Credit Stacking in Agri-Environmental Programs: Water

Quality Trading and Carbon Markets

Adriana Valcu Research Assistant

Iowa State University 573 Heady Hall Ames, IA 50011

amvalcu@iastate.edu (515) 6357-5767

Sergey S. Rabotyagov

Assistant Professor School of Environmental and Forest Sciences

University of Washington Box 352100

Seattle, WA 98195-2100 rabotyag@uw.edu (206)

685-3159 C.L. Kling Professor

Iowa State University 568D Heady Hall Ames, IA 50011

ckling@iastate.edu (515) 294-5767

Poster prepared for presentation at the Agricultural & Applied Economics Association’s 2013 AAEA&CAES Joint Annual Meeting, Washington, DC, August 4-6, 2013 Copyright 2013 by A Valcu, S.S. Rabotyagov, A. ,and Catherine.L. Kling All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies.

June 2013

Results

AcknowledgmentsThis research is part of a regional collaborative project supported by the USDA-NIFA, award number 2011-68002-30190. The authors appreciate support from a cooperative agreement with USDA' s Economic Research Service: "The Supply of Greenhouse Gas Offsets from Agriculture and Their Water Quality Effects in the Upper Mississippi River Basins", the US-EPA Targeted Watershed Grant Program WS97704701, and the Policy Research Center at Iowa State.

Figure 1. Placement of Boone watershed within the Upper Mississippi River Basin (UMRB)

WATER quality and improved soils are the necessary qualities of a healthy watershed, which provide local ecosystem services such as fishing and wildlife habitat. At the same time, soil carbon sequestration is a global ecosystem service and plays an important role in reducing GHGs.

We explore the conceptual links between a nonpoint source trading program for water quality and a carbon offset market

• There is substantial research that shows that agricultural soils have the potential for additional carbon sequestration.

• There is emerging research in the area of credit-stacking—where farmers are given incentives to participate in multiple agri-environmental programs.

Introduction Methods• Water quality goal 30% reductions in baseline

nitrogen/phosphorus emissions)• A set of 9 abatement actions• Price of carbon $5, $15, $25 per MteCO2

Analyze the impact of the existence of a carbon offset market on the efficiency of a water quality trading program for non-point sources.

• The water quality trading program is a local cap and trade program (i.e., at the watershed or state level).

• The carbon market is a wider market (i.e., nationwide) with no specific cap requirements at farm level.

Objective

• Consider a watershed impaired by agricultural pollution (nitrogen and phosphorus) and a set of abatement actions available for improving water quality

• For each abatement action estimate and assign a set of points that measure the efficiency in reducing field emissions (Sergey, Valcu and Kling 2012)

• For each field compute the amount of soil carbon sequestration associated with each abatement action using the EPIC model

Simulate the outcomes of a point-based trading market for water quality

xij is the abatement action j for field i, a

ij the

number of points associated with abatement action j; b

i0 is the initial allocation of points

requirement for a field. The market clearing condition is: ∑

i b

i = 0.

min {∑i ∑

j c

ij *

xij + p*

bi}

s.t. ∑i {∑

ij a

ij x

ij + b

i } ≥ ∑

i b

i0

x

ij , b

i

Simulate the outcomes of a point-based trading market for water quality in the presence of a market for carbon offsets

pc is price of a carbon offset and g

i (x

ij ) represents

the amount of soil carbon sequestration associated with abatement action j and field i

Compare the outcomes of the two trading settings.

min {∑i ∑

j c

ij *

xij − p

c * g

i (x

ij ) + p*

bi}

s.t. ∑i {∑

ij a

ij x

ij + b

i } ≥ ∑

i b

i0

x

ij , b

i

Iowa

Wisconsin

Illinois

Minnesota

• This research utilizes two different watershed based models (EPIC and SWAT) together with an optimization algorithm to evaluate the implications of credit stacking on the cost efficiency of a water quality trading and carbon sequestration program.

• The levels of post-trading water quality reductions are similar to the cases where there is no carbon market.

• The total program costs of a point-based trading program decreases as the price of carbon increases.

• As the price of carbon increases, the total program costs become negative, meaning that farmers obtain extra revenue by selling carbon offsets

• While the program cost is reduced, since more expensive abatement actions are adopted, the total cost of implementing the conservation practices increases.

Conclusions

Boone watershed landand use facts

• 237,000 ha divided into 30 subbasins and 2,900 homogenous field units

• Corn and soybean cover nearly 90% of the agricultural area

Boone watershed environmental facts

• Discharged some of the highest N leads during 2000-2002 amongst analyzed Iowa watersheds

• Corn and soybean cover nearly 90% of the agricultural area

Adriana ValcuCatherine L. Kling

Department of Economics

Center for Agricultural and

Rural Development

Sergey S. RabotyagovSchool of Environmental and Forest Sciences

Credit Stacking in Agri-environmental Programs: Water Quality Trading and Carbon Markets

Land use competition: water quality vs carbon benefits

Nitrogen

Phosphorus

Table 1. Credit stacking implications

Missouri