An Integrated Approach to Modeling Land Use Implication on the U.S. Agriculture Sector Under a Carbon Policy

by

Sugandha D. Tuladhar1

Mei Yuan Paul Bernstein

W. David Montgomery Julian Lamy Anne Smith

Charles River Associates Washington DC

Abstract

This paper studies impacts on fuels, forest, and food industries by introducing land as a separate factor of production for agriculture and domestic offset generation in Charles River Associates’ fully integrated top-down and bottom-up model, MRN-NEEM. We analyze the impact of the climate change bill, American Clean Energy and Security Act of 2009 (ACESA), on the agriculture and food markets resulting from competition for land. The model accounts for land use in agricultural goods production; in growing energy crops for biomass generation and biofuels production; and in afforestation and other carbon offset activities. The model results show that prices of fuel and land increase over time as the policy becomes more stringent. Increases in the afforestation activities and biomass generation draw down land available for agriculture goods production significantly beyond 2040. The effects of high fuel costs and land prices lead to higher agriculture, fertilizer, and food costs and ultimately discourage exports. A key insight of the analysis is that the U.S. becomes a net importer of food by 2035, a decade and a half earlier than in the baseline, which is a looming challenge for U.S. food security.

1 Corresponding author: Sugandha D Tuladhar. Associate Principal, 1201 F Street NW, Suite 700, Washington, DC 20004.

Tel + 1 202 662 3871; fax: +1 202 662 3910

Email address: stuladhar@crai.com

This paper describes the conclusions of the individual authors and does not necessarily represent the views of Charles River Associates or its clients.

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1. Introduction Over the last decade food prices in the United States have increased steadily. The food and beverage consumer price index increased by 27 percent while cereal and bakery products increased by 36 percent (BLS 2010). Increase in the price of food since 2006 is largely attributed to an increase in the energy prices resulting from higher oil prices and significant increase in biofuels production. The rapid increase in demand for energy crops has affected land use and introduced imbalance in the food market (Hertel et al. (2010), Golub et al. (2010), Gurgel et al. (2008), Reilley et al. (2007)). Land use is becoming a new flash point in the U.S. climate policy debate that purports to curb greenhouse gases (GHG) through a nationwide or through state-level cap-and-trade programs. Numerous climate change proposals that have been proposed over the past few years have shown an impact to the agriculture markets; see Paltsev et al. (2007), Tuladhar et al. (2009) for discussion of some of the climate change proposals. In addition to capping emissions, embedded in recent climate change proposals are provisions that combine efficiency and renewable electricity standards, incentives for low carbon emitting technologies, and terrestrial mitigation options.

In principle, biofuels, offsets, and low carbon emitting technologies such as biomass generation might offer a low net cost means of curbing greenhouse gas (GHG) emissions; yet questions still surround this concept. Would agriculture land be substituted to grow forest for afforestation offset activities that would add pressure to the land use and ultimately the agriculture and food markets? Would biofuels mandates and continued subsidies address energy and food security issues in the long run? Or might biofuels policies subvert the goals of offset programs?

These questions are not new and many studies have provided insights to help answer them. In particular, some recent studies have looked at the overall macroeconomic impact on the agriculture sector from a climate change policy proposal, American Clean Energy and Security Act of 2009 (ACESA), (Baker et al (2009), EPA (2009), EIA (2009), FAPRI (2010), Montgomery et al. (2009), Ugarte et al. (2009), USDA (2009)). Other studies have looked at more specific questions that pertain to land and biofuel interaction Gurgel et al. (2008), or land and offset interactions (Brown et al. (2009)). These recent studies indicate that there would be costs to the agriculture sector as a result of limiting GHG emissions. Gurgel et al. (2008) extended the EPPA model to include agricultural sectors and different land types to model the impacts on the agricultural sector from increased biofuels production, mainly cellulosic ethanol. Hertel et al. (2010) highlights the need for an analysis to include trade-offs between land use by crops, biofuels and offsets and proposes a Computable General Equilibrium (CGE) modeling framework to do this. An important provision that has emerged from the climate change discussion is the provision of offsets. Offsets provide an opportunity to reduce the costs of the policy if designed properly. Emission reduction costs can therefore be lowered by expanding the available pool of offsets. However, there could be potential unintended consequences on land use. Murray et al. (2005) performed analysis of offsets using the Forest and Agricultural Sector Optimization Model (FASOM) model. Lewandrowski et al. (2004) provides a detailed literature review on this topic. They extended the U.S. Agricultural Sector Model (USMP) to analyze the mitigation potential of carbon offsets using agricultural and forestry practices in the U.S and concluded that commodity prices would rise as cropland is substituted for afforestation activities.

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Sand et al. (2008) notes that determining a practical economic structure to model land competition is conceptually challenging. To address this challenge, we introduce land as a separate factor of production for agriculture production and domestic offset generation in a fully integrated top-down and bottom-up model to analyze the impact of ACESA on the agriculture sector and food market. The MRN-NEEM model accounts for land use for biomass generation, biofuels production, and afforestation and other carbon offset activities. These competing sources for land facilitate the evaluation of economic impacts of land competition on the agriculture sector. We illustrate some of these key trade-offs, and, by doing so, cast light on how certain provisions of a climate policy impacts the food and agriculture markets.

The rest of the paper is organized as follows. In the next section, we describe the modeling approach and then briefly describe how we expand the MRN-NEEM model to include land and model fuel, forest, and food interactions. We then summarize the underlying model assumptions and the baseline. Following a detailed discussion of the results from the model for a carbon policy and limitations of the model, we present some conclusions.

2. Analysis Approach and Model Description The MRN-NEEM model combines two economic models: the Multi-Region National (MRN) model and the North American Electricity and Environment Model (NEEM). MRN is a top-down model that characterizes the production technology and consumption preference in the economic system with smooth functions and captures the economy-wide effects through interrelated markets. NEEM is a bottom-up model that represents the electricity sector at the unit level and models the evolution of the North American power system taking account of the electricity demand growth, available generation, environmental technologies and environmental regulations both present and future.

Overview of the MRN Model The MRN model is a forward-looking, dynamic computable general equilibrium model of the United States economy. All states are aggregated and represented by a single region. Consumer behavior is represented by a single representative agent who faces an intertemporal budget constraint. The intertemporal budget constraint of the households equates the present value of consumption gross of taxes to the present value of income earned in the labor market and to the value of the initial capital stock minus the value of post-terminal capital. The infinitely lived representative agent optimally distributes wealth over the horizon by choosing how much output in a given period to consume and how much to save. The income-balance and zero-profit conditions ensure that the infinitely lived economic agent makes intertemporal decisions to optimize consumption, production, and investment in every period.

For this analysis, we expand the sectoral definition to include sectors that would enable us to analyze changes in land use, fertilizer costs, and agriculture costs. We separate fertilizer, agriculture, beverage, household products, and finished food products from the agriculture sector that is represented in the standard MRN-NEEM model. All sectors except electricity and coal are modeled as a nested Constant Elasticity of Substitution (CES) production function in the MRN model. The electricity and coal sectors are characterized by detailed supply structures in the NEEM model.

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The model also includes four different types of low carbon fuels for personal transportation that can be blended with gasoline: aggregate refined petroleum products or oil (blend of gasoline and diesel), conventional corn-based ethanol, Low-GHG biofuel (blend of bio-diesel and cellulosic and sugar-based ethanol), and a carbon-free transportation fuel. Each fuel is characterized by its emission factor, cost, and maximum allowable penetration. Corn-based ethanol production is associated with an emission factor of 76 % of that of gasoline measured in grams of CO2 per mega joule (gCO2/MJ), while for the low-GHG the emission factor is 20 % of conventional gasoline. The zero-carbon transportation fuel, as the name suggests, embodies no carbon.

Overview of the NEEM Model The NEEM model is a flexible, partial equilibrium model of the North American electricity market that can simultaneously model system expansion and environmental compliance over a 50-year time frame. The model employs detailed unit-level information on all of the large generating units in the United States and large portions of Canada and dispatches load based on a load duration curve. NEEM models the evolution of the North American power system, taking account of demand growth, available generation, environmental technologies, and environmental regulations both present and future. The North American interconnected power system is modeled as a set of regions connected by a network of transmission paths.

NEEM has a rich representation of technologies for electric power generation, which are characterized in terms of capital cost, operating cost, and heat rates, all of which improve overtime. New technologies like Integrated Gasification Combined Cycle (IGCC) with carbon capture and sequestration and biomass generation are characterized by dates of availability and introduction constraints. Since coal is the primary fuel used by the electricity sector, a detailed representation of the supply structure of this fuel is important. NEEM captures the coal market through 21 supply curves that represent different regional sources, ranks, sulfur, and mercury content.

MRN-NEEM Integration Methodology Following the approach outlined by Bohringer-Rutherford (Bohringer and Rutherford 2006), the MRN-NEEM integration methodology follows an iterative procedure to link top-down and bottom-up models. The method utilizes an iterative process where the MRN and the NEEM models are solved in succession, reconciling the equilibrium prices and quantities between the two models. The solution procedure, in general, involves an iterative solution of the top-down general equilibrium model given the net supplies from the bottom-up energy sector sub-model followed by the solution of the energy sector model based on a locally calibrated set of linear demand functions for the energy sector outputs. The two models are solved independently using different solution techniques, but linked through iterative solutions points.

Specifically, the NEEM model passes the electricity supply and non-electric coal supply and electric natural gas demand to the MRN model. The MRN model optimizes and returns to the NEEM model the electricity prices, natural gas prices, and the coal demand from the non-electric sectors. The iterative process involves NEEM resolving given MRN feedbacks. The iterating continues until energy prices and quantities converge. The same procedure applies

under a carbon policy analysis where carbon allowances are passed between the two models until there is equalization of marginal abatement costs between the two models.

The MRN model is formulated as a mixed complementary problem (MCP) using the Mathematical Programming Subsystem for General Equilibrium (MPSGE) software (Rutherford 1995) and solved using the PATH solver within the Generalized Algebraic Modeling System (GAMS) (Brooke, Kendrick, Meeraus, and Raman, 2003). The NEEM model is formulated in GAMS as a quadratic program and solved using MOSEK solver. The MRN-NEEM model is solved in five-year intervals to 2050 with 2010 as the first endogenous year and solved within GAMS.

Modeling Land in the MRN-NEEM Model To capture the interactions among land, food, and energy, we include land in the model as a separate factor of production. The total endowment of land is used for producing agriculture goods, producing feedstock for biomass generation and generating carbon emission offsets (see Figure 1). The agriculture commodity is an intermediate input to biofuels, agriculture and grocery manufacturing sectors (BEV, FOD, and HHP). Since the agriculture production requires the use of land, the competition for the agriculture commodity by these sectors such as biofuels and food can be translated into competition for agriculture land use. In addition, the imposition of the carbon policy induces more biomass generation and offset generation activities leading to further competition for land.

Figure 1: Land use Options

All production technologies are modeled as nested constant elasticity of substitution (CES) functions. The production structure of agriculture is similar to that in the EPPA model (Paltsev et al. 2005) and also adopts similar elasticities between inputs where applicable. Agriculture production represents crop production (NAICS 111), animal production (NAICS 112), forestry and logging (NAICS 113), and fishing, hunting and trapping (NAICS 114).2 The production

2 The North American Industry Classification System (NAICS) is used to classify business establishments to similar production process used

to produce goods or services in the United States. U.S. Census Bureau.

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structure combines land with an aggregate of materials and energy to form a resource-materials-energy bundle which substitutes against value added inputs of capital and labor to produce agriculture goods. The elasticity of substitution, labeled on top of each nest (see Figure 2) represents the ease of substitutability between inputs in the nest in response to changes in relative prices.

Figure 2: Nesting Structure of Agriculture Sector

Figure 3: Nesting Structure of Grocery Manufacture Goods

The grocery manufacturing sector represents beverage (NAICS 312), food (NAICS 311), and soap, cleaning compound, and toilet preparation (NAICS 3256) manufacturing. These

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industries and fertilizer production employ a generic production structure used for other industrial sectors in the MRN-NEEM model (Tuladhar et al. 2009, Smith 2008). Agriculture goods enter into the materials nest which substitutes against refined oil in the oil-materials aggregate. At the top nest, an oil-material aggregate is combined in fixed proportions with an energy and value added aggregate to produce grocery manufacture goods. Figure 3 illustrates the production structure and the underlying elasticities.

The production structure of offsets is a Leontief structure with capital, labor, and land as inputs. Biomass generation technology is modeled using a bottom-up structure that is consistent with engineering estimates on the NEEM side.

3. Model calibration and scenario design

Model data and assumptions MRN explicitly models the U.S. economy and energy sectors. Data that characterize the interrelationships of commodity uses within the economy therefore are of primary importance in quantifying the economic impacts of policies. As a starting point for characterizing the inputs and outputs of commodities in the U.S. economy, we use a Social Accounting Matrix (SAM) developed for each state by the Minnesota IMPLAN Group, Inc. (MIG). The IMPLAN 2004 database represents the activities in 509 sectors for all 50 states and the District of Columbia for the year 2002. We adjust the original SAM data to make it consistent with state level energy data from the U.S. Energy Information Administration (EIA). The EIA data is more accurate and provides detailed energy markets data than the corresponding IMPLAN data. The SAM that results from the combination of IMPLAN and EIA data fully tracks the intensities of commodity use for the modeled production and consumption sectors for any regional aggregation of states. In addition, the SAM completes the circular flow that accounts for factor incomes, household savings, trade, and institutional transfers.

Conceptually, the SAM represents a “snapshot” of the economy at the current point along a dynamic growth path. MRN simulates the growth path into the future to establish a “baseline” or a reference path. Calibration of the baseline is completed by incorporating growth forecasts for industries, population, and energy demand. For this study, the MRN-NEEM model was calibrated to achieve the same path for the total non-electric U.S. emissions by matching the total non-electric use of fossil fuels and economic growth projected in the Energy Information Administration’s Annual Energy Outlook 2009 (Early Release).

The regional detail of MRN can be specified at any level of disaggregation down to the state level, depending on the needs of the analysis. For this analysis, a single-region national model was used to avoid regional land use data complexity. However, the electricity sector was modeled in detail that captured 27 separate electricity supply and demand regions. This allowed for greater specificity in assessing impacts on the electric markets.

To better capture the impact of the policy on the agriculture and food sectors, we extend the model to include 15 sectors: 5 energy sectors and 10 non-energy sectors.3 Figure 4 summarizes input value shares for five key sectors that are analyzed in the paper. The input shares indicate how much of each input is required to produce a dollar’s worth of output. As shown in the figure, 3% of agriculture sector inputs is fertilizer, 19% agriculture, 6% energy, 16% land, 14% capital, 9% labor, and 34% all other materials. The food (FOD) sector demands a high share of agriculture product input, hence has the highest agriculture input intensity (25%). The Beverage sector (BEV) is the most capital intensive (27%) and the least energy intensive sector (1%), while the household products (HHP) and fertilizer (FRT) sectors are relatively energy intensive. The relative input intensity indicates how the sectoral impacts might change as input costs change. A sector will face larger impacts relative to the other sectors if the policy affects those factors that are intensively used in the sector’s production process. Therefore, energy intensive sectors (FRT and HHP) and land intensive sector (AGR) will be burdened under an energy and environment policy that increases energy and land prices.

Figure 4: Input value shares for some sectors

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We use land cover data from the National Resource Inventory NRI 2003, National Resource Conservation Services. It provides data on various surface lands types: cropland, Conservation Reserve Program land, pastureland, range land, forestland, other rural land, developed land, water areas, and federal land. For this analysis, we take only cropland, pastureland, and forestland and aggregate it into a single type of land. This land aggregate is available for growing agriculture products, growing biomass and biofuels feedstock, and offset activities. The

3 The energy sectors represented in the model are coal (COL), natural gas (GAS), petroleum products (OIL), crude oil (CRU), and electricity

(ELE). The non-energy sectors include agriculture (AGR), manufacturing (MAN), commercial transportation (TRN), motor vehicles (M_V), energy-intensive sectors (EIS), services (SRV), fertilizer (FRT), beverage (BEV), household products (HHP), and food (FOD).

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total land endowment remains constant over the model horizon at about 328 million hectares of which cropland, pastureland, and forestland accounts for 149, 14, and 164 million hectares, respectively.4

The Statistical Bulletin of United States Department of Agriculture (NASS 2004) provides data on cropland rented for cash and the rental price of pastureland for the year 2002. The rental price of cropland and pastureland is estimated to be $71.6 per acre and $9.2 per acre, respectively. We take the forestland rental price to be the same as the forest service fee of $23 per acre (GOA 2003). We average out the different rental prices to estimate a singe land rental price and hence value for the land in the model.

To compute land demand for biomass generation, we use an estimate of U.S. biomass productivity, oven-dry-tonnes (odt) of biomass per hectare (ha) reported in Grugel et al. (2008). Following IPCC (1996, 2001), Moreira (2004), Chou et al. (1977), Edmonds and Reilly (1985), and Bot et al. (2000), Grugel et al. (2008) report U.S. productivity to be half of Latin America’s tons per hectare of 10 to 15 odt/ha/year. We assume a higher estimate of 7.5 odt/ha/year. Furthermore, assuming a heating value of dry biomass of 8600 Btu/lb and a direct-fire technology heatrate of 13000 Btu/kWh, we estimate a biomass requirement of 756 odt per gigawatthour (GWH).5 Given odt/ha and odt/GWH information, we can estimate land demand for a given level of biomass generation.

Carbon policy scenario design We model a carbon policy as defined by the American Clean Energy and Security Act of 2009 (ACESA or H.R. 2454),6 which was passed by the U.S. House of Representatives. ACESA establishes a U.S. national cap on total GHG emissions. The bill has two major provisions: (i) a combined efficiency and renewable electricity standard; and (ii) a greenhouse gas cap-and-trade system. ACESA requires retail electric utilities to meet a specified percentage of their annual load through renewable electricity generation and energy efficiency savings.

Title III of the bill establishes a U.S. national cap on total GHG emissions. The cap would apply to electric utilities, oil companies, large industrial sources, and other covered entities. Entities covered by the act collectively contribute about 85% of U.S. greenhouse gas emissions, which are, in turn, approximately 17% of current global emissions. The program is designed to reduce covered emissions by 3% below 2005 levels in 2012, 17% below 2005 levels in 2020, 42% below 2005 levels in 2030, and 83% below 2005 levels in 2050.

Title III of the bill also provides for alternative compliance with the GHG emissions cap through offset credits and international emission allowances. However, it restricts the use of these measures. The total quantity of emissions that may be covered by rendering offsets to meet compliance obligations is limited to 2 billion metric tons of CO2 in each year, split evenly between domestic and international offsets.

We omit provisions of ACESA that would have negligible economic impact, whose effect would be too speculative or that lie beyond the scope of this analysis. Our analysis of the cap-and-

4 Since pastureland can be used for many purposes other than for growing feedstock, we account for only 30% of the U.S. total pastureland.

328 million hectares is about 810 million acres.

5 If a more efficient technology, such as gasification technology with a heatrate of 9650 Btu/kWh, is assumed then the odt requirement would be lower.

6 Bill released May 15, 2009.

trade program includes offset provisions, banking and borrowing, and a strategic reserve, all measures meant to ease the burdens expected to result from allowance price fluctuations. Our analysis also accounts for the full recycling of auction revenues in these ways. Montgomery et al. 2009 provides a detailed discussion of the bill and the provisions that are modeled.

4. Model Results

Carbon price is an additional cost under the ACESA The primary objective of the ACESA is to implement a GHG cap-and-trade policy that would reduce greenhouse gas emissions by decreasing the use of fossil fuel energy. This would be achieved by creating a limited supply of carbon permits (allowances) required for the use of carbon-emitting energy, thereby creating scarcity in the permit market. As the cap progressively tightens over time (i.e., allowances become scarcer), the marginal source of reducing emissions becomes more expensive as lower-cost sources of emissions reductions are exhausted. As a result, the price of an allowance increases with time as the cap becomes more stringent. The underlying cost driver of the ACESA policy is the carbon price. Figure 5 summarizes the carbon price for the model horizon. In 2015, the price of a carbon allowance is estimated to be $27 per metric ton of CO2. By 2030, the allowance price would increase to $56 per metric ton of CO2.7 The carbon price grows 5% per year; which reflects the banking behavior assumption of the policy.8

Figure 5: Projected CO2 allowance prices due to the ACESA

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7 All values in the paper are expressed in 2009 dollars.

8 The carbon price increases at the rate of the arbitrage rate in the model which is the interest rate. The interest rate is assumed to be 5% in the model.

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analyzed (2010 through 2050) as the measures become more stringent, with the largest changes projected over the 2030 to 2050 time period.

Agriculture land diverted to create energy crops In the baseline, land is used either to produce agriculture goods, or to grow energy crops for ethanol, advanced biofuels, and biodiesel fuels to meet the RFS, or to produce a small portion of biomass feedstock. In 2010, energy crops account for 5% of the total land use of 328 million hectares with 4% (12.3 million hectares) dedicated to corn farming for ethanol production, and 1% (2.4 million hectares) for biofuels feedstock farming.

Figure 6: Baseline land use

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The rest, 95% of the land (312 million hectares), is used for agricultural farming. By 2050, the share of land dedicated for advanced biofuels increases significantly to 20% (67 million hectares) at the expense of agricultural land whose share decreases to about 74% (244 million hectares) by 2050 (see Figure 6).

As mentioned already, ACESA allows cheap domestic and international offsets as a flexible mechanism to mitigate the costs of the policy. Since land is a factor of production to generate domestic offset, land demand increases as more offsets are generated. As the demand for offsets increase, agriculture land is converted into land for afforestation, forest management, and other offset activities. The amount of shift in the land use from agriculture production to offset generation depends upon the carbon price which drives the economics and supply of offsets. As seen in Figure 7, land demanded by offset activities increases from 11.3 million hectares in 2015 to about 33 million hectares by 2050. A large part of the land diversion is due to afforestation activities. In 2030, a total of 39 million hectares of agriculture land is diverted for biomass and offset activities of which 27 millions hectares are used for biomass, soil sequestration, and afforestation purpose and 2 million hectares for forest management (see Figure 8).

The amount of biofuels (ethanol and advanced biofuels production) does not change between the baseline and the scenario suggesting that the carbon costs are not high enough to make biofuel production cost-competitive compared to conventional transportation fuel. As a result, under the ACESA, the land devoted for energy crops production for biofuels remains the same as in the baseline.

Figure 7: Land use under the ACESA

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Figure 8: Land use by Biomass and Offsets types under the ACESA

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An important set of provisions of the ACESA are regulatory measures that go beyond the cap-and-trade program to require a certain percentage of electricity generation to come from renewable sources. Biomass generation that uses agriculture feedstocks qualify as renewables

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to meet the mandate. The land dedicated for biomass feedstock production is a function of the amount of biomass generation. As a result of carbon price and RPS, electricity generation shifts from fossil based generation to low or zero carbon emitting technologies such as nuclear, wind, solar, biomass or other renewables. In the near term, the carbon price is not high enough to induce new biomass capacity expansion. Till 2030, an increase in the online capacity of the existing biomass leads to an incremental increase in biomass generation. However, beyond 2030 as nuclear and coal with CCS are limited by their ability to expand capacity and carbon prices increase to over $100 per ton of CO2, new biomass capacity is added and biomass generation expands substantially. Over the model horizon, 47 GW of new biomass generation capacity are added. A large part of the addition occurs after 2030. This addition of biomass contributes in increasing its share in the total electricity generation from a small percentage (25 TeraWatt Hours) to about 7% (349 TeraWatt Hours) under the policy in 2050. Table 1 summarizes electricity generation by different types of technologies. The increase in biomass generation calls for an increase in the demand for biomass feedstocks and hence the demand for land to grow energy crops. The land use for biomass generation increases from 0.5 million hectares in 2010 to 35 million hectares by 2050. The amount of land devoted for increased biomass feedstock is carved out from the agriculture land.

Table 1: Electricity generation by types (TeraWatt Hours)

2010 2015 2020 2025 2030 2035 2040 2045Baseline Coal 2023 2269 2474 2868 3188 3572 3884 4290

Gas 775 650 565 365 247 231 217 241Coal‐CCS 0 5 12 19 54 83 108 123Gas‐CCS 0 0 0 0 0 0 0Nuclear 799 831 849 849 816 655 598 434Wind 85 88 125 136 154 174 176 176Biomass 6 12 13 12 22 25 25 25Other Renewables 340 371 400 416 431 431 431 431Total 4029 4225 4438 4664 4912 5170 5438 5719

ACESA Coal 2035 1992 1935 1747 1345 899 500 234Gas 763 813 783 739 650 612 540 466Coal‐CCS 0 5 5 190 414 637 860 1081Gas‐CCS 0 0 0 0 0 0 0Nuclear 799 831 867 985 1181 1282 1478 1579Wind 85 112 213 234 262 453 516 543Biomass 6 12 20 20 40 41 69 209Other Renewables 346 376 428 438 440 466 469 469Total 4034 4141 4250 4353 4330 4390 4431 4580

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0 016565673494694764

Scarcity in agriculture land increases land price The availability of agriculture land drops significantly as a result of offset generation activities and an increase in biomass generation. The share of agriculture land drops by 15% from the baseline value of 243 million hectares to 206 million hectares in 2030. The decrease by 2050 is even larger as more land is diverted for energy crop production. In 2050, available agriculture land drops by 27% from 243 million hectares to 177 million hectares. The share of agriculture land of the total land in 2050 amounts to only 54% (see Figure 7). The diversion of agriculture land puts additional pressure on the land price. The land price in the short run increases by about 20%; while in the long run, it increases by about 30% to 40% (see Figure 9). The impact on the land price would be even greater if the climate change bill calls for a low carbon fuels

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standard (LCFS), such as the California’s LCFS proposal.9 The LCFS mandate can be met by potentially increasing the share of low-carbon biofuels (corn-based ethanol, cellulosic ethanol, or others) that would divert even more agriculture land. The increase in land price induces farmers to adopt more efficient means of farming either by better improving farming techniques or through better crop rotation. The compounded annual growth rate of the agriculture yield improves by about 6% over the model horizon.10

Figure 9: Percent change in the land price

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Carbon policy leads to increase in energy prices Consumers and industries ultimately bear the added costs estimated from the cap-and-trade policy. The ACESA provisions are projected to result in fuel switching away from less costly conventional fuels (e.g., coal), towards more costly lower carbon alternatives (including natural gas) due to tightening GHG emission caps. Further, costs for all carbon-based energy sources (e.g., coal, oil, and natural gas) are projected to increase as allowances would need to be purchased for the emissions associated with the use of these fuels. Since coal has the highest carbon content per energy unit, followed by petroleum products and natural gas, coal would be disproportionately harmed under the policy. Products that are produced using coal extensively are harmed the most.

Relative to the baseline levels, retail natural gas rates for industries (i.e., the price paid by the industries per unit of gas energy used) would rise by an estimated 37% increase ($2.90 per

9 Executive Order S-1-07, the Low Carbon Fuel Standard (LCFS) (January 18, 2007), calls for a reduction of at least 10 percent in the carbon

intensity of California’s transportation fuels by 2020. We apply the mandate to cover only transportation fuel use for personal transportation.

10 The agriculture crop yield in the baseline and the scenario grows by 1.83% and 1.94%, respectively. Gurgel et al. (2009) assumes 1% productivity improvement in their model based on historical crop yields growth of 1% to 3% per year based on Reilly and Fuglie (1998).

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MMBtu) by 2020 and 47% ($4.60 per MMBtu) by the year 2030. Retail electricity rates faced by industries are estimated to increase by 26% (2.1 cents per kWh) relative to baseline levels in 2020 and by 41% (3.1 cents per kWh) in 2030. Retail energy prices continue to increase as the carbon cost adder increases over time.

Figure 10: Percent change in the energy prices

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Natural Gas Electricity

Impacts on industrial output and producer price Carbon costs force energy cuts in production and industries will react by substituting non-energy inputs which are relatively cheaper or by reducing output. The general equilibrium effects show that output decreases as the cost of production of goods and services rises and consumer purchasing power drops as a result of the policy. The fertilizer and agriculture industries which are exposed to increase in the input costs will experience larger impacts on the cost of production. As illustrated in Figure 11, the retail price of the fertilizer sector increases more than the beverage and household product prices. The increase in price amounts to 4% in 2020 and rises to 9% by 2050. This increase is primarily driven by an increase in the natural gas price which is a major energy input source for fertilizer production. Increases in the land price and fertilizer price result in higher agriculture output price. The agriculture output price increases by 5% in 2020 and to 7% by 2030. The steep rise in price beyond 2040 up to 13% increase by 2050 is largely due to substantial increase in the land price resulting from stiff competition for land. Other sectors that are relatively less intensive in land and agriculture goods have smaller increases in production costs. Food prices rise by about 2% to 4% and household products price increases by about 1% to 2% over the model horizon.

Increases in retail prices of agriculture, food, household products, and fertilizer discourage its consumption.11 Hence the overall demand for these goods decreases leading to a decrease in domestic production and imports of these goods. Figure 12 shows the percent change in

11 The imported price of these goods is relatively cheaper as a result of the policy hence the rise in the retail price of these armington goods would be lower than the producer price.

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sity

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cts

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industrial output of these industries. Since the fertilizer sector has the highest energy intenamong the sectors, as already seen in Figure 4, it will face significant increase in the productioncosts and hence output. The agriculture sector is relative energy intensive and highly land intensive; as a result, the sector will also see reduction in output as production costs increasThe fertilizer sector output declines by 9% in 2020, 12% in 2030, and 21% by 2050. In the same periods, agriculture output declines by 7%, 9%, and 17%. Food and household produoutput decline by 3% to 7%, respectively, over the model horizon.

Figure 11: Percent change in the output price of Agriculture, Fertilize, and F

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Overall, the policy reduces output of food and agriculture products. The decline in output has negative consequence for consumer welfare. The lower income from wage losses deteriorates consumers’ purchasing power, resulting in a negative macroeconomic impact on the economy.

U.S. becomes net importer of food a decade and a half earlier The ACESA analysis reveals that business and consumers would face higher energy and transportation costs. These costs affect terms of trade in agricultural and food commodities. The cost comparative advantage of these products erodes relative to the imported goods under the policy. The high cost of U.S. goods makes it less attractive in the international markets and hence depresses U.S. exports.

Figure 13: Percent change in the exports of Agriculture, Fertilizer, and Food industry output

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Beverage Food Household Products Agriculture Fertilizer Figure 13 shows that agriculture and fertilizer exports decrease by 30% in the mid-term and exports will continue to decline in the long run. Food exports would decrease by 8% to 22% in 2015 and 2050, respectively. Household products are also negatively impacted.

As the costs of goods and services rise, household disposable income and household consumption fall. Wages and returns on investment also fall, resulting in lower productivity growth and reduced employment opportunities. Reduction in the purchasing power of the consumer means that there is less demand for imported goods and services. Imports of agriculture and food commodities decline by about 2% to 5% (see Figure 14).

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Figure 14: Percent change in the imports of Agriculture, Fertilizer, and Food industry output

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Large reductions in exports with moderate decrease in imports have a significant impact on the U.S. trade pattern in these goods. This analysis provides an important insight about how the cap-and-trade policy impacts the food market in general and the U.S. food security issue in particular. Figure 15 summarizes the net trade pattern in the baseline and the scenario. The figure illustrates how the net trade index defined as the ratio of the net exports to net exports in 2010 changes over time. The positive half (upper half) of the figure indicates that the U.S. is a net food exporter while the negative half (lower half) indicates the U.S. is a net food importer. The blue line, with triangular markers, indicates that the U.S. slowly loses its net exporter of food status and barely remains a net food exporter by 2050. Under the scenario, represented by the red line, with square markers, in the figure, the U.S. becomes a net importer of food by 2035, a decade and a half earlier than in the baseline. The policy poses a challenge to the U.S. food security. Moreover, climate change policies could also have unintended consequences to the world food market as the U.S. transition from being a net food exporter to a net food importer. Although, we do not explicitly model international food market impacts, we can infer that this effect would certainly increase international food prices as the U.S. relies more on imported food. The rise in such a demand could potentially increase deforestation in developing countries to supply food for consumption in the U.S. or even compromise food security of developing countries by making food unaffordable. The potentially grave unintended consequences need further research.12

12 The net importer status of the U.S. could come even earlier than 2035 if the U.S. adopts a LCFS mandate; which is not a part of the current

analysis.

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Figure 15: Net exports of the Food Industry Output

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Model limitations As with all models, MRN-NEEM has its limitations. For this analysis, we use a single land type and represent the entire U.S. as one aggregate region in our model. This simple regional representation, although easy to work with, masks heterogeneity in the regional land use patterns, land productivity, and sequestration rates. These omissions are important as the U.S. has 11 Plant Hardiness Zones that affect soil conditions, which drive crop yields. This lack of distinction between different types of land omits competition between land types and hence these model results will either over or under estimate regional land and food impacts. In addition, the assumption of a single type of land does not allow us to determine the shift in land use patterns between cropland, pastureland, and forestland. A logical extension of the current model would be to include multiple land types that are substitutable. A single land type also prevents us from extending the domestic offsets to include avoided deforestation. Therefore, this paper only takes into account offset activities that take land away from other land demanders. A regional model with multiple land types would provide more refined insights at a regional level.

Since fuel, forest, and food interactions extend beyond the borders of the United States, a complete model should include other regions of the world as well. Our model does not explicitly represent other regions of the world. The lack of international linkages in the model, albeit an important dimension, is beyond the scope of the current analysis. Also missing from the current analysis are sensitivity results of key parameter values or assumptions.

5. Conclusion This paper integrates land as a factor of production in a fully integrated top-down and bottom-up model, MRN-NEEM. This model accounts for land use for agriculture goods, biomass generation, bio-fuels production, and other carbon offset activities such as afforestation. In principle, biofuels, offsets, and low carbon emitting technologies such as biomass generation

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might offer low net cost means of curbing greenhouse gas emissions; yet questions still remain as to how these activities may impact land use and ultimately the food and agriculture markets.

We simulate a representative carbon policy as defined by the American Clean Energy and Security Act of 2009 (ACESA) to analyze interactions between the fuels, forest, and food. ACESA establishes a U.S. national cap on total GHG emissions that allows a combined efficiency and renewable electricity standard and a greenhouse gas cap-and-trade system. The analysis of the cap-and-trade program includes offset provisions and banking. The cap analyzed imposes cost of carbon on fossil fuel sources, which directly increase the delivered cost of fossil fuels. The carbon policy also increases the use of cheap offsets to mitigate the cost of the policy. However, this low cost mitigating option, especially afforestation activities, diverts cropland from agriculture production. The land demand also increases as biomass generation increases because low carbon emitting technologies in the electric sector becomes cost-competitive. This increased demand for land causes land prices to increase. The effects of higher fuel costs and land price lead to increases in agriculture, fertilizer, and food costs. Furthermore, the rise in the domestic cost of agriculture and food products has adverse impacts on the U.S. trade pattern. A key insight of the analysis is that the U.S. becomes a net importer of food by 2035, a decade and a half earlier than in the baseline, a looming challenge for the U.S. food security.

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