Markets for Greenhouse Emissions

Cloé Garnache

Dept. of Agricultural, Food, and Resource EconomicsMichigan State University

CES LectureMunich July 27 2016

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Market-based approaches to reduce greenhouse gasemissions (GHG)

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A brief history of US national GHG regulations

• The Waxman-Markey Bill (American Clean Energy and SecurityAct) of 2009

• Approved by the House of Representatives in 2009, but neverbrought to the floor of the Senate for discussion or a vote

• First time either house of Congress had approved a bill meantto curb GHG emissions

• The Clean Power Plan: executive order signed by PresidentObama in 2015

• Aims at reducing GHG emissions from coal-burning powerplants by 32% by 2030 rel to 2005 levels

• Petition by 27 states to US DC Circuit Court of Appeals forupholding the law

• In early 2016, the Supreme Court ordered the EPA to haltenforcement of the plan until a lower court rules the lawsuit

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The Regional GHG Initiative (RGGI)

• A cap-and-trade approach initiated in 2009, involving 10northeast states

• Requirements for fossil fuel-fired electric power generators with acapacity of >25MW to hold CO2 allowances for their emissionsover a 3-year period

• Allocating allowances through quarterly, regional CO2 allowanceauctions

• Program reviewed in 2012 and 2016

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California AB 32

• Mandate: cap the state’s 2020 emissions (507Mt under BAU) totheir 1990 levels (427Mt)

• The cap-and-trade covers 85% of state’s emissions• Design of a price floor and ceiling

• ARB estimates:• 62Mt abated from standards (e.g., low carbon fuel standards,energy e�ciency, 33% renewable energy in electricitygeneration)

• 18Mt abated from cap-and-trade

• Many measures expected to yield small GHG savings, e.g., solarroofs, landfill methane control (

Regulation of pollutants

• Condition for e�cient regulation of global pollutants: Marginalabatement costs should be equalized across sources and sectors

(Baumol and Oates, 1988; Fowlie et al., 2012)

• First-best policies are e�cient but generally infeasible or toocostly under imperfect emissions monitoring

• Rich literature on the economic costs of second-best policies

• gasoline and vehicle taxes• driving restrictions and gasoline content regulations• payments for practice adoption in the AFOLU sector, e.g.,

Stavins (1999); Antle et al. (2003a); Lubowski et al. (2006)

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Studies show agriculture can cost-e↵ectively abateGHG emissions

• Globally agriculture emits about 50% of methane, 70% ofnitrous oxide, and 20 % of CO2

• Large abatement potential: carbon sequestration, reduction inN

2

O and CH4

(Lal et al., 1998)

• Economic studies conclude agriculture can cost-e↵ectivelyreduce GHG emissions (e.g.,McCarl and Schneider (2001))

• Contribution of the agricultural sector to AB 32?• GHG emissions from agricultural soil management: 8.8 MtCO

2

ein 2012

• ARB is reviewing the development of protocols to credit GHGo↵sets from agriculture (protocols already exist for forestry andbiodigesters)

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Payment design for agricultural o↵sets faces manychallenges

• GHG emissions unobservable or costly to monitor

• Many margins of adjustment

• Atomized and heterogenous sources of GHG emissions

• Quasi-rents

• Equity considerations

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Little is known about the GHG abatemente�ciency losses associated with second-bestpolicies

• GHG abatement supply curves under the first-best policy(McCarl and Schneider, 2001; Antle et al., 2007)

• GHG abatement supply curves using other cost measures(Stavins, 1999; Pautsch et al., 2001; Antle et al., 2003b;Lubowski et al., 2006; Choi and Sohngen, 2010)

• Average subsidy expenditures

• Marginal subsidy expenditures

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Overview of the literature on second-best MSCcurves

• Main approaches

• Engineering and programming models, e.g., Richards et al.

(1993), Adams (1993), and Park and Hardie (1995)

• Behavioral models, e.g., Stavins (1999); Lubowski et al. (2006)

• Issues with reported measures of e�ciency:

• Confusion between marginal, average, and total costs (Richards

and Stokes, 2004)

• Confusion between social costs (SC) and program expenditures

(PE)

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Distinction between social costs (SC) and programexpenditures (PE)

• Social costs:

• Opportunity costs of undertaking abatement actions• Transaction costs• Administrative and monitoring costs (Antle et al., 2003a)• Opportunity cost of public funds (Mason and Plantinga, 2013)

• Program expenditures: Total costs of the policy scheme

• All of the above, and• Inframarginal rents (income transfer) (Boomhower and Davis,

2014)

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Marginal social cost vs. program expenditure

l1HELl1HEL+El1¢ HEL

E E+dE

s

s+ds

E

l1

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The social cost of first-best and second-bestpolicies

• First-best GHG reduction program

max

z

P(z, r) subject to Âl

El(z) E0 � A [l⇤]

• Social cost for abatement target A: C⇤(A) = P⇤(0)� P⇤(A),and marginal social cost: l⇤

• Second-best policy when agency limited to the set P 2 P

max

P2PP(z, r) s.to

(Âl El(z) E0 � A [lP ]z 2 argmax {P(z, r) s.to P(z) 0}

• Social cost for abatement target A: CP (A) = P⇤(0)� PP (A),and marginal social cost: lP

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Research questions

• Estimate California Central Valley agriculture’s marginalabatement cost curve under the first-best GHG emissionreduction policy

• Estimate the restricted marginal social cost curves undersecond-best policies

1 Incentive schemes using spatially aggregated emission factors2 Incentive schemes targeting a single GHG3 Incentive schemes regulating a single input4 Incentive schemes regulating a mix of inputs

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Bioeconomic model of California Central Valley’sagriculture

• 7 crop groups (1.29 million hectares or 70% of the non-perennialacreage)

• Biophysical information comes from DAYCENT• process-based model calibrated to California conditions• simulates crop yield and GHG data for various input use andtillage practices

• Positive mathematical programming (PMP) model• allocatable inputs (simultaneous and continuous changes)• crop-specific CES production functions• regional land and water constraints• novelties:

• crop production functions calibrated to biophysically-derivedyield elasticities to water and nitrogen

• cost function calibrated to account for tillage choice15 / 37

Linkage between biophysical and economic models

DAYCENT ECON. OPT.

prod. fct.

policy instr.

DWL DP

Abatement DGHG

MAC

yield responses

emission factors

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Bioeconomic model of California Central Valley’sagriculture

• 7 crop groups (70% of the non-perennial acreage)

Crop Central Sacramento San Joaquin

Valley (%) Valley (%) Valley (%)

Alfalfa 22 24 19

Corn 21 22 21

Cotton 21 1 28

Grain (wheat) 12 21 9

Other field cr. (sunflower) 14 9 16

Proc. tomato 10 18 7

Sa✏ower 2 6 0

Total 100 100 100

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Estimation of yield responses to inputs

• fit crop yield response functions• N fertilizer application rate: aN (kg/ha)

y(aN) = y0 + aN [1 � exp (�bNaN)]

• Water application rate: aW (cm)

y(aW) =aW

1 + exp⇣� aW�a0bW

⌘

• compute yield elasticities ¯yiN, ¯yiW

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Fitted agronomic and calibrated CES responses toN fertilizer (Region 5)

Fitted agronomic and calibrated CES yieldresponses to water (Region 5)

Agronomic yield elasticities

Water Nitrogen

Crop ¯yiW ¯yiNAlfalfa 0.23 -

Corn 0.27 0.13

Cotton 0.58 0.01

Grain 0.24 0.01

Other field crops 0.66 0.00

Processing tomatoes 0.32 0.02

Sa✏ower 0.25 0.13

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Fitted partial CO2

and N2

O emission factors(Region 5)

Fitted partial emission factors for all GHGscombined (Region 5)

Model specification

max

0x, 0T1Âg Âi pgiqgi| {z }

revenue

�Âj

�cgij + µgij

�xgij

| {z }variable input cost

��cgiT(Tgi) + µgiTTgi

�xgi1| {z }

tillage cost

subj. to

8>>><

>>>:

qgi = agi⇣

Âj bgijxrgigij

⌘ dgirgi 8g, 8i

Âi xgij vgj [lgj] 8 g, j = 1, 2Âg Âi xgi1

hÂk egik

⇣xgi2xgi1

,

xgi3xgi1

, Tgi⌘i

ˆE [l⇤]

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Marginal abatement cost under the first-best policy

Regional GHG emission abatement at $20/tCO2

e

8

2

5

9

6

13

18

1

7

20

17

10

4

3B

15A

11

14A

12

19A

16

3A

21A

19B

14B

21B21C

15B

Average abatement (tCO2e/ha)

0.03 - 0.30

0.30 - 0.76

0.76 - 1.14

1.14 - 1.55

0 60 120 180 24030Kilometers

¯

Net emission rates (tCO2

e/ha) at the marginalcost of $20/tCO

2

e

Sacramento Valley San Joaquin Valley

Crop ˆE$0

ˆE$20

ˆE$0

ˆE$20

Alfalfa 1.40 1.34 -0.30 -0.40

Corn 2.88 2.37 1.42 1.16

Cotton 3.81 3.72 4.99 4.22

Wheat -0.15 -0.31 -0.34 -0.38

Sunflower 5.66 3.89 4.28 3.03

Processing tomatoes 6.17 3.81 6.22 4.60

Sa✏ower 1.04 0.23 0.03 -0.35

Valley average 2.63 1.75 2.63 1.73

Changes in the crop mix under the first-best policy

Marginal social abatement cost curve and intensiveand extensive margin contributions

Robustness check: Supply elasticities Robustness check: Reference shadow values29 / 37

Overestimation of abatement costs under myopicmodels

Second-best policy: Spatially aggregated emissionfactors

Second-best policy: Single GHG incentives

Second-best policy: Regulation of agriculturalinputs

GHG emissions abated in MtCO2

e at $20/tCO2

eunder the first-best and second-best policies

Second-best policies

First-best Spatially aggr. Regulation of Regulation

¯sgi policy emission factors a single GHG of inputs

Valley CA CO2

N2

O Tillage N Till. & N

0.1 1.08 1.04 1.00 0.86 0.88 0.47 0.16 0.60

0.2 1.22 1.15 1.13 0.90 0.97 0.49 0.25 0.71

0.5 1.49 1.40 1.36 1.00 1.22 0.52 0.43 0.96

Social cost of the first-best and second-bestpolicies at $20/tCO

2

e

Second-best policies

First-best Spatially aggreg. Regulation of Regulation

¯sgi policy emission factors a single GHG of inputs

Valley CA CO2

N2

O Tillage Tillage & N

0.1 0.40 0.43 0.45 0.57 0.54 1.04 0.83

0.2 0.43 0.47 0.48 0.69 0.63 1.28 0.91

0.5 0.46 0.54 0.54 0.97 0.70 1.73 0.99

Conclusion

• First-best policy• Unrealistic assumptions

• costless measurement of all relevant GHGs at each source or• perfect knowledge of the emission generation process

• Political considerations

• Multiple adjustment margins

• Crop agriculture could meaningfully contribute to California’sGHG reduction targets

• Second-best policies with reduced implementation costs mayreasonably approximate the first-best allocation

• spatially aggregated emission factors• single GHG• combination of inputs

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References

Antle, J. M., Capalbo, S. M., Mooney, S., Elliott, E., and Paustian, K. (2003a). Spatial heterogeneity, contract design, and thee�ciency of carbon sequestration policies for agriculture. Journal of Environmental Economics and Management,46:231–250.

Antle, J. M., Capalbo, S. M., Mooney, S., Elliott, E., and Paustian, K. (2003b). Spatial heterogeneity, contract design, and thee�ciency of carbon sequestration policies for agriculture. Journal of Environmental Economics and Management,46:231–250.

Antle, J. M., Capalbo, S. M., Paustian, K., and Ali, M. K. (2007). Estimating the economic potential for agricultural soil carbonsequestration in the Central United States using an aggregate econometric-process model. Climatic Change, 80:145–171.

Choi, S.-W. and Sohngen, B. (2010). The optimal choice of residue management, crop rotations, and cost of carbonsequestration: empirical results in the Midwest US. Climatic Change, 99(1-2):279–294.

Lal, R., Kimble, L., Follett, R., and Cole, C. (1998). The Potential of U.S. Cropland to Sequester C and Mitigate theGreenhouse E↵ect. Ann Arbor Press, Chelsea, MI.

Lubowski, R. N., Plantinga, A. J., and Stavins, R. N. (2006). Land-use change and carbon sinks: Econometric estimation of thecarbon sequestration supply function. Journal of Environmental Economics and Management, 51(2):135–152.

McCarl, B. A. and Schneider, U. A. (2001). Greenhouse gas mitigation in U.S. agriculture and forestry. Science,294(5551):2481–2482.

Parks, P. and Hardie, I. (1995). Least-cost forest carbon reserves: Cost-e↵ective subsidies to convert marginal agricultural landto forests. Land Economics, 71(1):122–136.

Pautsch, G., Kurkalova, L., Babcock, B. A., and Kling, C. (2001). The e�ciency of sequestering carbon in agricultural soils.Contemporary Economic Policy, 19(2):123–134.

Stavins, R. N. (1999). The costs of carbon sequestration: A revealed-preference approach. American Economic Review,89(4):994–1009.

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MotivationResearch questionsModelResultsConclusion