugar cane), cellulosic feedstocks (such as purpose grown grasses and short-rotation woody trees), and wood wastes (such as waste from pulp, paper and timber industries). Last week, SBFF promised readers that we would devote more time to understanding the assumptions and conclusions in WRI paper, and we address some of the main conclusions and assumptions employed in our discussion below.
The ‘Calorie Deficit’ Ignores Inequities Already Present in Food Systems The basic premise of the report is that no productive land should be directed towards biofuels crops, due to the looming issue of a worldwide food shortage by 2050. This is despite the fact that food currently is over-produced, and worldwide, approximately one third percent of food is wasted. In the United States, as much as 40 percent of food is wasted, and EPA reports that food waste is the number one ingredient in landfills – imagine, nearly every other bite of food is thrown away! Therefore, growing ever more crops is not the answer to issues of individual and community equity in food politics.
In fact, growing more and more food without addressing issues of food distribution, food waste (both in homes and across supply chains) will only exacerbate issues of environmental quality and do nothing to address affordable access to food. Instead, EESI and others argue – the time is ripe for a dramatic shift in the food production and distribution system. Growing ever more food on large farms won’t address the environmental and food justice issues the world faces. Instead, integrated farms where food, fiber, fuels, feedstocks for chemicals and animal husbandry is practiced in sustainable ways is the food revolution that is so badly needed. More farmers, with more equity in the food production process is key to the solution. This includes diversifying crop production and providing alternative revenue streams for producers by growing feedstocks for biofuels and biobased products. In the United States, a flowering regional food system, farmers’ markets and growing recognition of the importance of family farms is a start – but more is needed to provide Americans and people everywhere equitable access to affordable, healthy food.
As for Searchinger’s claim that food prices are affected by biofuels production, a World Bank analysis of the long-term drivers of food prices concluded that 66 percent of food price increases are thanks to oil prices. Additionally, only a small percentage of usable food crops goes towards biofuels production – globally, 2 percent of grain supplies go to ethanol production, according to the Global Renewable Fuels Alliance. Biofuels Suffers from a Carbon Accounting Error, Ignores Years of Research The WRI report continually implies that bioenergy’s potential is overblown due to a ‘carbon accounting error’. According to Dr. Searchinger, this double counting is a result of “assuming incorrectly that bioenergy can freely divert biomass or land that is already in use.” This same argument was raised by Dr. Searchinger in 2009, and since then, much research has been devoted to the topic. Instead of forcing more land into production – research has actually found the opposite.
Recent research from Dr. Bruce Babcock, an Iowa State University professor and a former California Air Resources Board consultant, finds that “the primary land use change response of the world's farmers from 2004 to 2012 has been to use available land resources more efficiently rather than to expand the amount of land brought into production.
This finding is not necessarily new however, this finding has not been recognized by regulators who calculate indirect land use.” And while drivers of land use change are complicated, and very different depending on a particular county’s forestry and land governance, the growing body of research encouragingly points to no net change in land use because of renewable fuels. This makes sense, since biofuels feedstocks fetch a lower value than food or feed products, their growth is not the number one driver of land-use decisions.
Additionally, the authors falsely conclude that there is an assumption that biofuels are ‘carbon free. ’ Not only does this ignore the complex science of carbon intensity calculations for all types of biofuels and biomass energy sources to identify the carbon footprint of biofuels, it assumes that crops devoted to biofuels growth are somehow removed from the carbon cycle. The science on land use change is constantly evolving, and new science is incorporated into updates to the Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) Model developed by Argonne National Laboratory, which is the standard for comparing the carbon intensity of diverse fuels.
Updates incorporated in more recent versions of GREET include significant reductions in the carbon intensity of ethanol production since 2008; these process improvements include greater energy efficiency, increasing yields per acre, and decreasing water and fertilizer inputs, among other things. Paints a Picture of an Inefficient Industry – Ignores Years of Progress in Renewable Fuels While Searchinger admits that other renewable technologies have seen great improvements in the last decade, he’s unwilling to give biofuels a second look. That’s despite the evidence of lower inputs and rising yields across the industry. Life-cycle assessment of both biofuels and traditional gasoline has found that while the carbon footprint of biofuels is dropping, it is steadily rising for traditional petroleum fuels. According to research from Dr. Steffen Mueller, Principal Economist at the University of Illinois at Chicago, Energy Resources Center, over the past 13 years, the amount of water necessary to produce one gallon of ethanol in factories has decreased from 5 gallons to 2.7 gallons of water. The amount of energy it takes to produce ethanol has also decreased from 1.09 kWh/gallon to 0.75 kWh/gallon, while crop yields have steadily increased.
Many new technologies have contributed to these efficiency gains, with even greater gains emerging as new biorefineries use corn kernel fiber (previously a waste byproduct) to produce cellulosic ethanol. According to scientists at Argonne, energy use for the production of corn-based ethanol dropped 25 percent, corn farming energy use has dropped 24 percent, and ethanol yields per bushel have risen three percent since 2008.
Soil research also finds that soil organic carbon in corn fields has risen due to increased use of no till and conservation tilling practices. This is backed up by findings from the U.S. Department of Agriculture’s (USDA) National Resource Conservation Service (NCRS), which also models soil carbon.
According to the most recent GREET model, corn ethanol may already be achieving greenhouse gas reductions much higher than the 20 percent reduction mandated by the Renewable Fuel Standard (RFS). Yet, none of this newer information is considered by Searchinger. Assumption that Other Renewable Technologies Will Save Us, Eventually Using solar energy as an example, the authors state that “PV systems today can generate more than 100 times the usable energy per hectare than bioenergy is likely to produce in the future even using optimistic assumptions.” While it is unclear how Dr. Searchinger calculated these numbers, the basic assumption is that we have time to wait for a perfect answer to our transportation needs. Currently, no country has the electric capacity, or the engine technology, to switch the entire vehicle fleet to plug-in electric. Most dangerously, Dr. Searchinger is content to wait for a future that’s several years away, at best guess, and ignore the very real benefits of utilizing biofuels today.
In the end, the multiple co-benefits of biofuels are ignored by the WRI study, and a narrow, outdated view of biofuels production is taken instead. Sustainable biofuels production is possible. The production of biofuels and biofuels feedstocks will not only reduce GHGs and lower dependence on petroleum but provide immense benefit farmers and communities. Regionally appropriate biofuels feedstocks have the potential to revitalize agricultural practices, reduce the use of toxic gasoline additives, such as benzene, and enhance rural economic opportunity, thereby increasing rural welfare and economic security.
Instead of dismantling the biofuels industry, our attention should turn instead to making biofuels production and biofuels feedstock growth even more sustainable and equitable, not only to reduce GHG and other toxic emissions, but to assist rural communities keep working lands free of development, provide economic benefit to local communities, and continue to seek ways to feed the world and provide fuels sustainably. In the search for a low carbon economy, an “all of the above” approach needs to be taken towards renewable electricity and fuels generation. And the world can’t afford to wait – sustainable biofuels are available now. For more information see:, The World Resources Institute, EESI, EESI Contact.
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What are the economic and policy factors influencing biofuel development?. 2.1 How are agricultural, energy and biofuels markets linked?.
What are the drivers of biofuel policies?. What policy measures are influencing biofuel development?. How costly are biofuel policies?. How viable are liquid biofuels? 2.1 How are agricultural, energy and biofuels markets linked? Agriculture both supplies and uses energy, so agriculture and energy markets are closely linked.The rapidly increasing demand for liquid is connecting agriculture and energy more closely than ever, both through market forces and government policies encouraging use. Liquid such as bioethanol and which are derived from agricultural crops compete with on energy markets.
Beggin strips ringtone free download. As volumes produced remain small compared to the global market of petroleum fuels, oil prices are an important driver of the prices of biofuels and of their agricultural. Agricultural crops grown for energy production also compete with food crops for resources. For example, a given plot of land can be used to grow maize for or for food. Farmers will sell their harvest to an ethanol or processor if the price received is higher than what they could obtain from other sources such as food processing. As a consequence, when the value of is high, prices for other agricultural crops tend to rise.
For this reason, producing second generation from non-food crops, such as wood or grasses, will not necessarily eliminate the competition between food and fuel. With the exception of bioethanol from sugar cane in Brazil, have not generally been competitive with without active government support to promote their development and subsidise their use, even at high crude oil prices.
In general, granting to a sector that cannot ultimately achieve economic viability is not and may simply transfer wealth from one group to another while imposing costs on the economy as a whole Subsidies can also have complex impacts on producers and consumers in other countries. 2.2 What are the drivers of biofuel policies? The main drivers behind government support for in countries are concerns about and energy security, and the political will to support the farm sector through increased demand for agricultural products. Energy Security Secure access to energy is a longstanding concern in many countries. The recent increases in oil and other energy prices have increased the incentive to promote alternative sources of energy. Strong demand from rapidly developing countries, especially China and India, is adding to concerns over future energy prices and supplies.
The transport sector depends mainly on oil. Liquid represent the main alternative source that can supply fuels suitable for use in current vehicles, without radical changes to transport technologies. There is increasing concern about human-induced climate change, and the effects of emissions on rising global temperatures. Bioenergy is often seen as a way to reduce greenhouse gas emissions. However, the extent to which the production and use of a given reduces emissions compared to the production and use of petroleum based fuels varies significantly depending on factors such as land-use change, type of, agricultural practices, conversion technology and end use. Recent analyses suggest that large-scale expansion of production could even cause a net increase in emissions. Farm Support Supporting the farm sector has been a key objective of policies in several developed countries, and rural development is also being cited as a driver by developing countries.
Bioreactor
In countries with heavily subsidised farm sectors, bioenergy is seen as a way of revitalising agriculture. The possibility of boosting farm incomes while reducing income support and has considerable appeal for policy-makers, although the latter part of this strategy has been difficult to achieve. Policies on agriculture, energy, transport, environment and trade all have an influence on production. Schemes to promote and support have been introduced both in and developing countries. Without these incentives, widespread biofuel production would in most cases not have been commercially viable.
The policies used by governments to promote and support development include various instruments. They can support the biofuel supply chain at different stages. Agricultural policies existed well before the introduction of. They include agricultural and price support mechanisms which directly affect production levels and prices of crops as well as production systems and methods. These policies also have implications at international level for agricultural trade and geographical pattern of agricultural production.
Blending mandates defining the overall amount or proportion of biofuel that must be blended with petrol and diesel are increasingly being imposed. Subsidies and support for the distribution and use of biofuels are key policy components in most countries that promote the use of biofuels. Several countries are subsidising or mandating investments in infrastructure for biofuel storage, transportation and use, especially towards bioethanol which requires major investments in equipment. Tariffs or import barriers are duties usually imposed on imported goods. They are widely used on biofuels to protect the national agriculture and biofuel sectors, support domestic prices of biofuels and provide an incentive for domestic production. Tax incentives or penalties are among the most widely used instruments for stimulating demand for biofuels and can drastically affect the competitiveness of biofuels compared to other energy sources. Research and development is generally aimed at improving the efficiency and cost-effectiveness of biofuel production, and identifying.
In developed countries an increasing proportion of public research and development funding is directed towards second-generation biofuel technologies, in particular and -derived alternatives to petroleum-based diesel. Support measures directly linked to levels of production and consumption are considered to have the greatest market-distorting effects, while support to research and development is likely to be the least distorting. 2.4 How costly are biofuel policies? Estimates of total support for in the countries show that are already relatively costly for taxpayers and consumers. In 2006, the USA spent an estimated 6.3 billion US$ to support biofuels, while the EU spent 4.7 billion US$. These figures include the cost of blending mandates, tax credits, import barriers, investment subsidies and general support such as public research investment, but exclude support to agricultural production.
Per litre of, support ranged from about US$0.20 to US$1.00 per litre in the countries in 2006. Because the level of support is to a large extent linked to production, expenditures will increase as biofuel output grows. In short, policies to promote and support have in most cases been costly. They have tended to introduce new distortions to national and international agricultural markets which are already severely distorted and protected.
This has not encouraged an efficient international production pattern for biofuels. In terms of litres of produced per hectare, sugar beet and sugar cane are currently the most productive crops. However, the costs of biofuel production vary widely depending on the type of, the country and various factors such as energy costs, processing costs and the value of co-products. Brazilian sugar-cane has a much lower total cost than other.
Costs for other liquid biofuels exceed the market price of and require. The feedstock accounts for the largest share of total biofuel production costs. The energy costs of biofuel production can be offset by the value of by-products which may be burned for energy or sold. Brazilian sugar-cane has a much lower total production cost than other due to the high efficiency of the production process. Sugar production costs have decreased over the last decades and bagasse, the major by-product of sugar-cane processing provides the energy necessary for the production process. In 2004 and 2007, costs for other liquid biofuels, such as sugar beet, wheat and maize ethanol in the EU or, rapeseed and soybean, all exceeded the market price of and required to remain competitive. Future profitability will depend on how these biofuels evolve in relation to fossil fuel prices.
The price that producers in the USA can pay for maize while remaining profitable varies both with and without government. It is estimated that for a crude oil price of US$60 per barrel, maize remains competitive on an energy basis as long as the market price for maize remains below US$79.52 per tonne, but the subsidies, which amount to about US$63 per tonne of maize, enable processors to pay up to US$142.51 per tonne and still remain profitable. When comparing observed monthly maize and crude oil prices with the maximum price that biofuel producers could pay for maize, it can be noted that maize ethanol in the USA has rarely been competitive with without subsidies. Prices usually change with crude oil prices because energy is a significant cost factor in agricultural production and transport and rising crude oil prices contribute to a surge in demand for agricultural crops as feedstock for. For instance, between 2003 and 2008, prices for maize, rapeseed, palm oil and soybean have been highest when crude oil prices were high. However, prices of agricultural products are also influenced by policies.
For instance, the price of maize in the USA rose steadily during 2006, partly because of increasing production, while the price of crude oil remained stable.
Aquatic Plant
Recent studies of the impact that land conversion has on the global warming pollution created by crop-based biofuels are changing the science of measuring biofuel risks and rewards. These studies demonstrate that when crop-based biofuels contribute to deforestation or other damaging land conversions, the pollution benefits can be compromised or even eliminated, potentially producing a net increase in pollution. The science behind these calculations is new, and the numbers can be expected to change as the science matures, but we can already conclude that biofuels must use both land and energy efficiently to ensure these fuels play a constructive role in addressing global warming.
Some biofuels can be produced without harmful changes in land use, and these have great potential to reduce global warming pollution. Examples include fuels made from biomass waste products or native perennials grown on land not currently used for or well suited to food crops.
On the other hand, there are types of land that should certainly not be used for biofuel production, especially forests high in stored carbon and rich in biodiversity. Converting a forest to cropland can result in much more global warming pollution than the amount that can be reduced by the biofuels grown on that land.
The Science of Land-use Changes A recent paper estimated that if peatlands in Southeast Asia were converted to palm oil plantations to make biodiesel, it would take 423 years to pay back the “carbon debt” from the land-use change. In the United States today, biofuels are mainly produced from corn and soybeans grown on existing agricultural land, so there is not necessarily a direct land-use change. But there can be an indirect land-use effect when the corn and soy are taken out of the market for food and animal feed. This increases corn and soy prices, stimulating land conversion in other parts of the world. A study by Searchinger et al. Of this indirect effect used agricultural economics models to estimate how global markets respond to the increased use of corn for biofuels. They used these models and historical data on land conversion to estimate where new crops will be planted, what land will be converted, and what emissions will result.
Based on these estimates they calculated that expanded use of corn ethanol will produce almost twice as much global warming pollution as gasoline. The federal energy bill passed in 2007 includes a Renewable Fuel Standard (RFS) that significantly accelerates use of biofuels including ethanol—from about 6 billion gallons in 2007 to 36 billion gallons in 2022.
The RFS requires most renewable fuels to reduce global warming pollution, including pollution from indirect land conversion, but exempts corn ethanol produced in existing plants or plants that were under construction prior to the law’s enactment. This loophole undermines the standard’s intended benefits.
If the estimates of indirect land-use impacts by Searchinger et al. Are accurate, the emissions from roughly 12 billion gallons of corn ethanol exempt from pollution limits set by the RFS could wipe out the benefits derived from the remaining 24 billion gallons of renewable fuels over the lifetime of the RFS. A Sensible Approach to Biofuel Production The Union of Concerned Scientists’ suggest how biofuels and bioenergy can be a productive part of a broader strategy to address climate change. Below, we offer additional recommendations that address land-use issues specifically and suggest how to avoid harmful unintended consequences, which can prevent biofuels from achieving their potential. Performance-based policies should reward reductions in global warming pollution over a fuel’s full life cycle, based on the best available information and vetted in an open and transparent process. The rule making currently under way for the federal RFS and the California low-carbon fuel standard (LCFS) will determine how global warming pollution is measured for compliance with these standards; their success requires that all significant inputs and impacts, including indirect land-use changes, be considered. Because the science of global warming pollution and indirect effects is still evolving, and new studies will improve our understanding over time, both standards must also include a mechanism to ensure that life cycle emissions metrics used for compliance can be improved accordingly, and that the process is open, transparent, and based on the best peer-reviewed science.
Biofuel life cycle analysis should include a non-zero estimate, based on the best available science, of emissions associated with indirect land-use changes. While there is no scientific consensus about the exact magnitude of indirect land-use effects, and details of the methodology used to measure these effects are debated, scientists generally agree that the impact is real and significant. Because the federal RFS and California LCFS will be implemented before a firm consensus on these details can be reached, both standards should take effect with a non-zero default value. Environmental Protection Agency and the California Air Resources Board are developing models fundamentally similar to Searchinger et al. In their use of global economic agricultural models; both agencies should use their best estimates and set a schedule for updating them as the science improves.
Even an indirect land-use value of one-fourth that predicted by Searchinger et al. Could have significant implications for near-term policy decisions, and setting that value to zero will send the wrong signal. The United States should promote biofuels that use both land and energy efficiently. Current life cycle accounting does a good job accounting for a fuel’s energy inputs, but the recent literature suggests we have not accounted for land use adequately.
In spite of the uncertainty, however, we can state with relative certainty that biofuels that use land more efficiently, such as those derived from agricultural, forest-product, and municipal waste streams, are a better bet than food-based biofuels from a land-use perspective. And bioenergy crops that improve land currently considered unsuitable for agriculture are likely to be the best bet of all. While these resources appear beneficial from a climate perspective, their broader impact must be considered before moving forward with their use, as outlined in the UCS Principles for Bioenergy Development. Additional funding should be directed to areas of research that can improve our ability to measure land-use changes globally. Satellite and aerial imagery, for example, can be used to accurately and objectively measure changes in land use and estimate the impact on carbon cycling, nitrogen and methane cycling, and carbon sequestration. Also, economic modeling of the impact biofuel production will have on land-use decisions worldwide (and how this affects food prices and availability, global warming pollution, deforestation, nutrient runoff, water use, and other important outcomes) will be critical to biofuel life cycle accounting and climate policy in general.
A long-term commitment to biofuels must be tempered by realistic expectations about the scope of biomass production. Biofuels derived from many resources can play a role in reducing global warming pollution. The federal RFS calls for 21 billion gallons of advanced ethanol, which would require about 300 million tons of biomass.
Based on current estimates, this amount of biomass can be obtained from waste products such as agricultural residues, forestry residues, and municipal and construction waste.5 Any significant expansion beyond this level, however, must be based on a sound scientific determination that the required volume of biomass can be produced in a sustainable manner. Biofuels will have to compete for biomass with electrical power generation, biogas and chemical production, and traditional agricultural uses such as food, feed, and fiber. Unexploited biomass production systems such as forests and prairies also play an important role in supporting needed ecosystem services including water purification, carbon sequestration, nutrient cycling, biodiversity, and recreation. If we over-utilize these resources to make fuel, we risk transforming a potential solution to our fuel challenges into a major problem for food supplies and ecosystem services. We need to ensure that renewable resource policies account for this risk and strike the right balance.
For more information, contact Jeremy Martin at (202) 223-6133.