# Unintended Consequences of Agricultural Participation in Voluntary Carbon Markets: Their Nature and Avoidance

Authors: Minglu Wang; Bruce McCarl; Hanlin Wei; Layla Shiva
Journal: Complexity
 (2021)

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## Abstract

Greenhouse gas (GHG) trading markets have been widely discussed for climate change mitigation. However in implementation coverage has not been universal. Agriculture, despite being the source of nearly 25% of net emissions, has not commonly been capped. But it has been mentioned as voluntary source of net emission offsets. Such offsets could arise from action reducing GHG emissions, enhancing sequestration, or producing feedstocks for low emitting bioenergy replacements for fossil based energy. This could be harnessed by setting up voluntary carbon markets that producers could join at their discretion. However, such a scheme could have unintended consequences. We conduct theoretical and empirical analyses of a voluntary “carbon” market examining both intended and unintended effects. We find certain participation rules can stimulate rebound effects from emitters and suppress participation from sequestration and bioenergy producing entities. To overcome this we develop and simulate offset participation limitations that could preclude unintended consequences.


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## Body

## 1. Introduction

Emission trading markets have been advanced as a mechanism reducing emissions in many settings including greenhouse gas (GHG) ones [1]. The Kyoto Protocol (KP) advocated trading markets and since then they have been prominently discussed in the climate change mitigation dialogue with a number of implementations. Such markets conceptually cap the emissions of an entity and then facilitate trading between expensive and cheaper net emissions reducers [1]. Typically capped entities include electricity generators, petroleum refiners, and cement manufacturers all of which generally face high costs to achieve large emission reductions. Sectors such as agriculture and forestry have typically not been capped. However, a number of markets and market designs include provisions where capped entities can buy offsets from uncapped sectors. One way of allowing such sales involves setting up a voluntary carbon offset market in which uncapped producers could make offers to sell. The project-based clean development mechanism (CDM) provides an example of this and allows capped producers in industrialized countries to buy certified emission reduction credits from developing countries. Allowing sales through offsets complements cap and trade programs and can reduce abatement costs relative to a program without access to offsets in uncapped sectors [2].Agriculture, forestry, and other land use activities have been estimated to have a 23% share of total global emissions in recent times and as such are a potential source of emissions reductions [3]. However, the agricultural sector has generally not been capped in GHG trading schemes but rather is often identified as an offset supplying sector [4]. Such offsets could be developed by adoption of agricultural actions that reduce GHG emissions, enhance carbon sequestration, or provide feedstocks for bioenergy that replace higher emitting fossil based energy [5]. Emission reducing possibilities include actions to lessen emissions from enteric fermentation, manure management, rice cultivation, fertilization, soil management, field burning, machinery operations, and avoided deforestation [5, 6]. Sequestration can be enhanced through deintensification of tillage, residue management, use of soil carbon enhancing crop rotations, cropland conversion to grasslands, improved grassland management, afforestation, reforestation, and restoration of degraded soils [6, 7]. Emissions also can be offset by replacing fossil fuels used in energy production with agriculturally produced feedstocks [8]. Sequestration strategies can also be price-competitive at low carbon prices as they are often complementary to current agricultural soil management practices but at higher carbon prices practices like bioenergy production and forestry can be more desirable as they yield larger net GHG emission reductions [9].Thus there are three basic situations where a farmer might be able to sell offsets. These are as follows:(1)
A cap and trade system allows covered individuals to purchase offsets from outside of the program or enrolled region. For example, the California Rice Protocol allows use of rice methane emission reducing practices in or out of California to satisfy needed emission reductions by capped parties [10].(2)
A program places emission limits on certain entities (like power plants) but they are allowed to satisfy the limits by purchasing offsets from others. For example, this might have happened under the proposed Clean Power Plan (CPP) that may be revisited in the near term.(3)
An organization offers a program where producers can sign up to sell offsets based on shifts in practices. For example, the Verified Carbon Standard (VCS) functions outside of compliance markets and facilitates purchases by firms that want to be demonstrably “greener” [11]. In recent years, more and more private companies such as Indigo Carbon and Noris Marketplace are getting involved by aggregating carbon offsets and then selling them to interested parties in a voluntary marketplace setting. Voluntary programs require less implementation cost but conceptually lead to lower levels of offsets than do mandatory programs [12].However, voluntary carbon markets suffer from substantial criticism regarding a large number of nonadditional offsets as a result of adverse selection. That problem stems from the fact that business as usual (BAU) emissions cannot be fully observed by regulators. To address this problem, baseline selection and manipulation have been extensively discussed regarding actions in the transportation [13], commercial building [14, 15], and forestry sectors [16]. In general there is a tradeoff between additionality and participation including payments to “good actors.”. In such cases a generous baseline can lead to credits to preexisting practices generating nonadditional offsets and implicitly relaxes the emission cap in compliance markets while a stringent baseline would eliminate many nonadditional offsets but also suppress the incentive to opt in and perhaps cause preexisting users of mitigation practices to reverse such practices stimulating emissions and then join the program [17, 18]. Carbon leakage is also a concern since most of current programs are regional and can displace traditional commodity production stimulating emission increase outside of the programs scope [19].Participation by those in agriculture also raises challenges. Lichtenfeld [20] indicated that land managers were reluctant to enter the carbon market for three main reasons: high transactions and management costs, imperfect information, and a lack of institutional support. Also farmers have concern over the long term effectiveness of weed control methods that are key to use of limited tillage strategies [21]. On the other hand, emission reductions have measurement issues in cases due to potentially high measurement cost, need for sophisticated equipment, their nonpoint source nature, and variable levels across time, locations, and climate conditions. Because of these factors and eligibility restrictions, the actual level of GHG mitigation is far below what many expected at the time of formation of the Kyoto Protocol [22].This investigates voluntary market design in an effort to evolve designs that better stimulate program participation and generate net emission reductions. Specifically, we conduct theoretical analysis and empirical simulations of the consequences of alternative voluntary market designs examining effects on net GHG emissions and possible market reactions that lead to unintended consequences.The significance of this study is fourfold. First, we integrate agricultural emission control activities, sequestration, bioenergy production, and voluntary carbon market participation into a single framework for study. Second, our study explores use of performance baselines, which utilize predefined emissions thresholds for a class of activities, and it differs from many prior studies that focused on setting costly, adverse selection, nonadditional prone project-based baselines [23]. Also, the performance baseline under limited private cost information and an uncertain market environment has been found to reduce leakage [15, 24]. Third, we use a baseline method in per unit participating, rate-based form instead of lump sum form which [25] argues is more realistic and responsive to external conditions. Fourth, since the credits are proportional to the output level, this may lead to unintended effects such as output expansion and emission increase [25, 26], but we introduce fairly simple constraints to limit unintended effects.This study is organized as follows. Section2 sets out a basic analytical model of participation and abatement decisions for a profit maximizing entity. Then analytical results are derived and they provide a basis for our empirical study. Section 3 describes an empirical test of our posed constraint effectiveness using an agricultural sector model. Section 4 discusses the simulation results. Section 5 concludes the paper.


## 2. Theoretical Analysis

In this section, a theoretical analysis will be conducted to investigate the effect of voluntary carbon crediting programs on net GHG emission reductions by examining cases for both emission reducers and sequestration enhancers. We evaluate the circumstances when net emission reductions are achieved and when unintended effects arise. This analytical model is elaborated in [27].


### 2.1. A No-Trading Base Scenario

A base scenario is set up to portray the situation in the absence of carbon payments. There we assume a producer can access two technologies producing a commodity but with different emissions consequences and costs (i.e., low and high emissions rice). The possibilities are the commonly used baseline strategy,x1, and a more costly “net emission reducing” alternative, x2. The producer maximizes profits by solving the following optimization problem where one of the two technologies are chosen:(1)Maxπ=px1+px2−x1C1x1−x2C2x2,(2)s.t.x1x2=0,(3)x1,x2≥0.p is the output price and Cixi is the cost function for producing xi. We assume either x1 or x2 is adopted but not both of them by imposing constraints (2) and (3). Additionally, we assume Cixi is a linear increasing cost function of producing the quantity xi:(4)Cixi=ci+dxi,where via this function x1 production is cheaper than x2 production since x2 required one-time investment for technology upgrades. For simplicity we structure this so both alternatives costs increase at the positive rate d as volume is expanded and both alternatives are initially profitable in the market c2>c1>0,p−ci>0;andd>0.If we use calculus to solve the choice problem we arrive at the base solution that onlyx1 will be produced since x1 is more cost-effective.(5)x1∗=p−c12d,x2∗=0.On the emissions side, we suppose net emissions ratesei for producing xi and e1>e2 so x2 is net emissions reducing relative to x1. We also will assume e1and e2 are of the same sign so they are both either positive or negative. The total net emissions in the absence of a carbon market are(6)EB∗=e1x1∗+e2x2∗=e1p−c12d,EB∗ is the total BAU net emissions, and e1 is the per unit baseline performance standard that the market must do better than under the voluntary market. Note that EB∗ could be negative if e1 is negative.


### 2.2. Adding in a Voluntary Carbon Market

Now we add carbon price into the model so that producers have an incentive to choosex2 and reduce emissions (a choice we call opt in). In setting the model up we add a baseline standard per unit produced for the net emission level, eb, to calculate the reduction of net emissions rate relative to the standard. We also assume payments are paid for the difference in net emissions. The resultant model is(7)Maxπ=px1+px2−x1C1x1−x2C2x2−Mx1e1−eb+x2e2−eb,(8)s.t.x1x2=0,(9)x1,x2≥0,where M is the carbon price. We set eb=e1 since x1 is adopted in the BAU without a carbon market and equation (7) becomes(10)Maxπ=px1+px2−x1C1x1−x2C2x2−Mx2e2−e1.From the equation, we see continuing to usex1 yields no carbon market payments, while using x2 results in a payment equaling Mx2e1−e2 but also a higher production cost. The optimal choice of x in the voluntary market is x1 for the carbon price below a threshold:(11)x1,v∗=p−c12d,x2,v∗=0,whenM<c2−c1e1−e2,and the choice shifts to x2 when the carbon price is above that threshold with the production level being(12)x2,v∗=p−c2−Me2−e12d,x1,v∗=0,whenM>c2−c1e1−e2,and when x2 is the choice the level of total net emissions is(13)Ev∗=e1x1,v∗+e2x2,v∗=e2p−c2−Me2−e12d.Then comparing with total BAU net emissions we get(14)Ev∗−EB∗=e2x2,v∗−e1x1∗=e2p−c2−Me2−e12d−e1p−c12d.In order to simplify the emission difference expression, we substitutec2=c1+Δc and e1=e2+Δe. Note that Δc>0andΔe>0.(15)Ev∗−EB∗=e2x2,v∗−e2+Δex1∗=e2p−c1−Δc+ΔeM2d−e2+Δep−c12d,and by simplifying we get(16)Ev∗−EB∗=e2x2,v∗−x1∗−Δex1∗=Δep−c1−e2Δc+ΔeMe22d.Note that all the items in equation (16) are positive except e2 which can be positive (when we are paying for reducing emissions) or negative (when we are paying for enhancing sequestration). Now, let us explore whether the program may cause increased net emissions. To do this, we consider two cases where (1) net emissions are positive and are being reduced (e1,e2>0, and e1>e2 so Δe>0) and (2) net emissions are negative implying sequestration or biofuel offsets that are becoming more negative when x2 is used (e1,e2<0, and as above e1>e2 so Δe>0).In the case of positive emitting strategies, note the last term+ΔeMe2 has all positive components and can cause an emissions increase that grows as the carbon price M does while the other terms Δep−c1−e2Δc are independent of the carbon price. Now, we solve for the size of M when alternative net emissions exceed the BAU net emissions. We find(17)Ev∗−EB∗>0,whenM>c2−c1e1−e2+p−c1e2.Here we find a rebound effect [28] where the carbon price stimulates more x2 production than in the baseline offsetting the gain from lower per unit emissions. An illustration of such an effect is that if per acre rice emission reductions were possible, the incentive may cause the acreage of “low emitting” rice x2,v∗ to expand substantially over the base levels x1∗ and the raw emission from rice expansion e2x2,v∗−x1∗ overcomes the emissions savings Δex1∗ from growing lower emitting rice relative to the standard. This finding is consistent with that in Fischer [25] and Strand and Rosendahl [26].In the case of negative emitting strategies the unintended consequence is that the incentive may be pretty ineffective. Under the assumption that we just want to get an increase in current sequestration or bioenergy activityx2>x1 where the technology is the same (Δc and Δe are both zero), the emission difference in equation (16) reduces to zero. In such case, the carbon price fails to provide any incentive to increase current sequestration or bioenergy production activity.Thus, for both positive and negative emitting strategies, the voluntary program can potentially give rise to unintended effects with it incurring the rebound effect or failing to provide an incentive to expand negative emission items.


### 2.3. Altering Payment Eligibility

Now we consider ways to alter the voluntary program to avoid the rebound effect for positive emitting strategies and encourage additional adoption of negative emitting strategies. As suggested by Strand and Rosendahl [26], production increase from positive emitting strategies should be restricted. To do this we add a new variable x3 that equals the amount of voluntary enrollmentbeyond the BAU amount that is paid but is not offset by the baseline rate e1 as follows:(18)Maxπ=px1+x2+x3−x1C1x1−x2+x3C2x2+x3−Mx2e2−e1+x3e2s.t.x2≤x1∗,fore2>0,x1+x2≥x1∗,fore2<0,x1x2=0,x1,x2,x3≥0.Here, bothx2 and x3 are net-emissions-reducing practices receiving carbon payments with x2 being paid on net emission difference e2−e1 and x3 receiving the full net emissions amount e2. Economically x3 will only enter the solution for negative cases (sequestration and bioenergy offsets) given higher or possibly equal costs for x3 in comparison with x1.The first constraint for positive emitters limits the amount that is paid forx2 to the BAU level of x1∗, avoiding the rebound effect caused by x2 being much greater than x1. In practice this means only preprogram rice producing acreage could receive payments not any new acreage.The second constraint for negative emitters requires that the production below the BAU level be either unpaid or paid by the net emission differencee2−e1. Then production expansion x3 can be paid for full credit e2 but this means additional new land or production must come in.We solve the new objective function and get the solution for positive emitting strategies:(19)x1,s∗=x1∗,x2,s∗=0,x3,s∗=0,whenM<c2−c1e1−e2,x1,s∗=0,x2,s∗=x1∗,x3,s∗=0,whenM>c2−c1e1−e2.x2 no longer increases with the carbon price and remains constant at its upper bound of x1∗. In this way, we are managed to restrict the total net emissions at(20)Ev,s∗=e2p−c12d,whenM>c2−c1e1−e2,Ev,s∗−EB∗=e2−e1p−c12d<0,whenM>c2−c1e1−e2.Comparing with the BAU net emissions, the voluntary program with the special payment design always yields lower total net emissions when the carbon price is above the critical value.The solutions for negative emitting strategies are:(21)x1,s∗=x1∗,x2,s∗=0,x3,s∗=0,whenM<A,x1,s∗=0,x2,s∗=x1∗,x3,s∗=c1−c2−Me22d,whenM>A,where A is the critical value of carbon price above which net-emissions-reducing alternatives will be adopted. We do not present the closed-form expression for A due to its complexity but note that A is smaller than the previously solved critical value c2−c1/e1−e2, because the payment for additional negative offsets is larger for alternative strategies. The total net emission level becomes(22)Ev,s∗=e2p−c2−Me22d,whenM>A,Ev,s∗−Ev∗=−Me1e22d<0,whenM>A.We can see the voluntary program with the special payment design always yields greater net emission reductions than that without alteration.Through our theoretical analysis, we find the modified payment limits the production of positive emitting strategies at or below the baseline amount and incentivizes producers to expand their production of strategies with negative emissions precluding the unintended consequences.


## 2.1. A No-Trading Base Scenario

A base scenario is set up to portray the situation in the absence of carbon payments. There we assume a producer can access two technologies producing a commodity but with different emissions consequences and costs (i.e., low and high emissions rice). The possibilities are the commonly used baseline strategy,x1, and a more costly “net emission reducing” alternative, x2. The producer maximizes profits by solving the following optimization problem where one of the two technologies are chosen:(1)Maxπ=px1+px2−x1C1x1−x2C2x2,(2)s.t.x1x2=0,(3)x1,x2≥0.p is the output price and Cixi is the cost function for producing xi. We assume either x1 or x2 is adopted but not both of them by imposing constraints (2) and (3). Additionally, we assume Cixi is a linear increasing cost function of producing the quantity xi:(4)Cixi=ci+dxi,where via this function x1 production is cheaper than x2 production since x2 required one-time investment for technology upgrades. For simplicity we structure this so both alternatives costs increase at the positive rate d as volume is expanded and both alternatives are initially profitable in the market c2>c1>0,p−ci>0;andd>0.If we use calculus to solve the choice problem we arrive at the base solution that onlyx1 will be produced since x1 is more cost-effective.(5)x1∗=p−c12d,x2∗=0.On the emissions side, we suppose net emissions ratesei for producing xi and e1>e2 so x2 is net emissions reducing relative to x1. We also will assume e1and e2 are of the same sign so they are both either positive or negative. The total net emissions in the absence of a carbon market are(6)EB∗=e1x1∗+e2x2∗=e1p−c12d,EB∗ is the total BAU net emissions, and e1 is the per unit baseline performance standard that the market must do better than under the voluntary market. Note that EB∗ could be negative if e1 is negative.


## 2.2. Adding in a Voluntary Carbon Market

Now we add carbon price into the model so that producers have an incentive to choosex2 and reduce emissions (a choice we call opt in). In setting the model up we add a baseline standard per unit produced for the net emission level, eb, to calculate the reduction of net emissions rate relative to the standard. We also assume payments are paid for the difference in net emissions. The resultant model is(7)Maxπ=px1+px2−x1C1x1−x2C2x2−Mx1e1−eb+x2e2−eb,(8)s.t.x1x2=0,(9)x1,x2≥0,where M is the carbon price. We set eb=e1 since x1 is adopted in the BAU without a carbon market and equation (7) becomes(10)Maxπ=px1+px2−x1C1x1−x2C2x2−Mx2e2−e1.From the equation, we see continuing to usex1 yields no carbon market payments, while using x2 results in a payment equaling Mx2e1−e2 but also a higher production cost. The optimal choice of x in the voluntary market is x1 for the carbon price below a threshold:(11)x1,v∗=p−c12d,x2,v∗=0,whenM<c2−c1e1−e2,and the choice shifts to x2 when the carbon price is above that threshold with the production level being(12)x2,v∗=p−c2−Me2−e12d,x1,v∗=0,whenM>c2−c1e1−e2,and when x2 is the choice the level of total net emissions is(13)Ev∗=e1x1,v∗+e2x2,v∗=e2p−c2−Me2−e12d.Then comparing with total BAU net emissions we get(14)Ev∗−EB∗=e2x2,v∗−e1x1∗=e2p−c2−Me2−e12d−e1p−c12d.In order to simplify the emission difference expression, we substitutec2=c1+Δc and e1=e2+Δe. Note that Δc>0andΔe>0.(15)Ev∗−EB∗=e2x2,v∗−e2+Δex1∗=e2p−c1−Δc+ΔeM2d−e2+Δep−c12d,and by simplifying we get(16)Ev∗−EB∗=e2x2,v∗−x1∗−Δex1∗=Δep−c1−e2Δc+ΔeMe22d.Note that all the items in equation (16) are positive except e2 which can be positive (when we are paying for reducing emissions) or negative (when we are paying for enhancing sequestration). Now, let us explore whether the program may cause increased net emissions. To do this, we consider two cases where (1) net emissions are positive and are being reduced (e1,e2>0, and e1>e2 so Δe>0) and (2) net emissions are negative implying sequestration or biofuel offsets that are becoming more negative when x2 is used (e1,e2<0, and as above e1>e2 so Δe>0).In the case of positive emitting strategies, note the last term+ΔeMe2 has all positive components and can cause an emissions increase that grows as the carbon price M does while the other terms Δep−c1−e2Δc are independent of the carbon price. Now, we solve for the size of M when alternative net emissions exceed the BAU net emissions. We find(17)Ev∗−EB∗>0,whenM>c2−c1e1−e2+p−c1e2.Here we find a rebound effect [28] where the carbon price stimulates more x2 production than in the baseline offsetting the gain from lower per unit emissions. An illustration of such an effect is that if per acre rice emission reductions were possible, the incentive may cause the acreage of “low emitting” rice x2,v∗ to expand substantially over the base levels x1∗ and the raw emission from rice expansion e2x2,v∗−x1∗ overcomes the emissions savings Δex1∗ from growing lower emitting rice relative to the standard. This finding is consistent with that in Fischer [25] and Strand and Rosendahl [26].In the case of negative emitting strategies the unintended consequence is that the incentive may be pretty ineffective. Under the assumption that we just want to get an increase in current sequestration or bioenergy activityx2>x1 where the technology is the same (Δc and Δe are both zero), the emission difference in equation (16) reduces to zero. In such case, the carbon price fails to provide any incentive to increase current sequestration or bioenergy production activity.Thus, for both positive and negative emitting strategies, the voluntary program can potentially give rise to unintended effects with it incurring the rebound effect or failing to provide an incentive to expand negative emission items.


## 2.3. Altering Payment Eligibility

Now we consider ways to alter the voluntary program to avoid the rebound effect for positive emitting strategies and encourage additional adoption of negative emitting strategies. As suggested by Strand and Rosendahl [26], production increase from positive emitting strategies should be restricted. To do this we add a new variable x3 that equals the amount of voluntary enrollmentbeyond the BAU amount that is paid but is not offset by the baseline rate e1 as follows:(18)Maxπ=px1+x2+x3−x1C1x1−x2+x3C2x2+x3−Mx2e2−e1+x3e2s.t.x2≤x1∗,fore2>0,x1+x2≥x1∗,fore2<0,x1x2=0,x1,x2,x3≥0.Here, bothx2 and x3 are net-emissions-reducing practices receiving carbon payments with x2 being paid on net emission difference e2−e1 and x3 receiving the full net emissions amount e2. Economically x3 will only enter the solution for negative cases (sequestration and bioenergy offsets) given higher or possibly equal costs for x3 in comparison with x1.The first constraint for positive emitters limits the amount that is paid forx2 to the BAU level of x1∗, avoiding the rebound effect caused by x2 being much greater than x1. In practice this means only preprogram rice producing acreage could receive payments not any new acreage.The second constraint for negative emitters requires that the production below the BAU level be either unpaid or paid by the net emission differencee2−e1. Then production expansion x3 can be paid for full credit e2 but this means additional new land or production must come in.We solve the new objective function and get the solution for positive emitting strategies:(19)x1,s∗=x1∗,x2,s∗=0,x3,s∗=0,whenM<c2−c1e1−e2,x1,s∗=0,x2,s∗=x1∗,x3,s∗=0,whenM>c2−c1e1−e2.x2 no longer increases with the carbon price and remains constant at its upper bound of x1∗. In this way, we are managed to restrict the total net emissions at(20)Ev,s∗=e2p−c12d,whenM>c2−c1e1−e2,Ev,s∗−EB∗=e2−e1p−c12d<0,whenM>c2−c1e1−e2.Comparing with the BAU net emissions, the voluntary program with the special payment design always yields lower total net emissions when the carbon price is above the critical value.The solutions for negative emitting strategies are:(21)x1,s∗=x1∗,x2,s∗=0,x3,s∗=0,whenM<A,x1,s∗=0,x2,s∗=x1∗,x3,s∗=c1−c2−Me22d,whenM>A,where A is the critical value of carbon price above which net-emissions-reducing alternatives will be adopted. We do not present the closed-form expression for A due to its complexity but note that A is smaller than the previously solved critical value c2−c1/e1−e2, because the payment for additional negative offsets is larger for alternative strategies. The total net emission level becomes(22)Ev,s∗=e2p−c2−Me22d,whenM>A,Ev,s∗−Ev∗=−Me1e22d<0,whenM>A.We can see the voluntary program with the special payment design always yields greater net emission reductions than that without alteration.Through our theoretical analysis, we find the modified payment limits the production of positive emitting strategies at or below the baseline amount and incentivizes producers to expand their production of strategies with negative emissions precluding the unintended consequences.


## 3. Empirical Study and Methods

Now, we do an empirical based study using a sector model to see if any of these unintended cases occur in an ideal modeling world. Here, we simulate a voluntary carbon offset crediting program, and we test whether we could avoid unintended consequences by adding constraints above. We also compare results to a mandatory program under alternative program designs to gain insight on net emissions reduction effectiveness.


### 3.1. Simplified Overview of the Sector Model

For the study we use the agricultural component of the Forest and Agriculture Sector Optimization Model-Greenhouse Gas version (FASOMGHG) [29–31]. That model will be set up to simulate farmers’ decisions under alternative forms of voluntary and mandatory carbon offset crediting programs.Elaborating, the agricultural part of FASOMGHG [9, 29, 31–33] is a dynamic, nonlinear, price endogenous programming model that maximizes the net present value of the sum of producer and consumers’ surplus across the US agricultural sector over time by optimizing consumption, trade, land/water/labor allocation, processing, and production. Mathematically, the objective function is represented by the area under the product demand curves being less than the area under the factor supply curves as described in McCarl and Spreen [34].Operationally, the model structure simulates multiperiod, multifactor, multicommodity market equilibria under perfect competition with the constraints of crop and livestock mix, resource limit for land, water, and labor, supply, and demand balances for primary and secondary commodities, trade balances for the US and the rest of world, and GHG balances. The model incorporates agricultural activities for over 100 commodity types across the conterminous US broken into 11 market regions and 28 foreign regions over up to 100 years on a 5-year time step basis. FASOMGHG has already been widely employed to evaluate both economic and environmental impacts of a variety of mitigation strategies and policies. Our contribution to the model is that we incorporate the opt in features into the model and construct different baselines for voluntary market settings.Equations (23) to (27) are a simplified representation of the model objective and constraints. We only include carbon payments structured for whether producers opt in or not in this brief mathematical exposition.(23)Max∫PdQdQ−∑ocoXo∗Xo−∑o,gPc∗Payo∗GWPg∗GHGo,g,(24)s.t.Q−∑ofoXo≤0,(25)∑oaoXo≤b,(26)eo,gXo∗Xo−GHGo,g=0,for allo,g,(27)Q,Xo≥0,whereg is a set of GHG accountso is an opt in set defining whether producers opt in or not to the program (base, yes_opt in)Q is a variable giving quantity of commodity consumptionPdQ is an inverse demand function giving price of commodities consumedXo is a variable giving production activity level under opt in choice ocoXo is cost function varying with the choice of opting inPc is the exogenous carbon pricePayo is a 0/1 indicator for whether to pay carbon payments under opt in choice oGHGo,g is a variable giving net GHG emissions of type g under opt in choice ofoXo is the yield of commodities produced by production under opt in choice oaoXo is the resource usage function of production activities under opt in choice ob is a vector of resource endowmentseo,gXo is the amount of net emissions reduction relative to the standard of GHG g when producing X under opt in choice oGWPg is the global warming potential of GHG type gHere, equation (23) is the objective function that is maximized where the maximand is the net present value of welfare including carbon payments and the costs of practices adopted including the cost of altering practices when opting in.Equation (24) is the supply-demand balance constraint by time period for commodities and reflects production balanced with sales including any alterations in production when opting in.Equation (25) is a resource endowment and reflects normal resource use under existing practices plus differences in resource use when opting in.Equation (26) is the balance of total net GHG emissions including sequestration and biofuel offsets by opt in choice. The GHG accounting adds net effects across all the full production domain separately by whether producers opt in or not. The accounting can result in either positive or negative net effects. Various forms of this constraint will be used depending on the structure of the program.More on the model is in Appendix A.


### 3.2. Scenarios to Run

In our empirical analysis, we setup a number of scenarios to look at different market setups. These span from no market to a mandatory to a couple of forms of a voluntary market. Details are as follows:(i)
Base (BAU) scenario—a base scenario is set up to simulate how farmers would behave in the absence of a carbon market. We set the carbon price equal to zero. The production level in the base scenario is denoted asXb.(ii)
Mandatory carbon program—the agricultural sector is treated as if it was capped with the carbon price applied to all changes in net emissions relative to a baseline level. We force all to participate and set the parameterPay to 1. This scenario is set up for comparison purposes only as we assume the agricultural sector will not be capped.(iii)
Voluntary carbon program—here, producers can opt in. We set a nonzero carbon pricePc>0, for participants, those opting in Payyes_optin=1, and no payments for nonparticipants Paybase=0 making only opting in producers eligible to receive carbon payments. Such a choice needs a baseline that encourages producers to voluntarily enroll and generate additional offsets. Two baseline specifications will be used.•
Per unit baseline: here we setup a per unit baseline level by practice corresponding to Section2.2. We establish a standard calculated from the base scenario that opting in producers must do better than on a per unit of the practice undertaken basis. Details on practices and the GHG account setup are in Appendix B. Under this per unit baseline, opting in producers get paid upon the difference in emissions per unit for the activity they choose less than the standard baseline rate and enrolled activity levels.•
Per unit baseline plus participation constraints: here, we impose constraints and add variables equivalent to x3 above as developed in the theory section. In that case activity in excess of base scenario receives full payments when the net emission nature of the activity is negative (generally sequestration or bioenergy fossil fuel offsets) or pays full cost when the net emission nature of the activity is positive (for those that on net emit GHGs).


## 3.1. Simplified Overview of the Sector Model

For the study we use the agricultural component of the Forest and Agriculture Sector Optimization Model-Greenhouse Gas version (FASOMGHG) [29–31]. That model will be set up to simulate farmers’ decisions under alternative forms of voluntary and mandatory carbon offset crediting programs.Elaborating, the agricultural part of FASOMGHG [9, 29, 31–33] is a dynamic, nonlinear, price endogenous programming model that maximizes the net present value of the sum of producer and consumers’ surplus across the US agricultural sector over time by optimizing consumption, trade, land/water/labor allocation, processing, and production. Mathematically, the objective function is represented by the area under the product demand curves being less than the area under the factor supply curves as described in McCarl and Spreen [34].Operationally, the model structure simulates multiperiod, multifactor, multicommodity market equilibria under perfect competition with the constraints of crop and livestock mix, resource limit for land, water, and labor, supply, and demand balances for primary and secondary commodities, trade balances for the US and the rest of world, and GHG balances. The model incorporates agricultural activities for over 100 commodity types across the conterminous US broken into 11 market regions and 28 foreign regions over up to 100 years on a 5-year time step basis. FASOMGHG has already been widely employed to evaluate both economic and environmental impacts of a variety of mitigation strategies and policies. Our contribution to the model is that we incorporate the opt in features into the model and construct different baselines for voluntary market settings.Equations (23) to (27) are a simplified representation of the model objective and constraints. We only include carbon payments structured for whether producers opt in or not in this brief mathematical exposition.(23)Max∫PdQdQ−∑ocoXo∗Xo−∑o,gPc∗Payo∗GWPg∗GHGo,g,(24)s.t.Q−∑ofoXo≤0,(25)∑oaoXo≤b,(26)eo,gXo∗Xo−GHGo,g=0,for allo,g,(27)Q,Xo≥0,whereg is a set of GHG accountso is an opt in set defining whether producers opt in or not to the program (base, yes_opt in)Q is a variable giving quantity of commodity consumptionPdQ is an inverse demand function giving price of commodities consumedXo is a variable giving production activity level under opt in choice ocoXo is cost function varying with the choice of opting inPc is the exogenous carbon pricePayo is a 0/1 indicator for whether to pay carbon payments under opt in choice oGHGo,g is a variable giving net GHG emissions of type g under opt in choice ofoXo is the yield of commodities produced by production under opt in choice oaoXo is the resource usage function of production activities under opt in choice ob is a vector of resource endowmentseo,gXo is the amount of net emissions reduction relative to the standard of GHG g when producing X under opt in choice oGWPg is the global warming potential of GHG type gHere, equation (23) is the objective function that is maximized where the maximand is the net present value of welfare including carbon payments and the costs of practices adopted including the cost of altering practices when opting in.Equation (24) is the supply-demand balance constraint by time period for commodities and reflects production balanced with sales including any alterations in production when opting in.Equation (25) is a resource endowment and reflects normal resource use under existing practices plus differences in resource use when opting in.Equation (26) is the balance of total net GHG emissions including sequestration and biofuel offsets by opt in choice. The GHG accounting adds net effects across all the full production domain separately by whether producers opt in or not. The accounting can result in either positive or negative net effects. Various forms of this constraint will be used depending on the structure of the program.More on the model is in Appendix A.


## 3.2. Scenarios to Run

In our empirical analysis, we setup a number of scenarios to look at different market setups. These span from no market to a mandatory to a couple of forms of a voluntary market. Details are as follows:(i)
Base (BAU) scenario—a base scenario is set up to simulate how farmers would behave in the absence of a carbon market. We set the carbon price equal to zero. The production level in the base scenario is denoted asXb.(ii)
Mandatory carbon program—the agricultural sector is treated as if it was capped with the carbon price applied to all changes in net emissions relative to a baseline level. We force all to participate and set the parameterPay to 1. This scenario is set up for comparison purposes only as we assume the agricultural sector will not be capped.(iii)
Voluntary carbon program—here, producers can opt in. We set a nonzero carbon pricePc>0, for participants, those opting in Payyes_optin=1, and no payments for nonparticipants Paybase=0 making only opting in producers eligible to receive carbon payments. Such a choice needs a baseline that encourages producers to voluntarily enroll and generate additional offsets. Two baseline specifications will be used.•
Per unit baseline: here we setup a per unit baseline level by practice corresponding to Section2.2. We establish a standard calculated from the base scenario that opting in producers must do better than on a per unit of the practice undertaken basis. Details on practices and the GHG account setup are in Appendix B. Under this per unit baseline, opting in producers get paid upon the difference in emissions per unit for the activity they choose less than the standard baseline rate and enrolled activity levels.•
Per unit baseline plus participation constraints: here, we impose constraints and add variables equivalent to x3 above as developed in the theory section. In that case activity in excess of base scenario receives full payments when the net emission nature of the activity is negative (generally sequestration or bioenergy fossil fuel offsets) or pays full cost when the net emission nature of the activity is positive (for those that on net emit GHGs).


## 4. Empirical Results

Now, we report on the results by major category and scenario. We examine the net cost of the carbon payment program as well as the effect on net GHG emissions plus other market effects including changes in agricultural commodity production and prices, land usage and value, bioenergy production, and welfare. Additionality and leakage implications will also be addressed.


### 4.1. GHG Mitigation

Figure1 presents annualized net GHG emissions by those opting in and not opting in under each alternative carbon market scenario under a CO2e price of $30/ton. Net offsets, as an indication of the truly additional ones after accounting for leakage and the rebound effect, are calculated as the difference in net GHG emissions between the base scenario and those occurring under each alternative scenario.Figure 1
Annualized GHG offsets by scenario at $30/tCO2e.In the results the net emission consequences are usually not equal to the gains paid to participants. This difference arises because of two factors. First, in most cases those not covered by the program choose to increase their net emissions or reduce sequestration relative to the baseline (a leakage effect) when responding to the market alterations caused by the actions of participants. Second, the per unit baseline may cause participants to expand qualifying “emission reductions” to levels beyond that in the base scenario (whereXo>Xb for positive emitters as discussed above).The mandatory participation scenario generates the most offsets as expected since it imposes the carbon price on all producers and does not pay for nonadditional production and penalizes for any within region leakage. This stimulates the greatest net emission reduction and penalizes for any and all emissions increase or sequestration/bioenergy offsets decrease while rewarding the opposite.Once we bring in the payment and constraint design, we find that both net offsets and participants’ offsets increase significantly. Also, we note this may be a second best solution as it is still less effective than the mandatory but likely hard to implement scenario.Figure2 shows more detailed GHG emission mitigation contributions by major strategy under the per unit and per unit plus constraints baselines across CO2 prices. Agricultural soil sequestration and bioenergy production are two primary categories of negative emission strategies. At low carbon prices, agricultural soil sequestration increases significantly compared to the base scenario and becomes the predominant strategy. That is because most of the agricultural soil sequestration practices are complementary with existing crop production and thus require relatively low costs as in [9]. When the carbon price gets higher, the share of agricultural soil sequestration declines due to the substantial increase in GHG offsets from bioelectricity production. Note that bioelectricity is not produced under the base scenario and at low carbon prices due to its high implementation costs but it has lifecycle GHG offset rates as high as 99%. Such high offset rates combined with higher carbon prices induce producers to shift land and other resources out of traditional crop production to bioelectricity feedstock production. In addition, the per unit plus constraints scenario achieves more GHG offsets from bioenergy production than the per unit baseline because of additional incentives for bioenergy production.Figure 2
GHG mitigation strategies used under per unit and per unit plus constraints baselines across CO2 prices.


### 4.2. Program Costs

We now turn to the average program cost per ton of mitigation. This is computed as annualized total carbon payments under the program divided by annualized tons of net CO2e offsets after considering both nonparticipants and participants net emissions. Although carbon credits are paid to participants only, we evaluate the effectiveness of carbon programs in mitigating GHG emissions considering possible leakage and nonadditionality. To accomplish this we use the total change in net CO2e offsets across all parties as the denominator instead of just participants’ offsets. The average program cost for each program setup is listed in Table 1.Table 1
Total and average program costs at $30/tCO2e.
MandatoryPer unitPer unit plus constraintsNet offsets (in million tons CO2e)169.3999.85124.33Participants’ offsets (in million tons CO2e)—146.33165.79Total program costs (in million $)5,081.686,266.106,555.77Average program costs (in $/ton CO2e)30.0062.7552.73The mandatory scenario has the lowest average program cost which also equals the CO2e price. Any emission change relative to the base case is rewarded or penalized according to the CO2e price so there is no leakage within the region associated with the mandatory program.On the other hand, the average cost of voluntary carbon crediting programs is not equal to the CO2e price as nonparticipants’ emissions are not restricted but are used in the calculation and leakage occurs. The per unit baseline also exhibits high average cost per effective ton CO2e net emissions reduction cost. In addition to the nonparticipants leakage, the rebound effect is playing a part. As noted above, the per unit baseline is paid upon the difference in emissions per unit and as long as producers could reduce their emission rate, they are rewarded. The rebound effects occur and for some subcategories lead to a total emission increase and higher program costs. For example, we saw increased annualized corn production relative to the base case yielding CO2e net emissions increases under the per unit baseline but declined production under the per unit plus constraints baseline.The per unit plus constraints case eliminates the rebound effects and provides more incentives toward expanding sequestration and bioenergy offsets. As previously shown, the per unit plus constraints baseline induces 8% more GHG net offset from ag soil sequestration and bioenergy at a $30 price. This results in achieving a lower average program cost than the per unit baseline. However, the existence of nonparticipants leakage still pushes up the average cost above the CO2 price.


### 4.3. Cropped Acres

Figure3 illustrates the effects on annualized national crop acres in million acres under per unit and per unit plus constraints baselines for alternative CO2e prices. As that price increases, the percentage of opt in acres increases, indicating the obvious result that higher carbon prices lead to higher participation rates. However, as the carbon price increases from $30/tCO2e to $50/tCO2e, the opt in acre percentage only increases slightly, showing that voluntary carbon programs are not able to encourage all producers to participate even at high carbon prices. In addition, agricultural soil sequestration practices are predominant mitigation strategies at low carbon prices. But as higher carbon prices, the agricultural soil sequestration no longer increases and bioenergy production begins to dominate as discussed in McCarl and Schneider [9]. In other words, energy crop production comes into play and competes for cropland. That is why we do not see use of less cropland even though we have less traditional crops produced. As shown in Figure 4, producers do not start producing more switchgrass based biopower at $15/tCO2e. As the carbon price gets higher, biopower production grows significantly, almost all of which comes from opt in producers. At each carbon price, the per unit plus constraints case exhibits a higher level of switchgrass based biopower production than the per unit case.Figure 3
Annualized national crop acres under per unit and per unit plus constraints scenarios across carbon prices.Figure 4
Annualized national switchgrass acres under per unit and per unit plus constraints scenarios across carbon prices.


### 4.4. Bioenergy Production

We mainly explore biodiesel and biopower production here because ethanol does not increase after 2020 due to the RFS assumptions in the model settings and the relatively lower CO2e offset rates. Figure 5 shows growth in the volume of biodiesel produced relative to production at the carbon price of $30/tCO2e by scenario.Figure 5
Biodiesel production additional to base at $30/tCO2e.We see the results that producers start to expand biodiesel production after 2025 because producers need time to react and construct biodiesel processing facilities. The per unit plus constraints baseline stimulates the largest amount of additional biodiesel production, indicating the per unit plus constraints setup provides effective incentives for biodiesel production. The mandatory scenario produces very large amounts of additional biodiesel in 2040 and 2045 and moderate amounts afterward. We also find that the per unit case has essentially the same level of biodiesel production as exhibited in the no carbon price because of the incentive lack as discussed in the theory section.The change in feedstock usage for biodiesel production relative to the base scenario at alternative carbon prices under the per unit and per unit plus cases baselines is shown in Figure6. At each carbon price, the per unit plus constraints baseline yields more production of biodiesel mainly from soybean oil. This result demonstrates that the per unit plus constraints case more effectively stimulates biodiesel production. In addition, we find biodiesel production increases with carbon prices at low prices. However, when the carbon price increases from $30/tCO2e to $50/tCO2e, biodiesel production no longer expands mainly because of the less use of corn oil under both per unit and per unit plus constraints cases.Figure 6
Additional biodiesel production composition from base across carbon prices.Figure7 presents bioelectricity production over time under all alternative scenarios at $30/tCO2e. The mandatory scenario consistently achieves a high level of bioelectricity production. The per unit plus constraints baseline exhibits high levels peaking its bioelectricity production from 2035 to 2055 but then later having it fall.Figure 7
Bioelectricity production over time at $30/tCO2e.We now look deeper into the breakdown of feedstock use for bioelectricity. For simplicity, we have combined processes with and without cofiring for each biofeedstock used. Bioelectricity production expands significantly with increased carbon prices. At the carbon price of $15/tCO2e, bioelectricity is primarily processed from manure (Table 2). As the carbon price increases to $30/tCO2e, we observe increasing use of switchgrass and crop residues especially wheat residues. When the carbon price is $50/tCO2e, switchgrass expands and is the predominant biofeedstock. We also see the use of corn residues increases substantially and willow comes into play. Note that biodiesel production stops expanding with less use of corn oil at high carbon prices. We observe a trend of shifting resources from biodiesel to bioelectricity due to the higher GHG reduction of bioelectricity.Table 2
Annualized electricity production breakdown in number of 100 megawatt plants.
Biofeedstock used$15/tCO2$30/tCO2$50/tCO2Per unitPer unit plus constraintsPer unitPer unit plus constraintsPer unitPer unit plus constraintsCorn residue0.314.8448.1952.85Sorghum residue2.454.7612.776.85Wheat residue0.010.029.0214.8017.1618.07Barley residue0.350.415.155.876.466.57Oats residue0.320.480.450.49Bagasse0.010.020.820.67Switchgrass0.110.1224.9034.4176.1981.30Willow0.100.36Lignin6.446.705.405.24Manure16.7316.2516.3716.1817.1316.54Beef manure0.593.010.673.182.622.91Dairy manure7.305.367.585.264.885.17


### 4.5. Market Effects

Table3 presents annualized national index numbers for commodity prices and quantities under all market design scenarios at $30/tCO2e. The results show that the introduction of voluntary carbon crediting programs leads to less conventional farm production and higher commodity prices with higher land values. This is because agricultural producers shift land use from agricultural production to bioenergy production to receive more carbon payments as in Schneider and McCarl [35]. In turn production falls and commodity prices generally increase with cropland becoming more valuable [8].Table 3
Annualized national index numbers at $30/tCO2e.
AgtypeItemBaseMandatoryPer unitPer unit plus constraintsAllFarmProdProduction100.0099.7796.3796.34AllFarmProdPrice100.0099.36100.32100.05AllFarmProdExports100.0099.63100.1699.53AllFarmProdImports100.0099.86100.06100.05CroplandPrice100.0099.37103.89104.54We illustrate the national index number changes across carbon prices under the per unit plus constraints scenario in Figure8. When the carbon price is low, impacts on price and trade indexes are quite minimal. When the carbon price is high, we do see higher commodity prices, fewer exports, and more imports due to less agricultural product supply.Figure 8
Annualized national index number changes under per unit plus constraints scenario across carbon prices.Welfare is also a concern. Table4 displays percentage changes from the base producers’ and consumers’ surplus for all the program setup scenarios. Note that the surplus discussed here does not cover: (1) the social cost and benefits of altered GHG emission reductions, erosion, or other environmentally related changes and (2) the government costs of raising the money for program payments and administration. The most noticeable change is the gain in domestic producers’ surplus. Despite less agricultural production, the domestic producers’ surplus increases under all scenarios since carbon payments provide an additional source of income for program participants and prices are somewhat higher. The mandatory scenario generates the largest increase in the domestic producers’ surplus which is directly related to the largest amount of carbon net emissions reductions and resulting payment applied to all producers in the agricultural sector. The per unit plus constraints scenario generates a higher domestic producers’ surplus than the per unit scenario does since it pays more for higher sequestration and bioenergy production in spite of actually having a lower average payment for net emission offsets. Changes in domestic and overseas consumers’ surpluses are negligible because the commodity price impact is quite small. Overseas producers gain as a result of fewer US exports. Overall, we achieve a net gain in world welfare under all scenarios.Table 4
Percentage change from base in welfare at $30/tCO2e.
Mandatory (%)Per unit (%)Per unit plus constraints (%)Domestic consumers’ surplus−0.050.00−0.01Domestic producers’ surplus3.502.012.63Overseas consumers’ surplus−0.010.00−0.01Overseas producers’ surplus0.29−0.050.40Total0.040.040.04


## 4.1. GHG Mitigation

Figure1 presents annualized net GHG emissions by those opting in and not opting in under each alternative carbon market scenario under a CO2e price of $30/ton. Net offsets, as an indication of the truly additional ones after accounting for leakage and the rebound effect, are calculated as the difference in net GHG emissions between the base scenario and those occurring under each alternative scenario.Figure 1
Annualized GHG offsets by scenario at $30/tCO2e.In the results the net emission consequences are usually not equal to the gains paid to participants. This difference arises because of two factors. First, in most cases those not covered by the program choose to increase their net emissions or reduce sequestration relative to the baseline (a leakage effect) when responding to the market alterations caused by the actions of participants. Second, the per unit baseline may cause participants to expand qualifying “emission reductions” to levels beyond that in the base scenario (whereXo>Xb for positive emitters as discussed above).The mandatory participation scenario generates the most offsets as expected since it imposes the carbon price on all producers and does not pay for nonadditional production and penalizes for any within region leakage. This stimulates the greatest net emission reduction and penalizes for any and all emissions increase or sequestration/bioenergy offsets decrease while rewarding the opposite.Once we bring in the payment and constraint design, we find that both net offsets and participants’ offsets increase significantly. Also, we note this may be a second best solution as it is still less effective than the mandatory but likely hard to implement scenario.Figure2 shows more detailed GHG emission mitigation contributions by major strategy under the per unit and per unit plus constraints baselines across CO2 prices. Agricultural soil sequestration and bioenergy production are two primary categories of negative emission strategies. At low carbon prices, agricultural soil sequestration increases significantly compared to the base scenario and becomes the predominant strategy. That is because most of the agricultural soil sequestration practices are complementary with existing crop production and thus require relatively low costs as in [9]. When the carbon price gets higher, the share of agricultural soil sequestration declines due to the substantial increase in GHG offsets from bioelectricity production. Note that bioelectricity is not produced under the base scenario and at low carbon prices due to its high implementation costs but it has lifecycle GHG offset rates as high as 99%. Such high offset rates combined with higher carbon prices induce producers to shift land and other resources out of traditional crop production to bioelectricity feedstock production. In addition, the per unit plus constraints scenario achieves more GHG offsets from bioenergy production than the per unit baseline because of additional incentives for bioenergy production.Figure 2
GHG mitigation strategies used under per unit and per unit plus constraints baselines across CO2 prices.


## 4.2. Program Costs

We now turn to the average program cost per ton of mitigation. This is computed as annualized total carbon payments under the program divided by annualized tons of net CO2e offsets after considering both nonparticipants and participants net emissions. Although carbon credits are paid to participants only, we evaluate the effectiveness of carbon programs in mitigating GHG emissions considering possible leakage and nonadditionality. To accomplish this we use the total change in net CO2e offsets across all parties as the denominator instead of just participants’ offsets. The average program cost for each program setup is listed in Table 1.Table 1
Total and average program costs at $30/tCO2e.
MandatoryPer unitPer unit plus constraintsNet offsets (in million tons CO2e)169.3999.85124.33Participants’ offsets (in million tons CO2e)—146.33165.79Total program costs (in million $)5,081.686,266.106,555.77Average program costs (in $/ton CO2e)30.0062.7552.73The mandatory scenario has the lowest average program cost which also equals the CO2e price. Any emission change relative to the base case is rewarded or penalized according to the CO2e price so there is no leakage within the region associated with the mandatory program.On the other hand, the average cost of voluntary carbon crediting programs is not equal to the CO2e price as nonparticipants’ emissions are not restricted but are used in the calculation and leakage occurs. The per unit baseline also exhibits high average cost per effective ton CO2e net emissions reduction cost. In addition to the nonparticipants leakage, the rebound effect is playing a part. As noted above, the per unit baseline is paid upon the difference in emissions per unit and as long as producers could reduce their emission rate, they are rewarded. The rebound effects occur and for some subcategories lead to a total emission increase and higher program costs. For example, we saw increased annualized corn production relative to the base case yielding CO2e net emissions increases under the per unit baseline but declined production under the per unit plus constraints baseline.The per unit plus constraints case eliminates the rebound effects and provides more incentives toward expanding sequestration and bioenergy offsets. As previously shown, the per unit plus constraints baseline induces 8% more GHG net offset from ag soil sequestration and bioenergy at a $30 price. This results in achieving a lower average program cost than the per unit baseline. However, the existence of nonparticipants leakage still pushes up the average cost above the CO2 price.


## 4.3. Cropped Acres

Figure3 illustrates the effects on annualized national crop acres in million acres under per unit and per unit plus constraints baselines for alternative CO2e prices. As that price increases, the percentage of opt in acres increases, indicating the obvious result that higher carbon prices lead to higher participation rates. However, as the carbon price increases from $30/tCO2e to $50/tCO2e, the opt in acre percentage only increases slightly, showing that voluntary carbon programs are not able to encourage all producers to participate even at high carbon prices. In addition, agricultural soil sequestration practices are predominant mitigation strategies at low carbon prices. But as higher carbon prices, the agricultural soil sequestration no longer increases and bioenergy production begins to dominate as discussed in McCarl and Schneider [9]. In other words, energy crop production comes into play and competes for cropland. That is why we do not see use of less cropland even though we have less traditional crops produced. As shown in Figure 4, producers do not start producing more switchgrass based biopower at $15/tCO2e. As the carbon price gets higher, biopower production grows significantly, almost all of which comes from opt in producers. At each carbon price, the per unit plus constraints case exhibits a higher level of switchgrass based biopower production than the per unit case.Figure 3
Annualized national crop acres under per unit and per unit plus constraints scenarios across carbon prices.Figure 4
Annualized national switchgrass acres under per unit and per unit plus constraints scenarios across carbon prices.


## 4.4. Bioenergy Production

We mainly explore biodiesel and biopower production here because ethanol does not increase after 2020 due to the RFS assumptions in the model settings and the relatively lower CO2e offset rates. Figure 5 shows growth in the volume of biodiesel produced relative to production at the carbon price of $30/tCO2e by scenario.Figure 5
Biodiesel production additional to base at $30/tCO2e.We see the results that producers start to expand biodiesel production after 2025 because producers need time to react and construct biodiesel processing facilities. The per unit plus constraints baseline stimulates the largest amount of additional biodiesel production, indicating the per unit plus constraints setup provides effective incentives for biodiesel production. The mandatory scenario produces very large amounts of additional biodiesel in 2040 and 2045 and moderate amounts afterward. We also find that the per unit case has essentially the same level of biodiesel production as exhibited in the no carbon price because of the incentive lack as discussed in the theory section.The change in feedstock usage for biodiesel production relative to the base scenario at alternative carbon prices under the per unit and per unit plus cases baselines is shown in Figure6. At each carbon price, the per unit plus constraints baseline yields more production of biodiesel mainly from soybean oil. This result demonstrates that the per unit plus constraints case more effectively stimulates biodiesel production. In addition, we find biodiesel production increases with carbon prices at low prices. However, when the carbon price increases from $30/tCO2e to $50/tCO2e, biodiesel production no longer expands mainly because of the less use of corn oil under both per unit and per unit plus constraints cases.Figure 6
Additional biodiesel production composition from base across carbon prices.Figure7 presents bioelectricity production over time under all alternative scenarios at $30/tCO2e. The mandatory scenario consistently achieves a high level of bioelectricity production. The per unit plus constraints baseline exhibits high levels peaking its bioelectricity production from 2035 to 2055 but then later having it fall.Figure 7
Bioelectricity production over time at $30/tCO2e.We now look deeper into the breakdown of feedstock use for bioelectricity. For simplicity, we have combined processes with and without cofiring for each biofeedstock used. Bioelectricity production expands significantly with increased carbon prices. At the carbon price of $15/tCO2e, bioelectricity is primarily processed from manure (Table 2). As the carbon price increases to $30/tCO2e, we observe increasing use of switchgrass and crop residues especially wheat residues. When the carbon price is $50/tCO2e, switchgrass expands and is the predominant biofeedstock. We also see the use of corn residues increases substantially and willow comes into play. Note that biodiesel production stops expanding with less use of corn oil at high carbon prices. We observe a trend of shifting resources from biodiesel to bioelectricity due to the higher GHG reduction of bioelectricity.Table 2
Annualized electricity production breakdown in number of 100 megawatt plants.
Biofeedstock used$15/tCO2$30/tCO2$50/tCO2Per unitPer unit plus constraintsPer unitPer unit plus constraintsPer unitPer unit plus constraintsCorn residue0.314.8448.1952.85Sorghum residue2.454.7612.776.85Wheat residue0.010.029.0214.8017.1618.07Barley residue0.350.415.155.876.466.57Oats residue0.320.480.450.49Bagasse0.010.020.820.67Switchgrass0.110.1224.9034.4176.1981.30Willow0.100.36Lignin6.446.705.405.24Manure16.7316.2516.3716.1817.1316.54Beef manure0.593.010.673.182.622.91Dairy manure7.305.367.585.264.885.17


## 4.5. Market Effects

Table3 presents annualized national index numbers for commodity prices and quantities under all market design scenarios at $30/tCO2e. The results show that the introduction of voluntary carbon crediting programs leads to less conventional farm production and higher commodity prices with higher land values. This is because agricultural producers shift land use from agricultural production to bioenergy production to receive more carbon payments as in Schneider and McCarl [35]. In turn production falls and commodity prices generally increase with cropland becoming more valuable [8].Table 3
Annualized national index numbers at $30/tCO2e.
AgtypeItemBaseMandatoryPer unitPer unit plus constraintsAllFarmProdProduction100.0099.7796.3796.34AllFarmProdPrice100.0099.36100.32100.05AllFarmProdExports100.0099.63100.1699.53AllFarmProdImports100.0099.86100.06100.05CroplandPrice100.0099.37103.89104.54We illustrate the national index number changes across carbon prices under the per unit plus constraints scenario in Figure8. When the carbon price is low, impacts on price and trade indexes are quite minimal. When the carbon price is high, we do see higher commodity prices, fewer exports, and more imports due to less agricultural product supply.Figure 8
Annualized national index number changes under per unit plus constraints scenario across carbon prices.Welfare is also a concern. Table4 displays percentage changes from the base producers’ and consumers’ surplus for all the program setup scenarios. Note that the surplus discussed here does not cover: (1) the social cost and benefits of altered GHG emission reductions, erosion, or other environmentally related changes and (2) the government costs of raising the money for program payments and administration. The most noticeable change is the gain in domestic producers’ surplus. Despite less agricultural production, the domestic producers’ surplus increases under all scenarios since carbon payments provide an additional source of income for program participants and prices are somewhat higher. The mandatory scenario generates the largest increase in the domestic producers’ surplus which is directly related to the largest amount of carbon net emissions reductions and resulting payment applied to all producers in the agricultural sector. The per unit plus constraints scenario generates a higher domestic producers’ surplus than the per unit scenario does since it pays more for higher sequestration and bioenergy production in spite of actually having a lower average payment for net emission offsets. Changes in domestic and overseas consumers’ surpluses are negligible because the commodity price impact is quite small. Overseas producers gain as a result of fewer US exports. Overall, we achieve a net gain in world welfare under all scenarios.Table 4
Percentage change from base in welfare at $30/tCO2e.
Mandatory (%)Per unit (%)Per unit plus constraints (%)Domestic consumers’ surplus−0.050.00−0.01Domestic producers’ surplus3.502.012.63Overseas consumers’ surplus−0.010.00−0.01Overseas producers’ surplus0.29−0.050.40Total0.040.040.04


## 5. Conclusion and Policy Implications

As an often noncapped sector in GHG trading markets, agriculture can provide offsets by reducing GHG emissions, enhancing sequestration, or producing feedstocks for bioenergy that replaces higher emitting fossil based energy forms but needs incentives to do so. Voluntary carbon markets can provide such incentives and allow producers to join at their discretion. We set up a baseline condition where participants have to do better at reducing emissions relative to a per unit standard or produce more sequestration or bioenergy feedstocks again relative to a standard. Herein, we analyze market designs under such a situation.In terms of market design, we first examine the case where a producer is paid the difference between emissions under an improved practice and the emissions under currently used practice (i.e., if current rice cultivation generatesx tons of emissions per hectare and an improved practice yields y we will pay for the x-y savings times the land area enrolled). In that case, we find there is an incentive to expand the participating land area beyond preexisting area, thus generating a rebound effect. We subsequently suggest imposition of a maximum participating land area constraint set at the preexisting area which could likely be established using remote sensing.Subsequently we turned to the sequestration/bioenergy offset case finding that payments relative to a standard discouraged participation. There we suggest only paying relative to the standard on the preexisting area then dropping the standard to zero paying for the full amount for additional participation. Therefore, adding proper constraints to per unit baseline could effectively reduce unintended effects. We also find as carbon prices increase, strategies switch from low-cost such as agricultural soil sequestration to higher-cost but greater per unit land emission offset strategies such as bioenergy production. This is consistent with Forest Trends’ Ecosystem Marketplace [36] observation that in 2019 over-the-counter transaction voluntary offset volumes for renewable energy surged by 78 percent while agriculture, forestry, and other land use (AFOLU) volumes dropped by 28 percent.Our empirical work basically shows imposing such constraints eliminates the rebound effect for emitters and the participation disincentives for those selling sequestration or bioenergy offsets. From a policy standpoint we believe imposing the constraints can help generate cheap, additional actions and feel that imposing the constraints can be done via a requirement that participants prove the amount of preexisting use of the emitting, sequestering, and bioenergy feedstock activities much as done in the US farm and insurance programs. This allows effective ways to harness sectoral potential to reduce emissions relative to current conditions and increase sequestration and bioenergy feedstock production well beyond current levels.Although our analysis focuses on agricultural sector, the policy design may also be applicable to other voluntary market settings for sectors such as forestry and wetland management. For example, expansions in reforestation may yield substantial carbon benefits but proper incentives or subsidies need to be provided to cause the expansion and reach the optimal harvest cycle with a reward to all of the extra carbon not a measure relative to gains under small implementations of such practices. Such incentives would also likely gain cobenefits in the form of environmental services [37]. Note however caution would be needed to not get carried away with the cobenefits when they make an alternative look better but if one looks at all alternatives there may be other cases where cobenefits are even greater [38]. As for wetland restoration, four project-specific carbon offsets methodologies are approved but no transactions have been made yet because of high cost and lack of proper incentives [39]. We believe that with our baseline design their attractiveness would increase.There are several limitations to our current study. Since FASOMGHG is used in the empirical phase of our study, our results and discussion depend heavily on its embodied assumptions, data, and model structure including projections of future crop and livestock yields along with ethanol mandates. Also, the uncertainty and permanence issues involved with mitigation are not fully considered in this analysis. Incorporating an uncertainty or permanence discount may alter our results.



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*Source: 9518135-2021-07-17.xml*